U.S. patent application number 17/495571 was filed with the patent office on 2022-06-23 for method and systems for prediction of hla class ii-specific epitopes and characterization of cd4+ t cells.
The applicant listed for this patent is BioNTech US Inc.. Invention is credited to Jennifer Grace Abelin, Dominik Barthelme, Robert Kamen, Michael Steven Rooney.
Application Number | 20220199198 17/495571 |
Document ID | / |
Family ID | 1000006181613 |
Filed Date | 2022-06-23 |
United States Patent
Application |
20220199198 |
Kind Code |
A1 |
Rooney; Michael Steven ; et
al. |
June 23, 2022 |
METHOD AND SYSTEMS FOR PREDICTION OF HLA CLASS II-SPECIFIC EPITOPES
AND CHARACTERIZATION OF CD4+ T CELLS
Abstract
Methods for preparing a personalized cancer vaccine and a method
to train a machine-learning HLA-peptide presentation prediction
model.
Inventors: |
Rooney; Michael Steven;
(Boston, MA) ; Abelin; Jennifer Grace; (Boston,
MA) ; Barthelme; Dominik; (Belmont, MA) ;
Kamen; Robert; (Sudbury, MA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
BioNTech US Inc. |
Cambridge |
MA |
US |
|
|
Family ID: |
1000006181613 |
Appl. No.: |
17/495571 |
Filed: |
October 6, 2021 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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16824331 |
Mar 19, 2020 |
11183272 |
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17495571 |
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PCT/US2019/068084 |
Dec 20, 2019 |
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16824331 |
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62891101 |
Aug 23, 2019 |
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62855379 |
May 31, 2019 |
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62826827 |
Mar 29, 2019 |
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62783914 |
Dec 21, 2018 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G16B 40/20 20190201;
G16B 40/00 20190201; G16B 30/00 20190201; C07K 16/2833 20130101;
A61K 39/39 20130101 |
International
Class: |
G16B 30/00 20060101
G16B030/00; G16B 40/00 20060101 G16B040/00; A61K 39/39 20060101
A61K039/39; C07K 16/28 20060101 C07K016/28; G16B 40/20 20060101
G16B040/20 |
Claims
1.-61. (canceled)
62. A system for selecting one or more peptide sequences for
preparing a pharmaceutical composition, the system comprising a
computer processor comprising: (a) an input module configured to
receive amino acid sequence information of a set of candidate
peptide sequences expressed by cells of a human subject, wherein
each candidate peptide sequence of the plurality of candidate
peptide sequences is encoded by a genome, transcriptome, or exome
of a human subject, or a pathogen or a virus in the human subject;
(b) a processing module operably linked to the input module, the
processing module comprising an executable code comprising a
trained machine learning class II HLA-peptide presentation
prediction model, wherein the trained machine learning class II
HLA-peptide presentation prediction model is configured to generate
output peptide sequences with a plurality of presentation
predictions, wherein each presentation prediction of the plurality
of presentation predictions is indicative of a presentation
likelihood that a peptide sequence of the set of candidate peptide
sequences is presented by one or more proteins encoded by a class
II HLA allele of a cell of the human subject, and wherein the
trained machine learning class II HLA-peptide presentation
prediction model comprises; (i) a plurality of parameters
identified at least based on training data comprising: (1)
sequences of training peptides, (2) an identity of a protein
encoded by an HLA class II allele associated with the training
peptide sequences, and (3) an observation by mass spectrometry that
one or more of the training peptides was presented by the protein
encoded by the HLA class II allele in training cells; and (ii) a
function representing a relation between the amino acid sequence
information received as input and the presentation likelihood
generated as an output based on the amino acid sequence information
and the plurality of parameters; wherein each peptide sequence of a
subset of the output peptide sequences is for preparing a
therapeutic composition for the human subject based on the
presentation likelihood generated as the output of the peptide
sequence being in a complex with the one or more proteins encoded
by a class II HLA allele of a cell of the human subject, and
wherein the trained machine learning class II HLA-peptide
presentation prediction model has a positive predictive value (PPV)
of at least 0.2 according to a presentation PPV determination
method.
63. The system of claim 62, wherein the input module is configured
to receive an identity of one or more proteins encoded by a class
II HLA allele of a cell of the human subject.
64. The system of claim 62, wherein the processing module is linked
to an output module configured to display the plurality of
presentation predictions.
65. The system of claim 62, wherein the trained machine learning
class II HLA-peptide presentation prediction model has a PPV of at
least 0.2 when amino acid information of a plurality of test
peptide sequences are processed to generate a plurality of test
presentation predictions, each test presentation prediction
indicative of a likelihood that the one or more proteins encoded by
a class II HLA allele of a cell of the subject can present a given
test peptide sequence of the plurality of test peptide sequences,
wherein the plurality of test peptide sequences comprises at least
500 test peptide sequences comprising: (i) at least one hit peptide
sequence identified by mass spectrometry to be presented by an HLA
protein expressed in cells, and (ii) at least 499 decoy peptide
sequences contained within a protein encoded by a genome of an
organism, wherein the organism and the subject are the same
species, wherein the plurality of test peptide sequences comprises
a ratio of 1:499 of the at least one hit peptide sequence to the at
least 499 decoy peptide sequences and a top 0.2% of the plurality
of test peptide sequences are predicted to be presented by the HLA
protein expressed in cells by the trained machine learning class II
HLA-peptide presentation prediction model.
66. The system of claim 62, wherein: (i) the at least one hit
peptide sequence comprises at least 10 hit peptide sequences, and
(ii) the at least 499 decoy peptide sequences comprise at least
4990 decoy peptide sequences.
67. The system of claim 62, wherein any nine contiguous amino acid
subsequences of any of the at least one hit peptides does not
overlap with any nine contiguous amino acid subsequences of the at
least 4990 decoy peptide sequences.
68. The system of claim 62, wherein the trained machine learning
class II HLA-peptide presentation prediction model has a PPV of at
least 0.2 at a recall rate of 10% according to a presentation PPV
determination method.
69. The system of claim 62, wherein the trained machine learning
class II HLA-peptide presentation prediction model has a PPV of at
least 0.2 at a recall rate of 20% according to a presentation PPV
determination method.
70. The system of claim 62, wherein the trained machine learning
class II HLA-peptide presentation prediction model has a PPV of at
least 0.3 according to a presentation PPV determination method.
71. The system of claim 62, wherein the trained machine learning
class II HLA-peptide presentation prediction model has a PPV of at
least 0.3 at a recall rate of 20% according to a presentation PPV
determination method.
72. The system of claim 62, wherein each peptide sequence of the
set of candidate peptide sequences is associated with a cancer.
73. The system of claim 72, wherein each peptide sequence of the
set of candidate peptide sequences (i) comprises a mutation, (ii)
is expressed in a cancer cell of the subject, and (iii) is not
encoded by a genome of a non-cancer cell of the human subject.
74. The system of claim 62, wherein each sequence of the one or
more of the training peptides sequences observed by mass
spectrometry to be presented by the protein encoded by the HLA
class II in training cells has a length of at least 15 amino
acids.
75. The system of claim 62, wherein the training cells comprise
training cells expressing a single MHC class II complex or a
protein encoded by a single allelic variant of a class II HLA locus
selected from the group consisting of DR, DP, and DQ, wherein the
single MHC class II complex or a protein encoded by the single
allelic variant of a class II HLA locus is expressed by a cell of
the subject.
76. The system of claim 62, wherein the training data comprises
training data obtained by deconvolution.
77. The system of claim 62, wherein the training cells express a
protein encoded by a class II HLA allele of a cell of the human
subject, wherein the protein encoded by a class II HLA allele
comprises an affinity tag.
78. The system of claim 62, wherein the protein encoded by a class
II HLA allele is selected from the group consisting of:
HLA-DPB1*01:01/HLA-DPA1*01:03, HLA-DPB1*02:01/HLA-DPA1*01:03,
HLA-DPB1*03:01/HLA-DPA1*01:03, HLA-DPB1*04:01/HLA-DPA1*01:03,
HLA-DPB1*04:02/HLA-DPA1*01:03, HLA-DPB1*06:01/HLA-DPA1*01:03,
HLA-DRB1*01:01, HLA-DRB1*01:02, HLA-DRB1*03:01, HLA-DRB1*03:02,
HLA-DRB1*04:01, HLA-DRB1*04:02, HLA-DRB1*04:03, HLA-DRB1*04:04,
HLA-DRB1*04:05, HLA-DRB1*04:07, HLA-DRB1*07:01, HLA-DRB1*08:01,
HLA-DRB1*08:02, HLA-DRB1*08:03, HLA-DRB1*08:04, HLA-DRB1*09:01,
HLA-DRB1*10:01, HLA-DRB1*11:01, HLA-DRB1*11:02, HLA-DRB1*11:04,
HLA-DRB1*12:01, HLA-DRB1*12:02, HLA-DRB1*13:01, HLA-DRB1*13:02,
HLA-DRB1*13:03, HLA-DRB1*14:01, HLA-DRB1*15:01, HLA-DRB1*15:02,
HLA-DRB1*15:03, HLA-DRB1*16:01, HLA-DRB3*01:01, HLA-DRB3*02:02,
HLA-DRB3*03:01, HLA-DRB4*01:01, HLA-DRB5*01:01, HLA-DRB1*01:01,
HLA-DRB1*01:02, HLA-DRB1*03:01, HLA-DRB1*04:01, HLA-DRB1*04:02,
HLA-DRB1*04:04, HLA-DRB1*04:05, HLA-DRB1*07:01, HLA-DRB1*08:01,
HLA-DRB1*08:02, HLA-DRB1*08:03, HLA-DRB1*09:01, HLA-DRB1*11:01,
HLA-DRB1*11:02, HLA-DRB1*11:04, HLA-DRB1*12:01, HLA-DRB1*13:01,
HLA-DRB1*13:02, HLA-DRB1*13:03, HLA-DRB1*14:01, HLA-DRB1*15:01,
HLA-DRB1*15:02, HLA-DRB1*15:03, HLA-DRB1*16:02, HLA-DRB3*01:01,
HLA-DRB3*02:01, HLA-DRB3*02:02, HLA-DRB3*03:01, HLA-DRB4*01:01,
HLA-DRB4*01:03, HLA-DRB5*01:01; HLA-DPB1*01:01, HLA-DPB1*02:01,
HLA-DPB1*02:02, HLA-DPB1*03:01, HLA-DPB1*04:01, HLA-DPB1*04:02,
HLA-DPB1*05:01, HLA-DPB1*06:01, HLA-DPB1*11:01, HLA-DPB1*13:01,
HLA-DPB1*17:01, HLA-DQA1*01:01/HLA-DQB1*05:01,
HLA-DQA1*01:02/HLA-DQB1*06:02, HLA-DQA1*01:02/HLA-DQB1*06:04,
HLA-DQA1*01:03/HLA-DQB1*06:03, HLA-DQA1*02:01/HLA-DQB1*02:02,
HLA-DQA1*02:01/HLA-DQB1*03:03, HLA-DQA1*03:01/HLA-DQB1*03:02,
HLA-DQA1*03:03/HLA-DQB1*03:01, HLA-DQA1*05:01/HLA-DQB1*02:01,
HLA-DQA1*05:05/HLA-DQB1*03:01, and any combination thereof.
79. The system of claim 62, wherein each peptide sequence of the
subset of the output peptide sequences binds to a protein encoded
by a class II HLA allele of a cell of the human subject with an
IC50 of 500 nM or less, or a predicted IC50 of 500 nM or less.
80. The system of claim 62, wherein each peptide sequence of the
subset of the output peptide sequences is for preparing a
therapeutic composition for the human subject that comprises one or
more polypeptides comprising at least two peptide sequence of the
subset of the output peptide sequences or one or more
polynucleotides encoding at least two of peptide sequence of the
subset of the output peptide sequences
81. The system of claim 62, wherein each candidate peptide sequence
of the plurality of candidate peptide sequences is encoded by a
genome, transcriptome, or exome of a human subject with cancer.
Description
CROSS-REFERENCE
[0001] This application is a divisional application of US
Non-Provisional application U.S. Ser. No. 16/824,331 filed Mar. 19,
2020, which is a continuation of International Application No.
PCT/US2019/068084 filed Dec. 20, 2019 which claims the benefit of
U.S. Provisional Application No. 62/891,101, filed on Aug. 23,
2019; U.S. Provisional Application No. 62/855,379, filed on May 31,
2019; U.S. Provisional Application No. 62/826,827, filed on Mar.
29, 2019; and 62/783,914, filed on Dec. 21, 2018; each of which is
incorporated herein by reference in its entirety.
SEQUENCE LISTING
[0002] The instant application contains a Sequence Listing which
has been submitted electronically in ASCII format and is hereby
incorporated by reference in its entirety. Said ASCII copy, created
on Oct. 6, 2021, is named 50401_735_401_SL.txt and is 27,451 bytes
in size.
BACKGROUND
[0003] The major histocompatibility complex (MHC) is a gene complex
encoding human leukocyte antigen (HLA) genes. HLA genes are
expressed as protein heterodimers that are displayed on the surface
of human cells to circulating T cells. HLA genes are highly
polymorphic, allowing them to fine-tune the adaptive immune system.
Adaptive immune responses rely, in part, on the ability of T cells
to identify and eliminate cells that display disease-associated
peptide antigens bound to human leukocyte antigen (HLA)
heterodimers.
[0004] In humans, endogenous and exogenous proteins can be
processed into peptides by the proteasome and by cytosolic and
endosomal/lysosomal proteases and peptidases and presented by two
classes of cell surface proteins encoded by MHC genes. These cell
surface proteins are referred to as human leukocyte antigens (HLA
class I and class II), and the group of peptides that bind them and
elicit immune responses are termed HLA epitopes. HLA epitopes are a
key component that enables the immune system to detect danger
signals, such as pathogen infection and transformation of self.
CD4+ T cells recognize class II MHC (HLA-DR, HLA-DQ, and HLA-DP)
epitopes displayed on antigen presenting cells (APCs), such as
dendritic cells and macrophages. The endogenous processing and
presentation of HLA class II-ligands is a complex procedure and
involves a variety of chaperones and a subset of enzymes that are
not all well characterized. HLA class II-peptide presentation
activates helper T cells, subsequently promoting B cell
differentiation and antibody production as well as CTL responses.
Activated helper T cells also secrete cytokines and chemokines that
activate and induce differentiation of other T cells.
[0005] Understanding the peptide-binding preferences of every HLA
class II heterodimer is the key to successfully predicting which
cancer or tumor-specific antigens are likely to elicit the cancer
or tumor-specific T cell responses. There is a need for methods of
identifying and isolating specific HLA class II-associated peptides
(e.g., neoantigen peptides). Such methodology and isolated
molecules are useful, e.g., for the development of therapeutics,
including but not limited to, immune based therapeutics.
SUMMARY
[0006] The methods and compositions described herein find uses in a
wide range of applications. For example, the methods and
compositions described herein can be used to identify immunogenic
antigen peptides and can be used to develop drugs, such as
personalized medicine drugs, and isolation and characterization of
antigen-specific T cells.
[0007] CD4+ T cell responses may have anti-tumor activity. A high
rate of CD4+ T cell responses may be shown without using Class II
prediction (e.g., 60% of SLP epitopes in NeoVax study (49% in
NT-001, see Ott et al., Nature, 2017 Jul. 13; 547(7662):217-221),
and 48% of mRNA epitopes in Biontech study, see Sahin et al.,
Nature, 2017 Jul. 13; 547(7662):222-226). It may not be clear
whether these epitopes are typically presented natively (by tumor
or by phagocytic DCs). It may be desirable to translate high CD4+ T
response rates into therapeutic efficacy by improving
identification of truly presented HLA class II binding
epitopes.
[0008] The roles of gene expression, enzymatic cleavage, and
pathway/localization bias may have not been robustly quantified. It
may be unclear whether autophagy (HLA class II presentation by
tumor cells) or phagocytosis (HLA class II presentation of tumor
epitopes by APCs) is the more relevant pathway, although most
existing MS data may be presumed to derive from autophagy.
NetMHCIIpan may be the current prediction standard, but it may not
be regarded as accurate. Of the three HLA class II loci (DR, DP,
and DQ), data may only exist for certain common alleles of
HLA-DR.
[0009] There may be different data generation approaches for
learning the rules of HLA Class II presentation, including the
field standard and the proposed approach. The field standard may
comprise affinity measurements, which may be the basis for the
NetMHCIIpan predictor, providing low throughput and requiring
radioactive reagents, and it misses the role of processing. The
proposed approach may comprise mass spectrometry, where data from
cell lines/tissues/tumors may help determine processing rules for
autophagy and mono-allelic MS may enable determination of
allele-specific binding rules (multi-allelic MS data is presumed
overly complex for efficient learning (Bassani-Sternberg. MCP.
2018)).
[0010] There may be different ways to validate the new HLA class II
predictors: validation on held-out MS data, which may be default
setting; retrospective of vaccine studies (e.g. NT-001), where
immune monitoring data may assess vaccine peptide loading on APCs
rather than tumor presentation and data may be thinly stretched
across many different alleles; biochemical affinity measurements,
which may be configured to get measurements for discordantly
predicted peptides (only for 2-3 alleles); T cell inductions, which
may be configured to test the rates at which Neon-preferred and
NetMHCIIpan-preferred epitopes induce ex vivo T cell responses.
[0011] For validation through T cell inductions, the default
approach may comprise assessing neoORFs from TCGA that are
discordantly predicted, wherein induction materials may comprise
healthy donor APCs and T cells and induction and readout may be via
SLP (.about.15mer peptides). Random peptides may give a high rate
of responses and SLP may insufficiently address processing.
Possible solutions may comprise induction via mRNA.
[0012] The methods disclosed herein may comprise generating
LC-MS/MS mono-allelic data for the training of allele-specific
machine learning methods for epitope prediction. Such methods may
comprise increasing LC-MS/MS data quality utilizing a set of
quality metrics to stringently remove false positives that
increases the performance of a prediction model; identifying
allele-specific HLA class II binding cores from HLA-ligandome
LC-MS/MS datasets; utilizing machine learning algorithms to improve
HLA class II-ligand and epitope prediction; and/or identifying
biological variables that impact HLA class II-ligand presentation
and improve HLA class II epitope prediction, such as gene
expression, cleavability, gene bias, cellular localization, and
secondary structure.
[0013] Provided herein is a method comprising: (a) processing amino
acid information of a plurality of candidate peptide sequences
using a machine learning HLA peptide presentation prediction model
to generate a plurality of presentation predictions, wherein each
candidate peptide sequence of the plurality of candidate peptide
sequences is encoded by a genome or exome of a subject, wherein the
plurality of presentation predictions comprises an HLA presentation
prediction for each of the plurality of candidate peptide
sequences, wherein each HLA presentation prediction is indicative
of a likelihood that one or more proteins encoded by a class II HLA
allele of a cell of the subject can present a given candidate
peptide sequence of the plurality of candidate peptide sequences,
wherein the machine learning HLA peptide presentation prediction
model is trained using training data comprising sequence
information of sequences of training peptides identified by mass
spectrometry to be presented by an HLA protein expressed in
training cells; and (b) identifying, based at least on the
plurality of presentation predictions, a peptide sequence of the
plurality of peptide sequences as being presented by at least one
of the one or more proteins encoded by a class II HLA allele of a
cell of the subject; wherein the machine learning HLA peptide
presentation prediction model has a positive predictive value (PPV)
of at least 0.07 according to a presentation PPV determination
method.
[0014] Provided herein is a method comprising: (a) processing amino
acid information of a plurality of peptide sequences of encoded by
a genome or exome of a subject using a machine learning HLA peptide
binding prediction model to generate a plurality of binding
predictions, wherein the plurality of binding predictions comprises
an HLA binding prediction for each of the plurality of candidate
peptide sequences, each binding prediction indicative of a
likelihood that one or more proteins encoded by a class II HLA
allele of a cell of the subject binds to a given candidate peptide
sequence of the plurality of candidate peptide sequences, wherein
the machine learning HLA peptide binding prediction model is
trained using training data comprising sequence information of
sequences of peptides identified to bind to an HLA class II protein
or an HLA class II protein analog; and (b) identifying, based at
least on the plurality of binding predictions, a peptide sequence
of the plurality of peptide sequences that has a probability
greater than a threshold binding prediction probability value of
binding to at least one of the one or more proteins encoded by a
class II HLA allele of a cell of the subject; wherein the machine
learning HLA peptide binding prediction model has a positive
predictive value (PPV) of at least 0.1 according to a binding PPV
determination method.
[0015] In some embodiments, the machine learning HLA peptide
presentation prediction model is trained using training data
comprising sequence information of sequences of training peptides
identified by mass spectrometry to be presented by an HLA protein
expressed in training cells.
[0016] In some embodiments, the method comprises ranking, based on
the presentation predictions, at least two peptides identified as
being presented by at least one of the one or more proteins encoded
by a class II HLA allele of a cell of the subject.
[0017] In some embodiments, the method comprises selecting one or
more peptides of the two or more ranked peptides.
[0018] In some embodiments, the method comprises selecting one or
more peptides of the plurality that were identified as being
presented by at least one of the one or more proteins encoded by a
class II HLA allele of a cell of the subject.
[0019] In some embodiments, the method comprises selecting one or
more peptides of two or more peptides ranked based on the
presentation predictions.
[0020] In some embodiments, the machine learning HLA peptide
presentation prediction model has a positive predictive value (PPV)
of at least 0.07 when amino acid information of a plurality of test
peptide sequences are processed to generate a plurality of test
presentation predictions, each test presentation prediction
indicative of a likelihood that the one or more proteins encoded by
a class II HLA allele of a cell of the subject can present a given
test peptide sequence of the plurality of test peptide sequences,
wherein the plurality of test peptide sequences comprises at least
500 test peptide sequences comprising (i) at least one hit peptide
sequence identified by mass spectrometry to be presented by an HLA
protein expressed in cells and (ii) at least 499 decoy peptide
sequences contained within a protein encoded by a genome of an
organism, wherein the organism and the subject are the same
species, wherein the plurality of test peptide sequences comprises
a ratio of 1:499 of the at least one hit peptide sequence to the at
least 499 decoy peptide sequences and a top percentage of the
plurality of test peptide sequences are predicted to be presented
by the HLA protein expressed in cells by the machine learning HLA
peptide presentation prediction model.
[0021] In some embodiments, the machine learning HLA peptide
presentation prediction model has a positive predictive value (PPV)
of at least 0.1 when amino acid information of a plurality of test
peptide sequences are processed to generate a plurality of test
binding predictions, each test binding prediction indicative of a
likelihood that the one or more proteins encoded by a class II HLA
allele of a cell of the subject binds to a given test peptide
sequence of the plurality of test peptide sequences, wherein the
plurality of test peptide sequences comprises at least 20 test
peptide sequences comprising (i) at least one hit peptide sequence
identified by mass spectrometry to be presented by an HLA protein
expressed in cells and (ii) at least 19 decoy peptide sequences
contained within a protein comprising at least one peptide sequence
identified by mass spectrometry to be presented by an HLA protein
expressed in cells, such as a single HLA protein expressed in cells
(e.g., mono-allelic cells), wherein the plurality of test peptide
sequences comprises a ratio of 1:19 of the at least one hit peptide
sequence to the at least 19 decoy peptide sequences and a top
percentage of the plurality of test peptide sequences are predicted
to bind to the HLA protein expressed in cells by the machine
learning HLA peptide presentation prediction model.
[0022] In some embodiments, no amino acid sequence overlap exist
among the at least one hit peptide sequence and the decoy peptide
sequences.
[0023] In some embodiments, the machine learning HLA peptide
presentation prediction model has a positive predictive value (PPV)
of at least 0.08, 0.09, 0.1, 0.11, 0.12, 0.13, 0.14, 0.15, 0.16,
0.17, 0.18, 0.19, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27,
0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38,
0.39, 0.4, 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49,
0.5, 0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6,
0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.7, 0.71,
0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.8, 0.81, 0.82,
0.83, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.9, 0.91, 0.92, 0.93,
0.94, 0.95, 0.96, 0.97, 0.98 or 0.99.
[0024] In some embodiments, the at least one hit peptide sequence
comprises at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,
16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32,
33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49,
50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66,
67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83,
84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99 or
100 hit peptide sequences.
[0025] In some embodiments, the at least 499 decoy peptide
sequences comprises at least 500 600, 700, 800, 900, 1000, 1100,
1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900, 2000, 2100, 2200,
2300, 2400, 2500, 2600, 2700, 2800, 2900, 3000, 3100, 3200, 3300,
3400, 3500, 3600, 3700, 3800, 3900, 4000, 4100, 4200, 4300, 4400,
4500, 4600, 4700, 4800, 4900, 5000, 5100, 5200, 5300, 5400, 5500,
5600, 5700, 5800, 5900, 6000, 6100, 6200, 6300, 6400, 6500, 6600,
6700, 6800, 6900, 7000, 7100, 7200, 7300, 7400, 7500, 7600, 7700,
7800, 7900, 8000, 8100, 8200, 8300, 8400, 8500, 8600, 8700, 8800,
8900, 9000, 9100, 9200, 9300, 9400, 9500, 9600, 9700, 9800, 9900,
10000, 11000, 12000, 13000, 14000, 15000, 16000, 17000, 18000,
19000, 20000, 21000, 22000, 23000, 24000, 25000, 26000, 27000,
28000, 29000, 30000, 31000, 32000, 33000, 34000, 35000, 36000,
37000, 38000, 39000, 40000, 41000, 42000, 43000, 44000, 45000,
46000, 47000, 48000, 49000, 50000, 52500, 55000, 57500, 60000,
62500, 65000, 67500, 70000, 72500, 75000, 77500, 80000, 82500,
85000, 87500, 90000, 92500, 95000, 97500, 100000, 125000, 150000,
175000, 200000, 225000, 250000, 275000, 300000, 325000, 350000,
375000, 400000, 425000, 450000, 475000, 500000, 600000, 700000,
800000, 900000 or 1000000 decoy peptide sequences. One of skill in
the art is able to recognize that changing the ratio of hit: decoy
changes the PPV.
[0026] In some embodiments, the at least 500 test peptide sequences
comprises at least 600, 700, 800, 900, 1000, 1100, 1200, 1300,
1400, 1500, 1600, 1700, 1800, 1900, 2000, 2100, 2200, 2300, 2400,
2500, 2600, 2700, 2800, 2900, 3000, 3100, 3200, 3300, 3400, 3500,
3600, 3700, 3800, 3900, 4000, 4100, 4200, 4300, 4400, 4500, 4600,
4700, 4800, 4900, 5000, 5100, 5200, 5300, 5400, 5500, 5600, 5700,
5800, 5900, 6000, 6100, 6200, 6300, 6400, 6500, 6600, 6700, 6800,
6900, 7000, 7100, 7200, 7300, 7400, 7500, 7600, 7700, 7800, 7900,
8000, 8100, 8200, 8300, 8400, 8500, 8600, 8700, 8800, 8900, 9000,
9100, 9200, 9300, 9400, 9500, 9600, 9700, 9800, 9900, 10000, 11000,
12000, 13000, 14000, 15000, 16000, 17000, 18000, 19000, 20000,
21000, 22000, 23000, 24000, 25000, 26000, 27000, 28000, 29000,
30000, 31000, 32000, 33000, 34000, 35000, 36000, 37000, 38000,
39000, 40000, 41000, 42000, 43000, 44000, 45000, 46000, 47000,
48000, 49000, 50000, 52500, 55000, 57500, 60000, 62500, 65000,
67500, 70000, 72500, 75000, 77500, 80000, 82500, 85000, 87500,
90000, 92500, 95000, 97500, 100000, 125000, 150000, 175000, 200000,
225000, 250000, 275000, 300000, 325000, 350000, 375000, 400000,
425000, 450000, 475000, 500000, 600000, 700000, 800000, 900000 or
1000000 test peptide sequences.
[0027] In some embodiments, the top percentage is a top 0.20%,
0.30%, 0.40%, 0.50%, 0.60%, 0.70%, 0.80%, 0.90%, 1.00%, 1.10%,
1.20%, 1.30%, 1.40%, 1.50%, 1.60%, 1.70%, 1.80%, 1.90%, 2.00%,
2.10%, 2.20%, 2.30%, 2.40%, 2.50%, 2.60%, 2.70%, 2.80%, 2.90%,
3.00%, 3.10%, 3.20%, 3.30%, 3.40%, 3.50%, 3.60%, 3.70%, 3.80%,
3.90%, 4.00%, 4.10%, 4.20%, 4.30%, 4.40%, 4.50%, 4.60%, 4.70%,
4.80%, 4.90%, 5.00%, 5.10%, 5.20%, 5.30%, 5.40%, 5.50%, 5.60%,
5.70%, 5.80%, 5.90%, 6.00%, 6.10%, 6.20%, 6.30%, 6.40%, 6.50%,
6.60%, 6.70%, 6.80%, 6.90%, 7.00%, 7.10%, 7.20%, 7.30%, 7.40%,
7.50%, 7.60%, 7.70%, 7.80%, 7.90%, 8.00%, 8.10%, 8.20%, 8.30%,
8.40%, 8.50%, 8.60%, 8.70%, 8.80%, 8.90%, 9.00%, 9.10%, 9.20%,
9.30%, 9.40%, 9.50%, 9.60%, 9.70%, 9.80%, 9.90%, 10%, 11%, 12%,
13%, 14%, 15%, 16%, 17%, 18%, 19% or 20%.
[0028] In some embodiments, the at least one hit peptide sequence
comprises at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,
16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32,
33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49,
50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66,
67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83,
84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99 or
100 hit peptide sequences.
[0029] In some embodiments, the at least 19 decoy peptide sequences
comprises at least 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130,
140, 150, 160, 170, 180, 190, 200, 210, 220, 230, 240, 250, 260,
270, 280, 290, 300, 310, 320, 330, 340, 350, 360, 370, 380, 390,
400, 410, 420, 430, 440, 450, 460, 470, 480, 490, 500 600, 700,
800, 900, 1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800,
1900, 2000, 2100, 2200, 2300, 2400, 2500, 2600, 2700, 2800, 2900,
3000, 3100, 3200, 3300, 3400, 3500, 3600, 3700, 3800, 3900, 4000,
4100, 4200, 4300, 4400, 4500, 4600, 4700, 4800, 4900, 5000, 5100,
5200, 5300, 5400, 5500, 5600, 5700, 5800, 5900, 6000, 6100, 6200,
6300, 6400, 6500, 6600, 6700, 6800, 6900, 7000, 7100, 7200, 7300,
7400, 7500, 7600, 7700, 7800, 7900, 8000, 8100, 8200, 8300, 8400,
8500, 8600, 8700, 8800, 8900, 9000, 9100, 9200, 9300, 9400, 9500,
9600, 9700, 9800, 9900, 10000, 11000, 12000, 13000, 14000, 15000,
16000, 17000, 18000, 19000, 20000, 21000, 22000, 23000, 24000,
25000, 26000, 27000, 28000, 29000, 30000, 31000, 32000, 33000,
34000, 35000, 36000, 37000, 38000, 39000, 40000, 41000, 42000,
43000, 44000, 45000, 46000, 47000, 48000, 49000, 50000, 52500,
55000, 57500, 60000, 62500, 65000, 67500, 70000, 72500, 75000,
77500, 80000, 82500, 85000, 87500, 90000, 92500, 95000, 97500,
100000, 125000, 150000, 175000, 200000, 225000, 250000, 275000,
300000, 325000, 350000, 375000, 400000, 425000, 450000, 475000,
500000, 600000, 700000, 800000, 900000 or 1000000 decoy peptide
sequences.
[0030] In some embodiments, the at least 20 test peptide sequences
comprises at least wherein the at least 500 test peptide sequences
comprises at least 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130,
140, 150, 160, 170, 180, 190, 200, 210, 220, 230, 240, 250, 260,
270, 280, 290, 300, 310, 320, 330, 340, 350, 360, 370, 380, 390,
400, 410, 420, 430, 440, 450, 460, 470, 480, 490, 500 600, 700,
800, 900, 1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800,
1900, 2000, 2100, 2200, 2300, 2400, 2500, 2600, 2700, 2800, 2900,
3000, 3100, 3200, 3300, 3400, 3500, 3600, 3700, 3800, 3900, 4000,
4100, 4200, 4300, 4400, 4500, 4600, 4700, 4800, 4900, 5000, 5100,
5200, 5300, 5400, 5500, 5600, 5700, 5800, 5900, 6000, 6100, 6200,
6300, 6400, 6500, 6600, 6700, 6800, 6900, 7000, 7100, 7200, 7300,
7400, 7500, 7600, 7700, 7800, 7900, 8000, 8100, 8200, 8300, 8400,
8500, 8600, 8700, 8800, 8900, 9000, 9100, 9200, 9300, 9400, 9500,
9600, 9700, 9800, 9900, 10000, 11000, 12000, 13000, 14000, 15000,
16000, 17000, 18000, 19000, 20000, 21000, 22000, 23000, 24000,
25000, 26000, 27000, 28000, 29000, 30000, 31000, 32000, 33000,
34000, 35000, 36000, 37000, 38000, 39000, 40000, 41000, 42000,
43000, 44000, 45000, 46000, 47000, 48000, 49000, 50000, 52500,
55000, 57500, 60000, 62500, 65000, 67500, 70000, 72500, 75000,
77500, 80000, 82500, 85000, 87500, 90000, 92500, 95000, 97500,
100000, 125000, 150000, 175000, 200000, 225000, 250000, 275000,
300000, 325000, 350000, 375000, 400000, 425000, 450000, 475000,
500000, 600000, 700000, 800000, 900000 or 1000000 test peptide
sequences test peptide sequences.
[0031] In some embodiments, the top percentage is a top 5%, 6%, 7%,
8%, 9%, 10%, 11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%, 19%, 20%, 21%,
22%, 23%, 24%, 25%, 26%, 27%, 28%, 29%, 30%, 31%, 32%, 33%, 34%,
35%, 36%, 37%, 38%, 39%, or 40%.
[0032] In some embodiments, the PPV is greater than the respective
PPV of column 2 of Table 11 for the protein encoded by the
corresponding HLA allele of Table 11. In some embodiments, the PPV
is at least equal to the respective PPV of column 3 of Table 11 for
the protein encoded by the corresponding HLA allele of Table
11.
[0033] In some embodiments, the PPV is equal to or greater than the
respective PPV of column 2 of Table 12 for the protein encoded by
an HLA class II allele.
[0034] In some embodiments, the PPV is greater than the respective
PPV of column 2 of Table 16 for the protein encoded by an HLA class
II allele.
[0035] In some embodiments, the subject is a single subject.
[0036] In some embodiments, the subject is a mammal.
[0037] In some embodiments, the subject is a human.
[0038] In some embodiments, the training cells are cells expressing
a single protein encoded by a class II HLA allele of a cell of the
subject.
[0039] In some embodiments, the training cells are monoallelic HLA
cells, or cells expressing an HLA allele with an affinity tag.
[0040] In some embodiments, the cell of the subject comprises
cancer cells.
[0041] In some embodiments, the method is for identifying peptide
sequences.
[0042] In some embodiments, the method is for selecting peptide
sequences.
[0043] In some embodiments, the method is for preparing a cancer
therapy.
[0044] In some embodiments, the method is for preparing a
subject-specific cancer therapy.
[0045] In some embodiments, the method is for preparing a cancer
cell-specific cancer therapy.
[0046] In some embodiments, each peptide sequence of the plurality
of peptide sequences is associated with a cancer.
[0047] In some embodiments, at least one peptide sequence of the
plurality of peptide sequences is overexpressed by a cancer cell of
the subject.
[0048] In some embodiments, each peptide sequence of the plurality
of peptide sequences is overexpressed by a cancer cell of the
subject.
[0049] In some embodiments, at least one peptide sequence of the
plurality of peptide sequences is a cancer cell-specific
peptide.
[0050] In some embodiments, each peptide sequence of the plurality
of peptide sequences is a cancer cell-specific peptide.
[0051] In some embodiments, each peptide sequence of the plurality
of peptide sequences is expressed by a cancer cell of the
subject.
[0052] In some embodiments, at least one peptide sequence of the
plurality of peptide sequences is not encoded by a non-cancer cell
of the subject.
[0053] In some embodiments, each peptide sequence of the plurality
of peptide sequences is not encoded by a non-cancer cell of the
subject.
[0054] In some embodiments, at least one peptide sequence of the
plurality of peptide sequences is not expressed by a non-cancer
cell of the subject.
[0055] In some embodiments, each peptide sequence of the plurality
of peptide sequences is not expressed by a non-cancer cell of the
subject.
[0056] In some embodiments, the method comprises obtaining the
plurality of peptide sequences of the subject.
[0057] In some embodiments, the method comprises obtaining a
plurality of polynucleotide sequences of the subject.
[0058] In some embodiments, the method comprises obtaining a
plurality of polynucleotide sequences of the subject that encodes
the plurality of peptide sequences encoded by a genome or exome of
a subject, or by a pathogen or virus in the subject.
[0059] In some embodiments, the method comprises obtaining a
plurality of polynucleotide sequences of the subject that encodes
the plurality of peptide sequences encoded by a genome or exome of
a subject by a computer processor.
[0060] In some embodiments, the method comprises obtaining a
plurality of polynucleotide sequences of the subject by genomic or
exomic sequencing.
[0061] In some embodiments, the method comprises obtaining a
plurality of polynucleotide sequences of the subject by whole
genome sequencing or whole exome sequencing.
[0062] In some embodiments, processing comprises processing by a
computer processor
[0063] In some embodiments, processing comprises generating a
plurality of predictor variables based at least on the amino acid
information of the plurality of peptide sequences
[0064] In some embodiments, processing the plurality of predictor
variables using the machine-learning HLA-peptide presentation
prediction model.
[0065] In some embodiments, the that one or more proteins encoded
by a class II HLA allele of a cell of the subject are one or more
proteins encoded by a class II HLA allele that are expressed by the
subject.
[0066] In some embodiments, the that one or more proteins encoded
by a class II HLA allele of a cell of the subject are one or more
proteins encoded by a class II HLA allele that are expressed by
cancer cells of the subject.
[0067] In some embodiments, the that one or more proteins encoded
by a class II HLA allele of a cell of the subject is a single
protein encoded by a class II HLA allele of a cell of the
subject.
[0068] In some embodiments, the that one or more proteins encoded
by a class II HLA allele of a cell of the subject is two, three,
four, five or six or more proteins encoded by a class II HLA allele
of a cell of the subject.
[0069] In some embodiments, the that one or more proteins encoded
by a class II HLA allele of a cell of the subject is each protein
encoded by a class II HLA allele of a cell of the subject.
[0070] In some embodiments, the method further comprises
administering to the subject a composition comprising one or more
of the selected sub-set of peptide sequences.
[0071] In some embodiments, identifying the plurality of peptide
sequences comprises comparing DNA, RNA, or protein sequences from
cancer cells of the subject to DNA, RNA, or protein sequences from
normal cells of the subject, wherein each of the plurality of the
peptides comprise at least one mutation, which is present in the
cancer cell of the subject, and not present in the normal cell of
the subject.
[0072] In some embodiments, the machine-learning HLA-peptide
presentation prediction model comprises a plurality of predictor
variables identified at least based on the training data, wherein
the training data comprises training peptide sequence information
comprising amino acid position information, wherein the training
peptide sequence information is associated with the HLA protein
expressed in cells; and a function representing a relation between
the amino acid position information and the presentation likelihood
generated as output based on the amino acid position information
and the plurality of predictor variables.
[0073] In some embodiments, identifying comprises identifying,
based at least on the plurality of presentation predictions, a
peptide sequence of the plurality of peptide sequences that has a
probability greater than a threshold presentation prediction
probability value of being presented by at least one of the one or
more proteins encoded by a class II HLA allele of a cell of the
subject.
[0074] In some embodiments, one or more of the 0.2% of the
plurality of test peptide sequences predicted to be presented by
the by the machine learning HLA peptide presentation prediction
model has a probability greater than the threshold presentation
prediction probability value of being presented by at least one of
the one or more proteins encoded by a class II HLA allele of a cell
of the subject.
[0075] In some embodiments, each of the 0.2% of the plurality of
test peptide sequences predicted to be presented by the by the
machine learning HLA peptide presentation prediction model has a
probability greater than the threshold presentation prediction
probability value of being presented by at least one of the one or
more proteins encoded by a class II HLA allele of a cell of the
subject.
[0076] In some embodiments, the number of positives is constrained
to be equal to the number of hits.
[0077] In some embodiments, the mass spectrometry is mono-allelic
mass spectrometry.
[0078] In some embodiments, the peptides are presented by a HLA
protein expressed in cells through autophagy.
[0079] In some embodiments, the peptides are presented by a HLA
protein expressed in cells through phagocytosis.
[0080] In some embodiments, the plurality of predictor variables
comprises expression level predictor of the source protein
comprising the peptide.
[0081] In some embodiments, the plurality of predictor variables
comprises stability predictor of the source protein comprising the
peptide.
[0082] In some embodiments, the plurality of predictor variables
comprises degradation rate predictor of the source protein
comprising the peptide.
[0083] In some embodiments, the plurality of predictor variables
comprises protein cleavability predictor of the source protein
comprising the peptide.
[0084] In some embodiments, the plurality of predictor variables
comprises cellular or tissue localization predictor of the source
protein comprising the peptide.
[0085] In some embodiments, the plurality of predictor variables
comprises a predictor for the intracellular processing mode of the
source protein comprising the peptide, wherein processing mode of
the source protein comprises predictor for whether the source
protein is subject to autophagy, phagocytosis, and intracellular
transport, among others.
[0086] In some embodiments, quality of the training data is
increased by using a plurality of quality metrics.
[0087] In some embodiments, the plurality of quality metrics
comprises common contaminant peptide removal, high scored peak
intensity, high score, and high mass accuracy.
[0088] In some embodiments, a scored peak intensity is at least
50%.
[0089] In some embodiments, the scored peak intensity is at least
60%.
[0090] In some embodiments, a score is at least 7.
[0091] In some embodiments, a mass accuracy is at most 5 ppm.
[0092] In some embodiments, the peptides presented by an HLA
protein expressed in cells are peptides presented by a single
immunoprecipitated HLA protein expressed in cells.
[0093] In some embodiments, the peptides presented by an HLA
protein expressed in cells are peptides presented by a single
exogenous HLA protein expressed in cells.
[0094] In some embodiments, the peptides presented by an HLA
protein expressed in cells are peptides presented by a single
recombinant HLA protein expressed in cells.
[0095] In some embodiments, the plurality of predictor variables
comprises a peptide-HLA affinity predictor variable.
[0096] In some embodiments, the peptides presented by the HLA
protein comprise peptides identified by searching a no-enzyme
specificity without modification peptide database.
[0097] In some embodiments, the peptides presented by the HLA
protein comprise peptides identified by searching a peptide
database using a reversed-database search strategy.
[0098] In some embodiments, the HLA protein comprises an HLA-DR,
HLA-DQ, or an HLA-DP protein.
[0099] In some embodiments, the HLA protein comprises an HLA class
II protein selected from the group consisting of:
HLA-DPB1*01:01/HLA-DPA1*01:03, HLA-DPB1*02:01/HLA-DPA1*01:03,
HLA-DPB1*03:01/HLA-DPA1*01:03, HLA-DPB1*04:01/HLA-DPA1*01:03,
HLA-DPB1*04:02/HLA-DPA1*01:03, HLA-DPB1*06:01/HLA-DPA1*01:03,
HLA-DQB1*02:01/HLA-DQA1*05:01, HLA-DQB1*02:02/HLA-DQA1*02:01,
HLA-DQB1*06:02/HLA-DQA1*01:02, HLA-DQB1*06:04/HLA-DQA1*01:02,
HLA-DRB1*01:01, HLA-DRB1*01:02, HLA-DRB1*03:01, HLA-DRB1*03:02,
HLA-DRB1*04:01, HLA-DRB1*04:02, HLA-DRB1*04:03, HLA-DRB1*04:04,
HLA-DRB1*04:05, HLA-DRB1*04:07, HLA-DRB1*07:01, HLA-DRB1*08:01,
HLA-DRB1*08:02, HLA-DRB1*08:03, HLA-DRB1*08:04, HLA-DRB1*09:01,
HLA-DRB1*10:01, HLA-DRB1*11:01, HLA-DRB1*11:02, HLA-DRB1*11:04,
HLA-DRB1*12:01, HLA-DRB1*12:02, HLA-DRB1*13:01, HLA-DRB1*13:02,
HLA-DRB1*13:03, HLA-DRB1*14:01, HLA-DRB1*15:01, HLA-DRB1*15:02,
HLA-DRB1*15:03, HLA-DRB1*16:01, HLA-DRB3*01:01, HLA-DRB3*02:02,
HLA-DRB3*03:01, HLA-DRB4*01:01, HLA-DRB5*01:01.
[0100] In some embodiments, the HLA-DR is paired with paired with
DRA*01:01.
[0101] In some embodiments, the HLA protein is a HLA class II
protein selected from the group consisting of: DPA*01:03/DPB*04:01,
DRB1*01:01, DRB1*01:02, DRB1*03:01, DRB1*04:01, DRB1*04:02,
DRB1*04:04, DRB1*04:05, DRB1*07:01, DRB1*08:01, DRB1*08:02,
DRB1*08:03, DRB1*09:01, DRB1*11:01, DRB1*11:02, DRB1*11:04,
DRB1*12:01, DRB1*13:01, DRB1*13:02, DRB1*13:03, DRB1*14:01,
DRB1*15:01, DRB1*15:02, DRB1*15:03, DRB1*16:02, DRB3*01:01,
DRB3*02:01, DRB3*02:02, DRB3*03:01, DRB4*01:01, DRB4*01:03 and
DRB5*01:01.
[0102] In some embodiments, the HLA-DR protein comprises a
DRA*01:01 in the dimer.
[0103] In some embodiments, the HLA protein comprises an HLA-DP
protein selected from the group consisting of: DPB1*01:01,
DPB1*02:01, DPB1*02:02, DPB1*03:01, DPB1*04:01, DPB1*04:02,
DPB1*05:01, DPB1*06:01, DPB1*11:01, DPB1*13:01, DPB1*17:01.
[0104] In some embodiments, the HLA-DP protein is paired comprising
DPA1*01:03.
[0105] In some embodiments, the HLA protein comprises an HLA-DQ
protein complex selected from the group consisting of:
A1*01:01+B1*05:01, A1*01:02+B1*06:02, A1*01:02+B1*06:04,
A1*01:03+B1*06:03, A1*02:01+B1*02:02, A1*02:01+B1*03:03,
A1*03:01+B1*03:02, A1*03:03+B1*03:01, A1*05:01+B1*02:01 and
A1*05:05+B1*03:01.
[0106] In some embodiments, the peptides presented by the HLA
protein comprise peptides identified by comparing a MS/MS spectra
of the HLA-peptides with MS/MS spectra of one or more peptides or
proteins in a peptide or protein database.
[0107] In some embodiments, the mutation is selected from the group
consisting of a point mutation, a splice site mutation, a
frameshift mutation, a read-through mutation, and a gene fusion
mutation.
[0108] In some embodiments, the peptides presented by the HLA
protein have a length of from 15-40 amino acids.
[0109] In some embodiments, the peptides presented by the HLA
protein comprise peptides identified by identifying peptides
presented by an HLA protein by comparing a MS/MS spectra of the
HLA-peptides with MS/MS spectra of one or more peptides or proteins
in a peptide or protein database.
[0110] In some embodiments, the personalized cancer therapy further
comprises an adjuvant.
[0111] In some embodiments, the personalized cancer therapy further
comprises an immune checkpoint inhibitor.
[0112] In some embodiments, the training data comprises structured
data, time-series data, unstructured data, relational data, or any
combination thereof.
[0113] In some embodiments, the unstructured data comprises image
data.
[0114] In some embodiments, the relational data comprises data from
a customer system, an enterprise system, an operational system, a
website, web accessible application program interface (API), or any
combination thereof.
[0115] In some embodiments, the training data is uploaded to a
cloud-based database.
[0116] In some embodiments, the training is performed using
convolutional neural networks.
[0117] In some embodiments, the convolutional neural networks
comprise at least two convolutional layers.
[0118] In some embodiments, the convolutional neural networks
comprise at least one batch normalization step.
[0119] In some embodiments, the convolutional neural networks
comprise at least one spatial dropout step.
[0120] In some embodiments, the convolutional neural networks
comprise at least one global max pooling step.
[0121] In some embodiments, the convolutional neural networks
comprise at least one dense layer.
[0122] In some embodiments, identifying peptide sequences comprises
identifying peptide sequences with a mutation expressed in cancer
cells of a subject.
[0123] In some embodiments, identifying peptide sequences comprises
identifying peptide sequences not expressed in normal cells of a
subject.
[0124] In some embodiments, identifying peptide sequences comprises
identifying viral peptide sequences.
[0125] In some embodiments, identifying peptide sequences comprises
identifying overexpressed peptide sequences.
[0126] Provided herein is a method for identifying HLA class II
specific peptides for immunotherapy for a subject, comprising:
obtaining, by a computer processor, a candidate peptide comprising
an epitope, and a plurality of peptide sequences, each comprising
the epitope; processing, by a computer processor, amino acid
information of the plurality of peptide sequences using a
machine-learning HLA-peptide presentation prediction model to
generate a presentation prediction for each of the plurality of
peptide sequences to an immune cell, each presentation prediction
indicative of a likelihood that one or more proteins encoded by an
HLA class II allele can present a given peptide sequence of the
plurality of peptide sequences, wherein the machine-learning
HLA-peptide presentation prediction model is trained using training
data comprising sequence information of sequences of peptides
presented by an HLA protein expressed in cells and identified by
mass spectrometry; selecting a protein from the one or more
proteins encoded by the HLA class II allele of a cell of the
subject, predicted to bind to the candidate peptide by the
machine-learning HLA-peptide presentation prediction model, wherein
the protein has a probability greater than a threshold presentation
prediction probability value for presenting the candidate peptide
to an immune cell; contacting the candidate peptide with the
selected protein, such that the candidate peptide competes with a
placeholder peptide associated with the selected protein; and
identifying the candidate peptide as a peptide for immunotherapy
specific for the selected protein based on whether the candidate
peptide displaces the placeholder
[0127] In some embodiments, obtaining comprises identifying the
candidate peptide, wherein identifying the candidate peptide
comprises comparing DNA, RNA, or protein sequences from cancer
cells of the subject to DNA, RNA, or protein sequences from normal
cells of the subject.
[0128] In some embodiments, processing comprises identifying a
plurality of predictor variables based at least on the amino acid
information of the plurality of peptide sequences, and processing
the plurality of predictor variables using the machine-learning
HLA-peptide presentation prediction model.
[0129] In some embodiments, the machine-learning HLA-peptide
presentation prediction model comprises a plurality of predictor
variables identified at least based on the training data, wherein
the training data comprises: training peptide sequence information
comprising amino acid position information, wherein the training
peptide sequence information is associated with the HLA protein
expressed in cells; and a function representing a relation between
the amino acid position information and the presentation likelihood
generated as output based on the amino acid position information
and the plurality of predictor variables.
[0130] In some embodiments, the number of positives is constrained
to be equal to the number of hits.
[0131] In some embodiments, the mass spectrometry is mono-allelic
mass spectrometry.
[0132] In some embodiments, the plurality of predictor variables
comprises any one or more of: expression level predictor, stability
predictor, degradation rate predictor, cleavability predictor,
cellular or tissue localization predictor, and intracellular
processing mode comprising autophagy, phagocytosis, and
intracellular transport predictor, of the source protein comprising
the peptide.
[0133] In some embodiments, quality of the training data is
increased by using a plurality of quality metrics.
[0134] In some embodiments, the plurality of quality metrics
comprises common contaminant peptide removal, high scored peak
intensity, high score, and high mass accuracy.
[0135] In some embodiments, a scored peak intensity is at least
50%.
[0136] In some embodiments, the scored peak intensity is at least
60%.
[0137] In some embodiments, the placeholder peptide is a CLIP
peptide.
[0138] In some embodiments, the placeholder peptide is a CMV
peptide.
[0139] In some embodiments, the 3 method further comprises
measuring the IC50 of displacement of the placeholder peptide by
the target peptide.
[0140] In some embodiments, the IC50 of displacement of the
placeholder peptide by the target peptide is less than 500 nM.
[0141] In some embodiments, the at least one protein from the one
or more proteins encoded by the HLA class II allele of a cell of
the subject is an HLA class II tetramer or multimer.
[0142] In some embodiments, the target peptide is further
identified by mass spectrometry.
[0143] In some embodiments, the at least one protein encoded by the
HLA class II allele of a cell of the subject is a recombinant
protein.
[0144] In some embodiments, the at least one protein encoded by the
HLA class II allele of a cell of the subject is expressed in a
eukaryotic cell.
[0145] In some embodiments, the peptides are presented by a HLA
protein expressed in cells through autophagy.
[0146] In some embodiments, the peptides are presented by a HLA
protein expressed in cells through phagocytosis.
[0147] In some embodiments, the peptides presented by a HLA protein
expressed in cells are peptides presented by a single
immunoprecipitated HLA protein expressed in cells.
[0148] In some embodiments, the peptides presented by a HLA protein
expressed in cells are peptides presented by a single exogenous HLA
protein expressed in cells.
[0149] In some embodiments, the peptides presented by a HLA protein
expressed in cells are peptides presented by a single recombinant
HLA protein expressed in cells.
[0150] In some embodiments, the plurality of predictor variables
comprises a peptide-HLA affinity predictor variable.
[0151] In some embodiments, the peptides presented by the HLA
protein comprise peptides identified by searching a no-enzyme
specificity without modification peptide database.
[0152] In some embodiments, the peptides presented by the HLA
protein comprise peptides identified by searching a peptide
database using a reversed-database search strategy.
[0153] In some embodiments, the HLA protein comprises an HLA-DR,
HLA-DQ, or an HLA-DP protein.
[0154] In some embodiments, the immunotherapy is cancer
immunotherapy.
[0155] In some embodiments, the epitope is a cancer specific
epitope.
[0156] In some embodiments, the at least one protein encoded by the
HLA class II allele comprises at least an alpha 1 subunit and a
beta 1 subunit of the HLA protein, present in dimer form.
[0157] In some embodiments, the identity of the peptide is
known.
[0158] In some embodiments, the identity of the peptide is not
known.
[0159] In some embodiments, the identity of the peptide is
determined by mass spectrometry.
[0160] In some embodiments, peptide exchange assay comprises
detection of peptide fluorescent probes or tags.
[0161] In some embodiments, in the placeholder peptide is a CLIP
peptide. In some embodiments, the placeholder peptide has an amino
acid sequence of PVSKMRMATPLLMQA (SEQ ID NO: 1).
[0162] In some embodiments, the polynucleic acid construct
comprises an expression vector, further comprising one or more of:
a promoter, a secretion signal, dimerization factors, ribosomal
skipping sequence, one or more tags for purification and/or
detection.
[0163] In some embodiments, the placeholder peptide sequence is
encoded by a nucleic acid sequence within the vector.
[0164] In some embodiments, a sequence encoding a cleavable domain
is placed in between the sequence encoding the placeholder peptide
and the HLA beta1 peptide.
[0165] Provided herein is a method for assaying immunogenicity of a
MHC class II binding peptide, comprising: selecting a protein
encoded by an HLA class II allele predicted by a machine-learning
HLA-peptide presentation prediction model to bind to the MHC class
II binding peptide, wherein the machine-learning HLA-peptide
presentation prediction model is configured to generate a
presentation prediction for a given peptide sequence, the
presentation prediction indicative of a likelihood that one or more
proteins encoded by the HLA class II allele can present the given
peptide sequence, and wherein the protein has a probability greater
than a threshold presentation prediction probability value for
presenting the MHC class II binding peptide; contacting the peptide
with the selected protein such that the peptide competes with a
placeholder peptide associated with the selected protein, and
displaces the placeholder peptide, thereby forming a complex
comprising the HLA class II protein and the MHC class II binding
peptide; contacting the complex with a CD4+ T cell, and assaying
for one or more of activation parameters of the CD4+ T cell,
selected from the group consisting of: induction of a cytokine,
induction of a chemokine, and expression of a cell surface
marker.
[0166] In some embodiments, the HLA class II allele is a tetramer
or multimer.
[0167] In some embodiments, the cytokine is IL-2.
[0168] Provided herein is a method for inducing a CD4+ T cells
activation in a subject for cancer immunotherapy, the method
comprising: identifying a peptide sequence associated with cancer
and comprising a cancer mutation, wherein identifying the peptide
sequence comprises comparing DNA, RNA, or protein sequences from
cancer cells of the subject to DNA, RNA, or protein sequences from
normal cells of the subject; selecting a protein encoded by an HLA
class II allele that is normally expressed by a cell of the
subject, and predicted by a machine-learning HLA-peptide
presentation prediction model to bind to the peptide; wherein the
prediction model has a positive predictive value of at least 0.1 at
a recall rate of at least 0.1%, from 0.1%-50% or at most 50%. and
wherein the protein has a probability greater than a threshold
presentation prediction probability value for presenting the
identified peptide sequence; contacting the identified peptide with
the selected protein encoded by the HLA class II allele to verify
whether the identified peptide competes with a placeholder peptide
associated with the selected protein encoded by the HLA class II
allele to displace the placeholder peptide with an IC50 value of
less than 500 nM; optionally, purifying the identified peptide; and
administering an effective amount of a polypeptide comprising a
sequence of the identified peptide or a polynucleotide encoding the
polypeptide to the subject.
[0169] Provided herein is a method of screening a drug comprising a
polypeptide sequence for immunogenicity in a subject, comprising:
obtaining, by a computer processor, a plurality of peptide
sequences of the polypeptide sequence; processing, by a computer
processor, amino acid information of the plurality of peptide
sequences using a machine-learning HLA-peptide presentation
prediction model to generate a presentation prediction for each of
the plurality of peptide sequences, each presentation prediction
indicative of a likelihood that one or more proteins encoded by a
class I or II MHC allele of a cell of the subject can present an
epitope sequence of a given peptide sequence of the plurality of
peptide sequences, wherein the machine-learning HLA-peptide
presentation prediction model is trained using training data
comprising sequence information associated with the HLA protein
expressed in cells; determining or predicting that each of the
plurality of peptide sequences of the polypeptide sequence would
not be immunogenic to the subject based on the plurality of
presentation predictions; and administering to the subject a
composition comprising the drug.
[0170] Provided herein is a method for manufacturing HLA class II
tetramers or multimers by conjugation of four individual HLA
protein alpha1 and beta1 heterodimers, the method comprising:
expressing in a eukaryotic cell, a vector comprising a nucleic acid
sequence encoding an alpha chain and a beta chain of HLA protein, a
secretion signal, a biotinylation motif and at least one tag for
identification or for purification, such that each HLA protein
alpha 1 and beta1 heterodimers is secreted in dimerized state,
wherein the heterodimer is associated with a placeholder peptide,
purifying the secreted heterodimer from cell medium, validating the
peptide binding activity using peptide exchange assay, adding
streptavidin thereby conjugating heterodimers into tetramers,
purifying the tetramers and having a yield of greater than 1 mg/L.
Multimers, for example pentamers, hexamers or octamers can also be
likewise generated, which are equally contemplated herein.
[0171] In some embodiments, the vector comprises a CMV
promoter.
[0172] In some embodiments, the vector comprises a sequence
encoding a placeholder peptide linked via a cleavable site to the
beta 1 chain.
[0173] In some embodiments, peptide exchange assay involves prior
cleavage of the placeholder peptide from the beta chain.
[0174] In some embodiments, the cleavable site is a thrombin
cleavage site.
[0175] In some embodiments, peptide exchange assay is a FRET
assay.
[0176] In some embodiments, the purification is by any one of:
column chromatography, ion exchange chromatography, size exclusion
chromatography, affinity chromatography, or LC-MS.
[0177] Provided herein is an HLA class II tetramer or multimer
comprising either HLA-DR, or HLA-DP, or HLA-DQ heterodimers, each
heterodimer comprising an alpha and a beta chain, wherein the
heterodimer is purified and present at a concentration of greater
than 1 mg/L.
[0178] In some embodiments, the HLA class II tetramers are selected
from Table 8A-8C.
[0179] In some embodiments, the HLA class II tetramer comprises
heterodimer pairs selected from the group consisting of: an HLA-DR,
an HLA-DP, and an HLA-DQ protein.
[0180] In some embodiments, the HLA protein is an HLA class II
protein selected from the group consisting of:
HLA-DPB1*01:01/HLA-DPA1*01:03, HLA-DPB1*02:01/HLA-DPA1*01:03,
HLA-DPB1*03:01/HLA-DPA1*01:03, HLA-DPB1*04:01/HLA-DPA1*01:03,
HLA-DPB1*04:02/HLA-DPA1*01:03, HLA-DPB1*06:01/HLA-DPA1*01:03,
HLA-DQB1*02:01/HLA-DQA1*05:01, HLA-DQB1*02:02/HLA-DQA1*02:01,
HLA-DQB1*06:02/HLA-DQA1*01:02, HLA-DQB1*06:04/HLA-DQA1*01:02,
HLA-DRB1*01:01, HLA-DRB1*01:02, HLA-DRB1*03:01, HLA-DRB1*03:02,
HLA-DRB1*04:01, HLA-DRB1*04:02, HLA-DRB1*04:03, HLA-DRB1*04:04,
HLA-DRB1*04:05, HLA-DRB1*04:07, HLA-DRB1*07:01, HLA-DRB1*08:01,
HLA-DRB1*08:02, HLA-DRB1*08:03, HLA-DRB1*08:04, HLA-DRB1*09:01,
HLA-DRB1*10:01, HLA-DRB1*11:01, HLA-DRB1*11:02, HLA-DRB1*11:04,
HLA-DRB1*12:01, HLA-DRB1*12:02, HLA-DRB1*13:01, HLA-DRB1*13:02,
HLA-DRB1*13:03, HLA-DRB1*14:01, HLA-DRB1*15:01, HLA-DRB1*15:02,
HLA-DRB1*15:03, HLA-DRB1*16:01, HLA-DRB3*01:01, HLA-DRB3*02:02,
HLA-DRB3*03:01, HLA-DRB4*01:01, and HLA-DRB5*01:01.
[0181] In some embodiments, the heterodimer pair is expressed in a
eukaryotic cell.
[0182] In some embodiments, the heterodimer pairs are encoded by a
vector.
[0183] Provided herein is a vector, wherein the vector comprises a
nucleic acid sequence encoding an alpha chain and a beta chain of
HLA protein described herein, a secretion signal, a biotinylation
motif and at least one tag for identification or for purification,
such that each HLA protein alpha 1 and beta1 heterodimers is
secreted in dimerized state, wherein the secreted heterodimer is
optionally associated with a placeholder peptide.
[0184] Provided herein is a cell, comprising a vector described
herein.
[0185] In some embodiments, the HLA class II heterodimers are
secreted from eukaryotic cells into cell culture medium, which is
further purified by any one of: column chromatography, ion exchange
chromatography, size exclusion chromatography, affinity
chromatography or LC-MS.
[0186] Provided herein is a method of screening a drug comprising a
polypeptide sequence for immunogenicity in a subject, comprising:
obtaining, by a computer processor, a plurality of peptide
sequences of the polypeptide sequence; processing, by a computer
processor, amino acid information of the plurality of peptide
sequences using a machine-learning HLA-peptide presentation
prediction model to generate a presentation prediction for each of
the plurality of peptide sequences, each presentation prediction
indicative of a likelihood that one or more proteins encoded by a
class I or II MHC allele of a cell of the subject can present an
epitope sequence of a given peptide sequence of the plurality of
peptide sequences, wherein the machine-learning HLA-peptide
presentation prediction model is trained using training data
comprising sequence information of sequences of peptides presented
by a HLA protein expressed in cells and identified by mass
spectrometry; and determining or predicting that at least one of
the plurality of peptide sequences of the polypeptide sequence
would be immunogenic to the subject based on the plurality of
presentation predictions.
[0187] Provided herein is a method of screening a drug comprising a
polypeptide sequence for immunogenicity in a subject, the method
comprising: inputting amino acid information of peptide sequences
of the polypeptide sequence, using a computer processor, into a
machine-learning HLA-peptide presentation prediction model to
generate a set of presentation predictions for the peptide
sequences, each presentation prediction representing a probability
that one or more proteins encoded by a class I or II MHC allele of
a cell of the subject will present an epitope sequence of a given
peptide sequence; wherein the machine-learning HLA-peptide
presentation prediction model comprises: a plurality of predictor
variables identified at least based on training data, wherein the
training data comprises: sequence information of sequences of
peptides presented by a HLA protein expressed in cells and
identified by mass spectrometry; training peptide sequence
information comprising amino acid position information, wherein the
training peptide sequence information is associated with the HLA
protein expressed in cells; and a function representing a relation
between the amino acid position information received as input and
the presentation likelihood generated as output based on the amino
acid position information and the predictor variables; determining
or predicting that each of the peptide sequences of the polypeptide
sequence would not be immunogenic to the subject based on the set
of presentation predictions; and administering to the subject a
composition comprising the drug.
[0188] Provided herein is a method of screening a drug comprising a
polypeptide sequence for immunogenicity in a subject, the method
comprising: inputting amino acid information of peptide sequences
of the polypeptide sequence, using a computer processor, into a
machine-learning HLA-peptide presentation prediction model to
generate a set of presentation predictions for the peptide
sequences, each presentation prediction representing a probability
that one or more proteins encoded by a class I or II MHC allele of
a cell of the subject will present an epitope sequence of a given
peptide sequence; wherein the machine-learning HLA-peptide
presentation prediction model comprises: a plurality of predictor
variables identified at least based on training data; wherein the
training data comprises: sequence information of sequences of
peptides presented by a HLA protein expressed in cells and
identified by mass spectrometry; training peptide sequence
information comprising amino acid position information, wherein the
training peptide sequence information is associated with the HLA
protein expressed in cells; and a function representing a relation
between the amino acid position information received as input and
the presentation likelihood generated as output based on the amino
acid position information and the predictor variables; determining
or predicting that at least one of the peptide sequences of the
polypeptide sequence would be immunogenic to the subject based on
the set of presentation predictions.
[0189] Provided herein is a method of screening a drug comprising a
polypeptide sequence for immunogenicity in a subject, comprising:
obtaining, by a computer processor, a plurality of peptide
sequences of the polypeptide sequence; processing, by a computer
processor, amino acid information of the plurality of peptide
sequences using a machine-learning HLA-peptide presentation
prediction model to generate a presentation prediction for each of
the plurality of peptide sequences, each presentation prediction
indicative of a likelihood that one or more proteins encoded by a
class I or II MHC allele of a cell of the subject can present an
epitope sequence of a given peptide sequence of the plurality of
peptide sequences, wherein the machine-learning HLA-peptide
presentation prediction model is trained using training data
comprising sequence information associated with the HLA protein
expressed in cells; determining or predicting that each of the
plurality of peptide sequences of the polypeptide sequence would
not be immunogenic to the subject based on the plurality of
presentation predictions; and administering to the subject a
composition comprising the drug.
[0190] In some embodiments, the method further comprises deciding
not to administer the drug to the subject.
[0191] In some embodiments, the drug comprises an antibody or
binding fragment thereof.
[0192] In some embodiments, the peptide sequences of the
polypeptide sequence have a length of 8, 9, 10, 11, or 12 amino
acids, and wherein the protein encoded by a class I or II MHC
allele of a cell of the subject is a protein encoded by a class I
MHC allele of a cell of the subject.
[0193] In some embodiments, the peptide sequences of the
polypeptide sequence have a length of 15, 16, 17, 18, 19, 20, 21,
22, 23, 24, or 25 amino acids, and wherein the protein encoded by a
class I or II MHC allele of a cell of the subject is a protein
encoded by a class II MHC allele of a cell of the subject.
[0194] Provided herein is a method of treating a subject with an
autoimmune disease or condition comprising: (a) identifying or
predicting an epitope of an expressed protein presented by a class
I or II MHC of a cell of the subject, wherein a complex comprising
the identified or predicted epitope and the class I or II MHC is
targeted by a CD8 or CD4 T cell of the subject; (b) identifying a T
cell receptor (TCR) that binds to the complex; (c) expressing the
TCR in a regulatory T cell from the subject or an allogeneic
regulatory T cell; and (d) administering the regulatory T cell
expressing the TCR to the subject.
[0195] In some embodiments, the autoimmune disease or condition is
diabetes.
[0196] In some embodiments, the cell is an islet cell.
[0197] Provided herein is a method of treating a subject with an
autoimmune disease or condition, comprising administering to the
subject a regulatory T cell expressing a T cell receptor (TCR) that
binds to a complex comprising: (i) an epitope of an expressed
protein identified or predicted to be presented by a class I or II
MHC of a cell of the subject, and (ii) the class I or II MHC,
wherein the complex is targeted by a CD8 or CD4 T cell of the
subject.
[0198] Provided herein is a computer system for identifying peptide
sequences for a personalized cancer therapy of a subject,
comprising: a database that is configured to store a plurality of
peptide sequences of the subject; and one or more computer
processors operatively coupled to said database, wherein said one
or more computer processors are individually collectively
programmed to: process amino acid information of the plurality of
peptide sequences using a machine-learning HLA-peptide presentation
prediction model to generate a presentation prediction for each of
the plurality of peptide sequences, each presentation prediction
indicative of a likelihood that one or more proteins encoded by a
class II MHC allele of a cell of the subject can present a given
peptide sequence of the plurality of peptide sequences, wherein the
machine-learning HLA-peptide presentation prediction model is
trained using training data comprising sequence information of
sequences of peptides presented by an HLA protein expressed in
cells and identified by mass spectrometry; and select a subset of
the plurality of peptide sequences for the personalized cancer
therapy of the subject based at least on the plurality of
presentation predictions.
[0199] Provided herein is a computer system for identifying HLA
class II specific peptides for immunotherapy for a subject,
comprising: a database that is configured to store a candidate
peptide comprising an epitope, and a plurality of peptide
sequences, each comprising the epitope; and one or more computer
processors operatively coupled to said database, wherein said one
or more computer processors are individually collectively
programmed to: process amino acid information of the plurality of
peptide sequences a machine-learning HLA-peptide presentation
prediction model to generate a presentation prediction for each of
the plurality of peptide sequences to an immune cell, each
presentation prediction indicative of a likelihood that one or more
proteins encoded by an HLA class II allele can present a given
peptide sequence of the plurality of peptide sequences, wherein the
machine-learning HLA-peptide presentation prediction model is
trained using training data comprising sequence information of
sequences of peptides presented by an HLA protein expressed in
cells and identified by mass spectrometry; select a protein from
the one or more proteins encoded by the HLA class II allele of a
cell of the subject, predicted to bind to the candidate peptide by
the machine-learning HLA-peptide presentation prediction model,
wherein the protein has a probability greater than a threshold
presentation prediction probability value for presenting the
candidate peptide to an immune cell; and identify the candidate
peptide as a peptide for immunotherapy specific for the selected
protein based on whether the candidate peptide displaces the
placeholder peptide, upon contacting the candidate peptide with the
selected protein, such that the candidate peptide competes with a
placeholder peptide associated with the selected protein.
[0200] Provided herein is a computer system for screening a drug
comprising a polypeptide sequence for immunogenicity in a subject,
comprising: a database that is configured to store a plurality of
peptide sequences of the polypeptide sequence; and one or more
computer processors operatively coupled to said database, wherein
said one or more computer processors are individually collectively
programmed to: process amino acid information of the plurality of
peptide sequences using a machine-learning HLA-peptide presentation
prediction model to generate a presentation prediction for each of
the plurality of peptide sequences, each presentation prediction
indicative of a likelihood that one or more proteins encoded by a
class I or II MHC allele of a cell of the subject can present an
epitope sequence of a given peptide sequence of the plurality of
peptide sequences, wherein the machine-learning HLA-peptide
presentation prediction model is trained using training data
comprising sequence information associated with the HLA protein
expressed in cells; and determine or predict that each of the
plurality of peptide sequences of the polypeptide sequence would
not be immunogenic to the subject based on the plurality of
presentation predictions, wherein a composition comprising the drug
is administered to the subject.
[0201] Provided herein is a computer system for screening a drug
comprising a polypeptide sequence for immunogenicity in a subject,
comprising: a database that is configured to store a plurality of
peptide sequences of the polypeptide sequence; and one or more
computer processors operatively coupled to said database, wherein
said one or more computer processors are individually collectively
programmed to: process amino acid information of the plurality of
peptide sequences using a machine-learning HLA-peptide presentation
prediction model to generate a presentation prediction for each of
the plurality of peptide sequences, each presentation prediction
indicative of a likelihood that one or more proteins encoded by a
class I or II MHC allele of a cell of the subject can present an
epitope sequence of a given peptide sequence of the plurality of
peptide sequences, wherein the machine-learning HLA-peptide
presentation prediction model is trained using training data
comprising sequence information of sequences of peptides presented
by a HLA protein expressed in cells and identified by mass
spectrometry; and determine or predict that at least one of the
plurality of peptide sequences of the polypeptide sequence would be
immunogenic to the subject based on the plurality of presentation
predictions.
[0202] Provided herein is a non-transitory computer readable medium
comprising machine-executable code that, upon execution by one or
more computer processors, implements a method for identifying
peptide sequences for a personalized cancer therapy of a subject,
said method comprising: obtaining a plurality of peptide sequences
of the subject; processing amino acid information of the plurality
of peptide sequences using a machine-learning HLA-peptide
presentation prediction model to generate a presentation prediction
for each of the plurality of peptide sequences, each presentation
prediction indicative of a likelihood that one or more proteins
encoded by a class II MHC allele of a cell of the subject can
present a given peptide sequence of the plurality of peptide
sequences, wherein the machine-learning HLA-peptide presentation
prediction model is trained using training data comprising sequence
information of sequences of peptides presented by an HLA protein
expressed in cells and identified by mass spectrometry; and
selecting a subset of the plurality of peptide sequences for the
personalized cancer therapy of the subject based at least on the
plurality of presentation predictions.
[0203] Provided herein is a non-transitory computer readable medium
comprising machine-executable code that, upon execution by one or
more computer processors, implements a method for identifying HLA
class II specific peptides for immunotherapy for a subject,
comprising: obtaining a candidate peptide comprising an epitope,
and a plurality of peptide sequences, each comprising the epitope;
processing amino acid information of the plurality of peptide
sequences a machine-learning HLA-peptide presentation prediction
model to generate a presentation prediction for each of the
plurality of peptide sequences to an immune cell, each presentation
prediction indicative of a likelihood that one or more proteins
encoded by an HLA class II allele can present a given peptide
sequence of the plurality of peptide sequences, wherein the
machine-learning HLA-peptide presentation prediction model is
trained using training data comprising sequence information of
sequences of peptides presented by an HLA protein expressed in
cells and identified by mass spectrometry; selecting a protein from
the one or more proteins encoded by the HLA class II allele of a
cell of the subject, predicted to bind to the candidate peptide by
the machine-learning HLA-peptide presentation prediction model,
wherein the protein has a probability greater than a threshold
presentation prediction probability value for presenting the
candidate peptide to an immune cell; and identifying the candidate
peptide as a peptide for immunotherapy specific for the selected
protein based on whether the candidate peptide displaces the
placeholder peptide, upon contacting the candidate peptide with the
selected protein, such that the candidate peptide competes with a
placeholder peptide
[0204] Provided herein is a non-transitory computer readable medium
comprising machine-executable code that, upon execution by one or
more computer processors, implements a method of screening a drug
comprising a polypeptide sequence for immunogenicity in a subject,
comprising: obtaining a plurality of peptide sequences of the
polypeptide sequence; processing amino acid information of the
plurality of peptide sequences using a machine-learning HLA-peptide
presentation prediction model to generate a presentation prediction
for each of the plurality of peptide sequences, each presentation
prediction indicative of a likelihood that one or more proteins
encoded by a class I or II MHC allele of a cell of the subject can
present an epitope sequence of a given peptide sequence of the
plurality of peptide sequences, wherein the machine-learning
HLA-peptide presentation prediction model is trained using training
data comprising sequence information associated with the HLA
protein expressed in cells; and determining or predicting that each
of the plurality of peptide sequences of the polypeptide sequence
would not be immunogenic to the subject based on the plurality of
presentation predictions, wherein a composition comprising the drug
is administered to the subject.
[0205] Provided herein is a non-transitory computer readable medium
comprising machine-executable code that, upon execution by one or
more computer processors, implements a method of screening a drug
comprising a polypeptide sequence for immunogenicity in a subject,
comprising: obtaining a plurality of peptide sequences of the
polypeptide sequence; processing amino acid information of the
plurality of peptide sequences using a machine-learning HLA-peptide
presentation prediction model to generate a presentation prediction
for each of the plurality of peptide sequences, each presentation
prediction indicative of a likelihood that one or more proteins
encoded by a class I or II MHC allele of a cell of the subject can
present an epitope sequence of a given peptide sequence of the
plurality of peptide sequences, wherein the machine-learning
HLA-peptide presentation prediction model is trained using training
data comprising sequence information of sequences of peptides
presented by a HLA protein expressed in cells and identified by
mass spectrometry; and determining or predicting that at least one
of the plurality of peptide sequences of the polypeptide sequence
would be immunogenic to the subject based on the plurality of
presentation predictions.
[0206] Provided herein is a method comprising: processing amino
acid information of a plurality of candidate peptide sequences
using a machine learning HLA peptide presentation prediction model
to generate a plurality of presentation predictions, wherein each
candidate peptide sequences of the plurality is encoded by a genome
or exome of a subject, wherein the plurality of presentation
predictions comprises an HLA presentation prediction for each of
the plurality of candidate peptide sequences, wherein each
presentation prediction indicative of a likelihood that one or more
proteins encoded by a class II HLA allele of a cell of the subject
can present a given candidate peptide sequence of the plurality,
wherein the machine learning HLA peptide presentation prediction
model is trained using training data comprising sequence
information of sequences of training peptides identified by mass
spectrometry to be presented by an HLA protein expressed in
training cells; and identifying, based at least on the plurality of
presentation predictions, a peptide sequence of the plurality of
peptide sequences that has a probability greater than a threshold
presentation prediction probability value of being presented by at
least one of the one or more proteins encoded by a class II HLA
allele of a cell of the subject; wherein the machine learning HLA
peptide presentation prediction model has a positive predictive
value (PPV) of at least 0.07 when amino acid information of a
plurality of test peptide sequences are processed to generate a
plurality of test presentation predictions, each test presentation
prediction indicative of a likelihood that the one or more proteins
encoded by a class II HLA allele of a cell of the subject can
present a given test peptide sequence of the plurality of test
peptide sequences, wherein the plurality of test peptide sequences
comprises at least 500 test peptide sequences comprising (i) at
least one hit peptide sequence identified by mass spectrometry to
be presented by an HLA protein expressed in cells and (ii) at least
499 decoy peptide sequences contained within a protein encoded by a
genome of an organism, wherein the organism and the subject are the
same species, wherein the plurality of test peptide sequences
comprises a ratio of 1:499 of the at least one hit peptide sequence
to the at least 499 decoy peptide sequences and 0.2% of the
plurality of test peptide sequences are predicted to be presented
by the HLA protein expressed in cells by the machine learning HLA
peptide presentation prediction model.
