U.S. patent application number 17/565659 was filed with the patent office on 2022-04-21 for systems and methods for sample preparation, sample sequencing, and sequencing data bias correction and quality control.
This patent application is currently assigned to BostonGene Corporation. The applicant listed for this patent is BostonGene Corporation. Invention is credited to Alexander Bagaev, Maksim Chelushkin, Yaroslav Lozinsky, Natalia Miheecheva, Ekaterina Nuzhdina, Aleksandr Zaitsev.
Application Number | 20220119881 17/565659 |
Document ID | / |
Family ID | 1000006127571 |
Filed Date | 2022-04-21 |
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United States Patent
Application |
20220119881 |
Kind Code |
A1 |
Nuzhdina; Ekaterina ; et
al. |
April 21, 2022 |
SYSTEMS AND METHODS FOR SAMPLE PREPARATION, SAMPLE SEQUENCING, AND
SEQUENCING DATA BIAS CORRECTION AND QUALITY CONTROL
Abstract
Described herein are various methods of collecting and
processing of tumor and/or healthy tissue samples to extract
nucleic acid and perform nucleic acid sequencing. Also described
herein are various methods of processing nucleic acid sequencing
data to remove bias from the nucleic acid sequencing data. Also
described herein are various methods of evaluating the quality of
nucleic acid sequence information. The identity and/or integrity of
nucleic acid sequence data is evaluated prior to using the sequence
information for subsequent analysis (for example for diagnostic,
prognostic, or clinical purposes). The methods enable a subject,
doctor, or user to characterize or classify various types of cancer
precisely, and thereby determine a therapy or combination of
therapies that may be effective to treat a cancer in a subject
based on the precise characterization.
Inventors: |
Nuzhdina; Ekaterina;
(Moscow, RU) ; Bagaev; Alexander; (Moscow, RU)
; Chelushkin; Maksim; (Moscow, RU) ; Lozinsky;
Yaroslav; (Moscow, RU) ; Miheecheva; Natalia;
(Moscow, RU) ; Zaitsev; Aleksandr; (Leninskiy
R-N,, RU) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
BostonGene Corporation |
Waltham |
MA |
US |
|
|
Assignee: |
BostonGene Corporation
Waltham
MA
|
Family ID: |
1000006127571 |
Appl. No.: |
17/565659 |
Filed: |
December 30, 2021 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
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PCT/IB2020/000928 |
Jul 3, 2020 |
|
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17565659 |
|
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62991570 |
Mar 18, 2020 |
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62870622 |
Jul 3, 2019 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
C12Q 1/6806 20130101;
G16H 50/30 20180101; C12Q 1/6869 20130101; G16B 30/10 20190201;
G16B 20/20 20190201; G16B 25/10 20190201; G16B 35/10 20190201 |
International
Class: |
C12Q 1/6869 20060101
C12Q001/6869; C12Q 1/6806 20060101 C12Q001/6806; G16B 20/20
20060101 G16B020/20; G16B 35/10 20060101 G16B035/10; G16H 50/30
20060101 G16H050/30 |
Claims
1. A method, comprising: using at least one computer hardware
processor to perform: obtaining nucleic acid data comprising:
sequence data indicating a nucleotide sequence for at least 5
kilobases (kb) of DNA and/or RNA from a previously obtained
biological sample of a subject having, suspected of having, or at
risk of having a disease; and asserted information indicating an
asserted source and/or an asserted integrity of the sequence data;
and validating the nucleic acid data by: processing the sequence
data to obtain determined information indicating a determined
source and/or a determined integrity of the sequence data; and
determining whether the determined information matches the asserted
information.
2. The method of claim 1, further comprising: processing the
sequence data to determine whether the sequence data is indicative
of one or more disease features when it is determined that the
asserted information matches the determined information.
3. The method of claim 1 or any other preceding claim, further
comprising: determining that the determined information matches the
asserted information; and processing the sequence data to determine
whether it is indicative of one or more disease features.
4. The method of claim 1 or any other preceding claim, further
comprising: generating an indication: that the determined
information does not match the asserted information, to not process
the sequence data in a subsequent analysis, and/or to obtain
additional sequence data and/or other information about the
biological sample and/or the subject, when it is determined that
the asserted information does not match the determined
information.
5. The method of claim 1 or any other preceding claim, further
comprising: determining that the asserted information does not
match the determined information; and generating an indication:
that the determined information does not match the asserted
information, to not process the sequence data in a subsequent
analysis, and/or to obtain additional sequence data and/or other
information about the biological sample and/or the subject.
6. The method of claim 1 or any other preceding claim, wherein the
asserted information indicates the asserted source of the sequence
data, the method further comprising: processing the sequence data
to obtain determined information indicative of a determined source
for the sequence data; and determining whether the determined
source matches the asserted source for the sequence data.
7. The method of claim 6 or any other preceding claim, wherein the
determined information indicative of the determined source for the
sequence data is indicative of an MHC genotype of the subject;
whether the nucleic acid data is RNA data or DNA data; a tissue
type of the biological sample; a tumor type of the biological
sample; a sequencing platform used to generate the sequence data;
SNP concordance, and/or a whether an RNA sample is polyA
enriched.
8. The method of claim 7 or any other preceding claim, wherein the
determined information indicative of the determined source for the
sequence data is indicative of at least two of an MHC genotype of
the subject; whether the nucleic acid data is RNA data or DNA data;
a tissue type of the biological sample; a tumor type of the
biological sample; a sequencing platform used to generate the
sequence data; SNP concordance, and a whether an RNA sample is
polyA enriched.
9. The method of claim 8 or any other preceding claim, wherein the
determined information indicative of the determined source for the
sequence data is indicative of at least three of an MHC genotype of
the subject; whether the nucleic acid data is RNA data or DNA data;
a tissue type of the biological sample; a tumor type of the
biological sample; a sequencing platform used to generate the
sequence data; SNP concordance, and a whether an RNA sample is
polyA enriched.
10. The method of claim 1 or any other preceding claim, wherein the
asserted information indicates the asserted integrity of the
sequence data, the method further comprising: processing the
sequence data to obtain determined information indicative of a
determined integrity of the sequence data; and determining whether
the determined integrity matches the asserted integrity for the
sequence data.
11. The method of claim 10 or any other preceding claim, wherein
the determined information indicative of the determined integrity
is indicative of total sequence coverage; exon coverage;
chromosomal coverage; a ratio of nucleic acids encoding two or more
subunits of a multimeric protein; species contamination; single
nucleotide polymorphisms (SNPs); complexity; and/or guanine (G) and
cytosine (C) percentage (%) of the sequence data.
12. The method of claim 11 or any other preceding claim, wherein
the determined information indicative of the determined integrity
is indicative of at least two of total sequence coverage; exon
coverage; chromosomal coverage; a ratio of nucleic acids encoding
two or more subunits of a multimeric protein; species
contamination; single nucleotide polymorphisms (SNPs); complexity;
and guanine (G) and cytosine (C) percentage (%) of the sequence
data.
13. The method of claim 12 or any other preceding claim, wherein
the determined information indicative of the determined integrity
is indicative of at least three of total sequence coverage; exon
coverage; chromosomal coverage; a ratio of nucleic acids encoding
two or more subunits of a multimeric protein; species
contamination; single nucleotide polymorphisms (SNPs); complexity;
and guanine (G) and cytosine (C) percentage (%) of the sequence
data.
14. The method of claim 1 or any other preceding claim, wherein the
asserted information for the sequence data comprises MHC allele
information for the subject.
15. The method of claim 14 or any other preceding claim, further
comprising: determining one or more MHC allele sequences from the
sequence data and determining whether the one or more MHC alleles
sequences match the asserted MHC allele information for the
subject.
16. The method of claim 15 or any other preceding claim, wherein
determining the one or more MHC allele sequences comprises
determining MHC allele sequences for six MHC loci from the sequence
data.
17. The method of claim 1 or any other preceding claim, wherein the
sequence data indicates the nucleotide sequence for RNA, the
asserted information indicates whether the RNA is polyA
enriched.
18. The method of claim 1 or any other preceding claim, further
comprising: determining, using the sequence data, a therapy for the
subject when it is determined that the asserted information matches
the determined information.
19. The method of claim 18 or any other preceding claim, wherein
determining the therapy comprises: determining a plurality of gene
group expression levels, the plurality of gene group expression
levels comprising a gene group expression level for each gene group
in a set of gene groups, wherein the set of gene groups comprises
at least one gene group associated with cancer malignancy, and at
least one gene group associated with cancer microenvironment;
identifying the therapy using the determined gene group expression
levels.
20. The method of claim 19 or any other preceding claim, further
comprising administering the therapy to the subject.
21-28. (canceled)
29. A system comprising: at least one computer hardware processor;
at least one non-transitory computer-readable storage medium
storing processor executable instructions that, when executed by
the at least one computer hardware processor, cause the at least
one computer hardware processor to perform a method, comprising:
obtaining nucleic acid data comprising: sequence data indicating a
nucleotide sequence for at least 5 kilobases (kb) of DNA and/or RNA
from a previously obtained biological sample of a subject having,
suspected of having, or at risk of having a disease; and asserted
information indicating an asserted source and/or an asserted
integrity of the sequence data; and validating the nucleic acid
data by: processing the sequence data to obtain determined
information indicating a determined source and/or a determined
integrity of the sequence data; and determining whether the
determined information matches the asserted information.
30. At least one non-transitory computer-readable storage medium
storing processor executable instructions that, when executed by at
least one computer hardware processor, cause the at least one
computer hardware processor to perform a method, comprising:
obtaining nucleic acid data comprising: sequence data indicating a
nucleotide sequence for at least 5 kilobases (kb) of DNA and/or RNA
from a previously obtained biological sample of a subject having,
suspected of having, or at risk of having a disease; and asserted
information indicating an asserted source and/or an asserted
integrity of the sequence data; and validating the nucleic acid
data by: processing the sequence data to obtain determined
information indicating a determined source and/or a determined
integrity of the sequence data; and determining whether the
determined information matches the asserted information.
31-56. (canceled)
Description
CROSS REFERENCE TO RELATED APPLICATIONS
[0001] This application claims the benefit under 35 U.S.C. .sctn.
119(e) of U.S. Provisional Application No. 62/870,622, filed Jul.
3, 2019, entitled "Compositions and Methods for Sample Preparation
and Characterization of Cancer Therefrom" and U.S. Provisional
Application No. 62/991,570, filed Mar. 18, 2020, entitled "Nucleic
Acid Data Quality Control," the entire disclosure of each is hereby
incorporated by reference.
FIELD
[0002] Some aspects of the technology described herein relate to
collecting and processing of tumor and/or healthy tissue samples to
extract nucleic acid and perform nucleic acid sequencing. Some
aspects of the technology described herein relate to processing
nucleic acid sequencing data to remove bias from the nucleic acid
sequencing data. Also described herein are various methods of
evaluating the quality of nucleic acid sequence information
obtained by sequencing.
BACKGROUND
[0003] Correctly characterizing the type or types of cancer a
patient or subject has and, potentially, selecting one or more
effective therapies for the patient based on the characterization
can be crucial for the survival and overall wellbeing of that
patient. The manner in which biological samples from a subject are
processed to obtain sequence data (e.g., RNA expression data) to
characterize the type or types of cancer, and the manner in which
the data is processed may have detrimental effects on the
characterization of the cancer or cancers. For example, high
throughput nucleic acid sequencing platforms (e.g., next generation
sequencing platforms) can generate large amounts of DNA and RNA
sequence data from patient samples. Advances in sample preparation,
data processing, and evaluation of sequence information from
different NGS platforms by custom software for characterizing
cancers, predicting prognoses, identifying effective therapies, and
otherwise aiding in personalized care of patients with cancer are
needed.
SUMMARY
[0004] Some embodiments provide for a system comprising: at least
one computer hardware processor; and at least one non-transitory
computer-readable storage medium storing processor executable
instructions that, when executed by the at least one computer
hardware processor, cause the at least one computer hardware
processor to perform a method, the method comprising:
[0005] obtaining nucleic acid data comprising: sequence data
indicating a nucleotide sequence for at least 5 kilobases (kb) of
DNA and/or RNA from a previously obtained biological sample of a
subject having, suspected of having, or at risk of having a
disease; and asserted information indicating an asserted source
and/or an asserted integrity of the sequence data; and validating
the nucleic acid data by: processing the sequence data to obtain
determined information indicating a
[0006] determined source and/or a determined integrity of the
sequence data; and determining whether the determined information
matches the asserted information.
[0007] Some embodiments provide at least one non-transitory
computer-readable storage medium storing processor executable
instructions that, when executed by at least one computer hardware
processor, cause the at least one computer hardware processor to
perform a method. The method comprising: obtaining nucleic acid
data comprising: sequence data indicating a nucleotide sequence for
at least 5 kilobases (kb) of DNA and/or RNA from a previously
obtained biological sample of a subject having, suspected of
having, or at risk of having a disease; and
[0008] asserted information indicating an asserted source and/or an
asserted integrity of the sequence data; and validating the nucleic
acid data by: processing the sequence data to obtain determined
information indicating a determined source and/or a determined
integrity of the sequence data; and determining whether the
determined information matches the asserted information.
[0009] Some embodiments using at least one computer hardware
processor to perform: obtaining nucleic acid data comprising:
sequence data indicating a nucleotide sequence for at least 5
kilobases (kb) of DNA and/or RNA from a previously obtained
biological sample of a subject having, suspected of having, or at
risk of having a disease; and asserted information indicating an
asserted source and/or an asserted integrity of the sequence data;
and validating the nucleic acid data by: processing the sequence
data to obtain determined information indicating a determined
source and/or a determined integrity of the sequence data; and
determining whether the determined information matches the asserted
information.
[0010] In some embodiments, the sequence data may include raw DNA
or RNA sequence data, DNA exome sequence data (e.g., from whole
exome sequencing (WES), DNA genome sequence data (e.g., from whole
genome sequencing (WGS)), RNA expression data, gene expression
data, bias-corrected gene expression data or any other suitable
type of sequence data comprising data obtained from a sequencing
platform and/or comprising data derived from data obtained from a
sequencing platform.
[0011] In some embodiments, the method further comprises processing
the sequence data to determine whether the sequence data is
indicative of one or more disease features when it is determined
that the asserted information matches the determined
information.
[0012] In some embodiments, the method further comprises
determining that the determined information matches the asserted
information; and processing the sequence data to determine whether
it is indicative of one or more disease features.
[0013] In some embodiments, the method further comprises generating
an indication: that the determined information does not match the
asserted information, to not process the sequence data in a
subsequent analysis, and/or to obtain additional sequence data
and/or other information about the biological sample and/or the
subject, when it is determined that the asserted information does
not match the determined information.
[0014] In some embodiments, the method further comprises:
determining that the asserted information does not match the
determined information; and generating an indication: that the
determined information does not match the asserted information, to
not process the sequence data in a subsequent analysis, and/or to
obtain additional sequence data and/or other information about the
biological sample and/or the subject.
[0015] In some embodiments, the asserted information indicates the
asserted source of the sequence data, the method further comprising
processing the sequence data to obtain determined information
indicative of a determined source for the sequence data; and
determining whether the determined source matches the asserted
source for the sequence data.
[0016] In some embodiments, the determined information indicative
of the determined source for the sequence data is indicative of an
MHC genotype of the subject; whether the nucleic acid data is RNA
data or DNA data; a tissue type of the biological sample; a tumor
type of the biological sample; a sequencing platform used to
generate the sequence data; SNP concordance, and/or a whether an
RNA sample is polyA enriched.
[0017] In some embodiments, the determined information indicative
of the determined source for the sequence data is indicative of at
least two of an MHC genotype of the subject; whether the nucleic
acid data is RNA data or DNA data; a tissue type of the biological
sample; a tumor type of the biological sample; a sequencing
platform used to generate the sequence data; SNP concordance, and a
whether an RNA sample is polyA enriched.
[0018] In some embodiments, the determined information indicative
of the determined source for the sequence data is indicative of at
least three of an MHC genotype of the subject; whether the nucleic
acid data is RNA data or DNA data; a tissue type of the biological
sample; a tumor type of the biological sample; a sequencing
platform used to generate the sequence data; SNP concordance, and a
whether an RNA sample is polyA enriched.
[0019] In some embodiments, the asserted information indicates the
asserted integrity of the sequence data, the method further
comprising: processing the sequence data to obtain determined
information indicative of a determined integrity of the sequence
data; and determining whether the determined integrity matches the
asserted integrity for the sequence data.
[0020] In some embodiments, the determined information indicative
of the determined integrity is indicative of total sequence
coverage; exon coverage; chromosomal coverage; a ratio of nucleic
acids encoding two or more subunits of a multimeric protein;
species contamination; single nucleotide polymorphisms (SNPs);
complexity; and/or guanine (G) and cytosine (C) percentage (%) of
the sequence data.
[0021] In some embodiments, the determined information indicative
of the determined integrity is indicative of at least two of total
sequence coverage; exon coverage; chromosomal coverage; a ratio of
nucleic acids encoding two or more subunits of a multimeric
protein; species contamination; single nucleotide polymorphisms
(SNPs); complexity; and guanine (G) and cytosine (C) percentage (%)
of the sequence data.
[0022] In some embodiments, the determined information indicative
of the determined integrity is indicative of at least three of
total sequence coverage; exon coverage; chromosomal coverage; a
ratio of nucleic acids encoding two or more subunits of a
multimeric protein; species contamination; single nucleotide
polymorphisms (SNPs); complexity; and guanine (G) and cytosine (C)
percentage (%) of the sequence data.
[0023] In some embodiments, the asserted information for the
sequence data comprises MHC allele information for the subject.
[0024] In some embodiments, the method further comprises
determining one or more MHC allele sequences from the sequence data
and determining whether the one or more MHC alleles sequences match
the asserted MHC allele information for the subject.
[0025] In some embodiments, determining the one or more MHC allele
sequences comprises determining MHC allele sequences for six MHC
loci from the sequence data.
[0026] In some embodiments, wherein the sequence data indicates the
nucleotide sequence for RNA, the asserted information indicates
whether the RNA is polyA enriched.