[0207] Provided herein is a method comprising: processing amino
acid information of a plurality of peptide sequences of encoded by
a genome or exome of a subject using a machine-learning HLA-peptide
binding prediction model to generate a plurality of binding
predictions, wherein the plurality of binding predictions comprises
an HLA binding prediction for each of the plurality of candidate
peptide sequences, each binding prediction indicative of a
likelihood that one or more proteins encoded by a class II HLA
allele of a cell of the subject binds to a given candidate peptide
sequence of the plurality of candidate peptide sequences, wherein
the machine learning HLA peptide binding prediction model is
trained using training data comprising sequence information of
sequences of peptides identified to bind to an HLA class II protein
or an HLA class II protein analog; and identifying, based at least
on the plurality of binding predictions, a peptide sequence of the
plurality of peptide sequences that has a probability greater than
a threshold binding prediction probability value of binding to at
least one of the one or more proteins encoded by a class II HLA
allele of a cell of the subject; wherein the machine learning HLA
peptide binding prediction model has a positive predictive value
(PPV) of at least 0.1 when amino acid information of a plurality of
test peptide sequences are processed to generate a plurality of
test binding predictions, each test binding prediction indicative
of a likelihood that the one or more proteins encoded by a class II
HLA allele of a cell of the subject binds to a given test peptide
sequence of the plurality of test peptide sequences, wherein the
plurality of test peptide sequences comprises at least 50 test
peptide sequences comprising (i) at least one hit peptide sequence
identified by mass spectrometry to be presented by an HLA protein
expressed in cells and (ii) at least 19 decoy peptide sequences
contained within a protein comprising a peptide sequence identified
by mass spectrometry to be presented by an HLA protein expressed in
cells, wherein the organism and the subject are the same species,
wherein the plurality of test peptide sequences comprises a ratio
of 1:19 of the at least one hit peptide sequence to the at least 19
decoy peptide sequences and 5% of the plurality of test peptide
sequences are predicted to bind to the HLA protein expressed in
cells by the machine learning HLA peptide presentation prediction
model.
[0208] In some embodiments, the machine learning HLA peptide
presentation prediction model is trained using training data
comprising sequence information of sequences of training peptides
identified by mass spectrometry to be presented by an HLA protein
expressed in training cells
[0209] In some embodiments, one or more of the 0.2% of the
plurality of test peptide sequences predicted to be presented by
the by the machine learning HLA peptide presentation prediction
model has a probability greater than the threshold presentation
prediction probability value of being presented by at least one of
the one or more proteins encoded by a class II HLA allele of a cell
of the subject.
[0210] In some embodiments, each of the 0.2% of the plurality of
test peptide sequences predicted to be presented by the by the
machine learning HLA peptide presentation prediction model has a
probability greater than the threshold presentation prediction
probability value of being presented by at least one of the one or
more proteins encoded by a class II HLA allele of a cell of the
subject.
[0211] In some embodiments, the PPV is greater than the respective
PPV of column 2 of Table 11 for the protein encoded by the
corresponding HLA allele of Table 13. In some embodiments, the PPV
is at least equal to the respective PPV of column 3 of Table 11 for
the protein encoded by the corresponding HLA allele of Table
11.
[0212] In some embodiments, the PPV is greater than the respective
PPV of column 2 of Table 12 for the protein encoded by an HLA class
II allele.
[0213] In some embodiments, the PPV is at least equal to the
respective PPV of column 2 of Table 16 for the protein encoded by
the corresponding HLA allele of Table 16.
[0214] Provided herein is a method for preparing a personalized
cancer therapy, the method comprising: identifying peptide
sequences, wherein the peptide sequences are associated with
cancer, wherein identifying comprises comparing DNA, RNA or protein
sequences from the cancer cells of the subject to DNA, RNA or
protein sequences from the normal cells of the subject; inputting
amino acid position information of the peptide sequences
identified, using a computer processor, into a machine-learning
HLA-peptide presentation prediction model to generate a set of
presentation predictions for the peptide sequences identified, each
presentation prediction representing a probability that one or more
proteins encoded by an HLA class II allele of a cell of the subject
will present a given sequence of a peptide sequence identified;
wherein the machine-learning HLA-peptide presentation prediction
model comprises: a plurality of predictor variables identified at
least based on training data wherein the training data comprises:
sequence information of sequences of peptides presented by an HLA
protein expressed in cells and identified by mass spectrometry;
training peptide sequence information comprising amino acid
position information, wherein the training peptide sequence
information is associated with the HLA protein expressed in cells;
and a function representing a relation between the amino acid
position information received as input and the presentation
likelihood generated as output based on the amino acid position
information and the predictor variables; and selecting a subset of
the peptide sequences identified based on the set of presentation
predictions for preparing the personalized cancer therapy; wherein
the prediction model has a positive predictive value of at least
0.1 at a recall rate of at least 0.1%, from 0.1%-50% or at the most
50%.
[0215] Provided herein is a method comprising training a
machine-learning HLA-peptide presentation prediction model, wherein
training comprises inputting amino acid position information
sequences of HLA-peptides isolated from one or more HLA-peptide
complexes from a cell expressing an HLA class II allele into the
HLA-peptide presentation prediction model using a computer
processor; the machine-learning HLA-peptide presentation prediction
model comprising: a plurality of predictor variables identified at
least based on training data that comprises: sequence information
of sequences of peptides presented by an HLA protein expressed in
cells and identified by mass spectrometry; training peptide
sequence information comprising amino acid position information of
training peptides, wherein the training peptide sequence
information is associated with the HLA protein expressed in cells;
and a function representing a relation between the amino acid
position information received as input and a presentation
likelihood generated as output based on the amino acid position
information and the predictor variables.
[0216] In some embodiments, the presentation model has a positive
predictive value of at least 0.25 at a recall rate at least 0.1%,
from 0.1%-50% or at the most 50%.
[0217] In some embodiments, the presentation model has a positive
predictive value of at least 0.4 at a recall rate of at least 0.1%,
from 0.1%-50% or at the most 50%.
[0218] In some embodiments, the presentation model has a positive
predictive value of at least 0.6 at a recall rate of at least 0.1%,
from 0.1%-50% or at the most 50%.
[0219] In some embodiments, the mass spectrometry is mono-allelic
mass spectrometry.
[0220] In some embodiments, the peptides are presented by an HLA
protein expressed in cells through autophagy.
[0221] In some embodiments, the peptides are presented by an HLA
protein expressed in cells through phagocytosis.
[0222] In some embodiments, quality of the training data is
increased by using a plurality of quality metrics.
[0223] In some embodiments, the plurality of quality metrics
comprises common contaminant peptide removal, high scored peak
intensity, high score, and high mass accuracy.
[0224] In some embodiments, the scored peak intensity is at least
50%.
[0225] In some embodiments, the scored peak intensity is at least
60%.
[0226] In some embodiments, a score is at least 7.
[0227] In some embodiments, a mass accuracy is at most 5 ppm.
[0228] In some embodiments, a mass accuracy is at most 2 ppm.
[0229] In some embodiments, a backbone cleavage score is at least
5.
[0230] In some embodiments, a backbone cleavage score is at least
8.
[0231] In some embodiments, the peptides presented by an HLA
protein expressed in cells are peptides presented by a single
immunoprecipitated HLA protein expressed in cells.
[0232] In some embodiments, the peptides presented by an HLA
protein expressed in cells are peptides presented by a single
exogenous HLA protein expressed in cells.
[0233] In some embodiments, the peptides presented by an HLA
protein expressed in cells are peptides presented by a single
recombinant HLA protein expressed in cells.
[0234] In some embodiments, the plurality of predictor variables
comprises a peptide-HLA affinity predictor variable.
[0235] In some embodiments, the plurality of predictor variables
comprises a source protein expression level predictor variable.
[0236] In some embodiments, the plurality of predictor variables
comprises a peptide cleavability predictor variable.
[0237] In some embodiments, the training peptide sequence
information comprises sequences from the peptides presented by the
HLA protein, which comprise peptides identified by searching a
no-enzyme specificity without modification to a peptide database.
In some embodiments, the peptides presented by the HLA protein
comprise peptides identified by searching the de novo peptide
sequencing tools.
[0238] In some embodiments, the peptides presented by the HLA
protein comprise peptides identified by searching a peptide
database using a reversed-database search strategy.
[0239] In some embodiments, the HLA protein comprises an HLA-DR,
and HLA-DP or an HLA-DQ protein.
[0240] In some embodiments, the HLA protein comprises an HLA-DR
protein selected from the group consisting of an HLA-DR, and HLA-DP
or an HLA-DQ protein. In some embodiments, the HLA protein
comprises an HLA-DR protein selected from the group consisting of:
HLA-DPB1*01:01/HLA-DPA1*01:03, HLA-DPB1*02:01/HLA-DPA1*01:03,
HLA-DPB1*03:01/HLA-DPA1*01:03, HLA-DPB1*04:01/HLA-DPA1*01:03,
HLA-DPB1*04:02/HLA-DPA1*01:03, HLA-DPB1*06:01/HLA-DPA1*01:03,
HLA-DQB1*02:01/HLA-DQA1*05:01, HLA-DQB1*02:02/HLA-DQA1*02:01,
HLA-DQB1*06:02/HLA-DQA1*01:02, HLA-DQB1*06:04/HLA-DQA1*01:02,
HLA-DRB1*01:01, HLA-DRB1*01:02, HLA-DRB1*03:01, HLA-DRB1*03:02,
HLA-DRB1*04:01, HLA-DRB1*04:02, HLA-DRB1*04:03, HLA-DRB1*04:04,
HLA-DRB1*04:05, HLA-DRB1*04:07, HLA-DRB1*07:01, HLA-DRB1*08:01,
HLA-DRB1*08:02, HLA-DRB1*08:03, HLA-DRB1*08:04, HLA-DRB1*09:01,
HLA-DRB1*10:01, HLA-DRB1*11:01, HLA-DRB1*11:02, HLA-DRB1*11:04,
HLA-DRB1*12:01, HLA-DRB1*12:02, HLA-DRB1*13:01, HLA-DRB1*13:02,
HLA-DRB1*13:03, HLA-DRB1*14:01, HLA-DRB1*15:01, HLA-DRB1*15:02,
HLA-DRB1*15:03, HLA-DRB1*16:01, HLA-DRB3*01:01, HLA-DRB3*02:02,
HLA-DRB3*03:01, HLA-DRB4*01:01, and HLA-DRB5*01:01.
[0241] In some embodiments, the peptides presented by the HLA
protein comprise peptides identified by comparing MS/MS spectra of
the HLA-peptides with MS/MS spectra of one or more HLA-peptides in
a peptide database.
[0242] In some embodiments, the mutation is selected from the group
consisting of a point mutation, a splice site mutation, a
frameshift mutation, a read-through mutation, and a gene fusion
mutation.
[0243] In some embodiments, the peptides presented by the HLA
protein have a length of 15-40 amino acids.
[0244] In some embodiments, the peptides presented by the HLA
protein comprise peptides identified by (a) isolating one or more
HLA complexes from a cell line expressing a single HLA class II
allele; (b) isolating one or more HLA-peptides from the one or more
isolated HLA complexes; (c) obtaining MS/MS spectra for the one or
more isolated HLA-peptides; and (d) obtaining a peptide sequence
that corresponds to the MS/MS spectra of the one or more isolated
HLA-peptides from a peptide database; wherein one or more sequences
obtained from step (d) identifies the sequence of the one or more
isolated HLA-peptides.
[0245] In some embodiments, the personalized cancer therapy further
comprises an adjuvant.
[0246] In some embodiments, the personalized cancer therapy further
comprises an immune checkpoint inhibitor.
[0247] In some embodiments, the training data comprises structured
data, time-series data, unstructured data, relational data, or any
combination thereof.
[0248] In some embodiments, the unstructured data comprises image
data.
[0249] In some embodiments, the relational data comprises data from
a customer system, an enterprise system, an operational system, a
website, web accessible application program interface (API), or any
combination thereof.
[0250] In some embodiments, the training data is uploaded to a
cloud-based database.
[0251] In some embodiments, the training is performed using
convolutional neural networks.
[0252] In some embodiments, the convolutional neural networks
comprise at least two convolutional layers.
[0253] In some embodiments, the convolutional neural networks (CNN)
comprise at least one batch normalization step.
[0254] In some embodiments, the convolutional neural networks
comprise at least one spatial dropout step.
[0255] In some embodiments, the convolutional neural networks
comprise at least one global max pooling step.
[0256] In some embodiments, the convolutional neural networks
comprise at least one dense layer.
[0257] In some embodiments, identifying peptide sequences comprises
identifying peptide sequences with a mutation expressed in cancer
cells of a subject.
[0258] In some embodiments, identifying peptide sequences comprises
identifying peptide sequences not expressed in normal cells of a
subject.
[0259] In some embodiments, identifying peptide sequences comprises
identifying overexpressed peptide sequences.
[0260] In some embodiments, identifying peptide sequences comprises
identifying viral peptide sequences. In one aspect, provided herein
is a method for identifying HLA class II specific peptides for
immunotherapy specific for a subject, the method comprising:
identifying a candidate peptide comprising an epitope; inputting
amino acid information of a plurality of peptide sequences, each
comprising an epitope, using a computer processor, into a
machine-learning HLA-peptide presentation prediction model to
generate a set of HLA presentation predictions for the peptide
sequence to an immune cell, each presentation prediction
representing a probability that one or more proteins encoded by an
HLA class II allele of a cell of the subject will present a given
peptide sequence comprising the epitope; wherein the prediction
model has a positive predictive value of at least 0.1 at a recall
rate of at least 0.1%, from 0.1%-50% or at the most 50%, selecting
a protein from the one or more proteins encoded by the HLA class II
allele of a cell of the subject, predicted to bind to the candidate
peptide by the prediction model, wherein the protein has a
probability greater than a threshold presentation prediction
probability value for presenting the candidate peptide to an immune
cell; contacting the candidate peptide with the protein encoded by
the HLA class II allele, such that the candidate peptide competes
with a placeholder peptide associated with the protein encoded by
the HLA class II allele; and, identifying the candidate peptide as
a peptide for immunotherapy specific for the protein encoded by an
HLA class II allele based on whether the candidate peptide
displaces the placeholder peptide.
[0261] In some embodiments, the immunotherapy is cancer
immunotherapy.
[0262] In some embodiments, identifying comprises comparing DNA,
RNA or protein sequences from the cancer cells of the subject to
DNA, RNA or protein sequences from the normal cells of the subject.
In some embodiments, the epitope is a cancer specific epitope.
[0263] In some embodiments, the at least one protein encoded by the
HLA class II allele comprises at least an alpha 1 subunit and a
beta 1 subunit of the HLA protein, or fragments thereof, present in
dimer form. In some embodiments, the placeholder peptide is a CLIP
peptide. In some embodiments, the placeholder peptide is a CMV
peptide. In some embodiments, the method further comprises
measuring the IC50 of displacement of the placeholder peptide by
the target peptide. In some embodiments, the IC50 of displacement
of the placeholder peptide by the target peptide is less than 500
nM. In some embodiments, the at least one protein from the one or
more proteins encoded by the HLA class II allele of a cell of the
subject is an HLA class II tetramer or multimer. In some
embodiments, the target peptide is further identified by mass
spectrometry. In some embodiments, the at least one protein encoded
by the HLA class II allele of a cell of the subject is a
recombinant protein. In some embodiments, the at least one protein
encoded by the HLA class II allele of a cell of the subject is
expressed in a eukaryotic cell.
[0264] In one aspect, provided herein is assay method for verifying
the specificity of a candidate peptide for binding an HLA class II
protein, the method comprising: expressing in a eukaryotic cell, a
polynucleic acid construct comprising a nucleic acid sequence
encoding an HLA class II protein comprising an alpha chain and beta
chain or portions thereof, capable of binding a peptide comprising
an MHC-II-binding epitope, and wherein the expressed HLA class II
protein or portions thereof remains associated with a placeholder
peptide; isolating the HLA class II protein or portions thereof
expressed in the eukaryotic cell; performing a peptide exchange
assay by (a) adding increasing amount of the candidate peptide to
determine whether the candidate peptide displaces the placeholder
peptide associated with the HLA class II protein or portions
thereof; and (b) calculating the IC50 of the displacement reaction
to determine the affinity of the candidate peptide to the HLA class
II protein or portions thereof relative to the placeholder peptide,
thereby verifying the specificity of the candidate peptide for
binding an HLA class II protein.
[0265] In some embodiments, the identity of the peptide is known.
In some embodiments, the identity of the peptide is not known. In
some embodiments, the identity of the peptide is determined by mass
spectrometry.
[0266] In some embodiments, the peptide exchange assay comprises
detection of peptide fluorescent probes or tags. In some
embodiments, the placeholder peptide is a CLIP peptide.
[0267] In some embodiments, the polynucleic acid construct
comprises an expression vector, further comprising one or more of:
a promoter, a linker, one or more protease cleavage sites, a
secretion signal, dimerization factors, ribosomal skipping
sequence, one or more tags for purification and or detection.
[0268] In one aspect, provided herein is a method for assaying
immunogenicity of a MHC class II binding peptide, the method
comprising: selecting a protein encoded by an HLA class II allele
predicted by a machine-learning HLA-peptide presentation prediction
model to bind to the peptide; wherein the prediction model has a
positive predictive value of at least 0.1 at a recall rate of at
least 0.1%, from 0.1%-50% or at the most 50% and wherein the
protein has a probability greater than a threshold presentation
prediction probability value for presenting the identified peptide
sequence; contacting the peptide with the selected protein encoded
by the HLA class II allele such that the peptide competes with a
placeholder peptide associated with the selected protein encoded by
the HLA class II allele, and displaces the placeholder peptide,
thereby forming a complex comprising the HLA class II protein and
the identified peptide; contacting the HLA class II protein and the
identified peptide complex with a CD4+ T cell, assaying for one or
more of activation parameters of the CD4+ T cell, selected from
induction of a cytokine, induction of a chemokine and expression of
a cell surface marker.
[0269] In some embodiments, the HLA class II allele is a tetramer
or multimer. In some embodiments, the cytokine is IL-2. In some
embodiments, the cytokine is IFN-gamma.
[0270] In one aspect, provided herein is a method for inducing a
CD4+ T cells activation in a subject for cancer immunotherapy, the
method comprising: identifying a peptide sequence associated with
cancer and comprising a cancer mutation, wherein identifying
comprises comparing DNA, RNA or protein sequences from the cancer
cells of the subject to DNA, RNA or protein sequences from the
normal cells of the subject; selecting a protein encoded by an HLA
class II allele that is normally expressed by a cell of the
subject, and predicted by a machine-learning HLA-peptide
presentation prediction model to bind to the peptide; wherein the
prediction model has a positive predictive value of at least 0.1 at
a recall rate of at least 0.1%, from 0.1%-50% or at the most 50%
and wherein the protein has a probability greater than a threshold
presentation prediction probability value for presenting the
identified peptide sequence; contacting the identified peptide with
the selected protein encoded by the HLA class II allele to verify
whether the identified peptide competes with a placeholder peptide
associated with the selected protein encoded by the HLA class II
allele to displace the placeholder peptide with an IC50 value of
less than 500 nM; purifying the identified peptide; and administer
an effective amount of the identified peptide to the subject.
[0271] In one aspect, provided herein is a method of manufacturing
HLA class II tetramers or multimers, the method comprising:
expressing in a eukaryotic cell, a vector comprising a nucleic acid
sequence encoding an alpha chain and a beta chain of HLA protein, a
linker, one or more protease cleavage sites, a secretion signal, a
biotinylation motif and at least one tag for identification or for
purification, such that each HLA protein alpha 1 and beta 1
heterodimers is secreted in dimerized state, wherein the
heterodimer is associated with a placeholder peptide, purifying the
secreted heterodimer from cell medium, validating the peptide
binding activity using peptide exchange assay, adding streptavidin
thereby conjugating heterodimers into tetramers, purifying the
tetramers and having an yield of greater than 1 mg/L.
[0272] In some embodiments, the vector comprises a CMV promoter. In
some embodiments, the vector comprises a sequence encoding a
placeholder peptide linked via a cleavable site to the beta1 chain.
In some embodiments, peptide exchange assay involves prior cleavage
of the placeholder peptide from the beta chain. In some
embodiments, the cleavable site is a thrombin cleavage site. In
some embodiments, peptide exchange assay is a FRET assay. In some
embodiments, the purification is by any one of: column
chromatography, batch chromatography, ion exchange chromatography,
size exclusion chromatography, affinity chromatography or
LC-MS.
[0273] In one aspect, provided herein is a composition comprising
HLA class II tetramers comprising either HLA-DR, or HLA-DP, or
HLA-DQ heterodimers, each heterodimer comprising an alpha and a
beta chain, purified and present at a concentration of greater than
0.25 mg/L. In some embodiments, the HLA class II tetramer comprises
heterodimer pairs selected from a group consisting of: protein may
be selected from the group consisting of an HLA-DR, and HLA-DP or
an HLA-DQ protein. In some embodiments, the HLA protein is selected
from the group consisting of: HLA-DPB1*01:01/HLA-DPA1*01:03,
HLA-DPB1*02:01/HLA-DPA1*01:03, HLA-DPB1*03:01/HLA-DPA1*01:03,
HLA-DPB1*04:01/HLA-DPA1*01:03, HLA-DPB1*04:02/HLA-DPA1*01:03,
HLA-DPB1*06:01/HLA-DPA1*01:03, HLA-DQB1*02:01/HLA-DQA1*05:01,
HLA-DQB1*02:02/HLA-DQA1*02:01, HLA-DQB1*06:02/HLA-DQA1*01:02,
HLA-DQB1*06:04/HLA-DQA1*01:02, HLA-DRB1*01:01, HLA-DRB1*01:02,
HLA-DRB1*03:01, HLA-DRB1*03:02, HLA-DRB1*04:01, HLA-DRB1*04:02,
HLA-DRB1*04:03, HLA-DRB1*04:04, HLA-DRB1*04:05, HLA-DRB1*04:07,
HLA-DRB1*07:01, HLA-DRB1*08:01, HLA-DRB1*08:02, HLA-DRB1*08:03,
HLA-DRB1*08:04, HLA-DRB1*09:01, HLA-DRB1*10:01, HLA-DRB1*11:01,
HLA-DRB1*11:02, HLA-DRB1*11:04, HLA-DRB1*12:01, HLA-DRB1*12:02,
HLA-DRB1*13:01, HLA-DRB1*13:02, HLA-DRB1*13:03, HLA-DRB1*14:01,
HLA-DRB1*15:01, HLA-DRB1*15:02, HLA-DRB1*15:03, HLA-DRB1*16:01,
HLA-DRB3*01:01, HLA-DRB3*02:02, HLA-DRB3*03:01, HLA-DRB4*01:01,
HLA-DRB5*01:01).
[0274] In some embodiments, the heterodimer pairs are expressed in
a eukaryotic cell. In some embodiments, the heterodimer pair is
encoded by a vector. In some embodiments, the vector comprises: a
nucleic acid sequence encoding an alpha chain and a beta chain of
HLA protein, a secretion signal, a biotinylation motif and at least
one tag for identification or for purification, such that each HLA
protein alpha 1 and beta1 heterodimers is secreted in dimerized
state, wherein the secreted heterodimer is associated with a
placeholder peptide. In some embodiments, the vector comprises: a
nucleic acid sequence encoding an alpha chain and a beta chain of
HLA protein, a secretion signal, a biotinylation motif and at least
one tag for identification or for purification, such that each HLA
protein alpha 1 and beta1 heterodimers is secreted in dimerized
state, wherein the secreted heterodimer is associated with a
placeholder peptide.
[0275] In some embodiments, HLA class II heterodimers secreted from
eukaryotic cells into cell culture medium, and is purified by any
one of: column or batch chromatography, ion exchange
chromatography, size exclusion chromatography, affinity
chromatography or LC-MS.
[0276] In one aspect, provided herein is a method of screening a
drug comprising a polypeptide sequence for immunogenicity in a
subject, the method comprising: inputting amino acid information of
peptide sequences of the polypeptide sequence, using a computer
processor, into a machine-learning HLA-peptide presentation
prediction model to generate a set of presentation predictions for
the peptide sequences, each presentation prediction representing a
probability that one or more proteins encoded by an HLA class I or
II allele of a cell of the subject will present an epitope sequence
of a given peptide sequence; wherein the machine-learning
HLA-peptide presentation prediction model comprises: a plurality of
predictor variables identified at least based on training data
wherein the training data comprises: sequence information of
sequences of peptides presented by an HLA protein expressed in
cells and identified by mass spectrometry; training peptide
sequence information comprising amino acid position information,
wherein the training peptide sequence information is associated
with the HLA protein expressed in cells; and a function
representing a relation between the amino acid position information
received as input and the presentation likelihood generated as
output based on the amino acid position information and the
predictor variables; (b) determining or predicting that each of the
peptide sequences of the polypeptide sequence would not be
immunogenic to the subject based on the set of presentation
predictions; and (c) administering to the subject a composition
comprising the drug.
[0277] In one aspect, provided herein is a method of screening a
drug comprising a polypeptide sequence for immunogenicity in a
subject, the method comprising: (a) inputting amino acid
information of peptide sequences of the polypeptide sequence, using
a computer processor, into a machine-learning HLA-peptide
presentation prediction model to generate a set of presentation
predictions for the peptide sequences, each presentation prediction
representing a probability that one or more proteins encoded by an
HLA class I or II allele of a cell of the subject will present an
epitope sequence of a given peptide sequence; wherein the
machine-learning HLA-peptide presentation prediction model
comprises: a plurality of predictor variables identified at least
based on training data; wherein the training data comprises:
sequence information of sequences of peptides presented by an HLA
protein expressed in cells and identified by mass spectrometry;
training peptide sequence information comprising amino acid
position information, wherein the training peptide sequence
information is associated with the HLA protein expressed in cells;
and a function representing a relation between the amino acid
position information received as input and the presentation
likelihood generated as output based on the amino acid position
information and the predictor variables; (b) determining or
predicting that at least one of the peptide sequences of the
polypeptide sequence would be immunogenic to the subject based on
the set of presentation predictions.
[0278] In one embodiment, the method further comprises deciding not
to administer the drug to the subject.
[0279] In one embodiment, the drug comprises and antibody or
binding fragment thereof.
[0280] In one embodiment, the peptide sequences of the polypeptide
sequences comprise each contiguous peptide sequence of the
polypeptide sequence that has a length of 8, 9, 10, 11 or 12 amino
acids, and wherein the protein encoded by an HLA class I or II
allele of a cell of the subject is a protein encoded by an HLA
class I allele of a cell of the subject.
[0281] In one embodiment, the peptide sequences of the polypeptide
sequences comprise each contiguous peptide sequence of the
polypeptide sequence that has a length of 15, 16, 17, 18, 19, 20,
21, 22, 23, 24, or 25 amino acids, and wherein the protein encoded
by an HLA class I or II allele of a cell of the subject is a
protein encoded by a class II MHC allele of a cell of the
subject.
[0282] In one aspect, provided herein is a method of treating a
subject with an autoimmune disease or condition comprising: (a)
identifying or predicting an epitope of an expressed protein
presented by an HLA class I or II of a cell of the subject, wherein
a complex comprising the identified or predicted epitope and the
HLA class I or II is targeted by a CD8 or CD4 T cell of the
subject; (b) identifying a T cell receptor (TCR) that binds to the
complex; (c) expressing the TCR in a regulatory T cell from the
subject or an allogeneic regulatory T cell; and (d) administering
the regulatory T cell expressing the TCR to the subject.
[0283] In one embodiment, the autoimmune disease or condition is
diabetes.
[0284] In one embodiment, the cell is an islet cell.
[0285] In one aspect, provided herein is a method of treating a
subject with an autoimmune disease or condition comprising
administering to the subject a regulatory T cell expressing a T
cell receptor (TCR) that binds to a complex comprising (i) an
epitope of an expressed protein identified or predicted to be
presented by an HLA class I or II of a cell of the subject and (ii)
the HLA class I or II, wherein the complex is targeted by a CD8 or
CD4 T cell of the subject.
[0286] Additional aspects and advantages of the present disclosure
will become readily apparent to those skilled in this art from the
following detailed description, wherein only illustrative
embodiments of the present disclosure are shown and described. As
will be realized, the present disclosure is capable of other and
different embodiments, and its several details are capable of
modifications in various obvious respects, all without departing
from the disclosure. Accordingly, the drawings and description are
to be regarded as illustrative in nature, and not as
restrictive.
[0287] MAPTAC.TM. can be used for high-throughput peptide binding
assays where peptides bound to HLA class II are measured after
isolation with MAPTAC.TM. constructs at different time points and
under different conditions, such as heating at 37.degree. C., to
obtain the sequences of populations of peptides with different
stabilities using LC-MS/MS.
[0288] In one aspect, provided herein is a method for treating a
cancer in a subject the method comprising: identifying peptide
sequences, wherein the peptide sequences are associated with
cancer, wherein identifying comprises comparing DNA, RNA or protein
sequences from the cancer cells of the subject to DNA, RNA or
protein sequences from the normal cells of the subject; inputting
amino acid information of the peptide sequences identified, using a
computer processor, into a machine-learning HLA-peptide
presentation prediction model to generate a set of presentation
predictions for the peptide sequences identified, each presentation
prediction representing a probability that one or more proteins
encoded by an HLA class II allele of a cell of the subject will
present a given sequence of a peptide sequence identified; wherein
the machine-learning HLA-peptide presentation prediction model
comprises: a plurality of predictor variables identified at least
based on training data wherein the training data comprises:
sequence information of sequences of peptides presented by an HLA
protein expressed in cells and identified by mass spectrometry;
training peptide sequence information comprising amino acid
position information, wherein the training peptide sequence
information is associated with the HLA protein expressed in cells;
and a function representing a relation between the amino acid
position information received as input and the presentation
likelihood generated as output based on the amino acid position
information and the predictor variables; and selecting a subset of
the peptide sequences identified based on the set of presentation
predictions for preparing the personalized cancer therapy; and
administering to the subject a composition comprising one or more
of the peptides, wherein the prediction model has a positive
predictive value of at least 0.1 at a recall rate of at least 0.1%,
from 0.1%-50% or at most 50%.
[0289] In some embodiments, the machine-learning HLA-peptide
presentation prediction model comprises sequence information of
sequences of peptides presented by an HLA protein expressed in
cells and identified by mass spectrometry after performing reverse
phase offline fractionation.
[0290] In some embodiments, the prediction model exhibits a
1.1.times. to 100.times. fold improvement compared to NetMHCIIpan.
In some embodiments, the prediction model exhibits a 1.1, 2, 3, 4,
5, 6, 7, 7.4, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 18, 20, 21,
22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38,
39, 40, 41, 42, 43, 44, 45, 50, 55, 60, 61, 62, 63, 64, 65, 66, 67,
68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 8, 83, 84,
85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99,
100-fold or more improvement compared to NetMHCIIpan.
INCORPORATION BY REFERENCE
[0291] All publications, patents, and patent applications mentioned
in this specification are herein incorporated by reference to the
same extent as if each individual publication, patent, or patent
application was specifically and individually indicated to be
incorporated by reference. To the extent publications and patents
or patent applications incorporated by reference contradict the
disclosure contained in the specification, the specification is
intended to supersede and/or take precedence over any such
contradictory material.
BRIEF DESCRIPTION OF THE DRAWINGS
[0292] The novel features of the invention are set forth with
particularity in the appended claims. A better understanding of the
features and advantages of the present invention will be obtained
by reference to the following detailed description that sets forth
illustrative embodiments, in which the principles of the invention
are utilized, and the accompanying drawings (also "FIG." herein),
of which:
[0293] FIG. 1A diagram representing a peptide docked onto MHC Class
I protein. Figure discloses SEQ ID NO: 36.
[0294] FIG. 1B depicts an exemplary diagram representing a peptide
docked onto MHC Class II protein. Figure discloses SEQ ID NO:
37.
[0295] FIG. 2 depicts an exemplary experimental approach for
generating mono-allelic HLA class II binding peptide data. HLA
class II peptides are introduced into any cell, including a cell
not expressing HLA class II so that specific HLA class II allele(s)
are expressed in the cell. Populations of genetically engineered
HLA expressing cells are harvested, lysed, and their HLA-peptide
complexes are tagged (e.g., biotinylated) and immunopurified (e.g.,
using the biotin-streptavidin interaction). HLA-associated peptides
specific to a single HLA can be eluted from their tagged (e.g.,
biotinylated) complexes and evaluated (e.g., sequenced using high
resolution LC-MS/MS).
[0296] FIG. 3 depicts an exemplary sequence logo representation of
HLA class II-DRB1*11:01-associated peptides across Neon BAP,
Expi293 cell line; Neon BAP, A375 cell line; IEDB, Affinity <50
nM; and Pan-HLA Class II Ab, Homozygous LCL. FIG. 3 shows that
examples of MS-derived motifs match known patterns and show
consistency across transfected cell lines.
[0297] FIG. 4 is an exemplary depiction of the HLA class II binding
predictor performance. FIG. 4 is a bar plot showing the performance
of the binding predictor (neonmhc2) and NetMHCIIpan applied to a
validation dataset consisting of observed mass spec peptides and
decoy peptides which are generated at a ratio of 1:19 (hits:decoys)
by randomly shuffling the hit peptides. For the NEON binding
predictor neonmhc2, a separate model is built for each MHC II
allele shown. The height of the bars shows the positive predictive
value (PPV), defined as the fraction of predicted binders in the
validation set which were indeed hit peptides. The alleles are
sorted by the model's performance when predicting for that
allele.
[0298] FIG. 5 depicts an exemplary effect of scored peak intensity
(SPI) thresholds on binding predictor validation. FIG. 5 shows the
performance of the HLA class II binding predictor when
trained/validated on sets of peptides with different scored peak
intensity (SPI) cutoffs. For each allele-specific model that is
trained, shown is the model's performance in 3 settings: trained
and evaluated on datasets using observed MS hit peptides of larger
than or equal to 70 SPI, trained on peptides with larger than or
equal to 50 SPI and validated on peptides with larger than or equal
to 70 SPI, and trained and validated on peptides with larger than
or equal to 50 SPI.
[0299] FIG. 6 depicts an exemplary bar plot showing representative
data from number of observed peptides by allele profiling by
LC-MS/MS with larger than or equal to 70 scored peak intensity
(SPI) cutoffs. Each bar represents the total number of observed
peptides of an allele. There are collected data for 35 HLA-DR
alleles. The collected data for 35 HLA-DR alleles have >95%
population coverage for HLA-DR (USA allele frequencies).
[0300] FIG. 7A shows the PPV of the model when applied to test
partition of data for the indicated HLA class II alleles. The decoy
peptides used were scrambled sequences of the positive (hit)
peptide sequences at a hit to decoy ratio of 1:19. PPV was
determined by identifying the top-scoring 5% of peptides in the
test partition and determining the fraction of them that were
positive for binding to the protein encoded by the respective HLA
class II allele.
[0301] FIGS. 7B-7D depict exemplary prediction performance as a
function of training set size (curves obtained by artificially
down-sampling the training set). FIGS. 7B, 7C and 7D show that,
generally, for the 35 HLA-DR alleles collected, when the training
set size increases, the value of PPV increases.
[0302] FIG. 8 depicts an exemplary graph, demonstrating that
processing-related variables can improve prediction further.
Distinguish MS-observed peptides random sequences selected from
protein-coding exome may be distinguished. On the training data
partition, a logistic regression may be fit to predict HLA class II
presentation using binding strength (NetMHCIIpan or Neon's
predictor) and processing features (RNA-Seq expression and a
derived gene-level bias term). On a separate evaluation partition,
exonic positions overlapping MS-observed MHC II peptides ("hits")
may be scored alongside random exonic positions not observed in MS
(1:499 ratio). The top 0.2% (1/500) may be called as positives, and
positive predictive value may be assessed this threshold.
[0303] FIG. 9 depicts an exemplary neural network architecture.
Input peptides are represented as 20mers, with shorter peptides
being filled in with "missing" characters. Each peptide has a
31-dimensional embedding, so the input into the neural network is a
20.times.31 matrix. Before being processed by the neural network,
feature normalization on the 20.times.31 matrix is performed based
on feature value means and standard deviations in the training set.
The first convolutional layer has a kernel of 9 amino acids and 50
filters (also called channels) with a Rectified Linear Unit (ReLU)
activation function. This is followed by batch normalization then
spatial dropout with a dropout rate of 20%. This is followed by
another convolutional layer with a kernel of 3 amino acids and 20
filters with a ReLU activation function and then again followed by
batch normalization and spatial dropout with a dropout rate of 20%.
Global max pooling is then applied, taking the maximally-activated
neuron in each of the 20 filters; then these 20 values are passed
into a fully connected (dense) layer with a single neuron using a
Sigmoid activation function. The output of this layer is treated as
the binding/non-binding prediction. L2 regularization is applied to
the weights of the first convolutional layer, second convolutional
layer, and dense layer with weights of 0.05, 0.1, and 0.01,
respectively. Additional models used have varied the number of
convolutional layers and the kernel size of each layer.
[0304] FIG. 10 depicts an exemplary computer control system that is
programmed or otherwise configured to implement methods provided
herein.
[0305] FIG. 11A depicts an exemplary overview of the MAPTAC.TM.
experimental workflow. Figure discloses SEQ ID NO: 38.
[0306] FIG. 11B depicts exemplary per-allele peptide counts, merged
across replicates.
[0307] FIG. 11C depicts exemplary peptide length distributions for
HLA class I and HLA class II alleles profiled by MAPTAC.TM..
[0308] FIG. 11D depicts exemplary per-residue cysteine frequencies
observed for MAPTAC.TM. and IEDB (alleles DRB1*01:01, DRB1*03:01,
DRB1*09:01, and DRB1*11:01), the human proteome, and multi-allelic
MS data from previous publications.
[0309] FIG. 12A depicts Caucasian frequencies for HLA-DR, -DP, and
-DQ alleles present in >1% of individuals and counts of peptides
from the indicated sources measured as strong binders (<50
nM).
[0310] FIG. 12B depicts exemplary length distributions of IEDB
peptides with associated HLA class II affinity measurements.
[0311] FIG. 12C depicts exemplary Western blots of (1) Expi293, (2)
HeLa, and (3) A375 cell lines individually transfected with two HLA
class I and two HLA class II alleles: HLA-A*02:01, HLA-B*45:01,
HLA-DRB1*01:01, and HLA-DRB1*11:01. Membranes were blotted with
anti-biotin ligase epitope tag to visualize biotin acceptor peptide
(BAP) and anti-beta-tubulin as a loading control. Lanes correspond
to the following fractions collected during the MAPTAC.TM.
protocol: lane 1 input, lane 2 biotinylated input, and lane 3 input
after pull-down.
[0312] FIG. 12D depicts exemplary per-residue amino acid
frequencies observed for MAPTAC.TM. and IEDB (alleles DRB1*01:01,
DRB1*03:01, DRB1*09:01, and DRB1*11:01), the human proteome, and
multi-allelic MS data from previous publications.
[0313] FIG. 12E depicts Caucasian frequencies for HLA-DR, -DP, and
-DQ alleles present in >1% of individuals and counts of peptides
from the indicated sources measured as strong binders (<50 nM).
This figure includes additional data relative to FIG. 12A. The
additional data were taken from: tools.iedb.org/main/datasets/.
[0314] FIG. 12F depicts exemplary per-residue amino acid
frequencies observed for MAPTAC.TM. (reduced and alkylated),
MAPTAC.TM. (no treatment) and IEDB (alleles DRB1*01:01, DRB1*03:01,
DRB1*09:01, and DRB1*11:01), the human proteome, and multi-allelic
MS data from previous publications
[0315] FIG. 13 depicts an exemplary representation of core binding
sequence logos for MHC II alleles per MAPTAC.TM. and IEDB. Sequence
logos are graphical representations wherein the height of each
amino acid is proportional to its frequency of occurrence in a
peptide that binds to the MHC protein encoded by the allele.
Positions with lowest entropy are represented by shading, where the
shadings correspond to amino acid properties. Peptides are derived
from the indicated data sets and are aligned according to a
CNN-based predictor (Methods). Logos represent all peptides
including those that did not closely match the overall motif (e.g.,
no peptides are sequestered in a "trash" cluster).
[0316] FIG. 14A depicts exemplary sequence logos for HLA-A*02:01
binding peptides (ligands) analyzed using different HLA-ligand
profiling technologies including binding assays, stability assays,
soluble HLA (sHLA) mass spectrometry, mono-allelic mass
spectrometry, and MAPTAC.TM. in two different cell lines (A375
& expi293).
[0317] FIG. 14B depicts an exemplary fraction of MAPTAC.TM.
peptides exhibiting 0, 1, 2, 3, and 4 of the heuristically defined
anchors.
[0318] FIG. 14C depicts an exemplary distribution of
NetMHCIIpan-predicted binding affinities for MAPTAC.TM.-observed
peptides (20 peptides per allele, each with SPI>70 and a nested
set of size >=2) and length-matched decoys sampled from the
proteome.
[0319] FIG. 15A depicts an exemplary architecture of a
convolutional neural network (CNN) trained to distinguish
mono-allelic MHC peptides from scrambled length-matched decoys. The
schematic indicates the usage of an amino acid feature embedding, 2
convolutional layers with different filter sizes, and the usage of
global max pooling as input to a final logistic output node.
[0320] FIG. 15B is an exemplary result that shows Kendall Tau
statistics for the correlation of measured IEDB affinities with
binding predictions either from neonmhc2 or NetMHCIIpan. Evaluated
peptides include only those posted to IEDB the year after
NetMHCIIpan was released.
[0321] FIG. 16 is an exemplary depiction of the performance of
neonmhc2 as a function of training data set size.
[0322] FIG. 17A depicts exemplary cluster assignments for
MAPTAC.TM. peptides (20 per allele) spiked into pan-DR and
pan-class II MHC MS datasets. Datasets were deconvolved using
GibbsCluster. Each box represents one MAPTAC.TM. peptide. The color
shading of the box indicates which cluster it was assigned to, and
gray bars indicate which allele the peptide actually came from. The
total number of clusters in the Gibbs cluster solution (right side)
was selected using a mutual information (MI) metric. The MI score
also determines how the samples are sorted; samples with high-MI
solutions appear at the top.
[0323] FIG. 17B depicts exemplary core-binding sequence logos for
multi-allelic MS data deconvolved by GibbsCluster. Each set of
peptides corresponds to the cluster that aligned best with the
MAPTAC.TM. spike-ins.
[0324] FIG. 17C depicts representative performance of models using
either MAPTAC.TM. data or deconvolved multi-allelic data to predict
hold-out MAPTAC.TM. peptides. For each allele, the larger of the
two data sources (usually MAPTAC.TM.) was down-sampled so that the
predictors would be based on an equal number of training examples.
NetMHCIIpan performance is shown as an additional comparison.
[0325] FIG. 17D depicts exemplary core binding sequence logos
derived from multi-allelic MS data from the indicated sources.
[0326] FIG. 18A depicts an exemplary graph of fraction of peptides
vs source gene expression (transcripts per million (TPM)) for
MS-observed peptides and random proteome decoys (data replotted
from Schuster et al. 2017).
[0327] FIG. 18B depicts exemplary observed vs. expected number of
Class II peptides per gene as determined by a joint analysis of
colorectal cancer, melanoma, and ovarian cancer datasets (Loffler
et al., 2018, and Schuster et al., 2017). The expected count is
derived by multiplying gene length by expression level. Expected
and observed counts were summed across relevant samples. Genes with
known presence in plasma are marked according to their
concentration (Inset).
[0328] FIG. 18C depicts exemplary distribution of enrichment scores
(ratio of observed to expected observations, as in FIG. 18B) for
genes associated with autophagy.
[0329] FIG. 18D depicts exemplary distribution of enrichment scores
according to the localization of each source gene. Source gene
localization was determined using Uniprot (uniprot_sprot.dat).
[0330] FIG. 18E depicts exemplary data representing comparison of
the expected versus observed frequency of fraction of total number
of peptides having MHC-II binding affinity, segregated based on
their cellular localization properties.
[0331] FIG. 18F depicts exemplary representative data of relative
concordance of peptides in observations with respect to two
different gene expression profiles. For each sample, gene-level
peptide counts were modeled as a linear combination of a bulk tumor
gene expression and professional APC (macrophage) gene expression
profile. The ratio of the coefficients determines the relative
concordance of each expression profile with the peptide repertoire.
Error bars correspond to a 95% confidence interval computed by
bootstrap resampling.
[0332] FIG. 19A depicts exemplary representative data of expression
levels of HLA-DRB1 in the five example studies. Each dot represents
expression in an individual cell type in an individual patient,
averaged over cells.
[0333] FIG. 19B depicts exemplary representative data of tumor and
stromal derived HLA-DRB1 expression as inputted from RNA-Seq of
TCGA patients. Horizontal bars correspond to individual patients
and are grouped by tumor type. Patients were included if they had a
mutation in HLA class II pathway gene (CIITA, CD74 or CTSSS) as
determined by DNA-based mutation calls. For each patient, the
fraction of HLA-DRB1 expression attributable to the tumor estimated
as min(1,2f), where f is the fraction of RNA-Seq reads in CIITA,
CD74, or CTSS exhibiting a mutation.
[0334] FIG. 19C depicts exemplary representative data of additional
single-cell RNA-Seq studies that include biopsies pre- and
post-checkpoint blockade immunotherapy.
[0335] FIG. 20 depicts exemplary representative experimental data
assessing prediction overall performance on natural donor
tissues.
[0336] FIG. 21A depicts exemplary representative data, showing that
the integrated presentation model predicts cellular HLA class II
ligandomes. It represents. PPV at a 1:499 hit-to-decoy ratio for
pan-DR datasets (also analyzed in FIG. 30B and FIG. 32E).
Predictors use binding prediction (NetMHCIIpan or neonmhc2) and
optionally employ gene expression, gene bias (per FIG. 32A), and
overlap with previously observed HLA-DQ peptides. For each
candidate peptide, the binding score was calculated as the maximum
across the HLA-DR alleles in the sample genotype.
[0337] FIG. 21B depicts exemplary representative data, showing
prediction performance for tumor-derived peptides as identified
using SILAC, presented by dendritic cells (analyzed from cell
lysates) using the same hit:decoy ratio and performance metrics as
in FIG. 21A, with and without use of processing features.
[0338] FIG. 21C depicts exemplary expression and gene bias scores
for heavy-labeled peptides observed in an UV treatment experiment
(plotted according to K562 expression) as compared to light-labeled
peptides (plotted according to DC expression).
[0339] FIG. 21D depicts an exemplary diagram representing overlap
of heavy-labeled peptide source genes according to the lysate and
UV-treatment experiments. Gene names are shaded by functional
class.
[0340] FIG. 22A depicts an exemplary flow diagram representing an
assay protocol disclosed herein, to validate HLA class II-driven
CD4+ T cells and T cell responses.
[0341] FIG. 22B depicts an exemplary HLA protein dimer construct
design for peptide exchange assay (upper panel) and a graphical
representation of an exemplary assay workflow (lower panel). Figure
discloses "10.times.His" as SEQ ID NO: 20.
[0342] FIG. 23 depicts an exemplary graphical illustration of an
exemplary vector design for MHC-II expression for screening new
binding peptides, and a representation of the expressed protein
product. Figure discloses SEQ ID NO: 39 and discloses
"10.times.His" as SEQ ID NO: 20.
[0343] FIG. 24 depicts an exemplary flow diagram of transfection,
purification and cleavage of placeholder peptide from beta
chain.
[0344] FIG. 25A depicts an exemplary graphical illustration showing
vector encoding CLIP peptides that are associated with increased
secretion of expressed MHC-II peptides. Figure discloses SEQ ID NO:
21.
[0345] FIG. 25B depicts an exemplary graphical representation with
the shorter and longer forms of the nucleic acids encoding CLIP0
and CLIP1 respectively. Figure discloses SEQ ID NOS 1 and 21,
respectively, in order of appearance.
[0346] FIG. 25C depicts an exemplary representative result of a
Coomassie gel analysis of the alpha and beta chains with or without
the longer clip.
[0347] FIG. 26A depicts an exemplary graphical illustration of the
TR-FRET assay.
[0348] FIG. 26B depicts exemplary representative polarization data
from an HLA class II peptide binding assay using Fluorescence
Resonance Energy Transfer (FRET) assay using specific peptides.
[0349] FIG. 26C depicts exemplary representative polarization data
from an HLA class II peptide binding assay using Fluorescence
Resonance Energy Transfer (FRET) assay using specific peptides.
[0350] FIG. 26D depicts an exemplary percent displacement of
MHC-construct bound peptide that was calculated from increase in
fluorescence.
[0351] FIG. 26E depicts an exemplary percent displacement of
MHC-construct bound peptide that was calculated from increase in
fluorescence.
[0352] FIG. 26F depicts an exemplary peptide exchange using assay
using differential scanning fluorometry (DSF). A graphical
representation is depicted showing an exemplary mechanism of
detecting peptide dissociation from MHC class II with heat which
also dissociates the MHC class II heterodimer, resulting in binding
of the fluorophore and high fluorescence. An exemplary schematic of
placeholder peptide dislodgement by epitope peptide is also
depicted. Exemplary melting curves plotted over temperature are
also depicted.
[0353] FIG. 26G depicts an exemplary soluble HLA-DM construct and
its use for the performance of MHC Class II peptide exchange. The
construct depicted contains a CMV promoter, a coding sequence for
HLA-DM beta chain and a coding sequence for a HLA-DM alpha chain
downstream of a secretion sequence (leader) and a BAP sequence at
the 3'end of the beta chain coding sequence; a His tag at the 3'end
of the alpha chain coding sequence. The two chains are be separated
by an intervening ribosomal skipping sequence. The construct was
expressed in Expi-CHO cells and the protein secreted into the
medium culture medium was purified. Figure discloses "10.times.His"
as SEQ ID NO: 20.
[0354] FIG. 2611 shows exemplary size exclusion chromatography data
using HLA-sDM to perform peptide exchange.
[0355] FIG. 27A depicts an exemplary graphical illustration of an
exemplary DRB tetramer repertoire build.
[0356] FIG. 27B depicts an exemplary graphical illustration of an
exemplary class II tetramer repertoire build.
[0357] FIG. 27C depicts an exemplary graphical illustration of a
summary of DRB tetramer repertoire coverage for the DRB1 allele for
peptide exchange.
[0358] FIG. 27D depicts exemplary coverage of human MHC class II
allele production.
[0359] FIG. 27E shows an exemplary result from tetramer staining of
samples induced with Flu epitopes (memory response) or HIV epitopes
(naive response).
[0360] FIG. 28A depicts an exemplary graphical representation of a
method of evaluation of peptides for HLA class II restriction by
fluorescence polarization assay that enables a screening method to
rapidly identify allele restriction for epitope peptides. The assay
principle depicted in FIG. 28A allows for affinity measurements,
and an unambiguous measurement of peptide exchange.
[0361] FIG. 28B depicts an exemplary summary of the multiple assay
conditions explored (upper panel) in the fluorescence polarization
assay with DRB1*01:01. Also depicted is an illustration of a
soluble MHC class II allele and a full-length MHC class II allele
with the transmembrane domain in a detergent micelle (lower panel),
both of which were constructed with placeholder peptide with the
cleavable linker for use in the assay.
[0362] FIG. 28C depicts an exemplary graphical representation of
the assays for investigating the full length and the soluble allele
previously shown in FIG. 28B lower panel. In short, both the full
length and the soluble alleles are expressed in cells. The membrane
bound full length allele form is harvested by permeabilizing the
membrane, while the secreted form is harvested from the cell
supernatant. The harvested Class II HLA allele proteins are
purified by passing through nickel (Ni.sup.2+) columns.
[0363] FIG. 28D depicts exemplary data showing that purification
method does not affect peptide potency. Shown on the left are
average IC50 values from experiments using L243 purified full
length HLA-DR1 and Ni.sup.2+ purified full-length HLA-DR1.
[0364] FIG. 28E depicts exemplary data showing choice of the
soluble form (sDR1) or the full-length form (fDR1) does not affect
the peptide potency. Shown on the left are average IC50 values from
experiments using sDR1 form or fDR1. FP, fluorescence
polarization.
[0365] FIG. 28F depicts an exemplary graphical view of an exemplary
evaluation of neonmhc2 and NetMHCIIpan predicted peptides in
binding assay and identification of discordant peptides.
[0366] FIG. 28G depicts exemplary fluorescence polarization binding
screen data for evaluation of neonmhc2 predicted peptides; shown as
heat map as also the percent inhibition of probe binding indicated
for each concentration of the peptide used.
[0367] FIG. 2811 depicts a summary of an evaluation of neonmhc2
predicted peptides in an exemplary binding assay.
[0368] FIG. 29 depicts an exemplary average count of peptides from
an average MAPTAC.TM. experimental replicate (50 million cells),
per each HLA allele.
[0369] FIGS. 30A-30C depict an exemplary binding core analysis for
HLA class II MAPTAC.TM. alleles+/- HLA-DM and multi-allelic
deconvolution fidelity. FIG. 30A depicts exemplary sequence logos
for one representative HLA-DR, -DQ, and -DP allele according to
MAPTAC.TM. with and without HLA-DM co-transfection (expi293 cell
line) and IEDB wherein the height of each amino acid is
proportional to its frequency. Amino acids with frequency greater
than 10% are shown in darker shading according to chemical
properties; all others are shown in lighter shading. Peptides were
aligned according to the GibbsCluster tool (Supplemental Methods),
and logos represent all peptides, including those that did not
closely match the overall motif (e.g. no peptides are sequestered
in a "trash" cluster). FIG. 30B depicts an exemplary description of
cluster assignments for MAPTAC.TM. peptides (20 per allele) spiked
into pan-DR MS datasets. Datasets were deconvolved using
GibbsCluster. Each colored box represents one MAPTAC.TM. peptide.
The shading of the box indicates which cluster it was assigned to,
and gray bars indicate which allele the peptide came from. FIG. 30C
depicts an exemplary graph showing that the share of peptides
exhibiting 0, 1, 2, 3, or 4 expected residues in anchor positions,
for alleles shown in FIG. 30B. Anchor positions were defined as the
four positions with lowest entropy, and the "expected" residues
were defined as those with .gtoreq.10% frequency in those
positions.
[0370] FIGS. 31A-31F depict an exemplary architecture and
benchmarking of the neonmhc2 binding prediction algorithm. FIG. 31A
depicts an exemplary architecture of a convolutional neural network
(CNN) trained to distinguish mono-allelic HLA class II peptides
from scrambled length-matched decoys. The schematic indicates the
usage of an amino acid feature embedding layer, 2 convolutional
layers of width 6, the presence of skip-to-end connections, and a
combination of average- and max-pooling operations as input to a
final logistic output node. FIG. 31B depicts an exemplary positive
predictive value (PPV) for NetMHCIIpan and neonmhc2 as evaluated on
a partition of MAPTAC.TM. data that was not used for training or
hyper-parameter optimization. For each allele, n MS-observed
peptides were scored in conjunction with 19n length-matched decoys
sampled from the same set of source genes, and each predictor's n
top-ranked peptides (e.g. the top 5%) were called as positives.
According to this evaluation protocol, PPV is identical to recall
because the number of false positives and false negatives is
necessarily equal. FIG. 31C depicts an exemplary PPV for
NetMHCIIpan and neonmhc2 on the TGEM data set. For each allele, the
n top-ranked peptides were called positives, where n is the number
of confirmed immunogenic epitopes in the evaluated set. FIG. 31D
depicts exemplary ex vivo T cell induction results for neoantigen
peptides. Peptides were selected based on high neonmhc2 scores and
weak NetMHCIIpan scores for HLA-DRB1*11:01. Figure discloses SEQ ID
NOS 87-89, 91, 90, 2, 92-94, 3, and 95-96, respectively, in order
of appearance. FIG. 31E depicts comparison of models trained on
monoallelic MAPTAC data versus deconvolved multiallelic data as
evaluated on hold-out monoallelic data. Values are as shown for
neonmhc2 where the training dataset is down-sampled to match the
size of the deconvolution training set. FIG. 31F shows PPV on the
TGEM dataset for NetMHCIIpan-v3.1, the deconvolution-trained
predictor, and neonmhc2 (with and without down-sampling). For each
allele, the n top-ranked peptides were called positives, where n is
the number of confirmed immunogenic epitopes in the evaluated
set.
[0371] FIGS. 32A-32E depict exemplary gene representation and
protein processing in HLA class II tumor peptidomes. FIG. 32A
depicts exemplary results of observed vs. expected number of HLA
class II peptides per gene as determined by a joint analysis of
colorectal cancer, melanoma, and ovarian cancer datasets. The
expected count is derived by multiplying gene length by expression
level. Expected and observed counts were summed across relevant
samples. Genes with known presence in plasma are marked according
to their concentration. FIG. 32B depicts exemplary results of
expected vs. observed frequency of peptides per cellular
localization. FIG. 32C depicts exemplary results of distribution of
enrichment scores (ratio of observed to expected observations, as
in part FIG. 32B) for genes regulated by the proteasome. Gene sets
include those with known ubiquitination sites and those that
increase in abundance upon application of a proteasome
inhibitor.
[0372] FIG. 32D depicts a diagram presenting three exemplary
working models for how HLA class II peptides are processed,
according to which i) cathepsins and other enzyme break cleave
proteins into peptide fragments that are subsequently bound by HLA,
ii) proteins or unfolded polypeptides bind HLA and are subsequently
cleaved to peptide length iii) proteins are partially digested
before binding and further trimmed after binding. Each model
corresponds to a different prediction approach. FIG. 32E depicts
absolute increase in PPV observed for logistic regression models
that included processing-related variables and neonmhc2 binding
predictions as compared to models that only used binding
predictions. Evaluation was conducted on eleven samples that were
profiled by HLA-DR antibody (the same samples analyzed in FIG.
30B); each point corresponds to one sample. Asterisks mark
significant improvements (*: p<0.01, **: p<0.001, ***:
p<0.0001) according to two-tailed paired t-tests. The same
analysis is shown in FIG. 40B but instead using NetMHCIIpan as the
base predictor. Methods for decoy selection and PPV calculation are
identical to those used in FIG. 31B.
[0373] FIGS. 33A-33G depict exemplary results of identification and
prediction tumor antigens presented by dendritic cells. FIG. 33A
depicts an exemplary graphical representation of experimental
workflow for identifying DC-presented HLA-II ligands that originate
from cancer cells (K562). Cancer cells were grown in SILAC media to
full incorporation, either lysed or irradiated, and then plated
with monocyte-derived dendritic cells. Presented peptides were
isolated by pan-DR antibody and sequenced by LC-MS/MS. FIG. 33B
depicts exemplary data representing prediction performance for
tumor-derived peptides presented by dendritic cells using the same
hit-to-decoy ratio and performance metrics as in FIG. 21A.
Performance is shown for NetMHCIIpan- and neonmhc2-based models
with and without use of processing features. FIG. 33C depicts
exemplary gene expression distribution for source genes of
heavy-labeled peptides observed in the UV-treatment experiment
(plotted according to K562 expression) as compared to the source
genes of light-labeled peptides (plotted according to DC
expression). FIG. 33D shows an exemplary graph of PPV at a 1:499
hit-to-decoy ratio for predicting presented tumor antigens using
NetMHCIIpan- and neonmhc2-based models with and without processing
features. Data points from left to right represent samples: Donor 1
HOC1 treated cells: NetMHCIIpan continuous expression; NetMHCIIpan
continuous expression+gene bias; NetMHCIIpan continuous
expression+gene bias+DQ overlap, full processing mode; Donor 1,
UV-treated: neonmhc2; neonmhc2+threshold expression;
neonmhc2+continuous expression; neonmhc2+continuous expression+gene
bias; neonmhc2+continuous expression+gene bias+DQ overlap. FIG. 33E
depicts significance of various gene localizations and functional
classes in predicting heavy (K562-derived and light (DC-derived)
peptides respectively. P-values are calculated according to
logistic regression that controls for neonmhc2 binding score and
source gene expression. Bar shading indicate sign associated with
coefficient in the regression. FIG. 33F depicts an exemplary
graphical representation of results showing overlap of tumor
cell-derived peptide source genes (shaded by functional class) in
the UV- and HOC1-treated experiments. FIG. 33G depicts exemplary
data showing PPV for predicting presented tumor antigens in a
second donor using logistic models fit on heavy-labeled peptides
observed in the first donor. Models were fit using neonmhc2 binding
alone; binding and expression; or binding, expression, and a binary
variable indicating if a peptide was from a mitochondria gene.
[0374] FIGS. 34A-34B depict exemplary characterization of
MAPTAC.TM. data related to FIG. 29. FIG. 34A depicts an exemplary
HLA cell surface analysis by FACS of Expi293 cell lines transfected
with MAPTAC.TM. constructs coding for affinity-tagged
HLA-A*02:01-BAP FIG. 34B depicts an exemplary HLA cell surface
analysis by FACS of Expi293 cell lines transfected with MAPTAC.TM.
constructs coding for affinity-tagged HLA-DRB1*11:01-BAP (bottom).
HLA cell surface expression of transfected Expi293 cells were
compared with stained untransfected Expi293, unstained
untransfected Expi293, stained PBMCs, and unstained PBMCs. All HLA
class I stains utilized W6/32 (pan-HLA class I), while HLA class II
stains utilized REA332 (pan-HLA class II).
[0375] FIG. 35 depicts an exemplary comparison of MAPTAC.TM. and
IEDB logos, related to FIG. 30A. Measured and NetMHCIIpan-predicted
affinities for MS-observed peptides that did not exhibit good
NetMHCIIpan scores but were well supported by MS (scored peak
intensity >70 and nested set size .gtoreq.1).
[0376] FIGS. 36A-36C depict an exemplary analysis of HLA-DR1
MAPTAC.TM. data fidelity, related to FIGS. 30A-30C. FIG. 36A
depicts exemplary NetMHCIIpan3.1 scores for HLA-DR1 MAPTAC.TM.
peptides (lengths 12-23) as compared to 50,000 length-matched decoy
peptides randomly sampled from the proteome, for common alleles.
FIG. 36B depicts exemplary measured and NetMHCIIpan-predicted
affinities for exemplary MS-observed peptides that did not exhibit
good NetMHCIIpan scores but were well-supported by MS (scored peak
intensity>70 and nested set size .gtoreq.1). Figure discloses
SEQ ID NOS 40-86, top to bottom, left to right, respectively, in
order of appearance. FIG. 36C depicts exemplary HLA class II
sequence logos for HLA-DRB1 alleles as determined by MAPTAC.TM. in
different cell types.
[0377] FIGS. 37A-37C and 37D (continuation of FIG. 37C) depict an
additional exemplary analysis of MAPTAC.TM. motifs, related to
FIGS. 30A-30C. FIG. 37A depicts MAPTAC.TM.-derived sequence logos
for experiments with and without HLA-DM co-transfection (expi293
cell line). FIG. 37B depicts sequence logos for several HLA class I
alleles according to MAPTAC.TM. and IEDB. Note that A*32:01 does
not show a high frequency Q at P2 and C*03:03 does not show a high
frequency Y at P9, differing with previous studies that used
multi-allelic deconvolution; the logo for B*52:01 is previously
unpublished. FIGS. 37C and 37D (continuation of FIG. 37C) depicts
an exemplary alignment of MAPTAC.TM.-observed peptides to the gene
sequence of CD74.
[0378] FIGS. 38X, 38Y, 38B-38D depict exemplary neonmhc2
performance statistics and T cell flow staining, related to FIGS.
31A-31D. FIG. 38X depicts an exemplary performance of neonmhc2 as a
function of training data set size. PPV was evaluated in the same
manner and using the same evaluation peptides as in FIG. 31B;
however, the training data was randomly down-sampled to mimic
smaller training data sets. FIG. 38Y depicts exemplary sequence
logos for peptide clusters derived from multi-allelic HLA-DR
ligandome using GibbsCluster (default settings; "trash cluster
allowed). FIG. 38B depicts exemplary representative flow cytometry
plots of IFN-.gamma. expression by CD4+ cells from induction
samples recalled with neoantigen peptides predicted with neonmhc2.
Delta values were calculated by subtracting the percent of CD4+
cells expressing IFN-.gamma. when recalled with neoantigen
(+Peptide) from the percent of CD4+expressing IFN-.gamma. when
recalled in the presence of no neoantigen (No Peptide). The left
two flow plots are representative of a neoantigen that induced a
CD4+ T cell T cell response (PEASLYGALSKGSGG (SEQ ID NO: 2)) and a
neoantigen that did not induce a T cell response (PATYILILKEFCLVG
(SEQ ID NO: 3)). FIG. 38C depicts exemplary delta values from wells
recalled with single neonmch2 neoantigen peptides. Peptides were
considered an induction hit if they had a positive response (delta
response above 3%, highlighted). Figure discloses SEQ ID NOS 87-91,
2, 92-94, 3, and 95-96, respectively, in order of appearance. FIG.
38D shows exemplary sequence logos for peptide clusters derived for
multi-allelic HLA-DR ligandomes using GibbsCluster (default
settings; "trash" cluster allowed).
[0379] FIGS. 39A-39C depict an additional exemplary cell-of-origin
analysis for HLA class II, related to FIGS. 32A-32E. FIG. 39A
depicts exemplary percent-rank neonmhc2 scores for HLA class II
peptides observed in 4 PBMC samples profiled by pan-DR antibody
(RG1248, RG1104, RG1095, and HDSC from FIG. 30B), according to
whether the peptide source gene is present in human plasma. For
each peptide, the best (lowest) percent rank was used across the
alleles present in the donor. Scores for random length-matched
proteome decoys are shown for comparison. Box plots mark the 5th,
25th, 50th, 75th, and 95th percentiles. FIG. 39B depicts exemplary
counts of observed vs. expected peptides per gene for HLA class I,
using the same methodology as in FIG. 32A. Data correspond to the
same tumor types (colorectal, ovarian, and melanoma). Genes present
in human plasma are highlighted and sized according to their
concentration. FIG. 39C depicts an exemplary relative concordance
of peptide observations with respect to two different gene
expression profiles. For each sample, gene-level peptide counts
were modeled as a linear combination of a bulk tumor gene
expression and professional APC gene expression profile. The ratio
of the coefficients determines the relative concordance of each
expression profile with the peptide repertoire. Error bars
correspond to a 95% confidence interval computed by bootstrap
resampling.
[0380] FIGS. 40A-40B depict an additional exemplary analysis of
processing motifs related to FIGS. 32A-32E. FIG. 40A depicts
exemplary amino acid frequencies near N-terminal and C-terminal
peptide cut sites relative to average proteome frequencies (applies
for upstream positions U3-U1 and downstream positions D1-D3) or
relative to average peptide frequencies (applies for internal
positions N1-C1) as observed in donor PBMC, monocyte-derived
dendritic cells, colorectal cancer, melanoma, ovarian cancer, and
the expi293 cell line (used for most MAPTAC.TM. data generation).
FIG. 40B depicts the same analysis as FIG. 32E but using
NetMHCIIpan as the base predictor. Absolute increase in PPV
observed for logistic regression models that included
processing-related variables in addition to NetMHCIIpan predictions
(as compared to NetMHCIIpan-only models) for eight samples profiled
by HLA-DR antibody (the same samples analyzed in FIG. 31B).
Asterisks mark significant improvements (*: p<0.01, **:
p<0.001, ***: p<0.0001) according to two-tailed paired
t-tests.
[0381] FIG. 41 depicts an exemplary naming system used to refer to
positions upstream of peptides, within peptides, and downstream of
peptides.
[0382] FIG. 42A depicts a diagram representing an exemplary
workflow for analysis of endogenously processed and HLA-1 and HLA
class II presented peptides by nLC-MS/MS.
[0383] FIG. 42B depicts a graph showing exemplary experimental
results from nLC-MS/MS analysis of tryptic peptides with or without
FAIMS. Representative overlap in the detections of HLA-1 and HLA
class II peptides by nLC-MS/MS analysis with or without FAIMS at
the analysis scale as indicated are also depicted.
[0384] FIG. 43A depicts exemplary HLA class I acidic and basic
reverse phase fractionated peptide detections with or without
FAIMS.
[0385] FIG. 43B depicts exemplary experimental results showing
detection of HLA class I bound unique peptides plotted over
retention time.
[0386] FIG. 44A depicts exemplary HLA class II acidic and basic
reverse phase fractionated peptide detections with or without
FAIMS.
[0387] FIG. 44B depicts exemplary experimental results showing
detection of HLA class II bound unique peptides plotted over
retention time.
[0388] FIGS. 45A and 45B depict an exemplary graph of intersection
size of HLA class I binding peptides detected using the methods
indicated (left) and a Venn diagram of an exemplary standard
workflow and an optimized workflow for LC-MS/MS detection of HLA
class I binding peptides (right).
[0389] FIGS. 46A and 46B depict an exemplary graph of intersection
size of HLA class II binding peptides detected using the methods
indicated (left) and a Venn diagram of an exemplary standard
workflow and an optimized workflow for LC-MS/MS detection of HLA
class II binding peptides (right).
DETAILED DESCRIPTION
[0390] All terms are intended to be understood as they would be
understood by a person skilled in the art. Unless defined
otherwise, all technical and scientific terms used herein have the
same meaning as commonly understood by one of ordinary skill in the
art to which the disclosure pertains.
[0391] The section headings used herein are for organizational
purposes only and are not to be construed as limiting the subject
matter described.
[0392] Although various features of the present disclosure can be
described in the context of a single embodiment, the features can
also be provided separately or in any suitable combination.
Conversely, although the present disclosure can be described herein
in the context of separate embodiments for clarity, the disclosure
can also be implemented in a single embodiment.
[0393] The present disclosure is based on the important finding
that the presentation of antigens, specifically cancer antigens by
specific HLA class II alpha and beta chain pairs can be predicted
with high degree of confidence using a new computer-based
machine-learning HLA-peptide presentation prediction model which
allows use of HLA class II specific peptides for improved
immunotherapy.
[0394] In one aspect, the present disclosure provides method for
predicting peptides that can accurately pair with, or bind to, a
specific HLA class II alpha and beta chain heterodimer, such that
the high fidelity binding of the peptide to HLA class II protein
(comprising the alpha and beta chain heterodimer) ensures
presentation of the specific peptide to the T lymphocytes, thereby
eliciting a specific immune response and avoid any cross-reactivity
or immune promiscuity. Several recent studies have shown that CD4+
T cells can also recognize HLA class II presented ligands and
contribute to tumor control. Cancer vaccines and other
immunotherapies would ideally take advantage of directing CD4+ T
cell responses, but current efforts have forgone HLA class II
antigen prediction entirely because the accuracy of current
prediction tools is inadequate.
[0395] In one aspect, the present disclosure provides method for
predicting peptides that can accurately bind to a specific HLA
class II protein, such that a more sustained and robust immune
response can be activated with the peptide, when the peptide is
administered therapeutically to a subject expressing the specific
cognate HLA class II protein, by means of the ability of HLA class
II protein's activation of CD4+ T cells and stimulate immunological
memory. In some embodiments, the method provided herein exhibits an
improvement in a specific HLA class II protein prediction over
currently available predictor. In some embodiments, the method
provided herein exhibits at least about a 1.1-fold improvement in a
specific HLA class II protein prediction over currently available
predictor. In some embodiments, the method provided herein exhibits
at least about a 2-fold improvement in a specific HLA class II
protein prediction over currently available predictor. In some
embodiments, the method provided herein exhibits at least about a
3-fold improvement in a specific HLA class II protein prediction
over currently available predictor. In some embodiments, the method
provided herein exhibits at least about a 4-fold improvement in a
specific HLA class II protein prediction over currently available
predictor. In some embodiments, the method provided herein exhibits
at least about a 5-fold improvement in a specific HLA class II
protein prediction over currently available predictor. In some
embodiments, the method provided herein exhibits at least about a
6-fold improvement in a specific HLA class II protein prediction
over currently available predictor. In some embodiments, the method
provided herein exhibits at least about a 7-fold improvement in a
specific HLA class II protein prediction over currently available
predictor. In some embodiments, the method provided herein exhibits
at least about a 8-fold improvement in a specific HLA class II
protein prediction over currently available predictor. In some
embodiments, the method provided herein exhibits at least about a
9-fold improvement in a specific HLA class II protein prediction
over currently available predictor. In some embodiments, the method
provided herein exhibits at least about a 10-fold improvement in a
specific HLA class II protein prediction over currently available
predictor. In some embodiments, the method provided herein exhibits
at least about a 15-fold improvement in a specific HLA class II
protein prediction over currently available predictor. In some
embodiments, the method provided herein exhibits at least about a
20-fold improvement in a specific HLA class II protein prediction
over currently available predictor. In some embodiments, the method
provided herein exhibits at least about a 30-fold improvement in a
specific HLA class II protein prediction over currently available
predictor. In some embodiments, the method provided herein exhibits
at least about a 40-fold improvement in a specific HLA class II
protein prediction over currently available predictor. In some
embodiments, the method provided herein exhibits at least about a
50-fold improvement in a specific HLA class II protein prediction
over currently available predictor. In some embodiments, the method
provided herein exhibits at least about a 60-fold improvement in a
specific HLA class II protein prediction over currently available
predictor.
[0396] In one aspect, presented herein are methods of immunotherapy
tailored or personalized for a specific subject. Every subject or
patient expresses a specific array of HLA class I and HLA class II
proteins. HLA typing is a well-known technique that allows
determination of the specific repertoire of HLA proteins expressed
by the subject. Once the HLA heterodimers expressed by a specific
subject is known, having an improved, sophisticated and reliable
method as described herein for predicting peptides that can bind to
a specific HLA class II alpha and beta chain heterodimer, with high
fidelity can ensure that a specific immune response can be
generated tailored specifically for the subject.
[0397] In this application, the use of the singular includes the
plural unless specifically stated otherwise. It must be noted that,
as used in the specification, the singular forms "a," "an" and
"the" include plural referents unless the context clearly dictates
otherwise. In this application, the use of "or" means "and/or"
unless stated otherwise. Furthermore, use of the term "including"
as well as other forms, such as "include", "includes," and
"included," is not limiting. The terms "one or more" or "at least
one," such as one or more or at least one member(s) of a group of
members, is clear per se, by means of further exemplification, the
term encompasses inter alia a reference to any one of said members,
or to any two or more of said members, such as, e.g., any
.gtoreq.3, .gtoreq.4, .gtoreq.5, .gtoreq.6 or .gtoreq.7 etc. of
said members, and up to all said members.
[0398] Reference in the specification to "some embodiments," "an
embodiment," "one embodiment" or "other embodiments" means that a
feature, structure, or characteristic described in connection with
the embodiments is included in at least some embodiments, but not
necessarily all embodiments, of the present disclosure.
[0399] As used in this specification and claim(s), the words
"comprising" (and any form of comprising, such as "comprise" and
"comprises"), "having" (and any form of having, such as "have" and
"has"), "including" (and any form of including, such as "includes"
and "include") or "containing" (and any form of containing, such as
"contains" and "contain") are inclusive or open-ended and do not
exclude additional, unrecited elements or method steps. It is
contemplated that any embodiment discussed in this specification
can be implemented with respect to any method or composition of the
disclosure, and vice versa. Furthermore, compositions of the
disclosure can be used to achieve methods of the disclosure.
[0400] The term "about" or "approximately" as used herein when
referring to a measurable value such as a parameter, an amount, a
temporal duration, and the like, is meant to encompass variations
of +/-20% or less, +/-10% or less, +/-5% or less, or +/-1% or less
of and from the specified value, insofar such variations are
appropriate to perform in the present disclosure. It is to be
understood that the value to which the modifier "about" or
"approximately" refers is itself also specifically disclosed.
[0401] The term "immune response" includes T cell mediated and/or B
cell mediated immune responses that are influenced by modulation of
T cell costimulation. Exemplary immune responses include T cell
responses, e.g., cytokine production, and cellular cytotoxicity. In
addition, the term immune response includes immune responses that
are indirectly affected by T cell activation, e.g., antibody
production (humoral responses) and activation of cytokine
responsive cells, e.g., macrophages.
[0402] A "receptor" is to be understood as meaning a biological
molecule or a molecule grouping capable of binding a ligand. A
receptor can serve to transmit information in a cell, a cell
formation or an organism. The receptor comprises at least one
receptor unit and can contain two or more receptor units, where
each receptor unit can consist of a protein molecule, e.g., a
glycoprotein molecule. The receptor has a structure that
complements the structure of a ligand and can complex the ligand as
a binding partner. Signaling information can be transmitted by
conformational changes of the receptor following binding with the
ligand on the surface of a cell. According to the present
disclosure, a receptor can refer to proteins of MHC classes I and
II capable of forming a receptor/ligand complex with a ligand,
e.g., a peptide or peptide fragment of suitable length. The class I
and class II MHC peptides that are encoded by HLA class I and class
II alleles are often referred to here as HLA class I and HLA class
II peptides respectively, or HLA class I and HLA class II peptides,
or HLA class I class II proteins, or HLA class I and HLA class II
proteins, or HLA class I and class II molecules, or such common
variants thereof, as is well understood within the context of the
discussion by one of ordinary skill in the art.
[0403] A "ligand" is a molecule which is capable of forming a
complex with a receptor. According to the present disclosure, a
ligand is to be understood as meaning, for example, a peptide or
peptide fragment which has a suitable length and suitable binding
motifs in its amino acid sequence, so that the peptide or peptide
fragment is capable of binding to and forming a complex with
proteins of MHC class I or MHC class II (i.e., HLA class I and HLA
class II proteins).
[0404] An "antigen" is a molecule capable of stimulating an immune
response, and can be produced by cancer cells or infectious agents
or an autoimmune disease. Antigens recognized by T cells, whether
helper T lymphocytes (T helper (TH) cells) or cytotoxic T
lymphocytes (CTLs), are not recognized as intact proteins, but
rather as small peptides in association with HLA class I or class
II proteins on the surface of cells. During the course of a
naturally occurring immune response, antigens that are recognized
in association with HLA class II molecules on antigen presenting
cells (APCs) are acquired from outside the cell, internalized, and
processed into small peptides that associate with the HLA class II
molecules. APCs can also cross-present peptide antigens by
processing exogenous antigens and presenting the processed antigens
on HLA class I molecules. Antigens that give rise to peptides that
are recognized in association with HLA class I MHC molecules are
generally peptides that are produced within the cells, and these
antigens are processed and associated with class I MHC molecules.
It is now understood that the peptides that associate with given
HLA class I or class II molecules are characterized as having a
common binding motif, and the binding motifs for a large number of
different HLA class I and II molecules have been determined.
Synthetic peptides that correspond to the amino acid sequence of a
given antigen and that contain a binding motif for a given HLA
class I or II molecule can also be synthesized. These peptides can
then be added to appropriate APCs, and the APCs can be used to
stimulate a T helper cell or CTL response either in vitro or in
vivo. The binding motifs, methods for synthesizing the peptides,
and methods for stimulating a T helper cell or CTL response are all
known and readily available to one of ordinary skill in the
art.
[0405] The term "peptide" is used interchangeably with "mutant
peptide" and "neoantigenic peptide" in the present specification.
Similarly, the term "polypeptide" is used interchangeably with
"mutant polypeptide" and "neoantigenic polypeptide" in the present
specification. By "neoantigen" or "neoepitope" is meant a class of
tumor antigens or tumor epitopes which arises from tumor-specific
mutations in expressed protein. The present disclosure further
includes peptides that comprise tumor specific mutations, peptides
that comprise known tumor specific mutations, and mutant
polypeptides or fragments thereof identified by the method of the
present disclosure. These peptides and polypeptides are referred to
herein as "neoantigenic peptides" or "neoantigenic polypeptides."
The polypeptides or peptides can be a variety of lengths, either in
their neutral (uncharged) forms or in forms which are salts, and
either free of modifications such as glycosylation, side chain
oxidation, phosphorylation, or any post-translational modification
or containing these modifications, subject to the condition that
the modification not destroy the biological activity of the
polypeptides as herein described. In some embodiments, the
neoantigenic peptides of the present disclosure can include: for
HLA class I, 22 residues or less in length, e.g., from about 8 to
about 22 residues, from about 8 to about 15 residues, or 9 or 10
residues; for HLA Class II, 40 residues or less in length, e.g.,
from about 8 to about 40 residues in length, from about 8 to about
24 residues in length, from about 12 to about 19 residues, or from
about 14 to about 18 residues. In some embodiments, a neoantigenic
peptide or neoantigenic polypeptide comprises a neoepitope.
[0406] The term "epitope" includes any protein determinant capable
of specific binding to an antibody, antibody peptide, and/or
antibody-like molecule (including but not limited to a T cell
receptor) as defined herein. Epitopic determinants typically
consist of chemically active surface groups of molecules such as
amino acids or sugar side chains and generally have specific
three-dimensional structural characteristics as well as specific
charge characteristics.
[0407] A "T cell epitope" is a peptide sequence which can be bound
by the MHC molecules of class I or II in the form of a
peptide-presenting MHC molecule or MHC complex and then, in this
form, be recognized and bound by cytotoxic T-lymphocytes or
T-helper cells, respectively.
[0408] The term "antibody" as used herein includes IgG (including
IgG1, IgG2, IgG3, and IgG4), IgA (including IgA1 and IgA2), IgD,
IgE, IgM, and IgY, and is meant to include whole antibodies,
including single-chain whole antibodies, and antigen-binding (Fab)
fragments thereof. Antigen-binding antibody fragments include, but
are not limited to, Fab, Fab' and F(ab')2, Fd (consisting of VH and
CH1), single-chain variable fragment (scFv), single-chain
antibodies, disulfide-linked variable fragment (dsFv) and fragments
comprising either a VL or VH domain. The antibodies can be from any
animal origin. Antigen-binding antibody fragments, including
single-chain antibodies, can comprise the variable region(s) alone
or in combination with the entire or partial of the following:
hinge region, CH1, CH2, and CH3 domains. Also included are any
combinations of variable region(s) and hinge region, CH1, CH2, and
CH3 domains. Antibodies can be monoclonal, polyclonal, chimeric,
humanized, and human monoclonal and polyclonal antibodies which,
e.g., specifically bind an HLA-associated polypeptide or an HLA-HLA
binding peptide (HLA-peptide) complex. A person of skill in the art
will recognize that a variety of immunoaffinity techniques are
suitable to enrich soluble proteins, such as soluble HLA-peptide
complexes or membrane bound HLA-associated polypeptides, e.g.,
which have been proteolytically cleaved from the membrane. These
include techniques in which (1) one or more antibodies capable of
specifically binding to the soluble protein are immobilized to a
fixed or mobile substrate (e.g., plastic wells or resin, latex or
paramagnetic beads), and (2) a solution containing the soluble
protein from a biological sample is passed over the antibody coated
substrate, allowing the soluble protein to bind to the antibodies.
The substrate with the antibody and bound soluble protein is
separated from the solution, and optionally the antibody and
soluble protein are disassociated, for example by varying the pH
and/or the ionic strength and/or ionic composition of the solution
bathing the antibodies. Alternatively, immunoprecipitation
techniques in which the antibody and soluble protein are combined
and allowed to form macromolecular aggregates can be used. The
macromolecular aggregates can be separated from the solution by
size exclusion techniques or by centrifugation.
[0409] The term "immunopurification (IP)" (or immunoaffinity
purification or immunoprecipitation) is a process well known in the
art and is widely used for the isolation of a desired antigen from
a sample. In general, the process involves contacting a sample
containing a desired antigen with an affinity matrix comprising an
antibody to the antigen covalently attached to a solid phase. The
antigen in the sample becomes bound to the affinity matrix through
an immunochemical bond. The affinity matrix is then washed to
remove any unbound species. The antigen is removed from the
affinity matrix by altering the chemical composition of a solution
in contact with the affinity matrix. The immunopurification can be
conducted on a column containing the affinity matrix, in which case
the solution is an eluent. Alternatively, the immunopurification
can be in a batch process, in which case the affinity matrix is
maintained as a suspension in the solution. An important step in
the process is the removal of antigen from the matrix. This is
commonly achieved by increasing the ionic strength of the solution
in contact with the affinity matrix, for example, by the addition
of an inorganic salt. An alteration of pH can also be effective to
dissociate the immunochemical bond between antigen and the affinity
matrix.
[0410] An "agent" is any small molecule chemical compound,
antibody, nucleic acid molecule, or polypeptide, or fragments
thereof.
[0411] An "alteration" or "change" is an increase or decrease. An
alteration can be by as little as 1%, 2%, 3%, 4%, 5%, 10%, 20%,
30%, or by 40%, 50%, 60%, or even by as much as 70%, 75%, 80%, 90%,
or 100%.
[0412] A "biologic sample" is any tissue, cell, fluid, or other
material derived from an organism. As used herein, the term
"sample" includes a biologic sample such as any tissue, cell,
fluid, or other material derived from an organism. "Specifically
binds" refers to a compound (e.g., peptide) that recognizes and
binds a molecule (e.g., polypeptide), but does not substantially
recognize and bind other molecules in a sample, for example, a
biological sample.
[0413] "Capture reagent" refers to a reagent that specifically
binds a molecule (e.g., a nucleic acid molecule or polypeptide) to
select or isolate the molecule (e.g., a nucleic acid molecule or
polypeptide).
[0414] As used herein, the terms "determining", "assessing",
"assaying", "measuring", "detecting" and their grammatical
equivalents refer to both quantitative and qualitative
determinations, and as such, the term "determining" is used
interchangeably herein with "assaying," "measuring," and the like.
Where a quantitative determination is intended, the phrase
"determining an amount" of an analyte and the like is used. Where a
qualitative and/or quantitative determination is intended, the
phrase "determining a level" of an analyte or "detecting" an
analyte is used.
[0415] A "fragment" is a portion of a protein or nucleic acid that
is substantially identical to a reference protein or nucleic acid.
In some embodiments, the portion retains at least 50%, 75%, or 80%,
or 90%, 95%, or even 99% of the biological activity of the
reference protein or nucleic acid described herein.
[0416] The terms "isolated," "purified", "biologically pure" and
their grammatical equivalents refer to material that is free to
varying degrees from components which normally accompany it as
found in its native state. "Isolate" denotes a degree of separation
from original source or surroundings. "Purify" denotes a degree of
separation that is higher than isolation. A "purified" or
"biologically pure" protein is sufficiently free of other materials
such that any impurities do not materially affect the biological
properties of the protein or cause other adverse consequences. That
is, a nucleic acid or peptide of the present disclosure is purified
if it is substantially free of cellular material, viral material,
or culture medium when produced by recombinant DNA techniques, or
chemical precursors or other chemicals when chemically synthesized.
Purity and homogeneity are typically determined using analytical
chemistry techniques, for example, polyacrylamide gel
electrophoresis or high performance liquid chromatography. The term
"purified" can denote that a nucleic acid or protein gives rise to
essentially one band in an electrophoretic gel. For a protein that
can be subjected to modifications, for example, phosphorylation or
glycosylation, different modifications can give rise to different
isolated proteins, which can be separately purified.
[0417] An "isolated" polypeptide (e.g., a peptide from an
HLA-peptide complex) or polypeptide complex (e.g., an HLA-peptide
complex) is a polypeptide or polypeptide complex of the present
disclosure that has been separated from components that naturally
accompany it. Typically, the polypeptide or polypeptide complex is
isolated when it is at least 60%, by weight, free from the proteins
and naturally-occurring organic molecules with which it is
naturally associated. The preparation can be at least 75%, at least
90%, or at least 99%, by weight, a polypeptide or polypeptide
complex of the present disclosure. An isolated polypeptide or
polypeptide complex of the present disclosure can be obtained, for
example, by extraction from a natural source, by expression of a
recombinant nucleic acid encoding such a polypeptide or one or more
components of a polypeptide complex, or by chemically synthesizing
the polypeptide or one or more components of the polypeptide
complex. Purity can be measured by any appropriate method, for
example, column chromatography, polyacrylamide gel electrophoresis,
or by HPLC analysis. In some cases, an HLA allele-encoded MHC Class
II protein (i.e., an MHC class II peptide) is interchangeably
referred to within this document as an HLA class II protein (or HLA
class II peptide).
[0418] The term "vectors" refers to a nucleic acid molecule capable
of transporting or mediating expression of a heterologous nucleic
acid. A plasmid is a species of the genus encompassed by the term
"vector." A vector typically refers to a nucleic acid sequence
containing an origin of replication and other entities necessary
for replication and/or maintenance in a host cell. Vectors capable
of directing the expression of genes and/or nucleic acid sequence
to which they are operatively linked are referred to herein as
"expression vectors". In general, expression vectors of utility are
often in the form of "plasmids" which refer to circular double
stranded DNA molecules which, in their vector form are not bound to
the chromosome, and typically comprise entities for stable or
transient expression or the encoded DNA. Other expression vectors
that can be used in the methods as disclosed herein include, but
are not limited to plasmids, episomes, bacterial artificial
chromosomes, yeast artificial chromosomes, bacteriophages or viral
vectors, and such vectors can integrate into the host's genome or
replicate autonomously in the cell. A vector can be a DNA or RNA
vector. Other forms of expression vectors known by those skilled in
the art which serve the equivalent functions can also be used, for
example, self-replicating extrachromosomal vectors or vectors
capable of integrating into a host genome. Exemplary vectors are
those capable of autonomous replication and/or expression of
nucleic acids to which they are linked.
[0419] The terms "spacer" or "linker" as used in reference to a
fusion protein refers to a peptide that joins the proteins
comprising a fusion protein. Generally, a spacer has no specific
biological activity other than to join or to preserve some minimum
distance or other spatial relationship between the proteins or RNA
sequences. However, in some embodiments, the constituent amino
acids of a spacer can be selected to influence some property of the
molecule such as the folding, net charge, or hydrophobicity of the
molecule. Suitable linkers for use in an embodiment of the present
disclosure are well known to those of skill in the art and include,
but are not limited to, straight or branched-chain carbon linkers,
heterocyclic carbon linkers, or peptide linkers. The linker is used
to separate two antigenic peptides by a distance sufficient to
ensure that, in some embodiments, each antigenic peptide properly
folds. Exemplary peptide linker sequences adopt a flexible extended
conformation and do not exhibit a propensity for developing an
ordered secondary structure. Typical amino acids in flexible
protein regions include Gly, Asn and Ser. Virtually any permutation
of amino acid sequences containing Gly, Asn and Ser would be
expected to satisfy the above criteria for a linker sequence. Other
near neutral amino acids, such as Thr and Ala, also can be used in
the linker sequence. Still other amino acid sequences that can be
used as linkers are disclosed in Maratea et al. (1985), Gene 40:
39-46; Murphy et al. (1986) Proc. Nat'l. Acad. Sci. USA 83:
8258-62; U.S. Pat. Nos. 4,935,233; and 4,751,180.
[0420] The term "neoplasia" refers to any disease that is caused by
or results in inappropriately high levels of cell division,
inappropriately low levels of apoptosis, or both. Glioblastoma is
one non-limiting example of a neoplasia or cancer. The terms
"cancer" or "tumor" or "hyperproliferative disorder" refer to the
presence of cells possessing characteristics typical of
cancer-causing cells, such as uncontrolled proliferation,
immortality, metastatic potential, rapid growth and proliferation
rate, and certain characteristic morphological features. Cancer
cells are often in the form of a tumor, but such cells can exist
alone within an animal, or can be a non-tumorigenic cancer cell,
such as a leukemia cell. Cancers include, but are not limited to, B
cell cancer (e.g., multiple myeloma, Waldenstrom's
macroglobulinemia), the heavy chain diseases (such as, for example,
alpha chain disease, gamma chain disease, and mu chain disease),
benign monoclonal gammopathy, and immunocytic amyloidosis,
melanomas, breast cancer, lung cancer, bronchus cancer, colorectal
cancer, prostate cancer (e.g., metastatic, hormone refractory
prostate cancer), pancreatic cancer, stomach cancer, ovarian
cancer, urinary bladder cancer, brain or central nervous system
cancer, peripheral nervous system cancer, esophageal cancer,
cervical cancer, uterine or endometrial cancer, cancer of the oral
cavity or pharynx, liver cancer, kidney cancer, testicular cancer,
biliary tract cancer, small bowel or appendix cancer, salivary
gland cancer, thyroid gland cancer, adrenal gland cancer,
osteosarcoma, chondrosarcoma, cancer of hematological tissues, and
the like. Other non-limiting examples of types of cancers
applicable to the methods encompassed by the present disclosure
include human sarcomas and carcinomas, e.g., fibrosarcoma,
myxosarcoma, liposarcoma, chondrosarcoma, osteogenic sarcoma,
chordoma, angiosarcoma, endotheliosarcoma, lymphangiosarcoma,
lymphangioendotheliosarcoma, synovioma, mesothelioma, Ewing's
tumor, leiomyosarcoma, rhabdomyosarcoma, colon carcinoma,
colorectal cancer, pancreatic cancer, breast cancer, ovarian
cancer, squamous cell carcinoma, basal cell carcinoma,
adenocarcinoma, sweat gland carcinoma, sebaceous gland carcinoma,
papillary carcinoma, papillary adenocarcinomas, cystadenocarcinoma,
medullary carcinoma, bronchogenic carcinoma, renal cell carcinoma,
hepatoma, bile duct carcinoma, liver cancer, choriocarcinoma,
seminoma, embryonal carcinoma, Wilms' tumor, cervical cancer, bone
cancer, brain tumor, testicular cancer, lung carcinoma, small cell
lung carcinoma, bladder carcinoma, epithelial carcinoma, glioma,
astrocytoma, medulloblastoma, craniopharyngioma, ependymoma,
pinealoma, hemangioblastoma, acoustic neuroma, oligodendroglioma,
meningioma, melanoma, neuroblastoma, retinoblastoma; leukemias,
e.g., acute lymphocytic leukemia and acute myelocytic leukemia
(myeloblastic, promyelocytic, myelomonocytic, monocytic and
erythroleukemia); chronic leukemia (chronic myelocytic
(granulocytic) leukemia and chronic lymphocytic leukemia); and
polycythemia vera, lymphoma (Hodgkin's disease and non-Hodgkin's
disease), multiple myeloma, Waldenstrom's macroglobulinemia, and
heavy chain disease. In some embodiments, the cancer is an
epithelial cancer such as, but not limited to, bladder cancer,
breast cancer, cervical cancer, colon cancer, gynecologic cancers,
renal cancer, laryngeal cancer, lung cancer, oral cancer, head and
neck cancer, ovarian cancer, pancreatic cancer, prostate cancer, or
skin cancer. In other embodiments, the cancer is breast cancer,
prostate cancer, lung cancer, or colon cancer. In still other
embodiments, the epithelial cancer is non-small-cell lung cancer,
nonpapillary renal cell carcinoma, cervical carcinoma, ovarian
carcinoma (e.g., serous ovarian carcinoma), or breast carcinoma.
The epithelial cancers can be characterized in various other ways
including, but not limited to, serous, endometrioid, mucinous,
clear cell, brenner, or undifferentiated. In some embodiments, the
present disclosure is used in the treatment, diagnosis, and/or
prognosis of lymphoma or its subtypes, including, but not limited
to, mantle cell lymphoma. Lymphoproliferative disorders are also
considered to be proliferative diseases.
[0421] The term "vaccine" is to be understood as meaning a
composition for generating immunity for the prophylaxis and/or
treatment of diseases (e.g., neoplasia/tumor/infectious
agents/autoimmune diseases). Accordingly, vaccines are medicaments
which comprise antigens and are intended to be used in humans or
animals for generating specific defense and protective substance by
vaccination. A "vaccine composition" can include a pharmaceutically
acceptable excipient, carrier or diluent. Aspects of the present
disclosure relate to use of the technology in preparing an
antigen-based vaccine. In these embodiments, vaccine is meant to
refer one or more disease-specific antigenic peptides (or
corresponding nucleic acids encoding them). In some embodiments,
the antigen-based vaccine contains at least two, at least three, at
least four, at least five, at least six, at least seven, at least
eight, at least nine, at least 10, at least 11, at least 12, at
least 13, at least 14, at least 15, at least 16, at least 17, at
least 18, at least 19, at least 20, at least 21, at least 22, at
least 23, at least 24, at least 25, at least 26, at least 27, at
least 28, at least 29, at least 30, or more antigenic peptides. In
some embodiments, the antigen-based vaccine contains from 2 to 100,
2 to 75, 2 to 50, 2 to 25, 2 to 20, 2 to 19, 2 to 18, 2 to 17, 2 to
16, 2 to 15, 2 to 14, 2 to 13, 2 to 12, 2 to 10, 2 to 9, 2 to 8, 2
to 7, 2 to 6, 2 to 5, 2 to 4, 3 to 100, 3 to 75, 3 to 50, 3 to 25,
3 to 20, 3 to 19, 3 to 18, 3 to 17, 3 to 16, 3 to 15, 3 to 14, 3 to
13, 3 to 12, 3 to 10, 3 to 9, 3 to 8, 3 to 7, 3 to 6, 3 to 5, 4 to
100, 4 to 75, 4 to 50, 4 to 25, 4 to 20, 4 to 19, 4 to 18, 4 to 17,
4 to 16, 4 to 15, 4 to 14, 4 to 13, 4 to 12, 4 to 10, 4 to 9, 4 to
8, 4 to 7, 4 to 6, 5 to 100, 5 to 75, 5 to 50, 5 to 25, 5 to 20, 5
to 19, 5 to 18, 5 to 17, 5 to 16, 5 to 15, 5 to 14, 5 to 13, 5 to
12, 5 to 10, 5 to 9, 5 to 8, or 5 to 7 antigenic peptides. In some
embodiments, the antigen-based vaccine contains 2, 3, 4, 5, 6, 7,
8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 antigenic
peptides. In some cases, the antigenic peptides are neoantigenic
peptides. In some cases, the antigenic peptides comprise one or
more neoepitopes.
[0422] The term "pharmaceutically acceptable" refers to approved or
approvable by a regulatory agency of the Federal or a state
government or listed in the U.S. Pharmacopeia or other generally
recognized pharmacopeia for use in animals, including humans. A
"pharmaceutically acceptable excipient, carrier or diluent" refers
to an excipient, carrier or diluent that can be administered to a
subject, together with an agent, and which does not destroy the
pharmacological activity thereof and is nontoxic when administered
in doses sufficient to deliver a therapeutic amount of the agent. A
"pharmaceutically acceptable salt" of pooled disease specific
antigens as recited herein can be an acid or base salt that is
generally considered in the art to be suitable for use in contact
with the tissues of human beings or animals without excessive
toxicity, irritation, allergic response, or other problem or
complication. Such salts include mineral and organic acid salts of
basic residues such as amines, as well as alkali or organic salts
of acidic residues such as carboxylic acids. Specific
pharmaceutical salts include, but are not limited to, salts of
acids such as hydrochloric, phosphoric, hydrobromic, malic,
glycolic, fumaric, sulfuric, sulfamic, sulfanilic, formic, toluene
sulfonic, methane sulfonic, benzene sulfonic, ethane disulfonic,
2-hydroxyethylsulfonic, nitric, benzoic, 2-acetoxybenzoic, citric,
tartaric, lactic, stearic, salicylic, glutamic, ascorbic, pamoic,
succinic, fumaric, maleic, propionic, hydroxymaleic, hydroiodic,
phenylacetic, alkanoic such as acetic, HOOC--(CH2)n-COOH where n is
0-4, and the like. Similarly, pharmaceutically acceptable cations
include, but are not limited to sodium, potassium, calcium,
aluminum, lithium and ammonium. Those of ordinary skill in the art
will recognize from this disclosure and the knowledge in the art
that further pharmaceutically acceptable salts for the pooled
disease specific antigens provided herein, including those listed
by Remington's Pharmaceutical Sciences, 17th ed., Mack Publishing
Company, Easton, Pa., p. 1418 (1985). In general, a
pharmaceutically acceptable acid or base salt can be synthesized
from a parent compound that contains a basic or acidic moiety by
any conventional chemical method. Briefly, such salts can be
prepared by reacting the free acid or base forms of these compounds
with a stoichiometric amount of the appropriate base or acid in an
appropriate solvent.
[0423] Nucleic acid molecules useful in the methods of the
disclosure include any nucleic acid molecule that encodes a
polypeptide of the disclosure or a fragment thereof. Such nucleic
acid molecules need not be 100% identical with an endogenous
nucleic acid sequence, but will typically exhibit substantial
identity. Polynucleotides having substantial identity to an
endogenous sequence are typically capable of hybridizing with at
least one strand of a double-stranded nucleic acid molecule.
"Hybridize" refers to when nucleic acid molecules pair to form a
double-stranded molecule between complementary polynucleotide
sequences, or portions thereof, under various conditions of
stringency. (See, e.g., Wahl, G. M. and S. L. Berger (1987) Methods
Enzymol. 152:399; Kimmel, A. R. (1987) Methods Enzymol. 152:507).
For example, stringent salt concentration can ordinarily be less
than about 750 mM NaCl and 75 mM trisodium citrate, less than about
500 mM NaCl and 50 mM trisodium citrate, or less than about 250 mM
NaCl and 25 mM trisodium citrate. Low stringency hybridization can
be obtained in the absence of organic solvent, e.g., formamide,
while high stringency hybridization can be obtained in the presence
of at least about 35% formamide, or at least about 50% formamide.
Stringent temperature conditions can ordinarily include
temperatures of at least about 30.degree. C., at least about
37.degree. C., or at least about 42.degree. C. Varying additional
parameters, such as hybridization time, the concentration of
detergent, e.g., sodium dodecyl sulfate (SDS), and the inclusion or
exclusion of carrier DNA, are well known to those skilled in the
art. Various levels of stringency are accomplished by combining
these various conditions as needed. In an exemplary embodiment,
hybridization can occur at 30.degree. C. in 750 mM NaCl, 75 mM
trisodium citrate, and 1% SDS. In another exemplary embodiment,
hybridization can occur at 37.degree. C. in 500 mM NaCl, 50 mM
trisodium citrate, 1% SDS, 35% formamide, and 100 .mu.g/ml
denatured salmon sperm DNA (ssDNA). In another exemplary
embodiment, hybridization can occur at 42.degree. C. in 250 mM
NaCl, 25 mM trisodium citrate, 1% SDS, 50% formamide, and 200
.mu.g/ml ssDNA. Useful variations on these conditions will be
readily apparent to those skilled in the art. For most
applications, washing steps that follow hybridization can also vary
in stringency. Wash stringency conditions can be defined by salt
concentration and by temperature. As above, wash stringency can be
increased by decreasing salt concentration or by increasing
temperature. For example, stringent salt concentration for the wash
steps can be less than about 30 mM NaCl and 3 mM trisodium citrate,
or less than about 15 mM NaCl and 1.5 mM trisodium citrate.
Stringent temperature conditions for the wash steps can include a
temperature of at least about 25.degree. C., of at least about
42.degree. C., or at least about 68.degree. C. In exemplary
embodiments, wash steps can occur at 25.degree. C. in 30 mM NaCl, 3
mM trisodium citrate, and 0.1% SDS. In other exemplary embodiments,
wash steps can occur at 42.degree. C. in 15 mM NaCl, 1.5 mM
trisodium citrate, and 0.1% SDS. In another exemplary embodiment,
wash steps can occur at 68.degree. C. in 15 mM NaCl, 1.5 mM
trisodium citrate, and 0.1% SDS. Additional variations on these
conditions will be readily apparent to those skilled in the art.
Hybridization techniques are well known to those skilled in the art
and are described, for example, in Benton and Davis (Science
196:180, 1977); Grunstein and Hogness (Proc. Natl. Acad. Sci., USA
72:3961, 1975); Ausubel et al. (Current Protocols in Molecular
Biology, Wiley Interscience, New York, 2001); Berger and Kimmel
(Guide to Molecular Cloning Techniques, 1987, Academic Press, New
York); and Sambrook et al., Molecular Cloning: A Laboratory Manual,
Cold Spring Harbor Laboratory Press, New York.
[0424] "Substantially identical" refers to a polypeptide or nucleic
acid molecule exhibiting at least 50% identity to a reference amino
acid sequence (for example, any one of the amino acid sequences
described herein) or nucleic acid sequence (for example, any one of
the nucleic acid sequences described herein). Such a sequence can
be at least 60%, 80% or 85%, 90%, 95%, 96%, 97%, 98%, or even 99%
or more identical at the amino acid level or nucleic acid to the
sequence used for comparison. Sequence identity is typically
measured using sequence analysis software (for example, Sequence
Analysis Software Package of the Genetics Computer Group,
University of Wisconsin Biotechnology Center, 1710 University
Avenue, Madison, Wis. 53705, BLAST, BESTFIT, GAP, or
PILEUP/PRETTYBOX programs). Such software matches identical or
similar sequences by assigning degrees of homology to various
substitutions, deletions, and/or other modifications. Conservative
substitutions typically include substitutions within the following
groups: glycine, alanine; valine, isoleucine, leucine; aspartic
acid, glutamic acid, asparagine, glutamine; serine, threonine;
lysine, arginine; and phenylalanine, tyrosine. In an exemplary
approach to determining the degree of identity, a BLAST program can
be used, with a probability score between e-3 and e-m.degree.
indicating a closely related sequence. A "reference" is a standard
of comparison.
[0425] The term "subject" or "patient" refers to an animal which is
the object of treatment, observation, or experiment. By way of
example only, a subject includes, but is not limited to, a mammal,
including, but not limited to, a human or a non-human mammal, such
as a non-human primate, murine, bovine, equine, canine, ovine, or
feline.
[0426] The terms "treat," "treated," "treating," "treatment," and
the like are meant to refer to reducing, preventing, or
ameliorating a disorder and/or symptoms associated therewith (e.g.,
a neoplasia or tumor or infectious agent or an autoimmune disease).
"Treating" can refer to administration of the therapy to a subject
after the onset, or suspected onset, of a disease (e.g., cancer or
infection by an infectious agent or an autoimmune disease).
"Treating" includes the concepts of "alleviating", which refers to
lessening the frequency of occurrence or recurrence, or the
severity, of any symptoms or other ill effects related to the
disease and/or the side effects associated with therapy. The term
"treating" also encompasses the concept of "managing" which refers
to reducing the severity of a disease or disorder in a patient,
e.g., extending the life or prolonging the survivability of a
patient with the disease, or delaying its recurrence, e.g.,
lengthening the period of remission in a patient who had suffered
from the disease. It is appreciated that, although not precluded,
treating a disorder or condition does not require that the
disorder, condition, or symptoms associated therewith be completely
eliminated.
[0427] The term "prevent", "preventing", "prevention" and their
grammatical equivalents as used herein, means avoiding or delaying
the onset of symptoms associated with a disease or condition in a
subject that has not developed such symptoms at the time the
administering of an agent or compound commences.
[0428] The term "therapeutic effect" refers to some extent of
relief of one or more of the symptoms of a disorder (e.g., a
neoplasia, tumor, or infection by an infectious agent or an
autoimmune disease) or its associated pathology. "Therapeutically
effective amount" as used herein refers to an amount of an agent
which is effective, upon single or multiple dose administration to
the cell or subject, in prolonging the survivability of the patient
with such a disorder, reducing one or more signs or symptoms of the
disorder, preventing or delaying, and the like beyond that expected
in the absence of such treatment. "Therapeutically effective
amount" is intended to qualify the amount required to achieve a
therapeutic effect. A physician or veterinarian having ordinary
skill in the art can readily determine and prescribe the
"therapeutically effective amount" (e.g., ED50) of the
pharmaceutical composition required. For example, the physician or
veterinarian can start doses of the compounds of the present
disclosure employed in a pharmaceutical composition at levels lower
than that required in order to achieve the desired therapeutic
effect and gradually increase the dosage until the desired effect
is achieved. Disease, condition, and disorder are used
interchangeably herein.
[0429] Those of ordinary skill in the art will recognize that the
terms "peptide tag," "affinity tag," "epitope tag," or "affinity
acceptor tag" are used interchangeably herein. As used herein, the
term "affinity acceptor tag" refers to an amino acid sequence that
permits the tagged protein to be readily detected or purified, for
example, by affinity purification. An affinity acceptor tag is
generally (but need not be) placed at or near the N- or C-terminus
of an HLA allele. Various peptide tags are well known in the art.
Non-limiting examples include poly-histidine tag (e.g., 4 to 15
consecutive His residues (SEQ ID NO: 4), such as 8 consecutive His
residues (SEQ ID NO: 5)); poly-histidine-glycine tag; HA tag (e.g.,
Field et al., Mol. Cell. Biol., 8:2159, 1988); c-myc tag (e.g.,
Evans et al., Mol. Cell. Biol., 5:3610, 1985); Herpes simplex virus
glycoprotein D (gD) tag (e.g., Paborsky et al., Protein
Engineering, 3:547, 1990); FLAG tag (e.g., Hopp et al.,
BioTechnology, 6:1204, 1988; U.S. Pat. Nos. 4,703,004 and
4,851,341); KT3 epitope tag (e.g., Martine et al., Science,
255:192, 1992); tubulin epitope tag (e.g., Skinner, Biol. Chem.,
266:15173, 1991); T7 gene 10 protein peptide tag (e.g.,
Lutz-Freyemuth et al., Proc. Natl. Acad. Sci. USA, 87:6393, 1990);
streptavidin tag (StrepTag.TM. or StrepTagII.TM.; see, e.g.,
Schmidt et al., J. Mol. Biol., 255(5):753-766, 1996 or U.S. Pat.
No. 5,506,121; also commercially available from Sigma-Genosys); or
a VSV-G epitope tag derived from the Vesicular Stomatis viral
glycoprotein; or a V5 tag derived from a small epitope (Pk) found
on the P and V proteins of the paramyxovirus of simian virus 5
(SV5). In some embodiments, the affinity acceptor tag is an
"epitope tag," which is a type of peptide tag that adds a
recognizable epitope (antibody binding site) to the HLA-protein to
provide binding of corresponding antibody, thereby allowing
identification or affinity purification of the tagged protein.
Non-limiting example of an epitope tag is protein A or protein G,
which binds to IgG. In some embodiments, the matrix of IgG
Sepharose 6 Fast Flow chromatography resin is covalently coupled to
human IgG. This resin allows high flow rates, for rapid and
convenient purification of a protein tagged with protein A.
Numerous other tag moieties are known to, and can be envisioned by,
the ordinarily skilled artisan, and are contemplated herein. Any
peptide tag can be used as long as it is capable of being expressed
as an element of an affinity acceptor tagged HLA-peptide
complex.
[0430] As used herein, the term "affinity molecule" refers to a
molecule or a ligand that binds with chemical specificity to an
affinity acceptor peptide. Chemical specificity is the ability of a
protein's binding site to bind specific ligands. The fewer ligands
a protein can bind, the greater its specificity. Specificity
describes the strength of binding between a given protein and
ligand. This relationship can be described by a dissociation
constant (KD), which characterizes the balance between bound and
unbound states for the protein-ligand system.
[0431] The term "affinity acceptor tagged HLA-peptide complex"
refers to a complex comprising an HLA class I or class
II-associated peptide or a portion thereof specifically bound to a
single allelic recombinant HLA class I or class II peptide
comprising an affinity acceptor peptide.
[0432] The terms "specific binding" or "specifically binding" when
used in reference to the interaction of an affinity molecule and an
affinity acceptor tag or an epitope and an HLA peptide mean that
the interaction is dependent upon the presence of a particular
structure (e.g., the antigenic determinant or epitope) on the
protein; in other words, the affinity molecule is recognizing and
binding to a specific affinity acceptor peptide structure rather
than to proteins in general.
[0433] As used herein, the term "affinity" refers to a measure of
the strength of binding between two members of a binding pair, for
example, an "affinity acceptor tag" and an "affinity molecule" and
an HLA-binding peptide and an HLA class I or II molecule. KD is the
dissociation constant and has units of molarity. The affinity
constant is the inverse of the dissociation constant. An affinity
constant is sometimes used as a generic term to describe this
chemical entity. It is a direct measure of the energy of binding.
Affinity can be determined experimentally, for example by surface
plasmon resonance (SPR) using commercially available Biacore SPR
units. Affinity can also be expressed as the inhibitory
concentration 50 (IC50), that concentration at which 50% of the
peptide is displaced. Likewise, lnIC50 refers to the natural log of
the IC50. K.sub.off refers to the off-rate constant, for example,
for dissociation of an affinity molecule from the affinity acceptor
tagged HLA-peptide complex.
[0434] In some embodiments, an affinity acceptor tagged HLA-peptide
complex comprises biotin acceptor peptide (BAP) and is
immunopurified from complex cellular mixtures using
streptavidin/NeutrAvidin beads. The biotin-avidin/streptavidin
binding is the strongest non-covalent interaction known in nature.
This property is exploited as a biological tool for a wide range of
applications, such as immunopurification of a protein to which
biotin is covalently attached. In an exemplary embodiment, the
nucleic acid sequence encoding the HLA allele implements biotin
acceptor peptide (BAP) as an affinity acceptor tag for
immunopurification. BAP can be specifically biotinylated in vivo or
in vitro at a single lysine residue within the tag (e.g., U.S. Pat.
Nos. 5,723,584; 5,874,239; and 5,932,433; and U.K Pat. No.
GB2370039). BAP is typically 15 amino acids long and contains a
single lysine as a biotin acceptor residue. In some embodiments,
BAP is placed at or near the N- or C-terminus of a single allele
HLA peptide. In some embodiments, BAP is placed in between a heavy
chain domain and .beta.2 microglobulin domain of an HLA class I
peptide. In some embodiments, BAP is placed in between .beta.-chain
domain and .alpha.-chain domain of an HLA class II peptide. In some
embodiments, BAP is placed in loop regions between .alpha.1,
.alpha.2, and .alpha.3 domains of the heavy chain of HLA class I,
or between .alpha.1 and .alpha.2 and .beta.1 and .beta.2 domains of
the .alpha.-chain and .beta.-chain, respectively of HLA class II.
Exemplary constructs designed for HLA class I and II expression
implementing BAP for biotinylation and immunopurification are
described in FIG. 2.
[0435] As used herein, the term "biotin" refers to the compound
biotin itself and analogues, derivatives and variants thereof.
Thus, the term "biotin" includes biotin
(cis-hexahydro-2-oxo-1H-thieno [3,4]imidazole-4-pentanoic acid) and
any derivatives and analogs thereof, including biotin-like
compounds. Such compounds include, for example, biotin-e-N-lysine,
biocytin hydrazide, amino or sulfhydryl derivatives of
2-iminobiotin and biotinyl-E-aminocaproic acid-N-hydroxysuccinimide
ester, sulfosuccinimideiminobiotin, biotinbromoacetylhydrazide,
p-diazobenzoyl biocytin, 3-(N-maleimidopropionyl)biocytin,
desthiobiotin, and the like. The term "biotin" also comprises
biotin variants that can specifically bind to one or more of a
Rhizavidin, avidin, streptavidin, tamavidin moiety, or other
avidin-like peptides.
[0436] As used herein, a "PPV determination method" can refer to a
presentation PPV determination method. For example, a "PPV
determination method" can refer to a method comprising (a)
processing amino acid information of a plurality of test peptide
sequences using an HLA peptide presentation prediction model, such
as a machine learning HLA peptide presentation prediction model, to
generate a plurality of test presentation predictions, each test
presentation prediction indicative of a likelihood that one or more
proteins encoded by a class II HLA allele of a cell, such as a
class II HLA allele of a cell of a subject, can present a given
test peptide sequence of the plurality of test peptide sequences,
wherein the plurality of test peptide sequences comprises at least
500 test peptide sequences comprising (i) at least one hit peptide
sequence identified by mass spectrometry to be presented by an HLA
protein expressed in cells and (ii) at least 499 decoy peptide
sequences contained within a protein encoded by a genome of an
organism, such as an organism that is the same species as the
subject, wherein the plurality of test peptide sequences comprises
a ratio of less than one of the number of hit peptide sequences to
the number of decoy peptide sequences, such as a ratio of 1:499 of
the at least one hit peptide sequences to the at least 499 decoy
peptide sequences; (b) identifying or calling a top percentage of
the plurality of test peptide sequences, such as a top 0.2% of the
plurality of test peptide sequences, as being presented by the
class II HLA allele of a cell; and (c) calculating a PPV of the HLA
peptide presentation prediction model, wherein the PPV is the
fraction of the test peptide sequences of the plurality that were
identified or called as being presented by the class II HLA allele
of a cell that are peptides observed by mass spectrometry as being
presented by the class II HLA allele of a cell. In some
embodiments, a decoy peptide is of the same length, i.e., comprises
the same number of amino acids as a hit peptide. In some
embodiments, a decoy peptide may comprise one more or one less
amino acid as compared to the hit peptide. In some embodiments the
decoy peptide is a peptide that is an endogenous peptide. In some
embodiments a decoy peptide is a synthetic peptide. In some
embodiments the decoy peptide is an endogenous peptide that has
been identified by mass spectrometry to bind to a first MHC class I
or class II protein, wherein the first MHC class I or class II
protein is distinct from a second MHC class I or class II protein
that binds to a hit peptide. In some embodiments, the decoy peptide
may be a scrambled peptide, e.g., the decoy peptide may comprise an
amino acid sequence in which the amino acid positions are
rearranged relative to that of the hit peptide within the length of
the peptide. In some embodiments, the PPV determination method can
be a presentation PPV determination method. In some embodiments,
the ratio of the number of hit peptide sequences to the number of
decoy peptide sequences is about 1:10, 1:20, 1:50, 1:100, 1:250,
1:500, 1:1000, 1:1500, 1:2000, 1:2500, 1:5000, 1:7500, 1:10000,
1:25000, 1:50000 or 1:100000. In some embodiments, the at least one
hit peptide sequence comprises at least 2, 3, 4, 5, 6, 7, 8, 9, 10,
11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27,
28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44,
45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61,
62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78,
79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95,
96, 97, 98, 99 or 100 hit peptide sequences. In some embodiments,
the at least 499 decoy peptide sequences comprises at least 500
600, 700, 800, 900, 1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700,
1800, 1900, 2000, 2100, 2200, 2300, 2400, 2500, 2600, 2700, 2800,
2900, 3000, 3100, 3200, 3300, 3400, 3500, 3600, 3700, 3800, 3900,
4000, 4100, 4200, 4300, 4400, 4500, 4600, 4700, 4800, 4900, 5000,
5100, 5200, 5300, 5400, 5500, 5600, 5700, 5800, 5900, 6000, 6100,
6200, 6300, 6400, 6500, 6600, 6700, 6800, 6900, 7000, 7100, 7200,
7300, 7400, 7500, 7600, 7700, 7800, 7900, 8000, 8100, 8200, 8300,
8400, 8500, 8600, 8700, 8800, 8900, 9000, 9100, 9200, 9300, 9400,
9500, 9600, 9700, 9800, 9900, 10000, 11000, 12000, 13000, 14000,
15000, 16000, 17000, 18000, 19000, 20000, 21000, 22000, 23000,
24000, 25000, 26000, 27000, 28000, 29000, 30000, 31000, 32000,
33000, 34000, 35000, 36000, 37000, 38000, 39000, 40000, 41000,
42000, 43000, 44000, 45000, 46000, 47000, 48000, 49000, 50000,
52500, 55000, 57500, 60000, 62500, 65000, 67500, 70000, 72500,
75000, 77500, 80000, 82500, 85000, 87500, 90000, 92500, 95000,
97500, 100000, 125000, 150000, 175000, 200000, 225000, 250000,
275000, 300000, 325000, 350000, 375000, 400000, 425000, 450000,
475000, 500000, 600000, 700000, 800000, 900000 or 1000000 decoy
peptide sequences. In some embodiments, the at least 500 test
peptide sequences comprises at least 600, 700, 800, 900, 1000,
1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900, 2000, 2100,
2200, 2300, 2400, 2500, 2600, 2700, 2800, 2900, 3000, 3100, 3200,
3300, 3400, 3500, 3600, 3700, 3800, 3900, 4000, 4100, 4200, 4300,
4400, 4500, 4600, 4700, 4800, 4900, 5000, 5100, 5200, 5300, 5400,
5500, 5600, 5700, 5800, 5900, 6000, 6100, 6200, 6300, 6400, 6500,
6600, 6700, 6800, 6900, 7000, 7100, 7200, 7300, 7400, 7500, 7600,
7700, 7800, 7900, 8000, 8100, 8200, 8300, 8400, 8500, 8600, 8700,
8800, 8900, 9000, 9100, 9200, 9300, 9400, 9500, 9600, 9700, 9800,
9900, 10000, 11000, 12000, 13000, 14000, 15000, 16000, 17000,
18000, 19000, 20000, 21000, 22000, 23000, 24000, 25000, 26000,
27000, 28000, 29000, 30000, 31000, 32000, 33000, 34000, 35000,
36000, 37000, 38000, 39000, 40000, 41000, 42000, 43000, 44000,
45000, 46000, 47000, 48000, 49000, 50000, 52500, 55000, 57500,
60000, 62500, 65000, 67500, 70000, 72500, 75000, 77500, 80000,
82500, 85000, 87500, 90000, 92500, 95000, 97500, 100000, 125000,
150000, 175000, 200000, 225000, 250000, 275000, 300000, 325000,
350000, 375000, 400000, 425000, 450000, 475000, 500000, 600000,
700000, 800000, 900000 or 1000000 test peptide sequences. In some
embodiments, identifying or calling a top percentage of the
plurality of test peptide sequences as being presented by the class
II HLA allele of a cell comprises identifying or calling a top
0.20%, 0.30%, 0.40%, 0.50%, 0.60%, 0.70%, 0.80%, 0.90%, 1.00%,
1.10%, 1.20%, 1.30%, 1.40%, 1.50%, 1.60%, 1.70%, 1.80%, 1.90%,
2.00%, 2.10%, 2.20%, 2.30%, 2.40%, 2.50%, 2.60%, 2.70%, 2.80%,
2.90%, 3.00%, 3.10%, 3.20%, 3.30%, 3.40%, 3.50%, 3.60%, 3.70%,
3.80%, 3.90%, 4.00%, 4.10%, 4.20%, 4.30%, 4.40%, 4.50%, 4.60%,
4.70%, 4.80%, 4.90%, 5.00%, 5.10%, 5.20%, 5.30%, 5.40%, 5.50%,
5.60%, 5.70%, 5.80%, 5.90%, 6.00%, 6.10%, 6.20%, 6.30%, 6.40%,
6.50%, 6.60%, 6.70%, 6.80%, 6.90%, 7.00%, 7.10%, 7.20%, 7.30%,
7.40%, 7.50%, 7.60%, 7.70%, 7.80%, 7.90%, 8.00%, 8.10%, 8.20%,
8.30%, 8.40%, 8.50%, 8.60%, 8.70%, 8.80%, 8.90%, 9.00%, 9.10%,
9.20%, 9.30%, 9.40%, 9.50%, 9.60%, 9.70%, 9.80%, 9.90%, 10%, 11%,
12%, 13%, 14%, 15%, 16%, 17%, 18%, 19% or 20% as being presented by
the class II HLA allele of a cell. In some embodiments, the cell is
a mono-allelic cell.
[0437] As used herein, a "PPV determination method" can refer to a
binding PPV determination method. For example, a "PPV determination
method" can refer to a method comprising (a) processing amino acid
information of a plurality of test peptide sequences using an HLA
peptide binding prediction model, such as a machine learning HLA
peptide binding prediction model, to generate a plurality of test
binding predictions, each test binding prediction indicative of a
likelihood that the one or more proteins encoded by a class II HLA
allele of a cell, such as a class II HLA allele of a cell of a
subject, binds to a given test peptide sequence of the plurality of
test peptide sequences, wherein the plurality of test peptide
sequences comprises at least 20 test peptide sequences comprising
(i) at least one hit peptide sequence identified by mass
spectrometry to be presented by an HLA protein expressed in cells
and (ii) at least 19 decoy peptide sequences contained within a
protein comprising at least one peptide sequence identified by mass
spectrometry to be presented by an HLA protein expressed in cells,
wherein the plurality of test peptide sequences comprises a ratio
of less than one of the number of hit peptide sequences to the
number of decoy peptide sequences, such as a ratio of 1:19 of the
at least one hit peptide sequences to the at least 19 decoy peptide
sequences; (b) identifying or calling a top percentage of the
plurality of test peptide sequences, such as a top 5% of the
plurality of test peptide sequences, as binding to the HLA protein;
and (c) calculating a PPV of the HLA peptide binding prediction
model, wherein the PPV is the fraction of the test peptide
sequences of the plurality that were identified or called as
binding to the class II HLA allele of a cell that are peptides
observed by mass spectrometry as being presented by the class II
HLA allele of a cell. In some embodiments, the ratio of the number
of hit peptide sequences to the number of decoy peptide sequences
is about 1:2, 1:3, 1:4, 1:5, 1:10, 1:20, 1:25, 1:30, 1:40, 1:50,
1:75, 1:100, 1:200, 1:250, 1:500 or 1:1000. In some embodiments,
the at least one hit peptide sequence comprises at least 2, 3, 4,
5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22,
23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,
40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56,
57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73,
74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90,
91, 92, 93, 94, 95, 96, 97, 98, 99 or 100 hit peptide sequences. In
some embodiments, the at least 19 decoy peptide sequences comprises
at least 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150,
160, 170, 180, 190, 200, 210, 220, 230, 240, 250, 260, 270, 280,
290, 300, 310, 320, 330, 340, 350, 360, 370, 380, 390, 400, 410,
420, 430, 440, 450, 460, 470, 480, 490, 500 600, 700, 800, 900,
1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900, 2000,
2100, 2200, 2300, 2400, 2500, 2600, 2700, 2800, 2900, 3000, 3100,
3200, 3300, 3400, 3500, 3600, 3700, 3800, 3900, 4000, 4100, 4200,
4300, 4400, 4500, 4600, 4700, 4800, 4900, 5000, 5100, 5200, 5300,
5400, 5500, 5600, 5700, 5800, 5900, 6000, 6100, 6200, 6300, 6400,
6500, 6600, 6700, 6800, 6900, 7000, 7100, 7200, 7300, 7400, 7500,
7600, 7700, 7800, 7900, 8000, 8100, 8200, 8300, 8400, 8500, 8600,
8700, 8800, 8900, 9000, 9100, 9200, 9300, 9400, 9500, 9600, 9700,
9800, 9900, 10000, 11000, 12000, 13000, 14000, 15000, 16000, 17000,
18000, 19000, 20000, 21000, 22000, 23000, 24000, 25000, 26000,
27000, 28000, 29000, 30000, 31000, 32000, 33000, 34000, 35000,
36000, 37000, 38000, 39000, 40000, 41000, 42000, 43000, 44000,
45000, 46000, 47000, 48000, 49000, 50000, 52500, 55000, 57500,
60000, 62500, 65000, 67500, 70000, 72500, 75000, 77500, 80000,
82500, 85000, 87500, 90000, 92500, 95000, 97500, 100000, 125000,
150000, 175000, 200000, 225000, 250000, 275000, 300000, 325000,
350000, 375000, 400000, 425000, 450000, 475000, 500000, 600000,
700000, 800000, 900000 or 1000000 decoy peptide sequences. In some
embodiments, the at least 20 test peptide sequences comprises at
least 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150,
160, 170, 180, 190, 200, 210, 220, 230, 240, 250, 260, 270, 280,
290, 300, 310, 320, 330, 340, 350, 360, 370, 380, 390, 400, 410,
420, 430, 440, 450, 460, 470, 480, 490, 500 600, 700, 800, 900,
1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900, 2000,
2100, 2200, 2300, 2400, 2500, 2600, 2700, 2800, 2900, 3000, 3100,
3200, 3300, 3400, 3500, 3600, 3700, 3800, 3900, 4000, 4100, 4200,
4300, 4400, 4500, 4600, 4700, 4800, 4900, 5000, 5100, 5200, 5300,
5400, 5500, 5600, 5700, 5800, 5900, 6000, 6100, 6200, 6300, 6400,
6500, 6600, 6700, 6800, 6900, 7000, 7100, 7200, 7300, 7400, 7500,
7600, 7700, 7800, 7900, 8000, 8100, 8200, 8300, 8400, 8500, 8600,
8700, 8800, 8900, 9000, 9100, 9200, 9300, 9400, 9500, 9600, 9700,
9800, 9900, 10000, 11000, 12000, 13000, 14000, 15000, 16000, 17000,
18000, 19000, 20000, 21000, 22000, 23000, 24000, 25000, 26000,
27000, 28000, 29000, 30000, 31000, 32000, 33000, 34000, 35000,
36000, 37000, 38000, 39000, 40000, 41000, 42000, 43000, 44000,
45000, 46000, 47000, 48000, 49000, 50000, 52500, 55000, 57500,
60000, 62500, 65000, 67500, 70000, 72500, 75000, 77500, 80000,
82500, 85000, 87500, 90000, 92500, 95000, 97500, 100000, 125000,
150000, 175000, 200000, 225000, 250000, 275000, 300000, 325000,
350000, 375000, 400000, 425000, 450000, 475000, 500000, 600000,
700000, 800000, 900000 or 1000000 test peptide sequences. In some
embodiments, identifying or calling a top percentage of the
plurality of test peptide sequences as being presented by the class
II HLA allele of a cell comprises identifying or calling a top 5%,
6%, 7%, 8%, 9%, 10%, 11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%, 19%,
20%, 21%, 22%, 23%, 24%, 25%, 26%, 27%, 28%, 29%, 30%, 31%, 32%,
33%, 34%, 35%, 36%, 37%, 38%, 39%, or 40% as being presented by the
class II HLA allele of a cell. In some embodiments, the cell is a
mono-allelic cell.
Human Leukocyte Antigen (HLA) System
[0438] The immune system can be classified into two functional
subsystems: the innate and the adaptive immune system. The innate
immune system is the first line of defense against infections, and
most potential pathogens are rapidly neutralized by this system
before they can cause, for example, a noticeable infection. The
adaptive immune system reacts to molecular structures, referred to
as antigens, of the intruding organism. Unlike the innate immune
system, the adaptive immune system is highly specific to a
pathogen. Adaptive immunity can also provide long-lasting
protection; for example, someone who recovers from measles is now
protected against measles for their lifetime. There are two types
of adaptive immune reactions, which include the humoral immune
reaction and the cell-mediated immune reaction. In the humoral
immune reaction, antibodies secreted by B cells into bodily fluids
bind to pathogen-derived antigens, leading to the elimination of
the pathogen through a variety of mechanisms, e.g.
complement-mediated lysis. In the cell-mediated immune reaction, T
cells capable of destroying other cells are activated. For example,
if proteins associated with a disease are present in a cell, they
are fragmented proteolytically to peptides within the cell.
Specific cell proteins then attach themselves to the antigen or
peptide formed in this manner and transport them to the surface of
the cell, where they are presented to the molecular defense
mechanisms, in T cells, of the body. Cytotoxic T cells recognize
these antigens and kill the cells that harbor the antigens.
[0439] The term "major histocompatibility complex (MHC)", "MHC
molecules", or "MHC proteins" refers to proteins capable of binding
peptides resulting from the proteolytic cleavage of protein
antigens and representing potential T cell epitopes, transporting
them to the cell surface and presenting the peptides to specific
cells, e.g., in cytotoxic T-lymphocytes or T-helper cells. The
human MHC is also called the HLA complex. Thus, the term "human
leukocyte antigen (HLA) system", "HLA molecules" or "HLA proteins"
refers to a gene complex encoding the MHC proteins in humans. The
term MHC is referred as the "H-2" complex in murine species. Those
of ordinary skill in the art will recognize that the terms "major
histocompatibility complex (MHC)", "MHC molecules", "MHC proteins"
and "human leukocyte antigen (HLA) system", "HLA molecules", "HLA
proteins" are used interchangeably herein.
[0440] HLA proteins are classified into two types, referred to as
HLA class I and HLA class II. The structures of the proteins of the
two HLA classes are very similar; however, they have very different
functions. HLA class I proteins are present on the surface of
almost all cells of the body, including most tumor cells. HLA class
I proteins are loaded with antigens that usually originate from
endogenous proteins or from pathogens present inside cells and are
then presented to naive or cytotoxic T-lymphocytes (CTLs). HLA
class II proteins are present on antigen presenting cells (APCs),
including but not limited to dendritic cells, B cells, and
macrophages. They mainly present peptides, which are processed from
external antigen sources, e.g. outside of the cells, to helper T
cells. Most of the peptides bound by the HLA class I proteins
originate from cytoplasmic proteins produced in the healthy host
cells of an organism itself, and do not normally stimulate an
immune reaction.
[0441] HLA class I molecules (FIG. 1) consist of two non-covalently
linked polypeptide chains, an HLA-encoded .alpha. chain (heavy
chain, 44 to 47 kD) and a non-HLA encoded subunit called .beta.2
microglobulin (or, .beta.2m), (12 kD). The .alpha. chain has three
extracellular domains, .alpha.1, .alpha.2 and .alpha.3 and a
transmembrane region, of which the .alpha.1 and .alpha.2 regions
are capable of binding a peptide of about 7 to 13 amino acids
(e.g., about 8 to 11 amino acids, or 9 or 10 amino acids). An HLA
class 1 molecule binds to a peptide that has the suitable binding
motifs, and presents it to cytotoxic T-lymphocytes. HLA class 1
heavy chains can be the protein product of an HLA-A allele, also
termed as an HLA-A monomer, or the protein product of HLA-B allele
(likewise, an HLA-B monomer) or the protein product of HLA-C allele
(an HLA-C monomer), each of which complexes with a
.beta.-2-microglobulin. The .alpha.1 rests upon the non-HLA protein
.beta.2m; .beta.2m is encoded by beta-2-microglobulin gene located
on human chromosome 15. The .alpha.3 domain is connected to the
transmembrane region, anchoring the HLA class I molecule to the
cell membrane. The peptide being presented is held by the floor of
the peptide-binding groove, in the central region of the
.alpha.1/.alpha.2 heterodimer (a molecule composed of two
non-identical subunits). HLA class I-A, HLA class I-B or HLA class
I-C are highly polymorphic. Each of a HLA class 1-A gene (termed
HLA-A gene), a HLA class I-B gene (termed HLA-B gene) and a HLA
class 1-C gene (termed HLA-C gene) contains 8 exons, exon 1 encodes
the leader peptide, exons 2 and 3 encode the .alpha.1 and .alpha.2
domains, exon 5 encodes the transmembrane region and exons 6 and 7
encode the cytoplasmic tail. Polymorphisms of exon 2 and exon 3 are
responsible for the peptide binding specificity of each class 1
molecule. HLA class I-B gene (HLA-B) has many possible variations,
expression patterns and presented antigens. This group is
subdivided into a group encoded within HLA loci, e.g., HLA-E,
HLA-F, HLA-G, as well as those not, e.g., stress ligands such as
ULBPs, Rae 1 and H60. The antigen/ligand for many of these
molecules remains unknown, but they can interact with each of CD8+
T cells, NKT cells, and NK cells.
[0442] In some embodiments, the present disclosure utilizes a
non-classical HLA class I-E allele. HLA-E molecules are recognized
by natural killer (NK) cells and CD8+ T cells. HLA-E is expressed
in almost all tissues including lung, liver, skin and placental
cells. HLA-E expression is also detected in solid tumors (e.g.,
osteosarcoma and melanoma). HLA-E molecule binds to TCR expressed
on CD8+ T cells, resulting in T cell activation. HLA-E is also
known to bind CD94/NKG2 receptor expressed on NK cells and CD8+ T
cells. CD94 can pair with several different isoforms of NKG2 to
form receptors with potential to either inhibit (NKG2A, NKG2B) or
promote (NKG2C) cellular activation. HLA-E can bind to a peptide
derived from amino acid residues 3-11 of the leader sequences of
most HLA-A, --B, --C, and -G molecules, but cannot bind to its own
leader peptide. HLA-E has also been shown to present peptides
derived from endogenous proteins similar to HLA-A, -B, and -C
alleles. Under physiological conditions, the engagement of
CD94/NKG2A with HLA-E, loaded with peptides from the HLA class I
leader sequences, usually induces inhibitory signals.
Cytomegalovirus (CMV) utilizes the mechanism for escape from NK
cell immune surveillance via expression of the UL40 glycoprotein,
mimicking the HLA-A leader. However, it is also reported that CD8+
T cells can recognize HLA-E loaded with the UL40 peptide derived
from CMV Toledo strain and play a role in defense against CMV. A
number of studies revealed several important functions of HLA-E in
infectious disease and cancer.
[0443] The peptide antigens attach themselves to the molecules of
HLA class I by competitive affinity binding within the endoplasmic
reticulum before they are presented on the cell surface. Here, the
affinity of an individual peptide antigen is directly linked to its
amino acid sequence and the presence of specific binding motifs in
defined positions within the amino acid sequence. If the sequence
of such a peptide is known, it is possible to manipulate the immune
system against diseased cells using, for example, peptide
vaccines.
[0444] MHC molecules are highly polymorphic, that is, there are
many MHC variants. Each variant is encoded by a variation of the
gene encoding the protein, and each such variant gene is called an
allele. For human beings, MHC is known as Human Leukocyte Antigens
(HLA), which involves three types of HLA class II molecules: DP, DQ
and DR. HLA class II peptides (FIG. 1) have two chains, .alpha. and
.beta., each having two domains--.alpha.1 and .alpha.2 and .beta.1
and .beta.2--each chain having a transmembrane domain, .alpha.2 and
.beta.2, respectively, anchoring the HLA class II molecule to the
cell membrane. The peptide-binding groove is formed from the
heterodimer of .alpha.1 and .beta.1. The most widely studied HLA-DR
molecules have DRA and DRB, corresponding to .alpha. and .beta.
domains, respectively. The DRB is diverse, DRA is almost identical.
Thus, the binding specificity of a DRB allele indicates that of the
corresponding HLA-DR. Each MHC protein has its own binding
specificity, meaning that a set of peptides binding to an MHC
molecule can be different from those to another MHC molecule.
Classic molecules present peptides to CD4+ lymphocytes. Nonclassic
molecules, accessories, with intracellular functions, are not
exposed on cell membranes but in internal membranes in lysosomes,
normally loading the antigenic peptides onto classic HLA class II
molecules.
[0445] In HLA class II system, phagocytes such as macrophages and
immature dendritic cells take up entities by phagocytosis into
phagosomes--though B cells exhibit the more general endocytosis
into endosomes--which fuse with lysosomes whose acidic enzymes
cleave the uptaken protein into many different peptides. Autophagy
is another source of HLA class II peptides. Via physicochemical
dynamics in molecular interaction with the HLA class II variants
borne by the host, encoded in the host's genome, a particular
peptide exhibits immunodominance and loads onto HLA class II
molecules. These are trafficked to and externalized on the cell
surface. The most studied subclasses of HLA class II genes are:
HLA-DPA1, HLA-DPB1, HLA-DQA1, HLA-DQB1, HLA-DRA, and HLA-DRB1.
[0446] Presentation of peptides by HLA class II molecules to CD4+
helper T cells is required for immune responses to foreign antigens
(Roche and Furuta, 2015). Once activated, CD4+ T cells promote B
cell differentiation and antibody production, as well as CD8+ T
cell (CTL) responses. CD4+ T cells also secrete cytokines and
chemokines that activate and induce differentiation of other immune
cells. HLA class II molecules are heterodimers of .alpha.- and
.beta.-chains that interact to form a peptide-binding groove that
is more open than HLA class I peptide-binding grooves (Unanue et
al., 2016). Peptides bound to HLA class II molecules are believed
to have a 9-amino acid binding core with flanking residues on
either N- or C-terminal side that overhang from the groove
(Jardetzky et al., 1996; Stern et al., 1994). These peptides are
usually 12-16 amino acids in length and often contain 3-4 anchor
residues at positions P1, P4, P6/7 and P9 of the binding register
(Rossjohn et al., 2015).
[0447] HLA alleles are expressed in codominant fashion, meaning
that the alleles (variants) inherited from both parents are
expressed equally. For example, each person carries 2 alleles of
each of the 3 class I genes, (HLA-A, HLA-B and HLA-C) and so can
express six different types of HLA class II. In the HLA class II
locus, each person inherits a pair of HLA-DP genes (DPA1 and DPB1,
which encode .alpha. and .beta. chains), HLA-DQ (DQA1 and DQB1, for
.alpha. and .beta. chains), one gene HLA-DR.alpha. (DRA1), and one
or more genes HLA-DR.beta. (DRB1 and DRB3, -4 or -5). HLA-DRB1, for
example, has more than nearly 400 known alleles. That means that
one heterozygous individual can inherit six or eight functioning
HLA class II alleles: three or more from each parent. Thus, the HLA
genes are highly polymorphic; many different alleles exist in the
different individuals inside a population. Genes encoding HLA
proteins have many possible variations, allowing each person's
immune system to react to a wide range of foreign invaders. Some
HLA genes have hundreds of identified versions (alleles), each of
which is given a particular number. In some embodiments, the HLA
class I alleles are HLA-A*02:01, HLA-B*14:02, HLA-A*23:01,
HLA-E*01:01 (non-classical). In some embodiments, HLA class II
alleles are HLA-DRB*01:01, HLA-DRB*01:02, HLA-DRB*11:01,
HLA-DRB*15:01, and HLA-DRB*07:01.
[0448] Subject specific HLA alleles or HLA genotype of a subject
can be determined by any method known in the art. In exemplary
embodiments, HLA genotypes are determined by any method described
in International Patent Application number PCT/US2014/068746,
published Jun. 11, 2015 as WO2015085147, which is incorporated
herein by reference in its entirety. Briefly, the methods include
determining polymorphic gene types that can comprise generating an
alignment of reads extracted from a sequencing data set to a gene
reference set comprising allele variants of the polymorphic gene,
determining a first posterior probability or a posterior
probability derived score for each allele variant in the alignment,
identifying the allele variant with a maximum first posterior
probability or posterior probability derived score as a first
allele variant, identifying one or more overlapping reads that
aligned with the first allele variant and one or more other allele
variants, determining a second posterior probability or posterior
probability derived score for the one or more other allele variants
using a weighting factor, identifying a second allele variant by
selecting the allele variant with a maximum second posterior
probability or posterior probability derived score, the first and
second allele variant defining the gene type for the polymorphic
gene, and providing an output of the first and second allele
variant.
[0449] In some embodiments the MHC class II peptide: antigenic
peptide binding and presenting prediction methods described herein
have the capacity to predict binders from a large repertoire MHC
class II peptides encoded by individual HLA alleles. In some
embodiments, the MAPTAC technology is trained with a large database
of mass spectrometry validated HLA-matched peptides. In some
embodiments, the large database of mass spectrometry validated
HLA-matched peptides comprise greater than 1.2.times.10{circumflex
over ( )}6 such HLA-matched peptides. In some embodiments, the
large database of mass spectrometry validated HLA-matched peptides
cover greater than 150 HLA alleles including both MHC Class I and
Class II allelic subtypes. In some embodiments, the database covers
at least 95% of US population for HLA-I and HLA-II (DR
subtype).
[0450] As described herein, there is a large body of evidence in
both animals and humans that mutated epitopes are effective in
inducing an immune response and that cases of spontaneous tumor
regression or long term survival correlate with CD8+ T cell
responses to mutated epitopes and that "immunoediting" can be
tracked to alterations in expression of dominant mutated antigens
in mice and man.
[0451] Sequencing technology has revealed that each tumor contains
multiple, patient-specific mutations that alter the protein coding
content of a gene. Such mutations create altered proteins, ranging
from single amino acid changes (caused by missense mutations) to
additions of long regions of novel amino acid sequences due to
frame shifts, read-through of termination codons or translation of
intron regions (novel open reading frame mutations; neoORFs). These
mutated proteins are valuable targets for the host's immune
response to the tumor as, unlike native proteins, they are not
subject to the immune-dampening effects of self-tolerance.
Therefore, mutated proteins are more likely to be immunogenic and
are also more specific for the tumor cells compared to normal cells
of the patient. In essence, short peptides (8-24 amino acids long)
containing a cancer associated mutation are candidates for cancer
immunotherapy.
[0452] In some embodiments the algorithm driving the prediction
method can be further utilized for mutation calling on a peptide.
In some embodiments, the prediction method may be used for
determining driver mutation status, and/or RNA expression status,
and/or cleavage prediction within the peptide.
[0453] The term "T cell" includes CD4+ T cells and CD8+ T cells.
The term T cell also includes both T helper 1 type T cells and T
helper 2 type T cells. T cells as used herein are generally
classified by function and cell surface antigens (cluster
differentiation antigens, or CDs), which also facilitate T cell
receptor binding to antigen, into two major classes: helper T (TH)
cells and cytotoxic T-lymphocytes (CTLs).
[0454] Mature helper T (TH) cells express the surface protein CD4
and are referred as CD4+ T cells. Following T cell development,
matured, naive T cells leave the thymus and begin to spread
throughout the body, including the lymph nodes. Naive T cells are
those T cells that have never been exposed to the antigen that they
are programmed to respond to. Like all T cells, they express the T
cell receptor-CD3 complex. The T cell receptor (TCR) consists of
both constant and variable regions. The variable region determines
what antigen the T cell can respond to. CD4+ T cells have TCRs with
an affinity for MHC class II, proteins and CD4 are involved in
determining MHC affinity during maturation in the thymus. MHC class
II proteins are generally only found on the surface of specialized
antigen-presenting cells (APCs). Specialized antigen presenting
cells (APCs) are primarily dendritic cells, macrophages and B
cells, although dendritic cells are the only cell group that
expresses MHC Class II constitutively (at all times). Some APCs
also bind native (or unprocessed) antigens to their surface, such
as follicular dendritic cells, but unprocessed antigens do not
interact with T cells and are not involved in their activation. The
peptide antigens that bind to HLA class I proteins are typically
shorter than peptide antigens that bind to HLA class II
proteins.
[0455] Cytotoxic T-lymphocytes (CTLs), also known as cytotoxic T
cells, cytolytic T cells, CD8+ T cells, or killer T cells, refer to
lymphocytes which induce apoptosis in targeted cells. CTLs form
antigen-specific conjugates with target cells via interaction of
TCRs with processed antigen (Ag) on target cell surfaces, resulting
in apoptosis of the targeted cell. Apoptotic bodies are eliminated
by macrophages. The term "CTL response" is used to refer to the
primary immune response mediated by CTL cells. Cytotoxic
T-lymphocytes have both T cell receptors (TCR) and CD8 molecules on
their surface. T cell receptors are capable of recognizing and
binding peptides complexed with the molecules of HLA class I. Each
cytotoxic T-lymphocyte expresses a unique T cell receptor which is
capable of binding specific MHC/peptide complexes. Most cytotoxic T
cells express T cell receptors (TCRs) that can recognize a specific
antigen. In order for the TCR to bind to the HLA class I molecule,
the former must be accompanied by a glycoprotein called CD8, which
binds to the constant portion of the HLA class I molecule.
Therefore, these T cells are called CD8+ T cells. The affinity
between CD8 and the MHC molecule keeps the T cell and the target
cell bound closely together during antigen-specific activation.
CD8+ T cells are recognized as T cells once they become activated
and are generally classified as having a pre-defined cytotoxic role
within the immune system. However, CD8+ T cells also have the
ability to make some cytokines.
[0456] "T cell receptors (TCR)" are cell surface receptors that
participate in the activation of T cells in response to the
presentation of antigen. The TCR is generally made from two chains,
alpha and beta, which assemble to form a heterodimer and associates
with the CD3-transducing subunits to form the T cell receptor
complex present on the cell surface. Each alpha and beta chain of
the TCR consists of an immunoglobulin-like N-terminal variable (V)
and constant (C) region, a hydrophobic transmembrane domain, and a
short cytoplasmic region. As for immunoglobulin molecules, the
variable regions of the alpha and beta chains are generated by
V(D)J recombination, creating a large diversity of antigen
specificities within the population of T cells. However, in
contrast to immunoglobulins that recognize intact antigen, T cells
are activated by processed peptide fragments in association with an
MHC molecule, introducing an extra dimension to antigen recognition
by T cells, known as MHC restriction. Recognition of MHC
disparities between the donor and recipient through the T cell
receptor leads to T cell proliferation and the potential
development of GVHD. It has been shown that normal surface
expression of the TCR depends on the coordinated synthesis and
assembly of all seven components of the complex (Ashwell and
Klusner 1990). The inactivation of TCR.alpha. or TCR.beta. can
result in the elimination of the TCR from the surface of T cells
preventing recognition of alloantigen and thus GVHD. However, TCR
disruption generally results in the elimination of the CD3
signaling component and alters the means of further T cell
expansion.
[0457] The term "HLA peptidome" refers to a pool of peptides which
specifically interacts with a particular HLA class and can
encompass thousands of different sequences. HLA peptidomes include
a diversity of peptides, derived from both normal and abnormal
proteins expressed in the cells. Thus, the HLA peptidomes can be
studied to identify cancer specific peptides, for development of
tumor immunotherapeutics and as a source of information about
protein synthesis and degradation schemes within the cancer cells.
In some embodiments, HLA peptidome is a pool of soluble HLA
peptides (sHLA). In some embodiments, HLA peptidome is a pool of
membrane associated HLA (mHLA).
[0458] "Antigen presenting cell" or "APC" includes professional
antigen presenting cells (e.g., B lymphocytes, macrophages,
monocytes, dendritic cells, Langerhans cells), as well as other
antigen presenting cells (e.g., keratinocytes, endothelial cells,
astrocytes, fibroblasts, oligodendrocytes, thymic epithelial cells,
thyroid epithelial cells, glial cells (brain), pancreatic beta
cells, and vascular endothelial cells). An "antigen presenting
cell" or "APC" is a cell that expresses the Major
Histocompatibility complex (MHC) molecules and can display foreign
antigen complexed with MHC on its surface.
Mono-Allelic HLA Cell Lines
[0459] A mono-allelic cell line expressing either a single HLA
class I allele, a single pair of HLA class II alleles, or a single
HLA class I allele and a single pair of HLA class II alleles can be
generated by transducing or transfecting a suitable cell population
with a polynucleic acid, e.g., a vector, coding a single HLA allele
(FIG. 2). Suitable cell populations include, e.g., HLA class I
deficient cells lines in which a single HLA class I allele is
exogenously expressed, HLA class II deficient cell lines in which a
single exogenous pair of HLA class II alleles are expressed, or
class I and class II deficient cell lines in which a single HLA
class I and/or single pair of class II alleles are exogenously
expressed. As an exemplary embodiment, the HLA class I deficient B
cell line is B721.221. However, it is clear to a skilled person
that other cell populations can be generated which are HLA class I
and/or HLA class II deficient. An exemplary method for
deleting/inactivating endogenous HLA class I or HLA class II genes
includes CRISPR-Cas9 mediated genome editing in, for example, THP-1
cells. In some embodiments, the populations of cells are
professional antigen presenting cells, such as macrophages, B
cells, and dendritic cells. The cells can be B cells or dendritic
cells. In some embodiments, the cells are tumor cells or cells from
a tumor cell line. In some embodiments, the cells are isolated from
a patient. In some embodiments, the cells contain an infectious
agent or a portion thereof. In some embodiments, the population of
cells comprises at least 107 cells. In some embodiments, the
population of cells are further modified, such as by increasing or
decreasing the expression and/or activity of at least one gene. In
some embodiments, the gene encodes a member of the
immunoproteasome. The immunoproteasome is known to be involved in
the processing of HLA class I binding peptides and includes the
LMP2 (.beta.1i), MECL-1 (.beta.2i), and LMP7 (.beta.5i) subunits.
The immunoproteasome can also be induced by interferon-gamma.
Accordingly, in some embodiments, the population of cells can be
contacted with one or more cytokines, growth factors, or other
proteins. The cells can be stimulated with inflammatory cytokines
such as interferon-gamma, IL-10, IL-6, and/or TNF-.alpha.. The
population of cells can also be subjected to various environmental
conditions, such as stress (heat stress, oxygen deprivation,
glucose starvation, DNA damaging agents, etc.). In some
embodiments, the cells are contacted with one or more of a
chemotherapy drug, radiation, targeted therapies, or immunotherapy.
The methods disclosed herein can therefore be used to study the
effect of various genes or conditions on HLA peptide processing and
presentation. In some embodiments, the conditions used are selected
so as to match the condition of the patient for which the
population of HLA-peptides is to be identified.
[0460] A single HLA-allele of the present disclosure can be encoded
and expressed using a viral based system (e.g., an adenovirus
system, an adeno associated virus (AAV) vector, a poxvirus, or a
lentivirus). Plasmids that can be used for adeno associated virus,
adenovirus, and lentivirus delivery have been described previously
(see e.g., U.S. Pat. Nos. 6,955,808 and 6,943,019, and U.S. Patent
application No. 20080254008, hereby incorporated by reference).
Among vectors that can be used in the practice of the present
disclosure, integration in the host genome of a cell is possible
with retrovirus gene transfer methods, often resulting in long term
expression of the inserted transgene. In an exemplary embodiment,
the retrovirus is a lentivirus. Additionally, high transduction
efficiencies have been observed in many different cell types and
target tissues. The tropism of a retrovirus can be altered by
incorporating foreign envelope proteins, expanding the potential
target population of target cells. A retrovirus can also be
engineered to allow for conditional expression of the inserted
transgene, such that only certain cell types are infected by the
lentivirus. Cell type specific promoters can be used to target
expression in specific cell types. Lentiviral vectors are
retroviral vectors (and hence both lentiviral and retroviral
vectors can be used in the practice of the present disclosure).
Moreover, lentiviral vectors are able to transduce or infect
non-dividing cells and typically produce high viral titers.
[0461] Selection of a retroviral gene transfer system can depend on
the target tissue. Retroviral vectors are comprised of cis-acting
long terminal repeats with packaging capacity for up to 6-10 kb of
foreign sequence. The minimum cis-acting LTRs are sufficient for
replication and packaging of the vectors, which are then used to
integrate the desired nucleic acid into the target cell to provide
permanent expression. Widely used retroviral vectors that can be
used in the practice of the present disclosure include those based
upon murine leukemia virus (MuLV), gibbon ape leukemia virus
(GaLV), Simian Immunodeficiency virus (SIV), human immunodeficiency
virus (HIV), and combinations thereof (see, e.g., Buchscher et al.,
(1992) J. Virol. 66:2731-2739; Johann et al., (1992) J. Virol.
66:1635-1640; Sommnerfelt et al., (1990) Virol. 176:58-59; Wilson
et al., (1998) J. Virol. 63:2374-2378; Miller et al., (1991) J.
Virol. 65:2220-2224; PCT/US94/05700). Also, useful in the practice
of the present disclosure is a minimal non-primate lentiviral
vector, such as a lentiviral vector based on the equine infectious
anemia virus (EIAV) (see, e.g., Balagaan, (2006) J Gene Med; 8:
275-285, Published online 21 Nov. 2005 in Wiley InterScience DOI:
10.1002/jgm.845). The vectors can have cytomegalovirus (CMV)
promoter driving expression of the target gene. Accordingly, the
present disclosure contemplates amongst vector(s) useful in the
practice of the present disclosure: viral vectors, including
retroviral vectors and lentiviral vectors.
[0462] Any HLA allele can be expressed in the cell population. In
an exemplary embodiment, the HLA allele is an HLA class I allele.
In some embodiments, the HLA class I allele is an HLA-A allele or
an HLA-B allele. In some embodiments, the HLA allele is an HLA
class II allele. Sequences of HLA class I and class II alleles can
be found in the IPD-IMGT/HLA Database. Exemplary HLA alleles
include, but are not limited to, HLA-A*02:01, HLA-B*14:02,
HLA-A*23:01, HLA-E*01:01, HLA-DRB*01:01, HLA-DRB*01:02,
HLA-DRB*11:01, HLA-DRB*15:01, and HLA-DRB*07:01.
[0463] In some embodiments, the HLA allele is selected so as to
correspond to a genotype of interest. In some embodiments, the HLA
allele is a mutated HLA allele, which can be non-naturally
occurring allele or a naturally occurring allele in an afflicted
patient. The methods disclosed herein have the further advantage of
identifying HLA binding peptides for HLA alleles associated with
various disorders as well as alleles which are present at low
frequency. Accordingly, in some embodiments, the method provided
herein can identify the HLA allele even if it is present at a
frequency of less than 1% within a population, such as within the
Caucasian population.