[0027] In some embodiments, determining, using the sequence data, a
therapy for the subject when it is determined that the asserted
information matches the determined information.
[0028] In some embodiments, determining the therapy comprises:
determining a plurality of gene group expression levels, the
plurality of gene group expression levels comprising a gene group
expression level for each gene group in a set of gene groups,
wherein the set of gene groups comprises at least one gene group
associated with cancer malignancy, and at least one gene group
associated with cancer microenvironment; and identifying the
therapy using the determined gene group expression levels.
[0029] In some embodiments, the method further comprises
administering the therapy to the subject.
[0030] In some embodiments, wherein it is determined that the
determined information matches the asserted information, the
sequence data is processed to determine a therapy for the subject,
and the therapy is administered to the subject.
[0031] In some embodiments, wherein the disease is cancer, and the
therapy is a cancer treatment. In some embodiments, the subject is
human.
[0032] In some embodiments, processing the sequence data to obtain
the determined source comprises determining one or more single
nucleotide polymorphisms (SNPs) in the sequence data, and
determining whether the one or more SNPs in the sequence data match
one or more SNPs in a reference sequence.
[0033] In some embodiments, the reference sequence is a sequence of
a nucleic acid in a second biological sample of the subject.
[0034] In some embodiments, processing the sequence data to obtain
a determined integrity comprises: determining a first level of a
first nucleic acid encoding a first subunit of a multimeric
protein, determining a second level of a second nucleic acid
encoding a second subunit of a multimeric protein, and determining
whether a ratio between the first level and the second level
matches an expected ratio. In some embodiments, the multimeric
protein is a dimer. In some embodiments, the first subunit and the
second subunits are first and second CD3 subunits, first and second
CD8 subunits, or first and second CD79 subunits.
[0035] Some embodiments provide for a system for identifying a
cancer treatment for a subject having, suspected having, or at risk
of having cancer, the system comprising: at least one sequencing
platform configured to generate gene expression data from enriched
RNA obtained from a first biological sample previously obtained
from the subject, wherein the enriched RNA was obtained by: (i)
extracting RNA from the first biological sample of the first tumor
to obtain extracted RNA; and (ii) enriching the extracted RNA for
coding RNA to obtain enriched RNA, wherein the RNA expression data
comprises at least 5 kilobases (kb); at least one computer hardware
processor; and at least one non-transitory computer-readable
storage medium storing processor-executable instructions that, when
executed by the at least one computer hardware processor, cause the
at least one computer hardware processor to perform: obtaining the
RNA expression data using the at least one sequencing platform;
converting the RNA expression data to gene expression data;
determining bias-corrected gene expression data from the gene
expression data at least in part by removing, from the gene
expression data, expression data for at least one gene that
introduces bias in the gene expression data; and identifying a
cancer treatment for the subject using the bias-corrected gene
expression data.
[0036] Some embodiments provide for a system for identifying a
cancer treatment for a subject having, suspected having, or at risk
of having cancer, the system comprising: at least one computer
hardware processor; and at least one non-transitory
computer-readable storage medium storing processor-executable
instructions that, when executed by the at least one computer
hardware processor, cause the at least one computer hardware
processor to perform: obtaining RNA expression data from at least
one sequencing platform, the RNA expression data comprising at
least 5 kilobases (5 kb), wherein the RNA expression data was
obtained, from a first biological sample of a first tumor
previously obtained from the subject, at least in part by: (i)
extracting RNA from the first biological sample of the first tumor
to obtain extracted RNA; and (ii) enriching the extracted RNA for
coding RNA to obtain enriched RNA; converting the RNA expression
data to gene expression data; determining bias-corrected gene
expression data from the gene expression data at least in part by
removing, from the gene expression data, expression data for at
least one gene that introduces bias in the gene expression data;
and identifying a cancer treatment for the subject using the
bias-corrected gene expression data. The system may further
comprise the at least one sequencing platform in some
embodiments.
[0037] Some embodiments provide for at least one non-transitory
computer-readable storage medium storing processor-executable
instructions that, when executed by at least one computer hardware
processor, cause the at least one computer hardware processor to
perform: obtaining RNA expression data from at least one sequencing
platform, the RNA expression data comprising at least 5 kilobases
(5 kb), wherein the RNA expression data was obtained, from a first
biological sample of a first tumor previously obtained from a
subject having, suspected of having or at risk of having cancer, at
least in part by: (i) extracting RNA from the first biological
sample of the first tumor to obtain extracted RNA; and (ii)
enriching the extracted RNA for coding RNA to obtain enriched RNA;
converting the RNA expression data to gene expression data;
determining bias-corrected gene expression data from the gene
expression data at least in part by removing, from the gene
expression data, expression data for at least one gene that
introduces bias in the gene expression data; and identifying a
cancer treatment for the subject using the bias-corrected gene
expression data.
[0038] Some embodiments provide for a method comprising: obtaining
a first biological sample of a first tumor, the first biological
sample previously obtained from a subject having, suspected of
having or at risk of having cancer; extracting RNA from the first
biological sample of the first tumor to obtain extracted RNA;
enriching the extracted RNA for coding RNA to obtain enriched RNA;
sequencing, using at least one sequencing platform, the enriched
RNA to obtain RNA expression data comprising at least 5 kilobases
(kb); using at least one computer hardware processor to perform:
obtaining the RNA expression data using the at least one sequencing
platform; converting the RNA expression data to gene expression
data; determining bias-corrected gene expression data from the gene
expression data at least in part by removing, from the gene
expression data, expression data for at least one gene that
introduces bias in the gene expression data; and identifying a
cancer treatment for the subject using the bias-corrected gene
expression data.
[0039] In some embodiments, the method further comprises
administering the identified cancer treatment to the subject.
[0040] In some embodiments, enriching the RNA for coding RNA
comprises performing polyA enrichment.
[0041] In some embodiments, the at least one gene that introduces
bias in the gene expression data comprises: a gene having an
average transcript length that is higher or lower than an average
length of transcripts in the gene expression data; a gene having at
least a threshold variation in average transcript expression level
based on transcript expression levels in reference samples;
[0042] and/or a gene that has a polyA tail that is at least a
threshold amount smaller in length compared to an average length of
polyA tails of genes from: the first biological sample from which
the RNA expression data was obtained and/or a reference sample.
[0043] In some embodiments, the at least one gene that introduces
bias in the gene expression data belongs to a family of genes
selected from the group consisting of: histone-encoding genes,
mitochondrial genes, interleukin-encoding genes, collagen-encoding
genes, B-cell receptor-encoding genes, and T cell receptor-encoding
genes.
[0044] In some embodiments, the at least one gene comprises at
least one histone-encoding gene selected from the group consisting
of: HIST1H1A, HIST1H1B, HIST1H1C, HIST1H1D, HIST1H1E, HIST1H1T,
HIST1H2AA, HIST1H2AB, HIST1H2AC, HIST1H2AD, HIST1H2AE, HIST1H2AG,
HIST1H2AH, HIST1H2AI, HIST1H2AJ, HIST1H2AK, HIST1H2AL, HIST1H2AM,
HIST1H2BA, HIST1H2BB, HIST1H2BC, HIST1H2BD, HIST1H2BE, HIST1H2BF,
HIST1H2BG, HIST1H2BH, HIST1H2BI, HIST1H2BJ, HIST1H2BK, HIST1H2BL,
HIST1H2BM, HIST1H2BN, HIST1H2BO, HIST1H3A, HIST1H3B, HIST1H3C,
HIST1H3D, HIST1H3E, HIST1H3F, HIST1H3G, HIST1H3H, HIST1H3I,
HIST1H3J, HIST1H4A, HIST1H4B, HIST1H4C, HIST1H4D, HIST1H4E,
HIST1H4F, HIST1H4G, HIST1H4H, HIST1H4I, HIST1H4J, HIST1H4K,
HIST1H4L, HIST2H2AA3, HIST2H2AA4, HIST2H2AB, HIST2H2AC, HIST2H2BE,
HIST2H2BF, HIST2H3A, HIST2H3C, HIST2H3D, HIST2H3PS2, HIST2H4A,
HIST2H4B, HIST3H2A, HIST3H2BB, HIST3H3, and HIST4H4.
[0045] In some embodiments, the at least one gene comprises at
least one mitochondrial gene selected from the group consisting of:
MT-ATP6, MT-ATP8, MT-CO1, MT-CO2, MT-CO3, MT-CYB, MT-ND1, MT-ND2,
MT-ND3, MT-ND4, MT-ND4L, MT-ND5, MT-ND6, MT-RNR1, MT-RNR2, MT-TA,
MT-TC, MT-TD, MT-TE, MT-TF, MT-TG, MT-TH, MT-TI, MT-TK, MT-TL1,
MT-TL2, MT-TM, MT-TN, MT-TP, MT-TQ, MT-TR, MT-TS1, MT-TS2, MT-TT,
MT-TV, MT-TW, MT-TY, MTRNR2L1, MTRNR2L10, MTRNR2L11, MTRNR2L12,
MTRNR2L13, MTRNR2L3, MTRNR2L4, MTRNR2L5, MTRNR2L6, MTRNR2L7, and
MTRNR2L8.
[0046] In some embodiments, determining the bias-corrected gene
expression data further comprises: after removing the expression
data for the at least one gene that introduces bias in the gene
expression data, renormalizing the gene expression data.
[0047] In some embodiments, converting the RNA expression data to
gene expression data comprises: removing non-coding transcripts
from the RNA expression data to obtain filtered RNA expression
data; and after removing the non-coding transcripts, normalizing
the filtered RNA expression data to obtain gene expression data in
transcripts per million (TPM) and/or any other suitable format.
[0048] In some embodiments, removing the non-coding transcripts
from the RNA expression data comprises removing non-coding
transcripts that belong to groups selected from the list consisting
of: pseudogenes, polymorphic pseudogenes, processed pseudogenes,
transcribed processed pseudogenes, unitary pseudogenes, unprocessed
pseudogenes, transcribed unitary pseudogenes, constant chain
immunoglobulin (IG C) pseudogenes, joining chain immunoglobulin (IG
J) pseudogenes, variable chain immunoglobulin (IG V) pseudogenes,
transcribed unprocessed pseudogenes, translated unprocessed
pseudogenes, joining chain T cell receptor (TR J) pseudogenes,
variable chain T cell receptor (TR V) pseudogenes, small nuclear
RNAs (snRNA), small nucleolar RNAs (snoRNA), microRNAs (miRNA),
ribozymes, ribosomal RNA (rRNA), mitochondrial tRNAs (Mt tRNA),
mitochondrial rRNAs (Mt rRNA), small Cajal body-specific RNAs
(scaRNA), retained introns, sense intronic RNA, sense overlapping
RNA, nonsense-mediated decay RNA, non-stop decay RNA, antisense
RNA, long intervening noncoding RNAs (lincRNA), macro long
non-coding RNA (macro lncRNA), processed transcripts, 3prime
overlapping non-coding RNA (3prime overlapping ncrna), small RNAs
(sRNA), miscellaneous RNA (misc RNA), vault RNA (vaultRNA), and TEC
RNA.
[0049] In some embodiments, information (e.g., sequence
information) for one or more transcripts for one of more of these
types of transcripts can be obtained in a nucleic acid database
(e.g., a Gencode database, for example Gencode V23, Genbank
database, EMBL database, or other database).
[0050] In some embodiments, the method further comprises, prior to
performing the removal of the non-coding transcripts, aligning the
RNA expression data to a reference; and annotating the RNA
expression data.
[0051] In some embodiments, the RNA expression data comprises at
least 25 million paired-end reads. In some embodiments, the RNA
expression data comprises at least 50 million paired-end reads,
with an average read length of at least 100 bp.
[0052] In some embodiments, identifying the cancer treatment for
the subject using the bias-corrected gene expression data
comprises: determining, using the bias-corrected gene expression
data, a plurality of gene group expression levels, the plurality of
gene group expression levels comprising a gene group expression
level for each gene group in a set of gene groups, wherein the set
of gene groups comprises at least one gene group associated with
cancer malignancy, and at least one gene group associated with
cancer microenvironment; and identifying the cancer treatment using
the determined gene group expression levels.
[0053] In some embodiments, the cancer treatment is selected from
the group consisting of a radiation therapy, a surgical therapy, a
chemotherapy, and an immunotherapy.
[0054] In some embodiments, the method further comprises obtaining
a second biological sample of a second tumor, the second biological
sample previously obtained from the subject.
[0055] In some embodiments, the method further comprises combining
the first biological sample and the second biological sample to
form a combined tumor sample, and extracting the RNA comprises
extracting the RNA from the combined tumor sample.
[0056] In some embodiments, the method further comprises extracting
RNA from the second biological sample; and combining the RNA
extracted from the second biological sample with the RNA extracted
from the first biological sample to form combined extracted RNA,
and enriching the RNA for coding RNA comprises enriching the
combined extracted RNA for coding RNA.
[0057] In some embodiments, the extracted RNA comprises at least 1
.mu.g of RNA upon RNA extraction.
[0058] In some embodiments, the extracted RNA is at least 1000-6000
ng in total mass, and has a purity corresponding to a ratio of
absorbance at 260 nm to absorbance at 280 nm of at least 2.0.
[0059] In some embodiments, the method further comprises performing
quality control assessment on the RNA expression data at least in
part by: obtaining asserted information indicating an asserted
source and/or an asserted integrity of the RNA expression data;
processing the RNA expression data to obtain determined information
indicating a determined source and/or a determined integrity of the
RNA expression data; and determining whether the determined
information matches the asserted information.
[0060] In some embodiments, processing the RNA expression data
comprises processing the RNA expression RNA to determine: a tissue
type of the first biological sample; a tumor type of the first
biological sample; and/or guanine (G) and/or cytosine (C)
percentage (%).
BRIEF DESCRIPTION OF THE DRAWINGS
[0061] The following drawings form part of the present
specification and are included to further demonstrate certain
aspects of the present disclosure, which can be better understood
by reference to one or more of these drawings in combination with
the detailed description of specific embodiments presented herein.
The drawings are not necessarily drawn to scale.
[0062] FIG. 1A and FIG. 1B provide exemplary flow charts that
illustrate processes of sample preparation and quality control.
FIG. 1A provides an example of a process pipeline that includes one
or more quality control assessments during biopsy sample
collection, DNA/RNA extraction and library construction, and/or
nucleic acid bioinformatic analysis. FIG. 1B provides an example of
a process for obtaining a biopsy sample of a subject, extracting
nucleic acid from the sample, sequencing the nucleic acid, and
processing the nucleic acid sequence to identify one or more cancer
therapies appropriate for the subject.
[0063] FIG. 2A and 2B provide graphical representations of the
levels and distribution of RNA transcripts depending on the type of
RNA enrichment methods used and whether stranded or non-stranded
RNA was used for sequencing. FIG. 2A provides a graphical
representation of distribution of RNA after RNA enrichment by
either depletion of ribosomal RNA (r-RNA) or by poly A enrichment.
FIG. 2B provides a graphical representation of levels of RNA
measured after RNA sequencing of either stranded or non-stranded
RNA for IL24, ICAM4, and GAPDH.
[0064] FIG. 3 is a graphical representation of the distribution of
different RNA transcripts for different types of RNA as shown in
the legend. Each column represents a unique sample. All samples
were prepared from the same tissue type, using the same RNA
enrichment method and the same sequencing service. The bottom panel
shows the data in the top panel in Transcripts per Kilobase Million
(TPM).
[0065] FIG. 4A shows the distribution of poly-A tails of RNA
transcripts from HeLa cell samples, and several examples of poly-A
tails for histone family genes.
[0066] FIG. 4B shows a comparison of expression of mitochondrial
RNA for samples that are either poly-A-enriched or enriched by rRNA
depletion (denoted by total RNA).
[0067] FIG. 4C shows a comparison of expression of Histone coding
RNA for samples that are either poly-A-enriched or enriched by rRNA
depletion (denoted by total RNA).
[0068] FIG. 5 shows a principal component analysis (PCA) of RNA
expression of cell samples containing different percentages of HeLa
cells, either with or without polyA enrichment, and with or without
data filtration. Data filtration included removal of non-coding RNA
transcripts, histone-coding transcripts, and mitochondrial
transcripts. The PCAO--component describes major differences
between poly-A and total-RNA sequencing. The PCA1 component
describes different cell line ratios. Samples were prepared as a
mixture of two different cell lines in 5 different ratios.
[0069] FIG. 6A is an exemplary flowchart that illustrates a process
200 for obtaining enriched RNA sequence data from a tumor of a
subject having, suspected of having, or at risk of having
cancer.
[0070] FIG. 6B is an exemplary flowchart that illustrates a process
210 for obtaining bias-corrected gene expression data from RNA
expression data to identify a cancer treatment for a subject
having, suspected of having, or at risk of having cancer.
[0071] FIG. 6C is an exemplary flowchart that illustrates a process
220 for processing RNA obtained from a tumor sample to identify a
cancer treatment for a subject having, suspected of having, or at
risk of having cancer.
[0072] FIG. 7 is flowchart of an illustrative process pipeline 300
comprising bioinformatic quality control processes for assessing
nucleic acid sequence data obtained from a tumor sample and using
the nucleic acid sequence data to identify a cancer treatment for a
subject having, suspected of having, or at risk of having
cancer.
[0073] FIG. 8 is an exemplary flowchart that illustrates a process
800 showing computerized processes for processing and validating
sequence data and related information.
[0074] FIG. 9 is a block diagram of an illustrative computer system
500 that may be used to implement one or more embodiments of a
process pipeline for preparing, assessing, and/or analyzing
sequence data.
[0075] FIG. 10 is a block diagram of an illustrative environment
600 in which one or more embodiments of the technology described in
this application may be implemented.
[0076] FIG. 11 shows the results of MHC allele analysis for
sequence information obtained from three nucleic acids (RNA-Seq,
WES Tumor, and WES Normal) for two subjects (103 and 105).
[0077] FIG. 12 shows an example of a bar graph representing the
probability of sequence information being from a particular type of
tumor (e.g., BRCA related to breast cancer).
[0078] FIGS. 13A-13B show graphs representing an example of the
relationship between protein subunit expression levels. FIGS.
14A-14B show examples of bar graphs representing the probability
that sequence information was obtained from samples that contained
only polyadenylated RNA or from samples that contained total or all
RNA (total RNA).
[0079] FIG. 15 shows an example of a principal component analysis
and illustrates the analysis of three batches of gene expression
data including tumor and normal samples.
DETAILED DESCRIPTION
[0080] Recent advances in personalized genomic sequencing and
cancer genomic sequencing technologies have made it possible to
obtain patient-specific information about cancer cells (e.g., tumor
cells) and cancer microenvironments from one or more biological
samples obtained from individual patients. The inventors have
appreciated that this information may be used to characterize the
type(s) of cancer a patient has and, potentially, select one or
more effective therapies for the patient. This information may also
be used to determine how a patient is responding over time to a
treatment and, if necessary, to select a new therapy or therapies
for the patient as necessary. This information may also be used to
determine whether a patient should be included or excluded from
participating in a clinical trial.
[0081] The inventors have recognized that the workflow used to
obtain sequence data for a patient strongly influences the
inferences that can be drawn about the patient's cancer. Such
inferences include, but are not limited to, determining whether the
patient will respond to a particular therapy or therapies, whether
the patient will have an adverse reaction to a particular therapy
or therapies, whether the patient is a candidate for enrollment in
a clinical trial, whether the patient has one or more particular
biomarkers (e.g., biomarkers indicative of potential response to a
therapy, biomarkers indicative of survival, etc.), whether the
patient's disease has progressed (e.g., from an earlier stage
cancer to a later stage cancer, relapsed from remission, etc.),
whether a different therapy or therapies should be selected for the
patient, and/or any other suitable prognostic, diagnostic, and/or
clinical inferences.
[0082] When the workflow used to obtain sequence data contains
errors, sub-optimal processing, sources of bias in the data, and
the like, it is often not possible to make inferences about the
subject's cancer with the desired or necessary confidence, or even
make any such inferences at all. Even worse, errors in the workflow
for producing sequence data may result in incorrect inferences
about the patient, potentially leading to incorrect treatment or
missed opportunities for better treatment. Moreover, workflow
errors lead to wasted resources in the laboratory (e.g., having to
reprocess samples) and wasted computing resources (e.g., performing
expensive computational processing on megabytes and gigabytes of
sequence data, taking up processor and networking resources, only
to discard the results at a later time and/or have to repeat the
processing).
[0083] A conventional workflow used to obtain sequence data for a
patient includes multiple steps including: obtaining a biological
sample from the patient (e.g., by performing a biopsy, obtaining a
blood sample, a salivary sample or any other suitable biological
sample from the patient), preparing the biological sample for
sequencing using a sequencing platform (e.g., a next generation
sequencing (NGS) platform), and obtaining raw data output by the
sequencing platform. Various conventional bio-informatics
processing pipelines and other algorithms may then use the raw data
output by the sequencing platform in an attempt to make one or more
of the above-described inferences.
[0084] However, such conventional workflows for obtaining
sequencing data are prone to errors at all stages. For example,
errors may be made in a laboratory when handling samples for
multiple patients. Indeed, it is not uncommon for a laboratory to
receive a biological sample asserted to be from one patient, when
that sample is from another patient. As another example, a
biological sample may not be processed properly by the laboratory
and may not have the concentration and/or quality of nucleic acid
needed for subsequent analysis. As yet another example, errors may
be introduced by the sequencing platform itself and/or subsequent
post processing steps (e.g., alignment and variant calling). As yet
another example, raw sequencing data produced by a sequencing
platform may contain artefacts and undesired sequences and/or
transcripts. Other examples of various errors are described
herein.
[0085] In some embodiments, sequence data or sequencing data may
include raw DNA or RNA sequence data, DNA exome sequence data
(e.g., from whole exome sequencing (WES), DNA genome sequence data
(e.g., from whole genome sequencing (WGS)), RNA expression data,
gene expression data, bias-corrected gene expression data or any
other suitable type of sequence data comprising data obtained from
a sequencing platform and/or comprising data derived from data
obtained from a sequencing platform.
[0086] To address shortcomings of conventional workflows for
obtaining sequencing data for patients, the inventors have
developed techniques that address various sources of error that may
be present in sequencing data. These techniques developed by the
inventors include: (1) novel sample preparation techniques to
prepare biological samples for sequencing using one or multiple
sequencing platforms; (2) novel techniques for post processing raw
data output by the sequencing platform(s) to filter out irrelevant
data and sources of bias (e.g., transcripts for non-coding regions
and expression data associated with genes that introduce bias in
the sequence data); and (3) novel quality control techniques that
facilitate the detection and remediation of errors in the sequence
data. In some embodiments, techniques from each of these three
categories may be utilized in a workflow to obtain sequence data
for a patient, though it should be appreciated that this is not a
limitation of the techniques described herein, and that, in some
embodiments, any one or more of the techniques (but not necessarily
all of them) may be used in a workflow.
[0087] As one example, in some embodiments, novel sample
preparation techniques and post-processing techniques include
obtaining sequencing data and removing sources of bias from the
sequencing data by: (1) obtaining a first biological sample of a
first tumor, the first biological sample previously obtained from a
subject having, suspected of having or at risk of having cancer;
(2) extracting RNA from the first biological sample of the first
tumor to obtain extracted RNA; (3) enriching the extracted RNA for
coding RNA to obtain enriched RNA; (4) sequencing, using at least
one sequencing platform, the enriched RNA to obtain RNA expression
data comprising at least 5 kilobases (kb); and (5) using at least
one computer hardware processor to perform: (a) obtaining the RNA
expression data using the at least one sequencing platform; (b)
converting the RNA expression data to gene expression data; (c)
determining bias-corrected gene expression data from the gene
expression data at least in part by removing, from the gene
expression data, expression data for at least one gene that
introduces bias in the gene expression data; and (d) identifying a
cancer treatment for the subject using the bias-corrected gene
expression data.
[0088] Removing bias from the gene expression data in this way
provides an improvement to sequencing technology for numerous
reasons. First, it removes artefacts and sources of bias from
sequencing data, resulting in fewer errors in any downstream
processing and a higher fidelity output. Second, the inventors have
recognized that removing sources of bias in this way allows for
more accurately and faithfully representing a patient's molecular
functional characteristics (e.g., via molecular functional
expression signatures described herein). The inventors have
recognized that the bias-corrected gene expression data may be used
to identify more effective therapies for a patient, improve ability
to determine whether one or more cancer therapies will be effective
if administered to the patient, improve the ability to identify
clinical trials in which the subject may participate, and/or
improvements to numerous other prognostic, diagnostic, and clinical
applications.
[0089] As another example, in some embodiments, novel quality
control techniques include using at least one computer hardware
processor to perform: (a) obtaining nucleic acid data comprising:
(i) sequence data indicating a nucleotide sequence for at least 5
kilobases (kb) of DNA and/or RNA from a previously obtained
biological sample of a subject having, suspected of having, or at
risk of having a disease; and (ii) asserted information indicating
an asserted source and/or an asserted integrity of the sequence
data; and (b) validating the nucleic acid data by: (i) processing
the sequence data to obtain determined information indicating a
determined source and/or a determined integrity of the sequence
data; and (ii) determining whether the determined information
matches the asserted information. Examples of various such
validation techniques are described herein, and they are important
examples of quality control techniques developed by the inventors
and described herein.
[0090] Employing such quality control techniques also provides an
improvement to sequencing technology and computer technology.
First, sequencing data that do not pass one or more quality control
checks are not used for some or all of downstream processing
reducing or eliminating errors in downstream applications (e.g.,
identifying biomarkers, tumor microenvironment types, possible
therapies for a patient, etc.). Often such downstream processing
requires performing expensive (frequently cloud-based)
computational processing of large data sets (e.g., sequencing data
may contain tens of millions of reads, which have to be aligned,
annotated and processed in other numerous ways). Using quality
control to prevent computationally expensive processes from
executing will reduce or eliminate wasteful use of computing
resources, saving processing power, memory, and networking
resources (which is an improvement to computing technology in
addition to being an improvement to sequencing technology).
Identifying errors will also reduce waste of resources at a
laboratory that processes multiple samples, by freeing up equipment
for processing biological samples that have passed initial quality
control checks. In addition, using sequence data for downstream
processing that has passed various quality control checks may be
used to identify more effective therapies for a patient, improve
ability to determine whether one or more cancer therapies will be
effective if administered to the patient, improve the ability to
identify clinical trials in which the subject may participate,
and/or improvements to numerous other prognostic, diagnostic, and
clinical applications.
[0091] FIGS. 1A and 1B illustrate examples of process pipelines for
sample preparation and quality control as described herein. The
process pipeline in FIG. 1 illustrate embodiments of methods and
systems provided in the present disclosure and are not to be
construed in any way as limiting their scope. The present
disclosure provides that a process pipeline does not need to
include all of the process steps or the order of process steps
illustrated in FIG. 1. One or more processes can be omitted,
repeated, or performed in a different order depending on the
application.
[0092] FIG. 1A illustrates a non-limiting process pipeline 100 that
includes one or more quality control assessments. A biological
sample (e.g., a tumor biopsy) is obtained for a subject (e.g., a
subject having, suspected of having, or at risk of having cancer)
in act 101. In some embodiments, the sample is obtained from a
physician, hospital, clinic, or other healthcare provider. One or
more sample quality control assessments at quality control act 102
can be performed on the biological sample. In some embodiments, a
quality control assessment on the biological sample (e.g., biopsy
material) comprises determining whether the sample is in an
appropriate form (e.g., fresh frozen or FFPE) and/or is accompanied
by sufficient information to identify the nature and source of the
sample. Subsequently, nucleic acid (e.g., DNA and/or RNA) can be
extracted from a biological sample that satisfies sample quality
control act 102. One or more nucleic acid quality control
assessments at act 103 then can be performed, for example to
evaluate one or more physical attributes of the extracted nucleic
acid, of a nucleic acid library prepared from the extracted nucleic
acid, and/or of pooled nucleic acids or libraries. Subsequently,
nucleic acid (e.g., DNA and/or RNA) that satisfies nucleic acid
quality control act 103 can be processed (e.g., to enrich for polyA
RNA) and/or sequenced to obtain raw DNA and/or RNA sequence data
(e.g., RNA expression data). In some embodiments, RNA expression
data can be processed to obtain gene expression data and optionally
to remove data for one or more types of genes that could interfere
with (e.g., bias) subsequent analysis of the gene expression data.
In some embodiments, gene expression data is normalized (e.g.,
after removal of the data for the one or more interfering genes).
In some embodiments, one or more sequence quality control
assessments are performed on DNA and/or RNA sequence data (e.g., on
processed, for example normalized, gene expression data) for
bioinformatic quality control act 104. In some embodiments, one or
more bioinformatic quality control assessments are performed to
determine whether sequence data is from an expected source (e.g.,
patient, tissue, tumor, etc.) and/or whether it has sufficient
integrity for further analysis. In some embodiments, sequence data
that satisfies bioinformatic quality control act 104 is further
processed, for example, to determine a diagnosis, prognosis, and/or
therapy for a subject, to evaluate and/or monitor a subject, and/or
for one or more clinical applications (e.g., to evaluate a
therapy).
[0093] In some embodiments, the sequence data may include raw DNA
or RNA sequence data, DNA exome sequence data (e.g., from whole
exome sequencing (WES), DNA genome sequence data (e.g., from whole
genome sequencing (WGS)), RNA expression data, gene expression
data, bias-corrected gene expression data or any other suitable
type of sequence data comprising data obtained from a sequencing
platform and/or comprising data derived from data obtained from a
sequencing platform including, but not limited to, examples of such
data described herein. FIG. 1B illustrates a non-limiting process
pipeline 110 for preparing nucleic acid from a biological sample
(e.g., a tumor biopsy) and obtaining and processing nucleic acid
sequence data for subsequent analysis (e.g., for diagnostic,
prognostic, therapeutic, and/or other clinical applications).
Process pipeline 110 is performed by obtaining a biological sample
(e.g., a tumor sample) from a subject having, suspected of having,
or at risk of having cancer at act 111. Nucleic acid (e.g., DNA
and/or RNA) is obtained (e.g., extracted) from the sample at act
112. One or more quality control assessments of the nucleic acid is
performed in act 113. One or more nucleic acid libraries is
prepared in act 114, for example using nucleic acid that satisfies
at least one quality control assessment of act 113. The nucleic
acid libraries are sequenced using at least one sequencing platform
in sequencing act 115 (e.g., to obtain RNA expression data for
RNA). In some embodiments, RNA expression data is converted to gene
expression data in act 116, and the gene expression data is
optionally bias-corrected, at least in part, by removing expression
data for at least one gene that introduces bias in the gene
expression data. One or more bioinformatic quality control
assessments are performed on the DNA sequence data or RNA sequence
data from act 115 and/or RNA sequence data (e.g., the
bias-corrected gene expression data from act 116) in bioinformatics
quality control act 117. In some embodiments, nucleic acid data
(e.g., that satisfies at least one bioinformatic quality control
assessments of act 117), is further processed in act 118 (e.g., to
determine one or more indicia of disease from the gene expression
data), to perform a diagnostic, prognostic, therapeutic, and/or
other clinical assessment of the subject (e.g., to identify a
treatment, for example a cancer treatment, for the subject) in act
119. In some embodiments, a treatment (e.g., a cancer treatment) is
administered to the subject.
[0094] In some embodiments, act 111 comprises obtaining bulk biopsy
tissues of a subject or a patient. In some embodiments, act 111
comprises obtaining a blood sample of a subject or a patient. In
some embodiments, act 111 comprises obtaining a single cell
suspension. In some embodiments, act 111 comprises obtaining any
types of sample that are suitable for preparing nucleic acids for
subsequent sequencing analysis. In some embodiments, act 111
comprises obtaining more than one type of samples.
[0095] In some embodiments, when the bulk biopsy tissues are
obtained, the tissues are processed (e.g., homogenized in the
presence of TriZol) to extract nucleic acids such as DNA or RNA at
act 112. In some embodiments, when a single cell suspension is
obtained, the suspension is processed to extract nucleic acids such
as DNA or RNA at act 112. In some embodiments, nucleic acids can be
extracted that are suitable for germline whole exome sequencing
(WES) at act 112. In some embodiments, nucleic acids can be
extracted that are suitable for tumor whole exome sequencing (WES)
at act 112. In some embodiments, nucleic acids can be extracted
that are suitable for tumor RNA sequencing at act 112. In some
embodiments, nucleic acids can be extracted that are suitable for
CYTOF (mass cytometry) at act 112. In some embodiments, nucleic
acids can be extracted that are suitable for any type of sequencing
known in the art at act 112.
[0096] At act 113, one or more quality control assessments can be
performed. Acceptable and/or target thresholds can be determined
and used as references. In some embodiments, the total amount of
extracted DNA or RNA can be used for quality control assessment. In
some embodiments, a spectrophotometer, for example a small volume
full-spectrum, UV-visible spectrophotometer (e.g., NanoDrop
spectrophotometer available from ThermoFisher Scientific,
www.thermofisher.com) can be used for quality control assessment of
DNA or RNA. In some embodiments, a fluorometer, for example for
quantification of DNA or RNA (e.g., a Qubit fluorometer available
from ThermoFisher Scientific, www.thermofisher.com) can be used for
quality control assessment of DNA or RNA. In some embodiments, an
automated electrophoresis system (e.g., TAPESTATION) can be used
for quality control assessment of DNA or RNA. In some embodiments,
a real-time PCR system (e.g., LIGHTCYCLER.RTM.) can be used for
quality control assessment of DNA or RNA.
[0097] In some embodiments, act 114 comprises preparing libraries
for the extracted nucleic acids that have satisfied at least one
quality control threshold at act 113. In some embodiments, act 114
comprises one or more methods described in Example 2.
[0098] In some embodiments, act 115 comprises sequencing nucleic
acid (e.g., the DNA, RNA, or related libraries of act 114) to
obtain DNA sequence data and/or RNA sequence data (e.g., RNA
expression data) using at least one nucleic acid sequencing
platform (e.g., a next generation nucleic acid sequencing
platform). Sequence data obtained at act 115 can be stored in any
suitable format (e.g., in the form of one or more FASTQ files).
[0099] In some embodiments, RNA expression data is converted to
gene expression data at act 116. In some embodiments, RNA
expression data is aligned to known genes in a database, for
example to a known assembled genome (e.g., a human genome) or to a
transcriptome in the database. In some embodiments, a program for
quantifying transcripts, for example from bulk and single-cell
RNA-Seq data, using high-throughput sequencing reads (e.g.,
Kallisto (hg38) available from Github, www.github.com, for example
as described in Nicolas L Bray, Harold Pimentel, Pall Melsted and
Lior Pachter, Near-optimal probabilistic RNA-seq quantification,
Nature Biotechnology 34, 525-527 (2016), doi:10.1038/nbt.3519),
and/or Gencode (e.g., Gencode V23) is used for sequence alignment,
and/or annotation. In some embodiments, act 116 comprises gene
aggregation. In some embodiments, act 116 comprises removing
expression data for one or more non-coding transcripts from the
gene expression data. In some embodiments, act 116 comprises
removing expression data for one or more genes that can bias the
gene expression data. In some embodiments, act 116 comprises
removing expression data for histone encoding genes and/or
mitochondrial-encoding genes. In some embodiments, act 116
comprises normalization (e.g., TPM normalization) after removal of
expression data for non-coding and/or bias-associated genes from
the gene expression data. This normalization may be termed
"renormalization" herein.
[0100] At act 117, one or more bioinformatic quality control
assessments is performed on nucleic acid sequence data, for example
DNA sequence data and/or RNA sequence data (e.g., bias corrected,
and/or normalized, gene expression data). In some embodiments, one
or more bioinformatic quality control assessments can be performed
to evaluate the source and/or integrity of the nucleic acid
sequence data. In some embodiments, one or more bioinformatic
quality control assessments described in this application are
performed.