[0464] In some embodiments, the nucleic acid sequence encoding the
HLA allele further comprises an affinity acceptor tag which can be
used to immunopurify the HLA-protein. Suitable tags are well-known
in the art. In some embodiments, an affinity acceptor tag is
poly-histidine tag, poly-histidine-glycine tag, poly-arginine tag,
poly-aspartate tag, poly-cysteine tag, poly-phenylalanine, c-myc
tag, Herpes simplex virus glycoprotein D (gD) tag, FLAG tag, KT3
epitope tag, tubulin epitope tag, T7 gene 10 protein peptide tag,
streptavidin tag, streptavidin binding peptide (SPB) tag,
Strep-tag, Strep-tag II, albumin-binding protein (ABP) tag,
alkaline phosphatase (AP) tag, bluetongue virus tag (B-tag),
calmodulin binding peptide (CBP) tag, chloramphenicol acetyl
transferase (CAT) tag, choline-binding domain (CBD) tag, chitin
binding domain (CBD) tag, cellulose binding domain (CBP) tag,
dihydrofolate reductase (DHFR) tag, galactose-binding protein (GBP)
tag, maltose binding protein (MBP), glutathione-S-transferase
(GST), Glu-Glu (EE) tag, human influenza hemagglutinin (HA) tag,
horseradish peroxidase (HRP) tag, NE-tag, HSV tag, ketosteroid
isomerase (KSI) tag, KT3 tag, LacZ tag, luciferase tag, NusA tag,
PDZ domain tag, AviTag, Calmodulin-tag, E-tag, S-tag, SBP-tag,
Softag 1, Softag 3, TC tag, VSV-tag, Xpress tag, Isopeptag, SpyTag,
SnoopTag, Profinity eXact tag, Protein C tag, 51-tag, S-tag,
biotin-carboxy carrier protein (BCCP) tag, green fluorescent
protein (GFP) tag, small ubiquitin-like modifier (SUMO) tag, tandem
affinity purification (TAP) tag, HaloTag, Nus-tag, Thioredoxin-tag,
Fc-tag, CYD tag, HPC tag, TrpE tag, ubiquitin tag, a VSV-G epitope
tag derived from the Vescular Stomatis viral glycoprotein, or a V5
tag derived from a small epitope (Pk) found on the P and V proteins
of the paramyxovirus of simian virus 5 (SV5). In some embodiments,
the affinity acceptor tag is an "epitope tag," which is a type of
peptide tag that adds a recognizable epitope (antibody binding
site) to the HLA-protein to provide binding of corresponding
antibody, thereby allowing identification or affinity purification
of the tagged protein. Non-limiting example of an epitope tag is
protein A or protein G, which binds to IgG. In some embodiments,
affinity acceptor tags include the biotin acceptor peptide (BAP) or
Human influenza hemagglutinin (HA) peptide sequence. Numerous other
tag moieties are known to, and can be envisioned by, the ordinarily
skilled artisan, and are contemplated herein. Any peptide tag can
be used as long as it is capable of being expressed as an element
of an affinity acceptor tagged HLA-peptide complex.
[0465] The methods provided herein comprise isolating HLA-peptide
complexes from the cells transfected or transduced with affinity
pulldown of HLA constructs (FIG. 3). In some embodiments, the
complexes can be isolated using standard immunoprecipitation
techniques known in the art with commercially available antibodies.
The cells can be first lysed. HLA class I-peptide complexes can be
isolated using HLA class I specific antibodies such as the W6/32
antibody, while HLA class II-peptide complexes can be isolated
using HLA class II specific antibodies such as the M5/114.15.2
monoclonal antibody. In some embodiments, the single (or pair of)
HLA alleles are expressed as a fusion protein with a peptide tag
and the HLA-peptide complexes are isolated using binding molecules
that recognize the peptide tags.
[0466] The methods further comprise isolating peptides from said
HLA-peptide complexes and sequencing the peptides. The peptides are
isolated from the complex by any method known to one of skill in
the art, such as acid elution. While any sequencing method can be
used, methods employing mass spectrometry, such as liquid
chromatography-mass spectrometry (LC-MS or LC-MS/MS, or
alternatively HPLC-MS or HPLC-MS/MS) are utilized in some
embodiments. These sequencing methods are well-known to a skilled
person and are reviewed in Medzihradszky K F and Chalkley R J. Mass
Spectrom Rev. 2015 January-February; 34(1):43-63.
[0467] In some embodiments, the population of cells expresses one
or more endogenous HLA alleles. In some embodiments, the population
of cells is an engineered population of cells lacking one or more
endogenous HLA class I alleles. In some embodiments, the population
of cells is an engineered population of cells lacking endogenous
HLA class I alleles. In some embodiments, the population of cells
is an engineered population of cells lacking one or more endogenous
HLA class II alleles. In some embodiments, the population of cells
is an engineered population of cells lacking endogenous HLA class
II alleles or an engineered population of cells lacking endogenous
HLA class I alleles and endogenous HLA class II alleles. In some
embodiments, the population of cells comprises cells that have been
enriched or sorted, such as by fluorescence activated cell sorting
(FACS). In some embodiments, fluorescence activated cell sorting
(FACS) is used to sort the population of cells. In some
embodiments, the population of cells is previously FACS sorted for
cell surface expression of either HLA class I or class II or both
HLA class I and class II. For example, FACS can be used to sort the
population of cells for cell surface expression of an HLA class I
allele, an HLA class II allele, or a combination thereof
Methods for Preparing a Personalized Cancer Vaccine
[0468] Once a mutation specific for a cancer is identified, such
that the mutation exists in the DNA in cancer cells but not in the
normal cells of the same human subject, and the mutation leads to a
change in one or more amino acids in the protein encoded by the
DNA, the mutation can be a target for the host immune response. A
natural immune response can be directed against the mutated protein
leading to the destruction of cancer cells expressing the protein.
Because of the natural tolerance response and immunocompromised
environment in the cancerous tissue, immunotherapy is a clinical
path that attempts augmenting such immune response to override the
body's tolerance and immunosuppressive effects. A protein or a
peptide comprising the mutation as described above is therefore a
suitable candidate for immunotherapy.
[0469] A mutated protein is ingested by professional phagocytes
acting as antigen presenting cells (APCs), chopped and displayed as
antigens on the cell surface for T cell activation in an antigen
presentation complex comprising a Major Histocompatibility Complex
(MHC) protein. Human MHC proteins are called Human Leukocytic
antigens, HLAs. The MHC protein can be a MHC-class I or a class II
protein, and while several functional distinctions are attributed
to the presentation of peptides by either class I or class II MHC
proteins (HLA class I and HLA class II proteins), one salient
distinction lies in the fact that HLA class I-peptide complexes
present antigens to cytotoxic CD8+ T cells, whereas the HLA class
II peptide complexes are also capable of activating CD4+ T cell
leading to prolonged immune response. CD8+ T cells are
indispensable in the task of cell-by-cell elimination of a diseased
cell, such as an infected cell or a tumor cell. CD4+ T cells have a
more sustained effects upon activation, the most important of those
being generation of immunological memory. CD4 subsets are
differentially recruited according to the type of immunologic
threat, and multiple subsets with overlapping or disparate
functions may be co-recruited. This helps in balancing the
immunological response with respect to the pathogenic threat. In
these respects, HLA class II peptide mediated antigen presentation
effects a sustained and tailored immune response. On the other
hand, HLA class II binding to peptides may be promiscuous and
therefore non-specific peptide binding and presentation to the
immune system leads to aberrant immune response, such as
autoimmunity.
[0470] In one aspect, the present disclosure provides method for
predicting peptides that can accurately pair with, or bind to, a
specific HLA class II alpha and beta chain heterodimer, such that
the high fidelity binding of the peptide to HLA class II protein
(comprising the alpha and beta chain heterodimer) ensures
presentation of the specific peptide to the T lymphocytes, thereby
eliciting a specific immune response and avoid any cross-reactivity
or immune promiscuity.
[0471] In one aspect, the present disclosure provides method for
predicting peptides that can accurately bind to a specific HLA
class II protein, such that a more sustained and robust immune
response can be activated with the peptide, when the peptide is
administered therapeutically to a subject expressing the specific
cognate HLA class II protein, by dint of the ability of HLA class
II protein's activation of CD4+ T cells and stimulate immunological
memory. In some embodiments, the given peptide that is predicted to
bind to a HLA class II protein with high specificity is a peptide
comprising a mutation, wherein the mutation is prevalent in a
cancer or a tumor cell of a subject; whereas the same HLA class II
protein predicted to bind the mutated peptide either (a) does not
bind, or (b) binds with distinctly lower affinity to the
corresponding non-mutated wild type peptide compared to the
affinity for binding to the mutated peptide of the subject. The
preferential binding of the HLA to the mutated peptide is
advantageous in the development of an immunotherapeutic, since the
cells expressing the wild type peptide will be spared from the
immune attack by the T cells reactive to the HLA-presented peptide.
In some embodiments, predicted peptides that bind specifically to
the HLA class II proteins are peptides that have post-translation
modifications. Exemplary post-translational modifications include
but are not limited to: phosphorylation, ubiquitylation,
dephosphorylation, glycosylation, methylation, or, acetylation. In
some embodiments, the predicted peptides are subjected to
post-translational modifications prior for use in
immunotherapy.
[0472] In some embodiments, the immunotherapy methods and
strategies disclosed herein could also be applicable in suppressing
unwanted immune activation, such as, in an autoimmune reaction.
Specifically, peptides identified as potential binders for specific
HLA subtypes could be tailored to bind to the specific HLA molecule
and induces tolerance rather than cause immunogenic response.
[0473] In one aspect, presented herein are methods of immunotherapy
tailored or personalized for a specific subject. Every subject or
patient expresses a specific array of HLA class I and HLA class II
proteins. HLA typing is a well-known technique that allows
determination of the specific repertoire of HLA proteins expressed
by the subject. Once the HLA heterodimers expressed by a specific
subject is known, having an improved, sophisticated and reliable
method as described herein for predicting peptides that can bind to
a specific HLA class II alpha and beta chain heterodimer, with high
fidelity can ensure that a specific immune response can be
generated tailored specifically for the subject.
[0474] The genes coding for HLA heterodimers are highly
polymorphic, with more 4,000 HLA class II allele variants
identified across the human population. From maternal and paternal
HLA haplotypes, an individual can inherit different alleles for
each of the HLA class II loci, and each HLA class II heterodimer is
made of an .alpha.- and .beta.-chain Because of the large number of
.alpha.- and .beta.-chain pairing combinations, especially for
HLA-DP and HLA-DQ alleles, the population of possible HLA
heterodimers is highly complex. HLA class II heterodimers are
translated in the endoplasmic reticulum (ER) and assembled into a
stable complex with the invariant chain (Ii) derived from the
protein CD74. The Ii stabilizes the class II complex by allowing
proper protein folding and enables the export of HLA class II
heterodimers into endosomal/lysosomal compartments. Inside these
HLA class II loading compartments, the Ii is proteolytically
cleaved by cathepsins into a placeholder peptide called CLIP. CLIP
is then exchanged for higher-affinity peptides in a low pH
environment by the chaperone HLA-DM, a non-classical HLA class II
heterodimer. High affinity peptide-loaded HLA class II complexes
are then to the trans-Golgi and finally to the cell surface for
display for CD4+ T cells.
[0475] Each HLA heterodimer is estimated to bind thousands of
peptides with allele-specific binding preferences. In fact, each
HLA allele is estimated to bind and present .about.1,000-10,000
unique peptides to T cells. Given such diversity in HLA binding,
accurate prediction of whether a peptide is likely to bind to a
specific HLA allele is highly challenging. Less is known about
allele-specific peptide-binding characteristics of HLA class II
molecules because of the heterogeneity of .alpha.- and .beta.-chain
pairing, complexity of data limiting the ability to confidently
assign core binding epitopes, and the lack of immunoprecipitation
grade, allele-specific antibodies required for high-resolution
biochemical analyses. Furthermore, analyzing peptide epitopes
derived from a given HLA allele raises ambiguity when multiple HLA
alleles are presented on a cell surface.
[0476] Predictions for candidate neoantigens are predominantly made
for HLA class I epitopes (given the availability of experimental
data for class I prediction algorithms compared to class II), yet
CD4+ T cell responses are often observed in both pre-clinical and
clinical personalized neoantigen vaccination studies. These
observations demonstrate that HLA class II epitope processing and
presentation may also play a critical role in cancer treatment.
Although HLA class II prediction algorithms exist, they are
inaccurate because the open-ended peptide-binding groove on HLA
class II heterodimers allows for longer peptides (generally 15-40
amino acids) to bind, which increases the heterogeneity and
complexity of epitope presentation. Further work to better
understand the characteristics of HLA class II peptide-binding
cores and the cellular processes involved in class II epitope
processing and presentation is therefore required. The proteomics
field is currently limited by the complexity of HLA class II
heterodimer formation and the availability of immunoprecipitation
grade antibodies for HLA class II-peptide complex isolation. To
overcome these challenges, a mono-allelic HLA profiling workflow
was developed that relies on LC-MS/MS for the characterization of
allele-specific HLA class II-ligandomes to class II epitope
prediction methods. The following definitions supplement those in
the art and are directed to the current application and are not to
be imputed to any related or unrelated case, e.g., to any commonly
owned patent or application. Although any methods and materials
similar or equivalent to those described herein can be used in the
practice for testing of the present disclosure, exemplary materials
and methods are described herein. Accordingly, the terminology used
herein is for the purpose of describing particular embodiments
only, and is not intended to be limiting.
[0477] Disclosed herein are methods to preparing a personalized
cancer vaccine. The method for preparing a personalized cancer
vaccine may comprise identifying peptide sequences with a mutation
expressed in cancer cells of a subject; inputting amino acid
position information of the peptide sequences identified, using a
computer processor, into a machine-learning HLA-peptide
presentation prediction model to generate a set of presentation
predictions for the peptide sequences identified, each presentation
prediction representing a probability that one or more proteins
encoded by a class II MHC allele of a cancer cell of the subject
will present a given sequence of a peptide sequence identified; and
selecting a subset of the peptide sequences identified based on the
set of presentation predictions for preparing the personalized
cancer vaccine.
[0478] In some embodiments, one or more results obtained from a
method described herein may provide a quantitative value or values
indicative of one or more of the following: a likelihood of
diagnostic accuracy, a likelihood of a presence of a condition in a
subject, a likelihood of a subject developing a condition, a
likelihood of success of a particular treatment, or any combination
thereof. In some embodiments, a method as described herein may
predict a risk or likelihood of developing a condition. In some
embodiments, a method as described herein may be an early
diagnostic indicator of developing a condition. In some
embodiments, a method as described herein may confirm a diagnosis
or a presence of a condition. In some embodiments, a method as
described herein may monitor the progression of a condition. In
some embodiments, a method as described herein may monitor the
efficacy of a treatment for a condition in a subject.
Method for Identification of MHC-II Peptides
[0479] In one aspect, presented herein is a method of identifying
one or more peptides that are presented by MHC-II proteins for
immune activation. In some embodiments, the one r more peptides
comprise an epitope. In some embodiments, the method involves
computational prediction of the likelihood that specific epitopes
are presented by an MHC-II protein. In some embodiments, the method
involves computational prediction of the specificity of an epitope
for MHC-II presentation. In some embodiments, the computational
prediction methods involve an assessment of peptide-MHC
interactions. In some embodiments, the computational prediction
methods involve an prediction of the allelic specificity of a
peptide for antigen presentation. In some embodiments, the
computational prediction methods involve integration of
bioinformatics information, for example, nucleotide sequences,
structural motifs of biomolecules, protein-protein interaction
features and functional potency such as immunogenicity. In some
embodiments, the computational prediction methods involve machine
learning. Many immunoinformatics methods for prediction of
peptide-MHC interactions have been developed for both MHC class I
and II, based on machine learning approaches such as simple pattern
motif, support vector machine (SVM), hidden Markov model (HMM),
neural network (NN) models, quantitative structure-activity
relationship (QSAR) analysis, structure-based methods, and
biophysical methods. These methods can be divided into two
categories, namely, intra-allele (allele-specific) and trans-allele
(pan-specific) methods. Intra-allelic methods are trained for a
specific MHC molecule on a limited set of experimental
peptide-binding data and applied for prediction of peptides binding
to that molecule. Because of the extreme polymorphism of MHC
molecules, the existence of thousands of allele variants, combined
with the lack of sufficient experimental binding data, it is
impossible to build a prediction model for each allele. Thus,
trans-allele and general purpose methods such as NetMHCIIpan
(Karosiene E et al., NetMHCIIpan-3.0, a common pan-specific MHC
class II prediction method including all three human MHC class II
isotypes, HLA-DR, HLA-DP and HLADQ. Immunogenetics (2013)
65(10):711-24), and TEPITOPEpan (Zhang L, et al., TEPITOPEpan:
extending TEPITOPE for peptide binding prediction covering over 700
HLA-DR molecules. PLoS One (2012) 7(2):e30483) have been developed
using peptide-binding data expanding over many alleles or across
species Similar methods for MHC-I are also available such as
NetMHCpan and KISS.
[0480] In some embodiments, ahe peptide sequences may not be
expressed in normal cells of the subject. In some embodiments, each
and every cell of the subject may not be cancer cells. The cancer
cells may be produced through different cancers, including, but not
limited to, thyroid cancer, adrenal cortical cancer, anal cancer,
aplastic anemia, bile duct cancer, bladder cancer, bone cancer,
bone metastasis, central nervous system (CNS) cancers, peripheral
nervous system (PNS) cancers, breast cancer, Castleman's disease,
cervical cancer, childhood Non-Hodgkin's lymphoma, lymphoma, colon
and rectum cancer, endometrial cancer, esophagus cancer, Ewing's
family of tumors (e.g. Ewing's sarcoma), eye cancer, gallbladder
cancer, gastrointestinal carcinoid tumors, gastrointestinal stromal
tumors, gestational trophoblastic disease, hairy cell leukemia,
Hodgkin's disease, Kaposi's sarcoma, kidney cancer, laryngeal and
hypopharyngeal cancer, acute lymphocytic leukemia, acute myeloid
leukemia, children's leukemia, chronic lymphocytic leukemia,
chronic myeloid leukemia, liver cancer, lung cancer, lung carcinoid
tumors, Non-Hodgkin's lymphoma, male breast cancer, malignant
mesothelioma, multiple myeloma, myelodysplastic syndrome,
myeloproliferative disorders, nasal cavity and paranasal cancer,
nasopharyngeal cancer, neuroblastoma, oral cavity and oropharyngeal
cancer, osteosarcoma, ovarian cancer, pancreatic cancer, penile
cancer, pituitary tumor, prostate cancer, retinoblastoma,
rhabdomyosarcoma, salivary gland cancer, sarcoma (adult soft tissue
cancer), melanoma skin cancer, non-melanoma skin cancer, stomach
cancer, testicular cancer, thymus cancer, uterine cancer (e.g.
uterine sarcoma), vaginal cancer, vulvar cancer, or Waldenstrom's
macroglobulinemia.
[0481] The identifying may comprise comparing DNA, RNA or protein
sequences from the cancer cells of the subject to DNA, RNA or
protein sequences from the normal cells of the subject. The DNA,
RNA or protein sequences from the cancer cells of the subject may
be different from the DNA, RNA or protein sequences from the normal
cells of the subject. The identifying may identify nucleic acid
variants with high sensitivity.
[0482] The machine-learning HLA-peptide presentation prediction
model may comprise a plurality of predictor variables identified at
least based on training data. The training data may comprises
sequence information of sequences of peptides presented by an HLA
protein expressed in cells and identified by mass spectrometry;
training peptide sequence information comprising amino acid
position information, wherein the training peptide sequence
information is associated with the HLA protein expressed in cells;
and a function representing a relation between the amino acid
position information received as input and the presentation
likelihood generated as output based on the amino acid position
information and the predictor variables.
[0483] In some embodiments, the training data may further comprise
structured data, time-series data, unstructured data, and
relational data. Unstructured data may comprise audio data, image
data, video, mechanical data, electrical data, chemical data, and
any combination thereof, for use in accurately simulating or
training robotics or simulations. Time-series data may comprise
data from one or more of a smart meter, a smart appliance, a smart
device, a monitoring system, a telemetry device, or a sensor.
Relational data comprises data from a customer system, an
enterprise system, an operational system, a website, web accessible
application program interface (API), or any combination thereof.
This may be done by a user through any method of inputting files or
other data formats into software or systems.
[0484] In some embodiments, the training data may be stored in a
database. A database can be stored in computer readable format. A
computer processor may be configured to access the data stored in
the computer readable memory. In some embodiments, the computer
system may be used to analyze the data to obtain a result. The
result may be stored remotely or internally on storage medium, and
communicated to personnel such as medication professionals. In some
embodiments, the computer system may be operatively coupled with
components for transmitting the result. Components for transmitting
can include wired and wireless components. Examples of wired
communication components can include a Universal Serial Bus (USB)
connection, a coaxial cable connection, an Ethernet cable such as a
Cat5 or Cat6 cable, a fiber optic cable, or a telephone line.
Examples or wireless communication components can include a Wi-Fi
receiver, a component for accessing a mobile data standard such as
a 3G or 4G LTE data signal, or a Bluetooth receiver. In some
embodiments, all these data in the storage medium is collected and
archived to build a data warehouse.
[0485] In some embodiments, the database comprises an external
database. The external database may be a medical database, for
example, but not limited to, Adverse Drug Effects Database, AHFS
Supplemental File, Allergen Picklist File, Average WAC Pricing
File, Brand Probability File, Canadian Drug File v2, Comprehensive
Price History, Controlled Substances File, Drug Allergy
Cross-Reference File, Drug Application File, Drug Dosing &
Administration Database, Drug Image Database v2.0/Drug Imprint
Database v2.0, Drug Inactive Date File, Drug Indications Database,
Drug Lab Conflict Database, Drug Therapy Monitoring System (DTMS)
v2.2/DTMS Consumer Monographs, Duplicate Therapy Database, Federal
Government Pricing File, Healthcare Common Procedure Coding System
Codes (HCPCS) Database, ICD-10 Mapping Files, Immunization
Cross-Reference File, Integrated A to Z Drug Facts Module,
Integrated Patient Education, Master Parameters Database, Medi-Span
Electronic Drug File (MED-File) v2, Medicaid Rebate File, Medicare
Plans File, Medical Condition Picklist File, Medical Conditions
Master Database, Medication Order Management Database (MOMD),
Parameters to Monitor Database, Patient Safety Programs File,
Payment Allowance Limit-Part B (PAL-B) v2.0, Precautions Database,
RxNorm Cross-Reference File, Standard Drug Identifiers Database,
Substitution Groups File, Supplemental Names File, Uniform System
of Classification Cross-Reference File, or Warning Label
Database.
[0486] In some embodiments, the training data may also be obtained
through other data sources. The data sources may include sensors or
smart devices, such as appliances, smart meters, wearables,
monitoring systems, data stores, customer systems, billing systems,
financial systems, crowd source data, weather data, social
networks, or any other sensor, enterprise system or data store.
Example of smart meters or sensors may include meters or sensors
located at a customer site, or meters or sensors located between
customers and a generation or source location. By incorporating
data from a broad array of sources, the system may be capable of
performing complex and detailed analyses. In some embodiments, the
data sources may include sensors or databases for other medical
platforms without limitation.
[0487] HLA-typing is conventionally carried out by either
serological methods using antibodies or by PCR-based methods such
as Sequence Specific Oligonucleotide Probe Hybridization (SSOP), or
Sequence Based Typing (SBT). While the first is hampered by the
potentially high degree of cross reactivity and limited resolution
capabilities, the second suffers from difficulties associated with
the efficiency of the PCR due to very limited possibilities for
positioning primers because of polymorphic positions.
[0488] In some embodiments, the sequence information is identified
by either sequencing methods or methods employing mass
spectrometry, such as liquid chromatography-mass spectrometry
(LC-MS or LC-MS/MS, or alternatively HPLC-MS or HPLC-MS/MS). These
sequencing methods may be well-known to a skilled person and are
reviewed in Medzihradszky K F and Chalkley R J. Mass Spectrom Rev.
2015 January-February; 34(1):43-63. In some embodiments, the mass
spectrometry is mono-allelic mass spectrometry. In some
embodiments, the mass spectrometry may be MS analysis, MS/MS
analysis, LC-MS/MS analysis, or a combination thereof. In some
embodiments, MS analysis may be used to determine a mass of an
intact peptide. For example, the determining can comprise
determining a mass of an intact peptide (e.g., MS analysis). In
some embodiments, MS/MS analysis may be used to determine a mass of
peptide fragments. For example, the determining can comprise
determining a mass of peptide fragments, which can be used to
determine an amino acid sequence of a peptide or portion thereof
(e.g., MS/MS analysis). In some embodiments, the mass of peptide
fragments may be used to determine a sequence of amino acids within
the peptide. In some embodiments, LC-MS/MS analysis may be used to
separate complex peptide mixtures. For example, the determining can
comprise separating complex peptide mixtures, such as by liquid
chromatography, and determining a mass of an intact peptide, a mass
of peptide fragments, or a combination thereof (e.g., LC-MS/MS
analysis). This data can be used, e.g., for peptide sequencing.
[0489] In some embodiments, the training peptide sequence
information comprises amino acid position information of training
peptides. In some embodiments, the training peptide sequence
information comprises at most about 90%, 80%, 70%, 60%, 50%, 40%,
30%, 20%, 10% or less sequence information of sequences of peptides
presented by an HLA protein expressed in cells and identified by
mass spectrometry. In some embodiments, the training peptide
sequence information may comprise at least about 10%, 20%, 30%,
40%, 50%, 60%, 70%, 80%, 90% or more sequence information of
sequences of peptides presented by an HLA protein expressed in
cells and identified by mass spectrometry.
[0490] Any information and data may be paired with a subject who is
the source of the information and data. The subject or medical
professional can retrieve the information and data from a storage
or a server through a subject identity. A subject identity may
comprise patient's photo, name, address, social security number,
birthday, telephone number, zip code, or any combination thereof. A
subject identity may be encrypted and encoded in a visual graphical
code. A visual graphical code may be a one-time barcode that can be
uniquely associated with a subject identity. A barcode may be a UPC
barcode, EAN barcode, Code 39 barcode, Code 128 barcode, ITF
barcode, CodaBar barcode, GS1 DataBar barcode, MSI Plessey barcode,
QR barcode, Datamatrix code, PDF417 code, or an Aztec barcode. A
visual graphical code may be configured to be displayed on a
display screen. A barcode may comprise QR that can be optically
captured and read by a machine. A barcode may define an element
such as a version, format, position, alignment, or timing of the
barcode to enable reading and decoding of the barcode. A barcode
can encode various types of information in any type of suitable
format, such as binary or alphanumeric information. A QR code can
have various symbol sizes as long as the QR code can be scanned
from a reasonable distance by an imaging device. A QR code can be
of any image file format (e.g. EPS or SVG vector graphs, PNG, TIF,
GIF, or JPEG raster graphics format).
[0491] In some embodiments, the function representing a relation
between the amino acid position information received as input and
the presentation likelihood generated as output based on the amino
acid position information and the predictor variables comprises a
linear or non-linear function. The function may be, for example, a
rectified linear unit (ReLU) activation function, a Leaky ReLu
activation function, or other function such as a saturating
hyperbolic tangent, identity, binary step, logistic, arcTan,
softsign, parameteric rectified linear unit, exponential linear
unit, softPlus, bent identity, softExponential, Sinusoid, Sinc,
Gaussian, or sigmoid function, or any combination thereof.
[0492] In some embodiments, the linear function is obtained through
linear regression. In some embodiments, the linear regression is a
method to predict a target variable by fitting the best linear
relationship between the dependent and independent variable. The
best fit may mean that the sum of all the distances between the
shape and the actual observations at each point is the least.
Linear regression may comprise simple linear regression or multiple
linear regression. The simple linear regression may use a single
independent variable to predict a dependent variable. The multiple
linear regressions may use more than one independent variables to
predict a dependent variable by fitting a best linear relationship.
The non-linear function may be obtained through non-linear
regression. The nonlinear regression may be a form of regression
analysis in which observational data are modeled by a function
which is a nonlinear combination of the model parameters and
depends on one or more independent variables. The nonlinear
regression may comprise a step function, piecewise function,
spline, and generalized additive model.
[0493] In some embodiments, the presentation likelihood is
presented by one-dimensional values (e.g., probabilities). In some
embodiments, the probability is configured to measure the
likelihood that an event may occur. In some embodiments, the
probability ranges from about 0 and 1, 0.1 to 0.9, 0.2 to 0.8, 0.3
to 0.7, or 0.4 to 0.6. The higher the probability of an event, the
more likely the event may occur. In some embodiments, the event
comprises any type of situation, including, by way of non-limiting
examples, whether the HLA-peptide will present some peptide with
certain amino acid position information, and whether a person will
be sick based on amino acid position information. In some
embodiments, the likelihood may be presented by multi-dimensional
values. The multi-dimensional values may be presented by
multi-dimensional space, heatmap, or spreadsheet.
[0494] In one embodiment, selecting a subset of the peptide
sequences identified based on the set of presentation predictions
is configured to prepare the personalized cancer vaccine. In some
embodiments, the subset comprises at most about 90%, 80%, 70%, 60%,
50%, 40%, 30%, 20%, 10% or less of the peptide sequences identified
based on the set of presentation predictions. In other cases, the
subset may comprise at least about 10%, 20%, 30%, 40%, 50%, 60%,
70%, 80%, 90% or more of the peptide sequences identified based on
the set of presentation predictions. A cancer vaccine may be a
vaccine that either treats existing cancer or prevents development
of a cancer. Vaccines may be prepared from samples taken from the
patient, and may be specific to that patient.
[0495] In some embodiments, a Poxvirus is used in the disease
(e.g., cancer) vaccine or immunogenic composition. These include
orthopoxvirus, avipox, vaccinia, MVA, NYVAC, canarypox, ALVAC,
fowlpox, TROVAC, etc. Advantages of the vectors may include simple
construction, ability to accommodate large amounts of foreign DNA
and high expression levels. Information concerning poxviruses that
can be used in the practice of the disclosure, such as
Chordopoxvirinae subfamily poxviruses (poxviruses of vertebrates),
for instance, orthopoxviruses and avipoxviruses, e.g., vaccinia
virus (e.g., Wyeth Strain, WR Strain (e.g., ATCC.RTM. VR-1354),
Copenhagen Strain, NYVAC, NYVAC.1, NYVAC.2, MVA, MVA-BN), canarypox
virus (e.g., Wheatley C93 Strain, ALVAC), fowlpox virus (e.g., FP9
Strain, Webster Strain, TROVAC), dovepox, pigeonpox, quailpox, and
raccoon pox, inter alia, synthetic or non-naturally occurring
recombinants thereof, uses thereof, and methods for making and
using such recombinants can be found in scientific and patent
literature.
[0496] In some embodiments, a vaccinia virus is used in the disease
vaccine or immunogenic composition to express an antigen. The
recombinant vaccinia virus may be able to replicate within the
cytoplasm of the infected host cell and the polypeptide of interest
may therefore induce an immune response.
[0497] In some embodiments, ALVAC is used as a vector in a disease
vaccine or immunogenic composition. ALVAC may be a canarypox virus
that can be modified to express foreign transgenes and has been
used as a method for vaccination against both prokaryotic and
eukaryotic antigens.
[0498] In some embodiments, a Modified Vaccinia Ankara (MVA) virus
is used as a viral vector for an antigen vaccine or immunogenic
composition. MVA may be a member of the Orthopoxvirus family and
has been generated by about 570 serial passages on chicken embryo
fibroblasts of the Ankara strain of Vaccinia virus (CVA). As a
consequence of these passages, the resulting MVA virus may comprise
31 kilobases fewer genomic information compared to CVA, and is
highly host-cell restricted. MVA may be characterized by its
extreme attenuation, namely, by a diminished virulence or
infectious ability, but still holds an excellent immunogenicity.
When tested in a variety of animal models, MVA may be proven to be
avirulent, even in immuno-suppressed individuals. Moreover,
MVA-BN.RTM.-HER2 may be a candidate immunotherapy designed for the
treatment of HER-2-positive breast cancer and is currently in
clinical trials.
[0499] In some embodiments, a positive predictive value (PPV) is
used as part of the prediction model. A PPV, also known as a
precision measurement, is the probability that an individual
diagnosed with a disease or condition through, for example, a test
or model, actually has the disease or condition. It can be
calculated by dividing the number of true positive results by the
total number of results that returned positive (results that
include false positives). PPV=True Positives/(True positives+False
positives). For example, if in a set of 100 patients, the model
identified a positive result in 50 patients, of which 25 were true
positives, the PPV would be 25/50=0.5. A PPV closer to 1 represents
a more accurate diagnosis method, such as a test or model. A PPV
may be used to determine the accuracy of the prediction model. A
PPV may be used to adjust the prediction model to accommodate for
false positive results that may be generated by the model.
[0500] A recall rate may be used as part of the prediction model. A
recall rate may be considered as the percentage of true positive
results out of the total number of positives in the sample set.
Recall=True Positives/(True positives+False Negatives). For
example, if in a set of 100 patients, the model identified a
positive result in 50 patients, of which 25 were true positives,
and there were a total of 75 positives in the set of patients, the
recall rate would be {25/(25+25)}.times.100=50%. A recall rate may
be used to determine the accuracy of the prediction model. A recall
rate may be used to adjust the prediction model to accommodate for
false positive results or false negative results that may be
generated by the model.
[0501] In some embodiments, the prediction model has a positive
predictive value of at least 0.05, 0.1, 0.2, 0.25, 0.3, 0.4, 0.5,
0.6, 0.7, 0.8, 0.9 or greater at a recall rate of from 0.1%-10%. In
some embodiments, the prediction model may have a positive
predictive value of at most 0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2,
0.1 or less at a recall rate of from 0.1%-10%. The prediction model
may have a positive predictive value of at least 0.05, 0.1, 0.2,
0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9 or greater at a recall rate less
than 0.1%. In some embodiments, the prediction model may have a
positive predictive value of at most 0.9, 0.8, 0.7, 0.6, 0.5, 0.4,
0.3, 0.2, 0.1 or less at a recall rate less than 0.1%. The
prediction model may have a positive predictive value of at least
0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9 or greater at a
recall rate more than 10%. In some embodiments, the prediction
model may have a positive predictive value of at most 0.9, 0.8,
0.7, 0.6, 0.5, 0.4, 0.3, 0.2, 0.1 or less at a recall rate more
than 10%.
[0502] In some embodiments, the prediction model has a positive
predictive value of at least 0.05, 0.1, 0.2, 0.25, 0.3, 0.4, 0.5,
0.6, 0.7, 0.8, 0.9 or greater at a recall rate of 0.1% to 10%. In
some embodiments, the prediction model has a positive predictive
value of at least 0.05, 0.1, 0.2, 0.25, 0.3, 0.4, 0.5, 0.6, 0.7,
0.8, 0.9 or greater at a recall rate of 0.1% to 0.5%, 0.1% to 1%,
0.1% to 2%, 0.1% to 3%, 0.1% to 4%, 0.1% to 5%, 0.1% to 6%, 0.1% to
7%, 0.1% to 8%, 0.1% to 9%, 0.1% to 10%, 0.5% to 1%, 0.5% to 2%,
0.5% to 3%, 0.5% to 4%, 0.5% to 5%, 0.5% to 6%, 0.5% to 7%, 0.5% to
8%, 0.5% to 9%, 0.5% to 10%, 1% to 2%, 1% to 3%, 1% to 4%, 1% to
5%, 1% to 6%, 1% to 7%, 1% to 8%, 1% to 9%, 1% to 10%, 2% to 3%, 2%
to 4%, 2% to 5%, 2% to 6%, 2% to 7%, 2% to 8%, 2% to 9%, 2% to 10%,
3% to 4%, 3% to 5%, 3% to 6%, 3% to 7%, 3% to 8%, 3% to 9%, 3% to
10%, 4% to 5%, 4% to 6%, 4% to 7%, 4% to 8%, 4% to 9%, 4% to 10%,
5% to 6%, 5% to 7%, 5% to 8%, 5% to 9%, 5% to 10%, 6% to 7%, 6% to
8%, 6% to 9%, 6% to 10%, 7% to 8%, 7% to 9%, 7% to 10%, 8% to 9%,
8% to 10%, or 9% to 10%. In some embodiments, the prediction model
has a positive predictive value of at least 0.05, 0.1, 0.2, 0.25,
0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9 or greater at a recall rate of
0.1%, 0.5%, 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, or 10%. In some
embodiments, the prediction model has a positive predictive value
of at least 0.05, 0.1, 0.2, 0.25, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9
or greater at a recall rate of at least 0.1%, 0.5%, 1%, 2%, 3%, 4%,
5%, 6%, 7%, 8%, or 9%. In some embodiments, the prediction model
has a positive predictive value of at least 0.05, 0.1, 0.2, 0.25,
0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9 or greater at a recall rate of at
most 0.5%, 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, or 10%.
[0503] In some embodiments, the prediction model has a positive
predictive value of at least 0.05, 0.1, 0.2, 0.25, 0.3, 0.4, 0.5,
0.6, 0.7, 0.8, 0.9 or greater at a recall rate of 10% to 20%. In
some embodiments, the prediction model has a positive predictive
value of at least 0.05, 0.1, 0.2, 0.25, 0.3, 0.4, 0.5, 0.6, 0.7,
0.8, 0.9 or greater at a recall rate of 10% to 11%, 10% to 12%, 10%
to 13%, 10% to 14%, 10% to 15%, 10% to 16%, 10% to 17%, 10% to 18%,
10% to 19%, 10% to 20%, 11% to 12%, 11% to 13%, 11% to 14%, 11% to
15%, 11% to 16%, 11% to 17%, 11% to 18%, 11% to 19%, 11% to 20%,
12% to 13%, 12% to 14%, 12% to 15%, 12% to 16%, 12% to 17%, 12% to
18%, 12% to 19%, 12% to 20%, 13% to 14%, 13% to 15%, 13% to 16%,
13% to 17%, 13% to 18%, 13% to 19%, 13% to 20%, 14% to 15%, 14% to
16%, 14% to 17%, 14% to 18%, 14% to 19%, 14% to 20%, 15% to 16%,
15% to 17%, 15% to 18%, 15% to 19%, 15% to 20%, 16% to 17%, 16% to
18%, 16% to 19%, 16% to 20%, 17% to 18%, 17% to 19%, 17% to 20%,
18% to 19%, 18% to 20%, or 19% to 20%. In some embodiments, the
prediction model has a positive predictive value of at least 0.05,
0.1, 0.2, 0.25, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9 or greater at a
recall rate of 10%, 11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%, 19%, or
20%. In some embodiments, the prediction model has a positive
predictive value of at least 0.05, 0.1, 0.2, 0.25, 0.3, 0.4, 0.5,
0.6, 0.7, 0.8, 0.9 or greater at a recall rate of at least 10%,
11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%, or 19%. In some
embodiments, the prediction model has a positive predictive value
of at least 0.05, 0.1, 0.2, 0.25, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9
or greater at a recall rate of at most 11%, 12%, 13%, 14%, 15%,
16%, 17%, 18%, 19%, or 20%.
[0504] In some embodiments, the prediction model has a positive
predictive value of at least 0.05, 0.1, 0.2, 0.25, 0.3, 0.4, 0.5,
0.6, 0.7, 0.8, 0.9 or greater at a recall rate of at least 0.1%,
0.5%, 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 11%, 12%, 13%, 14%,
15%, 16%, 17%, 18%, 19%, or 20%. For example, prediction model may
have a positive predictive value of at least 0.1 at a recall rate
of at least 10%. For example, prediction model may have a positive
predictive value of at least 0.2 at a recall rate of at least 10%.
For example, prediction model may have a positive predictive value
of at least 0.3 at a recall rate of at least 10%. For example,
prediction model may have a positive predictive value of at least
0.4 at a recall rate of at least 10%. For example, prediction model
may have a positive predictive value of at least 0.5 at a recall
rate of at least 10%. For example, prediction model may have a
positive predictive value of at least 0.6 at a recall rate of at
least 10%. For example, prediction model may have a positive
predictive value of at least 0.7 at a recall rate of at least 10%.
For example, prediction model may have a positive predictive value
of at least 0.8 at a recall rate of at least 10%. For example,
prediction model may have a positive predictive value of at least
0.9 at a recall rate of at least 10%. For example, prediction model
may have a positive predictive value of at least 0.1 at a recall
rate of at least 5%. For example, prediction model may have a
positive predictive value of at least 0.2 at a recall rate of at
least 5%. For example, prediction model may have a positive
predictive value of at least 0.3 at a recall rate of at least 5%.
For example, prediction model may have a positive predictive value
of at least 0.4 at a recall rate of at least 5%. For example,
prediction model may have a positive predictive value of at least
0.5 at a recall rate of at least 5%. For example, prediction model
may have a positive predictive value of at least 0.6 at a recall
rate of at least 5%. For example, prediction model may have a
positive predictive value of at least 0.7 at a recall rate of at
least 5%. For example, prediction model may have a positive
predictive value of at least 0.8 at a recall rate of at least 5%.
For example, prediction model may have a positive predictive value
of at least 0.9 at a recall rate of at least 5%. For example,
prediction model may have a positive predictive value of at least
0.1 at a recall rate of at least 20%. For example, prediction model
may have a positive predictive value of at least 0.2 at a recall
rate of at least 20%. For example, prediction model may have a
positive predictive value of at least 0.3 at a recall rate of at
least 20%. For example, prediction model may have a positive
predictive value of at least 0.4 at a recall rate of at least 20%.
For example, prediction model may have a positive predictive value
of at least 0.5 at a recall rate of at least 20%. For example,
prediction model may have a positive predictive value of at least
0.6 at a recall rate of at least 20%. For example, prediction model
may have a positive predictive value of at least 0.7 at a recall
rate of at least 20%. For example, prediction model may have a
positive predictive value of at least 0.8 at a recall rate of at
least 20%. For example, prediction model may have a positive
predictive value of at least 0.9 at a recall rate of at least
20%.
[0505] In some embodiments, the prediction model has a positive
predictive value of at least 0.05, 0.1, 0.2, 0.25, 0.3, 0.4, 0.5,
0.6, 0.7, 0.8, 0.9 or greater at a recall rate of about 0.1%, 0.5%,
1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 11%, 12%, 13%, 14%, 15%,
16%, 17%, 18%, 19%, or 20%. For example, prediction model may have
a positive predictive value of at least 0.1 at a recall rate of
about 10%. For example, prediction model may have a positive
predictive value of at least 0.2 at a recall rate of about 10%. For
example, prediction model may have a positive predictive value of
at least 0.3 at a recall rate of about 10%. For example, prediction
model may have a positive predictive value of at least 0.4 at a
recall rate of about 10%. For example, prediction model may have a
positive predictive value of at least 0.5 at a recall rate of about
10%. For example, prediction model may have a positive predictive
value of at least 0.6 at a recall rate of about 10%. For example,
prediction model may have a positive predictive value of at least
0.7 at a recall rate of about 10%. For example, prediction model
may have a positive predictive value of at least 0.8 at a recall
rate of about 10%. For example, prediction model may have a
positive predictive value of at least 0.9 at a recall rate of about
10%. For example, prediction model may have a positive predictive
value of at least 0.1 at a recall rate of about 5%. For example,
prediction model may have a positive predictive value of at least
0.2 at a recall rate of about 5%. For example, prediction model may
have a positive predictive value of at least 0.3 at a recall rate
of about 5%. For example, prediction model may have a positive
predictive value of at least 0.4 at a recall rate of about 5%. For
example, prediction model may have a positive predictive value of
at least 0.5 at a recall rate of about 5%. For example, prediction
model may have a positive predictive value of at least 0.6 at a
recall rate of about 5%. For example, prediction model may have a
positive predictive value of at least 0.7 at a recall rate of about
5%. For example, prediction model may have a positive predictive
value of at least 0.8 at a recall rate of about 5%. For example,
prediction model may have a positive predictive value of at least
0.9 at a recall rate of about 5%. For example, prediction model may
have a positive predictive value of at least 0.1 at a recall rate
of about 20%. For example, prediction model may have a positive
predictive value of at least 0.2 at a recall rate of about 20%. For
example, prediction model may have a positive predictive value of
at least 0.3 at a recall rate of about 20%. For example, prediction
model may have a positive predictive value of at least 0.4 at a
recall rate of about 20%. For example, prediction model may have a
positive predictive value of at least 0.5 at a recall rate of about
20%. For example, prediction model may have a positive predictive
value of at least 0.6 at a recall rate of about 20%. For example,
prediction model may have a positive predictive value of at least
0.7 at a recall rate of about 20%. For example, prediction model
may have a positive predictive value of at least 0.8 at a recall
rate of about 20%. For example, prediction model may have a
positive predictive value of at least 0.9 at a recall rate of about
20%.
[0506] In some embodiments, the prediction model has a positive
predictive value of at least 0.05, 0.1, 0.2, 0.25, 0.3, 0.4, 0.5,
0.6, 0.7, 0.8, 0.9 or greater at a recall rate of less than 0.1%,
0.5%, 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 11%, 12%, 13%, 14%,
15%, 16%, 17%, 18%, 19%, or 20%. For example, prediction model may
have a positive predictive value of at least 0.1 at a recall rate
of at most 10%. For example, prediction model may have a positive
predictive value of at least 0.2 at a recall rate of at most 10%.
For example, prediction model may have a positive predictive value
of at least 0.3 at a recall rate of at most 10%. For example,
prediction model may have a positive predictive value of at least
0.4 at a recall rate of at most 10%. For example, prediction model
may have a positive predictive value of at least 0.5 at a recall
rate of at most 10%. For example, prediction model may have a
positive predictive value of at least 0.6 at a recall rate of at
most 10%. For example, prediction model may have a positive
predictive value of at least 0.7 at a recall rate of at most 10%.
For example, prediction model may have a positive predictive value
of at least 0.8 at a recall rate of at most 10%. For example,
prediction model may have a positive predictive value of at least
0.9 at a recall rate of at most 10%. For example, prediction model
may have a positive predictive value of at least 0.1 at a recall
rate of at most 5%. For example, prediction model may have a
positive predictive value of at least 0.2 at a recall rate of at
most 5%. For example, prediction model may have a positive
predictive value of at least 0.3 at a recall rate of at most 5%.
For example, prediction model may have a positive predictive value
of at least 0.4 at a recall rate of at most 5%. For example,
prediction model may have a positive predictive value of at least
0.5 at a recall rate of at most 5%. For example, prediction model
may have a positive predictive value of at least 0.6 at a recall
rate of at most 5%. For example, prediction model may have a
positive predictive value of at least 0.7 at a recall rate of at
most 5%. For example, prediction model may have a positive
predictive value of at least 0.8 at a recall rate of at most 5%.
For example, prediction model may have a positive predictive value
of at least 0.9 at a recall rate of at most 5%. For example,
prediction model may have a positive predictive value of at least
0.1 at a recall rate of at most 20%. For example, prediction model
may have a positive predictive value of at least 0.2 at a recall
rate of at most 20%. For example, prediction model may have a
positive predictive value of at least 0.3 at a recall rate of at
most 20%. For example, prediction model may have a positive
predictive value of at least 0.4 at a recall rate of at most 20%.
For example, prediction model may have a positive predictive value
of at least 0.5 at a recall rate of at most 20%. For example,
prediction model may have a positive predictive value of at least
0.6 at a recall rate of at most 20%. For example, prediction model
may have a positive predictive value of at least 0.7 at a recall
rate of at most 20%. For example, prediction model may have a
positive predictive value of at least 0.8 at a recall rate of at
most 20%. For example, prediction model may have a positive
predictive value of at least 0.9 at a recall rate of at most
20%.
[0507] In some embodiments, at a recall rate of about 0.1%, 0.5%,
1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 11%, 12%, 13%, 14%, 15%,
16%, 17%, 18%, 19%, or 20% the prediction model has a positive
predictive value of 0.05% to 0.6%. At a recall rate of about 0.1%,
0.5%, 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 11%, 12%, 13%, 14%,
15%, 16%, 17%, 18%, 19%, or 20% the prediction model may have a
positive predictive value of 0.05% to 0.1%, 0.05% to 0.15%, 0.05%
to 0.2%, 0.05% to 0.25%, 0.05% to 0.3%, 0.05% to 0.35%, 0.05% to
0.4%, 0.05% to 0.45%, 0.05% to 0.5%, 0.05% to 0.55%, 0.05% to 0.6%,
0.1% to 0.15%, 0.1% to 0.2%, 0.1% to 0.25%, 0.1% to 0.3%, 0.1% to
0.35%, 0.1% to 0.4%, 0.1% to 0.45%, 0.1% to 0.5%, 0.1% to 0.55%,
0.1% to 0.6%, 0.15% to 0.2%, 0.15% to 0.25%, 0.15% to 0.3%, 0.15%
to 0.35%, 0.15% to 0.4%, 0.15% to 0.45%, 0.15% to 0.5%, 0.15% to
0.55%, 0.15% to 0.6%, 0.2% to 0.25%, 0.2% to 0.3%, 0.2% to 0.35%,
0.2% to 0.4%, 0.2% to 0.45%, 0.2% to 0.5%, 0.2% to 0.55%, 0.2% to
0.6%, 0.25% to 0.3%, 0.25% to 0.35%, 0.25% to 0.4%, 0.25% to 0.45%,
0.25% to 0.5%, 0.25% to 0.55%, 0.25% to 0.6%, 0.3% to 0.35%, 0.3%
to 0.4%, 0.3% to 0.45%, 0.3% to 0.5%, 0.3% to 0.55%, 0.3% to 0.6%,
0.35% to 0.4%, 0.35% to 0.45%, 0.35% to 0.5%, 0.35% to 0.55%, 0.35%
to 0.6%, 0.4% to 0.45%, 0.4% to 0.5%, 0.4% to 0.55%, 0.4% to 0.6%,
0.45% to 0.5%, 0.45% to 0.55%, 0.45% to 0.6%, 0.5% to 0.55%, 0.5%
to 0.6%, or 0.55% to 0.6%. At a recall rate of about 0.1%, 0.5%,
1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 11%, 12%, 13%, 14%, 15%,
16%, 17%, 18%, 19%, or 20% the prediction model may have a positive
predictive value of 0.05%, 0.1%, 0.15%, 0.2%, 0.25%, 0.3%, 0.35%,
0.4%, 0.45%, 0.5%, 0.55%, or 0.6%. At a recall rate of about 0.1%,
0.5%, 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 11%, 12%, 13%, 14%,
15%, 16%, 17%, 18%, 19%, or 20% the prediction model may have a
positive predictive value of at least 0.05%, 0.1%, 0.15%, 0.2%,
0.25%, 0.3%, 0.35%, 0.4%, 0.45%, 0.5%, or 0.55%. At a recall rate
of about 0.1%, 0.5%, 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 11%,
12%, 13%, 14%, 15%, 16%, 17%, 18%, 19%, or 20% the prediction model
may have a positive predictive value of at most 0.1%, 0.15%, 0.2%,
0.25%, 0.3%, 0.35%, 0.4%, 0.45%, 0.5%, 0.55%, or 0.6%.
[0508] At a recall rate of about 0.1%, 0.5%, 1%, 2%, 3%, 4%, 5%,
6%, 7%, 8%, 9%, 10%, 11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%, 19%,
or 20% the prediction model may have a positive predictive value of
0.45% to 0.98%. Ata recall rate of about 0.1%, 0.5%, 1%, 2%, 3%,
4%, 5%, 6%, 7%, 8%, 9%, 10%, 11%, 12%, 13%, 14%, 15%, 16%, 17%,
18%, 19%, or 20% the prediction model may have a positive
predictive value of 0.45% to 0.5%, 0.45% to 0.55%, 0.45% to 0.6%,
0.45% to 0.65%, 0.45% to 0.7%, 0.45% to 0.75%, 0.45% to 0.8%, 0.45%
to 0.85%, 0.45% to 0.9%, 0.45% to 0.96%, 0.45% to 0.98%, 0.5% to
0.55%, 0.5% to 0.6%, 0.5% to 0.65%, 0.5% to 0.7%, 0.5% to 0.75%,
0.5% to 0.8%, 0.5% to 0.85%, 0.5% to 0.9%, 0.5% to 0.96%, 0.5% to
0.98%, 0.55% to 0.6%, 0.55% to 0.65%, 0.55% to 0.7%, 0.55% to
0.75%, 0.55% to 0.8%, 0.55% to 0.85%, 0.55% to 0.9%, 0.55% to
0.96%, 0.55% to 0.98%, 0.6% to 0.65%, 0.6% to 0.7%, 0.6% to 0.75%,
0.6% to 0.8%, 0.6% to 0.85%, 0.6% to 0.9%, 0.6% to 0.96%, 0.6% to
0.98%, 0.65% to 0.7%, 0.65% to 0.75%, 0.65% to 0.8%, 0.65% to
0.85%, 0.65% to 0.9%, 0.65% to 0.96%, 0.65% to 0.98%, 0.7% to
0.75%, 0.7% to 0.8%, 0.7% to 0.85%, 0.7% to 0.9%, 0.7% to 0.96%,
0.7% to 0.98%, 0.75% to 0.8%, 0.75% to 0.85%, 0.75% to 0.9%, 0.75%
to 0.96%, 0.75% to 0.98%, 0.8% to 0.85%, 0.8% to 0.9%, 0.8% to
0.96%, 0.8% to 0.98%, 0.85% to 0.9%, 0.85% to 0.96%, 0.85% to
0.98%, 0.9% to 0.96%, 0.9% to 0.98%, or 0.96% to 0.98%. At a recall
rate of about 0.1%, 0.5%, 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%,
11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%, 19%, or 20% the prediction
model may have a positive predictive value of 0.45%, 0.5%, 0.55%,
0.6%, 0.65%, 0.7%, 0.75%, 0.8%, 0.85%, 0.9%, 0.96%, or 0.98%. At a
recall rate of about 0.1%, 0.5%, 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%,
9%, 10%, 11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%, 19%, or 20% the
prediction model may have a positive predictive value of at least
0.45%, 0.5%, 0.55%, 0.6%, 0.65%, 0.7%, 0.75%, 0.8%, 0.85%, 0.9%, or
0.96%. At a recall rate of about 0.1%, 0.5%, 1%, 2%, 3%, 4%, 5%,
6%, 7%, 8%, 9%, 10%, 11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%, 19%,
or 20% the prediction model may have a positive predictive value of
at most 0.5%, 0.55%, 0.6%, 0.65%, 0.7%, 0.75%, 0.8%, 0.85%, 0.9%,
0.96%, or 0.98%.
Methods of Training a Machine-Learning HLA-Peptide Presentation
Prediction Model
[0509] In an aspect, a method of training a machine-learning
HLA-peptide presentation prediction model may comprise inputting
amino acid position information sequences of HLA-peptides isolated
from one or more HLA-peptide complexes from a cell expressing an
HLA class II allele into the HLA-peptide presentation prediction
model using a computer processor; training the machine-learning
HLA-peptide presentation prediction model may comprise adjusting
weighted values on nodes of a neural network to best match the
provided training data.
[0510] The training data may comprise sequence information of
sequences of peptides presented by an HLA protein expressed in
cells and identified by mass spectrometry; training peptide
sequence information comprising amino acid position information of
training peptides, wherein the training peptide sequence
information is associated with the HLA protein expressed in cells;
and a function representing a relation between the amino acid
position information received as input and a presentation
likelihood generated as output based on the amino acid position
information and the predictor variables. The training data,
training peptide sequence information, function, and presentation
likelihood are disclosed elsewhere herein.
[0511] The trained algorithm may comprise one or more neural
networks. A neural network may be a type of computing system based
upon a graph of several connected neurons (or nodes) in a series of
layers. A neural network may comprise an input layer, to which data
is presented; one or more internal, and/or "hidden," layers; and an
output layer, from which results are presented. A neural network
may learn the relationships between an input data set and a target
data set by adjusting a series of connection weights. A neuron may
be connected to neurons in other layers via connections that have
weights, which are parameters that control the strength of a
connection. The number of neurons in each layer may be related to
the complexity of a problem to be solved. The minimum number of
neurons required in a layer may be determined by the problem
complexity, and the maximum number may be limited by the ability of
a neural network to generalize. Input neurons may receive data
being presented and then transmit that data to a node in the first
hidden layer through connection weights, which are modified during
training. The result node may sum up the products of all pairs of
inputs and their associated weights. The weighted sum may be offset
with a bias to adjust the value of the result node. The output of a
node or neuron may be gated using a threshold or activation
function. An activation function may be a linear or non-linear
function. An activation function may be, for example, a rectified
linear unit (ReLU) activation function, a Leaky ReLu activation
function, or other function such as a saturating hyperbolic
tangent, identity, binary step, logistic, arcTan, softsign,
parameteric rectified linear unit, exponential linear unit,
softPlus, bent identity, softExponential, Sinusoid, Sinc, Gaussian,
or sigmoid function, or any combination thereof.
[0512] A hidden layer in the neural network may process data and
transmit its result to the next layer through a second set of
weighted connections. Each subsequent layer may "pool" results from
previous layers into more complex relationships. Neural networks
may be trained with a known sample set of training data (data
collected from one or more sensors) by allowing them to modify
themselves during (and after) training so as to provide a desired
output from a given set of inputs, such as an output value. A
trained algorithm may comprise convolutional neural networks,
recurrent neural networks, dilated convolutional neural networks,
fully connected neural networks, deep generative models, and
Boltzmann machines.
[0513] Weighing factors, bias values, and threshold values, or
other computational parameters of a neural network, may be "taught"
or "learned" in a training phase using one or more sets of training
data. For example, parameters may be trained using input data from
a training data set and a gradient descent or backward propagation
method so that output value(s) from a neural network are consistent
with examples included in a training data set.
[0514] The number of nodes used in an input layer of a neural
network may be at least about 10, 50, 100, 200, 300, 400, 500, 600,
700, 800, 900, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000,
9000, 10,000, 20,000, 30,000, 40,000, 50,000, 60,000, 70,000,
80,000, 90,000, 100,000 or greater. In other instances, the number
of node used in an input layer may be at most about 100,000,
90,000, 80,000, 70,000, 60,000, 50,000, 40,000, 30,000, 20,000,
10,000, 9000, 8000, 7000, 6000, 5000, 4000, 3000, 2000, 1000, 900,
800, 700, 600, 500, 400, 300, 200, 100, 50, or 10 or smaller. In
some instance, the total number of layers used in a neural network
(including input and output layers) may be at least about 3, 4, 5,
10, 15, 20, or greater. In other instances, the total number of
layers may be at most about 20, 15, 10, 5, 4, 3 or less.
[0515] In some instances, the total number of learnable or
trainable parameters, e.g., weighting factors, biases, or threshold
values, used in a neural network may be at least about 10, 50, 100,
200, 300, 400, 500, 600, 700, 800, 900, 1000, 2000, 3000, 4000,
5000, 6000, 7000, 8000, 9000, 10,000, 20,000, 30,000, 40,000,
50,000, 60,000, 70,000, 80,000, 90,000, 100,000 or greater. In
other instances, the number of learnable parameters may be at most
about 100,000, 90,000, 80,000, 70,000, 60,000, 50,000, 40,000,
30,000, 20,000, 10,000, 9000, 8000, 7000, 6000, 5000, 4000, 3000,
2000, 1000, 900, 800, 700, 600, 500, 400, 300, 200, 100, 50, or 10
or smaller.
[0516] A neural network may comprise a convolutional neural
network. A convolutional neural network may comprise one or more
convolutional layers, dilated layers or fully connected layers. The
number of convolutional layers may be between 1-10 and dilated
layers between 0-10. The total number of convolutional layers
(including input and output layers) may be at least about 1, 2, 3,
4, 5, 10, 15, 20, or greater, and the total number of dilated
layers may be at least about 1, 2, 3, 4, 5, 10, 15, 20, or greater.
The total number of convolutional layers may be at most about 20,
15, 10, 5, 4, 3 or less, and the total number of dilated layers may
be at most about 20, 15, 10, 5, 4, 3 or less. In some embodiments,
the number of convolutional layers is between 1-10 and fully
connected layers between 0-10. The total number of convolutional
layers (including input and output layers) may be at least about 1,
2, 3, 4, 5, 10, 15, 20, or greater, and the total number of fully
connected layers may be at least about 1, 2, 3, 4, 5, 10, 15, 20,
or greater. The total number of convolutional layers may be at most
about 20, 15, 10, 5, 4, 3 or less, and the total number of fully
connected layers may be at most about 20, 15, 10, 5, 4, 3 or
less.
[0517] A convolutional neural network (CNN) may be a deep and
feed-forward artificial neural network. A CNN may be applicable to
analyzing visual imagery. A CNN may comprise an input, an output
layer, and multiple hidden layers. Hidden layers of a CNN may
comprise convolutional layers, pooling layers, fully connected
layers and normalization layers. Layers may be organized in 3
dimensions: width, height and depth.
[0518] Convolutional layers may apply a convolution operation to an
input and pass results of a convolution operation to a next layer.
For processing images, a convolution operation may reduce the
number of free parameters, allowing a network to be deeper with
fewer parameters. In a convolutional layer, neurons may receive
input from only a restricted subarea of a previous layer.
Convolutional layer's parameters may comprise a set of learnable
filters (or kernels). Learnable filters may have a small receptive
field and extend through the full depth of an input volume. During
a forward pass, each filter may be convolved across the width and
height of an input volume, compute a dot product between entries of
a filter and an input, and produce a 2-dimensional activation map
of that filter. As a result, a network may learn filters that
activate when it detects some specific type of feature at some
spatial position in an input.
[0519] Pooling layers may comprise global pooling layers. Global
pooling layers may combine outputs of neuron clusters at one layer
into a single neuron in the next layer. For example, max pooling
layers may use the maximum value from each of a cluster of neurons
at a prior layer; and average pooling layers may use an average
value from each of a cluster of neurons at the prior layer. Fully
connected layers may connect every neuron in one layer to every
neuron in another layer. In a fully-connected layer, each neuron
may receive input from every element of a previous layer. A
normalization layer may be a batch normalization layer. A batch
normalization layer may improve performance and stability of neural
networks. A batch normalization layer may provide any layer in a
neural network with inputs that are zero mean/unit variance.
Advantages of using batch normalization layer may include faster
trained networks, higher learning rates, easier to initialize
weights, more activation functions viable, and simpler process of
creating deep networks.
[0520] A neural network may comprise a recurrent neural network. A
recurrent neural network may be configured to receive sequential
data as an input, such as consecutive data inputs, and a recurrent
neural network software module may update an internal state at
every time step. A recurrent neural network can use internal state
(memory) to process sequences of inputs. A recurrent neural network
may be applicable to tasks such as handwriting recognition or
speech recognition, next word prediction, music composition, image
captioning, time series anomaly detection, machine translation,
scene labeling, and stock market prediction. A recurrent neural
network may comprise fully recurrent neural network, independently
recurrent neural network, Elman networks, Jordan networks, Echo
state, neural history compressor, long short-term memory, gated
recurrent unit, multiple timescales model, neural Turing machines,
differentiable neural computer, neural network pushdown automata,
or any combination thereof.
[0521] A trained algorithm may comprise a supervised or
unsupervised learning method such as, for example, SVM, random
forests, clustering algorithm (or software module), gradient
boosting, logistic regression, and/or decision trees. Supervised
learning algorithms may be algorithms that rely on the use of a set
of labeled, paired training data examples to infer the relationship
between an input data and output data. Unsupervised learning
algorithms may be algorithms used to draw inferences from training
data sets to output data. Unsupervised learning algorithms may
comprise cluster analysis, which may be used for exploratory data
analysis to find hidden patterns or groupings in process data. One
example of an unsupervised learning method may comprise principal
component analysis. Principal component analysis may comprise
reducing the dimensionality of one or more variables. The
dimensionality of a given variables may be at least 1, 5, 10, 50,
100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200 1300,
1400, 1500, 1600, 1700, 1800, or greater. The dimensionality of a
given variables may be at most 1800, 1600, 1500, 1400, 1300, 1200,
1100, 1000, 900, 800, 700, 600, 500, 400, 300, 200, 100, 50, 10 or
less.
[0522] A training algorithm may be obtained through statistical
techniques. In some embodiments, statistical techniques may
comprise linear regression, classification, resampling methods,
subset selection, shrinkage, dimension reduction, nonlinear models,
tree-based methods, support vector machines, unsupervised learning,
or any combination thereof.
[0523] A linear regression may be a method to predict a target
variable by fitting the best linear relationship between a
dependent and independent variable. The best fit may mean that the
sum of all distances between a shape and actual observations at
each point is the least. Linear regression may comprise simple
linear regression and multiple linear regression. A simple linear
regression may use a single independent variable to predict a
dependent variable. A multiple linear regression may use more than
one independent variable to predict a dependent variable by fitting
a best linear relationship.
[0524] A classification may be a data mining technique that assigns
categories to a collection of data in order to achieve accurate
predictions and analysis. Classification techniques may comprise
logistic regression and discriminant analysis. Logistic regression
may be used when a dependent variable is dichotomous (binary).
Logistic regression may be used to discover and describe a
relationship between one dependent binary variable and one or more
nominal, ordinal, interval or ratio-level independent variables. A
resampling may be a method comprising drawing repeated samples from
original data samples. A resampling may not involve a utilization
of a generic distribution tables in order to compute approximate
probability values. A resampling may generate a unique sampling
distribution on a basis of an actual data. In some embodiments, a
resampling may use experimental methods, rather than analytical
methods, to generate a unique sampling distribution. Resampling
techniques may comprise bootstrapping and cross-validation.
Bootstrapping may be performed by sampling with replacement from
original data, and take "not chosen" data points as test cases.
Cross validation may be performed by split training data into a
plurality of parts.
[0525] A subset selection may identify a subset of predictors
related to a response. A subset selection may comprise best-subset
selection, forward stepwise selection, backward stepwise selection,
hybrid method, or any combination thereof. In some embodiments,
shrinkage fits a model involving all predictors, but estimated
coefficients are shrunken towards zero relative to the least
squares estimates. This shrinkage may reduce variance. A shrinkage
may comprise ridge regression and a lasso. A dimension reduction
may reduce a problem of estimating n+1 coefficients to a simpler
problem of m+1 coefficients, where m<n. It may be attained by
computing n different linear combinations, or projections, of
variables. Then these n projections are used as predictors to fit a
linear regression model by least squares. Dimension reduction may
comprise principal component regression and partial least squares.
A principal component regression may be used to derive a
low-dimensional set of features from a large set of variables. A
principal component used in a principal component regression may
capture the most variance in data using linear combinations of data
in subsequently orthogonal directions. The partial least squares
may be a supervised alternative to principal component regression
because partial least squares may make use of a response variable
in order to identify new features.
[0526] A nonlinear regression may be a form of regression analysis
in which observational data are modeled by a function which is a
nonlinear combination of model parameters and depends on one or
more independent variables. A nonlinear regression may comprise a
step function, piecewise function, spline, generalized additive
model, or any combination thereof.
[0527] Tree-based methods may be used for both regression and
classification problems. Regression and classification problems may
involve stratifying or segmenting the predictor space into a number
of simple regions. Tree-based methods may comprise bagging,
boosting, random forest, or any combination thereof. Bagging may
decrease a variance of prediction by generating additional data for
training from the original dataset using combinations with
repetitions to produce multistep of the same carnality/size as
original data. Boosting may calculate an output using several
different models and then average a result using a weighted average
approach. A random forest algorithm may draw random bootstrap
samples of a training set. Support vector machines may be
classification techniques. Support vector machines may comprise
finding a hyperplane that best separates two classes of points with
the maximum margin. Support vector machines may constrain an
optimization problem such that a margin is maximized subject to a
constraint that it perfectly classifies data.
[0528] Unsupervised methods may be methods to draw inferences from
datasets comprising input data without labeled responses.
Unsupervised methods may comprise clustering, principal component
analysis, k-Mean clustering, hierarchical clustering, or any
combination thereof.
[0529] The mass spectrometry may be mono-allelic mass spectrometry.
In some embodiments, the mass spectrometry may be MS analysis,
MS/MS analysis, LC-MS/MS analysis, or a combination thereof. In
some embodiments, MS analysis may be used to determine a mass of an
intact peptide. For example, the determining can comprise
determining a mass of an intact peptide (e.g., MS analysis). In
some embodiments, MS/MS analysis may be used to determine a mass of
peptide fragments. For example, the determining can comprise
determining a mass of peptide fragments, which can be used to
determine an amino acid sequence of a peptide or portion thereof
(e.g., MS/MS analysis). In some embodiments, the mass of peptide
fragments may be used to determine a sequence of amino acids within
the peptide. In some embodiments, LC-MS/MS analysis may be used to
separate complex peptide mixtures. For example, the determining can
comprise separating complex peptide mixtures, such as by liquid
chromatography, and determining a mass of an intact peptide, a mass
of peptide fragments, or a combination thereof (e.g., LC-MS/MS
analysis). This data can be used, e.g., for peptide sequencing.
[0530] The peptides may be presented by an HLA protein expressed in
cells through autophagy. Autophagy may allow the orderly
degradation and recycling of cellular components. The autophagy may
comprise macroautophagy, microautophagy and Chaperone mediated
autophagy. The peptides may be presented by an HLA protein
expressed in cells through phagocytosis. The phagocytosis may be a
major mechanism used to remove pathogens and cell debris. For
example, when a macrophage ingests a pathogenic microorganism, the
pathogen becomes trapped in a phagosome which then fuses with a
lysosome to form a phagolysosome. In HLA class II, phagocytes such
as macrophages and immature dendritic cells may take up entities by
phagocytosis into phagosomes--though B cells exhibit the more
general endocytosis into endosomes--which fuse with lysosomes whose
acidic enzymes cleave the uptaken protein into many different
peptides.
[0531] The quality of the training data may be increased by using a
plurality of quality metrics. The plurality of quality metrics may
comprise common contaminant peptide removal, high scored peak
intensity, high score, and high mass accuracy. The scored peak
intensity may be used prior to performing scoring. The MS/MS Search
first screens the MS/MS spectrum against candidate sequences using
a simple filter. This filter may be minimum scored peak intensity.
Using the scored peak intensity may enhance search speed by
allowing candidate sequences to be rapidly and summarily rejected
once a sufficient number of spectral peaks are examined and found
not to meet the threshold established by this filter. The scored
peak intensity may be at least 50%. The scored peak intensity may
be at least 70%. The scored peak intensity may be at least 10%,
20%, 30%, 40%, 50%, 60%, 70%, 80%, 90% or greater. In some cases,
the scored peak intensity may be at most 90%, 80%, 70%, 60%, 50%,
40%, 30%, 20%, 10% or less. The score may be at least 7. The score
may be at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20 or greater.
In some cases, the score may be at most about 20, 15, 10, 9, 8, 7,
6, 5, 4, 3, 2, 1 or less. The mass accuracy may be at most 5 ppm.
The mass accuracy may be at most 10 ppm, 9 ppm, 8 ppm, 7 ppm, 6
ppm, 5 ppm, 4 ppm, 3 ppm, 2 ppm, 1 ppm or less. The mass accuracy
may be at least 1 ppm, 2 ppm, 3 ppm, 4 ppm, 5 ppm, 6 ppm, 7 ppm, 8
ppm, 9 ppm, 10 ppm or greater.
[0532] In some embodiments, a mass accuracy is at most 2 ppm. In
some embodiments, a backbone cleavage score is at least 5. In some
embodiments, a backbone cleavage score is at least 8.
[0533] The peptides presented by an HLA protein expressed in cells
may be peptides presented by a single immunoprecipitated HLA
protein expressed in cells Immunoprecipitation (IP) may be the
technique of precipitating a protein antigen out of solution using
an antibody that specifically binds to that particular protein.
This process can be used to isolate and concentrate a particular
protein from a sample containing many thousands of different
proteins Immunoprecipitation may require that the antibody be
coupled to a solid substrate at some point in the procedure.
[0534] The peptides presented by an HLA protein expressed in cells
may be peptides presented by a single exogenous HLA protein
expressed in cells. The single exogenous HLA protein may be created
by introducing one or more exogenous peptides to the population of
cells. In some embodiments, the introducing comprises contacting
the population of cells with the one or more exogenous peptides or
expressing the one or more exogenous peptides in the population of
cells. In some embodiments, the introducing comprises contacting
the population of cells with one or more nucleic acids encoding the
one or more exogenous peptides. In some embodiments, the one or
more nucleic acids encoding the one or more peptides is DNA. In
some embodiments, the one or more nucleic acids encoding the one or
more peptides is RNA, optionally wherein the RNA is mRNA. In some
embodiments, the enriching does not comprise use of a tetramer (or
multimer) reagent.
[0535] The peptides presented by an HLA protein expressed in cells
may be peptides presented by a single recombinant HLA protein
expressed in cells. The recombinant HLA protein may be encoded by a
recombinant HLA class I or HLA class II allele. The HLA class I may
be selected from the group consisting of HLA-A, HLA-B, HLA-C. The
HLA class I may be a non-classical class-I-b group. The HLA class I
may be selected from the group consisting of HLA-E, HLA-F, and
HLA-G. The HLA class I may be a non-classical class-I-b group
selected from the group consisting of HLA-E, HLA-F, and HLA-G. In
some embodiments, the HLA class II comprises an HLA class II
.alpha.-chain, an HLA class II .beta.-chain, or a combination
thereof.
[0536] The plurality of predictor variables may comprise a
peptide-HLA affinity predictor variable. The plurality of predictor
variables may comprise a source protein expression level predictor
variable. The source protein expression level may be the expression
level of the source protein of the peptide within a cell. In some
embodiments, the expression level may be determined by measuring
the amount of source protein or the amount of RNA encoding the
source protein. The plurality of predictor variables may comprise
peptide sequence, amino acid physical properties, peptide physical
properties, expression level of the source protein of a peptide
within a cell, protein stability, protein translation rate,
ubiquitination sites, protein degradation rate, translational
efficiencies from ribosomal profiling, protein cleavability,
protein localization, motifs of host protein that facilitate TAP
transport, host protein is subject to autophagy, motifs that favor
ribosomal stalling (e.g., polyproline or polylysine stretches),
protein features that favor NMD (e.g., long 3' UTR, stop codon
>50nt upstream of last exon:exon junction and peptide
cleavability).
[0537] The plurality of predictor variables may comprise a peptide
cleavability predictor variable. The peptide cleavability may be
associated with a cleavable linker or a cleavage sequence. In some
embodiments, the cleavable linker is a ribosomal skipping site or
an internal ribosomal entry site (IRES) element. In some
embodiments, the ribosomal skipping site or IRES is cleaved when
expressed in the cells. In some embodiments, the ribosomal skipping
site is selected from the group consisting of F2A, T2A, P2A, and
E2A. In some embodiments, the IRES element is selected from common
cellular or viral IRES sequences. A cleavage sequence, such as F2A,
or an internal ribosome entry site (IRES) can be placed between the
.alpha.-chain and .beta.2-microglobulin (HLA class I) or between
the .alpha.-chain and .beta.-chain (HLA class II). In some
embodiments, a single HLA class I allele is HLA-A*02:01,
HLA-A*23:01 and HLA-B*14:02, or HLA-E*01:01, and HLA class II
allele is HLA-DRB*01:01, HLA-DRB*01:02 and HLA-DRB*11:01,
HLA-DRB*15:01, or HLA-DRB*07:01. In some embodiments, the cleavage
sequence is a T2A, P2A, E2A, or F2A sequence. For example, the
cleavage sequence can be E G R G S L T C G D V E N P G P (SEQ ID
NO: 6)(T2A), A T N F S L K Q A G D V E N P G P(SEQ ID NO: 7)(P2A),
Q C T N Y A L K L A G D V E S N P G P (SEQ ID NO: 8)(E2A), or V K Q
T L N F D L K L A G D V E S N P G P (SEQ ID NO: 9)(F2A).
[0538] In some embodiments, the cleavage sequence may be a thrombin
cleavage site CLIP.
[0539] The peptides presented by the HLA protein may comprise
peptides that are identified by searching a no-enzyme specificity
without modification peptide database. The peptide database may be
a no-enzyme specificity peptide database, such as a without
modification database or a with modification (e.g., phosphorylation
or cysteinylation) database. In some embodiments, the peptide
database is a polypeptide database. In some embodiments, the
polypeptide database may be a protein database. In some
embodiments, the method further comprises searching the peptide
database using a reversed-database search strategy. In some
embodiments, the method further comprises searching a protein
database using a reversed-database search strategy. In some
embodiments, a de novo search is performed, e.g., to discover new
peptides that are not included in a normal peptide or protein
database. The peptide database may be generated by providing a
first and a second population of cells each comprising one or more
cells comprising an affinity acceptor tagged HLA, wherein the
sequence affinity acceptor tagged HLA comprises a different
recombinant polypeptide encoded by a different HLA allele
operatively linked to an affinity acceptor peptide; enriching for
affinity acceptor tagged HLA-peptide complexes; characterizing a
peptide or a portion thereof bound to an affinity acceptor tagged
HLA-peptide complex from the enriching; and generating an
HLA-allele specific peptide database.
[0540] The peptides presented by the HLA protein may comprise
peptides identified by comparing a MS/MS spectra of the
HLA-peptides with MS/MS spectra of one or more HLA-peptides in a
peptide database.
[0541] There may be mutation on either peptides or nucleic acid
that encodes peptides. The mutation may be selected from the group
consisting of a point mutation, a splice site mutation, a
frameshift mutation, a read-through mutation, and a gene fusion
mutation. The point mutation may be a genetic mutation where a
single nucleotide base is changed, inserted or deleted from a
sequence of DNA or RNA. The splice site mutation may be a genetic
mutation that inserts, deletes or changes a number of nucleotides
in the specific site at which splicing takes place during the
processing of precursor messenger RNA into mature messenger RNA.
The frameshift mutation may be a genetic mutation caused by indels
(insertions or deletions) of a number of nucleotides in a DNA
sequence that is not divisible by three. The mutation may also
comprise insertions, deletions, substitution mutations, gene
duplications, chromosomal translocations, and chromosomal
inversions.
[0542] In some embodiments, the HLA class II protein comprises an
HLA-DR protein.
[0543] In some embodiments, the HLA class II protein comprises an
HLA-DP protein.
[0544] In some embodiments, the HLA class II protein comprises an
HLA-DQ protein.