[0101] In some embodiments, a method comprises all processes
illustrated in FIG. 1. However, in some embodiments, a subset of
the processes is performed and any one or more of the processes may
be omitted, duplicated, and/or performed in a different order than
illustrated in FIG. 1. In some embodiments, a method comprises a
process, optionally including one or more quality control steps,
for preparing a nucleic acid from a biological sample, wherein the
nucleic acid is sequenced on at least one sequencing platform. In
some embodiments, a method comprises processing nucleic acid
information obtained (e.g., received) from a sequencing platform to
generate DNA or RNA sequence data for subsequent analysis (e.g., to
generate bias-corrected, optionally normalized gene expression data
for subsequent analysis). In some embodiments, one or more
processes of FIG. 1 are implemented on a computer. In some
embodiments, a method comprises identifying a treatment (e.g., a
cancer treatment) for a subject (e.g., a subject having, suspected
of having, or at risk of having cancer). In some embodiments, a
method comprises administering the treatment to the subject.
Biological Samples
[0102] Any of the methods, systems, or other claimed elements may
use or be used to analyze a biological sample from a subject. In
some embodiments, a biological sample is obtained from a subject
having or suspected of having cancer. One or more biological
samples from a subject may be analyzed as described herein to
obtain information about the subject's cancer. The biological
sample may be any type of biological sample including, for example,
a biological sample of a bodily fluid (e.g., blood, urine or
cerebrospinal fluid), one or more cells (e.g., from a scraping or
brushing such as a cheek swab or tracheal brushing), a piece of
tissue (cheek tissue, muscle tissue, lung tissue, heart tissue,
brain tissue, or skin tissue), or some or all of an organ (e.g.,
brain, lung, liver, bladder, kidney, pancreas, intestines, or
muscle), or other types of biological samples (e.g., feces or
hair).
[0103] In some embodiments, the biological sample is a sample of a
tumor from a subject. In some embodiments, the biological sample is
a sample of blood from a subject. In some embodiments, the
biological sample is a sample of tissue from a subject.
[0104] A sample of a tumor, in some embodiments, refers to a sample
comprising cells from a tumor. In some embodiments, the sample of
the tumor comprises cells from a benign tumor, e.g., non-cancerous
cells. In some embodiments, the sample of the tumor comprises cells
from a premalignant tumor, e.g., precancerous cells. In some
embodiments, the sample of the tumor comprises cells from a
malignant tumor, e.g., cancerous cells.
[0105] Examples of tumors include, but are not limited to,
adenomas, fibromas, hemangiomas, lipomas, cervical dysplasia,
metaplasia of the lung, leukoplakia, carcinoma, sarcoma, germ cell
tumors, and blastoma.
[0106] A sample of blood, in some embodiments, refers to a sample
comprising cells, e.g., cells from a blood sample. In some
embodiments, the sample of blood comprises non-cancerous cells. In
some embodiments, the sample of blood comprises precancerous cells.
In some embodiments, the sample of blood comprises cancerous cells.
In some embodiments, the sample of blood comprises blood cells. In
some embodiments, the sample of blood comprises red blood cells. In
some embodiments, the sample of blood comprises white blood cells.
In some embodiments, the sample of blood comprises platelets.
Examples of cancerous blood cells include, but are not limited to,
leukemia, lymphoma, and myeloma. In some embodiments, a sample of
blood is collected to obtain the cell-free nucleic acid (e.g.,
cell-free DNA) in the blood.
[0107] A sample of blood may be a sample of whole blood or a sample
of fractionated blood. In some embodiments, the sample of blood
comprises whole blood. In some embodiments, the sample of blood
comprises fractionated blood. In some embodiments, the sample of
blood comprises buffy coat. In some embodiments, the sample of
blood comprises serum. In some embodiments, the sample of blood
comprises plasma. In some embodiments, the sample of blood
comprises a blood clot.
[0108] A sample of a tissue, in some embodiments, refers to a
sample comprising cells from a tissue. In some embodiments, the
sample of the tumor comprises non-cancerous cells from a tissue. In
some embodiments, the sample of the tumor comprises precancerous
cells from a tissue. In some embodiments, the sample of the tumor
comprises precancerous cells from a tissue.
[0109] Methods of the present disclosure encompass a variety of
tissue including organ tissue or non-organ tissue, including but
not limited to, muscle tissue, brain tissue, lung tissue, liver
tissue, epithelial tissue, connective tissue, and nervous tissue.
In some embodiments, the tissue may be normal tissue or it may be
diseased tissue or it may be tissue suspected of being diseased. In
some embodiments, the tissue may be sectioned tissue or whole
intact tissue. In some embodiments, the tissue may be animal tissue
or human tissue. Animal tissue includes, but is not limited to,
tissues obtained from rodents (e.g., rats or mice), primates (e.g.,
monkeys), dogs, cats, and farm animals.
[0110] The biological sample may be from any source in the
subject's body including, but not limited to, any fluid [such as
blood (e.g., whole blood, blood serum, or blood plasma), saliva,
tears, synovial fluid, cerebrospinal fluid, pleural fluid,
pericardial fluid, ascitic fluid, and/or urine], hair, skin
(including portions of the epidermis, dermis, and/or hypodermis),
oropharynx, laryngopharynx, esophagus, stomach, bronchus, salivary
gland, tongue, oral cavity, nasal cavity, vaginal cavity, anal
cavity, bone, bone marrow, brain, thymus, spleen, small intestine,
appendix, colon, rectum, anus, liver, biliary tract, pancreas,
kidney, ureter, bladder, urethra, uterus, vagina, vulva, ovary,
cervix, scrotum, penis, prostate, testicle, seminal vesicles,
and/or any type of tissue (e.g., muscle tissue, epithelial tissue,
connective tissue, or nervous tissue).
[0111] Any of the biological samples described herein may be
obtained from the subject using any known technique. See, for
example, the following publications on collecting, processing, and
storing biological samples, each of which are incorporated herein
in its entirety: Biospecimens and biorepositories: from
afterthought to science by Vaught et al. (Cancer Epidemiol
Biomarkers Prey. 2012 February; 21(2):253-5), and Biological sample
collection, processing, storage and information management by
Vaught and Henderson (IARC Sci Publ. 2011; (163):23-42).
[0112] In some embodiments, the biological sample may be obtained
from a surgical procedure (e.g., laparoscopic surgery,
microscopically controlled surgery, or endoscopy), bone marrow
biopsy, punch biopsy, endoscopic biopsy, or needle biopsy (e.g., a
fine-needle aspiration, core needle biopsy, vacuum-assisted biopsy,
or image-guided biopsy). In some embodiments, the biological sample
may be obtained from an autopsy.
[0113] In some embodiments, one or more than one cell (i.e., a cell
biological sample) may be obtained from a subject using a scrape or
brush method. The cell biological sample may be obtained from any
area in or from the body of a subject including, for example, from
one or more of the following areas: the cervix, esophagus, stomach,
bronchus, or oral cavity. In some embodiments, one or more than one
piece of tissue (e.g., a tissue biopsy) from a subject may be used.
In certain embodiments, the tissue biopsy may comprise one or more
than one (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, or more than 10)
biological samples from one or more tumors or tissues known or
suspected of having cancerous cells.
[0114] Any of the biological samples from a subject described
herein may be stored using any method that preserves stability of
the biological sample. In some embodiments, preserving the
stability of the biological sample means inhibiting components
(e.g., DNA, RNA, protein, or tissue structure or morphology) of the
biological sample from degrading until they are measured so that
when measured, the measurements represents the state of the sample
at the time of obtaining it from the subject. In some embodiments,
a biological sample is stored in a composition that is able to
penetrate the same and protect components (e.g., DNA, RNA, protein,
or tissue structure or morphology) of the biological sample from
degrading. As used herein, degradation is the transformation of a
component from one from to another such that the first form is no
longer detected at the same level as before degradation.
[0115] In some embodiments, the biological sample is stored using
cryopreservation. Non-limiting examples of cryopreservation
include, but are not limited to, step-down freezing, blast
freezing, direct plunge freezing, snap freezing, slow freezing
using a programmable freezer, and vitrification. In some
embodiments, the biological sample is stored using lyophilisation.
In some embodiments, a biological sample is placed into a container
that already contains a preservant (e.g., RNALater to preserve RNA)
and then frozen (e.g., by snap-freezing), after the collection of
the biological sample from the subject. In some embodiments, such
storage in frozen state is done immediately after collection of the
biological sample. In some embodiments, a biological sample may be
kept at either room temperature or 4.degree. C. for some time
(e.g., up to an hour, up to 8 h, or up to 1 day, or a few days) in
a preservant or in a buffer without a preservant, before being
frozen.
[0116] Non-limiting examples of preservants include formalin
solutions, formaldehyde solutions, RNALater or other equivalent
solutions, TriZol or other equivalent solutions, DNA/RNA Shield or
equivalent solutions, EDTA (e.g., Buffer AE (10 mM Tris.Cl; 0.5 mM
EDTA, pH 9.0)) and other coagulants, and Acids Citrate Dextronse
(e.g., for blood specimens).
[0117] In some embodiments, special containers may be used for
collecting and/or storing a biological sample. For example, a
vacutainer may be used to store blood. In some embodiments, a
vacutainer may comprise a preservant (e.g., a coagulant, or an
anticoagulant). In some embodiments, a container in which a
biological sample is preserved may be contained in a secondary
container, for the purpose of better preservation, or for the
purpose of avoid contamination.
[0118] Any of the biological samples from a subject described
herein may be stored under any condition that preserves stability
of the biological sample. In some embodiments, the biological
sample is stored at a temperature that preserves stability of the
biological sample. In some embodiments, the sample is stored at
room temperature (e.g., 25.degree. C.). In some embodiments, the
sample is stored under refrigeration (e.g., 4.degree. C.). In some
embodiments, the sample is stored under freezing conditions (e.g.,
-20.degree. C.). In some embodiments, the sample is stored under
ultralow temperature conditions (e.g., -50.degree. C. to
-800.degree. C.). In some embodiments, the sample is stored under
liquid nitrogen (e.g., -1700.degree. C.). In some embodiments, a
biological sample is stored at -60.degree. C. to -8-.degree. C.
(e.g., -70.degree. C.) for up to 5 years (e.g., up to 1 month, up
to 2 months, up to 3 months, up to 4 months, up to 5 months, up to
6 months, up to 7 months, up to 8 months, up to 9 months, up to 10
months, up to 11 months, up to 1 year, up to 2 years, up to 3
years, up to 4 years, or up to 5 years). In some embodiments, a
biological sample is stored as described by any of the methods
described herein for up to 20 years (e.g., up to 5 years, up to 10
years, up to 15 years, or up to 20 years).
[0119] Methods of the present disclosure encompass obtaining one or
more biological samples from a subject for analysis. In some
embodiments, one biological sample is collected from a subject for
analysis. In some embodiments, more than one (e.g., 2, 3, 4, 5, 6,
7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, or more)
biological samples are collected from a subject for analysis. In
some embodiments, one biological sample from a subject will be
analyzed. In some embodiments, more than one (e.g., 2, 3, 4, 5, 6,
7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, or more)
biological samples may be analyzed. If more than one biological
sample from a subject is analyzed, the biological samples may be
procured at the same time (e.g., more than one biological sample
may be taken in the same procedure), or the biological samples may
be taken at different times (e.g., during a different procedure
including a procedure 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 days; 1, 2, 3,
4, 5, 6, 7, 8, 9, 10 weeks; 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 months,
1, 2, 3, 4, 5, 6, 7, 8, 9, 10 years, or 1, 2, 3, 4, 5, 6, 7, 8, 9,
10 decades after a first procedure).
[0120] A second or subsequent biological sample may be taken or
obtained from the same region (e.g., from the same tumor or area of
tissue) or a different region (including, e.g., a different tumor).
A second or subsequent biological sample may be taken or obtained
from the subject after one or more treatments and may be taken from
the same region or a different region. As a non-limiting example,
the second or subsequent biological sample may be useful in
determining whether the cancer in each biological sample has
different characteristics (e.g., in the case of biological samples
taken from two physically separate tumors in a patient) or whether
the cancer has responded to one or more treatments (e.g., in the
case of two or more biological samples from the same tumor or
different tumors prior to and subsequent to a treatment). In some
embodiments, each of the at least one biological sample is a bodily
fluid sample, a cell sample, or a tissue biopsy sample.
[0121] In some embodiments, one or more biological specimens are
combined (e.g., placed in the same container for preservation)
before further processing. For example, a first sample of a first
tumor obtained from a subject may be combined with a second sample
of a second tumor from the subject, wherein the first and second
tumors may or may not be the same tumor. In some embodiments, a
first tumor and a second tumor are similar but not the same (e.g.,
two tumors in the brain of a subject). In some embodiments, a first
biological sample and a second biological sample from a subject are
sample of different types of tumors (e.g., a tumor in muscle tissue
and brain tissue).
[0122] In some embodiments, a sample from which RNA and/or DNA is
extracted (e.g., a sample of tumor, or a blood sample) is
sufficiently large such that at least 2 .mu.g (e.g., at least 2
.mu.g, at least 2.5 .mu.g, at least 3 .mu.g, at least 3.5 .mu.g or
more) of RNA can be extracted from it. In some embodiments, the
sample from which RNA and/or DNA is extracted can be peripheral
blood mononuclear cells (PBMCs). In some embodiments, the sample
from which RNA and/or DNA is extracted can be any type of cell
suspension. In some embodiments, a sample from which RNA and/or DNA
is extracted (e.g., a sample of tumor, or a blood sample) is
sufficiently large such that at least 1.8 .mu.g RNA can be
extracted from it. In some embodiments, at least 50 mg (e.g., at
least 1 mg, at least 2 mg, at least 3 mg, at least 4 mg, at least 5
mg, at least 10 mg, at least 12 mg, at least 15 mg, at least 18 mg,
at least 20 mg, at least 22 mg, at least 25 mg, at least 30 mg, at
least 35 mg, at least 40 mg, at least 45 mg, or at least 50 mg) of
tissue sample is collected from which RNA and/or DNA is extracted.
In some embodiments, at least 20 mg of tissue sample is collected
from which RNA and/or DNA is extracted. In some embodiments, at
least 30 mg of tissue sample is collected. In some embodiments, at
least 10-50 mg (e.g., 10-50 mg, 10-15 mg, 10-30 mg, 10-40 mg, 20-30
mg, 20-40 mg, 20-50 mg, or 30-50 mg) of tissue sample is collected
from which RNA and/or DNA is extracted. In some embodiments, at
least 30 mg of tissue sample is collected. In some embodiments, at
least 20-30 mg of tissue sample is collected from which RNA and/or
DNA is extracted. In some embodiments, a sample from which RNA
and/or DNA is extracted (e.g., a sample of tumor, or a blood
sample) is sufficiently large such that at least 0.2 .mu.g (e.g.,
at least 200 ng, at least 300 ng, at least 400 ng, at least 500 ng,
at least 600 ng, at least 700 ng, at least 800 ng, at least 900 ng,
at least 1 .mu.g, at least 1.1 .mu.g, at least 1.2 .mu.g, at least
1.3 .mu.g, at least 1.4 .mu.g, at least 1.5 .mu.g, at least 1.6
.mu.g, at least 1.7 .mu.g, at least 1.8 .mu.g, at least 1.9 .mu.g,
or at least 2 .mu.g) of RNA can be extracted from it. In some
embodiments, a sample from which RNA and/or DNA is extracted (e.g.,
a sample of tumor, or a blood sample) is sufficiently large such
that at least 0.1 .mu.g (e.g., at least 100 ng, at least 200 ng, at
least 300 ng, at least 400 ng, at least 500 ng, at least 600 ng, at
least 700 ng, at least 800 ng, at least 900 ng, at least 1 .mu.g,
at least 1.1 .mu.g, at least 1.2 .mu.g, at least 1.3 .mu.g, at
least 1.4 .mu.g, at least 1.5 .mu.g, at least 1.6 .mu.g, at least
1.7 .mu.g, at least 1.8 .mu.g, at least 1.9 .mu.g, or at least 2
.mu.g) of RNA can be extracted from it.
Subjects
[0123] Aspects of this disclosure relate to a biological sample
that has been obtained from a subject. In some embodiments, a
subject is a mammal (e.g., a human, a mouse, a cat, a dog, a horse,
a hamster, a cow, a pig, or other domesticated animal). In some
embodiments, a subject is a human. In some embodiments, a subject
is an adult human (e.g., of 18 years of age or older). In some
embodiments, a subject is a child (e.g., less than 18 years of
age). In some embodiments, a human subject is one who has or has
been diagnosed with at least one form of cancer. In some
embodiments, a cancer from which a subject suffers is a carcinoma,
a sarcoma, a myeloma, a leukemia, a lymphoma, or a mixed type of
cancer that comprises more than one of a carcinoma, a sarcoma, a
myeloma, a leukemia, and a lymphoma. Carcinoma refers to a
malignant neoplasm of epithelial origin or cancer of the internal
or external lining of the body. Sarcoma refers to cancer that
originates in supportive and connective tissues such as bones,
tendons, cartilage, muscle, and fat. Myeloma is cancer that
originates in the plasma cells of bone marrow. Leukemias ("liquid
cancers" or "blood cancers") are cancers of the bone marrow (the
site of blood cell production). Lymphomas develop in the glands or
nodes of the lymphatic system, a network of vessels, nodes, and
organs (specifically the spleen, tonsils, and thymus) that purify
bodily fluids and produce infection-fighting white blood cells, or
lymphocytes. Non-limiting examples of a mixed type of cancer
include adenosquamous carcinoma, mixed mesodermal tumor,
carcinosarcoma, and teratocarcinoma. In some embodiments, a subject
has a tumor. A tumor may be benign or malignant. In some
embodiments, a cancer is any one of the following: skin cancer,
lung cancer, breast cancer, prostate cancer, colon cancer, rectal
cancer, cervical cancer, and cancer of the uterus. In some
embodiments, a subject is at risk for developing cancer, e.g.,
because the subject has one or more genetic risk factors, or has
been exposed to or is being exposed to one or more carcinogens
(e.g., cigarette smoke, or chewing tobacco).
Single Cell Suspensions
[0124] In some embodiments, methods (e.g., RNA sequencing, DNA
sequencing, or multiplexed flow cytometry) to characterize a cancer
that a subject has or is suspected of having is performed at a
single-cell level to capture the heterogeneity of a single tumor or
cancerous tissue, or multiple tumors or cancerous tissues. That is,
measurements and assessment of single cells in a tumor sample
provides information that is not confounded by genotypic or
phenotypic heterogeneity of bulk samples. In some embodiments, a
single cell-suspension is prepared from one or more biological
samples obtained from a subject for use in methods such as
single-cell RNA or DNA sequencing, or mass cytometry.
[0125] Accordingly, some embodiments of any one of the methods
described herein comprise forming a single-cell suspension of cells
from a sample of tumor (e.g., a first sample of tumor). In some
embodiments, forming a single-cell suspension of cells comprises
from a sample of tumor comprises dissecting a tumor sample to
obtain tumor sample fragments. A curved scissor may be used to
dissect a tumor tissue sample. In some embodiments, a tumor sample
fragment is 0.5-3 mm.sup.3 (e.g., 1-2 mm.sup.3). In some
embodiments, a tumor tissue sample or fragment thereof is kept
moist while dissecting.
[0126] A method of preparing a single-cell suspension from a tumor
sample may comprise any one or more of the following steps in any
order: fine mincing, enzymatic and/or non-enzymatic digestion,
vigorous pipetting, passage through a cell-strainer, washing, and
counting. In some embodiments, one or more of these steps are
repeated (e.g., 1 time, 2 times, 3 times, 4 times, or 5 or more
times).
[0127] In some embodiments, a tumor sample or tumor sample
fragment/s is incubated in an enzyme cocktail. Any number and
combination of enzymes can be used, see e.g., BioFiles: For Life
Science Research, Issue 2, 2006
www.sigmaaldrich.com/content/dam/sigma-aldrich/docs/Sigma/General_Informa-
tion/2/biofiles_issues2.pdf, which is incorporated herein by
reference in its entirety, and especially to incorporate herein any
of enzyme or other component (e.g., media) listed therein.
[0128] Quatromoni et al. (An optimized disaggregation method for
human lung tumors that preserves the phenotype and function of the
immune cells; J Leukoc Biol. 2015 January; 97(1): 201-209) provides
a comparison of different enzymatic cocktails and is incorporated
herein by reference in its entirety. In some embodiments, an enzyme
cocktail comprises any one or more of the following components:
media (e.g., L-15 media), anti-bacterials (e.g., penicillin and/or
streptomycin), anti-fungals (e.g., amphoterecin), collagenase
(e.g., collagenase I, collagenase II, collagenase IV), DNAse (e.g.,
DNAse I), elastase, hyaluronidase, and proteases (e.g., protease
XIV, trypsin, papain, or termolysin). Coll I has the original
balance of collagenase, caseinase, clostripain, and tryptic
activities; Coll II contains higher relative levels of protease
activity, particularly clostripain; and Coll IV is designed to be
especially low in tryptic activity (Quatromoni et al., J Leukoc
Biol. 2015 January; 97(1): 201-209). In some embodiments, only
collagenase I, collagenase II, or collagenase IV is used. In some
embodiments, a mixture of two collegenases is used (e.g.,
collagenase I and collagenase II, collagenase I and collagenase IV,
or collagenase II and collagenase IV). In some embodiments, more
than 2 collegenases are used (e.g., collagenase I, collagenase II,
and collagenase IV).
[0129] In some embodiments, an enzyme cocktail comprises one or
more of the following components: media (e.g., complete media),
penicillin, streptomycin, collagenase (e.g., collagenase I or
collagenase IV). Concentrations of enzymes in a cocktail can be
adjusted. A non-limiting example of an enzyme cocktail is as
follows: collagenase I (0.2 mg/ml), collagenase IV (1 mg/ml),
complete medium, penicillin (0.001%), and DNAse.
[0130] In some embodiments, at least 25 ml (e.g., at least 25 ml,
at least 26 ml, at least 27 ml, at least 28 ml, at least 29 ml, or
at least 30 ml) of enzyme cocktail is added per 0.5 gm of tumor
tissue. In some embodiments, a sample of tumor or fragments thereof
is incubated in enzyme cocktail while the sample is being shaken or
agitated (e.g., tumbled at 85 RPM, and/or vigorous pipetting). In
some embodiments, sample of tumor or fragments thereof is incubated
in enzyme cocktail at a temperature between 20-50.degree. C. (e.g.,
20-50.degree. C., 20-25.degree. C., 25-30.degree. C., 25-35.degree.
C., 30-40.degree. C., 35-45.degree. C., 40-50.degree. C., or
30-50.degree. C.). In some embodiments, a method of preparing a
single-cell suspension comprises filtering the enzyme cocktail,
e.g., through a cell strainer (e.g., 50 .mu.m, 70 .mu.m, or 100
.mu.m). In some embodiments, too fine of a filter may result in a
cell composition having a high concentration of fibroblast cells.
In some embodiments, too coarse a filter may result in clumps of
cells. In some embodiments, clumps of cells are disaggregated using
mechanical force (e.g., vigorous pipetting, or applying pressure
using a syringe).
[0131] In some embodiments, filtered cells are lysed using RBC
lysis buffer to lyse red blood cells. RBC lysis buffers are
available commercially (see e.g.,
www.abcam.com/red-blood-cell-rbc-lysis-buffer-ab204733.html).
[0132] In some embodiments, a method of preparing a single-cell
suspension comprises enzymatic and mechanical dissociation.
Examples of methods of dissociating cells from tissue can be found
in the following publications: Quatromoni et al., An optimized
disaggregation method for human lung tumors that preserves the
phenotype and function of the immune cells; J Leukoc Biol. 2015
January; 97(1): 201-209, Pennartz et al., Generation of Single-Cell
Suspensions from Mouse Neural Tissue; JOVE Issue 29; doi:
10.3791/1267; Published: Jul. 7, 2009, and
www.youtube.com/watch?v=N0jftyYqM38.
[0133] In some embodiments, cell-dissociation buffers that do not
contain enzymes are used. See e.g., ThermoFisher Scientific catalog
numbers 13151014 and 13150016, or Millipore Sigma Aldrich catalog
number S-014-B. Heng et al. (Biol Proced Online. 2009; 11: 161-169)
provides a comparison of enzymatic and non-enzymatic means of
dissociating cells and is incorporated herein by reference in its
entirety.
[0134] In some embodiments, the number of cells in a single-cell
suspension is counted and their viability tested. The Examples
below provide an example of an overall process of forming a
single-cell suspension from a sample of tumor tissue.
[0135] In some embodiments, a method comprises forming a
single-cell suspension of cells from a sample of tumor and
partitioning in into at least a first and second part. A first and
second part of a single-cells suspension may be of equal size or of
different sizes (e.g., comprising a different number of cells). In
some embodiments all the parts of a single-cell suspension (e.g., a
first part, a second part, and so on) are stored in separate
containers and stored under the same or similar conditions (e.g.,
in liquid nitrogen, or -80.degree. C.). In some embodiments, the
different parts of a single-cell suspension are stored under
different conditions, either before or after any further processing
(e.g., labeling with antibodies for protein expression studies). In
some embodiments, cells isolated from a biological sample are
cultured and expanded and then stored. In some embodiments, cells
isolated from a biological sample are cultured and expanded after
storage.
[0136] In some embodiments, any one of the methods described herein
further comprises forming a lysate from at least a part (e.g., a
first or second part) of the single-cell suspension. In some
embodiments, different parts of a single-cell suspension comprise
different types of cells. In some embodiments, a part of the
single-cell suspension from which a lysate is formed comprises at
least 1.times.10.sup.6 cells (e.g., at least 1.times.10.sup.6
cells, at least 2.times.10.sup.6 cells, at least 3.times.10.sup.6
cells, at least 4.times.10.sup.6 cells, or at least
5.times.10.sup.6 cells). In some embodiments, a part of the
single-cell suspension from which a lysate is formed comprises at
least 2.times.10.sup.6 cells. Lysate may be stored in storage
mediums that will prevent the degradation of DNA and/or RNA (e.g.,
RNALater). In some embodiments, a method comprises extracting RNA
from the lysate from a single-cell suspension or each part of a
single-cell suspension and performing RNA sequencing on the
extracted RNA to obtain RNA expression data. These RNA expression
data can be used to determine the heterogeneity of a tumor.
[0137] An overview of single-cell RNA sequencing can be found at
hemberg-lab.github.io/scRNA.seq.course/introduction-to-single-cell-rna-se-
q.html, FIG. 2.1 of which is incorporated by reference herein. In
some embodiments, a method of performing RNA sequencing a
single-cell suspension comprises single-cell RNA isolation, reverse
transcription cDNA pre-amplification, cDNA library preparation
(e.g., using Fluidigm C1 Protocol), and sequencing of sequenced
using platforms such as Illumina HiSeq 2500.
[0138] Methods of performing single-cell RNA sequencing are
described by the following references, each of which is
incorporated herein by reference in its entirety: Bagnoli et al.
(Studying Cancer Heterogeneity by Single-Cell RNA Sequencing;
Methods Mol Biol. 2019;1956:305-319); Sun et al. (Single-cell RNA
sequencing reveals gene expression signatures of breast
cancer-associated endothelial cells; Oncotarget. 2018 Feb. 16;
9(13): 10945-10961); Kulkarni et al. (Beyond bulk: a review of
single cell transcriptomics methodologies and applications; Curr
Opin Biotechnol. 2019 Apr. 9; 58:129-136); Huang et al (High
Throughput Single Cell RNA Sequencing, Bioinformatics Analysis and
Applications; Adv Exp Med Biol. 2018; 1068:33-43); Zilionis et al.
(Single-Cell Transcriptomics of Human and Mouse Lung Cancers
Reveals Conserved Myeloid Populations across Individuals and
Species; Immunity. 2019 Apr. 5. pii: S1074-7613(19)30126-8); and
Kashima et al. (An Informative Approach to Single-Cell Sequencing
Analysis; Adv Exp Med Biol. 2019; 1129:81-96. doi:
10.1007/978-981-13-6037-4_6); Seki et al. (Single-Cell DNA-Seq and
RNA-Seq in Cancer Using the C1 System; Adv Exp Med Biol. 2019;
1129:27-50. doi: 10.1007/978-981-13-6037-4_3); and See et al. (A
Single-Cell Sequencing Guide for Immunologists; Front Immunol.
2018; 9: 2425).
[0139] Gan et al. (Identification of cancer subtypes from
single-cell RNA-seq data using a consensus clustering method; BMC
Med Genomics. 2018; 11(Suppl 6): 117) describes a clustering method
for single-cell RNA sequencing data, which is incorporated herein
in its entirety by reference.
[0140] In some embodiments, any one of the following single-cell
RNA sequencing methods is used: Fluidigm C1 system (SMART-seq),
Fluidigm Cl system (mRNA Seq HT), SMART-seq2,10.times. Genomics
Chromium system, and MARS-seq. See et al. (Front Immunol. 2018; 9:
2425) provides a comparison of these methods and is incorporated
herein in its entirety by reference.
[0141] In some embodiments, any one of the methods described herein
further comprises performing measurement of a single-cell
suspension. In some embodiments, different measurements are made in
parallel on the same cells. Macaulay et al. (Trends Genet. 2017
February; 33(2): 155-168) describes methods of making multiple
measurements from single cells and is incorporated herein by
reference in its entirety.
[0142] In some embodiments, any one of the methods described herein
further comprises performing mass cytometry on at least a first
part of the single-cell suspension. Mass cytometry is a mass
spectrometry technique based on inductively coupled plasma mass
spectrometry and time of flight mass spectrometry used for the
determination of the properties of cells. In some embodiments, mass
cytometry comprises conjugating antibodies with isotopically pure
elements, and then using them to label cellular molecules (e.g.,
proteins). In some embodiments, cells are nebulized and sent
through an argon plasma, which ionizes the metal-conjugated
antibodies. The metal signals are then analyzed by a time-of-flight
mass spectrometer to identify and quantify the cellular molecules
in the cells.
[0143] In some embodiments, a single-cell suspension or part
thereof on which mass cytometry is performed comprises at least
1.times.10.sup.6 cells (e.g., at least 1.times.10.sup.6 cells, at
least 2.times.10.sup.6 cells, at least 3.times.10.sup.6 cells, at
least 4.times.10.sup.6 cells, at least 5.times.10.sup.6 cells, at
least 6.times.10.sup.6 cells, at least 7.times.10.sup.6 cells, at
least 8.times.10.sup.6 cells, at least 9.times.10.sup.6 cells, or
at least 10.times.10.sup.6 cells). In some embodiments, a
single-cell suspension or part thereof on which mass cytometry is
performed comprises at least 5.times.10.sup.6 cells.
[0144] Methods of performing mass cytometry are described by the
following references, each of which is incorporated herein by
reference in its entirety: Galli et al. (The end of omics? High
dimensional single cell analysis in precision medicine; Eur J
Immunol. 2019 February; 49(2):212-220); Brodin (The biology of the
cell--insights from mass cytometry; FEBS J. 2018 Nov. 3. doi:
10.1111/febs.14693); Olsen et al. (The anatomy of single cell mass
cytometry data; Cytometry A. 2019 February; 95(2):156-172);
Behbehani (Applications of Mass Cytometry in Clinical Medicine: The
Promise and Perils of Clinical CyTOF; Clin Lab Med. 2017 December;
37(4):945-964); Gondhalekar et al. (Alternatives to current flow
cytometry data analysis for clinical and research studies; Methods.
2018 Feb. 1; 134-135:113-129); and Soares et al. (Go with the flow:
advances and trends in magnetic flow cytometry; Anal Bioanal Chem.
2019 March; 411(9):1839-1862. doi: 10.1007/s00216-019-01593-9. Epub
2019 Feb. 19).
Other Assays
[0145] Any of the biological samples described herein can be used
for obtaining expression data using conventional assays or those
described herein. Expression data, in some embodiments, includes
gene expression levels. Gene expression levels may be detected by
detecting a product of gene expression such as mRNA and/or
protein.
[0146] In some embodiments, gene expression levels are determined
by detecting a level of a protein in a sample and/or by detecting a
level of activity of a protein in a sample. As used herein, the
terms "determining" or "detecting" may include assessing the
presence, absence, quantity and/or amount (which can be an
effective amount) of a substance within a sample, including the
derivation of qualitative or quantitative concentration levels of
such substances, or otherwise evaluating the values and/or
categorization of such substances in a sample from a subject.
[0147] The level of a protein may be measured using an immunoassay.
Examples of immunoassays include any known assay (without
limitation), and may include any of the following: immunoblotting
assay (e.g., Western blot), immunohistochemical analysis, flow
cytometry assay, immunofluorescence assay (IF), enzyme linked
immunosorbent assays (ELISAs) (e.g., sandwich ELISAs),
radioimmunoassays, electrochemiluminescence-based detection assays,
magnetic immunoassays, lateral flow assays, and related techniques.
Additional suitable immunoassays for detecting a level of a protein
provided herein will be apparent to those of skill in the art.
[0148] Such immunoassays may involve the use of an agent (e.g., an
antibody) specific to the target protein. An agent such as an
antibody that "specifically binds" to a target protein is a term
well understood in the art, and methods to determine such specific
binding are also well known in the art. An antibody is said to
exhibit "specific binding" if it reacts or associates more
frequently, more rapidly, with greater duration and/or with greater
affinity with a particular target protein than it does with
alternative proteins. It is also understood by reading this
definition that, for example, an antibody that specifically binds
to a first target peptide may or may not specifically or
preferentially bind to a second target peptide. As such, "specific
binding" or "preferential binding" does not necessarily require
(although it can include) exclusive binding. Generally, but not
necessarily, reference to binding means preferential binding. In
some examples, an antibody that "specifically binds" to a target
peptide or an epitope thereof may not bind to other peptides or
other epitopes in the same antigen. In some embodiments, a sample
may be contacted, simultaneously or sequentially, with more than
one binding agent that binds different proteins (e.g., multiplexed
analysis).
[0149] As used herein, the term "antibody" refers to a protein that
includes at least one immunoglobulin variable domain or
immunoglobulin variable domain sequence. For example, an antibody
can include a heavy (H) chain variable region (abbreviated herein
as VH), and a light (L) chain variable region (abbreviated herein
as VL). In another example, an antibody includes two heavy (H)
chain variable regions and two light (L) chain variable regions.
The term "antibody" encompasses antigen-binding fragments of
antibodies (e.g., single chain antibodies, Fab and sFab fragments,
F(ab')2, Fd fragments, Fv fragments, scFv, and domain antibodies
(dAb) fragments (de Wildt et al., Eur J Immunol. 1996;
26(3):629-39.)) as well as complete antibodies. An antibody can
have the structural features of IgA, IgG, IgE, IgD, IgM (as well as
subtypes thereof). Antibodies may be from any source including, but
not limited to, primate (human and non-human primate) and
primatized (such as humanized) antibodies.
[0150] In some embodiments, the antibodies as described herein can
be conjugated to a detectable label and the binding of the
detection reagent to the peptide of interest can be determined
based on the intensity of the signal released from the detectable
label. Alternatively, a secondary antibody specific to the
detection reagent can be used. One or more antibodies may be
coupled to a detectable label. Any suitable label known in the art
can be used in the assay methods described herein. In some
embodiments, a detectable label comprises a fluorophore. As used
herein, the term "fluorophore" (also referred to as "fluorescent
label" or "fluorescent dye") refers to moieties that absorb light
energy at a defined excitation wavelength and emit light energy at
a different wavelength. In some embodiments, a detection moiety is
or comprises an enzyme. In some embodiments, an enzyme is one
(e.g., .beta.-galactosidase) that produces a colored product from a
colorless substrate.