[0545] In some embodiments, the HLA class II protein may be
selected from the group consisting an HLA-DR, and HLA-DP or an
HLA-DQ protein. In some embodiments, the HLA protein is an HLA
class II protein selected from the group consisting of:
HLA-DPB1*01:01/HLA-DPA1*01:03, HLA-DPB1*02:01/HLA-DPA1*01:03,
HLA-DPB1*03:01/HLA-DPA1*01:03, HLA-DPB1*04:01/HLA-DPA1*01:03,
HLA-DPB1*04:02/HLA-DPA1*01:03, HLA-DPB1*06:01/HLA-DPA1*01:03,
HLA-DQB1*02:01/HLA-DQA1*05:01, HLA-DQB1*02:02/HLA-DQA1*02:01,
HLA-DQB1*06:02/HLA-DQA1*01:02, HLA-DQB1*06:04/HLA-DQA1*01:02,
HLA-DRB1*01:01, HLA-DRB1*01:02, HLA-DRB1*03:01, HLA-DRB1*03:02,
HLA-DRB1*04:01, HLA-DRB1*04:02, HLA-DRB1*04:03, HLA-DRB1*04:04,
HLA-DRB1*04:05, HLA-DRB1*04:07, HLA-DRB1*07:01, HLA-DRB1*08:01,
HLA-DRB1*08:02, HLA-DRB1*08:03, HLA-DRB1*08:04, HLA-DRB1*09:01,
HLA-DRB1*10:01, HLA-DRB1*11:01, HLA-DRB1*11:02, HLA-DRB1*11:04,
HLA-DRB1*12:01, HLA-DRB1*12:02, HLA-DRB1*13:01, HLA-DRB1*13:02,
HLA-DRB1*13:03, HLA-DRB1*14:01, HLA-DRB1*15:01, HLA-DRB1*15:02,
HLA-DRB1*15:03, HLA-DRB1*16:01, HLA-DRB3*01:01, HLA-DRB3*02:02,
HLA-DRB3*03:01, HLA-DRB4*01:01, HLA-DRB5*01:01). The peptides
presented by the HLA protein may have a length of from 15-40 amino
acids. The peptides presented by the HLA protein may have a length
of at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, or greater amino acids.
In some embodiments, the peptides presented by the HLA protein may
have a length of at most 30, 29, 28, 27, 26, 25, 24, 23, 22, 21,
20, 19, 18, 17, 16, 15, 14, 13, 12, 11, or less amino acids.
[0546] The peptides presented by the HLA protein may comprise
peptides identified by (a) isolating one or more HLA complexes from
a cell line expressing a single HLA class II allele; (b) isolating
one or more HLA-peptides from the one or more isolated HLA
complexes; (c) obtaining MS/MS spectra for the one or more isolated
HLA-peptides; and (d) obtaining a peptide sequence that corresponds
to the MS/MS spectra of the one or more isolated HLA-peptides from
a peptide database; wherein one or more sequences obtained from
steps (a, b, c) and (d) identifies the sequence of the one or more
isolated HLA-peptides.
[0547] The isolating may comprise isolating HLA-peptide complexes
from the cells transfected or transduced with affinity tagged HLA
constructs. In some embodiments, the complexes can be isolated
using standard immunoprecipitation techniques known in the art with
commercially available antibodies. The cells can be first lysed.
HLA class II-peptide complexes can be isolated using HLA class II
specific antibodies such as the M5/114.15.2 monoclonal antibody. In
some embodiments, the single (or pair of) HLA alleles are expressed
as a fusion protein with a peptide tag and the HLA-peptide
complexes are isolated using binding molecules that recognize the
peptide tags.
[0548] The isolating may comprise isolating peptides from the
HLA-peptide complexes and sequencing the peptides. The peptides are
isolated from the complex by any method known to one of skill in
the art, such as acid elution. While any sequencing method can be
used, methods employing mass spectrometry, such as liquid
chromatography-mass spectrometry (LC-MS or LC-MS/MS, or
alternatively HPLC-MS or HPLC-MS/MS) are utilized in some
embodiments. These sequencing methods may be well-known to a
skilled person and are reviewed in Medzihradszky K F and Chalkley R
J. Mass Spectrom Rev. 2015 January-February; 34(1):43-63.
[0549] Additional candidate components and molecules suitable for
isolation or purification may comprise binding molecules, such as
biotin (biotin-avidin specific binding pair), an antibody, a
receptor, a ligand, a lectin, or molecules that comprise a solid
support, including, for example, plastic or polystyrene beads,
plates or beads, magnetic beads, test strips, and membranes.
Purification methods such as cation exchange chromatography can be
used to separate conjugates by charge difference, which effectively
separates conjugates into their various molecular weights. The
content of the fractions obtained by cation exchange chromatography
can be identified by molecular weight using conventional methods,
for example, mass spectroscopy, SDS-PAGE, or other known methods
for separating molecular entities by molecular weight.
[0550] In some embodiments, the method further comprises isolating
peptides from the affinity acceptor tagged HLA-peptide complexes
before the characterizing. In some embodiments, an HLA-peptide
complex is isolated using an anti-HLA antibody. In some cases, an
HLA-peptide complex with or without an affinity tag is isolated
using an anti-HLA antibody. In some cases, a soluble HLA (sHLA)
with or without an affinity tag is isolated from media of a cell
culture. In some cases, a soluble HLA (sHLA) with or without an
affinity tag is isolated using an anti-HLA antibody. For example,
an HLA, such as a soluble HLA (sHLA) with or without an affinity
tag, can be isolated using a bead or column containing an anti-HLA
antibody. In some embodiments, the peptides are isolated using
anti-HLA antibodies. In some cases, a soluble HLA (sHLA) with or
without an affinity tag is isolated using an anti-HLA antibody. In
some cases, a soluble HLA (sHLA) with or without an affinity tag is
isolated using a column containing an anti-HLA antibody. In some
embodiments, the method further comprises removing one or more
amino acids from a terminus of a peptide bound to an affinity
acceptor tagged HLA-peptide complex.
[0551] The personalized cancer vaccine may further comprise an
adjuvant. For example, poly-ICLC, an agonist of TLR3 and the RNA
helicase-domains of MDA5 and RIG3, has shown several desirable
properties for a vaccine adjuvant. These properties may include the
induction of local and systemic activation of immune cells in vivo,
production of stimulatory chemokines and cytokines, and stimulation
of antigen-presentation by DCs. Furthermore, poly-ICLC can induce
durable CD4+ and CD8+ responses in humans. Importantly, striking
similarities in the upregulation of transcriptional and signal
transduction pathways may be seen in subjects vaccinated with
poly-ICLC and in volunteers who had received the highly effective,
replication-competent yellow fever vaccine. Furthermore, >90% of
ovarian carcinoma patients immunized with poly-ICLC in combination
with a NYESO-1 peptide vaccine (in addition to Montanide) showed
induction of CD4+ and CD8+ T cell, as well as antibody responses to
the peptide in a recent phase 1 study.
[0552] The personalized cancer vaccine may further comprise an
immune checkpoint inhibitor. The immune checkpoint inhibitor may
comprise a type of drug that blocks certain proteins made by some
types of immune system cells, such as T cells, and some cancer
cells. These proteins help keep immune responses in check and can
keep T cells from killing cancer cells. When these proteins are
blocked, the "brakes" on the immune system are released and T cells
are able to kill cancer cells better. Examples of checkpoint
proteins found on T cells or cancer cells include PD-1/PD-L1 and
CTLA-4/B7-1/B7-2. Some immune checkpoint inhibitors are used to
treat cancer.
[0553] The training data may further comprise structured data,
time-series data, unstructured data, and relational data.
Unstructured data may comprise audio data, image data, video,
mechanical data, electrical data, chemical data, and any
combination thereof, for use in accurately simulating or training
robotics or simulations. Time-series data may comprise data from
one or more of a smart meter, a smart appliance, a smart device, a
monitoring system, a telemetry device, or a sensor. Relational data
comprises data from a customer system, an enterprise system, an
operational system, a website, web accessible application program
interface (API), or any combination thereof. This may be done by a
user through any method of inputting files or other data formats
into software or systems.
[0554] The training data may be uploaded to a cloud-based database.
The cloud-based database may be accessible from local and/or remote
computer systems on which the machine learning-based sensor signal
processing algorithms are running. The cloud-based database and
associated software may be used for archiving electronic data,
sharing electronic data, and analyzing electronic data. The data or
datasets generated locally may be uploaded to a cloud-based
database, from which it may be accessed and used to train other
machine learning-based detection systems at the same site or a
different site. Sensor device and system test results generated
locally may be uploaded to a cloud-based database and used to
update the training data set in real time for continuous
improvement of sensor device and detection system test
performance.
[0555] The training may be performed using convolutional neural
networks. The convolutional neural network (CNN) is described
elsewhere herein. The convolutional neural networks may comprise at
least two convolutional layers. The number of convolutional layers
may be between 1-10 and the dilated layers between 0-10. The total
number of convolutional layers (including input and output layers)
may be at least about 1, 2, 3, 4, 5, 10, 15, 20, or greater, and
the total number of dilated layers may be at least about 1, 2, 3,
4, 5, 10, 15, 20, or greater. The total number of convolutional
layers may be at most about 20, 15, 10, 5, 4, 3 or less, and the
total number of dilated layers may be at most about 20, 15, 10, 5,
4, 3 or less. In some embodiments, the number of convolutional
layers is between 1-10 and the fully connected layers between 0-10.
The total number of convolutional layers (including input and
output layers) may be at least about 1, 2, 3, 4, 5, 10, 15, 20, or
greater, and the total number of fully connected layers may be at
least about 1, 2, 3, 4, 5, 10, 15, 20, or greater. The total number
of convolutional layers may be at most about 20, 15, 10, 5, 4, 3 or
less, and the total number of fully connected layers may be at most
about 20, 15, 10, 5, 4, 3 or less.
[0556] The convolutional neural networks may comprise at least one
batch normalization step. The batch normalization layer may improve
the performance and stability of neural networks. The batch
normalization layer may provide any layer in a neural network with
inputs that are zero mean/unit variance. The total number of batch
normalization layers may be at least about 3, 4, 5, 10, 15, 20 or
more. The total number of batch normalization layers may be at most
about 20, 15, 10, 5, 4, 3 or less
[0557] The convolutional neural networks may comprise at least one
spatial dropout step. The total number of spatial dropout steps may
be at least about 3, 4, 5, 10, 15, 20 or more, and the total number
of spatial dropout steps may be at most about 20, 15, 10, 5, 4, 3
or less.
[0558] The convolutional neural networks may comprise at least one
global max pooling step. The global pooling layers may combine the
outputs of neuron clusters at one layer into a single neuron in the
next layer. For example, max pooling layers may use the maximum
value from each of a cluster of neurons at the prior layer; and
average pooling layers may use the average value from each of a
cluster of neurons at the prior layer. The convolutional neural
networks may comprise at least about 1, 2, 3, 4, 5, 10, 15, 20, or
greater global max pooling steps. The convolutional neural networks
may comprise at most about 20, 15, 10, 5, 4, 3 or less global max
pooling steps.
[0559] The convolutional neural networks may comprise at least one
dense layer. The convolutional neural networks may comprise at
least about 1, 2, 3, 4, 5, 10, 15, 20, or greater dense layers. The
convolutional neural networks may comprise at most about 20, 15,
10, 5, 4, 3 or less dense layers.
Therapeutic Methods
[0560] Personalized immunotherapy using tumor-specific peptides has
been described. Tumor neoantigens, which arise as a result of
genetic change (e.g., inversions, translocations, deletions,
missense mutations, splice site mutations, etc.) within malignant
cells, represent the most tumor-specific class of antigens.
Neoantigens have rarely been used in cancer vaccine or immunogenic
compositions due to technical difficulties in identifying them,
selecting optimized antigens, and producing neoantigens for use in
a vaccine or immunogenic composition. Efficiently choosing which
particular peptides to utilize as an immunogen requires the ability
to predict which tumor-specific peptides would efficiently bind to
the HLA alleles present in a patient and would be effectively
presented to the patient's immune system for inducing anti-tumor
immunity. One of the critical barriers to developing curative and
tumor-specific immunotherapy is the identification and selection of
highly specific and restricted tumor antigens to avoid
autoimmunity. This is particularly important in case of candidate
tumor specific peptides for immunotherapy that are presented by MHC
class II antigens, because there is a certain level of promiscuity
in MHC class II-peptide binding and presentation to the immune
system. At the same time, MHC class II presented peptides are
required for activation of not only cytotoxic cells but also CD4+ve
memory T cells. MHC class II mediated immunogenic response is
therefore needed for a robust, offer long term immunogenicity for
greater effectiveness in tumor protection. These problems can be
addressed by: having a reliable peptide-MHC predicting algorithm
and having a reliable system for assaying and validating the
peptide-MHC interaction and immunogenicity. Therefore, in some
embodiments, a highly efficient and immunogenic cancer vaccine may
be produced by identifying candidate mutations in neoplasias/tumors
which are present at the DNA level in tumor but not in matched
germline samples from a high proportion of subjects having cancer;
analyzing the identified mutations with one or more peptide-MHC
binding prediction algorithms to identify which MHC (human
leukocytic antigen or HLA in case of humans) bind to a high
proportion of patient HLA alleles; and synthesizing the plurality
of neoantigenic peptides selected from the sets of all neoantigen
peptides and predicted binding peptides for use in a cancer vaccine
or immunogenic composition suitable for treating a high proportion
of subjects having cancer.
[0561] For example, translating peptide sequencing information into
a therapeutic vaccine can include prediction of mutated peptides
that can bind to HLA peptides of a high proportion of individuals.
Efficiently choosing which particular mutations to utilize as
immunogen requires the ability to predict which mutated peptides
would efficiently bind to a high proportion of patient's HLA
alleles. Recently, neural network based learning approaches with
validated binding and non-binding peptides have advanced the
accuracy of prediction algorithms for the major HLA-A and -B
alleles. However, although using advanced neural network-based
algorithms has helped to encode HLA-peptide binding rules, several
factors limit the power to predict peptides presented on HLA
alleles.
[0562] For example, translating peptide sequencing information into
a therapeutic vaccine can include formulating the drug as a
multi-epitope vaccine of long peptides. Targeting as many mutated
epitopes as practically possible takes advantage of the enormous
capacity of the immune system, prevents the opportunity for
immunological escape by down-modulation of an immune targeted gene
product, and compensates for the known inaccuracy of epitope
prediction approaches. Synthetic peptides provide a useful means to
prepare multiple immunogens efficiently and to rapidly translate
identification of mutant epitopes to an effective vaccine. Peptides
can be readily synthesized chemically and easily purified utilizing
reagents free of contaminating bacteria or animal substances. The
small size allows a clear focus on the mutated region of the
protein and also reduces irrelevant antigenic competition from
other components (unmutated protein or viral vector antigens).
[0563] For example, translating peptide sequencing information into
a therapeutic vaccine can include a combination with a strong
vaccine adjuvant. Effective vaccines can require a strong adjuvant
to initiate an immune response. For example, poly-ICLC, an agonist
of TLR3 and the RNA helicase-domains of MDA5 and RIG3, has shown
several desirable properties for a vaccine adjuvant. These
properties include the induction of local and systemic activation
of immune cells in vivo, production of stimulatory chemokines and
cytokines, and stimulation of antigen-presentation by DCs.
Furthermore, poly-ICLC can induce durable CD4+ and CD8+ responses
in humans. Importantly, striking similarities in the upregulation
of transcriptional and signal transduction pathways were seen in
subjects vaccinated with poly-ICLC and in volunteers who had
received the highly effective, replication-competent yellow fever
vaccine. Furthermore, >90% of ovarian carcinoma patients
immunized with poly-ICLC in combination with a NYESO-1 peptide
vaccine (in addition to Montanide) showed induction of CD4+ and
CD8+ T cell, as well as antibody responses to the peptide in a
recent phase 1 study. At the same time, poly-ICLC has been
extensively tested in more than 25 clinical trials to date and
exhibited a relatively benign toxicity profile.
[0564] In some embodiments, immunogenic peptides can be identified
from cells from a subject with a disease or condition. In some
embodiments, immunogenic peptides can be specific to a subject with
a disease or condition. In some embodiments, immunogenic peptides
can bind to an HLA that is matched to an HLA haplotype of a subject
with a disease or condition.
[0565] In some embodiments, a library of peptides can be expressed
in the cells. In some embodiments, the cells comprise the peptides
to be identified or characterized. In some embodiments, the
peptides to be identified or characterized are endogenous peptides.
In some embodiments, the peptides are exogenous peptides. For
example, the peptides to be identified or characterized can be
expressed from a plurality of sequences encoding a library of
peptides.
[0566] Prior to disclosure of the instant specification, the
majority of LC-MS/MS studies of the HLA peptidome have used cells
expressing multiple HLA peptides, which requires peptides to be
assigned to 1 of up to 6 HLA class I alleles using pre-existing
bioinformatic predictors or "deconvolution" (Bassani-Sternberg and
Gfeller, 2016). Thus, peptides that do not closely match known
motifs could not confidently be reported as binders to a given HLA
allele.
[0567] Provided herein are methods of prediction of peptides, such
as mutated peptides, that can bind to HLA peptides of individuals.
In some embodiments, the application provides methods of
identifying from a given set of antigen comprising peptides the
most suitable peptides for preparing an immunogenic composition for
a subject, said method comprising selecting from a given set of
peptides the plurality of peptides capable of binding an HLA
protein of the subject, wherein said ability to bind an HLA protein
is determined by analyzing the sequence of peptides with a machine
which has been trained with peptide sequence databases
corresponding to the specific HLA-binding peptides for each of the
HLA-alleles of said subject. Provided herein are methods of
identifying from a given set of antigen comprising peptides the
most suitable peptides for preparing an immunogenic composition for
a subject, said method comprising selecting from a given set of
peptides the plurality of peptides determined as capable of binding
an HLA protein of the subject, ability to bind an HLA protein is
determined by analyzing the sequence of peptides with a machine
which has been trained with a peptide sequence database obtained by
carrying out the methods described herein above. Thus, in some
embodiments, the present disclosure provides methods of identifying
a plurality of subject-specific peptides for preparing a
subject-specific immunogenic composition, wherein the subject has a
tumor and the subject-specific peptides are specific to the subject
and the subject's tumor, said method comprising: sequencing a
sample of the subject's tumor and a non-tumor sample of the
subject; determining based on the nucleic acid sequencing:
non-silent mutations present in the genome of cancer cells of the
subject but not in normal tissue from the subject, and the HLA
genotype of the subject; and selecting from the identified
non-silent mutations the plurality of subject-specific peptides,
each having a different tumor epitope that is specific to the tumor
of the subject and each being identified as capable of binding an
HLA protein of the subject, as determined by analyzing the sequence
of peptides derived from the non-silent mutations in the methods
for predicting HLA binding described herein.
[0568] In some embodiments, disclosed herein, is a method of
characterizing HLA-peptide complexes specific to an individual.
[0569] In some embodiments, a method of characterizing HLA-peptide
complexes specific to an individual is used to develop an
immunotherapeutic in an individual in need thereof, such as a
subject with a condition or disease.
[0570] Provided herein is a method of providing an anti-tumor
immunity in a mammal comprising administering to the mammal a
polynucleic acid comprising a sequence encoding a peptide
identified according to a method described. Provided herein is a
method of providing an anti-tumor immunity in a mammal comprising
administering to the mammal an effective amount of a peptide with a
sequence of a peptide identified according to a method described
herein. Provided herein is a method of providing an anti-tumor
immunity in a mammal comprising administering to the mammal a cell
comprising a peptide comprising the sequence of a peptide
identified according to a method described herein. Provided herein
is a method of providing an anti-tumor immunity in a mammal
comprising administering to the mammal a cell comprising a
polynucleic acid comprising a sequence encoding a peptide
comprising the sequence of peptide identified according to a method
described herein. In some embodiments, the cell presents the
peptide as an HLA-peptide complex.
[0571] Provided herein is a method of treating a disease or
disorder in a subject, the method comprising administering to the
subject a polynucleic acid comprising a sequence encoding a peptide
identified according to a method described herein. Provided herein
is a method of treating a disease or disorder in a subject, the
method comprising administering to the subject an effective amount
of a peptide comprising the sequence of a peptide identified
according to a method described herein. Provided herein is a method
of treating a disease or disorder in a subject, the method
comprising administering to the subject a cell comprising a peptide
comprising the sequence of a peptide identified according to a
method described herein. Provided herein is a method of treating a
disease or disorder in a subject, the method comprising
administering to the subject a cell comprising a polynucleic acid
comprising a sequence encoding a peptide comprising the sequence of
a peptide identified according to a method described herein. In
some embodiments, the disease or disorder is cancer. In some
embodiments, the method further comprises administering an immune
checkpoint inhibitor to the subject.
[0572] Disclosed herein, in some embodiments, are methods of
developing an immunotherapeutic for an individual in need thereof
by characterizing HLA-peptide complexes comprising: a) providing a
population of cells derived from the individual in need thereof
wherein one or more cells of the population of cells comprise a
polynucleic acid comprising a sequence encoding an affinity
acceptor tagged HLA class I or HLA class II allele, wherein the
sequence encoding an affinity acceptor tagged HLA comprises: i) a
sequence encoding a recombinant HLA class I or HLA class II allele
operatively linked to ii) a sequence encoding an affinity acceptor
peptide; b) expressing the affinity acceptor tagged HLA in at least
one cell of the one or more cells of the population of cells,
thereby forming affinity acceptor tagged HLA-peptide complexes in
the at least one cell; c) enriching for the affinity acceptor
tagged HLA-peptide complexes, characterizing HLA-peptide complexes
specific to the individual in need thereof; and d) developing the
immunotherapeutic based on an HLA-peptide complex specific to the
individual in need thereof; wherein the individual has a disease or
condition.
[0573] In some embodiments, the immunotherapeutic is a nucleic acid
or a peptide therapeutic.
[0574] In some embodiments, the method comprises introducing one or
more peptides to the population of cells. In some embodiments, the
method comprises contacting the population of cells with the one or
more peptides or expressing the one or more peptides in the
population of cells. In some embodiments, the method comprises
contacting the population of cells with one or more nucleic acids
encoding the one or more peptides.
[0575] In some embodiments, the method comprises developing an
immunotherapeutic based on peptides identified in connection with
the patient-specific HLAs. In some embodiments, the population of
cells is derived from the individual in need thereof.
[0576] In some embodiments, the method comprises expressing a
library of peptides in the population of cells. In some
embodiments, the method comprises expressing a library of affinity
acceptor tagged HLA-peptide complexes. In some embodiments, the
library comprises a library of peptides associated with the disease
or condition. In some embodiments, the disease or condition is
cancer or an infection with an infectious agent or an autoimmune
disease. In some embodiments, the method comprises introducing the
infectious agent or portions thereof into one or more cells of the
population of cells. In some embodiments, the method comprises
characterizing one or more peptides from the HLA-peptide complexes
specific to the individual in need thereof, optionally wherein the
peptides are from one or more target proteins of the infectious
agent or the autoimmune disease. In some embodiments, the method
comprises characterizing one or more regions of the peptides from
the one or more target proteins of the infectious agent or
autoimmune disease. In some embodiments, the method comprises
identifying peptides from the HLA-peptide complexes derived from an
infectious agent or an autoimmune disease.
[0577] In some embodiments, the infectious agent is a pathogen. In
some embodiments, the pathogen is a virus, bacteria, or a
parasite.
[0578] In some embodiments, the virus is selected from the group
consisting of: BK virus (BKV), Dengue viruses (DENV-1, DENV-2,
DENV-3, DENV-4, DENV-5), cytomegalovirus (CMV), Hepatitis B virus
(HBV), Hepatitis C virus (HCV), Epstein-Barr virus (EBV), an
adenovirus, human immunodeficiency virus (HIV), human T cell
lymphotrophic virus (HTLV-1), an influenza virus, RSV, HPV, rabies,
mumps rubella virus, poliovirus, yellow fever, hepatitis A,
hepatitis B, Rotavirus, varicella virus, human papillomavirus
(HPV), smallpox, zoster, and combinations thereof.
[0579] In some embodiments, the bacteria is selected from the group
consisting of: Klebsiella spp., Tropheryma whipplei, Mycobacterium
leprae, Mycobacterium lepromatosis, and Mycobacterium tuberculosis.
In some embodiments, the bacteria is selected from the group
consisting of: typhoid, pneumococcal, meningococcal, haemophilus B,
anthrax, tetanus toxoid, meningococcal group B, bcg, cholera, and
combinations thereof.
[0580] In some embodiments, the parasite is a helminth or a
protozoan. In some embodiments, the parasite is selected from the
group consisting of: Leishmania spp. (e.g. L. major, L. infantum,
L. braziliensis, L. donovani, L. chagasi, L. mexicana), Plasmodium
spp. (e.g. P. falciparum, P. vivax, P. ovale, P. malariae),
Trypanosoma cruzi, Ascaris lumbricoides, Trichuris trichiura,
Necator americanus, and Schistosoma spp. (S. mansoni, S.
haematobium, S. japonicum).
[0581] In some embodiments, the immunotherapeutic is an engineered
receptor. In some embodiments, the engineered receptor is a
chimeric antigen receptor (CAR), a T cell receptor (TCR), or a B
cell receptor (BCR), an adoptive T cell therapy (ACT), or a
derivative thereof. In other aspects, the engineered receptor is a
chimeric antigen receptor (CAR). In some aspects, the CAR is a
first generation CAR. In other aspects, the CAR is a second
generation CAR. In still other aspects, the CAR is a third
generation CAR.
[0582] In some aspects, the CAR comprises an extracellular portion,
a transmembrane portion, and an intracellular portion. In some
aspects, the intracellular portion comprises at least one T cell
co-stimulatory domain. In some aspects, the T cell co-stimulatory
domain is selected from the group consisting of CD27, CD28, TNFRS9
(4-1BB), TNFRSF4 (OX40), TNFRSF8 (CD30), CD40LG (CD40L), ICOS,
ITGB2 (LFA-1), CD2, CD7, KLRC2 (NKG2C), TNFRS18 (GITR), TNFRSF14
(HVEM), or any combination thereof.
[0583] In some aspects, the engineered receptor binds a target. In
some aspects, the binding is specific to a peptide identified from
the method of characterizing HLA-peptide complexes specific to an
individual suffering from a disease or condition.
[0584] In some aspects, the immunotherapeutic is a cell as
described in detail herein. In some aspects, the immunotherapeutic
is a cell comprising a receptor that specifically binds a peptide
identified from the method characterizing HLA-peptide complexes
specific to an individual suffering from a disease or condition. In
some aspects, the immunotherapeutic is a cell used in combination
with the peptides/nucleic acids of this invention. In some
embodiments, the cell is a patient cell. In some embodiments, the
cell is a T cell. In some embodiments, the cell is tumor
infiltrating lymphocyte.
[0585] In some aspects, a subject with a condition or disease is
treated based on a T cell receptor repertoire of the subject. In
some embodiments, an antigen vaccine is selected based on a T cell
receptor repertoire of the subject. In some embodiments, a subject
is treated with T cells expressing TCRs specific to an antigen or
peptide identified using the methods described herein. In some
embodiments, a subject is treated with an antigen or peptide
identified using the methods described herein specific to TCRs,
e.g., subject specific TCRs. In some embodiments, a subject is
treated with an antigen or peptide identified using the methods
described herein specific to T cells expressing TCRs, e.g., subject
specific TCRs. In some embodiments, a subject is treated with an
antigen or peptide identified using the methods described herein
specific to subject specific TCRs.
[0586] In some embodiments, an immunogenic antigen composition or
vaccine is selected based on TCRs identified in a subject. In one
embodiment, identifying a T cell repertoire and testing it in
functional assays is used to determine an immunogenic composition
or vaccine to be administered to a subject with a condition or
disease. In some embodiments, the immunogenic composition is an
antigen vaccine. In some embodiments, the antigen vaccine comprises
subject specific antigen peptides. In some embodiments, antigen
peptides to be included in an antigen vaccine are selected based on
a quantification of subject specific TCRs that bind to the
antigens. In some embodiments, antigen peptides are selected based
on a binding affinity of the peptide to a TCR. In some embodiments,
the selecting is based on a combination of both the quantity and
the binding affinity. For example, a TCR that binds strongly to an
antigen in a functional assay but is not highly represented in a
TCR repertoire can be a good candidate for an antigen vaccine
because T cells expressing the TCR would be advantageously
amplified.
[0587] In some embodiments, antigens are selected for administering
to a subject based on binding to TCRs. In some embodiments, T
cells, such as T cells from a subject with a disease or condition,
can be expanded. Expanded T cells that express TCRs specific to an
immunogenic antigen peptide identified using the method described
herein can be administered back to a subject. In some embodiments,
suitable cells, e.g., PBMCs, are transduced or transfected with
polynucleotides for expression of TCRs specific to an immunogenic
antigen peptide identified using the method described herein and
administered to a subject. T cells expressing TCRs specific to an
immunogenic antigen peptide identified using the method described
herein can be expanded and administered back to a subject. In some
embodiments, T cells that express TCRs specific to an immunogenic
antigen peptide identified using the method described herein that
result in cytolytic activity when incubated with autologous
diseased tissue can be expanded and administered to a subject. In
some embodiments, T cells used in functional assays result in
binding to an immunogenic antigen peptide identified using the
method described herein can be expanded and administered to a
subject. In some embodiments, TCRs that have been determined to
bind to subject specific immunogenic antigen peptides identified
using the method described herein can be expressed in T cells and
administered to a subject.
[0588] The methods described herein can involve adoptive transfer
of immune system cells, such as T cells, specific for selected
antigens, such as tumor or pathogen associated antigens. Various
strategies can be employed to genetically modify T cells by
altering the specificity of the T cell receptor (TCR), for example
by introducing new TCR .alpha.- and .beta.-chains with specificity
to an immunogenic antigen peptide identified using the method
described herein (see, e.g., U.S. Pat. No. 8,697,854; PCT Patent
Publications: WO2003020763, WO2004033685, WO2004044004,
WO2005114215, WO2006000830, WO2008038002, WO2008039818,
WO2004074322, WO2005113595, WO2006125962, WO2013166321,
WO2013039889, WO2014018863, WO2014083173; U.S. Pat. No.
8,088,379).
[0589] Chimeric antigen receptors (CARs) can be used to generate
immunoresponsive cells, such as T cells, specific for selected
targets, such a immunogenic antigen peptides identified using the
method described herein, with a wide variety of receptor chimera
constructs (see, e.g., U.S. Pat. Nos. 5,843,728; 5,851,828; 5,912,
170; 6,004,811; 6,284,240; 6,392,013; 6,410,014; 6,753,162;
8,211,422; and, PCT Publication WO9215322). Alternative CAR
constructs can be characterized as belonging to successive
generations. First-generation CARs typically consist of a
single-chain variable fragment of an antibody specific for an
antigen, for example comprising a VL linked to a VH of a specific
antibody, linked by a flexible linker, for example by a CD8a hinge
domain and a CD8a transmembrane domain, to the transmembrane and
intracellular signaling domains of either CD3.zeta. or FcRy or
scFv-FcRy (see, e.g., U.S. Pat. Nos. 7,741,465; 5,912,172;
5,906,936). Second-generation CARs incorporate the intracellular
domains of one or more costimulatory molecules, such as CD28, OX40
(CD134), or 4-1BB (CD137) within the endodomain, e.g.,
scFv-CD28/OX40/4-1BB-CD3 (see, e.g., U.S. Pat. Nos. 8,911,993;
8,916,381; 8,975,071; 9,101,584; 9,102,760; 9,102,761).
Third-generation CARs include a combination of costimulatory
endodomains, such a CD3C-chain, CD97, GDI la-CD18, CD2, ICOS, CD27,
CD154, CDS, OX40, 4-1BB, or CD28 signaling domains, e.g.,
scFv-CD28-4-1BB-CD3C or scFv-CD28-OX40-CD3Q (see, e.g., U.S. Pat.
Nos. 8,906,682; 8,399,645; 5,686,281; PCT Publication No.
WO2014134165; PCT Publication No. WO2012079000). In some
embodiments, costimulation can be coordinated by expressing CARs in
antigen-specific T cells, chosen so as to be activated and expanded
following, for example, interaction with antigen on professional
antigen-presenting cells, with costimulation. Additional engineered
receptors can be provided on the immunoresponsive cells, e.g., to
improve targeting of a T cell attack and/or minimize side
effects.
[0590] Alternative techniques can be used to transform target
immunoresponsive cells, such as protoplast fusion, lipofection,
transfection or electroporation. A wide variety of vectors can be
used, such as retroviral vectors, lentiviral vectors, adenoviral
vectors, adeno-associated viral vectors, plasmids or transposons,
such as a Sleeping Beauty transposon (see U.S. Pat. Nos. 6,489,458;
7,148,203; 7,160,682; 7,985,739; 8,227,432), can be used to
introduce CARs, for example using 2nd generation antigen-specific
CARs signaling through CD3 and either CD28 or CD137. Viral vectors
can, for example, include vectors based on HIV, SV40, EBV, HSV or
BPV.
[0591] Cells that are targeted for transformation can, for example,
include T cells, Natural Killer (NK) cells, cytotoxic T lymphocytes
(CTL), regulatory T cells, human embryonic stem cells,
tumor-infiltrating lymphocytes (TIL) or a pluripotent stem cell
from which lymphoid cells can be differentiated. T cells expressing
a desired CAR can, for example, be selected through co-culture with
.gamma.-irradiated activating and propagating cells (APC), which
co-express the cancer antigen and co-stimulatory molecules. The
engineered CAR T cells can be expanded, for example, by co-culture
on APC in presence of soluble factors, such as IL-2 and IL-21. This
expansion can, for example, be carried out so as to provide memory
CAR T cells (which, for example, can be assayed by non-enzymatic
digital array and/or multi-panel flow cytometry). In this way, CAR
T cells that have specific cytotoxic activity against
antigen-bearing tumors can be provided (optionally in conjunction
with production of desired chemokines such as interferon-.gamma.).
CAR T cells of this kind can, for example, be used in animal
models, for example to threaten tumor xenografts.
[0592] Approaches such as the foregoing can be adapted to provide
methods of treating and/or increasing survival of a subject having
a disease, such as a neoplasia or pathogenic infection, for example
by administering an effective amount of an immunoresponsive cell
comprising an antigen recognizing receptor that binds a selected
antigen, wherein the binding activates the immunoresponsive cell,
thereby treating or preventing the disease (such as a neoplasia, a
pathogen infection, an autoimmune disorder, or an allogeneic
transplant reaction). Dosing in CAR T cell therapies can, for
example, involve administration of from 106 to 109 cells/kg, with
or without a course of lymphodepletion, for example with
cyclophosphamide.
[0593] To guard against possible adverse reactions, engineered
immunoresponsive cells can be equipped with a transgenic safety
switch in the form of a transgene that renders the cells vulnerable
to exposure to a specific signal. For example, the herpes simplex
viral thymidine kinase (TK) gene can be used in this way, for
example by introduction into allogeneic T lymphocytes used as donor
lymphocyte infusions following stem cell transplantation. In such
cells, administration of a nucleoside prodrug such as ganciclovir
or acyclovir causes cell death. Alternative safety switch
constructs include inducible caspase 9, for example triggered by
administration of a small-molecule dimerizer that brings together
two nonfunctional icasp9 molecules to form the active enzyme. A
wide variety of alternative approaches to implementing cellular
proliferation controls have been described (see, e.g., U.S. Patent
Publication No. 20130071414; PCT Patent Publication WO2011146862;
PCT Patent Publication W0201401 1987; PCT Patent Publication
WO2013040371). In a further refinement of adoptive therapies,
genome editing can be used to tailor immunoresponsive cells to
alternative implementations, for example providing edited CAR T
cells.
[0594] Cell therapy methods can also involve the ex vivo activation
and expansion of T cells. In some embodiments, T cells can be
activated before administering them to a subject in need thereof.
Examples of these type of treatments include the use tumor
infiltrating lymphocyte (TIL) cells (see U.S. Pat. No. 5,126,132),
cytotoxic T cells (see U.S. Pat. Nos. 6,255,073; and 5,846,827),
expanded tumor draining lymph node cells (see U.S. Pat. No.
6,251,385), and various other lymphocyte preparations (see U.S.
Pat. Nos. 6,194,207; 5,443,983; 6,040,177; and 5,766,920).
[0595] An ex vivo activated T cell population can be in a state
that maximally orchestrates an immune response to cancer,
infectious diseases, or other disease states, e.g., an autoimmune
disease state. For activation, at least two signals can be
delivered to the T cells. The first signal is normally delivered
through the T cell receptor (TCR) on the T cell surface. The TCR
first signal is normally triggered upon interaction of the TCR with
peptide antigens expressed in conjunction with an MHC complex on
the surface of an antigen-presenting cell (APC). The second signal
is normally delivered through co-stimulatory receptors on the
surface of T cells. Co-stimulatory receptors are generally
triggered by corresponding ligands or cytokines expressed on the
surface of APCs.
[0596] It is contemplated that the T cells specific to immunogenic
antigen peptides identified using the method described herein can
be obtained and used in methods of treating or preventing disease.
In this regard, the disclosure provides a method of treating or
preventing a disease or condition in a subject, comprising
administering to the subject a cell population comprising cells
specific to immunogenic antigen peptides identified using the
method described herein in an amount effective to treat or prevent
the disease in the subject. In some embodiments, a method of
treating or preventing a disease in a subject, comprises
administering a cell population enriched for disease-reactive T
cells to a subject in an amount effective to treat or prevent
cancer in the mammal. The cells can be cells that are allogeneic or
autologous to the subject.
[0597] The disclosure further provides a method of inducing a
disease specific immune response in a subject, vaccinating against
a disease, treating and/or alleviating a symptom of a disease in a
subject by administering the subject an antigenic peptide or
vaccine.
[0598] The peptide or composition of the disclosure can be
administered in an amount sufficient to induce a CTL response. An
antigenic peptide or vaccine composition can be administered alone
or in combination with other therapeutic agents. Exemplary
therapeutic agents include, but are not limited to, a
chemotherapeutic or biotherapeutic agent, radiation, or
immunotherapy. Any suitable therapeutic treatment for a particular
disease can be administered. Examples of chemotherapeutic and
biotherapeutic agents include, but are not limited to, aldesleukin,
altretamine, amifostine, asparaginase, bleomycin, capecitabine,
carboplatin, carmustine, cladribine, cisapride, cisplatin,
cyclophosphamide, cytarabine, dacarbazine (DTIC), dactinomycin,
docetaxel, doxorubicin, dronabinol, epoetin alpha, etoposide,
filgrastim, fludarabine, fluorouracil, gemcitabine, granisetron,
hydroxyurea, idarubicin, ifosfamide, interferon alpha, irinotecan,
lansoprazole, levamisole, leucovorin, megestrol, mesna,
methotrexate, metoclopramide, mitomycin, mitotane, mitoxantrone,
omeprazole, ondansetron, paclitaxel (Taxol.RTM.), pilocarpine,
prochloroperazine, rituximab, tamoxifen, taxol, topotecan
hydrochloride, trastuzumab, vinblastine, vincristine and
vinorelbine tartrate. In addition, the subject can be further
administered an anti-immunosuppressive or immunostimulatory agent.
For example, the subject can be further administered an anti-CTLA
antibody or anti-PD-1 or anti-PD-L1.
[0599] The amount of each peptide to be included in a vaccine
composition and the dosing regimen can be determined by one skilled
in the art. For example, a peptide or its variant can be prepared
for intravenous (i.v.) injection, sub-cutaneous (s.c.) injection,
intradermal (i.d.) injection, intraperitoneal (i.p.) injection,
intramuscular (i.m.) injection. Exemplary methods of peptide
injection include s.c, i.d., i.p., i.m., and i.v. Exemplary methods
of DNA injection include i.d., i.m., s.c, i.p. and i.v. Other
methods of administration of the vaccine composition are known to
those skilled in the art.
[0600] A pharmaceutical composition can be compiled such that the
selection, number and/or amount of peptides present in the
composition is/are disease and/or patient-specific. For example,
the exact selection of peptides can be guided by expression
patterns of the parent proteins in a given tissue to avoid side
effects. The selection can be dependent on the specific type of
disease, the status of the disease, earlier treatment regimens, the
immune status of the patient, and the HLA-haplotype of the patient.
Furthermore, the vaccine according to the present disclosure can
contain individualized components, according to personal needs of
the particular patient. Examples include varying the amounts of
peptides according to the expression of the related antigen in the
particular patient, unwanted side-effects due to personal allergies
or other treatments, and adjustments for secondary treatments
following a first round or scheme of treatment.
Computer Control Systems
[0601] The present disclosure provides computer control systems
that are programmed to implement methods of the disclosure. FIG. 10
shows a computer system (1001) that is programmed or otherwise
configured to train a machine-learning HLA-peptide presentation
prediction model. The computer system (1001) can regulate various
aspects of the present disclosure, such as, for example, inputting
amino acid position information, transferring imputed information
into datasets, and generating a trained algorithm with the
datasets. The computer system (1001) can be an user electronic
device or a remote computer system. The electronic device can be a
mobile electronic device.
[0602] The computer system (1001) includes a central processing
unit (CPU, also "processor" and "computer processor" herein)
(1005), which can be a single core or multi core processor, either
through sequential processing or parallel processing. The computer
system (1001) also includes a memory unit or device (1010) (e.g.,
random-access memory, read-only memory, flash memory), a storage
unit (1015) (e.g., hard disk), a communication interface (1020)
(e.g., network adapter) for communicating with one or more other
systems, and peripheral devices (1025), either external or internal
or both, such as a printer, monitor, USB drive and/or CD-ROM drive.
The memory (1010), storage unit (1015), interface (1020) and
peripheral devices (1025) are in communication with the CPU (1005)
through a communication bus (solid lines), such as a motherboard.
The storage unit (1015) can be a data storage unit (or data
repository) for storing data. The computer system (1001) can be
operatively coupled to a computer network ("network") (1030) with
the aid of the communication interface (1020). The network (1030)
can be the Internet, an internet and/or extranet, or an intranet
and/or extranet that is in communication with the Internet. The
network (1030) in some cases is a telecommunication and/or data
network. The network (1030) can include one or more computer
servers, which can enable a peer-to-peer network that supports
distributed computing. The network (1030), in some cases with the
aid of the computer system (1001), can implement a client-server
structure, which may enable devices coupled to the computer system
(1001) to behave as a client or a server.
[0603] The CPU (1005) can execute a sequence of machine-readable
instructions, which can be embodied in a program or software. The
instructions may be stored in memory (1010). The instructions can
be directed to the CPU (1005), which can subsequently program or
otherwise configure the CPU (1005) to implement methods of the
present disclosure. Examples of operations performed by the CPU
(1005) can include fetch, decode, execute, and writeback.
[0604] The CPU (1005) can be part of a circuit, such as an
integrated circuit. One or more other components of the system
(1001) can be included in the circuit. In some cases, the circuit
is an application specific integrated circuit (ASIC).
[0605] The storage unit (1015) can store files, such as drivers,
libraries and saved programs. The storage unit (1015) can store
user data, e.g., user preferences and user programs. The computer
system (1001) in some cases can include one or more additional data
storage units that are external to the computer system (1001), such
as located on a remote server that is in communication with the
computer system (1001) through an intranet or the Internet.
[0606] The computer system (1001) can communicate with one or more
remote computer systems through the network (1030). For instance,
the computer system (1001) can communicate with a remote computer
system or user. Examples of remote computer systems include
personal computers (e.g., portable PC), slate or tablet PC's (e.g.,
Apple.RTM. iPad, Samsung.RTM. Galaxy Tab), telephones, Smart phones
(e.g., Apple.RTM. iPhone, Android-enabled device, Blackberry.RTM.),
or personal digital assistants. The user can access the computer
system (1001) via the network (1030).
[0607] Methods as described herein can be implemented by way of
machine (e.g., computer processor) executable code stored on an
electronic storage location of the computer system (1001), such as,
for example, in memory (1010) or a data storage unit (1015). The
machine executable or machine readable code can be provided in the
form of software. During use, the code can be executed by the
processor (1005). In some cases, the code can be retrieved from the
storage unit (1015) and stored in memory (1010) for ready access by
the processor (1005). In some situations, the storage unit (1015)
can be precluded, and machine-executable instructions are stored in
memory (1010).
[0608] The code can be pre-compiled and configured for use with a
machine having a processer adapted to execute the code, or it can
be compiled during runtime. The code can be supplied in a
programming language that can be selected to enable the code to
execute in a pre-compiled or as-compiled fashion.
[0609] Aspects of the systems and methods provided herein, such as
the computer system (1001), can be embodied in programming Various
aspects of the technology may be thought of as "products" or
"articles of manufacture" typically in the form of machine (or
processor) executable code and/or associated data that is carried
on or embodied in a type of machine readable medium.
Machine-executable code can be stored on a storage unit, such as a
hard disk, or in memory (e.g., read-only memory, random-access
memory, flash memory). "Storage" type media can include any or all
of the tangible memory of the computers, processors or the like, or
associated modules thereof, such as various semiconductor memories,
tape drives, disk drives and the like, which may provide
non-transitory storage at any time for the software programming.
All or portions of the software may at times be communicated
through the Internet or various other telecommunication networks.
Such communications, for example, may enable loading of the
software from one computer or processor into another, for example,
from a management server or host computer into the computer
platform of an application server. Thus, another type of media that
may bear the software elements includes optical, electrical and
electromagnetic waves, such as used across physical interfaces
between local devices, through wired and optical landline networks
and over various air-links. The physical elements that carry such
waves, such as wired or wireless links, optical links or the like,
also may be considered as media bearing the software. As used
herein, unless restricted to non-transitory, tangible "storage"
media, terms such as computer or machine "readable medium" refer to
any medium that participates in providing instructions to a
processor for execution.
[0610] Hence, a machine readable medium, such as
computer-executable code, may take many forms, including but not
limited to, a tangible storage medium, a carrier wave medium or
physical transmission medium. Non-volatile storage media include,
for example, optical or magnetic disks, such as any of the storage
devices in any computer(s) or the like, such as may be used to
implement the databases, etc. shown in the drawings. Volatile
storage media include dynamic memory, such as main memory of such a
computer platform. Tangible transmission media include coaxial
cables; copper wire and fiber optics, including the wires that
comprise a bus within a computer system. Carrier-wave transmission
media may take the form of electric or electromagnetic signals, or
acoustic or light waves such as those generated during radio
frequency (RF) and infrared (IR) data communications. Common forms
of computer-readable media therefore include for example: a floppy
disk, a flexible disk, hard disk, magnetic tape, any other magnetic
medium, a CD-ROM, DVD or DVD-ROM, any other optical medium, punch
cards paper tape, any other physical storage medium with patterns
of holes, a RAM, a ROM, a PROM and EPROM, a FLASH-EPROM, any other
memory chip or cartridge, a carrier wave transporting data or
instructions, cables or links transporting such a carrier wave, or
any other medium from which a computer may read programming code
and/or data. Many of these forms of computer readable media may be
involved in carrying one or more sequences of one or more
instructions to a processor for execution.
[0611] The computer system (1001) can include or be in
communication with an electronic display (1035) that comprises a
user interface (UI) (1040) for providing, for example, probability
that one or more proteins encoded by a class II MHC allele of a
cancer cell of the subject will present a given sequence of a
peptide sequence identified. Examples of UI's include, without
limitation, a graphical user interface (GUI) and web-based user
interface.
[0612] Methods and systems of the present disclosure can be
implemented by way of one or more algorithms. An algorithm can be
implemented by way of software upon execution by the central
processing unit (1005). The algorithm can, for example, input amino
acid position information, transfer imputed information into
datasets, and generate a trained algorithm with the datasets.
EXAMPLES
[0613] The examples provided below are for illustrative purposes
only and do not limit the scope of the claims provided herein.
Example 1. HLA Class II Binding Predictor Performance
[0614] In this example, a validation dataset comprising observed
mass spec peptides and decoy peptides which are generated at a
ratio of 1:19 (hits:decoys) by randomly shuffling the hit peptides
were used to analyze the performance of the binding predictor
neonmhc2 (NEON) and NetMHCIIpan (FIG. 4). For the NEON binding
predictor, a separate model was built for each MHC II allele shown.
The height of the bars showed the positive predictive value (PPV).
The alleles are sorted by the model's performance when predicting
for that allele. The NEON binding predictor showed higher PPV
across all the alleles when compared with NetMHCIIpan.
[0615] In this example, the effect of SPI thresholds on binding
predictor validation was also tested (FIG. 5). The performance of
the HLA class II binding predictor was shown when trained/validated
on sets of peptides with different scored peak intensity (SPI)
cutoffs. The different SPI cutoffs conditions were used: trained
and evaluated on datasets using observed MS hit peptides of larger
than or equal to 70 SPI, trained on peptides with larger than or
equal to 50 SPI and validated on peptides with larger than or equal
to 70 SPI, and trained and validated on peptides with larger than
or equal to 50 SPI.
[0616] In this example, data for 35 HLA-DR alleles, which had
>95% population coverage for HLA-DR (USA allele frequencies),
were collected to show the number of observed peptides by allele
profiling by LC-MS/MS with larger than or equal to 70 scored peak
intensity (SPI) cutoffs (FIG. 6).
[0617] In one exemplary set up, a model PPV analysis was applied to
test partition data for each class II allele that were generated
thus far for Neonmhc2 program. The test partition data was composed
of positive example (e.g. a hit sample peptide) that are
MS-observed class II binders and negative examples (e.g. a decoy
sample peptide) that are scrambled versions of the positive
examples. The hit: decoy ratio was kept 1:19, for example, for each
positive sample, 19 negative samples were included (i.e., 5%
positive sample) and test partition was performed for validation.
PPV scores were generated by selecting the best-scoring 5% of the
peptides, in the test partition and interrogating what fraction of
those are positive. Results are indicated in FIG. 7A.
[0618] It was observed that for the HLA-DR alleles collected, when
the training set size increased, the value of PPV increased (FIGS.
7B-7D).
[0619] In this example, the processing-related variables improved
prediction further (FIG. 8). On the training data partition, a
logistic regression was fit to predict HLA class II presentation
using binding strength (NetMHCIIpan or Neon's predictor) and
processing features (RNA-Seq expression and a derived gene-level
bias term). On a separate evaluation partition, exonic positions
overlapping MS-observed MHC II peptides ("hits") was scored
alongside random exonic positions not observed in MS (1:499 ratio).
In general, Neon with processing-related variables showed higher
PPV than NetMHCIIpan, Neon's predictor, and NetMHCIIpan with
processing-related variables.
Example 2. A Neural Network Architecture
[0620] In this example, a neural network was used to obtain the
training algorithm (FIG. 9). Input peptides were represented as
20mers, with shorter peptides being filled in with "missing"
characters. Each peptide had a 31-dimensional embedding, so the
input into the neural network was a 20.times.31 matrix. Before
being processed by the neural network, feature normalization on the
20.times.31 matrix was performed based on feature value means and
standard deviations in the training set. The first convolutional
layer had a kernel of 9 amino acids and 50 filters (also called
channels) with a ReLU activation function. This was followed by
batch normalization and then spatial dropout with a dropout rate of
20%. This was followed by another convolutional layer with a kernel
of 3 and with 20 filters and a ReLU activation function and then
again followed by batch normalization and spatial dropout with a
dropout rate of 20%. Global max pooling was then applied, taking
the maximally-activated neuron in each of the 20 filters and then
these 20 values were passed into a fully connected (dense) layer
with a single neuron using a Sigmoid activation function. This
output was treated as the binding/non-binding prediction. L2
regularization was applied to the weights of the first
convolutional layer, second convolutional layer, and dense layer
with weights of 0.05, 0.1, and 0.01, respectively.
Example 3. A Scalable Protocol for Mono-Allelic MHC Class II Ligand
Profiling
[0621] Currently knowledge of MHC Class II binding motifs can be
based on two in vitro binding assays, one that calculates an EC50
using cellular MHC and another that calculates an IC50 using
purified MHC. The leading HLA class II prediction algorithm
NetMHCIIpan is trained exclusively on these data.
[0622] Limited number of human HLA class II alleles are currently
supported by more than 200 examples of confirmed binding peptides
(affinity <100 nM) (FIG. 12E), which are nearly all 15mers.
These experiments cover only the most common Caucasian HLA-DR
alleles with limited coverage of alleles specific to non-Caucasian
populations (e.g., HLA-DRB1*15:02) and almost no coverage for
common HLA-DP and HLA-DQ alleles. Current HLA class II prediction
performance, even on the common Caucasian alleles, significantly
lags the accuracy of MHC class I; ROC curves are only modestly
better than random.
[0623] With these limitations in mind, a novel biotechnology was
developed herein that was termed Mono-Allelic Capture by Tagged
Allele capture (MAPTAC.TM.) that enables efficient isolation of HLA
class II binding peptides binding an MHC protein encoded by a
single allele for MS-based identification (FIGS. 11A and 11B); this
approach works for HLA class I as well. As applied to HLA class II,
the alpha and beta chains of a chosen allele are encoded on a
genetic construct, with a biotin-acceptor peptide (BAP) sequence
placed at the C-terminus of the beta chain. These cells are then
lysed and incubated with BirA enzyme to biotinylate the C-terminus
of the beta chain of the capture allele. NeutrAvidin pulldown
purifies a population of MHC-bound peptides, which are further
isolated by size exclusion and sequenced with best-in-class
LC-MS/MS protocols.
[0624] In some embodiments, the LC-MS/MS analysis is evaluated
using high field asymmetric waveform ion mobility spectrometry
(FAIMS). In some embodiments, peptides are subjected to both acidic
reverse-phase (aRP) and basic reverse-phase (bRP) offline
fractionation prior to analysis by nLC-MS/MS.
[0625] A two-day transfection was sufficient to achieve robust
expression of the construct (FIG. 12B) with appropriate cell
surface localization in three distinct cell lines (expi293, A375,
and B721) for four different alleles (FIG. 12C).
[0626] Because HLA-DRA is functionally invariant, this approach
achieves single-allele resolution even if the capture beta chain
pairs with endogenous alpha chain. This means that the approach can
be used to profile HLA-DR alleles regardless of pre-existing HLA
genotype and expression level in the given cell line.
[0627] For HLA-DP and HLA-DQ, the alpha and beta chains are both
variable and both contribute to peptide binding, so single-allele
resolution is expected only if the native alpha chain is not
expressed or if the native allele is homozygous and matches the
capture allele. Alternatively, one can use a beta chain-only
capture to establish a background of peptides corresponding to the
native alpha chain.
[0628] Profiled alleles included five HLA-DR alleles (DRB1*03:01,
DRB1*09:01, DRB1*11:01, DRB3*01:01, and DRB3*02:02) as well as one
HLA-DP allele (DPB1*01:01/DPA1*01:03), one HLA-DQ allele
(DQB1*06:02/DQA1*01:02), and two Class I alleles (Table 1). In all
cases, 2-3 replicates were sufficient to observe at least 1500
unique peptides (FIG. 11B). Among the alleles profiled, only a
small percentage of hits corresponded to known contaminants or
perfect tryptics; on the other hand, mock transfections returned
relatively few peptides, which were mostly identifiable as known
contaminants or perfect tryptics (FIG. 11B). Table 1 shows a
summary of the samples used in the exemplary experiments.
TABLE-US-00001 TABLE 1 Donor DR1 DR3/4/5 D001000763 DRB1*03:01,
DRB1*11:01 DRB3*01:01, DRB3*02:02 HD84 DRB1*01:01 Not present
RG1248 DRB1*03:01 DRB3*01:01 RG1095 DRB1*03:01, DRB1*11:01
DRB3*01:01, DRB3*02:02 RG1104 DRB1*01:01, DRB1*11:01 DRE3*02:02
2010113472 DRB1*01:01, DRB1*11:01 DRE3*02:02 2010113438 DRB1*03:01,
DRB1*11:01 DRB3*01:01, DRB3*02:02
[0629] Since the ends of MHC II binding peptides do not need to fit
within the MHC binding groove, multiple distinct peptide species
can bind equally well if they share the same core binding sequence.
When the peptides were pooled with overlapping sequence into
"nested sets", 500-700 unique nested sets per HLA class II allele
were observed; these were typically derived from 500-600 unique
genes. Length distributions for HLA class I and HLA class II
binding peptides match those observed in previous MS studies that
used antibody-based pulldowns (FIG. 11C).
[0630] Among the putative MHC-binding peptides, most amino acids
were represented at levels consistent with their source proteome
frequencies. Exceptions included cysteine, methionine, and
tryptophan, which were depleted, consistent with previous MS-based
studies of MHC II peptides. Depletions of cysteine, methionine, and
tryptophan were not observed in allele-matched high-affinity
peptides (<50 nM) from IEDB; however, the IEDB peptides did show
enrichments in leucine and methionine and depletions of proline,
aspartic acid, and glutamic acid with respect to the proteome.
Example 4. MAPTAC.TM. Protocol Uncovers Known and Novel MHC II
Binding Motifs
[0631] Since the MHC-binding subsequence of Class II peptides are
not at a fixed position with respect to the N- or C-terminus,
accurate Class II motif discovery must dynamically consider
different binding register possibilities for each binder peptide.
The Gibb's Cluster tool addresses this challenge through an
expectation maximization (EM) algorithm. The use of a novel motif
discovery approach using convolutional neural networks (CNNs) was
explored. CNNs have been successful in the field of computer
vision, which similarly seeks to achieve translationally invariant
pattern recognition. CNNs were trained to distinguish MHC binding
peptides from scrambled versions of themselves and then aligned the
positive examples according to the subsequences that had achieved
maximum node activation in the penultimate network layer. As
applied to the mono-allelic MS data, this approach yielded motifs
consistent with Gibbs clustering and showed anchors at relative
positions 1, 4, 6, and 9 (FIG. 13). These motifs were highly
consistent with CNN-derived motifs observed for high affinity
binders from IEDB (affinity <50 nM; FIG. 13). For DRB1*11:01,
was further validated that the motif was stable across cell lines
and consistent with a DRB1*11:01 homozygous cell line previously
profiled with a pan-DR antibody. Similarly, motifs derived for MHC
Class I alleles were consistent with those from affinity-based
methods and previous MS-based studies (FIG. 14A).
[0632] Although all the MHC class II alleles showed discernable
motifs, the entropy at anchor positions was notably higher than
that observed for MHC class I alleles. Accordingly, preferred amino
acids at each anchor position for each MHC class II allele were
defined and it was observed that only 10-20% of peptides exhibit
ideal residues in all four anchor positions and as many as 60%
exhibit two or fewer expected anchors (FIG. 14B and FIG. 30C).
13-17mers were scored for binding potential using NetMHCIIpan, and
while the MS-observed peptides were enriched for predicted binding
potential in all cases, there was significant overlap with scores
of length-matched random peptides (FIG. 14C, and FIG. 36A).
Example 5. Algorithms Trained on Mono-Allelic MHC II MS Data
Predict Immunogenicity
[0633] Next, whether data from the mono-allelic MS platform could
generate improved MHC class II binding predictors were considered.
Building on the CNN approach, a multi-layer network with filter
sizes, skip connections, and a total receptive field were created
(FIG. 31A). To train and assess this deep learning model, termed
neonmhc2, the proteome were partitioned into three partitions
representing 75%, 12.5%, and 12.5% of genes. The first partition
was used to train CNNs via stochastic gradient descent, and the
second was used for architecture and hyper-parameter optimization.
The third partition was used to evaluate performance only once at
the end of analysis. To ensure the integrity of the evaluation,
care was taken to place all genes in paralogous gene groups in the
same partition.
[0634] Since MS exhibits some degree of residue bias, particularly
against cysteine (FIG. 12D), this problem was mitigated by using
negative training examples (termed decoys) generated by randomly
permuting the sequences of positive examples. Since this approach
carries the risk of learning sequence properties of natural
proteins, which could artificially inflate prediction performance,
model evaluation employed a distinct decoy generation strategy
wherein decoys were sampled randomly from non-observed subsequences
of peptide source genes. Calculating positive predictive value
(PPV) at a 1:19 hit-to-decoy ratio showed that neonmhc2 has
improved PPV relative to NetMHCIIpan in predicting MS peptides in
the evaluation partition (FIG. 4 and FIG. 31B). Experiments
artificially down-sampling the size of neonmhc2's training dataset
suggest that its performance is data-limited and would improve with
deeper coverage data (FIG. 16).
[0635] The ability of neonmhc2 was explored to predict binding
affinity, the data type on which NetMHCIIpan is trained. To deprive
NetMHCpan the benefit of training and evaluating on the same
peptide measurements, the evaluation was run using a slightly older
version of NetMHCIIpan scoring peptides deposited to IEDB. Using a
Kendall Tau statistic to assess prediction accuracy, NetMHCIIpan
score similarly or slightly better than the MS-based predictor in
all cases (FIG. 15B). Interestingly, performance depended on the
type of affinity assay performed. While the neonmhc2 modestly
lagged NetMHCIIpan when predicting affinity measurements from Sette
and colleagues, it more substantially lagged NetMHCIIpan when
predicting measurements from Buus and colleagues. Considering these
results collectively, there appeared to be intrinsic differences
between these platforms, but it was not immediately clear which
approach was more correct.
[0636] To achieve improved clarity, the ability to predict natural
CD4 T cell responses was assessed. Data from IEDB was generally
unsuitable for this purpose since the allele restriction of
responses is almost always either undefined or imputed. Therefore,
a large dataset of tetramer-guided epitope mapping (TGEM) data was
assembled. These studies all used comprehensive overlapping peptide
screening rather than prediction prioritization, removing
observation bias in favor of NetMHCIIpan. Meanwhile, the allele
restriction is unambiguous. For all alleles for which there was
sufficient data for assessment, the neonmhc2 substantially
out-performed NetMHCpan, which performed only slightly better than
random. Thus, MAPTAC.TM. platform may be the best-in-class for
training models that identify immunogenic MHC class II
epitopes.
Example 6. Algorithms Trained on Multi-Allelic MS Data are
Inferior
[0637] Given that there are numerous multiallelic class II
databases in the public domain based on standard pan-DR and pan-II
antibody purification, whether a suitable predictor could have been
trained using multi-allelic data only was tested. Several groups
have shown success in deconvolving MHC class I allele motifs from
multi-allelic Class I data, though these efforts have not yet
translated into a publicly available predictor. Deconvolution of
Class II motifs is additionally complicated by the need to
simultaneously resolve both the binding register and cluster
membership of each peptide. While the Gibbs Cluster tool has been
used to explore the possibility of Class II deconvolution, the
fidelity of this approach has not been extensively validated.
[0638] To assess the accuracy of Class II deconvolution, publicly
available pan-DR datasets with known genotype were selected. For
each dataset, twenty peptides of our mono-allelic data were spiked
in for each allele in the donor's genotype (1-2 DR1 alleles plus
0-2 DR3/4/5 alleles, depending on haplotype and zygosity). Gibbs
Clustering tool was run on each dataset and whether the spike-in
peptides were appropriately co-clustered were observed according to
their known allele of origin. In early versions of this analysis,
either the cluster number to the allele number was fixed or the
Gibbs cluster was allowed to automatically determine the most
optimal number of clusters; however, neither approach appeared to
deconvolve the peptides accurately). To give the algorithm an
assist, the most optimal cluster count was selected by calculating
the adjusted mutual information between the true source alleles of
the spike-in peptides and their assigned clusters. Nonetheless, in
all but several cases, peptides were distributed across diverse
clusters without respect to their source allele (FIG. 17A). These
results suggest that current deconvolution protocols may not be
reliably accurate for MHC Class II.
[0639] One caveat to this analysis is that some peptides may be
capable of binding more than one allele. In line with that, the
next question is whether binding motifs derived from multi-allelic
data may nonetheless reasonably match those observed from
mono-allelic data. To assess this, clusters with the best
correspondence to the capture peptides of each single allele were
selected and motifs based on these populations were built. (see,
for example, FIG. 17B). While many motifs clearly demonstrated some
of the known anchors, other positions were discordant with the
mono-allelic motif or discordant between source data sets.
Additionally, there were clear cases in which spurious anchors had
emerged. Finally, we assessed whether the deconvolved data could be
used to train CNNs that could predict peptides in the evaluation
partition of our mono-allelic dataset. Models trained on
deconvoluted multi-allelic data fell short of MAPTAC.TM.-trained
models in all cases (FIG. 17C).
Example 7. Source Protein Features Influence Presentation
Likelihood
[0640] For MHC Class I, the proteasome plays an important role in
determining the repertoire of presented epitopes; therefore, how
protein-to-peptide processing shapes the Class II repertoire that
was characterized.
[0641] First, the exact positions of the N- and C-termini of MHC
Class II peptides observed in several tissue-based peptide
profiling data sets were focused on. Comparing position-based amino
acid frequencies with respect to decoy peptides, significant
enrichments and depletions was observed. This pattern is consistent
with recent observations. Interestingly, the overall pattern does
not match the known cleavage preference of Cathepsin S
([RPI][FMLW][KQTR][ALS]), the best characterized Class II
processing enzyme.
[0642] To determine the predictive potential of this motif,
NN-based predictors for the N- and C-termini were built and a
logistic regression that used the two cleavage variables along with
predicted binding potential (per MS-trained CNN) was fit to
distinguish true MS peptides from length-matched decoy peptides
sampled from the same source genes.
[0643] This predictor provided a modest improvement in peptide
prediction over a model that considered binding potential alone;
however, since the immunogenicity of MHC class II binding epitopes
(interchangeably termed, Class II epitopes) may not depend on the
exact position of peptide cleavage, the question is whether the
model would still add value if the exact site of cleavage was
unknown. Therefore, the prediction scheme was run a second time,
withholding the exact cleavage positions of hits and decoys,
instead scoring composite cleavability scores across protein
positions in the vicinity (+/-15 AA) of the imputed binding core.
Interestingly, there was no improvement in performance over the
binding-only predictor. These results are consistent with previous
work, which showed that the addition of Class II cleavage
prediction could improve prediction of MS-observed ligands, but not
T cell recognition, which is presumably agnostic to the exact
peptide termini.
[0644] A model was suggested in which a significant fraction of MHC
II peptides are "chewed back" from their N- and C-termini after MHC
binding. Under this model, the penultimate proline signature arises
because proline blocks the procession of exopeptidases. In this
scenario, the motif derived from direct analysis MHC ligand termini
is potentially misleading because it reflects downstream editing
rather than the initial step of peptide fragment generation.
Therefore, other sequence features were determined in the vicinity
of Class II peptides that might be able to explain their
generation. First, the canonical Cathepsin S signature was searched
for, but there was no enrichment in Cathepsin S sites near
MS-observed Class II peptides vs. length-matched decoy peptides
sampled from the peptide source genes. Because this processing
signature may reflect a complex ensemble of enzymes, a de novo CNN
was trained based on the upstream and downstream protein context
(+-25 AAs) around observed peptides and decoys.
[0645] A third model in which peptide availability is determined by
the folded or semi-unfolded state of the protein rather than its
primary sequence was considered. Homology-based ACCPRO was used to
predict secondary structure and regions of solvent accessibility,
and an ensemble of predictors was used to identify intrinsically
disordered domains.
[0646] If processing-preferred regions are inherently difficult to
predict, it might be possible to simply build a catalog of all
protein regions covered by at least one peptide in a large
collection of previously published multi-allelic Class II MS data
and use overlap as a prediction feature. Admittedly, the overlap
feature is contaminated with binding information since the alleles
represented in the previously published data may have the same or
similar binding motifs. Nonetheless, even this feature only
modestly improved the prediction of presented peptides suggesting
that MHC Class II peptides may not be subject to strong processing
hotspots.
[0647] The next question was which genes contribute the most to the
Class II binding peptides repertoire. Gene-level features, such as
expression level, are already known to provide a large boost when
predicting MHC Class I ligands. Leveraging previously published MS
datasets profiling the Class II binding repertoires of human
tissues, it was observed that MS-observed peptides are more highly
expressed than random decoy peptides (sampled from the proteome) by
an order of magnitude (FIG. 18A). Nonetheless, it was noticed that
about 5% of Class II peptides map to a gene that is ostensibly not
expressed according to representative RNA-Seq data. Based on this
pattern, the degree to which each gene was over- or
under-represented in the Class II peptide repertoire was sought to
be quantified by proposing a baseline expectation that the number
of observations for each gene should be proportional to the product
of its length and expression level (FIG. 18B). Among the
over-represented genes, there was a clear enrichment for proteins
expressed in human tissue serum, which produced many Class
II-binding peptides but were ostensibly not expressed in the native
tissue. This is consistent with the known role of MHC Class II in
presenting antigens sampled from the extracellular environment.
[0648] Since autophagy is another well-established Class II
processing pathway, the ratio of observed to expected peptides for
each gene (excluding any gene with fewer than five observed
peptides and fewer than five expected peptides) was determined and
determined if there was enrichment with respect to the physical
partners of known autophagy genes or genes stabilized by Atg5
knockout in mice (FIG. 18C). Neither gene set appeared to be
enriched in the Class II data; in fact, physical partners of
autophagy genes seemed to be modestly under-represented.
[0649] Looking across all cellular localizations (FIG. 18D and FIG.
18E), few compartments were definitively over- or
under-represented. The two most enriched compartments were the cell
membrane and the lysosome, each generating approximately twice the
expected number of Class II peptides. It is not clear whether the
enrichment of membrane proteins relates to membrane recycling into
autophagosomes or Golgi routing of membrane proteins directly into
the autophagy pathway. The apparent contradiction between the
enrichment of lysosomal proteins and the previously observed
depletion of autophagy genes indicated that these trends are highly
sensitive to the specific subset of autophagy-related genes being
considered. FIG. 18F shows relative concordance of peptide
observations with respect to two different gene expression
profiles, bulk tumor and professional antigen presenting cells.
Example 8. Accurate MHC II Prediction Requires Understanding the
Endocytic Pathway
[0650] In addition to understanding the source pathway of Class II
genes, it may be critical to understand which cell types are
responsible for most Class II presentation. In the case of cancer,
non-professional APCs, including fibroblasts and the tumor itself,
are thought to present Class II within inflamed tumor
microenvironments (TMEs). To gain further insight, HLA-DRB1
expression was analyzed in three recently published single-cell
RNA-Seq datasets that profiled lung cancer, head and neck cancer,
and melanoma. Averaging across cells to the patient-cell type
level, it was clear that canonical APCs (macrophages, dendritic
cells, and B cells) present much greater levels of Class II than
the tumor and other stromal cell types, and this trend is
consistent across multiple patients and tumor types.
[0651] To probe whether immunotherapy disrupted this trend,
additional single-cell RNA-Seq from checkpoint blockade-responsive
tumor types were analyzed, and HLA-DRB1 expression was assessed
before and after treatment. A melanoma cohort, which included one
confirmed responder, showed uniformly low HLA-DRB1 expression by
tumor cells in both the pre-therapy and post-therapy biopsies (FIG.
19C). A basal cell carcinoma cohort which showed a 55% clinical
response rate to anti-PD-1 therapy, likewise exhibited low tumor
cell-derived HLA-DRB1 expression regardless of time point (FIG.
19C).
[0652] These results suggested that most intra-tumoral HLA class II
presentation is driven primarily by professional APCs and "hot" TME
conditions do not guarantee divergence from the general
pattern.
[0653] Because tumor cells can outnumber APCs in the tumor
microenvironment, their lower levels of MHC class II expression may
nonetheless be immunologically relevant. To assess how much of
overall Class II expression comes from tumor cells vs. stroma, TCGA
patients with mutations in Class II-specific genes (focusing on
CIITA, CD74, and CTSS) were identified and the fraction of RNA-Seq
reads exhibited the somatic (tumor-specific) variant was
determined. This information was used to impute what fraction of
HLA-DRB1 expression derived from tumor vs. stroma (FIG. 19B). Based
on mutations identified in 153 patients representing 17 distinct
tumor types, a dominant pattern was observed in which most Class II
expression appears to arise from non-tumor cells. Focusing on just
the patients with highest levels of T cell infiltration (top 10%,
as identified using a previously published 18-gene signature (Ayers
et al., 2017), low tumor HLA-DR expression still appears to be the
norm, with only 3 of 16 patients expressing >1000 TPM (tumor
progression and metastasis).
[0654] To probe whether immunotherapy disrupted this trend,
additional single-cell RNA-Seq from checkpoint blockade-responsive
tumor types were analyzed, and HLA-DRB1 expression was assessed
before and after treatment. A melanoma cohort, which included one
confirmed responder, showed uniformly low HLA-DRB1 expression by
tumor cells in both the pre-therapy and post-therapy biopsies (FIG.
19C). A basal cell carcinoma cohort which showed a 55% clinical
response rate to anti-PD-1 therapy, likewise exhibited low tumor
cell-derived HLA-DRB1 expression regardless of time point (FIG.
19C).
[0655] These results suggested that most intra-tumoral HLA class II
presentation is driven primarily by professional APCs and "hot" TME
conditions do not guarantee divergence from the general
pattern.
Example 9. New Prediction Concepts Enable More Accurate
Identification of Immunogenic Neoantigens
[0656] In order to explore the utility of neonmhc2 and associated
processing rules, the performance in several prediction scenarios
was considered. First, the ability to predict MS-identified
peptides was assessed on PMBC from seven healthy donors profiled
with a pan-DR antibody. This analysis can control for any
systematic biases inherent to the MAPTAC.TM. system or our
production cell lines. Using a 1:499 ratio of hits to decoys and
sampling decoys at random from the protein-coding exome, the
positive predictive value of neonmhc2 and NetMHCIIpan base models
as well as models that incorporated additional processing features
were assessed (expression, gene-level bias per FIG. 18B, and
overlap with a previous MHC II peptide). These models confirmed
substantial improvements in both binding and processing prediction
(FIG. 20).
[0657] FIG. 21A shows a comparison of the NetMHCIIpan and neonmhc2
with further processing parameter or features as indicated.
Prediction performance for eight MS samples profiled by HLA-DR
antibody (the same samples analyzed in Example 6, FIG. 17A).
Predictors minimally employ HLA-binding prediction (either
NetMHCIIpan or neonmhc2) and optionally employ additional
processing related variables: gene expression, gene bias (e.g., per
FIG. 18B, FIG. 18C, FIG. 19B), and overlap with a previously
observed HLA-DQ peptide. In this example, decoys were sampled from
the proteome at random (including genes that never produced an
MS-observed peptide) to achieve a 1:499 ratio of hits to decoys,
which nearly saturates available decoy sequences. Positive
predictive value was calculated in a manner analogous to FIG. 4,
e.g., the top 0.2% of peptides were called as positives and PPV is
the fraction of positives that are true MS-observed peptides. For
each candidate peptide in each sample, the binding score was
calculated as the maximum across the HLA-DR alleles present in the
sample genotype. Although there is a fair correlation in the trends
of peptides found by the two methods, the model described herein
shows a more robust outcome. FIG. 21B (see also, FIG. 33B)
represents prediction performance for tumor-derived peptides
presented by dendritic cells (Lysate) using the same hit: decoy
ratio and performance metrics as in FIG. 21A. Performance is shown
for NetMHCIIpan and models described herein with and without use of
processing features. FIG. 21C shows the expression level and gene
bias score for each heavy-labeled peptide. FIG. 21D is a diagram
representing overlap of heavy-labeled peptide source genes
according to the lysate and UV-treatment experiments.
Example 10. Expression of Class-II HLA Peptides in Cell Lines and
Isolating MHC-II-Bound Peptides
Construct Design, Cell Culture and HLA-Peptide
Immunoprecipitation
[0658] In this exemplary study, mono-allelic cell lines were
generated by transfecting a single affinity-tagged HLA construct
into cell lines (A375, HEK293T, Expi293, HeLa) and affinity-tagged
HLA-peptide complexes were immunoprecipitated. In FIGS. 12A and
12E, MHC Class II allele frequencies are allele frequencies
obtained from allelefrequencies.net/unless otherwise noted. Allele
frequencies for the U.S. population were imputed by assuming an
admixture of 62.3% European, 13.3% African, 6.8% Asian, and 17.6%
Hispanic.
[0659] With regards to FIG. 12A and FIG. 12E, the
mhc_ligand_full.csv dataset was downloaded from IEDB data on Sep.
21, 2018. Valid affinity measurements were required to have a
"Method/Technique" equal to "cellular
MHC/competitive/fluorescence", "cellular
MHC/competitive/radioactivity", "cellular MHC/direct/fluorescence",
"purified MHC/competitive/fluorescence", "purified
MHC/competitive/radioactivity", or "purified
MHC/direct/fluorescence" and an "Assay Group" equal to
"dissociation constant KD", "dissociation constant KD
(.about.EC50)", "dissociation constant KD (IC50)", "half maximal
effective concentration (EC50)", or "half maximal inhibitory
concentration (IC50)". A measurement was attributed to the Soren
Buus group (University of Copenhagen, Denmark) if the string "Buus"
appeared in the "Authors" field. Otherwise, if the authors field
included the strings "Sette" or "Sidney", a measurement was
attributed to the Alessandro Sette group (La Jolla Institute for
Immunology, U.S.A). All other measurements were labeled as "Other".
For the purposes of enumerating strong binders, only peptides with
a measured affinity stronger than 50 nM were counted (FIG. 12A).
FIG. 12E includes additional data from
toolsiedb.org/main/datasets/, and strong binders with affinity
<100 nM are enumerated.
DNA Construct Design
[0660] The gene sequences for HLA class I and HLA class II alleles
were identified by the IPD-IMGT/HLA webpage
(ebi.ac.uk/ipd/imgt/hla) and used to design recombinant expression
constructs. For HLA class I, the .alpha.-chain was fused with a
C-terminal GSGGSGGSAGG linker (SEQ ID NO: 10), followed by the
biotin-acceptor-peptide (BAP) tag sequence GLNDIFEAQKIEWHE (SEQ ID
NO: 11), a stop codon, and a variable DNA barcode, and cloned into
the pSF Lenti vector (Oxford Genetics, Oxford, UK) via the NcoI and
XbaI restriction sites. The HLA class II constructs were similarly
cloned into pSF Lenti via the NcoI and XbaI restriction sites and
consisted of the .beta.-chain sequence fused on the C-terminus to
the linker-BAP sequence from the class I construct
(SGGSGGSAGGGLNDIFEAQKIEWHE (SEQ ID NO: 12)), followed by another
short GSG linker an a F2A ribosomal skipping sequence
(VKQTLNFDLLKLAGDVESNPGP (SEQ ID NO: 13)), the sequence of the
.alpha.-chain, an HA tag (GSYPYDVPDYA (SEQ ID NO: 14)), a stop
codon, and a variable DNA barcode. The identity of all DNA
sequences was verified by Sanger sequencing.
Cell Culture and Transient Transfections
[0661] Expi293 cells (Thermo Scientific) were grown in Expi293
medium (Thermo Scientific) with 8% CO.sub.2 at 37.degree. C. with
shaking at 125 rpm. Expi293 cells were maintained at cell densities
between 0.5.times.10.sup.6/mL and 6.times.10.sup.6/mL with regular
biweekly passaging. 30 mL of the Expi293 cell suspension was used
for transient transfections at a cell density of approximately
3.times.10.sup.6/mL and >90% viability. Briefly, 30 ug DNA (1
.mu.g/mL DNA per mL cell suspension) was diluted into 1.5 mL
Opti-MEM medium (Thermo Scientific) in one tube while 80 .mu.L
ExpiFectamine.TM. 293 transfection reagent (Thermo Scientific) was
diluted into a second tube containing 1.5 mL Opti-MEM. These two
tubes were incubated at room temperature for five minutes,
combined, mixed gently, and incubated at room temperature for 30
minutes. The DNA and ExpiFectamine mixture was added to Expi293
cells and incubated at 37.degree. C., 8% CO.sub.2, 80% relative
humidity. After 48 h, transfected cells were harvested in four
technical replicates at 50.times.10.sup.6 cells per tube,
centrifuged, washed once with 1.times. Gibco DPBS (Thermo
Scientific), and flash frozen in liquid nitrogen for mass
spectrometric analysis. An aliquot of 1.times.10.sup.6 cells was
collected from each transfection batch and analyzed via anti-BAP
(Rockland Immunochemicals Inc., Limerick, Pa.) or anti-HA (Bio-Rad,
Hercules, Calif.) western blot to verify affinity-tagged HLA
protein expression.
[0662] A375 cells (ATCC) were grown in DMEM with 10% FBS and
maintained at cultures at no greater than 80% confluence with
regular passaging. For mass spectrometry experiments A375 cells
were cultured in a 500 cm.sup.2 plate at a seeding density of
18.5.times.10.sup.6 cells/mL in 100 mL, as calculated from a 70%
confluent cell number. After 24 hours, cells were transfected with
TransIT-X2 (Mirus Bio, Madison, Wis.) by following the TransIT
system protocol adjusted for the total culture volume. After 48 h,
cell medium was aspirated, and cells were washed with 1.times.
Gibco DPBS (Thermo Scientific). For harvest, A375 cells were
incubated for 10 minutes at 37.degree. C. with 30 mL non-enzymatic
cell dissociation solution (Sigma-Aldrich), centrifuged, washed
with 1.times. DPBS, and aliquoted at 50.times.10.sup.6 cells per
sample. 293T and HeLa cells were purchased from ATCC and were
cultured at 37.degree. C. at 5% CO2 in DMEM, 10% FBS, 2 mM
L-glutamine or DMEM+10% FBS, respectively. Both cell lines were
transfected with the HLA constructs using the TransIT LT1 reagent
(Mirus Bio, Madison, Wis.) following the manufactures instructions
and processed 48h after transfection as described for the A375
cells. From all samples, an aliquot of 1.times.10.sup.6 cells was
collected from each transfection and analyzed via anti-BAP
(Rockland Immunochemicals Inc., Limerick, Pa.) or anti-HA (Bio-Rad,
Hercules, Calif.) western blot to verify affinity-tagged HLA
protein expression.
BirA Protein Expression and Purification
[0663] The pET19 vector encoding E. coli BirA fused to a C-terminal
hexa-histidine tag (SEQ ID NO: 15) was used. Chemical competent E.
coli BL21 (DE3) cells (New England Biolabs) were transformed with
the BirA expression plasmid, grown at 37.degree. C. in LB broth
plus 100 .mu.g/ml ampicillin to an OD.sub.600 of 0.6-0.8 and cooled
to 30.degree. C. before expression was induced by adding 0.4 mM
isopropyl-.beta.-D-thiogalactopyranoside. E. coli cell growth
continued at 30.degree. C. for 4 h. E. coli cells were harvested by
centrifugation at 8000.times.g for 30 minutes at 4.degree. C. and
stored at -80.degree. C. until use. Frozen cell pellets expressing
recombinant BirA were resuspended in IMAC buffer (50 mM
NaH.sub.2PO.sub.4 pH 8.0, 300 mM NaCl) with 5 mM Imidazole,
incubated with 1 mg/ml lysozyme for 20 minutes on ice and the lysed
by sonication. Cellular debris and insoluble materials were removed
by centrifugation at 16,000.times.g for 30 minutes at 4.degree. C.
The cleared supernatant was subsequently loaded on a HisTrap HP 5
mL column using the AKTA pure chromatography system (GE
Healthcare), washed with IMAC buffer plus 25 mM and 50 mM imidazole
before elution with 500 mM imidazole. Fractions containing BirA
were pooled and dialyzed against 20 mM Tris-HCl pH 8.0 with 25 mM
NaCl and were loaded on a HiTrap Q HP 5 mL column (GE Healthcare)
and eluted by applying a linear gradient from 25 to 600 mM NaCl.
Fractions containing highly pure BirA were pooled, buffer exchanged
in storage buffer (20 mM Tris-HCl pH 8.0 100 mM NaCl, 5% glycerol)
and concentrated to around 5-10 mg/mL, aliquoted, and flash frozen
in liquid nitrogen for storage at -80.degree. C. BirA protein
concentration was determined by UV spectroscopy at OD.sub.280 nm
using a calculated extinction coefficient of .epsilon.=47,440
M.sup.-1 cm.sup.-1.
Western Blotting Protocol
[0664] Samples were added to XT Sample Buffer and XT Reducing Agent
(Bio-Rad, Hercules, Calif.), heated at 95.degree. C. for five
minutes, then a volume corresponding to 100,000 cells was loaded
into 10% Criterion XT Bis-Tris gels (Bio-Rad) and electrophoresed
at 200 V for 35 minutes using a PowerPac Basic Power Supply
(Bio-Rad, Hercules, Calif.) with XT MES Running Buffer (Bio-Rad,
Hercules, Calif.). The gels were rinsed briefly with water, then
proteins were transferred to PVDF membranes within Invitrogen iBlot
Transfer Stacks (Thermo Fisher Scientific) using setting P3 on an
Invitrogen iBlot2 Gel Transfer Device (Thermo Scientific). The
Precision Plus Protein All Blue Standard (Bio-Rad, Hercules,
Calif.) was used to monitor molecular weights. Next, membranes were
washed 3.times. five minutes with Pierce TBS Tween 20 (TBST) buffer
(25 mM Tris, 0.15 mM NaCl, 0.05% (v/v) Tween 20, pH 7.5), blocked
for 1 h at room temperature in TBST-M (TBST containing 5% (w/v)
nonfat instant dry milk), then incubated overnight at 4.degree. C.
in TBST-B (TBST containing 5% (w/v) Bovine Serum Albumin (Sigma
Aldrich)] and a 1:5,000 dilution of both rabbit anti-beta tubulin
antibody (catalog # ab6046, Abcam) and rabbit anti-biotin ligase
epitope tag antibody (catalog #100-401-B21, Rockland
Immunochemicals). Next, the membranes were washed 3.times. five
minutes with TBST, incubated for 1 h at room temperature in TBST-M
containing a 1:10,000 dilution of goat anti-rabbit IgG
(H+L-horseradish peroxidase-conjugated antibody (catalog #170-6515,
Bio-Rad, Hercules, Calif.), then washed at room temperature
3.times. five minutes with TBST. Finally, membranes were bathed
with Pierce ECL Western Blotting Substrate (Thermo Fisher
Scientific, Rockford, Ill.), developed using a ChemiDoc XRS+ Imager
(Bio-Rad), and visualized using Image Lab software (Bio-Rad).
Affinity-Tagged HLA-Peptide Complex Isolation
[0665] Affinity-tagged HLA-peptide complex isolations were
performed from cells expressing BAP-tagged HLA alleles and negative
control cell lines that expressed only endogenous HLA-peptide
complexes without BAP tags. The NeutrAvidin beaded agarose resin
was washed three times with 1 mL cold PBS before use in HLA-peptide
affinity purification. Frozen pellets containing 50.times.10.sup.6
cells expressing BAP-tagged HLA peptides were thawed on ice for 20
minutes and gently lysed by hand pipetting in 1.2 mL cold lysis
buffer [20 mM Tris-Cl pH 8, 100 mM NaCl, 6 mM MgCl.sub.2, 1.5%
(v/v) Triton X-100, 60 mM octyl glucoside, 0.2 mM of
2-Iodoacetamide, 1 mM EDTA pH 8, 1 mM PMSF, 1.times. complete
EDTA-free protease inhibitor cocktail (Roche, Basel, Switzerland)].
Lysates were incubated end/over/end at 4.degree. C. for 15 minutes
with .gtoreq.250 units Benzonase nuclease (Sigma-Aldrich) to
degrade DNA/RNA and centrifuged at 15,000.times.g at 4.degree. C.
for 20 minutes to remove cellular debris and insoluble materials.
Cleared supernatants were transferred to new tubes and BAP-tagged
HLA peptides were biotinylated by incubating end/over/end at room
temperature for 10 minutes in a 1.5 mL tube with 0.56 .mu.M biotin,
1 mM ATP, and 3 .mu.M BirA. The supernatants were incubated
end/over/end at 4.degree. C. for 30 minutes with a volume
corresponding to 200 .mu.L of Pierce high-capacity NeutrAvidin
beaded agarose resin (Thermo Scientific) slurry to affinity-enrich
biotinylated-HLA-peptide complexes. Finally, the HLA-bound resin
was washed four times with 1 mL of cold wash buffer (20 mM Tris-Cl
pH 8, 100 mM NaCl, 60 mM octyl glucoside, 0.2 mM of
2-Iodoacetamide, 1 mM EDTA pH 8), then washed four times with 1 mL
of cold 10 mM Tris-Cl pH 8. Between washes, the HLA-bound resin was
gently mixed by hand then pelleted by centrifugation at
1,500.times.g at 4.degree. C. for one minute. The washed HLA-bound
resin was stored at -80.degree. C. or immediately subjected to
HLA-peptide elution and desalting.
Antibody-Based HLA-Peptide Complex Isolation
[0666] HLA class II DR-peptide complexes were isolated from healthy
donor peripheral blood mononuclear cells (PBMCs). A volume
corresponding to 75 .mu.L of GammaBind Plus Sepharose resin was
washed three times with 1 mL cold PBS, incubated end/over/end with
10 .mu.g of the antibody at 4.degree. C. overnight, then washed
with three times with 1 mL cold PBS before use in HLA-peptide
immunoprecipitation. Frozen PBMC pellets containing
50.times.10.sup.6 cells were thawed on ice for 20 minutes and
gently lysed by pipetting in 1.2 mL cold lysis buffer [20 mM
Tris-Cl pH 8, 100 mM NaCl, 6 mM MgCl2, 1.5% (v/v) Triton X-100, 60
mM octyl glucoside, 0.2 mM of 2-Iodoacetamide, 1 mM EDTA pH 8, 1 mM
PMSF, 1.times. complete EDTA-free protease inhibitor cocktail
(Roche, Basel, Switzerland)]. Lysates were incubated end/over/end
at 4.degree. C. for 15 minutes with >250 units Benzonase
nuclease (Sigma-Aldrich) to degrade DNA/RNA and centrifuged at
15,000.times.g at 4.degree. C. for 20 minutes to remove cellular
debris and insoluble materials. The supernatants were then
incubated end/over/end at 4.degree. C. for 3 hours with an anti-HLA
DR antibody (TAL 1B5, product # sc-53319; Santa Cruz Biotechnology,
Dallas, Tex.) bound to GammaBind Plus Sepharose resin (GE Life
Sciences) to immunoprecipitate HLA DR-peptide complexes. Finally,
the HLA-bound resin was washed four times with 1 mL of cold wash
buffer (20 mM Tris-Cl pH 8, 100 mM NaCl, 60 mM octyl glucoside, 0.2
mM of 2-Iodoacetamide, 1 mM EDTA pH 8), then washed four times with
1 mL of cold 10 mM Tris-Cl pH 8. Between washes, the HLA-bound
resin was gently mixed then pelleted by centrifugation at
1,500.times.g at 4.degree. C. for 1 minute. The washed HLA-bound
resin was stored at -80.degree. C. or immediately subjected to
HLA-peptide elution and desalting.
HLA-Peptide Elution and Desalting
[0667] HLA-peptides were eluted from affinity-tagged and endogenous
HLA complexes and simultaneously desalted using a Sep-Pak (Waters,
Milford, Mass.) solid-phase extraction system. In brief, Sep-Pak
Vac 1 cc (50 mg) 37-55 .mu.m particle size tC18 cartridges were
attached to a 24-position extraction manifold (Restek,), activated
two times with 200 .mu.L MeOH followed by 100 .mu.L of 50% (v/v)
ACN/1% (v/v) FA, then washed four times with 500 .mu.L 1% (v/v) FA.
To dissociate HLA-peptides from affinity-tagged HLA peptides and
facilitate peptide binding to the tC18 solid-phase, 400 .mu.L of 3%
(v/v) ACN/5% (v/v) FA was added to the tubes containing HLA-bound
beaded agarose resin. The slurry was mixed by pipetting, then
transferred to the Sep-Pak cartridges. The tubes and pipette tips
were rinsed with 1% (v/v) FA (2.times.200 .mu.L) and the rinsate
was transferred to the cartridges. 100 fmol of Pierce Peptide
Retention Time Calibration (PRTC) mixture (Thermo Scientific) was
added to the cartridges as a loading control. The beaded agarose
resin was incubated two times for five minutes with 200 .mu.L of
10% (v/v) AcOH to further dissociate HLA-peptides from the
affinity-tagged HLA peptides, then washed four times with 500 .mu.L
1% (v/v) FA. HLA-peptides were eluted off the tC18 into new 1.5 mL
micro tubes (Sarstedt,) by step fractionating with 250 .mu.L of 15%
(v/v) ACN/1% (v/v) FA followed by 2.times.250 .mu.L of 30% (v/v)
ACN/1% (v/v) FA. The solutions used for activation, sample loading,
washing, and elution flowed via gravity, but vacuum (.ltoreq.-2.5
PSI) was used to remove the remaining eluate from the cartridges.
Eluates containing HLA-peptides were frozen, dried via vacuum
centrifugation, and stored at -80.degree. C. before being subjected
to a second desalting workflow.
[0668] Secondary desalting of the HLA-peptide samples was performed
with in-house built StageTips packed using two 16-gauge punches of
Empore C18 solid phase extraction disks (3M, St. Paul, Minn.) as
previously described. StageTips were activated two times with 100
.mu.L of MeOH followed by 50 .mu.L of 50% (v/v) ACN/0.1% (v/v) FA,
then washed three times with 100 .mu.L of 1% (v/v) FA. The dried
HLA-peptides were solubilized by adding 200 .mu.L of 3% (v/v)
ACN/5% (v/v) then and loaded onto StageTips. The tubes and pipette
tips were rinsed with 1% (v/v) FA (2.times.100 .mu.L) and the rinse
volume was transferred to the StageTips, then the StageTips were
washed five times with 100 .mu.L 1% (v/v) FA. Peptides were eluted
using a step gradient of 20 .mu.L 15% (v/v) ACN/0.1% (v/v) FA
followed by two 20 .mu.L cuts of 30% (v/v) ACN/0.1% (v/v) FA.
Sample loading, washes, and elution were performed on a tabletop
centrifuge with a maximum speed of 1,500-3,000.times.g. Eluates
were frozen, dried via vacuum centrifugation, and stored at
-80.degree. C.
HLA-Peptide Sequencing by Tandem Mass Spectrometry
[0669] All nanoLC-ESI-MS/MS analyses employed the same LC
separation conditions described below. Samples were
chromatographically separated using a Proxeon Easy NanoLC 1200
(Thermo Scientific, San Jose, Calif.) fitted with a PicoFrit (New
Objective, Inc., Woburn, Mass.) 75 .mu.m inner diameter capillary
with a 10-.mu.m emitter was packed at 1000 psi of pressure with He
to .about.30-40 cm with 1.9 .mu.m particle size/200 .ANG. pore size
of C18 Reprosil beads and heated at 60.degree. C. during
separation. The column was equilibrated with 10.times. bed volume
of buffer A (0.1% (v/v) FA and 3% (v/v) ACN), samples were loaded
in 4 .mu.L 3% (v/v) ACN/5% (v/v) FA, and peptides were eluted with
a linear gradient from 7-30% of Buffer B (0.1% (v/v) FA and 80%
(v/v) ACN) over 82 minutes, 30-90% Buffer B over six minutes, then
held at 90% Buffer B for 15 minutes to wash the column A subset of
samples was eluted with a linear gradient from 6-40% of Buffer B
over 84 minutes 40-60% Buffer B over nine minutes, then held at 90%
Buffer B for five minutes and 50% Buffer B for nine minutes to wash
the column Linear gradients for sample elution were run at a rate
of 200 nL/min and yielded .about.13 sec median peak widths.
[0670] During data-dependent acquisition, eluted peptides were
introduced into an Orbitrap Fusion Lumos mass spectrometer (Thermo
Scientific) equipped with a Nanospray Flex Ion source (Thermo
Scientific) at 2.2-2.5 kV. A full-scan MS was acquired at a
resolution of 60,000 from 300 to 1,700 m/z (AGC target 4e5, 50 ms
max IT). Each full scan was followed by a 2 sec cycle time, or top
10, of data-dependent MS2 scans at resolution 15,000, using an
isolation width of 1.0 m/z, a collision energy of 34 (HLA class I
data) and 38 (HLA class II data), an ACG Target of 5e4, and a max
fill time of 250 ms max ion time. An isolation width of 1.0 m/z was
used because HLA class II peptides tend to be longer (median 16
amino acids with a subset of peptides >40 amino acids), so the
monoisotopic peak is not always the tallest peak in the isotope
cluster and the mass spectrometer acquisition software places the
tallest isotopic peak in the center of the isolation window in the
absence of a specified offset. The 1.0 m/z isolation window will
therefore allow for the co-isolation of the monoisotopic peak even
when it is not the tallest peak in the isotopic cluster as the
charge states of class II peptides are often +2 or higher. Dynamic
exclusion was enabled with a repeat count of 1 and an exclusion
duration of 5 sec to enable .about.3 PSMs per precursor selected.
Isotopes were excluded while dependent scans on a single charge
state per precursor was disabled because HLA-peptide identification
relies on PSM quality, so multiple PSMs of different charge states
further increases our confidence of peptide identifications. Charge
state screening for HLA class II data collection was enabled along
with monoisotopic precursor selection (MIPS) using Peptide Mode to
prevent triggering of MS/MS on precursor ions with charge state 1
(only for alleles with basic anchor residues), >7, or
unassigned. For HLA class I data collection, precursor ions with
charge state 1 (mass range 800-1700 m/z) and 2-4 were selected,
while charge states >4 and unassigned were excluded.
[0671] Detection of peptides using High field asymmetric waveform
ion mobility spectrometry (FAIMS) was assessed using the following
protocol. Endogenously processed and presented HLA class I and HLA
class II peptides from A375 cells were subjected to both acidic
reverse phase (aRP) and basic reverse phase (bRP) offline
fractionation prior to analysis by nLC-MS/MS using orbitrap fusion
lumos tribid mass spectrometer equipped without or with FAIMS
interface. FIG. 42A demonstrates the workflow. FIG. 42B, shows
benchmarking of FAIMS with low amounts of tryptic samples Jurkat
cell, HeLa cells. Both HLA class I binding and HLA class II binding
peptides were analyzed using FAIMS (FIG. 43A and FIG. 43B; and FIG.
44A and FIG. 44B respectively). In each case, a slight improvement
in peptide detection, especially with bRP fractionated peptides.
FIGS. 45A and 45B and FIGS. 46 A and 46B show the intersection
size.
Interpretation of LC-MS/MS Data
[0672] This section is related to, for example, FIG. 29. Mass
spectra were interpreted using the Spectrum Mill software package
v6.0 pre-Release (Agilent Technologies, Santa Clara, Calif.). MS/MS
spectra were excluded from searching if they did not have a
precursor MH+ in the range of 600-2000 (Class I)/600-4000 (Class
II), had a precursor charge >5 (Class I)/>7 (Class II), or
had a minimum of <5 detected peaks. Merging of similar spectra
with the same precursor m/z acquired in the same chromatographic
peak was disabled. MS/MS spectra were searched against a database
that contained all UCSC Genome Browser genes with hg19 annotation
of the genome and its protein coding transcripts (63,691 entries;
10,917,867 unique 9mer peptides) combined with 264 common
contaminants. Prior to the database search, all MS/MS had to pass
the spectral quality filter with a sequence tag length >2, e.g.,
minimum of 3 masses separated by the in-chain mass of an amino
acid. A minimum backbone cleavage score (BCS) of 5 was set, and ESI
QExactive HLAv2 scoring scheme was used. All spectra from native
HLA-peptide samples, not reduced and alkylated, were searched using
a no-enzyme specificity, fixed modification of cysteine as
cysteinylation, with the following variable modifications: oxidized
methionine (m), pyroglutamic acid (N-term q), carbamidomethylation
(c). Reduced and alkylated HLA-peptide samples were searched using
a no-enzyme specificity, fixed modification of cysteine as
carbamidomethylation, with the following variable modifications:
oxidized methionine (m), pyroglutamic acid (N-term q),
cysteinylation (c). A precursor mass tolerance of .+-.10 ppm,
product mass tolerance of .+-.10 ppm, and a minimum matched peak
intensity of 30% was used for both native and reduced and alkylated
HLA-peptide datasets. Peptide spectrum matches (PSMs) for
individual spectra were automatically designated as confidently
assigned using the Spectrum Mill autovalidation module to apply
target-decoy based FDR estimation at the PSM rank to set scoring
threshold criteria. An auto thresholds strategy using a minimum
sequence length of 7, automatic variable range precursor mass
filtering, and score and delta Rank1-Rank2 score thresholds
optimized across all LC-MS/MS runs for an HLA allele yielding a PSM
FDR estimate of <1.0% for each precursor charge state.
[0673] Identified peptides that passed the PSM FDR estimate of
<1.0% were further filtered for contaminants by removing all
peptides assigned to the 264 common contaminants proteins in the
reference database and by removing peptides identified in the
negative control MAPTAC.TM. affinity pulldowns. Additionally, all
peptide identifications that mapped to an in silico tryptic digest
of the reference database were removed, as these peptides cannot be
ruled out as tryptic contaminants from sample carry-over on the
uPLC column.
Monoallelic Assignment of HLA-DR, -DQ, DP Heterodimers Using
MAPTAC.TM. Protocol
[0674] Mono-allelic HLA assignment to LC-MS/MS identified peptides
followed two approaches. Because allelic variation in HLA-DRA1 is
limited and not considered to influence peptide binding, all data
from DR experiments (profiling DRB1, 3, 4 and 5) were considered as
mono-allelic meaning peptides were most likely bound to HLA class
II heterodimers comprising capture beta chains paired with the
capture alpha chains However, the possibility remains that some
peptides may have bound to HLA II heterodimers comprising knock-in
the beta chains paired with a distinct endogenously expressed alpha
chains.
[0675] Conversely, for HLA-DP and HLA-DQ loci, the alpha chains
exhibit important allelic variants such that the presence of both
knock-in and endogenous alpha chain alleles creates the potential
for multiple heterodimers. For example, knock-in alpha and beta
chains coding for distinct HLA-DP and HLA-DQ heterodimers can each
pair with endogenously expressed alpha and beta chains making up to
four unique heterodimers for each HLA-DP and HLA-DQ MAPTAC.TM.
construct. Therefore, binding specificities among the purified
MAPTAC.TM. peptide population are not mono-allelic. To mitigate
this endogenous pairing problem, a construct that lacked the alpha
chain was used that (sans-alpha knock-ins) enabled us to identify
the population of peptides that likely bind to HLA heterodimers
comprising endogenously alpha chains and MAPTAC.TM. beta chains.
These peptides were computationally subtracted from the
corresponding alpha+beta chain MAPTAC.TM. experiments to
approximate a population of peptides specific to the mono-allelic
MAPTAC.TM. alpha+beta combination.
[0676] Each peptide was assigned to one or more protein-coding
transcripts within the UCSC hg19 gene annotation. Since many
peptide identifications overlap others and thus constitute mostly
redundant information, the peptides were grouped into "nested
sets", each meant to correspond to -1 unique binding event, as
shown in FIG. 11C. For instance, the peptides GKAPILIATDVASRGLDV
(SEQ ID NO: 16), GKAPILIATDVASRGLD (SEQ ID NO: 17), and
KAPILIATDVASRGLDV (SEQ ID NO: 18) all contain the conserved
sequence KAPILIATDVASRGLD (SEQ ID NO: 19), and probably all bind
MHC in the same register. In order to nest peptides of a given data
set, a graph was built in which each node corresponded to a unique
peptide, and an edge was created between any pair of peptides
sharing at least one 9mer and mappable to at least one common
transcript. The clusters command in the R package igraph was used
to identify clusters of connected nodes, and each cluster was
defined as a nested set. This procedure guarantees that any two
peptides that meet the edge criteria (.gtoreq.1 common 9mer and
.gtoreq.1 common transcript) are placed within the same nested
set.
Analysis of Previously Published MS Data
[0677] The following section relates to at least FIGS. 12A-12F,
FIG. 35, FIG. 36A-36B, FIG. 38D, FIGS. 39A-39C, FIGS. 40A-40B.
Published LC-MS/MS datasets that provided .raw files were
reprocessed using the Spectrum Mill software package v6.0
pre-Release (Agilent Technologies, Santa Clara, Calif.). Datasets
that were collected on Thermo Orbitrap instruments (e.g. Velos,
QExactive, Fusion, Lumos) that utilized HCD fragmentation and MS
and MS/MS data collection in the orbitrap (high resolution) were
analyzed using the parameters described in the above section
"Interpretation of LC-MS/MS Data". For MS and MS/MS high resolution
datasets that utilized CID fragmentation, the same parameters as
above were used with an ESI Orbitrap scoring scheme. For datasets
with MS data collection in the orbitrap and MS/MS data collection
in the ion trap, the following same parameters above were also used
with the following deviations. For HCD data, the ESI QExactive
HLAv2 scoring scheme was used, while the ESI Orbitrap scoring
scheme was used for CID data. A precursor mass tolerance of .+-.10
ppm, product mass tolerance of .+-.0.5 Da was used. For both high-
and low-resolution MS/MS datasets, peptide spectrum matches (PSMs)
for individual spectra were automatically designated as confidently
assigned using the Spectrum Mill autovalidation module to apply
target-decoy based FDR estimation at the PSM rank to set scoring
threshold criteria. An auto thresholds strategy using a minimum
sequence length of 7, automatic variable range precursor mass
filtering, and score and delta Rank1-Rank2 score thresholds
optimized across all LC-MS/MS runs for an HLA allele yielding a PSM
FDR estimate of <1.0% for each precursor charge state.
[0678] Amino acid frequencies in the human proteome were calculated
based on sequences for all protein-coding genes in the UCSC hg19
annotation (selecting one transcript at random for genes
represented by multiple transcript isoforms), as shown in FIG. 11D.
IEDB frequencies were determined by identifying the unique set of
peptides with at least one affinity observation .ltoreq.50 nM
(excluding some peptides with hexavalent polyhistidine at their
C-terminus). MAPTAC.TM. frequencies were first considered in the
context of the standard forward-phase protocol across five DRB1
alleles alleles (DRB1*01:01, DRB1*03:01, DRB1*09:01, and
DRB1*11:01), using only one peptide (the longest) per nested set.
In addition, MAPTAC.TM. frequencies were separately calculated for
the basic reverse-phase protocol across several alleles. MS data
from external datasets were analyzed without respect to potential
allele of origin and likewise using the longest peptide per nested
set.
Building Class I (HLA Class I Binding Peptide) Sequence Logos
[0679] For each Class I allele (as depicted in FIG. 14A), a
length-9 sequence logo was created by profiling amino acid
frequencies in the first five positions (mapping to logo positions
1-5) and last four positions (mapping to logo positions 6-9) of
corresponding peptides. In this manner, peptides contributed to the
sequence logo regardless of their length. As in the Class II logos,
letter heights are proportional to the frequency of each amino acid
in each position, and darker shading is used for low-entropy
positions.
Predicted Affinities of MS-Observed Peptides
[0680] This section is at least related to FIG. 14C. For each class
II allele, all unique peptides length 14 through 17 were identified
and scored for binding potential using NetMHCIIpan. For comparison,
random length-matched peptides were sampled from the human
proteome. Density distributions (as depicted in FIG. 14C) were
determined based on log-transformed values. Some alleles were
excluded from the analysis since NetMHCIIpan does not support their
prediction.
Measured Affinities for MS-Observed Peptides
[0681] Peptides were selected for affinity measurement if they had
poor predicted NetMHCIIpan binding affinity (>100 nM for
DRB1*01:01 or >500 nM for DRB1*09:01 and DRB1*11:01) or if they
exhibited .ltoreq.2 of the heuristically defined anchors to be
testing in a previously published biochemical MHC-peptide affinity
assay.
Establishment of Training, Tuning, and Testing Proteome
Partitions
[0682] This section is related to at least FIG. 15A (see also, FIG.
31A). A graph was created in which each node represents a
protein-coding transcript and edges are present between all pairs
of transcripts sharing at least 5 unique 9mers of amino sequence
content (UCSC hg19 gene annotation). The clusters command in the R
package igraph
(cran.r-project.org/web/packages/igraph/citation.html) was used to
identify clusters of connected nodes, and each cluster was defined
as a "transcript group". In this manner, if two transcripts shared
an edge (.gtoreq.5 shared 9mers), they were guaranteed to be placed
in the same transcript group. Transcript groups were randomly
sampled, placing 75% in the train partition, 12.5% in the tune
partition, and 12.5% in the test partition. In all analyses,
MS-observed peptides (and non-observed decoy peptides) were placed
in partitions (train, tune, or test) according to the partition of
their source transcripts. In very rare instances in which the
source transcripts mapped to multiple different partitions, the
assignment preference order was train (most preferred), tune, and
then test (least preferred). The same partitioning of the proteome
was used in all partition-based analyses. The graph-based approach
of partitioning the proteome was used to minimize the likelihood
that similar peptide sequences would appear during training and
evaluation, which could artificially inflate prediction
performance.
Architecture and Training of a CNN-Based Class II Binding
Predictor
[0683] In relation to at least FIG. 15A, while amino acids may be
represented by a "one-hot" encoding, others have opted to encode
amino acids using the PMBEC matrix and the BLOSUM matrix, in which
similar amino acids have similar feature profiles. For the purposes
of our peptide featurization, a unique matrix based amino acid
proximities was generated in solved protein structures. The concept
of this approach is that the typical neighbors of an amino acid
should reflect its chemical properties. For each amino acid in each
of .about.100,000 DSSP protein structures
(cdn.rcsb.org/etl/kabschSander/ss.txt.gz,), the residue that was
closest in 3D space but at least 10 amino acids away in primary
sequence was determined. Using this data, the number of times the
nearest neighbor of alanine was alanine was determined, the number
of times the nearest neighbor of alanine was a cysteine, etc., to
create a 20.times.20 matrix of proximity counts. Each element of
the matrix was divided by the product of its corresponding column
and row sums, and the entire matrix was log-transformed. Finally,
the mean value of the entire matrix was subtracted from each
element. Three additional physical features--hydrophobicity,
charge, and size--were added as additional columns such that each
amino acid was represented by 23 input features.
Benchmarking Prediction Performance on MAPTAC.TM.-Observed
Peptides, Related to FIGS. 21A-B
[0684] For the purpose of assessing prediction performance for a
given allele, it was necessary to define a set of peptides that
could have been observed (because they are present in the proteome)
but were not observed in the MS data. These negative examples were
termed "natural decoys" (in contrast to the "scrambled decoys"
described above). As guiding principles, it was decided:
1. The length distribution of natural decoys should match the
length distribution of MS-observed hits. 2. Natural decoys should
not contain sequence redundant with other natural decoys. 3.
Natural decoys should not overlap hits. 4. Natural decoys should
come from genes that produced at least one hit.
[0685] The following pseudocode represents the process an
evaluation satisfying these principles was created:
TABLE-US-00002 Initialize two empty lists of hits, H.sub.minimal
and H.sub.exhaustive For each nested set S of MS-observed peptides:
If none of the peptides in S can be mapped to a transcript in the
train or tune partition: Add the shortest peptide in S to
H.sub.minimal Add all peptides in S to H.sub.exhaustive Initialize
an empty list of decoy peptides, D For each protein-coding
transcript (longest first, shortest last) in the test partition: If
no peptides in H.sub.exhaustive map to the transcript: Skip to the
next transcript Cover the transcript's protein sequence with a set
of overlapping peptides P, where the peptide lengths are randomly
sampled from the length distribution of H.sub.minimal. The overlap
is 8 amino acids. (The last peptide in P will typically dangle over
the end of the protein.) While the last peptide in P still dangles:
Subtract 1 amino acid from the length of the longest peptide in P
For each peptide in P: If it does not share a 9mer with a peptide
in H.sub.exhaustive and it does contain a 9mer not observed in any
peptide in D: Add the peptide to D Otherwise: Reject the peptide
H.sub.minimal and D constitute the evaluation data set
[0686] To evaluate performance on this set, all n hit peptides were
evaluated by the predictor (neonmhc2 or NetMHCIIpan) and scored
along with a set of 19n decoys (randomly sampled without
replacement from the complete set of decoys). The top 5% of
peptides in the combined set were labeled as positive calls, and
the positive predictive value (PPV) was calculated as the fraction
of positive calls that were hits. Note that since the number of
positives is constrained to be equal to the number of hits, recall
is equal to PPV in this evaluation scenario. The application of a
consistent 1:19 ratio across alleles helps stabilize the
performance values, which are otherwise highly influenced by the
number of hits observed for each allele. This was deemed
appropriate since it was assumed the number of hits relates more to
experimental conditions and replicate count than intrinsic
properties of the allele. The 1:19 ratio is not far from what was
to be used if down-sampling was not implemented.
Benchmarking Prediction Performance on IEDB Affinity
Measurements
[0687] As related to FIG. 15B, since NetMHCIIpan is trained on IEDB
affinity data, its out-of-sample performance was evaluated using a
slightly outdated version and IEDB measurements. The correspondence
of predicted and measured affinities was determined by Kendall's
tau coefficient. The same statistic was used to assess the
performance of neonmhc2 on the same set of measurements.
Evaluations were conducted separately by allele and by publishing
group (Sette or Buus).
Benchmarking Prediction Performance of Natural CD4+ T Cell
Responses
[0688] Since the vast majority of CD4+ T cell responses documented
in IEDB have an unknown or computationally imputed Class II allele
restriction, the subset of records was focused on that were
confirmed experimentally by Class II tetramer. Nearly all such
records were deposited by the William Kwok Laboratory (Benaroya
Research Institute, Seattle, Wash.), which uses the blood of
immune-reactive individuals to perform tetramer-guided epitope
mapping (TGEM) of diverse pathogens and allergens. Since negative
peptides were posted for some studies but not others, the source
publications were reviewed to reconstruct the complete set of
positive and negative peptide reactivities. All 20-mer peptides
were scored by neonmhc2 and by NetMHCIIpan. To calculate positive
predictive value (PPV) across alleles in a comparable way across
alleles, the negative examples for each allele were randomly
down-sampled until there was 1:19 ratio of positives to negatives.
PPV was calculated as the fraction of experimentally confirmed
positives among the top-scored 5% of peptides. Performance was also
evaluated by receiver-operator curves.
Assessing the Performance of MHC II Peptide Deconvolution
[0689] To assess the ability the GibbsCluster (v2.0) tool to
cluster multi-allelic MHC Class II peptide data by allele of
origin, peptides from a diverse set of published DR-specific
experiments on subjects of known DR genotype (Table 2) were first
curated. In some cases, the original publication provided HLA-DRB1
typing but omitted typing for HLA-DRB3/4/5. To address these cases,
it was assumed the DR1:DR3/4/5 linkages provided by IMGT, and if
that was insufficient to resolve four-digit typing, the linkages
observed in the population "USASanFranciscoCaucasian"
(allelefrequencies.net, population ID 3098: Table 2 were used.
TABLE-US-00003 TABLE 2 DRB1 DRB3/4/5 Haplotype Freq. (%) DRB1*03:01
DRB3*01:01 23.2 DRB1*04:01 DRB4*01:01 9 DRB1*04:02 DRB4*01:01 2.2
DRB1*04:04 DRB4*01:01 3.2 DRB1*07:01 DRB4*01:01 13.2 DRB1*09:01
DRB4*01:01 2.8 DRB1*11:01 DRB3*02:02 7.2 DRB1*11:04 DRB3*02:02 2.8
DRB1*13:01 DRB3*02:02 2.6 DRB1*13:02 DRB3*03:01 2.6 DRB1*14:01
DRB3*02:02 2.2 DRB1*15:01 DRB5*01:01 28.6 Note that DRB4*01:01 has
been shown to be identical to DRB4*01:03
(ebi.ac.uk/cgi-bin/ipd/imgt/hla/get_allele.cgi?DRB4*0101)
[0690] For each DRB1/3/4/5 allele present in each (imputed)
genotype, twenty peptides from our mono-allelic MAPTAC.TM. data
were spiked in. These augmented datasets were then submitted to
GibbsCluster-v2.0.
Characterizing Observed Cleavage Sites of MHC II Peptides
[0691] Disclosed herein is a large dataset of naturally processed
and presented peptides MHC II peptides by merging peptide
identifications across several studies that used immunopurification
to profile human tissues (Table 2). Since many peptides share the
same N-terminus (e.g. GKAPILIATDVASRGLDV (SEQ ID NO: 16) and
GKAPILIATDVASRGLD (SEQ ID NO: 17)) or the same C-terminus (e.g.
GKAPILIATDVASRGLD (SEQ ID NO: 17) and KAPILIATDVASRGLD (SEQ ID NO:
19)), two sets of non-redundant cut sites were curated, one for
N-termini and one for C-termini. Then, an equivalent number of
unique non-observed N-terminal and C-terminal cut sites were
sampled at random from the set of genes that had produced at least
one MHC II peptide. These four data sets were referred to as
N-terminal hits, C-terminal hits, N-terminal decoys, and C-terminal
decoys. In addition, a naming system was used to refer to positions
upstream of peptides, within peptides, and downstream of peptides
which is shown in FIG. 41.
[0692] The frequency of each amino acid was determined for
positions U10 through N3 for N-terminal hits, and these frequencies
were compared to those observed for N-terminal decoys. To determine
whether hits and decoys showed a significant difference in the rate
of a given amino acid at a given position, a 2.times.2 table (e.g.
count of hits for which U1 is lysine, count of decoys for which U1
is lysine; count of hits for which U1 is not lysine, and count of
decoys for which U1 is not lysine) was created and scored by a
Chi-square test. An analogous approach was use for analyzing amino
acid frequencies in positions C3 through D10 of C-terminal hits and
decoys.
[0693] A second analysis considered statistical linkages between
residues immediately preceding and following cleavage events.
First, the count of U1:N1 pairs (A:A, A:C, A:D, Y:V, Y:W, Y:Y) was
compared for N-terminal hits vs. N-terminal decoys, and
significance of enrichment/depletion for each pair was determined
by a Chi-square test of a 2.times.2 contingency table (e.g. count
of hits with P:K, count of decoys with P:K; count of hits without
P:K, count of decoys without P:K). An analogous approach was used
for analyzing C1:D1 pair frequencies of C-terminal hits and
decoys.
Benchmarking the Performance of Various Class II Cleavage
Predictors
[0694] Peripheral blood from healthy donors was profiled for
DR-binding peptides. These samples were used to benchmark the
ability of cleavage-related variables/predictors to enhance the
identification of presented Class II epitopes.
[0695] To build integrated predictors that predict peptide
presentation using both binding potential and cleavage potential, a
dataset was first constructed using the same approach described for
FIG. 4 but using the "tune" partition rather than the "test"
partition. In short, this meant using a 1:20 ratio of hits to
decoys, where decoys are length-matched to hits and are randomly
sampled from the set of genes that generated at least one hit.
Binding potential was calculated using neonmhc2, and because these
samples were multi-allelic, the binding score for each peptide
candidate peptide was taken to be the average of the 1-4 DR alleles
indicated by each donor's genotype. This fitting process determined
the relative weights that would be placed on the binding and
cleavage variables in forward prediction.
[0696] To determine the performance of forward prediction,
evaluation hits and decoys (1:19 ratio) were obtained from the
"test" partition using the same protocol just described. PPV was
calculated in the same manner as for FIG. 4. Several different
cleavage predictors were assessed as shown in Table 3.
TABLE-US-00004 TABLE 3 PSM with known cleavage site PSM with
unknown cleavage site To score a candidate peptide, the 9mer core
was first imputed. Next, the maximum PSM score was calculated
across three regions: the 10 amino acids upstream of the core
(regardless of the location of the true N- terminal cleavage site),
the core sequence, and the 10 amino acids after core. A logistic
regression was trained on the tune partition (see above) using the
neonmhc2 binding score as well as the three region-specific PSM
scores. Novel neural network, unknown cleavage site Cathepsin
motifs, unknown cleavage site Structural features Within the
SCRATCH suite (Cheng J, Randall A Z, Sweredoski M J & Baldi P
(2005) SCRATCH: a protein structure and structural feature
prediction server. Nucleic Acids Res. 33: W72-6 Available at:
dx.doi.org/10.1093/nar/gki396), the tool ACCpro20 was used to
predict relative solvent accessibility (RSA), and the tool SSpro8
was used to predict features of secondary structure (H: alpha-helix
G: 310-helix I: pi-helix E: extended strand B: beta-bridge T: turn
S: bend C: other). Per-residue scores of sequence disorder
(d2p2.pro/download) were likewise determined over the entirety of
the proteome, scoring on a 0-5 scale according to the number of
prediction engines labeling the position as disordered (servers
used: anchor, espritz-d, espritz-n, espritz-x, iupred-l, and
iupred-s). Finally, topological domains (transmembrane, signal,
extracellular, lumenal, and disulfide) were determined at the
residue level using Uniprot (uniprot_sprot.dat). For each candidate
peptide, average RSA and average disorder score were calculated
over a 21mer window centered on the predicted 9mer core. Over the
same 21- residue window, percent composition was calculated for
secondary structure features and topological features. Four
logistic regression models were trained on the "tune" partition
using the following input features: 1. Neonmhc2 binding score and
solvent Accessibility (RSA) 2. Neonmhc2 binding score and disorder
score 3. Neonmhc2 binding score and eight SSpro8-derived features
representing secondary structure 4. Neonmhc2 binding score and five
Uniprot features representing topology Overlap with a previously
observed peptide MS-based peptide identifications were pooled
across many multi- allelic Class II experiments from the public
domain. A new feature was created representing whether a new
candidate peptide overlapped with one of these previously observed
peptides. Specifically, the feature was set to 1 if it shared at
least one 9mer with any peptide in the large set of previously
observed MHC II ligands; otherwise the feature was set to 0. A
logistic regression was trained on the "tune" partition using the
neonmhc2 binding score and the overlap feature.
Relationship Between MHC Class II Presentation and Expression
[0697] Peptides were pooled across previously published MS
experiments that profiled the HLA-DR ligandomes of human ovarian
tissues. For each sample with available RNA-Seq data, the raw
fastqs were downloaded from SRA and aligned to the UCSC hg19
transcriptome using bowtie2. Transcript level gene quantification
was performed using transcripts per million (TPM) as calculated by
RSEM. The expression estimates were further processed by summing to
the gene level, dropping non-coding genes, and renormalizing such
that the total TPM summed to 1000000 (renormalizing across
protein-coding genes accounts for library-to-library variation in
ncRNA abundance).
[0698] For each gene in each tissue sample, its expression level in
the sample and whether it produced at least one peptide in the
sample was considered. Across all MS experiments, these
observations were binned according to expression level and peptide
generating status (see FIG. 18A).
Identification of Over- and Under-Represented Genes
[0699] To identify genes over- and under-represented in MHC II
ligandomes, data was compiled from five previous studies that
profiled ovarian tissue, colorectal tissue, and cutaneous
melanomas, lung cancers and head and neck cancers. For each gene,
our baseline assumption was that it should yield peptides in
proportion to its length multiplied by its expression level. To
determine the length of each gene, the unique 9mers across all
transcript isoforms were enumerated. Gene-level expression was
obtained by summing across transcript isoforms. The observed number
of peptides mapping to each gene was determined at the nested set
level (e.g. peptides GKAPILIATDVASRGLDV (SEQ ID NO: 16),
GKAPILIATDVASRGLD (SEQ ID NO: 17), and KAPILIATDVASRGLDV (SEQ ID
NO: 18) counted as a single observation).
[0700] Many samples from the ovarian study had corresponding
RNA-Seq data, but some did not. In those cases, expression was
estimated averaging across the samples with available RNA-Seq data.
For the colorectal and melanoma studies, there was no corresponding
RNA-Seq for any samples, so averages were calculated across
surrogate samples using data from GTEx and TCGA. In all cases, raw
fastqs were obtained and aligned and quantified them according to
the same protocols as described above for the ovarian study's
RNA-Seq.
[0701] Two matrices were created representing expected and observed
counts, referred to as E and O, respectively, wherein rows
correspond to genes and columns correspond to samples. The matrix E
was first populated by multiplying each gene's length by its
expression in each sample; then the columns of E were rescaled to
make the column sums of E match the column sums of O. Finally,
analysis was made at the gene level by comparing the row sums of E
to the row sums of O. Genes were highlighted according to their
presence and concentration in human plasma.
Analysis of Genes Related to Autophagy
[0702] Two autophagy-related gene sets were defined. The first set
comprised proteins experimentally identified as physical
interaction partners of known autophagy-related genes. For each
canonical autophagy-related gene
(genenames.org/cgi-bin/genefamilies/set/1022) used as bait in an
IP-MS experiment deposited in the Autophagy Interaction Network
data base (accessed from
besra.hms.harvard.edu/ipmsmsdbs/cgi-bin/downloads.cgi), the top 100
protein identifications according to the "WD" confidence score
(besra.hms.harvard.edu/ipmsmsdbs/cgi-bin/tutorial.cgi) were
identified. Pooling across 22 experiments, a set of 1004 unique
genes were obtained confidently associated with at least one
canonical autophagy-related gene. (FIG. 18C)
[0703] A second set of autophagy-related genes were identified
using a study that measured pan-proteome protein abundance in baby
mouse kidney epithelial (iBMK) cells pre- and post-ATG5 knockout
using SILAC
(sciencedirect.com/science/article/pii/S1097276514006121). Genes
with a t-statistic >5 were classified as being stabilized by
ATG5 knockout (pre-starvation conditions; variable "Intercept_t" in
supplemental data file mmc2.xls). To map each mouse Uniprot ID to
an hg19 UCSC ID, the human UCSC protein sequence was determined
with which the mouse Uniprot sequence shared the most 9mers. (FIG.
18C)
[0704] Based on FIG. 18B, the ratio R of observed to expected
peptides per gene was calculated, adding a pseudocount of one to
both the numerator and the denominator, e.g. R=(O+1)/(E+1). Log(R)
was taken to represent the relative enrichment (Log(R)>0) or
depletion (Log(R)<0) of each gene felt that there was
insufficient information to quantify relative enrichment/depletion
for these genes. Among the genes with valid Log(R) calculations,
Log(R) distributions were plotted for those in the IP-MS dataset,
those in the SILAC dataset, and those in neither dataset (FIG.
18C).
Analysis of Source Gene Localization, Related to FIG. 18D
[0705] Using the same log(R) score as described above,
distributions according to the localization of each source gene was
plotted (FIG. 18D). Source gene localization was determined using
Uniprot (uniprot_sprot.dat).
Analysis of Class II Expression Data in Single-Cell RNA-Seq Data,
Related to FIG. 19A
[0706] Single-cell RNA-Seq data were obtained from three previously
published data sets that profiled human tumor samples.
[0707] The first study included data from cutaneous melanomas. The
file "GSE72056_melanoma_single_cell_revised_v2.txt" was downloaded
from Gene Expression Omnibus (ncbi.nlm.nih.gov/geo/; accession:
GSE72056). Cells with tumor status flag "2" were treated as tumor
cells, and cells labeled with tumor status flag "1" and immune cell
type flag equal to "1" through "6" were treated as T cells, B
cells, Macrophages, Endothelium, Fibroblasts, and NKs,
respectively. All other cells were dropped. Data were natively
presented in units of log 2(TPM/10+1) and were thus mathematically
converted to a TPM scale. Once on the TPM scale, the data for each
cell was renormalized to sum to 1,000,000 over the set of
protein-coding UCSC gene symbols (protein-coding genes not
appearing in the expression matrix were implicitly treated as
having zero expression). Finally, single-cell observations
corresponding to the same cell type and same source biopsy where
averaged to produce expression estimates at the patient-cell type
level.
[0708] The second study included data from head and neck tumors.
The file "GSE103322_HNSCC_all_data.txt" was downloaded from the
Gene Expression Omnibus (ncbi.nlm.nih.gov/geo/; accession:
GSE103322). The data in this table are in units of log 2(TPM/10+1);
therefore, the values were mathematically converted to TPM units.
As with the melanoma study, the data for each cell was renormalized
to sum to 1,000,000 over the set of protein-coding UCSC gene
symbols, and single-cell observations corresponding to the same
cell type and same source biopsy where averaged. Data corresponding
to the lymph node biopsies were excluded.
[0709] The third study included data from untreated non-small cell
lung. The files "RawDataLung.table.rds" and "metadata.xlsx" were
downloaded from ArrayExpress (ebi.ac.uk/arrayexpress/; accessions:
E-MTAB-6149 and E-MTAB-6653). The data (already in TPM) units, were
re-scaled to sum to 1,000,000 over the set of protein-coding genes
as previously described. Finally, single-cell observations
corresponding to the same cell type and same source biopsy where
averaged to produce expression estimates at the patient-cell type
level. Similar studies in colorectal and ovarian cancers were
performed. Results are indicated in FIG. 19A.
[0710] For simplicity, cell types were merged to a coarser
granularity than natively reported in Table 4.
TABLE-US-00005 TABLE 4 Coarse designation Constituent cell types
Alveolar "Alveolar", excluding "cuboidal alveolar type 2 (AT2)
cells" FO B cells "follicular B cells" Plasma cells "plasma B
cells" CLEC9A+ DCs "cross-presenting dendritic cells" monoDCs
"monocyte-derived dendritic cells" pDCs "plasmacytoid dendritic
cells" Langerhans "Langerhans cells" Macrophages "macrophages"
Granulocytes "granulocytes" Endothelium "normal endothelial cell",
"tumor endothelial cell", and "lower quality endothelial cell",
excluding "lymphatic EC" Epithelium "epithelial cell" and "lower
quality epithelial cell" Fibroblasts "COL12A1-expressing
fibroblasts", "COL4A2-expressin fibroblasts", "GABARAP- expressing
fibroblasts", "lower quality fibroblasts", "normal
lungfibroblasts", "PLA2G2A-expressing fibroblasts", and
"TFPI2-expressing fibroblasts" T cells "regulatory T cells", "CD4+
T cells" and "CD8+ T cells" NKs "natural killer cells" Tumor
"cancer cells" Excluded "erythroblasts" and "MALT B cells" from
analysis
[0711] Expression levels of HLA-DRB1 in the five studies are
plotted in FIG. 19A.
Characterization of Tumor-Derived Vs. Stroma-Derived Class II
Expression
[0712] To determine the relative amount of MHC class II binding
peptide expression attributable to tumor vs. stroma, mutations were
identified in Class II pathways genes in TCGA patients (called
based on DNA), and for each patient bearing a Class II mutation,
the relative expression of the mutated and non-mutated copies were
quantified of the gene the corresponding RNA-Seq. Further, it was
assumed:
1. Mutated reads arise from the tumor 2. Non-mutated reads arise
for the stroma or the wildtype allele in the tumor 3. The tumor
retains a wildtype copy with expression approximately equal to the
mutated copy
[0713] Based on this, it was determined that for an observed mutant
allele fraction of f, the fraction of Class II expression
attributable to tumor was approximately 2f and not greater than
100%. Three genes--CIITA, CD74, and CTSS--were selected as core
Class II pathway genes and assessed for mutations (not excluding
synonymous and UTR mutations) in TCGA (data downloaded from
TumorPortal (tumorportal.org/): BRCA, CRC, HNSC, DLBCL, MM, LUAD;
TCGA bulk download (tcga-data.nci.nih.gov): CESC, LIHC, PAAD, PRAD,
KIRP, TGCT, UCS; Synapse (synapse.org/#!Synapse:syn1729383): GBM,
KIRC, LAML, UCEC, LUSC, OV, SKCM; or the original TCGA publication
(cancergenome.nih.gov/publications): BLCA, KICH, STAD, and THCA).
These genes were selected based on their known roles in Class II
expression and their tight correlation with HLA-DRB1 across a
cohort of 8500 GTEx samples. Other genes with equivalent
correlation with HLA-DRB1 (HLA-DRA1, HLA-DPA1, HLA-DQA1, HLA-DQB1,
and HLA-DPB1) were excluded because their polymorphic nature makes
them prone to false positive mutation calls. Naturally, only a
small fraction of patients had a mutation in CIITA, CD74, or CTSS,
and for some tumor types, there were no patients available to
analyze.
[0714] Sequences of original whole exome sequencing (WES) in Binary
Sequence Alignment/Map (BAM) format were visually assessed (IGV
tool) to confirm that the mutation was present in the tumor sample
and not present in the normal sample. Mutant vs. wildtype read
counts were obtained from corresponding RNA-Seq using pysam.
Overall HLA-DRB1 expression was determined based on expression data
downloaded from the Genomic Data Commons (gdc.cancer.gov), which
was renormalized to sum to 1,000,000 over the set of protein-coding
genes. The fraction of HLA-DRB1 expression attributable to the
tumor (FIG. 19B) was estimated as min(1,2F), where f is the
fraction of RNA-Seq reads in CIITA, CD74, or CTSS exhibiting a
mutation.
Assessing Prediction Overall Performance on Natural Donor
Tissues
[0715] Peripheral blood from seven healthy donors was profiled with
a DR-specific antibody as described in the section "Antibody-based
HLA-peptide complex isolation" above. Based on these results, two
datasets were defined: one for fitting multivariate logistic
regressions and another for evaluating the prediction performance
of the regressions.
[0716] The first dataset was built by using the hit and decoy
selection algorithm previously described in relation to FIG. 4. In
short, this means representing each nested set with one hit peptide
(the shortest peptide in the nested set) and tiling length-matched
decoys over genes such that they overlap minimally with hits and
minimally with each other. However, two important details differ
from algorithm outlined for FIG. 4. First, the hits and decoys are
selected from genes in the "tune" partition (rather than the "test"
partition), and second, decoys were allowed to map to genes that
showed zero hits. Logistic regression models with MHC binding
scores (from NetMHCIIpan or neonmhc2) as well as other input
features (expression, gene bias, etc.) were trained on this
dataset.
[0717] The second data set (used for evaluation), was built in an
identical manner, except it used the hits and decoys drawn from the
"test" partition. In addition to binding scores, the following
variables were used in a subset of the regressions, as shown in
Table 5.
TABLE-US-00006 TABLE 5 NetMHCIIpan Derived from NetMHCIIpan. For
each candidate Affinity peptide, the strongest score was taken
across all DR alleles in the donor's genotype. MS-based Derived
from neonmhc2. For each candidate binding score peptide, the
strongest score was taken across all DR alleles in the donor's
genotype. Expression Gene expression estimates were obtained by
analyzing data from bowtie2, RSEM, and renormalization over
protein-coding genes only, values averaged over N samples Hotspot
Indicator variable (0/1) for whether the candidate peptide shares
at least one 9mer with any of the previously published multi-
allelic datasets Gene bias (1 + observed)/(1 + expected) per the
analysis Cross Indicator variable (0/1) for whether the
presentation candidate peptide comes from a gene observed to be
cross-presented on MHC class II by DCs.
[0718] For the purpose of performance evaluation, all n hit
peptides were evaluated by the given logistic regression and scored
along with a set of 499n decoys (randomly sampled without
replacement from the complete set of decoys). The top 0.2% of
peptides in the combined set were labeled as positive calls, and
the positive predictive value (PPV) was calculated as the fraction
of positive calls that were hits. Note that since the number of
positives is constrained to be equal to the number of hits, recall
is exactly equal to PPV in this evaluation scenario. The
application of a consistent 1:499 ratio across alleles helps
stabilize the performance values, which are otherwise highly
influenced by the number of hits observed for each donor. This was
deemed appropriate since it was assumed the number of hits relates
more to experimental conditions than intrinsic properties of the
donor's cells. The 1:499 ratio is not far from what would be used
if down-sampling was not implemented.
[0719] While preferred embodiments of the present invention have
been shown and described herein, it will be obvious to those
skilled in the art that such embodiments are provided by way of
example only. It is not intended that the invention be limited by
the specific examples provided within the specification. While the
invention has been described with reference to the aforementioned
specification, the descriptions and illustrations of the
embodiments herein are not meant to be construed in a limiting
sense. Numerous variations, changes, and substitutions will now
occur to those skilled in the art without departing from the
invention. Furthermore, it shall be understood that all aspects of
the invention are not limited to the specific depictions,
configurations or relative proportions set forth herein which
depend upon a variety of conditions and variables. It should be
understood that various alternatives to the embodiments of the
invention described herein may be employed in practicing the
invention. It is therefore contemplated that the invention shall
also cover any such alternatives, modifications, variations or
equivalents. It is intended that the following claims define the
scope of the invention and that methods and structures within the
scope of these claims and their equivalents be covered thereby.
Example 11: High-Throughput Identification and Validation of HLA
Class II Allele Binding Epitopes
[0720] In this example, a representative reliable, high-throughput
method using time resolved fluorescence energy transfer (TR-FRET)
for identification and validation of novel MHC-II allele-binding
peptides is described. The assay has several parts, (1)
transfecting cells with a vector construct suitable for expressing
and secreting MHC-II .alpha. and .beta. chains having a
fluorescence tag for the FRET assay, (2) purifying the secreted
MHC-II construct protein products, (3) performing a peptide
exchange assay (FIG. 22A). FIG. 22B and FIG. 23 further exemplify
the design and the procedure. The assay as described herein
promotes fast and efficient detection and validation protocol, as
it may not require stable cell lines, and encompasses simple
isolation strategies. In addition, the tetramer or multimer can be
used to detect antigen-specific CD4 cells, for example, after
neonmhc2 predicted epitopes are administered in vivo, and the
immune response generated thereafter is used to verify CD4+ T cell
response.
CLIP-TR-FRET Assay for Identifying High Affinity MHC Class-II
Binding Peptides
[0721] Presented herein are exemplary vectors for expression of HLA
class II .alpha. and .beta. chains driven by a CMV promoter in a
single construct, the protein product of which yields a properly
folded .alpha. and .beta. chain pairs. In a properly folded .alpha.
and .beta. chain form, the ccl subunit and the .beta.1 subunit are
in dimer form, the ccl subunit and the .beta.1 subunits forming the
open accepting end, capable of accepting a peptide, resembling
physiological configurations. For the purpose of this assay, these
vector expressed HLA protein products with the properly folded
.alpha. and .beta. chain form are called HLA monomers. The
expression construct comprises a linker, one or more peptide
cleavage sites, secretion signal, dimerization factors, for example
c-Fos and Jun, linked with a biotinylation motif (BAP) and a
10.times.-His-Tag (SEQ ID NO: 20). A placeholder peptide is used to
stabilize the monomers and help in secretion. A placeholder peptide
can be a CMV peptide. A placeholder peptide can be a CLIP peptide.
A placeholder peptide can be a peptide identified via MS based
ligandome for the alleles. A placeholder peptide can be bound
covalently to the HLA peptides at the open .alpha.1-.beta.1 peptide
accepting end.
[0722] An exemplary construct used herein encodes a CLIP
placeholder peptide with a thrombin cleavage moiety placed between
the CLIP and the .beta. chain, as shown in FIG. 23 (upper panel).
Upon transfection and culture of the transfected cells, such that
they reach optimal growth, the cell culture supernatant (medium)
comprising the secreted proteins (monomers) were collected and
passed through nickel (Ni.sup.2+) columns for purification (FIG.
24). Expression levels and purification was examined by Coomassie
staining. The 28-mer CLIP peptide remains associated with the
.beta. chain, which is cleaved by treatment with thrombin (FIG. 24)
and thereafter may be dislodged by competing with test (e.g.,
candidate) HLA-Class-II binding peptide. A test peptide that
successfully dislodges the CLIP peptide is accountably a cognate
peptide for binding to the MHC-II heterodimer of the construct,
based on ability to displace the CLIP peptide as measured by its
IC50. A de novo test peptide could be used for a competitive
displacement reaction as described above can then be identified by
mass spectrometry (MS).
[0723] A large collection of HLA-DR heterodimer constructs were
made with CLIP placeholder peptides which were successfully
secreted and peptide exchange assays were performed.
[0724] It was observed that the peptide placeholder CLIP, derived
from CD74, has significant effect on the secretion of HLA class II
monomers. The edited canonical CLIP peptide having the CD74
sequence PVSKMRMATPLLMQA (SEQ ID NO: 1) (designated as CLIP0 in
FIG. 25A-25C) was generally used as placeholder sequence. However,
it was seen that some HLA-DR peptides, for example, DRB1*12:01 and
DRB1*13:02 had low yield with the canonical peptide (Table 6). It
was observed that certain HLA DRB allelic dimers have a binding
sequence longer sequence covering the whole or parts of the amino
acids in the sequence: LPKPPKPVSKMRMATPLLMQALPM (SEQ ID NO: 21)
(CLIP1) (FIG. 25A). Indeed, using CLIP1 sequence instead of CLIP0
sequence in case of DRB1*12:01 and DRB1*13:02 improved the
secretion yield of the HLA dimers (FIG. 25B-25C).
TABLE-US-00007 TABLE 6 SEQ ID Yield DRB1* Peptide placeholder NO:
(mg/L) 01:01 PVSKMRMATPLLMQA (CLIP) 1 20 LPLKMLNIPSINVH (CMV) 22
100 PKYVKQNTLKLAT (HA) 23 85 12:01 PVSKMRMATPLLMQA (CLIP) 1 <3
13:02 PVSKMRMATPLLMQA (CLIP) 1 <3
De Novo Screen of Peptides by Successful Peptide Exchange Assay
Using STII-TR-FRET
[0725] Peptides can be screened de novo using the assay involving
expressing HLA-monomer proteins described above in cell lines, such
as Expi293 cells, collected and purified from the supernatant, and
subjected to peptide exchange assay. HLA class II binding peptides
predicted by the prediction algorithms were tested using peptide
exchange assay. Peptides exchange assay can be performed using a
method involving fluorescence polarization. For example, any
fluorophore can be used to label either the placeholder peptide, or
to label the test peptide, or to label both using two different
fluorophores. Change is florescence either by loss of the bound
placeholder peptide that was previously labeled with a fluorophore,
or by fluorescence emission of a released fluorophore that was
otherwise quenched by biochemical reactions in its HLA bound form,
can be recorded for quantitative assessment of the displacement
reaction. Alternatively, replacement of a non-fluorescent
placeholder peptide with a labeled fluorescent peptide could be
recorded to quantitatively determine the displacement reaction. In
an exemplary assay, FITC-labeled placeholder CLIP peptide was used
to displace an existing covalently bound peptide such as a CMV
peptide. The FITC-labeled peptide when bound with HLA induces high
polarization. When the FITC-placeholder peptide is titrated with a
test peptide, the test peptide displaces the FITC-CLIP, which leads
to lowering of fluorescence.
[0726] A peptide exchange assay can also be performed using time
resolved FRET (TR-FRET) technology instead of fluorescence
polarization as described herein. In an exemplary TR-FRET assay
described herein, cells were transfected with an HLA monomer
construct having a placeholder peptide that comprises a Streptag II
(STII) moiety. The STII moiety was detected by an Alexa-647-tagged
antibody for STII. At the same time, the His-tag attached to the
Jun terminal of the monomer construct described earlier in this
example, which is present close to the .alpha.2-.beta.2 end of the
HLA peptides, was detected by an Europium III (Eu) compound coupled
anti-His antibody (FIG. 26A). The Eu complex acts as an energy
donor, whereas the Alexa647 acts as the acceptor in the FRET
reaction when the placeholder peptide remains bound to the HLA
monomer. When a test peptide displaced the STII-CLIP placeholder,
Alexa-647-.alpha.STII peptide is freed but it can no longer be
detected by fluorescence. The TR-FRET assay was found to be more
reliable than the fluorescence polarization. Additionally, the
assay had much reduced background signal. (Fluorescence readout
data are shown in FIGS. 26B-26E). The assay provides high
throughput identification platform for HLA-peptide pairs. As shown
in Table 7 below, the test peptide (or candidate peptides) P-156 to
P-191 exhibited a wide range of displacement capabilities, and
binding affinity as determined by the calculated IC50 with each
run. Lower IC50 demonstrates higher displacement capability, and
higher binding affinity.
TABLE-US-00008 TABLE 7 IC50 (nm) IC50 (nm) Avg IC50 Std. Dev
Peptide Run 1 Run 2 (nM) (nM) Comments P- 156 36294 38246 37270 976
P- 157 2550 2243 2397 154 P- 158 5786 5815 5800 15 P-159 58 94 76
18 P-160 1668 2020 1844 176 P-161 13541 14401 13971 430 P-162 3298
3636 3467 169 P-163 >40,000 >40,000 >40,000 N/A <50%
displacement at 40 .mu.M P-164 4553 4353 4453 100 P-165 357 422 389
32 P-166 3448 3104 3276 172 P-167 6612 5906 6259 353 P-189 4597 --
-- -- P-190 1137 -- -- -- P-191 5167 -- -- -- P-192 >40,000 No
displacement up to 40 .mu.M HA 23 23 23 0 ASP51 >40,000
>40,000 N/A N/A No displacement up to 40 .mu.M.
Peptide Exchange Validation Using Differential Scanning Fluorometry
(DSF)
[0727] In this method a high throughput assay for screening
peptides that can bind to a particular HLA allele and also, the
intensity of the peptide binding to the HLA dimer is determined
(FIG. 26F). In this assay a fluorescent probe is used, which binds
to the hydrophobic residues of a protein and therefore can bind to
the MHC alleles, only when the alleles dissociate from each other
by application of heat. When an MHC class II dimer binds a cognate
peptide, the dimers are held together in its dimeric form. When
heat is applied to an MHC dimer-peptide in bound form, the weak
binding peptides dissociate faster from the MHC class II protein
dimer, allowing the fluorophore to bind to the dissociated MHC
alpha and beta chains and producing high fluorescence. Fluorescence
is recorded as a function of temperature. Representative melting
curves are shown in FIG. 26F. Melting curves can be compared to
determine the strong binders (fluorescence detected at higher
temperature) from weak binders (fluorescence detected at lower
temperature).
[0728] Use of soluble HLA-DM (HLA-sDM) as a catalyst for MHC class
H peptide exchange: HLA-DM is a natural chaperone and peptide
exchange catalyst for HLA-DR, -DP, and -DQ molecules. It is an
integral membrane protein and occurs as a heterodimer of alpha and
beta polypeptide chains (DMA and DMB). Peptide exchange as
described in this section is performed using a soluble form of
HLA-DM (e.g., HLA-sDM protein) as chaperon for the HLA-DR, -DP, and
-DQ exchanges. HLA-sDM protein is produced via a transient
transfection in Expi-CHO cells as shown in FIG. 26G. Briefly, a
recombinant HLA-sDM construct is designed, as shown graphically in
FIG. 26G upper half. The recombinant HLA-sDM construct comprises a
CMV constitutive promoter, upstream of a leader sequence and
operably linked with the promoter. The leader sequence helps in the
secretion of the product (secretion signal). At the 3'-end of the
leader sequence a coding sequence for HLA-DM beta chain ectodomain
(and lacks a transmembrane domain) is introduced. A sequence
encoding a biotinylation motif (BAP) is ligated 3' of the beta
chain-encoding sequence. A sequence encoding the HLA-DM alpha chain
ectodomain (and lacks a transmembrane domain) is placed with a
secretion sequence (leader) at its 5' end, separated from the BAP
sequence by an intervening ribosomal skipping sequence. The HLA-DM
alpha chain sequence is ligated at the 3' end with a
10.times.HIS-tag (SEQ ID NO: 20). Once formed the heterodimeric
HLA-sDM is secreted outside the cell. When this construct is
expressed in Expi-CHO cells the HLA-sDM protein is secreted into
the medium culture medium.
[0729] Expi-CHO cells were transfected with a plasmid vector
expressing the HLA-sDM construct, and cultured over a period of
about 14 days. The protein was secreted into the culture medium
over the period of culture. The HLA-sDM protein was purified from
the culture in a process very similar to purifying MHC-II proteins.
MHC-II peptide exchange can be performed efficiently with acid and
HLA-sDM, or without acid, and with octyl glucoside. Size exclusion
chromatography was performed to assess peptide exchange, results
were as shown in FIG. 2611. All peptide exchange assays were
performed using of HLA-sDM or octyl-glucoside as the catalyst.
HLA-Class II Tetramer (or Multimer) Repertoire
[0730] A large repertoire of HLA class II tetramers were generated
for the purpose of testing epitope: HLA binding and dissociation
kinetics in a biochemical assay. These class II tetramers thus
generated are used for assaying peptide binding and presentation.
For example, the tetramers were used in peptide exchange assay. As
shown in FIG. 27A, 12 tetramers were generated and stored at a
concentration of greater than 15 mg/ml; six tetramers and four at
<5 mg/ml. HLA tetramers are used for flow cytometry to identify
neo-antigen reactive CD4+ T cells. Influenza virus epitope (HA) and
HIV epitopes were tested for T cell recognition when presented by
HLA tetramers (FIG. 27E).
[0731] FIGS. 27B-27D depict various subsets of HLA class II
tetramers that were generated and purified. As shown in FIG. 27B, a
large repertoire of DRB1 heterodimer tetramers were constructed and
purified at greater than 15 mg/L concentration. FIGS. 27C and 27D
summarize the coverage of human MHC class II allele constructs
produced and validated for fluorescent based peptide binding
assays. Table 8A, Table 8B and Table 8C provide lists of the
allelic tetramers manufactured, with corresponding secretion yield
concentrations of the purified product.
TABLE-US-00009 TABLE 8A HLA heterodimer Secretion Yield DRB1*01:01
>15 mg/L DRB1*04:01 >15 mg/L DRB1*04:02 >15 mg/L
DRB1*04:04 >15 mg/L DRB1*04:05 >15 mg/L DRB1*08:01 >15
mg/L DRB1*09:01 >15 mg/L DRB1*11:01 >15 mg/L DRB1*13:03
>15 mg/L DRB1*14:01 >15 mg/L DRB1*15:03 >15 mg/L
DRB1*01:02 >15 mg/L DRB1*11:04 >15 mg/L DRB1*15:02 >15
mg/L DRB4*01:01 >15 mg/L DRB1*07:01 5-15 mg/L DRB1*13:01 5-15
mg/L DRB1*13:02 5-15 mg/L DRB1*15:01 5-15 mg/L DRB1*15:02 5-15 mg/L
DRB3*01:01 5-15 mg/L DRB1*08:03 5-15 mg/L DRB1*11:02 5-15 mg/L
DRB1*16:02 5-15 mg/L DRB3*02:01 5-15 mg/L DRB3*02:02 5-15 mg/L
DRB3*03:01 5-15 mg/L murine I-Ab 5-15 mg/L DRB1*03:01 <5 mg/L
DRB1*12:01 <5 mg/L DRB5*01:01 <5 mg/L DPA*01:03/DPB*04:01
<5 mg/L
TABLE-US-00010 TABLE 8B HLA heterodimer Secretion Yield DPB1*05:01
>15 mg/L DPB1*13:01 >15 mg/L DPB1*03:01 5-15 mg/L DPB1*04:02
5-15 mg/L DPB1*06:01 5-15 mg/L DPB1*11:01 5-15 mg/L DPB1*01:01
<5 mg/L DPB1*02:01 <5 mg/L DPB1*02:02 <5 mg/L DPB1*04:01
<5 mg/L DPB1*17:01 <5 mg/L
TABLE-US-00011 TABLE 8C HLA heterodimer Secretion Yield A1*02:02 +
B1*06:02 >15 mg/L A1*02:01 + B1*02:02 >15 mg/L A1*01:03 +
B1*06:03 >15 mg/L A1*02:01 + B1*03:03 >15 mg/L A1*01:02 +
B1*06:04 5-15 mg/L A1*05:01 + B1*02:01 <5 mg/L A1*05:05 +
B1*03:01 <5 mg/L A1*01:01 + B1*05:01 <5 mg/L A1*03:01 +
B1*03:02 <5 mg/L A1*03:03 + B1*03:01 <5 mg/L
[0732] The MHC-II tetramer product pipeline further includes DRB3,
4, and 5 alleles, and DP and DQ alleles.
Peptide Exchange Validation Using Fluorescence Polarization
(FP)
[0733] Fluorescence polarization microscopy was used in an assay to
distinguish peptide bound to MHC class II proteins versus free
peptides. A fluorescence-tagged placeholder peptide when bound to
an MHC class II dimer, results in high polarized light by
fluorescence polarization (FP) microscopy, compared to its released
form, when a non-fluorophore tagged competing epitope peptide
remains bound to the MHC class II dimer by displacing the
placeholder peptide. FIG. 28A exhibits the principle via a
graphical representation. In brief, the assay is performed in the
following generalized method, and variations are either indicated
in the respective descriptions or are easily understood by one of
skill in the art.
[0734] Reagents as described in Table 9 (below) are assembled in a
reaction tube (e.g., 1.5 ml Eppendorf tube), mixed well and
incubated at 37.degree. C. for 2 hours. 25 ml of 10.times.PBS is
added to the mixture at the end of incubation time to neutralize
the peptide exchange reaction.
TABLE-US-00012 TABLE 9 Stock Final Ingredients Concentrations
Concentration Thrombin digested MHC Variable 5 .mu.M class II
allele Exchange Peptide/ 10 mM 50 .mu.M peptide of interest Sodium
Acetate, pH 5.2 1M 100 mM Sodium Chloride 5M 50 mM Soluble DM
variable 5 .mu.M MiliQ water Up to 100 .mu.l
[0735] The exchanged peptide is detected, for example, by staining;
or stored at -80.degree. C. by snap freezing in liquid nitrogen for
evaluation later.
[0736] FIGS. 28B and 28C provide an overview of the assay
development for HLA DRB1*01:01, using FP, and the various
conditions used. In some examples, the effect of pH on the assay
was determined. In short, both the full length and the soluble
alleles are expressed in cells. The membrane bound full length
allele form is harvested by permeabilizing the membrane, while the
secreted form is harvested from the cell supernatant. The harvested
HLA class II proteins are purified by passing through nickel
(Ni.sup.2+) columns. In some examples, effect of detergent (1%
Octyl glucoside vs 1.6% NP40 was evaluated in membrane
permeabilization for harvesting full length MHCII alleles. In some
examples, effect of temperature, or the probe used, or the
purification methods or the target format were individually
evaluated (FIG. 28B).
[0737] Effect of purification method using either conformation
specific antibody L243 or His-tag purification were evaluated. The
results are shown in FIG. 28D. Each data point is depicted in the
table on the left and is represented as a single dot in the graph
on the right. The dots in the graph align roughly along a 45-degree
angle to either axes, and with a r value of 0.9621, which indicate
that the IC50 values from both the purification methods are in
agreement with each other. It also shows that the rank order of
peptide potencies does not change between the purification
methods.
[0738] Effect of the choice of the HLA class II proteins in soluble
form (sDR1) versus the full-length form (fDR1) was evaluated and
FIG. 28E shows that choice of target format does not affect the
peptide potency. Shown on the left are average IC50 values from
experiments using sDR1 form or fDR1. These data are plotted to
obtain the graph on the right hand side. The data points, each
depicted by a single dot, align roughly along a 45 degree angle to
either axes, and with a r value of 0.9365, which indicate that the
IC50 values from both the forms used correlate well with each
other. It also shows that the rank order of peptide potencies does
not change between the purification methods.
[0739] FIG. 28F shows a graphical view of an exemplary evaluation
method of neonmhc2 and NetMHCIIpan predicted peptides in binding
assay and identification of discordant peptides. Fluorescence
polarization assay was used to evaluate the Neonmhc2 and
NetMHCIIpan predicted peptides in actual peptide binding assays.
For the assay, 60 nM of thrombin digested soluble HLA-DRB1*15:01
was incubated with an FITC-tagged super binder probe peptide
(PVVHFFK(FITC)NIVTPRTPPY (SEQ ID NO: 24)) (10 nM per assay) and the
assay peptide for 5 hours at 37.degree. C. in an assay buffer (pH
5.2). Fluorescence polarization was examined from which, % probe
displacement was calculated. As shown in FIG. 28G, inhibition of
the super binder fluorescent peptide was proportional to the
concentration of the predicted peptide indicating good specificity
of the assay. Striking differences are seen between the
performances of the Neonmhc2 and the NetMHCIIpan predicted
peptides. With Neonmhc2 predicted peptides more peptides were
positively bound, and with higher degree of inhibition; whereas the
NetMHCIIpan predicted peptides were overall poor performers in
comparison to the Neonmhc2 peptides. FIG. 2811 summarizes the
evaluation of Neonmhc2 predicted peptides in binding assay. As
depicted by the pie charts, of the double negative peptides
(peptides that were not predicted by any of the NetMHCIIpan or
Neonmhc2) only 5% turned out to be binders and 95% non-binders. Of
the NetMHCII predicted peptides, 40% were binders by fluorescence
polarization detection of probe displacement assay, and of the
Neonmhc2 predicted peptides to be binders, 100% were found to be
true binders by the probe displacement assay.
[0740] FITC-labelled probes were prepared by reviewing previously
published peptides shown by Sette et al., to bind specific alleles.
These peptide sequences were then analyzed using predicted class II
binding core to identify the minimal 9-mer core of the peptide and
the anchor residues. This information was then considered when
selecting a residue position for lysine substitution and FITC
labelling. For example, in the table below (Table 10) the sequences
as described in Sette et al. (Sidney J, Southwood S, Moore C, et
al. Measurement of MHC/peptide interactions by gel filtration or
monoclonal antibody capture. Curr Protoc Immunol. 2013; Chapter
18:Unit-18.3. doi:10.1002/0471142735.im1803s100) (hereinafter
"Sette's Sequences")) are listed. The predicted class II binding
core for each peptide were underlined in the context of a specific
allele. The bold font denotes anchor positions that were identified
as a result of epitope improvement. In some cases, the same peptide
sequence can be used for different alleles.