[0151] It will be apparent to those of skill in the art that this
disclosure is not limited to immunoassays. Detection assays that
are not based on an antibody, such as mass spectrometry, are also
useful for the detection and/or quantification of a protein and/or
a level of protein as provided herein. Assays that rely on a
chromogenic substrate can also be useful for the detection and/or
quantification of a protein and/or a level of protein as provided
herein.
[0152] Alternatively, the level of nucleic acids encoding a gene in
a sample can be measured via a conventional method. In some
embodiments, measuring the expression level of nucleic acid
encoding the gene comprises measuring mRNA. In some embodiments,
the expression level of mRNA encoding a gene can be measured using
real-time reverse transcriptase (RT) Q-PCR or a nucleic acid
microarray. Methods to detect nucleic acid sequences include, but
are not limited to, polymerase chain reaction (PCR), reverse
transcriptase-PCR (RT-PCR), in situ PCR, quantitative PCR (Q-PCR),
real-time quantitative PCR (RT Q-PCR), in situ hybridization,
Southern blot, Northern blot, sequence analysis, microarray
analysis, detection of a reporter gene, or other DNA/RNA
hybridization platforms.
[0153] In some embodiments, the level of nucleic acids encoding a
gene in a sample can be measured via a hybridization assay. In some
embodiments, the hybridization assay comprises at least one binding
partner. In some embodiments, the hybridization assay comprises at
least one oligonucleotide binding partner. In some embodiments, the
hybridization assay comprises at least one labeled oligonucleotide
binding partner. In some embodiments, the hybridization assay
comprises at least one pair of oligonucleotide binding partners. In
some embodiments, the hybridization assay comprises at least one
pair of labeled oligonucleotide binding partners.
[0154] Any binding agent that specifically binds to a desired
nucleic acid or protein may be used in the methods and kits
described herein to measure an expression level in a sample. In
some embodiments, the binding agent is an antibody or an aptamer
that specifically binds to a desired protein. In other embodiments,
the binding agent may be one or more oligonucleotides complementary
to a nucleic acid or a portion thereof. In some embodiments, a
sample may be contacted, simultaneously or sequentially, with more
than one binding agent that binds different proteins or different
nucleic acids (e.g., multiplexed analysis).
[0155] To measure an expression level of a protein or nucleic acid,
a sample can be in contact with a binding agent under suitable
conditions. In general, the term "contact" refers to an exposure of
the binding agent with the sample or cells collected therefrom for
suitable period sufficient for the formation of complexes between
the binding agent and the target protein or target nucleic acid in
the sample, if any. In some embodiments, the contacting is
performed by capillary action in which a sample is moved across a
surface of the support membrane.
[0156] In some embodiments, an assay may be performed in a
low-throughput platform, including single assay format. In some
embodiments, an assay may be performed in a high-throughput
platform. Such high-throughput assays may comprise using a binding
agent immobilized to a solid support (e.g., one or more chips).
Methods for immobilizing a binding agent will depend on factors
such as the nature of the binding agent and the material of the
solid support and may require particular buffers. Such methods will
be evident to one of ordinary skill in the art.
Extraction of DNA and/or RNA
[0157] In some embodiments of any one of the methods described
herein, RNA is extracted from a biological sample to prevent it
from being degraded and/or to prevent the inhibition of enzymes in
downstream processing, e.g., the preparation of DNA (i.e., a cDNA
library from RNA). In some embodiments of any one of the methods
described herein, DNA is extracted from a biological sample to
prevent it from being degraded and/or to prevent the inhibition of
enzymes in downstream processing, e.g., the preparation of DNA. In
some embodiments, the term "extraction" in the context of obtaining
DNA or RNA from a biological sample is used interchangeably with
the term "isolation."
[0158] Methods described herein involve extraction of RNA and/or
DNA from a biological sample (e.g., a tumor sample or sample of
blood). As described above, a biological sample may be comprised of
more than one sample from one or more than one tissues (e.g., one
or more than one different tumors). In some embodiments, RNA and/or
DNA are extracted from a combined sample. In some embodiments, RNA
and or DNA is extracted from multiple biological samples from a
subject, and then then combined before further processing (e.g.,
storage, or DNA library preparation). In some embodiments, more
than one sample of extracted RNA and/or DNA are combined with each
other after retrieval from storage. In some embodiments, at least
tumor DNA is extracted from one or more tumor tissues. In some
embodiments, at least tumor RNA is extracted from one or more tumor
tissues. In some embodiments, at least normal DNA is extracted from
one of more normal tissues to serve as a control. In some
embodiments, at least normal RNA is extracted from one of more
normal tissues to serve as a control. Protocols of DNA/RNA
extraction can be found at least in Example 2.
[0159] Methods for extracting DNA and/or RNA from biological
samples are known in the art, and reagents and kits for doing so
are commercially available. Gomez-Acata et al. (Methods for
extracting 'omes from microbialites, J Microbiol Methods. 2019 Mar.
12; 160:1-10) describes methods for extracting applied for DNA and
RNA extraction from microbialites and describes their advantages
and disadvantages and is incorporated herein by reference in its
entirety. The methods described in Gomez-Acata et al. are generally
applicable for RNA and/or DNA extracted from tissue. Moore (Curr
Protoc Immunol. 2001 May; Chapter 10:Unit 10.1) describes
purification and concentration of DNA from aqueous solutions and is
also incorporated by reference herein in its entirety.
[0160] In some embodiments, extracting DNA and/or RNA comprises
lysing cells of a biological sample and isolating DNA and/or RNA
from other cellular components. Examples of methods for lysing
cells include, but are not limited to, mechanical lysis, liquid
homogenization, sonication, freeze-thaw, chemical lysis, alkaline
lysis, and manual grinding.
[0161] Methods for extracting DNA and/or RNA include, but are not
limited to, solution phase extraction methods and solid-phase
extraction methods. In some embodiments, a solution phase
extraction method comprises an organic extraction method, e.g., a
phenol chloroform extraction method. In some embodiments, a
solution phase extraction method comprises a high salt
concentration extraction method, e.g., guanidinium thiocyantate
(GuTC) or guanidinium chloride (GuCl) extraction method. In some
embodiments, a solution phase extraction method comprises an
ethanol precipitation method. In some embodiments, a solution phase
extraction method comprises an isopropanol precipitation method. In
some embodiments, a solution phase extraction method comprises an
ethidium bromide (EtBr)-Cesium Chloride (CsCl) gradient
centrifugation method. In some embodiments, extracting DNA and/or
RNA comprises a nonionic detergent extraction method, e.g., a
cetyltrimethylammonium bromide (CTAB) extraction method.
[0162] In some embodiments, extracting DNA and/or RNA comprises a
solid phase extraction method. Any solid phase that binds to DNA
and/or RNA may be used for extracting DNA and/or RNA in methods and
systems described herein. Examples of solid phases that bind DNA
and/or RNA include, but are not limited to, silica matrices, ion
exchange matrices, glass particles, magnetizable cellulose beads,
polyamide matrices, and nitrocellulose membranes.
[0163] In some embodiments, a solid phase extraction method
comprises a spin-column based extraction method. In some
embodiments, a solid phase extraction method comprises a bead-based
extraction method. In some embodiments, a solid phase extraction
method comprises a cation exchange resin, e.g., a styrene
divinylbenzene copolymer resin.
[0164] Systems and methods described herein encompass extracting
DNA and/or RNA from a single biological sample or a plurality of
biological samples. In some embodiments, extracting DNA comprises
extracting DNA from a single sample. In some embodiments,
extracting DNA comprises extracting DNA from a plurality of
samples. In some embodiments, extracting DNA comprises extracting
DNA from a first sample and a second sample. In some embodiments,
extracting DNA comprises extracting DNA from one or more, two or
more, three or more, four or more, five or more, six or more, seven
or more, eight or more, nine or more, or ten or more samples.
[0165] In some embodiments, extracting RNA comprises extracting RNA
from a single sample. In some embodiments, extracting RNA comprises
extracting RNA from a plurality of samples. In some embodiments,
extracting RNA comprises extracting RNA from a first sample and a
second sample. In some embodiments, extracting RNA comprises
extracting RNA from one or more, two or more, three or more, four
or more, five or more, six or more, seven or more, eight or more,
nine or more, or ten or more samples.
[0166] Extracted DNA and/or RNA from a biological sample may be
combined with extracted DNA and/or RNA from another biological
sample. This may be accomplished by combining one or more
biological samples and extracting nucleic acids or by combining
nucleic acids extracted from one or more biological samples. In
some embodiments, a first biological sample is combined with a
second biological sample to form a combined sample and extracting
DNA and/or RNA from the combined sample. In some embodiments,
extracted DNA and/or RNA from a first biological sample may be
combined with extracted DNA and/or RNA from a second biological
sample.
[0167] Systems and methods described herein encompass extracting
any type of DNA and/or RNA from a biological sample. In some
embodiments, extracting DNA comprises extracting genomic DNA
(gDNA). In some embodiments, extracting DNA comprises extracting
mitochondrial DNA. In some embodiments, extracting RNA comprises
extracting messenger RNA (mRNA). In some embodiments, extracting
RNA comprises extracting precursor mRNA (pre-mRNA). In some
embodiments, extracting RNA comprises extracting ribosomal RNA
(rRNA). In some embodiments, extracting RNA comprises extracting
transfer RNA (tRNA).
[0168] In some embodiments, a single kit is used to purity DNA and
RNA from the same sample. A non-limiting example of kit for doing
so is the Qiagen AllPrep DNA/RNA kit. In some embodiments, robotics
is employed to carry out DNA and/or RNA extraction.
[0169] In some embodiments, if a sample of extracted RNA is not of
sufficient yield and/or quality, anyone of the following outcome
may occur. First, there may be an overrepresentation of common
transcripts in RNA sequencing data, and underrepresentation of low
abundance transcripts. Second, poor quality RNA can lead to
insufficient read lengths (i.e., reads are shorter) and/or
inadequate read quality leading to potential misidentification of
RNA.
[0170] For whole exome sequencing, poor quantity and quality of DNA
can lead to misidentification of base pairs leading to false
variant discovery (e.g., a false positive) or incidences where
variants are not identified (e.g., a false negative). Another
problem that can arise resulting from low DNA quantity and/or
quality is inadequate coverage of the exome (e.g., missing
sequences).
[0171] In some embodiments, before extracted RNA and/or DNA is
processed further for RNA sequencing or whole exome sequencing
(WES), the quality and/or quantity of RNA or DNA is checked. In
some embodiments, a sample of extracted RNA is at least 1000-6000
ng in total mass. In some embodiments, a sample of extracted RNA is
at least 100-60000 ng (e.g., 100-60000 ng, 500-30000 ng, 800-20000
ng, 1000-15000 ng, 1000-10000 ng, 1000-8000 ng, 1000-6000 ng,
10000-20000 ng, 20000-60000 ng) in total mass. In some embodiments,
the acceptable total RNA amount for further sequencing is at least
100-1,000 ng (e.g., 100-1,000 ng, 500-1,000 ng, or 300-900 ng). In
some embodiments, the target total RNA amount for further
sequencing is more than 200-1,000 ng (e.g., 200-1,000 ng, 500-1,000
ng, or 300-1,000 ng). In some embodiments, the purity of a sample
of extracted RNA is such that it corresponds to a ratio of
absorbance at 260 nm to absorbance at 280 nm of at least 1 (e.g.,
at least 1, at least 1.2, at least 1.4, at least 1.6, at least 1.8,
or at least 2). In some embodiments, the purity of a sample of
extracted RNA is such that it corresponds to a ratio of absorbance
at 260 nm to absorbance at 280 nm of at least 2. The ratio of
absorbance at 260 nm and 280 nm is used to assess the purity of DNA
and RNA. A ratio of .about.1.8 is generally accepted as "pure" for
DNA; a ratio of .about.2.0 is generally accepted as "pure" for RNA.
If the ratio is appreciably lower in either case, it may indicate
the presence of protein, phenol or other contaminants that absorb
strongly at or near 280 nm. Absorbances can be measured using a
spectrophotometer.
[0172] In some embodiments, the purity or integrity of extracted
RNA or DNA (e.g., a DNA fragment library) by any one of the methods
described herein is such that it corresponds to a RNA integrity
number (RIN) of at least 4 (e.g., at least 4, at least 5, at least
6, at least 7, at least 8, or at least 9). In some embodiments, the
purity of extracted nucleic acid (e.g., RNA or DNA) by any one of
the methods described herein is such that it corresponds to a RNA
integrity number (RIN) of at least 7. RIN has been demonstrated to
be robust and reproducible in studies comparing it to other RNA
integrity calculation algorithms, cementing its position as a
preferred method of determining the quality of RNA to be analyzed
(Imbeaud et al., Towards standardization of RNA quality assessment
using user-independent classifiers of microcapillary
electrophoresis traces; Nucleic Acids Research. 33 (6): e56).
[0173] In some embodiments, a sample of extracted DNA is at least
100-20000 ng (e.g., 100-20000 ng, 500-15000 ng, 800-10000 ng,
1000-15000 ng, 1000-10000 ng, 1000-8000 ng, 1000-6000 ng, or
1000-2000 ng) in total mass. In some embodiments, a sample of
extracted DNA is at least 1000-2000 ng in total mass. In some
embodiments, the acceptable total DNA amount for further sequencing
is at least 20-200 ng (e.g., 20-200 ng, 30-200 ng, or 50-150 ng).
In some embodiments, the target total DNA amount for further
sequencing is more than 30-200 ng (e.g., 30-200 ng, 50-200 ng, or
100-200 ng). In some embodiments, the target purity of a sample of
extracted DNA is such that it corresponds to a range of a ratio of
absorbance at 260 nm to absorbance at 280 nm of at least 1.8-2
(e.g., at least 1.8-2, at least 1.8-1.9). In some embodiments, the
purity of a sample of extracted DNA is such that it corresponds to
a ratio of absorbance at 260 nm to absorbance at 280 nm of at least
1 (e.g., at least 1, at least 1.2, at least 1.4, at least 1.6, at
least 1.8, or at least 2). In some embodiments, the acceptable
purity of a sample of extracted DNA is such that it corresponds to
a ratio of absorbance at 260 nm to absorbance at 280 nm of at least
1.5 (e.g., at least 1.5, at least 1.7, at least 2). In some
embodiments, the target purity of a sample of extracted DNA is such
that it corresponds to a range of a ratio of absorbance at 260 nm
to absorbance at 230 nm of at least 2-2.2 (e.g., at least 2-2.2, at
least 2-2.1). In some embodiments, the acceptable purity of a
sample of extracted DNA is such that it corresponds to a ratio of
absorbance at 260 nm to absorbance at 230 nm of at least 1.5 (e.g.,
at least 1.5, at least 1.7, at least 2). In some embodiments, the
purity of a sample of extracted DNA as described herein is analyzed
by a spectrophotometer, for example a small volume full-spectrum,
UV-visible spectrophotometer (e.g., Nanodrop spectrophotometer
available from ThermoFisher Scientific, www.thermofisher.com).
[0174] In some embodiments, a sample of extracted DNA has a target
concentration of at least 4.5 ng/.mu.l (e.g., 4.5 ng/.mu.l, 5.5
ng/.mu.l, 6.5 ng/.mu.l). In some embodiments, a sample of extracted
DNA has an acceptable concentration of at least 3 ng/.mu.l (e.g., 3
ng/.mu.l, 5 ng/.mu.l, 10 ng/.mu.l). In some embodiments, the
concentration of the extracted DNA is performed by a fluorometer,
for example for quantification of DNA or RNA (e.g., a Qubit
fluorometer available from ThermoFisher Scientific,
www.thermofisher.com).
[0175] In some embodiments, a sample of extracted DNA has a target
concentration of at least 4 ng/.mu.l (e.g., 4 ng/.mu.l, 6 ng/.mu.l,
8 ng/.mu.l). In some embodiments, a sample of extracted DNA has an
acceptable concentration of at least 2.5 ng/.mu.l (e.g., 2.5
ng/.mu.l, 4.5 ng/.mu.l, 5.5 ng/.mu.l). In some embodiments, the
concentration of the extracted DNA is performed by Tapestation.
[0176] In some embodiments, a sample of extracted RNA has a target
concentration of at least 2 ng/.mu.l (e.g., 2 ng/.mu.l, 4 ng/.mu.l,
6 ng/.mu.l). In some embodiments, a sample of extracted RNA has an
acceptable concentration of at least 4 ng/.mu.l (e.g., 4 ng/.mu.l,
6 ng/.mu.l, 10 ng/.mu.l). In some embodiments, the concentration of
the extracted DNA is performed by a fluorometer, for example for
quantification of DNA or RNA (e.g., a Qubit fluorometer available
from ThermoFisher Scientific, www.thermofisher.com).
[0177] In some embodiments, a sample of extracted RNA has a target
concentration of at least 4 ng/.mu.l (e.g., 4 ng/.mu.l, 6 ng/.mu.l,
8 ng/.mu.l). In some embodiments, a sample of extracted RNA has an
acceptable concentration of at least 1.5 ng/.mu.l (e.g., 1.5
ng/.mu.l, 3.5 ng/.mu.l, 5.5 ng/.mu.l). In some embodiments, the
concentration of the extracted RNA is performed by Tapestation. In
some embodiments, the acceptable RNA integrity number (RIN) is at
least 5 (e.g., 5, 6, 7). In some embodiments, the target RNA
integrity number (RIN) is at least 8 (e.g., 8, 9, 10). In some
embodiments, the RIN is performed by Tapestation. In some
embodiments, the target purity of a sample of extracted RNA is such
that it corresponds to a range of a ratio of absorbance at 260 nm
to absorbance at 280 nm of at least 1.8-2 (e.g., at least 1.8-2, at
least 1.8-1.9). In some embodiments, the purity of a sample of
extracted RNA is such that it corresponds to a ratio of absorbance
at 260 nm to absorbance at 280 nm of at least 1.8. In some
embodiments, the acceptable purity of a sample of extracted RNA is
such that it corresponds to a ratio of absorbance at 260 nm to
absorbance at 280 nm of at least 1.5 (e.g., at least 1.5, at least
1.7, at least 2). In some embodiments, the target purity of a
sample of extracted RNA is such that it corresponds to a range of a
ratio of absorbance at 260 nm to absorbance at 230 nm of at least
2-2.2 (e.g., at least 2-2.2, at least 2-2.1). In some embodiments,
the acceptable purity of a sample of extracted RNA is such that it
corresponds to a ratio of absorbance at 260 nm to absorbance at 230
nm of at least 1.5 (e.g., at least 1.5, at least 1.7, at least 2).
In some embodiments, the purity of a sample of extracted RNA as
described herein is analyzed by a spectrophotometer, for example a
small volume full-spectrum, UV-visible spectrophotometer (e.g.,
Nanodrop spectrophotometer available from ThermoFisher Scientific,
www.thermofisher.com). In some embodiments, the concentration of
extracted DNA is at least 10-2000 ng/.mu.l (e.g., 10-2000 ng/.mu.l,
10-1000 ng/.mu.l, 10-200 ng/.mu.l, 1-200 ng/.mu.l, 0.5-400
ng/.mu.l, 0.5-200 ng/.mu.l, 100-200 ng/.mu.l, 100-400 ng/.mu.l,
100-500 ng/.mu.l, 50-500 ng/.mu.l, or 50-250 ng/.mu.l).
[0178] Protocols for quality control of a sample of extracted RNA
or DNA can be found at least in Example 6. In some embodiments, the
purity of a sample of extracted DNA and/or RNA as described herein
can be analyzed by any other suitable technologies or tools. In
some embodiments, a sample of extracted RNA or DNA is not processed
further if it does not meet a particular quantity or purity
standard as described above. In some embodiments, if a sample of
extracted RNA or DNA does not meet a particular quantity or purity
standard, it is combined with another sample.
Library Preparation for RNA Sequencing
[0179] Methods of preparing cDNA libraries from a sample of RNA are
known in the art. For example,
www.illumina.com/content/dam/illumina-marketing/documents/applications/ng-
s-library-prep/for-all-you-seq-rna.pdf provides illustrations of
different methods for preparing cDNA libraries for RNA sequencing.
Non-limiting examples of cDNA library preparation include ClickSeq,
3Seq, and cP-RNA-Seq. In some embodiments, preparing a cDNA library
from RNA comprises purifying mRNA from the sample of RNA (RNA
enrichment). In some embodiments, enriched RNA is fragmented. In
some embodiments, after selection of the appropriate RNA fraction
is completed, the molecules are fragmented into smaller pieces, to
a size between 50-1000 bp (e.g., 50-100 bp, 100-800 bp, 100-500 bp,
or 200-500 bp) depending on the sequencing platform being used.
This fragmentation can be achieved either by fragmenting
double-stranded (ds) cDNA or by fragmenting RNA. Both methods
result in the same end product of a double stranded cDNA library in
which each fragment has an adapter attached.
[0180] In some embodiments, a library preparation method comprises
one or more amplification steps to add function elements (e.g.,
sample indices, molecular barcodes or flow cell oligo binding
sites), enrich for sequencing-competent DNA fragments, and/or
generate a sufficient amount of library DNA for downstream
processing. In some embodiments, enriched RNA (e.g., fragmented
enriched RNA) is amplified using random primers (e.g., random
hexamers). In some embodiments, enriched RNA (e.g., fragmented
enriched RNA) is amplified using oligodTs. In some embodiments, RNA
is then removed from the formed cDNA. In some embodiments, cDNA is
amplified to include sequencing adapters and indices (i.e., a
plurality of indexes). An adapter is a DNA sequence of 10-100 bp
(e.g., 10-20, 10-100, 20-80, 30-70, 40-60, 20-100, 40-100, 40-80,
30-60, or 45-65 bp) that can bind to a flow cell for sequencing.
Adapters also allow for PCR enrichment of adapter-ligated DNA
fragments. Adapters also can allow for indexing or barcoding of
samples so that multiple cDNA libraries can be mixed together into
one sequencing sample (or lane); i.e., it allows for multiplexing.
In some embodiments, an index or a barcode is 4-20 bp long (e.g.,
4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 4-20,
5-15, 6-12, or 4-12bp long).
tucf-genomics.tufts.edu/documents/protocols/TUCF_Understanding_Illumina_T-
ruSeq_Adapters.pdf provides example protocols for preparing cDNA
libraries using adapters and indexing and is incorporated herein by
reference in its entirety. Protocols for constructing a DNA or RNA
library can at least be found in Example 3 and Example 5.
[0181] RNA Enrichment
[0182] Methods for RNA enrichment to enrich for mRNA (herein also
described as "RNA enrichment") during cDNA library preparation are
known in the art. RNA enrichment can be targeted or non-targeting.
Targeted methods of RNA enrichment include use of sequence-specific
capture probes. A non-limiting example of targeted mRNA enrichment
includes CaptureSeq
(sapac.illumina.com/science/sequencing-method-explorer/kits-and-arrays/ca-
ptureseq.html), which makes use of capture probes specific to
sequences of interest. Other platforms or tools suitable for
targeted mRNA enrichment can also be used.
[0183] Examples of non-targeted mRNA enrichment methods include
polyA capture using oligodT (e.g., those on conjugated to beads),
and rRNA depletion. Petrova et al. (Scientific Reports volume 7,
Article number: 41114 (2017) provides a comparison of various rRNA
depletion methods and is incorporated by reference herein in its
entirety. In some embodiments, rRNA depletion can be performed
using enzymatic approaches (e.g., using an exonuclease that does
not process mRNA). In some embodiments, rRNA depletion method
comprises subtractive hybridization, whereby rRNA is captured using
sequence specific probes (see e.g.,
www.sciencedirect.com/topics/immunology-and-microbiology/subtractive-hybr-
idization)
[0184] In some embodiments, polyA capture comprises capturing of
mRNA bearing a polyA tail by using polyA-specific capture probes
(oligodT). In some embodiments, capture probes are immobilized for
ease of purification. In some embodiments, capture probes are
immobilized on beads (e.g., magnetic beads). In some embodiments, a
commercial kit is used to prepare a DNA library from an RNA sample.
In some embodiments, an Illumina TruSeq RNA Library Prep kit is
used.
[0185] The choice of mRNA enrichment can have a huge impact on the
selection of transcripts that are sequenced. For example, in some
embodiments, compared to rRNA depletion methods, cDNA libraries
prepared using polyA enrichment result in libraries comprising a
higher fraction of protein-coding transcripts (e.g., greater than
80%, greater than 90%, greater than 95%, greater than 96%, greater
than 97%, greater than 98%, greater than 99%, or greater than
99.9%) compared to non-coding transcripts (e.g., rRNA, miRNA, and
IncRNA).
[0186] In some embodiments, prepared cDNA libraries are tested for
quality. In some embodiments, quantification of libraries for use
in sequencing is generally performed before the libraries are
pooled for target enrichment or amplification to ensure equal
representation of indexed libraries in multiplexed applications. In
some embodiments, quantification is also used to confirm that
individual libraries or library pools are diluted optimally prior
to sequencing. Accurate and reproducible quantification of
adapter-ligated library molecules contributes to obtaining
consistent and reproducible results, and for maximizing sequencing
yields. Loading more than the recommended amount of DNA could lead
to saturation of the flowcell or increased cluster density while
loading too little DNA can lead to decreased cluster density and
reduced coverage and depth.
[0187] Methods of quantifying DNA libraries include
electrophoresis, fluorometry, spectrophotometry, digital PCR,
droplet-digital PCR and qPCR. Various instruments for measuring the
quantity and/or quality of DNA libraries exist, e.g., the Agilent
High Sensitivity D1000 ScreenTape System.
[0188] Aspects of the present disclosure provide quality control of
nucleic acids for the sequencing analysis. Aspects of the present
disclosure provide quality control of DNA for the sequencing
analysis. Aspects of the present disclosure provide quality control
of RNA for the sequencing analysis. In some embodiments, the
nucleic acids can include any suitable types of DNA or RNA. In some
embodiments, the quality control of nucleic acid comprises the
confirmation of biopsy condition and documents. In some
embodiments, the confirmation of biopsy condition and documents can
include, but are not limited to the inventory and registration of
the nucleic acid materials. In some embodiments, the confirmation
of biopsy condition and documents include nucleic acid material
acceptance. By way of example, patient samples received from a
healthcare provider are confirmed whether the patient tissues are
in the condition of fresh frozen or formalin-fixed
paraffin-embedded. The laboratory personnel verify the compliance
of the biopsy of the registered entity. The laboratory personnel
verify the proper storage of the biopsy sample during
transportation. The laboratory personnel verify the physical
condition of the biopsy samples. In the event that the laboratory
personnel identify any errors regarding the biopsy samples, the
source of the biopsy samples (e.g., a healthcare provider) may be
notified. In some embodiments, if the received biopsy samples are
patient tissue cell lines, the samples are prepared for extraction.
In some embodiments, if the received biopsy samples are extracted
DNA or RNA, the samples are stored at -80.degree. C. for further
sequencing. In some embodiments, the extracted DNA can be a
reference gDNA. In some embodiments, the extracted RNA can be a
reference RNA.
[0189] In some embodiments, the quality control procedures provide
a target range. The target range may represent the most ideal
quality of a given step (e.g., extraction). In some embodiments,
the quality control procedures provide an acceptable range. The
acceptable range may represent ideal or acceptable quality of a
given step. In some embodiments, the quality control of nucleic
acid comprises ensuring the quality in the process of constructing
a DNA library. In some embodiments, the quality control of nucleic
acid comprises ensuring the quality in the process of constructing
an RNA library. As shown in FIG. 7 and Example 6, the preparation
of the DNA or RNA libraries comprises the extraction of the DNA or
RNA from the patient tissue samples. In some embodiments, a
spectrophotometer, fo example a small volume full-spectrum,
UV-visible spectrophotometer (e.g., Nanodrop spectrophotometer
available from ThermoFisher Scientific, www.thermofisher.com) can
be used for determining the quality of the DNA or RNA extraction.
By way of example, the extracted DNA at >100 ng/.mu.l shows that
the extracted DNA passes the quality control test. The extracted
RNA at >500 ng/.mu.l shows that the extracted RNA passes the
quality control test. In another example, the ratio of absorbance
at 260 nm and 280 nm (260/280) of the extracted DNA at 1.8-2.0
shows that the extracted DNA passes the quality control test. The
ratio of absorbance at 260 nm and 280 nm (260/280) of the extracted
RNA at 2.0 shows that the extracted RNA passes the quality control
test. In another example, the ratio of absorbance at 260 nm and 230
nm (260/230) of the extracted DNA at 2.0-2.2 shows that the
extracted DNA passes the quality control test. The ratio of
absorbance at 260 nm and 230 nm (260/230) of the extracted RNA at
2.0-2.2 shows that the extracted RNA passes the quality control
test. In some embodiments, a fluorometer, for example for
quantification of DNA or RNA (e.g., a Qubit fluorometer available
from ThermoFisher Scientific, www.thermofisher.com) can be used for
determining the quality of the DNA or RNA extraction. In some
embodiments, an electrophoresis device, for example an automated
electrophoresis device (e.g., a TapeStation System available from
Agilent, www.agilent.com) can be used for determining the quality
of the DNA or RNA extraction. In some embodiments, any suitable
technology or tool can be used for determining the quality of the
DNA or RNA extraction.
[0190] In some embodiments, the acceptable total DNA amount for
further DNA library construction is at least 200-1,000 ng (e.g.,
200-1,000 ng, 300-1,000 ng, or 300-1,000 ng). In some embodiments,
the target total DNA amount for further sequencing is more than
500-1,000 ng (e.g., 500-1,000 ng, 600-1,000 ng, or 800-1,000 ng).
In some embodiments, the acceptable total RNA amount for further
RNA library construction is at least 0.5-4 nmol/l (e.g., 200-1,000
ng, 300-1,000 ng, or 300-1,000 ng). In some embodiments, the target
total RNA amount for further RNA library construction is at least
0.5-4 nmol/l (e.g., 500-1,000 ng, 600-1,000 ng, or 800-1,000
ng).
[0191] In some embodiments, the acceptable DNA concentration for
further DNA library construction is at least 17 ng/.mu.l (e.g., 17
ng/.mu.l, 25 ng/.mu.l, 35 ng/.mu.l). In some embodiments, the
target DNA concentration for further DNA library construction is at
least 42 ng/.mu.l (e.g., 42 ng/.mu.l, 50 ng/.mu.l, 80 ng/.mu.l). In
some embodiments, the acceptable RNA concentration for further RNA
library construction is at least 0.1 ng/.mu.l (e.g., 0.1 ng/.mu.l,
1 ng/.mu.l, 3 ng/.mu.l). In some embodiments, the target RNA
concentration for further RNA library construction is at least 0.1
ng/.mu.l (e.g., 0.1 ng/.mu.l, 1 ng/.mu.l, 3 ng/.mu.l). In some
embodiments, the DNA and RNA concentration is detected by a
fluorometer, for example for quantification of DNA or RNA (e.g., a
Qubit fluorometer available from ThermoFisher Scientific,
www.thermofisher.com). In some embodiments, the acceptable DNA
concentration for further DNA library construction is at least 15
ng/.mu.l (e.g., 15 ng/.mu.l, 25 ng/.mu.l, 35 ng/.mu.l). In some
embodiments, the target DNA concentration for further DNA library
construction is at least 402 ng/.mu.l (e.g., 40 ng/.mu.l, 50
ng/.mu.l, 80 ng/.mu.l). In some embodiments, the acceptable RNA
concentration for further RNA library construction is at least 0.1
ng/.mu.l (e.g., 0.1 ng/.mu.l, 1 ng/.mu.l, 3 ng/.mu.l). In some
embodiments, the target RNA concentration for further RNA library
construction is at least 0.1 ng/.mu.l (e.g., 0.1 ng/.mu.l, 1
ng/.mu.l, 3 ng/.mu.l). In some embodiments, the acceptable RNA
concentration for further RNA library construction is at least 0.5
nmol/l (e.g., 0.5 nmol/l nmol/l, 5 nmol/l). In some embodiments,
the target RNA concentration for further RNA library construction
is at least 0.5 nmol/l (e.g., 0.5 nmol/l, 1 nmol/l, 5 nmol/l). In
some embodiments, the DNA and RNA concentrations are detected by
Tapestation.
[0192] In some embodiments, the acceptable RNA concentration for
further RNA library construction is at least 0.5 nmol/l (e.g., 0.5
nmol/l, 1 nmol/l, 5 nmol/l). In some embodiments, the target RNA
concentration for further RNA library construction is at least 0.5
nmol/l (e.g., 0.5 nmol/l, 1 nmol/l, 5 nmol/l). In some embodiments,
the DNA and RNA concentration is detected by a nucleic acid
amplification device (e.g., a PCR system), for example a real-time
PCR system (e.g., a LightCycler Instrument available from Roche,
www.lifescience.roche.com). In some embodiments, the DNA and RNA
concentration can be detected by any suitable technologies or
tools.
[0193] In some embodiments, if RNA is extracted, reverse
transcription can be performed. In some embodiments, an RNA library
can be constructed after the reverse transcription is performed. In
some embodiments, a fluorometer, for example for quantification of
DNA or RNA (e.g., a Qubit fluorometer available from ThermoFisher
Scientific, www.thermofisher.com) can be used for determining the
quality of the DNA or RNA libraries. In some embodiments, any
suitable method can be used for determining the quality of the DNA
or RNA libraries. In some embodiments, an electrophoresis device,
for example an automated electrophoresis device (e.g., a
TapeStation System available from Agilent, www.agilent.com) can be
used for determining the quality of the DNA or RNA libraries. In
some embodiments, a fluorometer, for example for quantification of
DNA or RNA (e.g., a Qubit fluorometer available from ThermoFisher
Scientific, ww.thermofisher.com) can be used for determining the
quality of the DNA or RNA extraction. In some embodiments, a
nucleic acid amplification device (e.g., a PCR system), for example
a real-time PCR system (e.g., a LightCycler Instrument available
from Roche, www.lifescience.roche.com) can be used for determining
the quality of the RNA library. In some embodiments, one or more
RNA libraries can be pooled. In some embodiments, if DNA is
extracted, the extracted DNA can be used for a DNA library
construction. In some embodiments, the DNA fragments in the
constructed DNA library can be hybridized and/or captured. In some
embodiments, a fluorometer, for example for quantification of DNA
or RNA (e.g., a Qubit fluorometer available from ThermoFisher
Scientific, www.thermofisher.com) can be used for determining the
quality of the DNA hybridization and capture step. In some
embodiments, an electrophoresis device, for example an automated
electrophoresis device (e.g., a TapeStation System available from
Agilent, www.agilent.com) can be used for determining the quality
of the DNA hybridization and capture step. In some embodiments, a
nucleic acid amplification device (e.g., a PCR system), for example
a real-time PCR system (e.g., a LightCycler Instrument available
from Roche, www.lifescience.roche.com) can be used for determining
the quality of the DNA hybridization and capture step. In some
embodiments, any suitable method can be used for determining the
quality of the DNA hybridization and capture step. In some
embodiments, one or more DNA libraries can be pooled. In some
embodiments, an electrophoresis device, for example an automated
electrophoresis device (e.g., a TapeStation System available from
Agilent, www.agilent.com) can be used for determining the quality
of the DNA or RNA library pooling. In some embodiments, any
suitable methods can be used for determining the quality of the DNA
or RNA library pooling. In some embodiments, the acceptable and/or
target final DNA concentration range for pooling is at least 0.5-4
nmol/l (e.g., 0.5-4 nmol/l, 0.5-3 nmol/l, 2-4 nmol/l). In some
embodiments, the acceptable DNA concentration for pooling is at
least 0.1 ng/.mu.l (e.g., 0.1 ng/.mu.l, 0.8 ng/.mu.l, 4 ng/.mu.l)
when a fluorometer, for example for quantification of DNA or RNA
(e.g., a Qubit fluorometer available from ThermoFisher Scientific,
www.thermofisher.com) is used. In some embodiments, the target DNA
concentration for pooling is at least 0.1 ng/.mu.l (e.g., 0.1
ng/.mu.l, 0.8 ng/.mu.l, 4 ng/.mu.l) when a fluorometer, for example
for quantification of DNA or RNA (e.g., a Qubit fluorometer
available from ThermoFisher Scientific, www.thermofisher.com) is
used.