TABLE-US-00013 TABLE 10 Short- SEQ Probe SEQ hand Sette's ID
Sequences ID ID Allele Sequences NO: Selected NO: SB- DRB1*
YATFFIKAN 25 YATFFI AN 25 DR7/11 07:01 SKFIGITE SKFIGITE SB- DRB1*
YATFFIKAN 25 YATFFI AN 25 DR7/11 11:01 SKFIGITE SKFIGITE SB- DRB1*
TLSVTFIGA 26 TLSVTFIGAA 29 DR9 09:01 APLILSY P ILSY SB- DRB1*
PVVHFFKNI 27 PVVHFF NIV 27 DR4/15 15:01 VTPRTPPY TPRTPPY SB- DRB1*
PVVHFFKNI 27 PVVHFF NIV 27 DR4/15 04:01 VTPRTPPY TPRTPPY SB-DR3
DRB1* YARIRRDGC 28 YARI RDGCL 30 03:01 LLRLVD LRLVD
[0741] Based on positioning as described above, an internal lysine
for FITC conjugation was chosen by focusing on positions within the
binding core (underlined); italized were these positions as
appropriate positions for FITC conjugation. For sequences that did
not have an internal lysine for FITC conjugation, a manual approach
was undertaken where a comparison to an allele's binding motif to
the peptide sequence was performed, and a position for internal
lysine substitution was selected for the DRB1*09:01 and DRB1*03:01
peptides (see above table). More specifically, a leucine residue
for DRB1*09:01, and an arginine residue for DRB1*03:01 were
substituted with lysines to allow for FITC conjugation. This
substitution strategy was based on the MAPTAC-derived motifs, where
manual identification of positions with no strong amino acid
preference (also in the middle of the neonmhc2 predicted 9-mer
core) because the conjugated fluorophore may be more likely to emit
polarized light when bound (i.e., more restricted motion of the
fluorophore).
Example 12: HLA Class II Binding and Processing Rules for
Identifying Therapeutically Targetable Cancer Antigens
[0742] Increasing evidence indicates CD4+ T cells can recognize
cancer-specific antigens and control tumor growth. However, it
remains difficult to predict the antigens that will be presented by
human leukocyte antigen class II molecules (HLA class II)-hindering
efforts to optimally target them therapeutically. Obstacles include
inaccurate peptide-binding prediction and unsolved complexities of
the HLA class II pathway. In this Example, an improved technology
for discovering HLA class II binding motifs is described. Further,
described herein is a comprehensive analysis of tumor-ligandomes
conducted to learn processing rules relevant in the tumor
microenvironment (TME).
[0743] 40 HLA class II alleles were profiled and it was shown that
binding motifs are highly sensitive to HLA-DM, a peptide loading
chaperone. The intratumoral HLA class II presentation was revealed
to be dominated by professional antigen presenting cells (APCs),
rather than cancer cells. Integrating these observations,
algorithms were developed as described herein, that accurately
predict APC ligandomes, including peptides from phagocytosed cancer
cells. These tools and biological insights can enhance HLA class II
directed cancer therapies.
[0744] A promising new class of therapies seeks to treat cancer by
inducing T cell responses against cancer antigens and somatically
mutated sequences called neoantigens. At present, these efforts
have focused primarily on eliciting CD8+ T cell responses toward
HLA class I (HLA class I) presented ligands. However, several
recent studies have shown that CD4+ T cells can also recognize HLA
class II presented ligands and contribute to tumor control. Cancer
vaccines and other immunotherapies would ideally take advantage of
directing CD4+ T cell responses, but current efforts have forgone
HLA class II antigen prediction entirely because the accuracy of
current prediction tools is inadequate.
[0745] A key factor preventing the accurate identification of HLA
class II cancer antigens is the availability of comprehensive,
high-quality data required to learn the rules of peptide binding.
Data are needed for the three highly polymorphic canonical HLA
class II loci, HLA-DR, -DP, and -DQ, wherein each allelic variant
exhibits distinct peptide binding preferences. A widely used method
to define peptide-binding motifs is a biochemical assay that
measures the affinity of a single peptide in the absence of
physiological chaperones, such as HLA-DM. Measured affinity data
coverage is limited to common Caucasian HLA-DR alleles, and even
for these alleles, prediction accuracy significantly lags that of
HLA class I. In principle, mass spectrometry (MS)-based ligandomics
should enable improved prediction by offering scalability and
endogenous peptide-loading conditions. Nonetheless, natural
ligandomes are multi-allelic, concealing the peptide-to-allele
mapping information required to obtain accurate training data.
There has been progress solving this problem for HLA class I, which
uses both deconvolution and mono-allelic HLA class II cell lines
mono-allelic HLA class II ligandome datasets have been generated
using low-throughput transgenic mouse models HLA class II deficient
cell lines, or cell lines that have homozygous HLA-DR allele.
[0746] Another challenge is the ambiguity around which tumor
antigens are most likely to enter the HLA class II presentation
pathway. Recent MS-based studies have surveyed the HLA class II
ligandomes of tumor samples but have not addressed if professional
APCs or the cancer cells are presenting the therapeutically
relevant HLA class II antigens. Furthermore, it is not currently
known whether HLA class II processing of tumor antigens is
primarily dependent on phagocytosis or autophagy. Depending on
which pathway dominates in the relevant cell type, there could be
drastic differences in terms of which proteins are preferred as
sources for HLA class II peptide ligands. Compounding the problem,
there is no systematic approach for determining which regions
within proteins are most likely to produce HLA class II ligands,
even though prevailing theories hold that protein sequence features
should influence HLA class II processing potential.
[0747] To investigate the processing and presentation rules of
therapeutically targetable HLA class II antigens, a two-pronged
approach of i) improving peptide-binding prediction and ii)
determining how HLA class II ligands are processed and presented in
the TME was followed. In order to learn allele-specific peptide
binding rules, a scalable mono-allelic HLA ligandome profiling
workflow called MAPTAC.TM. (Mono-Allelic Purification with Tagged
Allele Constructs) was developed, that utilizes MS to sequence
endogenously presented HLA class Ii ligands. MAPTAC.TM. allowed to
clearly resolve peptide binding motifs for 40 HLA class II alleles
and train binding prediction algorithms that could accurately
identify immunogenic viral epitopes and neoantigens. To improve HLA
class II processing prediction, tumor samples were analyzed,
establishing professional APCs as the primary source of
intratumoral HLA class II expression and defining the set of genes
and gene regions preferentially processed by these cells. It was
then demonstrated that algorithms that integrate binding and
processing features can predict natural APC ligandomes and, more
importantly, the subset of HLA class II ligands derived from
endocytosed cancer cells. These advances in understanding the
processing and presentation rules of therapeutically relevant HLA
class II antigens will enable therapies that aim to harness CD4+ T
cell responses.
Experimental Procedures
MAPTAC.TM. Construct Design and Cell Culture
[0748] For HLA class I, the .alpha.-chain was fused with a
C-terminal GSG linker, followed by the biotin-acceptor-peptide
(BAP) sequence, a stop codon, and a variable DNA barcode, and
cloned into the pSF Lenti vector (Oxford Genetics). The HLA class
II constructs were similarly cloned into pSF Lenti and consisted of
the .beta.-chain sequence with the same linker-BAP sequence fused
on the C-terminus, followed by another short GSG linker, an F2A
ribosomal skipping sequence, the sequence of the .alpha.-chain with
a C-terminal HA tag, a stop codon, and a variable DNA barcode.
MAPTAC.TM. constructs were transfected or transduced into Expi293,
HEK293T, A375, HeLa, KG-1, K562 and B721.221 cells.
HLA-Peptide Isolation Protocols
[0749] Flash frozen cell pellets containing 50.times.10.sup.6 cells
expressing BAP-tagged HLA were thawed on ice for 20 minutes and
gently lysed by hand pipetting in 1.2 mL cold lysis buffer. After
clearing DNA, RNA, and cellular debris, supernatants were
transferred to new 1.5 mL tubes and BAP-tagged HLA were
biotinylated by incubation at room temperature for 10 minutes with
0.56 .mu.M biotin, 1 mM ATP, and 3 .mu.M BirA. The biotinylated
lysates were incubated with 200 .mu.L of NeutrAvidin resin at
4.degree. C. for 30 minutes to affinity-enrich biotinylated
HLA-peptide complexes. After washes, the HLA-bound resin was
pelleted by centrifugation at 1,500.times.g at 4.degree. C. for one
minute and stored at -80.degree. C. or immediately subjected to
HLA-peptide elution and desalting using Sep-Pak solid-phase
extraction. For profiling the endogenous HLA class II ligandomes of
healthy donor materials, HLA-peptide complexes were isolated using
in-house generated anti-HLA-DR antibody L243 or with the
commercially available TAL 1B5 antibody.
HLA-Peptide Sequencing by Tandem Mass Spectrometry
[0750] All nanoLC-ESI-MS/MS analyses employed the same LC
separation conditions, instrument parameters, and data analytics.
Briefly, samples were chromatographically separated using a Proxeon
Easy NanoLC 1200 fitted with a PicoFrit column packed in-house with
C18 Reprosil beads and heated at 60.degree. C. During
data-dependent acquisition, eluted peptides were introduced into an
Orbitrap Fusion Lumos mass spectrometer equipped with a Nanospray
Flex Ion source. Mass spectra were interpreted using the Spectrum
Mill software package v6.0 pre-Release. Identified peptides that
passed the PSM FDR estimate of <1% were filtered for
contaminants by removing all peptides assigned to the 264 common
contaminants proteins in the reference database and by removing
peptides identified negative control MAPTAC.TM. affinity pulldowns.
Additionally, all peptide that mapped to an in silico tryptic
digest of the reference database were removed to account for
tryptic sample carry-over. Raw mass spectrometry datasets will be
deposited in MassIVE upon acceptance (massive.ucsd.edu).
Machine Learning Approaches for Binding Motifs and Binding
Prediction
[0751] For each allele, an ensemble of convolution neural networks
was trained to distinguish MAPTAC.TM. peptides from scrambled
decoys. Each network comprised two ReLU-activated convolutional
layers, each with 50 6-wide filters. The maximum and average
activation per filter per layer were routed into a final dense
layer with sigmoid activation. Regularization was achieved through
L2-norm, 20% spatial dropout after each convolutional layer, and
early stopping, and tuned per allele according to a hold-out
partition of non-redundant peptides (.about.12.5%). In performance
benchmarking, NetMHCIIpan-v3.1 predictions were calculated as the
maximum-scoring 15mer within each query peptide, an approach which
performed uniformly better than the native NetMHCIIpan-v3.1
predictions.
CD4+ T Cell Induction Assay
[0752] PBMCs were co-cultured with peptide pulsed mDCs at a 1:10
ratio for a total of 3 stimulations. Induced T cells were then
labelled with a unique two-color barcode as described previously
and cultured overnight at a 1:10 ratio with peptide pulsed and
matured autologous mDCs. Cells were subsequently assessed for
production of IFN-.gamma. in response to peptide by flow cytometry.
Induction samples that positively responded to peptide were samples
that induced IFN-.gamma. production at 3% higher than the no
peptide control.
APC Endocytosis of SILAC-Labeled Tumor Cells
[0753] K562 cells (ATCC, Manassas, Va.) were grown for 5 doublings
in RPMI media for SILAC (ThermoFisher) containing the heavy
isotopically amino acids, L-Lysine 2HC1 .sup.13C6 .sup.15N2 (Life
Technologies) and L-leucine .sup.13C6 (Life Technologies).
Monocytic derived dendritic cells (mDCs) were co-cultured at a 1:3
ratio either overnight with UV-treated K562 cells or for 5h with
lysate generated following HOC1 treatment. Cells were harvested,
pelleted, and flash frozen in liquid nitrogen for proteomic
analysis.
Results
MAPTAC.TM.: A Scalable Platform for Mono-Allelic HLA Class II
Ligand Profiling
[0754] Current knowledge of HLA class II binding motifs is based
primarily on data generated using two biochemical binding assays.
In one such former approach, an assay peptide and a radio-labeled
competitor peptide are co-incubated with cellularly-derived HLA
extracts to determine an IC50. In another approach, a
conformationally specific antibody measures the proportion of HLA
bound to the assay peptide in order to determine an EC50. Data from
these assays are compiled in the Immune Epitope Database (IEDB) and
used to train HLA class II prediction algorithms such as
NetMHCIIpan. The five most common Caucasian HLA-DRB1 alleles are
well-supported in IEDB (3326-8967 peptides each), though only about
29% of these are strong binders (affinity <100 nM), and 85% of
IEDB peptides overall are exact 15mers (FIG. 12B, FIG. 12E). HLA-DP
and HLA-DQ alleles and non-Caucasian HLA-DR alleles (e.g.
HLA-DRB1*15:02) are supported by considerably less data.
[0755] To create a high-quality dataset with the allelic breadth to
support a diverse patient population, the MAPTAC.TM. was developed,
a technology that enables efficient isolation of HLA class II
peptides binding a single allele for MS-based identification (FIG.
11A). The alpha and beta chains of a chosen HLA class II
heterodimer are encoded by a genetic construct with a
biotin-acceptor peptide (BAP) sequence placed at the C-terminus of
the beta chain. Since HLA-DRA is functionally invariant, MAPTAC.TM.
yields mono-allelic HLA-DR results regardless of potential pairing
between exogenous beta chain and endogenous alpha chain. For HLA-DP
and HLA-DQ, which exhibit a limited set of functional alpha chain
variants, the cell line is chosen to have matching or non-expressed
alpha chain alleles. Importantly, this approach also works for HLA
class I, with the BAP tag appended to the HLA class I heavy
chain.
[0756] A 48-hour transfection achieved robust expression of the
MAPTAC.TM. construct (FIG. 12C) with normal levels of cell surface
presentation (FIGS. 34A and 34B). This was demonstrated in 7
distinct cell lines (expi293, A375, KG-1, K562, HeLa, HEK293, and
B72.221) and for 40 HLA class II alleles, providing data for all
three canonical HLA class II loci: HLA-DR, -DP, and -DQ. The
average number of unique peptide identifications per replicate
(.about.50 million cells) that passed quality control filters
ranged from 236 to 2580 across alleles (FIG. 29), with a median of
1319 peptides. Several process variations were employed to increase
data depth, including HLA-DM over-expression, and peptide reduction
and alkylation. Only a small percentage of MS hits corresponded to
known contaminants, tryptic peptides, and mock transfections (empty
plasmid) (FIG. 11B and FIG. 29). Length distributions for
MAPTAC.TM. HLA class I and HLA class II peptides match those
observed in previous MS studies utilizing antibody-based pulldowns
(FIG. 11C).
[0757] Among the MAPTAC.TM. HLA class II peptides, most amino acids
were represented at levels consistent with source proteome
frequencies (FIG. 12D and FIG. 12F). Exceptions included C, M, and
W, which were depleted by 85%, 34%, and 42%, respectively,
consistent with previous MS-based studies of HLA class II peptides.
Reduction and alkylation of HLA class II peptides nearly tripled
the frequency of C, though it was still under-represented with
respect to the proteome (FIG. 12F). Depletions of C, M, and W were
not observed in allele-matched high-affinity peptides (<100 nM)
from IEDB. Conversely, IEDB binders exhibited depletions of D
(-39%) and E (-37%) as well as an enrichment in M (+65%) when
compared to IEDB non-binders (>5000 nM). Thus, MAPTAC.TM.
exhibits defined biases that are in line with those observed with
other technologies.
MAPTAC.TM. Resolves HLA Class II Peptide Binding Motifs
[0758] MAPTAC.TM. was used to resolve allele-specific HLA class II
binding motifs. 40 HLA class II alleles were profiled, 15 of which
were previously uncharacterized (<30 peptides with <100 nM
affinity in IEDB) including alleles common in non-Caucasian
populations (DRB1*12:02, DRB1*15:03, and DRB1*04:07). Since HLA
class II peptides can be longer than the number of residues in the
binding groove, it is not immediately evident which portion of each
peptide is HLA-interacting (the "core") vs. overhanging; however,
resolving the binding core is critical to characterizing binding
motifs. To identify the binding core, peptides to a consensus
binding core were aligned using the tool GibbsCluster-2.0, which
uses an expectation maximization algorithm to iteratively nominate
a binding register for each peptide and re-learn the binding motif
across peptides. With few exceptions, binding core motifs for
common HLA-DR alleles showed strong agreement with IEDB-based
motifs (FIG. 35). MAPTAC.TM.-observed peptides did not always show
strong NetMHCIIpan scores for common alleles (FIG. 36A); yet,
observed binders that were poorly predicted by NetMHCIIpan were
shown to have very strong measured affinities (FIG. 36B) indicating
that these observations are unlikely to be false positives.
Notably, MAPTAC.TM. motifs were always stable across multiple cell
lines (FIG. 36C).
[0759] Typically, MAPTAC.TM. and IEDB agreed on the highest
frequency amino acids at anchor positions (.about.4 most highly
conserved positions), but MAPTAC.TM. motifs generally showed lower
entropy (manifested by taller letter heights in sequence logos).
Interestingly, when cells were co-transfected with MAPTAC.TM.
constructs and HLA-DM, the entropy at anchor positions decreased
even further for most alleles (FIG. 30A and FIG. 37A). This was
consistently observed across 12 HLA-DR alleles, showing HLA-DM's
pervasive effect as a repertoire "editor" and suggesting that
models based on affinity assays that lack HLA-DM and other loading
chaperones may learn binding rules that don't apply in vivo. The
effect of HLA-DM was also evident with respect to the presence of
CLIP peptides. Without HLA-DM co-transfection, CD74-derived
peptides were observed in 10 HLA-DR alleles and matched known CLIP
variants (FIGS. 37C and 37D); meanwhile, CLIP peptides were not
observed in any of our HLA-DM co-transfection experiments.
[0760] The effect of HLA-DM was not evident for the HLA-DP alleles
analyzed (FIG. 30A and FIG. 37A), which may relate to the presence
of an unusual positively charged P1 anchor not previously reported.
HLA-DM is thought to act primarily on N-terminal side of bound
peptides, as such, the unusual P1 anchor was not a marker for
HLA-DM insensitivity as HLA-DP motifs with hydrophobic P1 anchors
were also unaltered by the presence of HLA-DM (FIG. 37A). On the
other hand, HLA-DQB1*06:04/A1*01:02 was profoundly affected by
HLA-DM (FIG. 30A). Without HLA-DM co-transfection, this allele's
binding motif was not discernable, indicating that chaperone-free
loading onto some HLA-DQ alleles yields a large proportion of
non-physiological binders.
[0761] Given the availability of published multi-allelic HLA class
II datasets, whether our allele-specific peptides could have been
effectively identified was investigated, using in silico
deconvolution methods. Several groups have shown success in
deconvolving HLA class I allele motifs from multi-allelic HLA class
I data; however, deconvolution of HLA class II motifs is
complicated by the need to simultaneously resolve both the binding
core and allele assignment of each peptide. To assess the accuracy
of HLA class II deconvolution, the HLA-DR ligandomes were analyzed
from eight samples profiled by pan-DR antibody (PBMCs and published
cell lines. For each dataset, twenty peptides were spiked in of
mono-allelic data matching each allele in the sample's genotype
(1-2 DR1 alleles plus 0-2 DR3/4/5 alleles, depending on haplotype
and zygosity. GibbsCluster tool (which can also be used for
deconvolution; was used to partition peptides into groups and
observed whether the spike-in peptides were appropriately
co-clustered according to their known origin allele. In all cases,
peptides were distributed across diverse clusters, showing only
modest association with the correct source alleles (FIG. 30B) and
indicating that HLA class II training data based on deconvolution
is likely to bear significant errors.
[0762] To understand the poor performance of the deconvolution, the
mono-allelic MAPTAC.TM. data was reviewed to determine the
frequency of "obvious" anchors that could serve as guideposts for
GibbsCluster. Accordingly, obvious amino acids (those with
frequency >10%) at each anchor position (the four positions with
lowest entropy) for each HLA class II allele were defined. Only
10-20% of peptides exhibit ideal residues in all four anchor
positions and as many as 50% exhibit two or fewer obvious anchors
(FIG. 30C). Given the low frequency of peptides that exhibit most
of the expected anchors, it is not surprising that a large fraction
of peptides would be hard to classify on a purely computational
basis. Thus, MAPTAC.TM. addresses a major source of ambiguity that
is non-trivial to resolve with in silico methods.
[0763] The motifs for HLA class I alleles could also be defined
using MAPTAC.TM.. This included alleles whose binding profiles were
previously undefined (e.g. B*52:01, common in Japan). For
previously characterized alleles, it was seen that there was good
correspondence in the motifs derived from affinity-based methods
and previous mono-allelic MS studies. Nonetheless, it was noted
that some discrepancies exist with respect to multi-allelic
MS-based studies that employed deconvolution methods to define
motifs (FIG. 37B).
Algorithms Trained on MAPTAC.TM. Data Predict Immunogenicity
[0764] Whether MAPTAC.TM. data could generate HLA class II binding
predictors with improved accuracy was considered. Since the
HLA-binding subsequence of HLA class II peptides are not at a fixed
position with respect to the N- or C-terminus, the learning
algorithm must dynamically consider different binding core
possibilities for each peptide. To address this constraint,
convolutional neural networks (CNNs) were employed, which have been
successful in the field of computer vision because of their
proficiency in translationally invariant pattern recognition. For
each allele, an ensemble of CNNs were trained (FIG. 31A), calling
the overall predictor "neonmhc2."
[0765] To account for the fact that MS exhibits some degree of
amino acid residue bias, particularly against C, negative training
examples (termed decoys) were generated by randomly permuting the
sequences of observed binders (termed hits). As this approach
carries the risk of learning sequence properties of natural
proteins, decoys were sampled randomly from non-observed
subsequences of peptide source genes of HLA class II ligands. To
calculate positive predictive value (PPV) for each allele, n
MS-observed peptides were scored in conjunction with 19n
length-matched decoys sampled from the same set of source genes,
and each predictor's n top-ranked peptides (i.e. the top 5%) were
called as positives. PPV in this case is identical to recall
because the number of false positives and the number of false
negatives is equal. Calculating positive predictive value (PPV) at
a 1:19 hit-to-decoy ratio showed that neonmhc2 improved PPV
relative to NetMHCIIpan in predicting MAPTAC.TM.-observed peptides
(FIG. 31B; Table 11).
TABLE-US-00014 TABLE 11 PPV for PPV for MHC Class II allele
NetMHCIIpan neonmhc2 DRB1*16:01 0.13 0.66 DRB1*15:01 0.17 0.61
DRB4*01:01 0.23 0.62 DPB1*02:01/DPA1*01:03 0.12 0.52 DRB1*11:04
0.22 0.60 DRB1*14:01 0.14 0.59 DRB1*13:03 0.08 0.58
DPB1*06:01/DPA1*01:03 0.01 0.48 DRB3*03:01 0.21 0.57 DRB1*03:01
0.35 0.55 DRB1*01:01 0.34 0.56 DRB5*01:01 0.35 0.54 DRB1*01:02 0.27
0.54 DRB3*01:01 0.43 0.54 DPB1*01:01/DPA1*01:03 0.08 0.53
DRB1*07:01 0.29 0.54 DRB1*04:04 0.22 0.55 DRB1*11:01 0.17 0.52
DRB1*15:03 0.02 0.50 DPB1*04:01/DPA1*01:03 0.27 0.52
DPB1*04:02/DPA1*01:03 0.25 0.53 DRB1*15:02 0.17 0.50 DRB1*10:01
0.15 0.48 DRB1*08:02 0.21 0.50 DRB1*13:01 0.16 0.49 DRB1*04:05 0.13
0.50 DRB1*09:01 0.19 0.47 DQB1*06:02/DQA1*01:02 0.14 0.49
DRB1*11:02 0.07 0.46 DRB1*12:01 0.1 0.46 DRB1*04:01 0.27 0.45
DRB1*04:02 0.1 0.46 DRB1*04:03 0.18 0.45 DRB3*02:02 0.22 0.45
DRB1*08:04 0.1 0.44 DRB1*12:02 0.12 0.44 DPB1*03:01/DPA1*01:03 0.03
0.48 DRB1*04:07 0.21 0.40 DRB1*08:01 0.11 0.37
DQB1*06:04/DQA1*01:02 0.06 0.37 DRB1*03:02 0.31 0.29 DRB1*08:03 0.1
0.24 DRB1*13:02 0.06 0.22
[0766] Saturation experiments, in which the training dataset size
is down-sampled to varying degrees, suggests that neonmhc2's
performance is data-limited and would likely improve with more data
(FIG. 38X).
[0767] Analysis of the observation of low fidelity of HLA class II
deconvolution in FIG. 30B suggest that comparable prediction
performance could not have been achieved without mono-allelic data.
To test this, a recently published computational workflow that uses
deconvolution to train allele-specific binding predictors on
multi-allele MS data (Barra et al., 2018) was followed. Inspecting
GibbsCluster logos for eleven multi-allelic samples (the same
samples in FIG. 30B), it was observed that many clusters (13/32)
did not bear any resemblance to an allele known to be in the sample
(FIG. 38Y). Using pre-existing knowledge of what the motifs should
look like, only the legitimate clusters (marked in FIG. 38Y) were
selected and predictors with the same CNN architecture was built.
These models were then evaluated alongside neonmhc2 on bona fide
mono-allelic data (a hold-out partition of MAPTAC.TM. data not used
for training) The models trained on deconvolved multi-allelic data
usually exceeded NetMHCIIpan but were inferior to
MAPTAC.TM.-trained neonmhc2 (FIG. 31E). The superiority of the
monoallelic data was maintained even when the MAPTAC.TM. dataset
was down sampled such that the size of the respective training data
sets was identical.
[0768] In order to ensure that the apparent prediction improvements
would hold when evaluated on non-MS data, a large dataset of
allele-specific CD4+ memory T cell responses were curated which
were detected by tetramer-guided epitope mapping (TGEM). Notably,
these tetramer data rely on chaperone-free peptide exchange, so
they may be subject to the same biases as conventional affinity
assays (Archila and Kwok, 2017). Nonetheless, neonmhc2
out-performed NetMHCIIpan for all alleles with sufficient data for
assessment (at least 20 positive examples) (FIG. 31C). The
performance (measured by PPV) of NetMHCIIpan was variable, dropping
as low as 5% for DRB1*15:01 (in contrast, neonmhc2's performance
never fell below 30% PPV), and approached that of neonmhc2 on only
two alleles, including the well-studied HLA-DRB1*01:01. On the
other hand, neonmhc2 showed convincing improvement on all other
evaluated alleles, including the two most common Caucasian HLA-DR
alleles (DRB1*07:01 and DRB1*15:01). These results indicate that
prediction improvements of neonmhc2 over NetMHCIIpan can be
validated in a non-MS-based benchmark and likely extend across most
alleles.
[0769] To assess the therapeutic relevance of neonmhc2, it was
determined whether neonmhc2 could identify neoantigens capable of
eliciting CD4+ T cell responses in an ex vivo induction assay (see
Methods). Focusing on DRB1*11:01, which is a common allele with
many affinity assay-confirmed binders in IEDB (only surpassed by
DRB1*01:01 and DRB1*07:01; FIG. 12E), a set of The Cancer Genome
Atlas (TCGA)-observed neoantigen sequences was scored and a subset
was selected that were preferred by neonmhc2 (top 1% of
predictions) but were not selected by NetMHCIIpan (bottom 90% of
predictions). This set was further refined by removing peptides
that may bind other HLA-DR alleles present in the induction
materials. Most neonmhc2-selected peptides (8/12) yielded CD4+ T
cell responses as measured by IFN.gamma. expression in response to
recall with the peptide (FIG. 31D, FIG. 38B and FIG. 38C). These
results demonstrate that MAPTAC.TM.-trained predictor can identify
immunogenic HLA class II neoantigen sequences not identified by
NetMHCIIpan.
Professional APCs are the Dominant HLA Class II Presenters in the
Tumor Microenvironment
[0770] Having developed a technology that enabled both
characterization and prediction of HLA class II allele-specific
peptide binding preferences, it was sought to complement the
binding prediction improvements with further insights into antigen
processing, which are critical for prioritizing the protein
sequences most likely to produce HLA class II cancer antigens. To
address these questions in the context of the TME, non-MAPTAC.TM.
datasets were analyzed including single cell RNA-Seq and published
MS-based studies that surveyed HLA class II ligandomes in tumors.
Which cell types in the microenvironment are most likely to present
therapeutically targetable cancer antigens was considered.
Currently, there is no consensus as to whether cancer antigens are
presented by professional APCs that have endocytosed tumor proteins
or by the tumor cells themselves. To that end, HLA-DRB1 expression
was analyzed in five published single-cell RNA-Seq datasets that
profiled lung cancer, head and neck cancer, colorectal cancer,
ovarian cancer, and melanoma, and found that canonical APCs
(macrophages, dendritic cells, and B cells) express much greater
levels of HLA class II than the tumor cells and other stromal cell
types in the TME. This observation is consistent across multiple
patients and tumor types (FIG. 19A). Because tumor cells can
outnumber APCs in the TME, their lower levels of HLA class II
expression may nonetheless be immunologically relevant. To assess
how much of the overall HLA class II expression comes from tumor
cells vs. stroma, TCGA patients with mutations in HLA class
II-specific genes (focusing on CIITA, CD74, and CTSS) were
identified and determined what fraction of RNA-Seq reads exhibited
the somatic variant in order to impute what fraction of HLA-DRB1
expression derived from tumor vs. stroma (FIG. 19B, see Methods).
Based on mutations identified in 153 patients representing 17
distinct tumor types, most HLA class II expression appeared to
arise from non-tumor cells. In fact, 45% percent of patients showed
zero tumor-derived HLA class II expression. Focusing on the
patients with highest levels of T cell infiltration (top 10%, as
identified using a previously published 18-gene signature (Ayers et
al., 2017), low tumor HLA-DR expression still appeared to be the
norm, with only 3 of 16 patients expressing >1000 TPM. To probe
whether immunotherapy disrupted this trend, we analyzed additional
single-cell RNA-Seq from checkpoint blockade-responsive tumor types
and assessed HLA-DRB1 expression before and after treatment. A
melanoma cohort, which included one confirmed responder, showed
uniformly low HLA-DRB1 expression by tumor cells in both the
pre-therapy and post-therapy biopsies (FIG. 19C). A basal cell
carcinoma cohort which showed a 55% clinical response rate to
anti-PD1 therapy, likewise exhibited low tumor cell-derived
HLA-DRB1 expression regardless of time point (FIG. 19C). These
results suggest that most intra-tumoral HLA class II presentation
is driven primarily by professional APCs and "hot" TME conditions
do not guarantee divergence from this general pattern.
Specific Genes have Privileged Access to the HLA Class II
Presentation Pathway
[0771] In order to determine source genes of epitopes that are
preferentially presented by tumor-resident APCs and whether they
arise from autophagy or endocytosis three published HLA class II
ligandome studies were analyzed, that were performed using tumor
tissues.
[0772] First, the degree that each gene was represented in tumor
HLA class II ligandomes was quantified assuming that the number of
observations for each gene should be proportional to the product of
its length and expression level (FIG. 18B). A clear enrichment for
proteins expressed in human plasma was observed, especially
albumin, fibrinogen, complement factors, apolipoprotein, and
transferrin, despite not being expressed in the native tissue.
Concerned that these identifications represent non-specific binding
in HLA ligandomes, neonmhc2 binding scores were assessed for
plasma-derived peptides in four PBMC HLA-DR ligandomes (FIG. 39A);
the peptides displayed strong binding scores, suggesting that they
were HLA binding. Plasma-derived proteins were not significantly
enriched in tumor HLA class I ligandome data (FIG. 39B). The
enrichment of plasma genes in HLA class II ligandomes is consistent
APCs "sipping" extracellular proteins from tissue serum via
micropinocytosis. Additional enrichments for genes involved in
leukocyte cellular adhesion was also observed, such as ITGAM
(11.times.-enriched), LCP1 (8.times.), ITGAV (6.times.), and ICAM1
(6.times.) suggesting that APCs are actively recycling their own
membranes. MUC16, which was recently reported as enriched in
ovarian cancer HLA class I ligandomes, was not
over-represented.
[0773] Cellular localization was also considered to further
interrogate gene bias in the HLA class II antigen presentation
pathway. When genes were grouped by localization, secreted and
membrane genes were represented twice as often as expected based on
gene expression, underscoring an important role for
macropinocytosis in shaping HLA class II ligandomes. Nonetheless,
more than half of HLA class II peptides arise from compartments
inconsistent with macropinocytosis, such as the nucleus and
cytoplasm. It was reasoned that if many of these genes are
presented via autophagy, then there should be a corresponding
deficit of genes known to be cleared by the proteasome. Indeed,
proteins known to contain ubiquitin sites generated peptides less
often that would have been expected based on their length and
expression (FIG. 32C). Depletion was also observed for proteins
whose levels are known to increase upon proteasome inhibition.
These are patterns that would be expected for autophagy but not
necessarily for phagocytosis, suggesting that APC peptide
ligandomes partially represent their own intracellular
proteomes.
[0774] To address the origin of HLA class II antigens presented by
APCs in the TME, it was considered whether it might be possible to
directly deconvolve the origin of source genes by determining
whether nuclear and cytosolic peptide identifications were more
consistent with an APC-specific or a bulk tumor gene expression
profile (FIG. 39C). Though there was significant uncertainty in the
estimates (assessed by regression-based model and bootstrap
resampling; Supplemental Methods), HLA class II ligandomes were
best explained by a mixture of both tumor and APC gene expression
profiles. Taken together with the observed depletion of
proteasome-cleared proteins, this result suggests that intratumoral
APCs present a mixture of exogenous and endogenous proteins.
Some Gene Regions are Preferentially Processed but Lack Evident
Cleavage Motifs
[0775] There are multiple theories about which sequences are
preferred for antigen processing (FIG. 32D). According to one
model, enzymes cleave source proteins before they bind HLA class
II, as is the case with HLA class I (Sercarz and Maverakis, 2003).
A second model poses that the peptide binding occurs first and
bound peptides are subsequently trimmed by exopeptidase enzymes
until they are sterically hindered by HLA class II. In a third
model, peptide cleavage events occur both before and after
HLA-binding. Because there are competing models for how HLA class
II peptides are generated, three different frameworks for
prediction were generated (FIG. 32D). The first assumes that
endopeptidases dominate ("cleave first"); a second model assumes
that HLA class II engages full-length proteins that are
subsequently trimmed inward by exopeptidases ("bind first"); and a
third model poses that enzymatic digestion occurs both before and
after HLA binding ("hybrid"). Each model required a different
algorithmic approach. Specifically, an algorithm motivated by the
cleave-first perspective should focus on the amino acids motifs at
the edges of MS-observed ligands); however, an algorithm motivated
by bind-first perspective would do better to ignore these motifs
and focus on local protein structural properties that dictate HLA
binding accessibility. A hybrid model-inspired algorithm should
look upstream and downstream of observed HLA class II peptides for
candidate precursor cut sites.
[0776] Of the three approaches considered, only the cleave-first
algorithm yielded a measurable improvement over baseline models
(FIG. 32E and FIG. 40B). However, it appears that this approach
learns hallmarks of exopeptidase trimming present in the positive
example peptides (e.g., a penultimate proline signature (Barra et
al., 2018)) as it failed to add value if the exact cut sites of
query peptides were masked (STAR Methods).
[0777] Pivoting to a purely empirical approach, protein regions
observed in published HLA-DQ ligandomes (Bergseng et al., 2015)
were catalogued and used overlap to predict HLA-DR ligands. The
overlap variable yielded a modest improvement in prediction
performance (3.1% increase in PPV on average over neonmhc2 alone)
(FIG. 32E). Assuming that HLA-DQ and HLA-DR alleles share the same
HLA-II processing environment but do not share binding motifs, this
result indicates certain gene regions are indeed favored for
processing but are not tied to cleavage motifs or conformational
properties in obvious ways.
[0778] Groups have reported positive results using the observed
termini of MS-observed peptides to train processing algorithms, an
approach that assumes the "cleave-first" model. However, in
reviewing amino acid enrichments adjacent to peptide termini in
multiple distinct cell lines and tissue types (FIG. 40A), patterns
were observed that seemed more consistent with post-binding
trimming. These included the lack of correspondence with the known
motif of the HLA class II processing enzyme, cathepsin S, and the
enrichment of poorly-cleavable P at penultimate peptide positions,
a motif that could arise if P blocks the procession of trimming
enzymes. To test whether the "cleave-first" assumption is correct,
neural network models were trained on peptide termini and evaluated
them in two different ways: i) scoring cleavability on the exact N-
and C-termini of each peptide or ii) scoring the best site in a
range .+-.15AA of each peptide's predicted binding core
(Supplemental Methods). It was hypothesized that both approaches
should add predictive value if the cleave-first model is correct,
but only the first approach did (FIG. 32E and FIG. 40B). Thus, the
neural network can discern HLA class II from decoys based on
telltale features in ligands (e.g. penultimate P) but it is
irrelevant when the cut sites are not known a priori--as is always
the case when predicting immunogenic peptides from a primary
protein sequence. This subtle distinction has the potential to
cause confusion in the field.
[0779] With the "bind first" theory, MS-observed and decoy peptides
were scored for solvent accessibility, as well as for intrinsically
disordered domains. Solvent accessible or disordered domains could
be enriched in HLA class II ligands if protein structure dictates
availability for HLA binding. However, these features also proved
non-predictive (FIG. 32E). A hybrid model was then considered in
which enzymes partially digest the protein before peptide binding,
after which additional trimming occurs. In this model, precursor
cleavage sites exist further upstream and downstream of the
observed termini of MS ligands. Accordingly, a CNN was trained
based on the extended protein context (.+-.30 AAs) to detect distal
signals corresponding to precursor cuts. This model did not show
predictive value either (FIG. 32E). Finally, as
processing-preferred regions proved difficult to predict based on
primary sequence, protein regions observed in published HLA-DQ
ligandomes were catalogued and used overlap to predict HLA-DR
ligands. The overlap variable yielded a modest improvement in
prediction performance (3.1% increase in PPV on average over
neonmhc2 alone) (FIG. 32E). Assuming that HLA-DQ and HLA-DR alleles
share the same HLA class II processing environment but do not share
peptide binding motifs, this result indicates certain gene regions
are indeed favored for processing but do not show obvious cleavage
motifs or special conformational properties.
Integrating Presentation Rules Greatly Enhances HLA-DR Ligandome
Prediction
[0780] To quantify how binding rules synergize with
processing-related features, a multi-variate models was created for
predicting HLA-DR ligandomes of HLA class II-presenting cell lines,
dendritic cells, and healthy donor peripheral blood mononuclear
cells (PBMCs). Although the presented peptides are not mutated, the
prediction scenario mimics that of neoantigen prediction, in which
randomly sampled genomic loci must be evaluated in terms of their
ability to produce HLA class II peptides. Using a 1:499 ratio of
hits to decoys and sampling decoys at random from the
protein-coding exome, the performance of neonmhc2- and
NetMHCIIpan-based models was assessed as well as models that
incorporated additional processing features including
RNA-Seq-derived expression, gene-level bias (per FIG. 32A, see
related FIG. 39B), and overlap with a previously observed HLA-DQ
peptides. To make the model consistent with how mutated tumor
epitope targets are prioritized for the treatment of cancer, the
gene-level bias feature was modified to neutralize preference for
plasma genes that are not relevant sources of neoantigens.
[0781] These integrative algorithms confirmed substantial
improvements in both binding and processing prediction (FIG. 21A).
Specifically, the full model showed a 7.4.times. to 61.times.
fold-change improvement over a model using NetMHCIIpan binding
predictions alone, depending on the dataset being evaluated.
Expression and gene bias both provided substantial independent
contributions to prediction accuracy. The DQ overlap feature made
smaller contribution but consistently provided a positive
improvement. Importantly, affinity-based models were only half as
accurate as MAPTAC.TM.-based models even when provided the full
benefit of processing-related prediction variables.
Benchmarking Prediction Accuracy Using Tumor-Derived HLA Class II
Peptides Presented by Professional APCs
[0782] Having assessed our accuracy in predicting HLA class II
ligandomes, attention was shifted to testing whether tumor-derived
ligands endocytosed by professional APCs could be predicted. Our
observation that most HLA class II expression in the TME is from
professional APC's indicates that this processing route is likely
the most relevant pathway for tumor antigen presentation.
Unfortunately, conventional MS-based ligandomes of tumor tissues do
not identify which peptides originate from endocytosed tumor
proteins. Therefore, an experiment was devised in which were
profiled the HLA-DR ligandomes of dendritic cells (DCs) that had
been "fed" SILAC-labeled tumor cells (FIG. 33A).
[0783] To label tumor-derived proteins, an HLA class II-deficient
cancer cell line (K562) was grown in media containing
isotopically-labeled L and K achieving greater than 95% labeling
efficiency. DCs were fed either lysed tumor cells (to mimic
macropinocytosis of tumor debris) or UV-treated whole tumor cells
(to mimic phagocytosis of whole cells). HLA-DR binding peptides
were profiled using MS to identify peptides bearing heavy- or
light-labeled amino acids. The experiment yielded 29 heavy-labeled
peptides and the whole-cell experiment yielded 56 heavy-labeled
peptides for the lysate and UV experiments, respectively (Table
10). Peptides bearing more than one L or K showed complete labeling
in all but two cases indicating that the heavy-labeled peptides
originated from tumor cells and not from newly translated DC
proteins, which would show discordant labeling. Both untreated DCs
and DCs that were harvested after incubating 10 minutes with lysate
yielded no heavy-labeled peptides.
[0784] Using the integrated prediction algorithm disclosed here,
the ability to predict tumor-derived peptides was assessed.
Consistent with our previous result in predicting natural HLA class
II ligandomes, neonmhc2-based models achieved much greater
prediction accuracy than NetMHCIIpan-based models (FIG. 33D).
[0785] Unlike gene expression, the gene bias and DQ-overlap
features did not improve prediction of the endocytosed antigens
suggesting that the patterns that were learned from bulk tissue
ligandomes were not as relevant for this class of epitopes.
Analyzing the source genes of heavy-labeled peptides, the
RNA-binding proteins (RBPs) DNA-binding proteins (DBPs) heat shock
proteins (HSPs) and mitochondrial proteins (FIG. 21D) were noticed
as opposed to the predominance of secreted and membrane proteins
seen in the ligandome experiments (FIG. 32A). It was not clear
whether this represented distinct processing preferences. Indeed,
the source proteins were typically highly expressed in K562 (median
expression 430 TPM compared to 130 TPM for unlabeled peptides),
suggesting the detection limit might drive the observed gene
preferences.
[0786] To gain clarity, logistic regression models were built to
test whether gene localization and functional categories could
improve peptide prediction beyond models that already account for
gene expression. RBPs, DBPs and HSPs were no longer significant
when the binding and expression were accounted for, but
mitochondrial proteins remained significant (p=2.6e-4: FIG. 33E).
Notably, the pattern of enrichment was completely distinct from
what was observed in the light labeled peptides.
[0787] To determine whether mitochondrial enrichment could improve
prediction, data were collected from new donor with the aim for
deeper coverage by increasing the cellular input, focusing on the
UV-treatment protocol only, and adding a 24-hour incubation
timepoint in addition to the overnight timepoint. This experiment
yielded 77 and 59 heavy labeled peptides for the overnight and
24-hour timepoints, respectively, and jointly identified 78 unique
source genes. Using a logistic regression model that accounts for
mitochondrial preference (trained on the original SILAC data), we
were able to improve PPV by a net increase of 8-12% over models
that include binding and expression only (FIG. 33G). These
improvements were significant (p=1.1e-9 and p=1.5e=-8, for 16h and
24h, respectively). These preferences could not have been learned
from bulk ligandomes and can be used to enable more accurate
epitope prediction.
[0788] The presence of HLA class II presentation in the TME has
been associated with positive outcomes in patients treated with
cancer immunotherapies. Unfortunately, the inaccuracy of HLA class
II ligand prediction and the ambiguities around how tumor antigens
are presented in the TME have slowed the development of therapies
that target HLA class II antigens. Therefore, a
mono-allelic-profiling technology called MAPTAC.TM. was developed
as described herein, and comprehensively analyzed tumor ligandomes
to define HLA class II ligand processing rules. MAPTAC.TM. enabled
rapid profiling of 40 HLA class II alleles, including 35 HLA-DRB1
alleles that cover 95% of U.S. patients. Furthermore, neonmhc2, our
binding prediction algorithm trained on MAPTAC.TM. data,
outperformed NetMHCIIpan in predicting memory CD4+ T cell
responses, even for the alleles with the most pre-existing affinity
measurements available for NetMHCIIpan training. It was observed
that neonmhc2 was superior in performance to NetMHCIIpan in
identifying memory CD4.sup.+ T cell responses in the TGEM
validation dataset. Furthermore, the algorithms disclosed herein
also excelled at predicting ex vivo induced CD4.sup.+ T cell
responses against neoantigens, successfully identifying immunogenic
neoepitopes which would not have been prioritized by NetMHCIIpan.
Meanwhile, analysis of single-cell RNA-Seq tumor data revealed that
the most relevant tumor antigens are likely dominantly expressed by
infiltrating APCs phagocytosing tumor cells. Thus, which genes and
gene regions are preferentially presented in the TME was
investigated and multivariate models were created that accurately
predicted HLA-DR ligandomes and tumor-derived ligands presented by
phagocytic APCs. These models greatly exceed the positive
predictive value of NetMHCIIpan.
[0789] An advantage of directly profiling endogenously processed
and presented HLA class II ligands using MAPTAC.TM. in contrast to
conventional peptide binding assays is that peptide loading
chaperones such as HLA-DM are present. HLA-DM is known to play a
role in editing the HLA class II peptide repertoire of APCs, which
motivated us to study the effects of its differential expression on
HLA class II ligandomes. When HLA-DM was over-expressed in HLA-DR
MAPTAC.TM. experiments, the binding motifs were more clearly
resolved than in the experiments without HLA-DM over-expression.
Surprisingly, HLA-DM had a profound effect on
HLA-DQB1*06:04/A1*01:02, demonstrating that learning accurate
peptide binding rules for some HLA-DQ alleles may require the
presence of this peptide loading chaperone. Conversely, two HLA-DP
alleles showed no effect (Yin et al., 2015), suggesting a
relationship between HLA-DM sensitivity and P1 anchor preferences
that were unusual for these two HLA-DP alleles. Beyond HLA-DM, the
MAPTAC.TM. platform provides a way to rapidly learn how other key
chaperones and proteins involved in the HLA class II pathway, such
as CD74 or HLA-DO, may impact the peptide binding repertoires of
HLA class II alleles.
[0790] With respect to tumor biology, our most consequential
observation was that APCs are responsible for dominant HLA class II
expression in the TME for the tumor types evaluated. This suggests
that the presentation of therapeutically relevant tumor antigens
likely depends on the phagocytosis of apoptotic tumor cells or
macropinocytosis of secreted tumor proteins. Although there are
reports of direct CD4 T cell killing, the data provided suggests
that CD4 T cells more typically play a supportive role in the TME,
primarily recognizing tumor antigens presented on infiltrating
leukocytes. Thus, the anti-tumor effects of CD4 T cells are
probably mostly mediated by the secretion of chemokines and
cytokines that regulate the trafficking and activation of other
immune cells, including those with direct cytolytic function. While
this is more mechanistically complicated, one benefit is that the
tumor has less control over whether HLA class II antigens get
presented, suggesting that immune escape via loss-of-function
mutations, a common mechanism by which tumors avoid HLA class I
presentation, may not be as frequent with HLA class II. Future
studies that carefully define which APC populations are responsible
for presenting endocytosed tumor antigens and whether there are
ways to enhance recruitment of these phagocytic cells to the TME
will be beneficial for the field. Additionally, it would be useful
to understand how different modes of tumor cell death, such as
hypoxia, chemotherapy, and radiation, result in various levels of
tumor antigen capture by these APCs, which may lead to optimal
therapeutic combinations with HLA class II targeting therapies.
[0791] Finally, a comprehensive analysis of HLA class II ligandomes
led to the observation that certain genes appear to be presented
more often than their transcript expression levels would predict.
Learning gene level biases from tumor cells facilitated improved
prediction of APC HLA class II ligandomes; however, it is possible
that some of these signals are less relevant for neoantigen
prediction. For example, enrichments were detected that appear to
relate to autophagy and membrane recycling in APCs rather than the
uptake of exogenous antigens. Interestingly, when ""tumor cells
were "fed" to dendritic cells in vitro, the source gene
identifications instead showed enrichment for RNA-binding proteins.
It is tempting to speculate that RNA-binding proteins are
preferentially presented since such a mechanism would promote the
presentation of pathogen epitopes and potentially explain
reactivities against RNA-binding proteins observed in systemic
lupus erythematosus and other autoimmune conditions. In any case,
it is important to note that the utility of our SILAC-based HLA
ligandomics workflow is not limited to tumor antigens, as it can
also be applied to study antigens involved in infectious disease
and autoimmunity.
[0792] In summary, the rules of HLA class II processing and
presentation are significantly more complex than for HLA class I.
For this reason, the antigens that drive CD4+ T cell responses
often remain undefined. Our advances in defining HLA class II
binding and processing rules will enable the identification of
targetable cancer antigens and other disease-related epitopes that
can be translated to more effective therapies.
Example 13: Supplemental Information
[0793] Summary of Experiments and Data Sources with Associated Meta
Data
[0794] Exhaustive list of data sets, including MAPTAC.TM. data,
non-MAPTAC.TM. manuscript data, and previously published data.
Relevant associated features, such as sample genotype, are provided
where appropriate. B) Unique peptide identifications merged across
experimental MAPTAC.TM. replicates, PBMC donors, cell lines, and
SILAC-feeding experiments. Contaminants and perfect tryptic
peptides are removed. See for example, at least FIGS. 12E-12F,
34A-34B, among others.
Spike-In Peptides for Deconvolution Analysis
[0795] An exemplary list of 20 example peptides used per allele in
the spike-in analysis. Peptides were selected by requiring a
minimum SPI of 70, length between 12 and 20 amino acids, and by not
allowing a 9mer overlap with any binders observed for other
MAPTAC.TM.-profiled DR alleles. Additionally, no two spike-in
peptides for a given allele share a 9mer. See for example, at least
FIGS. 35, 36A-36C, among others.
Collated TGEM Data Set for Selected Alleles Supplemental
Experimental Procedures
[0796] HLA class II tetramer results for DRB1*01:01, DRB1*03:01,
DRB1*04:01, DRB1*07:01, DRB1*11:01, and DRB1*15:01 for diverse
pathogen and allergen peptides and their corresponding NetMHCIIpan
and neonmhc2 predictions. Data were curated from papers published
by Kwok and colleagues. See FIG. 38X, 38Y, 38B-38C, among
others.
Supplemental Methods
HLA Class II Allele Frequencies and Affinity Data Statistics,
Related to FIGS. 12A and 12E
[0797] Allele frequencies were obtained from resource,
bioinformatics.bethematchclinical.org/hla-resources/haplotype-frequencies-
/high-resolution-hla-alleles-and-haplotypes-in-the-us-population.
The mhc_ligand_full.csv dataset was downloaded from IEDB data
(iedb.org/database_export_v3.php) on Sep. 21, 2018. Valid affinity
measurements were required to have a "Method/Technique" equal to
"cellular MHC/competitive/fluorescence", "cellular
MHC/competitive/radioactivity", "cellular MHC/direct/fluorescence",
"purified MHC/competitive/fluorescence", "purified
MHC/competitive/radioactivity", or "purified
MHC/direct/fluorescence" and an "Assay Group" equal to
"dissociation constant KD", "dissociation constant KD
(.about.EC50)", "dissociation constant KD (IC50)", "half maximal
effective concentration (EC50)", or "half maximal inhibitory
concentration (IC50)". A measurement was attributed to the Soren
Buus group (University of Copenhagen, Denmark) if the string "Buus"
appeared in the "Authors" field. Otherwise, if the authors field
included the strings "Sette" or "Sidney", a measurement was
attributed to the Alessandro Sette group (La Jolla Institute for
Immunology, U.S.A). All other measurements were labeled as "Other".
For the purposes of enumerating strong binders, only peptides with
a measured affinity stronger than 100 nM were counted
MAPTAC.TM. Protocol Overview, Related to FIG. 2: DNA Construct
Design
[0798] The gene sequences for HLA class I and HLA class II alleles
were identified by the IPD-IMGT/HLA webpage
(ebi.ac.uk/ipd/imgt/hla) and used to design recombinant expression
constructs. For HLA class I, the .alpha.-chain was fused with a
C-terminal GSGGSGGSAGG linker (SEQ ID NO: 10), followed by the
biotin-acceptor-peptide (BAP) tag sequence GLNDIFEAQKIEWHE (SEQ ID
NO: 11), a stop codon, and a variable DNA barcode, and cloned into
the pSF Lenti vector (Oxford Genetics, Oxford, UK) via the NcoI and
XbaI restriction sites. The HLA class II constructs (DR, DP and DQ)
were similarly cloned into pSF Lenti via the NcoI and XbaI
restriction sites and consisted of the .beta.-chain sequence fused
on the C-terminus to the linker-BAP sequence from the HLA class I
construct (SGGSGGSAGGGLNDIFEAQKIEWHE (SEQ ID NO: 12)), followed by
another short GSG linker an a F2A ribosomal skipping sequence
(VKQTLNFDLLKLAGDVESNPGP (SEQ ID NO: 13)), the sequence of the
.alpha.-chain, an HA tag (GSYPYDVPDYA (SEQ ID NO: 14)), a stop
codon, and a variable DNA barcode. For all DR alleles the
beta-chain was paired with DRA*01:01. The HLA-DM construct was
cloned similarly to the HLA class II constructs except that it
lacked the BAP-sequence and the HA-tag. HLA-DM was added to a
subset of the HLA class II experiments. The identity of all DNA
sequences was verified by Sanger sequencing.
Cell Culture and Transient Transfections
[0799] Expi293 cells (Thermo Scientific) were grown in Expi293
medium (Thermo Scientific) with 8% CO.sub.2 at 37.degree. C. on an
orbital shaker at 125 rpm. Expi293 cells were maintained at cell
densities between 0.5.times.10.sup.6/mL and 6.times.10.sup.6/mL
with regular biweekly passaging. 30 mL of the Expi293 cell
suspension was used for transient transfections at a cell density
of approximately 3.times.10.sup.6/mL and >90% viability.
Briefly, 30 ug DNA (1 .mu.g DNA per mL cell suspension) was diluted
into 1.5 mL Opti-MEM medium (Thermo Scientific) in one tube while
80 .mu.l ExpiFectamine.TM. 293 transfection reagent (Thermo
Scientific) was diluted into a second tube containing 1.5 mL
Opti-MEM. These two tubes were incubated at room temperature for
five minutes, combined, mixed gently, and incubated at room
temperature for 30 minutes. The DNA and ExpiFectamine mixture were
added to Expi293 cells and incubated at 37.degree. C., 8% CO.sub.2,
80% relative humidity. After 48 h, transfected cells were harvested
in four technical replicates at 50.times.10.sup.6 cells per tube,
centrifuged, washed once with 1.times. Gibco DPBS (Thermo
Scientific), and flash frozen in liquid nitrogen for mass
spectrometric analysis. An aliquot of 1.times.10.sup.6 cells was
collected from each transfection batch and analyzed via anti-BAP
(Rockland Immunochemicals Inc., Limerick, Pa.) or anti-HA (Bio-Rad,
Hercules, Calif.) using western blot analysis to verify
affinity-tagged HLA protein expression. Expi293's endogenous HLA
class II genotype was determined to be DRB1*15:01, DRB1*01:01,
DPB1*04:02, DPA1*01:03, DQB1*06:02, DQA1*01:02 (Laboratory
Corporation of America, Burlington, N.C.). In some experiments, the
HLA class II alleles were co-transfected with HLA-DM, in which case
the DNA concentration used for both plasmids was dropped to 0.5
.mu.g DNA per mL cell suspension.
[0800] A375 cells (ATCC) were grown in DMEM with 10% FBS and
maintained at cultures at no greater than 80% confluence with
regular passaging. For mass spectrometry experiments A375 cells
were cultured in a 500 cm.sup.2 plate at a seeding density of
18.5.times.10.sup.6 cells/mL in 100 mL, as calculated from a 70%
confluent cell number. After 24 hours, cells were transfected with
TransIT-X2 (Mirus Bio) by following the TransIT system protocol
adjusted for the total culture volume. After 48 h, cell medium was
aspirated, and cells were washed with 1.times. Gibco DPBS (Thermo
Scientific). For harvest, A375 cells were incubated for 10 minutes
at 37.degree. C. with 30 mL non-enzymatic cell dissociation
solution (Sigma-Aldrich), centrifuged, washed with 1.times.DPBS,
and aliquoted at 50.times.10.sup.6 cells per sample. 293T and HeLa
cells were purchased from ATCC and were cultured at 37.degree. C.
at 5% CO2 in DMEM, 10% FBS, 2 mM L-glutamine or DMEM+10% FBS,
respectively. Both cell lines were transfected with the HLA
constructs using the TransIT LT1 reagent (Mirus Bio) following the
manufactures instructions and processed 48h after transfection as
described for the A375 cells. From all samples, an aliquot of
1.times.10.sup.6 cells was collected from each transfection and
analyzed via anti-BAP (Rockland Immunochemicals Inc., Limerick,
Pa.) or anti-HA (Bio-Rad, Hercules, Calif.) western blot to verify
affinity-tagged HLA protein expression. B721.221 cells were
obtained from Fred Hutchison Cancer Center (Seattle, Wash.) and
were cultured in RPMI-1640 plus glutamax (Thermo Fisher Scientific)
with 10% heat inactivated fetal bovine serum plus 1%
penicillin/streptomycin (both Thermo Fisher Scientific). Cells were
cultured twice weekly and discarded after 25 passages. K562 cells
and KG-1 cells (ATCC, Manassas, Va.) were grown in IMDM (Thermo
Fisher Scientific) media plus 10% heat inactivated FBS, 1%
penicillin/streptomycin, 1% sodium pyruvate, and 1% MEM-NEAA. Cells
were cultured twice weekly and discarded after 25 passages.
[0801] Lentivirus for transduction of B721.221, KG-1, and K562
cells were produced in HEK293T cells grown to 80% confluency. Six
micrograms of the genome vector psFLenti encoding HLA class I or
HLA class II (described in previous sections) was mixed with 5.3 ug
of the lentivirus packaging vector psPAX2 and 1.8 1 ug of the
envelope vector pMD.2. DNA was mixed with Opti-MEM (Thermo Fisher
Scientific) and the transfection reagent, Fugene HD (Promega,
Madison, Wis.), and the mixture was incubated at room temperature
for 15 minutes. The mixture was then added dropwise onto the dish
of HEK293T cells and incubated for 72 hours. Supernatant was then
harvested, and lentiviral titers were tested using Lenti-X GoStix
(Takara Bio Inc., Japan). For transduction, cells were seeded in
12-well flat bottom plates (Corning Inc., Corning, N.Y.) and mixed
with lentiviral supernatant with 6 ug/mL polybrene (Sigma-Aldrich).
Cells mixed with lentivirus were spun at 32.degree. C. at
800.times.g for 90 minutes. Cells were resuspended in warm media
and incubated in a 37.degree. C. incubator at 5% CO.sub.2 for 72
hours. Cells were then selected using 1 ug/ml puromycin for 2
weeks. After selection, at least 50 million cells were harvested,
centrifuged, washed once with 1.times. Gibco DPBS (Thermo
Scientific), and flash frozen in liquid nitrogen for mass
spectrometric analysis.
BirA Protein Expression and Purification
[0802] The pET19 vector encoding E. coli BirA fused to a C-terminal
hexa-histidine tag (SEQ ID NO: 15) was used. Chemical competent E.
coli BL21 (DE3) cells (New England Biolabs) were transformed with a
BirA expression plasmid (pET19 vector encoding E. coli BirA fused
to a C-terminal hexa-histidine (SEQ ID NO: 15)), grown at
37.degree. C. in LB broth plus 100 .mu.g/ml ampicillin to an
OD.sub.600 of 0.6-0.8 and cooled to 30.degree. C. before expression
was induced by adding 0.4 mM
isopropyl-.beta.-D-thiogalactopyranoside. E. coli cell growth
continued at 30.degree. C. for 4 h. E. coli cells were harvested by
centrifugation at 8000.times.g for 30 minutes at 4.degree. C. and
stored at -80.degree. C. until use. Frozen cell pellets expressing
recombinant BirA were resuspended in IMAC buffer (50 mM
NaH.sub.2PO.sub.4 pH 8.0, 300 mM NaCl) with 5 mM Imidazole,
incubated with 1 mg/ml lysozyme for 20 minutes on ice and the lysed
by sonication. Cellular debris and insoluble materials were removed
by centrifugation at 16,000.times.g for 30 minutes at 4.degree. C.
The cleared supernatant was subsequently loaded on a HisTrap HP 5
mL column using the AKTA pure chromatography system (GE
Healthcare), washed with IMAC buffer plus 25 mM and 50 mM imidazole
before elution with 500 mM imidazole. Fractions containing BirA
were pooled and dialyzed against 20 mM Tris-HCl pH 8.0 with 25 mM
NaCl and were loaded on a HiTrap Q HP 5 mL column (GE Healthcare,
Chicago, Ill.) and eluted by applying a linear gradient from 25 to
600 mM NaCl. Fractions containing highly pure BirA were pooled,
buffer exchanged in storage buffer (20 mM Tris-HCl pH 8.0 100 mM
NaCl, 5% glycerol) and concentrated to around 5-10 mg/mL,
aliquoted, and flash frozen in liquid nitrogen for storage at
-80.degree. C. BirA protein concentration was determined by UV
spectroscopy at OD.sub.280 nm using a calculated extinction
coefficient of .epsilon.=47,440 M.sup.1 cm.sup.-1.
Western Blotting Protocol
[0803] Samples were added to XT Sample Buffer and XT Reducing Agent
(Bio-Rad, Hercules, Calif.), heated at 95.degree. C. for five
minutes, then a volume corresponding to .about.100,000 cells was
loaded into 10% Criterion XT Bis-Tris gels (Bio-Rad, Hercules,
Calif.) and electrophoresed at 200 V for 35 minutes using a
PowerPac Basic Power Supply (Bio-Rad, Hercules, Calif.) with XT MES
Running Buffer (Bio-Rad, Hercules, Calif.). The gels were rinsed
briefly with water, then proteins were transferred to PVDF
membranes within Invitrogen iBlot Transfer Stacks (Thermo Fisher
Scientific) using setting P3 on an Invitrogen iBlot2 Gel Transfer
Device (Thermo Scientific). The Precision Plus Protein All Blue
Standard (Bio-Rad, Hercules, Calif.) was used to monitor molecular
weights. Next, membranes were washed 3.times. five minutes with
Pierce TBS Tween 20 buffer [(TBST) 25 mM Tris, 0.15 mM NaCl, 0.05%
(v/v) Tween 20, pH 7.5, Thermo Fisher Scientific)], blocked for 1 h
at room temperature in TBST-M [TBST containing 5% (w/v) nonfat
instant dry milk], then incubated overnight at 4.degree. C. in
TBST-B [TBST containing 5% (w/v) Bovine Serum Albumin (Sigma
Aldrich)] and a 1:5,000 dilution of both rabbit anti-beta tubulin
antibody (catalog # ab6046, Abcam, Cambridge, Mass.) and rabbit
anti-biotin ligase epitope tag antibody (catalog #100-401-B21,
Rockland Immunochemicals, Limerick, Pa.). Next, the membranes were
washed 3.times. five minutes with TBST, incubated for 1 h at room
temperature in TBST-M containing a 1:10,000 dilution of goat
anti-rabbit IgG (H+L- horseradish peroxidase-conjugated antibody
(catalog #170-6515, Bio-Rad), then washed at room temperature
3.times. five minutes with TBST. Finally, membranes were bathed
with Pierce ECL Western Blotting Substrate (Thermo Fisher
Scientific), developed using a ChemiDoc XRS+Imager (Bio-Rad), and
visualized using Image Lab software (Bio-Rad).
Affinity-Tagged HLA-Peptide Complex Isolation
[0804] Affinity-tagged HLA-peptide complex isolations were
performed from cells expressing BAP-tagged HLA alleles and negative
control cell lines that expressed only endogenous HLA-peptide
complexes without BAP tags. The NeutrAvidin beaded agarose resin
was washed three times with 1 mL cold PBS before use in HLA-peptide
affinity purification. Frozen pellets containing 50.times.10.sup.6
cells expressing BAP-tagged HLA molecules were thawed on ice for 20
minutes and gently lysed by hand pipetting in 1.2 mL cold lysis
buffer [20 mM Tris-Cl pH 8, 100 mM NaCl, 6 mM MgCl.sub.2, 1.5%
(v/v) Triton X-100, 60 mM octyl glucoside, 0.2 mM of
2-Iodoacetamide, 1 mM EDTA pH 8, 1 mM PMSF, 1.times. complete
EDTA-free protease inhibitor cocktail (Roche). Ly sates were
incubated end/over/end at 4.degree. C. for 15 minutes with
.gtoreq.250 units benzonase nuclease (Sigma-Aldrich) to degrade
DNA/RNA and centrifuged at 15,000.times.g at 4.degree. C. for 20
minutes to remove cellular debris and insoluble materials. Cleared
supernatants were transferred to new tubes and BAP-tagged HLA
molecules were biotinylated by incubating end/over/end at room
temperature for 10 minutes in a 1.5 mL tube with 0.56 .mu.M biotin,
1 mM ATP, and 3 .mu.M BirA. The supernatants were incubated
end/over/end at 4.degree. C. for 30 minutes with a volume
corresponding to 200 .mu.L of Pierce high-capacity NeutrAvidin
beaded agarose resin (Thermo Scientific) slurry to affinity-enrich
biotinylated-HLA-peptide complexes. Finally, the HLA-bound resin
was washed four times with 1 mL of cold wash buffer (20 mM Tris-Cl
pH 8, 100 mM NaCl, 60 mM octyl glucoside, 0.2 mM of
2-Iodoacetamide, 1 mM EDTA pH 8), then washed four times with 1 mL
of cold 10 mM Tris-Cl pH 8. Between washes, the HLA-bound resin was
gently mixed by hand then pelleted by centrifugation at
1,500.times.g at 4.degree. C. for one minute. The washed HLA-bound
resin was stored at -80.degree. C. or immediately subjected to
HLA-peptide elution and desalting.
Antibody-Based HLA-Peptide Complex Isolation
[0805] HLA DR-peptide complexes were isolated from healthy donor
peripheral blood mononuclear cells (PBMCs). A volume corresponding
to 75 .mu.L of GammaBind Plus Sepharose resin was washed three
times with 1 mL cold PBS, incubated end/over/end with 10 .mu.g of
the antibody at 4.degree. C. overnight, then washed with three
times with 1 mL cold PBS before use in HLA-peptide
immunoprecipation. Frozen PBMC pellets containing 50.times.10.sup.6
cells were thawed on ice for 20 minutes and gently lysed by
pipetting in 1.2 mL cold lysis buffer [20 mM Tris-Cl pH 8, 100 mM
NaCl, 6 mM MgCl2, 1.5% (v/v) Triton X-100, 60 mM octyl glucoside,
0.2 mM of 2-Iodoacetamide, 1 mM EDTA pH 8, 1 mM PMSF, 1.times.
complete EDTA-free protease inhibitor cocktail (Roche). Lysates
were incubated end/over/end at 4.degree. C. for 15 minutes with
>250 units benzonase nuclease (Sigma-Aldrich) to degrade DNA/RNA
and centrifuged at 15,000.times.g at 4.degree. C. for 20 minutes to
remove cellular debris and insoluble materials. The supernatants
were then incubated end/over/end at 4.degree. C. for 3 hours with
an anti-HLA DR antibody (TAL 1B5, product # sc-53319; Santa Cruz
Biotechnology, Dallas, Tex.) bound to GammaBind Plus Sepharose
resin (GE Life Sciences) to immunoprecipitate HLA DR-peptide
complexes. Finally, the HLA-bound resin was washed four times with
1 mL of cold wash buffer (20 mM Tris-Cl pH 8, 100 mM NaCl, 60 mM
octyl glucoside, 0.2 mM of 2-Iodoacetamide, 1 mM EDTA pH 8), then
washed four times with 1 mL of cold 10 mM Tris-Cl pH 8. Between
washes, the HLA-bound resin was gently mixed then pelleted by
centrifugation at 1,500.times.g at 4.degree. C. for 1 minute. The
washed HLA-bound resin was stored at -80.degree. C. or immediately
subjected to HLA-peptide elution and desalting.
HLA-Peptide Elution and Desalting
[0806] HLA-peptides were eluted from affinity-tagged and endogenous
HLA complexes and simultaneously desalted using a Sep-Pak (Waters)
solid-phase extraction system. In brief, Sep-Pak Vac 1 cc (50 mg)
37-55 .mu.m particle size tC18 cartridges were attached to a
24-position extraction manifold (Restek), activated two times with
200 .mu.L MeOH followed by 100 .mu.L of 50% (v/v) ACN/1% (v/v) FA,
then washed four times with 500 .mu.L 1% (v/v) FA. To dissociate
HLA-peptides from affinity-tagged HLA molecules and facilitate
peptide binding to the tC18 solid-phase, 400 .mu.L of 3% (v/v)
ACN/5% (v/v) FA was added to the tubes containing HLA-bound beaded
agarose resin. The slurry was mixed by pipetting, then transferred
to the Sep-Pak cartridges. The tubes and pipette tips were rinsed
with 1% (v/v) FA (2.times.200 .mu.L) and the rinsate was
transferred to the cartridges. 100 fmol of Pierce Peptide Retention
Time Calibration (PRTC) mixture (Thermo Scientific) was added to
the cartridges as a loading control. The beaded agarose resin was
incubated two times for five minutes with 200 .mu.L of 10% (v/v)
AcOH to further dissociate HLA-peptides from the affinity-tagged
HLA molecules, then washed four times with 500 .mu.L 1% (v/v) FA.
HLA-peptides were eluted off the tC18 into new 1.5 mL micro tubes
(Sarstedt) by step fractionating with 250 .mu.L of 15% (v/v) ACN/1%
(v/v) FA followed by 2.times.250 .mu.L of 30% (v/v) ACN/1% (v/v)
FA. The solutions used for activation, sample loading, washing, and
elution flowed via gravity, but vacuum (.ltoreq.-2.5 PSI) was used
to remove the remaining eluate from the cartridges. Eluates
containing HLA-peptides were frozen, dried via vacuum
centrifugation, and stored at -80.degree. C. before being subjected
to a second desalting workflow. Secondary desalting of the
HLA-peptide samples was performed with in-house built StageTips
packed using two 16-gauge punches of Empore C18 solid phase
extraction disks (3M, St. Paul, Minn.) as previously described.
StageTips were activated two times with 100 .mu.L of MeOH followed
by 50 .mu.L of 50% (v/v) ACN/0.1% (v/v) FA, then washed three times
with 100 .mu.L of 1% (v/v) FA. The dried HLA-peptides were
solubilized by adding 200 .mu.L of 3% (v/v) ACN/5% (v/v) then and
loaded onto StageTips. The tubes and pipette tips were rinsed with
1% (v/v) FA (2.times.100 .mu.L) and the rinse volume was
transferred to the StageTips, then the StageTips were washed five
times with 100 .mu.L 1% (v/v) FA. Peptides were eluted using a step
gradient of 20 .mu.L 15% (v/v) ACN/1% (v/v) FA followed by two 20
.mu.L cuts of 30% (v/v) ACN/1% (v/v) FA. Sample loading, washes,
and elution were performed on a tabletop centrifuge with a maximum
speed of 1,500-3,000.times.g. Eluates were frozen, dried via vacuum
centrifugation, and stored at -80.degree. C.
HLA-Peptide Sequencing by Tandem Mass Spectrometry
[0807] All nanoLC-ESI-MS/MS analyses employed the same LC
separation conditions described below.
[0808] Samples were chromatographically separated using a Proxeon
Easy NanoLC 1200 (Thermo Scientific, San Jose, Calif.) fitted with
a PicoFrit (New Objective, Inc., Woburn, Mass.) 75 .mu.m inner
diameter capillary with a 10-.mu.m emitter was packed at 1000 psi
of pressure with He to .about.30-40 cm with 1.9 .mu.m particle
size/200 .ANG. pore size of C18 Reprosil beads (Dr. Maisch GmbH,
Ammerbuch, Germany) and heated at 60.degree. C. during separation.
The column was equilibrated with 10.times. bed volume of buffer A
[0.1% (v/v) FA and 3% (v/v) ACN], samples were loaded in 4 .mu.L 3%
(v/v) ACN/5% (v/v) FA, and peptides were eluted with a linear
gradient from 7-30% of Buffer B [0.1% (v/v) FA and 80% (v/v) ACN]
over 82 minutes, 30-90% Buffer B over six minutes, then held at 90%
Buffer B for 15 minutes to wash the column. A subset of samples was
eluted with a linear gradient from 6-40% of Buffer B over 84
minutes 40-60% Buffer B over nine minutes, then held at 90% Buffer
B for five minutes and 50% Buffer B for nine minutes to wash the
column Linear gradients for sample elution were run at a rate of
250 nL/min and yielded .about.13 sec median peak widths.
[0809] During data-dependent acquisition, eluted peptides were
introduced into an Orbitrap Fusion Lumos mass spectrometer (Thermo
Scientific, San Jose, Calif.) equipped with a Nanospray Flex Ion
source (Thermo Scientific, San Jose, Calif.) at 2.2-2.5 kV. A
full-scan MS was acquired at a resolution of 60,000 from 300 to
1,700 m/z (AGC target 4e5, 50 ms max IT). Each full scan was
followed by a 2 sec cycle time, or top 10, of data-dependent MS2
scans at resolution 15,000, using an isolation width of 1.0 m/z, a
collision energy of 34 (HLA class I data) and 38 (HLA class II
data), an ACG Target of 5e4, and a max fill time of 250 ms max ion
time. An isolation width of 1.0 m/z was used because HLA class II
peptides tend to be longer (median 16 amino acids with a subset of
peptides >40 amino acids), so the monoisotopic peak is not
always the tallest peak in the isotope cluster and the mass
spectrometer acquisition software places the tallest isotopic peak
in the center of the isolation window in the absence of a specified
offset. The 1.0 m/z isolation window will therefore allow for the
co-isolation of the monoisotopic peak even when it is not the
tallest peak in the isotopic cluster as the charge states of HLA
class II peptides are often +2 or higher. Dynamic exclusion was
enabled with a repeat count of 1 and an exclusion duration of 5 sec
to enable .about.3 PSMs per precursor selected. Isotopes were
excluded while dependent scans on a single charge state per
precursor was disabled because HLA-peptide identification relies on
PSM quality, so multiple PSMs of different charge states further
increases our confidence of peptide identifications. Charge state
screening for HLA class II data collection was enabled along with
monoisotopic precursor selection (MIPS) using Peptide Mode to
prevent triggering of MS/MS on precursor ions with charge state 1
(only for alleles with basic anchor residues), >7, or
unassigned. For HLA class I data collection, precursor ions with
charge state 1 (mass range 800-1700 m/z) and 2-4 were selected,
while charge states >4 and unassigned were excluded.
Interpretation of LC-MS/MS Data, Related to FIG. 29
[0810] Mass spectra were interpreted using the Spectrum Mill
software package v6.0 pre-Release (Agilent Technologies, Santa
Clara, Calif.). MS/MS spectra were excluded from searching if they
did not have a precursor MH+ in the range of 600-2000 (HLA class
1)/600-4000 (HLA class II), had a precursor charge >5 (HLA class
I)/>7 (HLA class II), or had a minimum of <5 detected peaks.
Merging of similar spectra with the same precursor m/z acquired in
the same chromatographic peak was disabled. MS/MS spectra were
searched against a database that contained all UCSC Genome Browser
genes with hg19 annotation of the genome and its protein coding
transcripts (63,691 entries; 10,917,867 unique 9mer peptides)
combined with 264 common contaminants Prior to the database search,
all MS/MS had to pass the spectral quality filter with a sequence
tag length >2, e.g., minimum of 3 masses separated by the
in-chain mass of an amino acid. A minimum backbone cleavage score
(BCS) of 5 was set, and ESI QExactive HLAv2 scoring scheme was
used. All spectra from native HLA-peptide samples, not reduced and
alkylated, were searched using a no-enzyme specificity, fixed
modification of cysteine as cysteinylation, with the following
variable modifications: oxidized methionine (m), pyroglutamic acid
(N-term q), carbamidomethylation (c). Reduced and alkylated
HLA-peptide samples were searched using a no-enzyme specificity,
fixed modification of cysteine as carbamidomethylation, with the
following variable modifications: oxidized methionine (m),
pyroglutamic acid (N-term q), cysteinylation (c). A precursor mass
tolerance of .+-.10 ppm, product mass tolerance of .+-.10 ppm, and
a minimum scored peak intensity of 30% was used for both native and
reduced and alkylated HLA-peptide datasets. Peptide spectrum
matches (PSMs) for individual spectra were automatically designated
as confidently assigned using the Spectrum Mill autovalidation
module to apply target-decoy based FDR estimation at the PSM rank
to set scoring threshold criteria. An auto thresholds strategy
using a minimum sequence length of 7, automatic variable range
precursor mass filtering, and score and delta Rank1-Rank2 score
thresholds optimized across all LC-MS/MS runs for an HLA allele
yielding a PSM FDR estimate of <1% for each precursor charge
state.
[0811] Identified peptides that passed the PSM FDR estimate of
<1.0% were further filtered for contaminants by removing all
peptides assigned to the 264 common contaminants proteins in the
reference database and by removing peptides identified in the
negative control MAPTAC.TM. affinity pulldowns. Additionally, all
peptide identifications that mapped to an in silico tryptic digest
of the reference database were removed, as these peptides cannot be
ruled out as tryptic contaminants from sample carry-over on the
uPLC column.
[0812] To remove potential false positive PSM identifications from
the SILAC DC-feeding experiment, it was applied additional quality
filters to PSMs identified using the methods described above. All
peptides with FDR<1% were filtered for high quality PSMs using
the following thresholds: i) scored peak intensity >60% ii)
backbone cleavage score 8 and iii) ppm mass tolerance off 1 ppm
from the median ppm observed across all PSM identifications in the
same LC-MS/MS replicate.
Monoallelic Assignment of HLA-DR, -DQ, -DP Heterodimers Using
MAPTAC.TM. Protocol
[0813] Since only the beta chain of HLA class II is tagged in the
MAPTAC.TM. protocol, the pull-down step isolates peptide-MHC
complexes regardless of whether they contain knock-in or endogenous
alpha chain. In the case of HLA-DR, the allelic variation in the
alpha chain is not considered to influence peptide binding;
therefore, the relative degree of pairing with endogenous alpha
pairing is irrelevant to data interpretation--the data is
effectively mono-allelic. However, for HLA-DP and HLA-DQ loci, the
alpha chains exhibit important allelic variants such that the
presence of both knock-in and endogenous alpha chain alleles
creates the potential for 1-3 distinct specificities (depending on
whether the cell line has one or two alpha chain alleles and
whether either matches the knock-in allele). In principle, this
problem can be mitigated by running the protocol with and without a
knock-in alpha chain and identifying the set of peptides specific
to the with-alpha experiment. The approach of using a cell line was
taken herein that expresses a single alpha allele that matches the
knock-in alpha allele.
Analysis of Previously Published MS Data, Related to FIGS. 12A-12F,
FIGS. 30A-30C, FIGS. 31A-31D, FIG. 21A, FIG. 39A-39B, and FIG.
40A-B
[0814] Published LC-MS/MS datasets that provided.raw files were
reprocessed using the Spectrum Mill software package v6.0
pre-Release (Agilent Technologies, Santa Clara, Calif.). Datasets
that were collected on Thermo Orbitrap instruments (e.g., Velos,
QExactive, Fusion, Lumos) that utilized HCD fragmentation and MS
and MS/MS data collection in the orbitrap (high resolution) were
analyzed using the parameters described in the above section
"Interpretation of LC-MS/MS Data". For MS and MS/MS high resolution
datasets that utilized CID fragmentation, the same parameters as
above were used with an ESI Orbitrap scoring scheme. For datasets
with MS data collection in the orbitrap and MS/MS data collection
in the ion trap, the following same parameters above were also used
with the following deviations. For HCD data, the ESI QExactive
HLAv2 scoring scheme was used, while the ESI Orbitrap scoring
scheme was used for CID data. A precursor mass tolerance of .+-.10
ppm, product mass tolerance of .+-.0.5 Da was used. For both high-
and low-resolution MS/MS datasets, peptide spectrum matches (PSMs)
for individual spectra were automatically designated as confidently
assigned using the Spectrum Mill auto validation module to apply
target-decoy based FDR estimation at the PSM rank to set scoring
threshold criteria. An auto thresholds strategy using a minimum
sequence length of 7, automatic variable range precursor mass
filtering, and score and delta Rank1-Rank2 score thresholds
optimized across all LC-MS/MS runs for an HLA allele yielding a PSM
FDR estimate of <1.0% for each precursor charge state. Analysis
of peptide identifications from some previously published data
revealed a high rate of 9mers (>10%). Since these could
potentially represent contaminating HLA class I ligands, short
peptides were dropped (length <12) from all external data
sets.
Mapping Peptides to Genes and "Nested Sets", Related to FIGS.
30A-30C, FIGS. 31A-31D, and FIGS. 32A-32E
[0815] Each peptide was assigned to one or more protein-coding
transcripts within the UCSC hg19 gene annotation
(genome.ucsc.edu/cgi-bin/hgTables). Since many peptide
identifications overlap others and thus constitute mostly redundant
information, peptides were grouped into "nested sets", each meant
to correspond to .about.1 unique binding event. For instance, the
peptides GKAPILIATDVASRGLDV (SEQ ID NO: 16), GKAPILIATDVASRGLD (SEQ
ID NO: 17), and KAPILIATDVASRGLDV (SEQ ID NO: 18) all contain the
conserved sequence KAPILIATDVASRGLD (SEQ ID NO: 19), and probably
all bind MHC in the same register. In order to nest peptides of a
given data set, a graph was built in which each node corresponded
to a unique peptide, and an edge was created between any pair of
peptides sharing at least one 9mer and mappable to at least one
common transcript. The clusters command in the R package igraph
(Team, 2014) (cran.r-project.org/web/packages/igraph/citation.html)
was used to identify clusters of connected nodes, and each cluster
was defined as a nested set. This procedure guarantees that any two
peptides that meet the edge criteria (.gtoreq.1 common 9mer and
.gtoreq.1 common transcript) are placed within the same nested set.
The nests were used for sequence logo generation (logos were
generated using the shortest peptide in each nested set; FIG.
30A-30C, machine learning (importance weights across peptides in a
nested set sum to one; FIG. 31A-31D), and the gene bias analysis
(each nested set was counted as one observation rather than each
individual peptide; FIG. 32A-32E).
Analysis of Amino Acid Frequencies, Related to FIG. 12F
[0816] Amino acid frequencies in the human proteome were calculated
based on sequences for all protein-coding genes in the UCSC hg19
annotation (selecting one transcript at random for genes
represented by multiple transcript isoforms). IEDB frequencies were
determined by identifying the unique set of peptides with at least
one affinity observation .ltoreq.100 nM (excluding peptides with
hexavalent polyhistidine at their C-terminus). MAPTAC.TM.
frequencies were first considered in the context of the standard
forward-phase protocol across five DRB1 alleles (DRB1*01:01,
DRB1*03:01, DRB1*09:01, and DRB1*11:01), using only one peptide
(the longest) per nested set. In addition, MAPTAC.TM. frequencies
were separately calculated for the subset of samples processed by
the reduction and alkylation protocol. MS data from external
datasets were analyzed without respect to potential allele of
origin and likewise using the longest peptide per nested set.
Building HLA Class I Sequence Logos, Related to FIG. 37B
[0817] For each HLA class I allele, a length-9 sequence logo was
created by profiling amino acid frequencies in the first five
positions (mapping to logo positions 1-5) and last four positions
(mapping to logo positions 6-9) of corresponding peptides. In this
manner, peptides contributed to the sequence logo regardless of
their length. As in the HLA class II logos, letter heights are
proportional to the frequency of each amino acid in each position,
and darker shading is used for amino acids with frequency
.gtoreq.10%.
Assessing the Performance of HLA Class II Peptide Deconvolution,
Related to FIG. 30B
[0818] To assess the ability the GibbsCluster (v2.0) tool to
cluster multi-allelic HLA class II peptide data by allele of
origin, its performance on eight samples were analyzed, including 4
PBMC samples, 1 melanoma cell line (A375), and 3 previously
published lymphoblastoid cell lines. For each DRB1/3/4/5 allele
present in each sample genotype, twenty peptides were spiked in
from our mono-allelic MAPTAC.TM. data. The spiked peptides were
restricted to 12-20mers with SPI .gtoreq.70 that did not share a
9mer with any peptides in MAPTAC.TM. data for other HLA-DR alleles
or with any spiked peptides for the allele of interest. These
augmented datasets were then submitted to GibbsCluster-v2.0 using
default HLA class II settings except that was enforced a
hydrophobic preference at position 1, as others have previously for
deconvolution. For each sample, the number of clusters in the
solution was manually specified and set equal to the number of
HLA-DR alleles present in the genotype.
Calculating the Fraction of Peptides with Preferred Anchor
Residues, Related to FIG. 30C
[0819] Anchor positions were defined as the four positions with the
lowest entropy, and within those positions, "preferred" amino acids
included all those with frequency .gtoreq.10%. When calculating the
fraction of peptides with preferred amino acids at n positions,
only one peptide was used per nested set (the shortest).
Predicted Affinities of MS-Observed Peptides, Related to FIG.
36A
[0820] For each HLA class II allele, all unique peptides length 14
through 17 were identified and scored for binding potential using
NetMHCIIpan-v3.1. For comparison, 50,000 random length-matched
peptides were sampled from the human proteome. Density
distributions were determined based on log-transformed values.
Measured Affinities for MS-Observed Peptides, Related to FIG.
36B
[0821] Peptides were selected for affinity measurement if they had
poor predicted NetMHCIIpan-v3.1 binding affinity (>100 nM for
DRB1*01:01 or >500 nM for DRB1*11:01) or if they exhibited
.ltoreq.2 of the heuristically defined anchors.
Establishment of Cross-Validation Partitions, Related to FIG.
31A
[0822] A graph was created in which each node represents a
protein-coding transcript and edges are present between all pairs
of transcripts sharing at least 5 unique 9mers of amino sequence
content (UCSC hg19 gene annotation). The clusters command in the R
package igraph(Team, 2014)
(cran.r-project.org/web/packages/igraph/citation.html) was used to
identify clusters of connected nodes, and each cluster was defined
as a "transcript group". In this manner, if two transcripts shared
an edge (.gtoreq.5 shared 9mers), they were guaranteed to be placed
in the same transcript group. Transcript groups were randomly
sampled, dividing the proteome into eight roughly equally sized
partitions. MS-observed peptides (and non-observed decoy peptides)
were placed in partitions according to the partition of their
source transcripts, and these partitions were used for
cross-validation and hyper-parameter tuning. The graph-based
approach of partitioning the proteome was used to minimize the
likelihood that similar peptide sequences would appear during
training and evaluation, which could artificially inflate
prediction performance.
Architecture and Training of a CNN-Based HLA Class II Binding
Predictor, Neonmhc2, Related to FIG. 31A
[0823] Negative examples (decoys) were generated for training by
randomly shuffling the sequences of hit peptides. It was chosen
this method of decoy generation, rather than selecting unobserved
regions from the proteome, in order to eliminate MS biases that
could result in a general amino acid preference. In this way, our
binding predictor is unaware of the relative depletion of cysteine,
for example (FIG. 12F). Similarly, this prevents our model from
learning MS biases related to global properties of the peptides,
such as the overall hydrophobicity. This method is related to
results depicted in FIG. 31A.
[0824] Models were trained for two application scenarios:
validating on internal MAPTAC.TM. data (FIG. 31B) and on external
data (FIG. 31C, FIG. 21A, and FIG. 21B). When training models for
the former, it was adopted a simple training procedure where
network weight optimization was learned using six partitions of the
data (train partitions), hyper-parameter optimization and early
stopping was performed using the seventh partition (tune
partition), and the final validation was performed on the eighth
partition (evaluation partition) after the model design was
finalized. In the case of external validation, cross-validation was
employed, building an ensemble of models for each partition of data
whereby it was held out that partition for hyper-parameter tuning
and early stopping and used the remaining seven partitions for
network weight optimization. Additionally, when scoring non-MS data
(FIG. 31C and FIG. 31D), each 12-20mer substring of the target
peptide was scored and the highest score was kept.
[0825] When training our models, each hit and decoy was
down-weighted in the loss function by the size of its source nested
set such that each nested set as a whole carried equal weight. When
evaluating the model for hyper-parameter tuning, the shortest
peptide from each nested set was used in the relevant partition as
the positive examples and scrambled versions of those hits as the
decoys. Additionally, an overall weighting factor was applied such
that the summed weight of the hits equaled the summed weight of the
decoys when training. For the final evaluation of the model, as
shown in FIG. 31B, the shortest peptide was again selected from
each nested set in the evaluation partition (partition 8) but
sampled decoys randomly from non-observed subsequences of peptide
source genes ("natural decoys", described in a subsequent section).
In this way, any biases learned by the model in order to simply
discriminate natural sequences from scrambled ones did not inflate
our performance on the evaluation partition.
[0826] Models were trained using an Adam optimizer with an initial
learning rate of 0.003, beta_1 value of 0.9, beta_2 value of 0.999
and no decay (default Keras parameters, except for the learning
rate) and used a binary cross-entropy loss function. The initial
model weights were set using He initialization. After every 5
epochs of training, the positive predictive value (PPV, described
in subsequent section) on the tune partition was measured and the
maximum value was tracked. After each epoch, if the training loss
did not decrease, the learning rate was multiplied by 1/3.
Similarly, each time the PPV was measured on the tune partition, if
it did not increase compared to the running maximum the learning
rate by 1/3 was multiplied. An early stopping scheme was
implemented where, if the training loss failed to decrease for
three consecutive epochs or the tune PPV failed to increase above
the running maximum for 3 consecutive checks, then training was
stopped. When training the model, a fixed hit-to-decoy ratio of
1:39 was used in the training set, and 1:19 in the tune
partition.
[0827] Featurization: While amino acids may be represented by a
"one-hot" encoding, others have opted to encode amino acids using
the PMBEC matrix and the BLOSUM matrix (Henikoff and Henikoff,
1992), in which similar amino acids have similar feature profiles.
For the purposes of our peptide featurization, a novel matrix based
on amino acid proximities was generated in solved protein
structures. The concept of this approach is that the typical
neighbors of an amino acid should reflect its chemical properties.
For each amino acid in each of .about.100,000 DSSP protein
structures (cdn.rcsb.org/etl/kabschSander/ss.txt.gz), the residue
that was closest in 3D space but at least 10 amino acids away in
primary sequence was determined. Using this data, the number of
times the nearest neighbor of alanine was alanine was determined,
the number of times the nearest neighbor of alanine was a cysteine,
etc., to create a 20.times.20 matrix of proximity counts. Each
element of the matrix was divided by the product of its
corresponding column and row sums, and the entire matrix was
log-transformed. Finally, the mean value of the entire matrix was
subtracted from each element.
[0828] Each amino acid was also encode with 11 binary features
describing properties of the amino acid, such as whether it is:
acidic (N, Q), aliphatic (I, L, V), aromatic (H, F, W, Y), basic
(H, K, R), charged (D, E, H, K, R), hydrophobic (A, C, F, H, I, K,
L, M, T, V, W, Y), hydroxylic (S, T), polar (C, S, N, Q, T, D, E,
H, K, R, Y, W), small (V, P, A, G, C, S, T, N, D), very small (A,
G, C, S), or contains sulfur (M, C). Two features were used to
describe the position of each amino acid, one monotonically
increasing across the peptide and one indicating an absolute
distance from the center of the peptide, both in units of position
(not physical distance). Lastly, a single binary feature was
included to indicate whether an amino acid was "missing" from that
position, which would happen beyond the edges of shorter peptides.
The result is that each amino acid is encoded by 20 amino acid
proximity features, 11 amino acid property features, 2 position
features, and 1 missing character feature for a total of 34
features. All peptides were encoded as 20mers where the central 20
amino acids were used for longer peptides and the missing character
value was added symmetrically to the edges of peptides shorter than
20 amino acids.
[0829] When examples are input into the neural network, both for
training and evaluating, each of the 34 features are normalized by
subtracting their mean and dividing by their standard deviation.
The mean and standard deviation are calculated based solely on the
training set and without regard to position within the peptide.
[0830] For each allele, an ensemble of convolutional neural
networks was trained in order to predict binding. A sketch of the
model architecture is shown in FIG. 31A, depicting two
convolutional layers with a kernel size of 6 and 50 filters each.
After each layer, global max and mean pooling was applied and the
resulting values were input into a final output neuron with sigmoid
activation. It is implied but not shown that ReLU activation, batch
normalization (Ioffe and Szegedy, 2015), and 20% spatial dropout
were applied immediately after each convolutional layer.
When training an ensemble of models for each allele, the
architecture was fixed but the amount of L2 regularization was
varied. A base L2 regularization weight of 0.05 was used for the
first convolutional layer and 0.1 for the second convolutional
layer. To vary the amount of L2 regularization, these values were
multiplied by 0.1, 0.5, and 1. For each iteration in the ensemble,
one model per regularization level was trained and kept the best
based on performance on the tune partition. Benchmarking prediction
performance on MAPTAC.TM.-observed peptides, related to FIG. 7A
[0831] In some exemplary assessments of prediction performance
value for a given peptide or protein encoded by an HLA allele, a
method comprising "scrambled decoys" can be used. The scrambled
decoys are peptides having the same peptide length and amino acids
as a peptide that is known to bind to given HLA peptide or protein
based on, for example, mass spectroscopy data, but the sequence of
the amino acids are scrambled. For every single peptide that was
identified by mass spectrometry, 19 such scrambled peptide decoys
were employed (hit: decoy is 1:19) as shown in FIG. 7A. The
presentation prediction model was tested and PPV was determined by
analyzing the best-scoring 5% of peptides in the test partition and
interrogating what fraction of these were positive. The PPV thus
generated is shown in FIG. 7A and Table 12 below.
TABLE-US-00015 TABLE 12 MHC Class II allele PPV for neonmhc2
DPB1-0101_DPA1-0103 0.48 DPB1-0101_DPA1-0201 0.41
DPB1-0101_DPA1-0202 0.56 DPB1-0201_DPA1-0103 0.48
DPB1-0202_DPA1-0103 0.27 DPB1-0301_DPA1-0103 0.36
DPB1-0401_DPA1-0103 0.52 DPB1-0402_DPA1-0103 0.54
DPB1-0501_DPA1-0201 0.59 DPB1-0501_DPA1-0202 0.46
DPB1-0601_DPA1-0103 0.43 DPB1-0901_DPA1-0201 0.53
DPB1-1001_DPA1-0201 0.49 DPB1-1101_DPA1-0201 0.31
DPB1-1301_DPA1-0201 0.58 DPB1-1401_DPA1-0201 0.49
DPB1-1701_DPA1-0201 0.39 DQB1-0201_DQA1-0201 0.49
DQB1-0201_DQA1-0501 0.08 DQB1-0202_DQA1-0201 0.3
DQB1-0301_DQA1-0501 0.27 DQB1-0301_DQA1-0505 0.13
DQB1-0301_DQA1-0601 0.28 DQB1-0302_DQA1-0301 0.1
DQB1-0303_DQA1-0201 0.46 DQB1-0303_DQA1-0301 0.39
DQB1-0401_DQA1-0301 0.47 DQB1-0402_DQA1-0401 0.53
DQB1-0501_DQA1-0101 0.62 DQB1-0502_DQA1-0101 0.26
DQB1-0502_DQA1-0102 0.19 DQB1-0601_DQA1-0102 0.17
DQB1-0601_DQA1-0103 0.35 DQB1-0602_DQA1-0102 0.42
DQB1-0603_DQA1-0103 0.56 DQB1-0604_DQA1-0102 0.28 DRB1_0101 0.48
DRB1_0102 0.49 DRB1_0301 0.47 DRB1_0302 0.29 DRB1_0401 0.38
DRB1_0402 0.49 DRB1_0403 0.43 DRB1_0404 0.42 DRB1_0405 0.41
DRB1_0407 0.36 DRB1_0410 0.61 DRB1_0701 0.49 DRB1_0801 0.36
DRB1_0802 0.46 DRB1_0803 0.30 DRB1_0804 0.36 DRB1_0901 0.35
DRB1_1001 0.54 DRB1_1101 0.43 DRB1_1102 0.42 DRB1_1104 0.51
DRB1_1201 0.47 DRB1_1202 0.55 DRB1_1301 0.42 DRB1_1302 0.23
DRB1_1303 0.56 DRB1_1401 0.56 DRB1_1501 0.53 DRB1_1502 0.43
DRB1_1503 0.41 DRB1_1601 0.61 DRB3_0101 0.57 DRB3_0201 0.6
DRB3_0202 0.48 DRB3_0301 0.53 DRB4_0103 0.67 DRB5_0101 0.54
DRB5_0102 0.65 DRB5_0202 0.63
Benchmarking Prediction Performance on MAPTAC.TM.-Observed
Peptides, Related to FIG. 31B
[0832] For the purpose of assessing prediction performance for a
given allele, it was necessary to define a set of peptides that
could have been observed (because they are present in the proteome)
but were not observed in the MS data. These negative examples were
trained "natural decoys" (in contrast to the "scrambled decoys"
described above). As guiding principles, it was decided: the length
distribution of natural decoys should match the length distribution
of MS-observed hits, natural decoys should not contain sequence
redundant with other natural decoys, natural decoys should not
overlap hits, and/or natural decoys should come from genes that
produced at least one hit.
[0833] The following pseudocode represents the process implemented
to create an evaluation satisfying these principles:
[0834] Initialize two empty lists of hits, H.sub.minimal and
H.sub.exhaustive
[0835] For each nested set S of MS-observed peptides:
[0836] If none of the peptides in S can be mapped to a transcript
in the train or tune partition:
[0837] Add the shortest peptide in S to H.sub.minimal
[0838] Add all peptides in S to H.sub.exhaustive
[0839] Initialize an empty list of decoy peptides, D
[0840] For each protein-coding transcript (longest first, shortest
last) in the test partition:
[0841] If no peptides in H.sub.exhaustive map to the
transcript:
[0842] Skip to the next transcript
[0843] Cover the transcript's protein sequence with a set of
overlapping peptides P, where the peptide lengths are randomly
sampled from the length distribution of H.sub.minimal. The overlap
is 8 amino acids. (The last peptide in P will typically dangle over
the end of the protein.)
[0844] While the last peptide in P still dangles:
[0845] Subtract 1 amino acid from the length of the longest peptide
in P
[0846] For each peptide in P:
[0847] If it does not share a 9mer with a peptide in
H.sub.exhaustive nor with any 9mer observed in any peptide in
D:
[0848] Add the peptide to D
[0849] Otherwise:
[0850] Reject the peptide
[0851] H.sub.minimal and D constitute the evaluation data set
[0852] To evaluate performance on this set, all n hit peptides were
evaluated by the predictor (neonmhc2 or NetMHCIIpan) and scored
along with a set of 19n decoys (randomly sampled without
replacement from the complete set of decoys). The top 5% of
peptides in the combined set were labeled as positive calls, and
the positive predictive value (PPV) was calculated as the fraction
of positive calls that were hits. Note that since the number of
positives is constrained to be equal to the number of hits, recall
is exactly equal to PPV in this evaluation scenario. The
application of a consistent 1:19 ratio across alleles helps
stabilize the performance values, which are otherwise influenced by
the number of hits observed for each allele. This was deemed
appropriate since it was assumed the number of hits relates more to
experimental conditions and replicate count than intrinsic
properties of the allele.
Calculation of NetMHCIIpan Affinities for Non-15Mers, Related to
FIG. 31A-D, FIGS. 40A-B, and 33A-D
[0853] In early analyses, NetMHCIIpan-v3.1 affinity and percent
rank predictions for non-15mers performed poorly on benchmarks.
However, the following approach markedly improved performance: If a
peptide was longer than 15 amino acids all constituent were scored
15mers and selected the strongest prediction as the overall peptide
score; if a peptide was shorter than 15 amino acids, G's were
padded on the N-terminus to force the peptide to length 15 and
scored the resulting extended peptide.
Performance as a Function of Training Set Size, Related to FIGS.
38X and 38Y
[0854] To understand how our model's performance is limited by the
size of our datasets, a saturation analysis was performed. This
involved retraining ensembles of models while varying the fraction
of the training data used in order to understand how this affects
performance on a hold-out partition. FIGS. 38X and 38Y shows the
evaluation partition (partition 8) PPV as a function of the number
of hit peptides used in the training set. Each datapoint shows the
mean PPV across a collection of 10 models, with the error bar
indicating the standard deviation.
Benchmarking Prediction Performance of Natural CD4+ T Cell
Responses, Related to FIG. 31C
[0855] Since the vast majority of CD4+ T cell responses documented
in IEDB (tcell_full_v3.zip at iedb.org/database_export_v3.php) have
an unknown or computationally imputed HLA class II allele
restriction, the subset of records that were confirmed
experimentally by HLA class II tetramer were focused on. Nearly all
such records were deposited by the William Kwok Laboratory
(Benaroya Research Institute, Seattle, Wash.), which uses the blood
of immune-reactive individuals to perform tetramer-guided epitope
mapping (TGEM) of diverse pathogens and allergens. Since negative
peptides were posted for some studies but not others, the source
publications were reviewed to reconstruct the complete set of
positive and negative peptide reactivities. In some cases, the
source publication explicitly listed the negative peptides. In
other cases, the negatives were imputed by following the tiling
procedure specified in the publication's methods and confirming
that the peptide boundaries were consistent with the known positive
examples. In this assay depicted in FIG. 31C, viral epitopes were
mapped from viral genes of influenza and rhinovirus, and peptide
sequences comprising the epitopes were used to predict HLA class II
protein binders to each epitope. In this case, a PPV for CD4+
memory T cell response to the peptides was predicted for respective
HLA-DRB1 protein. The PPV was determined by asking, of the positive
binders, what fraction of the top-ranking epitopes were true hits.
Given that the positive pairing of an HLA class II protein molecule
with a peptide, and where the HLA molecule is present in the
subject infected by the respective virus, a CD4 response will be
generated in the subject. A comparison of the predictive efficiency
(PPV, in other words predicting the number of true hits) between
Neonmhc2 and a publicly available predictor (NetMHCIIpan) is shown
in FIG. 31C per each DRB1 protein tested in this exemplary study.
Neonmhc2 outperformed NetMHCIIpan for each of the six alleles
tested.
[0856] All 20mer peptides were scored by neonmhc2 and by
NetMHCIIpan-v3.1. PPV was calculated as the fraction of
experimentally confirmed positives among the n top-scored peptides,
where there were n experimentally confirmed peptides total (FIG.
31C).
T Cell Induction Protocol and Immunogenicity Readouts, Related to
FIG. 31D
[0857] To generate monocyte derived dendritic cells (mDCs), CD14+
monocytes were isolated from HLA-DRB1*11:01+ healthy donor
peripheral blood monocytes (PBMCs) by magnetic separation using
human CD14 microbeads as per manufacturer's protocol (Miltenyi
Biotec). Isolated CD14+ cells were differentiated for 5 days in
Cellgenix GMP DC media supplemented with 800 U/ml rh GM-CSF and 400
U/ml rh IL-4 (Cellgenix). On day 5, mDCs were harvested and pulsed
with 0.40/1 peptide for 1 hour at 37 degrees Celsius followed by
maturation using 10 ng/ml TNF-.alpha., long/ml IL-1.beta., long/ml
IL-6 (Cellgenix), and 0.5 ug/ml PGE1 (Cayman Pharma). After
forty-eight hours, mDCs were co-cultured with autologous PBMCs, at
a 1:10 ratio in media containing AIMV/RPMI (ThermoFisher), 10%
human serum (Sigma-Aldrich), 1% Pen/Strep (ThermoFisher) and
supplemented with 5 ng/ml of IL7 and IL15 (Cellgenix). On day 12, T
cells were harvested and restimulated on 0.40/1 peptide pulsed
matured DCs for 7 days for two additional stimulations, for a total
of 3 stimulations.
[0858] Induced T cells were labelled with a unique two-color
barcode labelling system as described previously and cultured
overnight at a 1:10 ratio with peptide pulsed and matured
autologous mDCs derived from CD14+ monocytes as described above.
The next morning, cells were assessed for production of IFN-.gamma.
in response to peptide by flow cytometry. Cells were treated with
Golgi Plug/Golgi Stop (BD Biosciences) for four hours at 37.degree.
C. Cells were then stained with surface marker antibodies against
CD19, CD16, CD14, CD3, CD4, CD8 (BD Biosciences, San Jose, Calif.),
as well as Live/Dead Fixable Dead Cell stain (ThermoFisher); see
Table 13 below. Samples were then permeabilized and fixed with BD
Cytofix/Cytoperm kit (BD Biosciences) per manufacturer's protocol
and stained with intracellular antibodies against IFN-.gamma. (BD
Biosciences). Samples were run on a BD Fortessa X-20 flow cytometer
and analyzed using FlowJo software (Treestar). Induction samples
that positively responded to peptide were samples that induced
IFN-gamma production at 3% higher than the no peptide control.
TABLE-US-00016 TABLE 13 Marker Fluorophore Vendor Cat# Clone Live
dead stain IR dye ThermoFisher L34976 CD19 BUV395 BD 563551 SJ25C1
CD16 BUV395 BD 563784 3G8 CD14 BUV395 BD 563562 M.phi.P9 CD3 BUV805
BD 565511 SK7 CD4 AF700 BD 557922 RPA-T4 CD8 PerCP-Cy5.5 BD 565310
SK1 IFN-.gamma. APC BD 554702 B27
Analysis of HLA Class II Expression Data in Single-Cell RNA-Seq,
Related to FIG. 19A
[0859] Single-cell RNA-Seq data were obtained from three previously
published data sets that profiled human tumor samples. The first
study included data from cutaneous melanomas. The file
"GSE72056_melanoma_single_cell_revised_v2.txt" was downloaded from
Gene Expression Omnibus (ncbi.nlm.nih.gov/geo/; accession:
GSE72056). Cells with tumor status flag "2" were treated as tumor
cells, and cells labeled with tumor status flag "1" and immune cell
type flag equal to "1" through "6" were treated as T cells, B
cells, Macrophages, Endothelium, Fibroblasts, and NKs,
respectively. All other cells were dropped. Data were natively
presented in units of log 2 (TPM/10+1) and were thus mathematically
converted to a TPM scale. Once on the TPM scale, the data for each
cell was renormalized to sum to 1,000,000 over the set of
protein-coding UCSC gene symbols (protein-coding genes not
appearing in the expression matrix were implicitly treated as
having zero expression). Finally, single-cell observations
corresponding to the same cell type and same source biopsy where
averaged to produce expression estimates at the patient-cell type
level.
[0860] The second study included data from head and neck tumors.
The file "GSE103322_HNSCC_all_data.txt" was downloaded from the
Gene Expression Omnibus (ncbi.nlm.nih.gov/geo/; accession:
GSE103322). Per personal correspondence with Itay Tirosh (Aug. 22,
2018), the data in this table were also in units of log 2
(TPM/10+1); therefore, the values were mathematically converted to
TPM units. As with the melanoma study, the data for each cell was
renormalized to sum to 1,000,000 over the set of protein-coding
UCSC gene symbols, and single-cell observations corresponding to
the same cell type and same source biopsy where averaged. Data
corresponding the lymph node biopsies were excluded.
[0861] The third study included data from untreated non-small cell
lung. The files "RawDataLung.table.rds" and "metadata.xlsx" were
downloaded from ArrayExpress (ebi.ac.uk/arrayexpress/; accessions:
E-MTAB-6149 and E-MTAB-6653). The data (already in TPM) units, were
re-scaled to sum to 1,000,000 over the set of protein-coding genes
as previously described. Finally, single-cell observations
corresponding to the same cell type and same source biopsy where
averaged to produce expression estimates at the patient-cell type
level. For simplicity, cell types were merged to a coarser
granularity than natively reported in Table 14 below.
TABLE-US-00017 TABLE 14 Coarse designation Constituent cell types
Alveolar "Alveolar", excluding "cuboidal alveolar type 2 (AT2)
cells" FO B cells "follicular B cells" Plasma cells "plasma B
cells" CLEC9A+ DCs "cross-presenting dendritic cells" monoDCs
"monocyte-derived dendritic cells" pDCs "plasmacytoid dendritic
cells" Langerhans "Langerhans cells" Macrophages "macrophages"
Granulocytes "granulocytes" Endothelium "normal endothelial cell",
"tumor endothelial cell", and "lower quality endothelial cell",
excluding "lymphatic EC" Epithelium "epithelial cell" and "lower
quality epithelial cell" Fibroblasts "COL12A1-expressing
fibroblasts", "COL4A2- expressing fibroblasts", "GABARAP-expressing
fibroblasts", "lower quality fibroblasts", "normal lung
fibroblasts", "PLA2G2A-expressing fibroblasts", and
"TFPI2-expressing fibroblasts" T cells "regulatory T cells", "CD4+
T cells" and "CD8+ T cells" NKs "natural killer cells" Tumor
"cancer cells" Excluded "erythroblasts" and "MALT B cells" from
analysis
[0862] A fourth study included data from colorectal tumors. The
file "GSE81861_CRC_tumor_all_cells_FPKM.csv" was downloaded from
the Gene Expression Omnibus (ncbi.nlm.nih.gov/geo/; accession:
GSE81861). The data (already in TPM) units, were re-scaled to sum
to 1,000,000 over the set of protein-coding genes as previously
described. Finally, single-cell observations corresponding to the
same cell type and same source biopsy where averaged to produce
expression estimates at the patient-cell type level. For this
study, cells labeled as "epithelium" are presumed to represent a
mixture of tumor cells and normal epithelium.
[0863] A fifth study included data from serous ovarian cancer
tumors. Single-cell RNA sequencing data of 6 ovarian epithelial
cancer of two low-grade serous ovarian cancer patients (LG1,LG2)
and 4 high-grade serous ovarian cancer patients (HG1,HG2F, HG3,HG4)
were obtained from elsewhere. Quality filtering, clustering and
analysis followed the steps outlined by Shih et al., 2018. Briefly,
the Seurat analysis tool was used to cluster cells passing quality
filtering (minimum of 200 expressed genes, where each gene must be
detected in at least 3 different cells; in total, 2258 cells). The
effects of cell-cycle and the unique transcript count were
regressed out. Cells were clustered following principal component
analysis, and clusters were assigned to cell types based on their
expression of the gene signatures from the original publication.
The TPM for the HLA-DRB1 gene was calculated from the normalized
unique transcript count of protein coding genes for each cell type
for each patient.
[0864] Expression levels of HLA-DRB1 in the four studies are
plotted in FIG. 19A.
Characterization of Tumor-Derived Vs. Stroma-Derived HLA Class II
Expression, Related to FIG. 19B
[0865] To determine the relative amount of HLA class II expression
attributable to tumor vs. stroma, mutations called from DNA
sequencing in HLA class II pathways genes in TCGA patients were
identified, and for each patient bearing an HLA class II mutation,
the relative expression of the mutated and non-mutated copies of
the gene were quantified in the corresponding RNA-Seq. Further, it
was assumed mutated reads arise from the tumor, non-mutated reads
arise for the stroma or the wildtype allele in the tumor, and the
tumor retains a wildtype copy with expression approximately equal
to the mutated copy.
[0866] Based on this, it was determined that for an observed mutant
allele fraction off the fraction of HLA class II expression
attributable to tumor was approximately 2f and not greater than
100%. Three genes--CIITA, CD74, and CTSS--were selected as core HLA
class II pathway genes and assessed for mutations (not excluding
synonymous and UTR mutations) in TCGA (data downloaded from
TumorPortal (tumorportal.org/): BRCA, CRC, HNSC, DLBCL, MM, LUAD;
TCGA bulk download (tcga-data.nci.nih.gov): CESC, LIHC, PAAD, PRAD,
KIRP, TGCT, UCS; Synapse (synapse.org/#!Synapse:syn1729383): GBM,
KIRC, LAML, UCEC, LUSC, OV, SKCM; or the original TCGA publication
(cancergenome.nih.gov/publications): BLCA, KICH, STAD, and THCA).
These genes were selected based on their known roles in HLA class
II expression and their tight correlation with HLA-DRB1 across a
cohort of 8500 GTEx samples. Other genes with equivalent
correlation with HLA-DRB1 (HLA-DRA1, HLA-DPA1, HLA-DQA1, HLA-DQB1,
and HLA-DPB1) were excluded because their polymorphic nature makes
them prone to false positive mutation calls. Naturally, only a
small fraction of patients had a mutation in CIITA, CD74, or CTSS,
and for some tumor types, there were no patients available to
analyze.
[0867] Original whole exome sequencing (WES) BAMS were visually
assessed (IGV) to confirm that the mutation was present in the
tumor sample and not present in the normal sample. Mutant vs.
wildtype read counts were obtained from corresponding RNA-Seq using
pysam. Overall HLA-DRB1 expression was determined based on
expression data downloaded from the Genomic Data Commons
(gdc.cancer.gov/), which was renormalized to sum to 1,000,000 over
the set of protein-coding genes. The fraction of HLA-DRB1
expression attributable to the tumor (FIG. 19B) was estimated as
min(1,2f), where f is the fraction of RNA-Seq reads in CIITA, CD74,
or CTSS exhibiting a mutation.
Identification of Over- and Under-Represented Genes, Related to
FIG. 32A and FIG. 39B
[0868] Samples were analyzed from previously published MS
experiments that profiled the MHC-II ligandomes of ovarian cancer,
colorectal cancer, and melanoma. Many samples from the ovarian
cancer dataset had available RNA-Seq; data for these samples was
downloaded from SRA (NCBI BioProject PRJNA398141) and aligned to
the UCSC hg19 transcriptome using STAR aligner. For ovarian samples
that did not have available RNA-Seq, expression was estimated
averaging across the samples with available RNA-Seq. For the
colorectal and melanoma studies, there was no corresponding RNA-Seq
for any samples, so averages were calculated across surrogate
samples using data from TCGA (The Cancer Genome Atlas Network).
Transcript level gene quantification was performed using
transcripts per million (TPM) as calculated by RSEM version-1.2.31.
The expression estimates were further processed by summing to the
gene level, dropping non-coding genes, and renormalizing such that
the total TPM summed to 1000000 (renormalizing across
protein-coding genes accounts for library-to-library variation in
ncRNA abundance).
[0869] To identify genes over- and under-represented in HLA class
II ligandomes, it was analyzed the same three datasets used in the
expression analysis. For each gene, our baseline assumption was
that it should yield peptides in proportion to its length
multiplied by its expression level. To determine the length of each
gene, the unique 9mers across all transcript isoforms were
enumerated. Gene-level expression was obtained by summing across
transcript isoforms. The observed number of peptides mapping to
each gene was determined at the nested set level (e.g. peptides
GKAPILIATDVASRGLDV (SEQ ID NO: 16), GKAPILIATDVASRGLD (SEQ ID NO:
17), and KAPILIATDVASRGLDV (SEQ ID NO: 18) counted as a single
observation).
[0870] Two matrices were created representing expected and observed
counts, referred to as E and O, respectively, wherein rows
correspond to genes and columns correspond to samples. The values
in O were determined by counting peptides per sample at the nested
set level. The matrix E was first populated by multiplying each
gene's length by its expression in each sample; then the columns of
E were resealed to make the column sums of E match the column sums
of O. Finally, analysis was made at the gene level by comparing the
row sums of E to the row sums of O (FIG. 32A). Genes were
highlighted according to their presence and concentration in human
plasma. An identical approach was used for identifying over- and
under-represented gene in HLA class I data, using melanoma,
colorectal cancer, and ovarian cancer data from the same set of
studies. For the HLA class I analysis, no nesting was applied, but
only unique peptides were counted.
Assessment of Binding Scores in Over-Represented Genes, Related to
FIG. 39A
[0871] It was observed that many of the over-represented genes were
plasma genes. A comprehensive list of serum genes was obtained and
the neonmhc2 binding scores were compared for HLA DR-bound peptides
derived from plasma genes with HLA-DR-bound peptides derived from
non-serum genes, as well as with length-matched, non-binding (e.g.
not observed in MS) peptides sampled from genes that were
represented in the immunopeptidome. For genotyped, multi-allelic
datasets that had HLA class II peptides profiled with a pan-DR
antibody (the same samples analyzed in FIG. 30B), peptides with
neonmhc2 for each DR allele that the sample expressed were scored.
The best score output by neonmhc2 over all expressed alleles was
taken as the representative score for each peptide. The data was
pooled across all usable datasets, and the distributions of the
scores for each category of peptide were visualized with a
boxplot.
Analysis of Genes Related to Protein Turnover, Related to FIG.
32C
[0872] Two gene sets were identified meant to represent proteins
whose turnover is regulated by the proteasome. The first gene set
comprised genes with at least one observed ubiquitination site in
the cell lines KG1, Jurkat, or MM1S. The second set comprised genes
whose levels increased upon application of the proteasome inhibitor
Bortezomib (BTZ) of a published paper, applying a p-value filter of
0.01 and selecting the 300 genes with the largest upward fold
change.
Comparing Explanatory Power of Bulk Tumor Vs. Antigen Presenting
Cell Gene Expression, Related to FIG. 39C
[0873] Four gene expression profiles were created. The first was
meant to represent APCs and estimated by averaging cell
type-specific profiles from the above-described single-cell RNA-Seq
experiments. The average included "macrophages" (from the head and
neck study, the lung study, and the melanoma study), "CLEC9A DCs"
(from the lung study), and "monoDCs" (from the lung study). The
three other expression profiles correspond to bulk tumor profiles
from ovarian cancer, colorectal cancer, and melanoma (Data FIG.
19A). The ovarian profile was an average of the samples published
by Schuster et al, and the other profiles are derived from the five
TCGA samples with the highest tumor cellularity, per tumor type, as
previously inferred using the "Absolute" algorithm. For each tumor
type, the number of peptides (at the nested-set level) per gene
were counted and modeled each gene's peptide count as a function of
gene length, APC-specific gene expression, and tumor-specific gene
expression using linear regression. The output variable and all
input variables were transformed via log(x+1). Using the model's
parameter estimates, the contribution of tumor was calculated as
.beta..sub.tumor/.beta..sub.tumor+.beta..sub.APC), and the
contribution of APCs was calculated as
.beta..sub.APC(.beta..sub.tumor+.beta..sub.APC). For each sample,
bootstrap re-sampling (M=100) at the gene level was used to
calculate confidence intervals for the explanatory proportions.
Characterizing Observed Cleavage Sites of HLA Class II Peptides,
Related to FIG. 40A
[0874] Naturally processed and presented HLA class II peptides were
analyzed from six datasets: PBMC draws, the DC-like MUTZ3 cell
line, colorectal cancer tissue, melanoma, ovarian cancer, and the
expi293 cell line. Since many peptides share the same N-terminus
(e.g. GKAPILIATDVASRGLDV (SEQ ID NO: 16) and GKAPILIATDVASRGLD (SEQ
ID NO: 17)) or the same C-terminus (e.g. GKAPILIATDVASRGLD (SEQ ID
NO: 17) and KAPILIATDVASRGLD (SEQ ID NO: 19)), two sets of
non-redundant cut sites were curated, one for N-termini and one for
C-termini. The naming system shown in FIG. 41 was used to refer to
positions upstream of peptides, within peptides, and downstream of
peptides. Upstream and downstream frequencies ( . . . U1 and D1 . .
. ), were compared against proteome amino acid frequencies and
scored significant deviations via Chi-square test. Peptide
positions (N1 . . . C1), were compared against frequencies as
observed in MS peptides.
Benchmarking the Performance of Various HLA Class II Cleavage
Predictors, Related to FIG. 40B
[0875] Four PBMC samples and published datasets were used to
benchmark the ability of cleavage-related variables/predictors to
enhance the identification of presented HLA class II epitopes.
[0876] To build integrated predictors that predict peptide
presentation using both binding potential and cleavage potential,
constructed datasets were first using the same approach described
for FIG. 31B. This meant using a 1:19 ratio of hits to decoys,
where decoys are length-matched to hits and are randomly sampled
from the set of genes that generated at least one hit. Different
datasets were built in this manner for three different
purposes:
1. For the solvent accessibility- and disorder-based cleavage
predictors, logistic models were fit using HLA class II ligandome
data from human tumor tissues. It was presumed that for a peptide
to have been observed in a ligandome experiment, it must have been
successfully processed. (For the neural network and CNN-based
cleavage predictors, training data was generated using the same
datasets in a distinct fashion, as explained in the table below.)
2. To evaluate if a given cleavage predictor boosted performance
over binding alone, models were fit using mono-allelic MAPTAC.TM.
data generated with B721 and KG1 cells, the most functionally
APC-like cell lines were interrogated. Binding potential was
calculated using neonmhc2, and a logistic regression determined the
relative weights that would be placed on the binding and cleavage
variables in forward prediction. 3. To evaluate the performance of
forward prediction, datasets were constructed for the PBMC samples
and published datasets in the same manner as before. However,
because these samples were multi-allelic, the binding score for
each peptide candidate peptide was taken to be the maximum scoring
of the 1-4 DR alleles indicated by each donor's genotype. PPV was
calculated as described for FIG. 31B.
[0877] Several different cleavage predictors were assessed
Cleave First Model, Cut Site Known (Neural Network)
[0878] To learn a cleavage signal from the MS-observed cut sites,
all unique 6mer amino acid sequences from U3 to N3, and C3 to D3
(using the nomenclature introduced in the section, "Characterizing
observed cleavage sites of HLA class II peptides, related to FIG.
40A") in the tumor tissue-derived HLA class II ligandomes from were
used as positive examples for training two distinct neural
networks, modeling N-terminal cuts and C-terminal cuts,
respectively. As before, an equivalent number of unique
non-observed N-terminal and C-terminal cut sites (negative
examples) were synthetically generated by drawing from the amino
acid frequency of the proteome for the context, and from the
MS-observed ligandome for the peptide. The amino acid sequences
were encoded with a subset of the same features used in neonmhc2,
specifically, the amino acid proximities based on protein
structures, and amino acid properties (e.g. acidic, aliphatic,
etc.). For each of the N-terminal and C-terminal observed cut site
models, (lr=0.0005, Adam optimizer, binary cross-entropy as loss
function) a fully connected neural network was then trained with
two hidden layers (20 neurons in one layer, followed by 10 neurons
in the next) with ReLu activations, followed by a final sigmoid
layer. For regularization, a dropout rate of 20% was used L2 norm
of 0.001 (for C-terminal model only), and max norm constraint of
4.
[0879] To score a candidate peptide, the N-terminal model was
applied to the 6mer sequence U3 to N3 with respect to the peptide,
and the C-terminal model was applied to C3 to D3. Both N-terminal
and C-terminal models were also applied to 6mer sequences tiling
across the candidate peptide to evaluate the cleavage propensity of
the sequence within the peptide itself. A logistic regression was
trained on the MAPTAC.TM. data using the neonmhc2 binding score as
well as four neural network outputs, corresponding to the
N-terminus, C-terminus, and maximum scoring cut sites for the
N-terminal and C-terminal models within the peptide.
Cleave First Model, Cut Site Unknown (+/-15AAs) (Neural
Network)
[0880] To determine if the cleavage models learned from observed
cut sites would be predictive when the precise termini of peptides
was not known, the same neural networks learned above was applied
to extended context, 15 amino acids beyond the peptide termini. To
score a candidate peptide in this case, the maximum score was
calculated across three regions: the 15 amino acids upstream of the
peptide (regardless of the location of the true N-terminal cleavage
site), which was scored with the N-terminal model, the peptide
sequence, which was scored with both the N-terminal and C-terminal
models, and the 15 amino acids downstream of the peptide, which was
scored with the C-terminal model. A logistic regression was trained
on the MAPTAC.TM. data using the neonmhc2 binding score as well as
the four region-specific (since the peptide itself contributes two
sets of values, from the N-terminal and C-terminal models)
scores.
Bind First Model, Solvent Accessibility
[0881] Within the SCRATCH suite, the tool ACCpro20 was used to
predict relative solvent accessibility. The likelihood of a peptide
being processed given the peptide's mean solvent accessibility
score was then fit with a logistic regression using the tumor
tissue data. Finally, a logistic regression was trained on the
mono-allelic data using the neonmhc2 binding score and the output
from the tumor tissue-trained predictor.
Bind First Model, Disorder
[0882] Per-residue scores of sequence disorder were determined over
the entirety of the proteome, scoring on a 0-5 scale according to
the number of prediction engines labeling the position as
disordered (servers used: anchor, espritz-d, espritz-n, espritz-x,
iupred-1, and iupred-s). The average disorder score was calculated
over each candidate peptide, with the six disorder predictor
outputs summed. As with solvent accessibility, first a logistic
model was fit using this overall disorder score with the tumor
tissue data. This was followed by training a logistic regression on
the mono-allelic data using the neonmhc2 binding score and the
output from the tumor tissue-trained predictor.
[0883] Hybrid Model, Precursor Cut Scan (+/-30AAs) (CNN)
[0884] Training data for hits was generated as described for the
`Cleave first, cut site known` cleavage predictor, with the
exception that instead of using the unique 6mer sequences from U3
to N3, and C3 to D3, the 30 amino acids flanking the peptides (U30
to U1, and D1 to D30) were taken from as model input. Furthermore,
whether a 30mer sequence came from the N-terminal or C-terminal
flank was not distinguished, and instead the data was pooled to
train a single model to learn a precursor cut signal that was
assumed may occur on either side of an observed peptide. In this
setting, instead of using synthetic decoys, flanking sequences from
unobserved peptides drawn from the same source genes was used as
negative examples. Sequences were encoded as before, using the
amino acid proximities based on protein structures, and amino acid
properties (e.g. acidic, aliphatic, etc.). The architecture of the
CNN consisted of two convolutional layers, the first layer with a
kernel size of 2 with 48 filters, followed by a layer with a kernel
size of 3 and 40 filters. These layers had ReLu activations. The
convolutional layers were followed with a global max pooling layer,
after which was a final dense layer with a sigmoid activation. The
CNN was trained with a learning rate of 0.001, with Adam
optimization and binary cross-entropy as the loss function.
[0885] To score a candidate peptide, the CNN was applied to the 30
amino acids upstream and 30 amino acids downstream of the peptide,
producing an N-terminal flank score and a C-terminal flank score. A
logistic regression was trained on the MAPTAC.TM. data using the
neonmhc2 binding score and the two CNN scores.
DQ Overlap
[0886] MS-based peptide identifications were pooled across HLA-DQ
ligandomes from Bergseng et al., 2015. A new feature was created
representing whether a new candidate peptide overlapped with one of
these previously observed peptides. Specifically, the feature was
set to 1 if it shared at least one 9mer with any peptide in the set
of previously observed HLA-DQ ligands; otherwise the feature was
set to 0. A logistic regression was trained on the mono-allelic
data using the neonmhc2 binding score and the overlap feature.
[0887] The integrated binding and cleavage models were also all fit
and evaluated using NetMHCIIpan as the binding predictor instead in
FIG. 40B.
Assessing Prediction Overall Performance on Natural Donor Tissues,
Related to FIG. 21A-21B
[0888] Peripheral blood from seven healthy donors was profiled with
a DR-specific antibody as described in the section "Antibody-based
HLA-peptide complex isolation" above. Training and evaluation
datasets were constructed using the hit and decoy selection
algorithm previously described in relation to FIG. 31B. In short,
this means representing each nested set with one hit peptide (the
shortest peptide in the nested set) and tiling length-matched
decoys over genes such that they overlap minimally with hits and
minimally with each other. In this setting, the decoy selection was
not constrained to MS-observed genes, and decoys were instead
randomly sampled without replacement from the entire proteome. A
1:499 hit to decoy rate was utilized, reflecting a rough estimate
of the frequency of HLA-DR-presented peptides in the proteome.
Logistic regression models with MHC binding scores (from
NetMHCIIpan or neonmhc2) as well as other input features
(expression, gene bias, and DQ overlap) were trained on MAPTAC.TM.
data from KG1 and B721 cell lines.
[0889] The following variables in Table 15 were used in a subset of
the regressions.
TABLE-US-00018 TABLE 15 NetMHCIIpan Derived from NetMHCIIpan-v3.1.
For each candidate percent rank peptide, the strongest score was
taken across all DR alleles in the donor's genotype. neonmhc2
Derived from neonmhc2. For each candidate peptide, percent rank the
strongest score was taken across all DR alleles in the donor's
genotype. Expression Gene expression estimates were obtained by
analyzing data from (bowtie2, RSEM, and renormalization over
protein-coding genes only), values averaged over N samples.
Expression was either thresholded (0/1, indicating if expression
was non-zero) or treated as a continuous variable. Gene bias (1 +
observed)/(1 + expected) per the analysis in FIG. 32A DQ overlap
Indicator variable (0/1) for whether the candidate peptide shares
at least one 9mer with any of the HLA-DQ datasets. HLA-DQ-binding
peptides have distinct binding motifs from HLA-DR-binding peptides,
so binding propensity should not be learned with this feature.
[0890] The performance of these models on HLA-DR ligandomes from
natural donor tissue (PBMC samples, etc.) were then evaluated.
Decoys are sampled from the proteome at random (including genes
that never produced an MS-observed peptide) to achieve a 1:499
ratio of hits to decoys, which nearly saturates available decoy
sequences. A 1:499 hit to decoys rate was used for evaluation (as
well as training). The top 0.2% scored peptides in the evaluated
dataset were labeled s positive calls, and the PPV was calculated
as the fraction of positive calls that were hits (see, e.g., FIG.
21A and Table 15). Note that since the number of positives is
constrained to be equal to the number of hits, recall is exactly
equal to PPV in this evaluation scenario. The application of a
consistent 1:499 ratio across alleles helps stabilize the
performance values, which are otherwise highly influenced by the
number of hits observed for each donor. This was deemed appropriate
since the number of hits was assumed to relate more to experimental
conditions than intrinsic properties of the donor's cells.
TABLE-US-00019 TABLE 16 PPV for PPV for MS Sample NetMHCIIpan
neonmhc2 Lung 0.05 >0.3 Spleen 0.025 ~0.3 Heyder LCLs 0.01 ~0.2
A375 0.01 ~0.3 mDCs 0.05 >0.44 PBMC sample #1 0.01 ~0.3 PBMC
sample #2 0.01 >0.3 PBMC sample #3 0.025 >0.3 PBMC sample #4
0.0025 ~0.275 Ritz DOHH2 0.01 0.3 Ritz Maver1 0.015 ~0.175
SILAC-Based Identification of DC-Presented Tumor Peptides, Related
to FIG. 33A
[0891] To generate monocyte derived dendritic cells (mDCs), CD14+
monocytes were isolated from healthy donor peripheral blood
monocytes (PBMCs) by magnetic separation using human CD14
microbeads as per manufacturer's protocol (Miltenyi Biotec).
Isolated cells were differentiated for 6 days in CellGenix GMP DC
media supplemented with 800 U/ml rh GM-CSF and 400 U/ml rh IL-4
(CellGenix, Germany). K562 cells (ATCC, Manassas, Va.) were
isotopically labeled using Stable Isotope Labeling with Amino acids
in Cell culture (SILAC). Cells were grown for 5 doublings in the
presence of RPMI 1640 media for SILAC (ThermoFisher) containing the
heavy isotopically amino acids, L-Lysine 2HCl13C6 15N2 (Life
Technologies, Carlsbad, Calif.) and L-leucine 13C6 (Life
Technologies, Carlsbad, Calif.) with 15% heat inactivated, dialyzed
fetal bovine serum (ThermoFisher). SILAC labeled K562 cells were
lysed using 60 .mu.M hypochlorous acid (HOC1) as described
previously or treated with UV for 3 hours at room temperature to
induce apoptosis and rested overnight. Seventy-five million mDCs
were co-cultured with UV treated SILAC labelled K562 cells at a 1:3
ratio for 14 hours at 37.degree. C. or cultured with a 1:3 ratio of
K562 lysed with HOC1 for 10 minutes or 5 hours at 37.degree. C.
After co-culture, cells were harvested, pelleted and flash frozen
in liquid nitrogen for proteomic analysis.
Prediction and Expression Analysis of DC-Presented Tumor Peptides,
Related to FIG. 33B and FIG. 21C
[0892] To calculate PPV for the prediction of heavy-labeled
(tumor-derived) peptides, the same model was used and evaluation
approach as used in FIG. 33B. Expression for the K562 cell lines
was determined based on data from ENCODE
(encodeproject.org/experiments/ENCSR545DKY/; libraries ENCLB075GEK
and ENCLB365AUY; (ENCODE Project Consortium, 2012)). Expression for
dendritic cells was determined based on GSE116412 (averaging of
accessions GSM3231102, GSM3231111, GSM3231121, GSM3231133,
GSM3231145.
Example 14. Benchmarking FAIMS with Tryptic Peptides
[0893] In this example, a standard HLA-peptidomic workflow for
using high field asymmetric waveform ion mobility spectrometry
(FAIMS) is described. Endogenously processed and presented HLA
class I and HLA class II peptides from A375 cells were
characterized. The peptides were subjected to both acidic
reverse-phase (aRP) and basic reverse-phase (bRP) offline
fractionation prior to analysis by nLC-MS/MS using a Thermo
Scientific Orbitrap Fusion Lumos Tribrid mass spectrometer equipped
without (-) and with (+) the FAIMS Pro interface. The workflow is
indicated in a diagram depicted in FIG. 42A. FIG. 42B shows results
indicating the FAIMS improves peptide detection in tryptic samples
as low as 10 ng. FAIMS increases HLA-1 and HLA class II peptide
detections throughout the LC gradient despite the lower MS1
intensity (FIG. 43A and FIG. 44A, data for HLA-1 and HLA class II
peptides, respectively). Strikingly, an increase in unique peptide
detection is observed in the acidic and basic reverse phase
samples, with FAIMS evaluation (FIG. 43B, and FIG. 44B, data for
HLA-1 and HLA class II peptides, respectively). This study
indicated that Detections of HLA class I and HLA class II peptides
increase throughout the LC gradient with FAIMS despite lower MS1
intensity. Combining offline fractionation and FAIMS increases the
analysis depth of HLA peptide repertoires, as shown in FIGS. 45A
and 45B and FIGS. 46 A and 46B, (HLA-1 and HLA class II peptides,
respectively).
Example 15--Differential Scanning Fluorometry (DSF) Peptide
Exchange Assay
[0894] A peptide exchange assay was performed as follows: The
following reagents (Table 17) were combined and mixed at 37.degree.
C. for 18 hours.
TABLE-US-00020 TABLE 16 Stock Final Ingredients Concentrations
Concentrations Thrombin digested DRB1*15:01 85.6 mM 5 mM (CLIP0),
"DR15" Exchange Pepride/ 10 mM 100 mM peptide of interest.sup.A
Sodium Acetate 1M 100 mM Sodium Chloride 5M 50 mM Octyl glucoside
10% 1% MiliQ water -- Up to 100 ml .sup.ADMSO or peptides with the
following sequences were used: PPIDGYPNHPCFEPE (SEQ ID NO: 31)
(M230), PQILPYPAPEEAQEN (SEQ ID NO: 32) (M231), PQLRQWWAQGADPLA
(SEQ ID NO: 33) (M247), LLRPGQIVAFDSTAQ (SEQ ID NO: 34) (M248) or
ASLRSWPSTWAPWAS (SEQ ID NO: 35) (M371.
[0895] The buffer was then exchanged using a PD minitrap G-25
desalting column Sypro orange dye (Fisher S6651) was diluted to
1000.times. in 100% DMSO. 50 .mu.L working stock of Sypro orange
dye at 100.times. was prepared in desalting buffer. 2 .mu.L of
100.times. sypro orange dye and 18 .mu.L of desalted peptide
exchanged sample was transferred to wells of a 384 white PCR
microplate and mixed. The plate was then covered with a transparent
plate sealer and the plate was subjected to the following program
in a Roche lightcycler 480: (1) heat to 25.degree. C., hold for 10
seconds; (2) increase the temperature to 99.degree. C., read plate
20 times/1.degree. C. (3) bring the temperature down to 25.degree.
C. and hold for 10 seconds. Melting temperatures were then
calculated. Exemplary results are shown in Table 18 below.
TABLE-US-00021 TABLE 18 DT.sub.m (sample Sample T.sub.m - control
T.sub.m) Average T.sub.m Slope Initial Low Peak DR15 + M230 21.4
79.7 0.5 2.8 2.0 4.8 DR15 + M231 15.6 73.9 0.3 2.7 1.9 5.8 DR15 +
M247 20.1 78.4 0.4 3.0 2.3 5.0 DR15 + M248 5.6 63.9 0.4 1.8 1.4 5.1
DR15 + M371 19.9 78.2 0.4 2.5 1.9 4.9 DR15 + DMSO (Control) NA 58.3
0.8 2.1 1.7 9.2
Sequence CWU 1
1
96115PRTArtificial SequenceDescription of Artificial Sequence
Synthetic peptide 1Pro Val Ser Lys Met Arg Met Ala Thr Pro Leu Leu
Met Gln Ala1 5 10 15215PRTArtificial SequenceDescription of
Artificial Sequence Synthetic peptide 2Pro Glu Ala Ser Leu Tyr Gly
Ala Leu Ser Lys Gly Ser Gly Gly1 5 10 15315PRTArtificial
SequenceDescription of Artificial Sequence Synthetic peptide 3Pro
Ala Thr Tyr Ile Leu Ile Leu Lys Glu Phe Cys Leu Val Gly1 5 10
15415PRTArtificial SequenceDescription of Artificial Sequence
Synthetic peptideMISC_FEATURE(1)..(15)This sequence may encompass
4-15 residues 4His His His His His His His His His His His His His
His His1 5 10 1558PRTArtificial SequenceDescription of Artificial
Sequence Synthetic peptide 5His His His His His His His His1
5616PRTArtificial SequenceDescription of Artificial Sequence
Synthetic peptide 6Glu Gly Arg Gly Ser Leu Thr Cys Gly Asp Val Glu
Asn Pro Gly Pro1 5 10 15717PRTArtificial SequenceDescription of
Artificial Sequence Synthetic peptide 7Ala Thr Asn Phe Ser Leu Lys
Gln Ala Gly Asp Val Glu Asn Pro Gly1 5 10 15Pro819PRTArtificial
SequenceDescription of Artificial Sequence Synthetic peptide 8Gln
Cys Thr Asn Tyr Ala Leu Lys Leu Ala Gly Asp Val Glu Ser Asn1 5 10
15Pro Gly Pro921PRTArtificial SequenceDescription of Artificial
Sequence Synthetic peptide 9Val Lys Gln Thr Leu Asn Phe Asp Leu Lys
Leu Ala Gly Asp Val Glu1 5 10 15Ser Asn Pro Gly Pro
201011PRTArtificial SequenceDescription of Artificial Sequence
Synthetic peptide 10Gly Ser Gly Gly Ser Gly Gly Ser Ala Gly Gly1 5
101115PRTArtificial SequenceDescription of Artificial Sequence
Synthetic peptide 11Gly Leu Asn Asp Ile Phe Glu Ala Gln Lys Ile Glu
Trp His Glu1 5 10 151225PRTArtificial SequenceDescription of
Artificial Sequence Synthetic peptide 12Ser Gly Gly Ser Gly Gly Ser
Ala Gly Gly Gly Leu Asn Asp Ile Phe1 5 10 15Glu Ala Gln Lys Ile Glu
Trp His Glu 20 251322PRTArtificial SequenceDescription of
Artificial Sequence Synthetic peptide 13Val Lys Gln Thr Leu Asn Phe
Asp Leu Leu Lys Leu Ala Gly Asp Val1 5 10 15Glu Ser Asn Pro Gly Pro
201411PRTArtificial SequenceDescription of Artificial Sequence
Synthetic peptide 14Gly Ser Tyr Pro Tyr Asp Val Pro Asp Tyr Ala1 5
10156PRTArtificial SequenceDescription of Artificial Sequence
Synthetic peptide 15His His His His His His1 51618PRTArtificial
SequenceDescription of Artificial Sequence Synthetic peptide 16Gly
Lys Ala Pro Ile Leu Ile Ala Thr Asp Val Ala Ser Arg Gly Leu1 5 10
15Asp Val1717PRTArtificial SequenceDescription of Artificial
Sequence Synthetic peptide 17Gly Lys Ala Pro Ile Leu Ile Ala Thr
Asp Val Ala Ser Arg Gly Leu1 5 10 15Asp1817PRTArtificial
SequenceDescription of Artificial Sequence Synthetic peptide 18Lys
Ala Pro Ile Leu Ile Ala Thr Asp Val Ala Ser Arg Gly Leu Asp1 5 10
15Val1916PRTArtificial SequenceDescription of Artificial Sequence
Synthetic peptide 19Lys Ala Pro Ile Leu Ile Ala Thr Asp Val Ala Ser
Arg Gly Leu Asp1 5 10 152010PRTArtificial SequenceDescription of
Artificial Sequence Synthetic peptide 20His His His His His His His
His His His1 5 102124PRTArtificial SequenceDescription of
Artificial Sequence Synthetic peptide 21Leu Pro Lys Pro Pro Lys Pro
Val Ser Lys Met Arg Met Ala Thr Pro1 5 10 15Leu Leu Met Gln Ala Leu
Pro Met 202214PRTArtificial SequenceDescription of Artificial
Sequence Synthetic peptide 22Leu Pro Leu Lys Met Leu Asn Ile Pro
Ser Ile Asn Val His1 5 102313PRTArtificial SequenceDescription of
Artificial Sequence Synthetic peptide 23Pro Lys Tyr Val Lys Gln Asn
Thr Leu Lys Leu Ala Thr1 5 102417PRTArtificial SequenceDescription
of Artificial Sequence Synthetic peptide 24Pro Val Val His Phe Phe
Lys Asn Ile Val Thr Pro Arg Thr Pro Pro1 5 10
15Tyr2517PRTArtificial SequenceDescription of Artificial Sequence
Synthetic peptide 25Tyr Ala Thr Phe Phe Ile Lys Ala Asn Ser Lys Phe
Ile Gly Ile Thr1 5 10 15Glu2616PRTArtificial SequenceDescription of
Artificial Sequence Synthetic peptide 26Thr Leu Ser Val Thr Phe Ile
Gly Ala Ala Pro Leu Ile Leu Ser Tyr1 5 10 152717PRTArtificial
SequenceDescription of Artificial Sequence Synthetic peptide 27Pro
Val Val His Phe Phe Lys Asn Ile Val Thr Pro Arg Thr Pro Pro1 5 10
15Tyr2815PRTArtificial SequenceDescription of Artificial Sequence
Synthetic peptide 28Tyr Ala Arg Ile Arg Arg Asp Gly Cys Leu Leu Arg
Leu Val Asp1 5 10 152916PRTArtificial SequenceDescription of
Artificial Sequence Synthetic peptide 29Thr Leu Ser Val Thr Phe Ile
Gly Ala Ala Pro Lys Ile Leu Ser Tyr1 5 10 153015PRTArtificial
SequenceDescription of Artificial Sequence Synthetic peptide 30Tyr
Ala Arg Ile Lys Arg Asp Gly Cys Leu Leu Arg Leu Val Asp1 5 10
153115PRTArtificial SequenceDescription of Artificial Sequence
Synthetic peptide 31Pro Pro Ile Asp Gly Tyr Pro Asn His Pro Cys Phe
Glu Pro Glu1 5 10 153215PRTArtificial SequenceDescription of
Artificial Sequence Synthetic peptide 32Pro Gln Ile Leu Pro Tyr Pro
Ala Pro Glu Glu Ala Gln Glu Asn1 5 10 153315PRTArtificial
SequenceDescription of Artificial Sequence Synthetic peptide 33Pro
Gln Leu Arg Gln Trp Trp Ala Gln Gly Ala Asp Pro Leu Ala1 5 10
153415PRTArtificial SequenceDescription of Artificial Sequence
Synthetic peptide 34Leu Leu Arg Pro Gly Gln Ile Val Ala Phe Asp Ser
Thr Ala Gln1 5 10 153515PRTArtificial SequenceDescription of
Artificial Sequence Synthetic peptide 35Ala Ser Leu Arg Ser Trp Pro
Ser Thr Trp Ala Pro Trp Ala Ser1 5 10 15369PRTArtificial
SequenceDescription of Artificial Sequence Synthetic peptide 36Gly
Lys Ser Val Val Cys Glu Ala Leu1 53715PRTArtificial
SequenceDescription of Artificial Sequence Synthetic peptide 37Leu
Pro Asn Gly Gly Phe Ala Ser Ile Leu Leu Tyr Lys Ile Glu1 5 10
153816PRTArtificial SequenceDescription of Artificial Sequence
Synthetic peptide 38Phe Cys Lys Gly Ser Phe Ala Ser Ile Leu Lys Leu
Leu Gly Glu Phe1 5 10 153942PRTArtificial SequenceDescription of
Artificial Sequence Synthetic polypeptideMOD_RES(6)..(19)Any amino
acid 39Gly Asp Thr Gly Leu Xaa Xaa Xaa Xaa Xaa Xaa Xaa Xaa Xaa Xaa
Xaa1 5 10 15Xaa Xaa Xaa Gly Gly Gly Gly Ser Leu Val Pro Arg Gly Ser
Gly Gly 20 25 30Gly Gly Ser Gly Asp Thr Arg Pro Arg Phe 35
404013PRTArtificial SequenceDescription of Artificial Sequence
Synthetic peptide 40Asp Arg Tyr Glu Met Glu Asp Gly Lys Val Ile Glu
Arg1 5 104116PRTArtificial SequenceDescription of Artificial
Sequence Synthetic peptide 41Gly Gly His Met Thr Thr Leu Ser Gly
Glu Glu Ile Ser Tyr Thr Gly1 5 10 154214PRTArtificial
SequenceDescription of Artificial Sequence Synthetic peptide 42Lys
Thr Phe Asp Gln Leu Thr Pro Glu Glu Ser Lys Glu Arg1 5
104314PRTArtificial SequenceDescription of Artificial Sequence
Synthetic peptide 43Leu Pro Arg Tyr Glu Ala Leu Arg Gly Glu Gln Pro
Pro Asp1 5 104416PRTArtificial SequenceDescription of Artificial
Sequence Synthetic peptide 44Asp Lys Lys Asn Ile Ile Leu Glu Glu
Gly Lys Glu Ile Leu Val Gly1 5 10 154514PRTArtificial
SequenceDescription of Artificial Sequence Synthetic peptide 45Lys
Glu Ala Ala Tyr His Pro Glu Val Ala Pro Asp Val Arg1 5
104615PRTArtificial SequenceDescription of Artificial Sequence
Synthetic peptide 46Asp Ala Glu Phe Arg His Asp Ser Gly Tyr Glu Val
His His Gln1 5 10 154716PRTArtificial SequenceDescription of
Artificial Sequence Synthetic peptide 47Gly His Pro Asp Leu Gln Gly
Gln Pro Ala Glu Glu Ile Phe Glu Ser1 5 10 154816PRTArtificial
SequenceDescription of Artificial Sequence Synthetic peptide 48Leu
Gly Lys Asn Phe Asp Phe Gln Lys Ser Asp Arg Ile Asn Ser Glu1 5 10
154914PRTArtificial SequenceDescription of Artificial Sequence
Synthetic peptide 49Met Pro Ser Phe Val Pro Ser Asp Gly Arg Gln Ala
Ala Asp1 5 105015PRTArtificial SequenceDescription of Artificial
Sequence Synthetic peptide 50Gly Leu Arg Tyr Lys Lys Leu His Asp
Pro Lys Gly Trp Ile Thr1 5 10 155115PRTArtificial
SequenceDescription of Artificial Sequence Synthetic peptide 51Thr
Ile Glu Lys Phe Glu Lys Glu Ala Ala Glu Met Gly Lys Gly1 5 10
155213PRTArtificial SequenceDescription of Artificial Sequence
Synthetic peptide 52Gly Val Gln Arg Gly Leu Val Gly Glu Ile Ile Lys
Arg1 5 105311PRTArtificial SequenceDescription of Artificial
Sequence Synthetic peptide 53Thr Trp Phe Asn Gln Pro Ala Arg Lys
Ile Arg1 5 105414PRTArtificial SequenceDescription of Artificial
Sequence Synthetic peptide 54Glu Phe Lys Lys Tyr Glu Met Met Lys
Glu His Glu Arg Arg1 5 105515PRTArtificial SequenceDescription of
Artificial Sequence Synthetic peptide 55Ser Pro His Ala Phe Lys Thr
Glu Ser Gly Glu Glu Thr Asp Leu1 5 10 155613PRTArtificial
SequenceDescription of Artificial Sequence Synthetic peptide 56Asn
Asn Leu Cys Pro Ser Gly Ser Asn Ile Ile Ser Asn1 5
105714PRTArtificial SequenceDescription of Artificial Sequence
Synthetic peptide 57Glu Arg Pro Tyr Trp Asp Met Ser Asn Gln Asp Val
Ile Asn1 5 105814PRTArtificial SequenceDescription of Artificial
Sequence Synthetic peptide 58Val Gly Gly Thr Met Val Arg Ser Gly
Gln Asp Pro Tyr Ala1 5 105913PRTArtificial SequenceDescription of
Artificial Sequence Synthetic peptide 59Lys Glu Ala Leu Glu Pro Ser
Gly Glu Asn Val Ile Gln1 5 106015PRTArtificial SequenceDescription
of Artificial Sequence Synthetic peptide 60Arg Gln Arg Arg Leu Leu
Gly Ser Val Gln Gln Asp Leu Glu Arg1 5 10 156114PRTArtificial
SequenceDescription of Artificial Sequence Synthetic peptide 61Ile
Asp Arg Ala Leu Asn Glu Ala Cys Glu Ser Val Ile Gln1 5
106216PRTArtificial SequenceDescription of Artificial Sequence
Synthetic peptide 62Asn Glu Asn Asn Leu Glu Ser Ala Lys Gly Leu Leu
Asp Asp Leu Arg1 5 10 156316PRTArtificial SequenceDescription of
Artificial Sequence Synthetic peptide 63Asp Ser Lys Ser Leu Arg Thr
Ala Leu Gln Lys Glu Ile Thr Thr Arg1 5 10 156413PRTArtificial
SequenceDescription of Artificial Sequence Synthetic peptide 64Thr
Gly Gly Asp Ile Asn Ala Ala Ile Glu Arg Leu Leu1 5
106516PRTArtificial SequenceDescription of Artificial Sequence
Synthetic peptide 65Ser Leu Asp Asn Leu Lys Ala Ser Val Ser Gln Val
Glu Ala Asp Leu1 5 10 156614PRTArtificial SequenceDescription of
Artificial Sequence Synthetic peptide 66Asp Glu Arg Arg Phe Lys Ala
Ala Asp Leu Asn Gly Asp Leu1 5 106715PRTArtificial
SequenceDescription of Artificial Sequence Synthetic peptide 67Lys
Pro Ala Pro Ala Leu Arg Ser Ala Arg Ser Ala Pro Glu Asn1 5 10
156814PRTArtificial SequenceDescription of Artificial Sequence
Synthetic peptide 68Arg Thr Ser Tyr Val Thr Ser Val Glu Glu Asn Thr
Val Asp1 5 106915PRTArtificial SequenceDescription of Artificial
Sequence Synthetic peptide 69Arg Ala Val Glu Phe Gln Glu Ala Gln
Ala Tyr Ala Asp Asp Asn1 5 10 157014PRTArtificial
SequenceDescription of Artificial Sequence Synthetic peptide 70Arg
Asp Leu Ala Gln Tyr Asp Ala Ala His His Glu Glu Phe1 5
107113PRTArtificial SequenceDescription of Artificial Sequence
Synthetic peptide 71Gly Gln Gln Trp Thr Tyr Glu Gln Arg Lys Ile Val
Glu1 5 107214PRTArtificial SequenceDescription of Artificial
Sequence Synthetic peptide 72Glu Gly Glu Tyr Gln Gly Ile Pro Arg
Ala Glu Ser Gly Gly1 5 107316PRTArtificial SequenceDescription of
Artificial Sequence Synthetic peptide 73Ser Pro Glu Glu Phe Asp Glu
Val Ser Arg Ile Val Gly Ser Val Glu1 5 10 157415PRTArtificial
SequenceDescription of Artificial Sequence Synthetic peptide 74Ile
Ala Gly Glu Trp Gln Val Leu His Arg Glu Gly Ala Ile Thr1 5 10
157515PRTArtificial SequenceDescription of Artificial Sequence
Synthetic peptide 75Glu Pro Ala Glu Phe Ile Ile Asp Thr Arg Asp Ala
Gly Tyr Gly1 5 10 157615PRTArtificial SequenceDescription of
Artificial Sequence Synthetic peptide 76Asp Leu Glu Glu Leu Glu Val
Leu Glu Arg Lys Pro Ala Ala Gly1 5 10 157716PRTArtificial
SequenceDescription of Artificial Sequence Synthetic peptide 77Gly
Met Asn Ile Val Glu Ala Met Glu Arg Phe Gly Ser Arg Asn Gly1 5 10
157815PRTArtificial SequenceDescription of Artificial Sequence
Synthetic peptide 78Gly Phe Asn Trp Asn Trp Ile Asn Lys Gln Gln Gly
Lys Arg Gly1 5 10 157913PRTArtificial SequenceDescription of
Artificial Sequence Synthetic peptide 79Gly Pro Phe Ser Phe Ser Val
Ile Asp Lys Pro Pro Gly1 5 108014PRTArtificial SequenceDescription
of Artificial Sequence Synthetic peptide 80Leu Asn Glu Leu Lys Pro
Ile Ser Lys Gly Gly His Ser Ser1 5 108115PRTArtificial
SequenceDescription of Artificial Sequence Synthetic peptide 81Asp
Val Asn Glu Tyr Ala Pro Val Phe Lys Glu Lys Ser Tyr Lys1 5 10
158215PRTArtificial SequenceDescription of Artificial Sequence
Synthetic peptide 82Leu Ala Pro Thr Trp Glu Glu Leu Ser Lys Lys Glu
Phe Pro Gly1 5 10 158315PRTArtificial SequenceDescription of
Artificial Sequence Synthetic peptide 83Thr Arg Asn Glu Val Ile Pro
Met Ser His Pro Gly Ala Val Asp1 5 10 158414PRTArtificial
SequenceDescription of Artificial Sequence Synthetic peptide 84Glu
Asn Pro Tyr Phe Ala Pro Asn Pro Lys Ile Ile Arg Gln1 5
108515PRTArtificial SequenceDescription of Artificial Sequence
Synthetic peptide 85Tyr Glu Asp Lys Phe Arg Asn Asn Leu Lys Gly Lys
Arg Leu Asp1 5 10 158615PRTArtificial SequenceDescription of
Artificial Sequence Synthetic peptide 86Lys Asn Gly Ala Tyr Lys Val
Glu Thr Lys Lys Tyr Asp Phe Tyr1 5 10 158715PRTArtificial
SequenceDescription of Artificial Sequence Synthetic peptide 87Gly
Glu Gly Leu Phe Gln Pro Ala His Arg Tyr Pro Asp Ala Gly1 5 10
158815PRTArtificial SequenceDescription of Artificial Sequence
Synthetic peptide 88Arg Asn Gln Trp Lys Cys Leu Gly Lys Pro Val Gly
Ala Glu Met1 5 10 158915PRTArtificial SequenceDescription of
Artificial Sequence Synthetic
peptide 89Val Pro Ala Trp Thr Arg Ala Trp Arg Asn Ser Ser Pro Lys
Gly1 5 10 159015PRTArtificial SequenceDescription of Artificial
Sequence Synthetic peptide 90Leu His Pro Glu Leu Leu Pro Leu Trp
Arg Leu Leu Pro Asp Gly1 5 10 159115PRTArtificial
SequenceDescription of Artificial Sequence Synthetic peptide 91Asp
Gly Gly Ser Tyr Phe Ser Leu Trp Lys Ile Trp Thr Gln Val1 5 10
159215PRTArtificial SequenceDescription of Artificial Sequence
Synthetic peptide 92His Glu Gly Gly Phe Pro Pro Leu Leu Arg Arg Ala
Ala Glu Asp1 5 10 159315PRTArtificial SequenceDescription of
Artificial Sequence Synthetic peptide 93Leu Ser Pro Val Trp Cys Leu
Gln Trp Lys Leu Ser Gly Thr Asp1 5 10 159415PRTArtificial
SequenceDescription of Artificial Sequence Synthetic peptide 94Lys
Asn Thr Ile Val Tyr Thr Thr Lys Gln Val Gln Ser Cys Gln1 5 10
159515PRTArtificial SequenceDescription of Artificial Sequence
Synthetic peptide 95Asn Asn Glu Gln Phe Gln Trp Lys Ile Arg His Val
Gly Pro Glu1 5 10 159615PRTArtificial SequenceDescription of
Artificial Sequence Synthetic peptide 96Tyr Lys Gly Gly Tyr Glu Leu
Val Lys Lys Ser Gln Thr Glu Leu1 5 10 15
* * * * *