[0194] In some embodiments, the acceptable DNA concentration for
pooling is at least 0.1 ng/.mu.l (e.g., 0.1 ng/.mu.l, 0.8 ng/.mu.l,
4 ng/.mu.l) when an electrophoresis device, for example an
automated electrophoresis device (e.g., a TapeStation System
available from Agilent, www.agilent.com) is used. In some
embodiments, the target DNA concentration for pooling is at least
0.1 ng/.mu.l (e.g., 0.1 ng/.mu.l, 0.8 ng/.mu.l, 4 ng/.mu.l) when an
electrophoresis device, for example an automated electrophoresis
device (e.g., a TapeStation System available from Agilent,
www.agilent.com) is used. In some embodiments, the acceptable DNA
concentration for pooling is at least 0.5 nmol/l (e.g., 0.5 nmol/l,
0.8 nmol/l, 3 nmol/l) when an electrophoresis device, for example
an automated electrophoresis device (e.g., a TapeStation System
available from Agilent, www.agilent.com) is used. In some
embodiments, the target DNA concentration for pooling is at least
0.5 nmol/l (e.g., 0.5 nmol/l, 0.8 nmol/l, 3 nmol/l) when an
electrophoresis device, for example an automated electrophoresis
device (e.g., a TapeStation System available from Agilent,
www.agilent.com) is used. In some embodiments, the acceptable
and/or concentration of DNA is in the range of 380-440 ng (e.g.,
380-440 ng, 400-440 ng, 420-440 ng) when an electrophoresis device,
for example an automated electrophoresis device (e.g., a
TapeStation System available from Agilent, www.agilent.com) is
used. In some embodiments, the acceptable DNA concentration for
pooling is at least 0.5 nmol/l (e.g., 0.5 nmol/l, 0.8 nmol/l, 3
nmol/l) when a nucleic acid amplification device (e.g., a PCR
system), for example a real-time PCR system (e.g., a LightCycler
Instrument available from Roche, www.lifescience.roche.com) is
used. In some embodiments, the target DNA concentration for pooling
is at least 0.5 nmol/l (e.g., 0.5 nmol/l, 0.8 nmol/l, 3 nmol/l)
when LightCycler is used.
[0195] In some embodiments, the quality control of nucleic acid
comprises ensuring the quality after DNA or RNA library
construction such as during the sequencing process. In some
embodiments, cluster density can be a parameter for quality control
of the sample run (Example 6). Cluster density is an important
factor in optimizing data quality and yield of the sequencing.
Without wishing to be bound by any theory, an optimal cluster
density at least shows the DNA or RNA libraries are balanced. In
some embodiments, quality score and signal/noise ratio can be
parameters for quality control of the sample run.
[0196] In some embodiments, the quality control of nucleic acid
comprises ensuring the quality of sequencing. In some embodiments,
the quality control of sequencing comprises bioinformatics quality
control. In some embodiments, the sequencing can be a DNA
sequencing. In some embodiments, the sequencing can be RNA
sequencing. In some embodiments, the sequencing can be any type of
sequencing technologies known in the art for determining the DNA or
RNA expression profiles of a given biological sample. By way of
example, the sequencing can be a whole-exome sequencing. The
sequencing can be a transcriptome sequencing. The sequencing can be
a Sanger sequencing.
[0197] In some embodiments, up to 1 ng (e.g., up to 0.1, up to 0.2,
up to 0.3, up to 0.4, up to 0.5, up to 0.6, up to 0.7, up to 0.8,
up to 0.9, or up to 1 ng) of library in up to 2 .mu.l (e.g., up to
0.1 .mu.l, up to 0.5 .mu.l, up to 0.8 .mu.l, up to 0.9 .mu.l, up to
1 .mu.l, up to 1.2 .mu.l, up to 1.4 .mu.l, up to 1.5 .mu.l, up to
1.8 .mu.l, or up to 2 .mu.l) of solution is used for quality
control testing. In some embodiments, the parameters that are
tested include sizes and size distribution of DNA molecules, and
purity.
[0198] In some embodiments, a standard method of preparing a
library of cDNA fragments from RNA fails to preserve the
information pertaining to which DNA strand was the original
template during transcription and subsequent synthesis of the mRNA
transcript. Since antisense transcripts are likely to have
regulatory roles that are distinctly different from their protein
coding complement, this loss of strand information results in an
incomplete understanding of the transcriptome. Strand-specific
RNA-Seq can be performed to preserve this strandedness. Methods of
preserving strandedness and preparing cDNA fragment libraries for
it are known in the art (see e.g., Mills et al. Strand-Specific
RNA-Seq Provides Greater Resolution of Transcriptome Profiling;
Curr Genomics. 2013 May; 14(3): 173-181). In some embodiments,
library preparation for stranded RNA-seq makes use of known
orientation strand-specific adapters. In some embodiments, strands
are chemically modified to preserve knowledge of their origin.
[0199] In some embodiments, methods making use of adapters include
strand-specific 3'-end RNA-Seq. In some embodiments,
strand-specific 3'-end RNA-Seq comprises anchored oligo(dT) primers
are first used to select for mRNA, which results in production of
double-stranded cDNA molecules. Adapters for paired-end sequencing
are then ligated to each end of the cDNA molecule. Subsequently,
the fragments are sequenced generating pair-end reads that are
aligned to a reference genome. Any aligned read that contains a
stretch of adenines at the end of the transcript must be a
transcript that originated from the DNA antisense strand, while any
reads that align with a stretch of thymines at the front must be a
transcript from the DNA sense strand.
[0200] In some embodiments, methods making use of adapters makes
use of single-stranded (ss) cDNA and Illumina adapters and 4 DNA
ligase that allows for linking of 3' and 5' adapters to ssDNA. As
the second strand is never synthesized and does not proceed to
sequencing, strand information is retained.
[0201] In some embodiments, any suitable technologies or tools can
be used for preserving strandedness. For example, Flowcell reverse
transcription sequencing (FRT-Seq) can be used to preserve
strandedness. In some embodiments, FRT-Seq or the equivalent
technologies comprises ligation of adapters to either end of
fragmented and purified polyadenylated mRNA. In some embodiments,
each adapter comprises two regions; a region to which the
sequencing primers anneal and a region that is complementary to the
oligonucleotides present on the flowcell. The complementary region
allows the mRNA fragment to hybridize to the flowcell. The mRNA
fragments are then reverse transcribed on the flowcell surface.
[0202] Other non-limiting adapter-based methods of preserving
strandedness include direct strand-specific sequencing (DSSS) and
SOLiD.RTM. Total RNA-Seq Kit
(tools.thermofisher.com/content/sfs/manuals/cms_078610.pdf) that
preserves strand specificity through the addition of adapters in a
directional manner.
[0203] In some embodiments, chemical modification of strands to
preserve knowledge of their origin comprises marking the original
RNA template through the use of bisulfite treatment. In some
embodiments, dUTPs are incorporated into reverse transcription
reaction, resulting in ds cDNA where the original strand has
deoxythymidine residues while the complementary strand contains
deoxyuridine residues. Uracil-DNA-Glycosylase (UDG) treatment can
then be used to degrade complementary strands.
Library Preparation of WES
[0204] An "exome" is the sum of all regions in the genome comprised
of exons. Exons are DNA regions that are transcribed into messenger
RNA, as opposed to introns which are removed by splicing proteins.
Exome sequencing is a capture-based method developed to identify
variants in the coding region of genes that affect protein
function. As the coding portion of the genome encompasses only 1-2%
of the entire genome, this approach represents a cost-effective
strategy to detect DNA alterations that may alter protein function,
compared to whole genome sequencing. In some embodiments, whole
exome sequencing (WES) comprises preparation of a library of DNA
fragments for sequencing from a sample of DNA. In some embodiments,
DNA is first fragmented to the appropriate size (depending on the
sequencing platform used) and then sequencing platform-specific
adapters are added. In some embodiments, libraries are amplified
before the next step in the process (target enrichment or
sequencing).
[0205] Kits are commercially available for the preparation of
libraries, non-limiting examples of which include KAPA HyperPrep
Kits, Agilent HaloPlex, Agilent SureSelect QXT, IDT xGEN Exome,
Illumina Nextera Rapid Capture Exome, Roche Nimblegen SeqCap, and
MYcroarray MYbaits. In some embodiments, any kit that can prepare a
DNA library for WES can be used. For example, an Agilent Human All
Exon V6 Capture Kit
(www.agilent.com/cs/library/datasheets/public/SureSelect%20V6%20DataSheet-
%205991-5572EN.pdf) is used to prepare a DNA library for WES. In
some embodiments, a Clinical Research Exome kit
(www.agilent.com/en/promotions/clinical-research-exome-v2) is used.
Quantities of DNA needed depend on the specific reagents used to
prepare the library. For example, 100 ng of genomic DNA is
sufficient for Agilent SureSelect XT2 V6 Exome, but 500 ng of
genomic DNA is required for IDT xGEN Exome Panel. A comparison of
various capture kits are provided in
www.genohub.com/exome-sequencing-library-preparation/.
[0206] In some embodiments, a library preparation method comprises
one or more amplification steps to add function elements (e.g.,
sample indices, molecular barcodes or flow cell oligo binding
sites), enrich for sequencing-competent DNA fragments, and/or
generate a sufficient amount of library DNA for downstream
processing. By way of example, a library preparation method is
shown in Example 3 and Example 5.
[0207] In some embodiments, prepared DNA libraries are tested for
quality. In some embodiments, quantification of libraries for use
in sequencing is generally performed before the libraries are
pooled for target enrichment or amplification to ensure equal
representation of indexed libraries in multiplexed applications. In
some embodiments, quantification is also used to confirm that
individual libraries or library pools are diluted optimally prior
to sequencing. Accurate and reproducible quantification of
adapter-ligated library molecules contributes to obtaining
consistent and reproducible results, and for maximizing sequencing
yields. Loading more than the recommended amount of DNA could lead
to saturation of the flowcell or increased cluster density while
loading too little DNA can lead to decreased cluster density and
reduced coverage and depth.
[0208] Methods of quantifying DNA libraries include
electrophoresis, fluorometry, spectrophotometry, digital PCR,
droplet-digital PCR and qPCR. Various instruments for measuring the
quantity and/or quality of DNA libraries exist, e.g., the Agilent
High Sensitivity D1000 ScreenTape System.
[0209] In some embodiments, prepared DNA libraries are tested for
quality. In some embodiments up to 1 ng (e.g., up to 0.1, up to
0.2, up to 0.3, up to 0.4, up to 0.5, up to 0.6, up to 0.7, up to
0.8, up to 0.9, or up to 1 ng) of library in up to 2 .mu.l (e.g.,
up to 0.1 .mu.l, up to 0.5 .mu.l, up to 0.8 .mu.l, up to 0.9 .mu.l,
up to 1 .mu.l, up to 1.2 .mu.l, up to 1.4 .mu.l, up to 1.5 .mu.l,
up to 1.8 .mu.l, or up to 2 .mu.l) of solution is used for quality
control testing. In some embodiments, the parameters that are
tested include sizes and size distribution of DNA molecules, and
purity.
RNA Sequencing
[0210] RNA sequencing is a tool to measure the transcriptome. The
transcriptome is comprised of different populations of RNA
molecules, including mRNA, rRNA, tRNA, and other non-coding RNA
(such as microRNA, lncRNA). In some embodiments, RNA sequencing is
used to profile the transcriptome (e.g., the coding and/or
non-coding regions). In some embodiments, it is used to identify
genes that are differentially expressed in different biological
samples (e.g., cells, tissue, or bodily fluid). In some
embodiments, RNA sequencing is used to determine the genetic
effects of splicing events, identify novel transcripts, detect
structural variations (e.g., gene fusions and isoforms), and/or to
detect single nucleotide variants.
[0211] In some embodiments, the term "RNA sequencing" can be used
interchangeably with "RNA seq," "RNA-seq," or the variations
thereof as known in the art referring to any technologies, tools,
or platforms that interrogate the transcriptome. It is noted that
when "RNA sequencing," "RNA seq," "RNA-seq," or the variations
thereof is referred in the present disclosure, it does not refer to
a specific technology or tool that is associated with a particular
platform or company, unless indicated otherwise by way of
non-limiting examples for demonstrating the processes or systems as
described herein. In some embodiments, RNA sequencing can be
conducted by using any suitable sequencing platforms and/or
sequencing methods. Non-limiting examples of high-throughput
sequencing platforms include mRNA-seq, total RNA-seq, targeted
RNA-seq, single-cell RNA-Seq, RNA exome capture platform, or small
RNA-seq (e.g., Illumina, www.illumina.com), SMRT (single molecule,
real-time) sequencing (e.g., Pacific Biosciences,
https://www.pacb.com), and RNA sequencing (e.g., ThermoFisher,
https://www.thermofisher.com).
[0212] As described above, RNA sequencing can be targeted or
untargeted. Targeted approaches include using sequence-specific
probes or oligonucleotides to sequence one or more specific regions
of the transcriptome. In some embodiments, targeted RNA sequencing
includes methods such as mRNA enrichment (e.g., by polyA enrichment
or rRNA depletion).
[0213] In some embodiments, RNA sequencing is whole transcriptome
sequencing. Whole transcriptome sequencing comprises measurement of
the complete complement of transcripts in a sample. In some
embodiments, whole transcriptome sequencing is used to determine
global expression levels of each transcript (e.g., both coding and
non-coding), identify exons, introns and/or their junctions.
[0214] In some embodiments, RNA is sequenced directly without
preparing cDNA from a sample of RNA. In some embodiments, direct
RNA sequencing comprises single molecule RNA sequencing
(DRSTM).
[0215] In some embodiments, RNA sequencing is mRNA sequencing. In
some embodiments, mRNA sequencing is the sequencing of only coding
transcripts with the goal to exclude non-coding regions. In some
embodiments, mRNA sequencing is independent of polyA enrichment. In
some embodiments, mRNA sequencing depends on polyA enrichment.
[0216] In some embodiments, RNA is extracted from a biological
sample, mRNA is enriched from the extracted RNA, cDNA libraries are
constructed from the enriched mRNA. In some embodiments, single
pieces of cDNA from a cDNA library are attached to a solid matrix.
In some embodiments, single pieces of cDNA from a cDNA library are
attached to a solid matrix by limited dilution. In some
embodiments, cDNA pieces attached to a matrix are then sequenced
(e.g., using Pacbio or Pacifbio technology). In some embodiments,
cDNA pieces that are attached to a matrix are amplified and
sequenced (e.g., using a specialized emulsion PCR (emPCR) in SOLiD,
454 Pyrosequencing, Ion Torrent, or a connector based on the
bridging reaction (Illumina) platforms).
[0217] In some embodiments, cDNA transcripts can be sequenced in
parallel, either by measuring the incorporation of fluorescent
nucleotides (for example, Illumina), fluorescent short linkers (for
example, SOLiD), by the release of the by-products derived from the
incorporation of normal nucleotides (454), by measuring
fluorescence emissions, or by measuring pH change (for example, Ion
Torrent). In some embodiments, cDNA transcripts can be sequenced
using any known sequencing platform. Jazayeri et al. (RNA-seq: a
glance at technologies and methodologies; Acta biol. Colomb. vol.
20 no. 2 Bogota May/August 2015) provides a comparison of different
RNA-seq platforms, and is incorporated herein by reference in its
entirety, including Table 3 and Table 4. Mestan et al. (Genomic
sequencing in clinical trials; Journal of Translational Medicine
2011, 9:222) provides a similar analysis for sequencing in clinical
trials.
[0218] In some embodiments, RNA sequencing is stranded or
strand-specific. cDNA synthesis from RNA results in loss of
strandedness. In some embodiments, strandedness is preserved by
chemically labeling either or both the RNA strand and the cDNA
strand that is formed by reverse transcription or antisense
transcription, or by using adapter-based techniques to distinguish
the original RNA strand from the complementary DNA strand, as
described above.
[0219] In some embodiments, nonstranded RNA sequencing is
performed. In some embodiments, stranded RNA-seq should be avoided
for clinical samples. In some embodiments, nonstranded RNA-seq is
used to compare data obtained from a biological sample to RNA
sequencing data in established data sets (e.g., The Cancer Genome
Atlas (TCGA) and International Cancer Genome Consortium
(ICGC)).
[0220] In some embodiments, RNA sequencing yields paired-end reads.
Paired-end reads are reads of the same nucleic acid fragment and
are reads that start from either end of the fragment. In some
embodiments, RNA sequencing is performed with paired-end reads of
at least 2.times.25 (2.times.25, 2.times.50, 2.times.75,
2.times.100, 2.times.125, 2.times.150, 2.times.175, 2.times.200,
2.times.225, 2.times.250, 2.times.275, 2.times.300, 2.times.325, or
2.times.350) paired-end reads. In some embodiments, RNA sequencing
is performed with paired-end reads of at least 2.times.75
paired-end reads. RNA sequencing with 2.times.75 paired-end reads
means that on average each read, which is paired-end, reads 75 base
pairs. In some embodiments, RNA sequencing is performed with a
total of at least 20 million (e.g., at least 20 million, at least
30 million, at least 40 million, at least 50 million, at least 60
million, at least 70 million at least 80 million, at least 90
million, at least 100 million, at least 120 million, at least 140
million, at least 150 million, at least 160 million, at least 180
million, at least 200 million, at least 250 million, at least 300
million, at least 350 million, or at least 400 million) paired-end
reads. In some embodiments, RNA sequencing is performed with a
total of at least 50 million paired-end reads. In some embodiments,
RNA sequencing is performed with a total of at least 100 million
paired-end reads.
[0221] In some embodiments, quality control is performed for RNA
sequencing. In some embodiments, cluster density or cluster PF % is
a parameter for determining the quality of the sample run. In some
embodiments, the target range of cluster density or cluster PF % is
at least 170-220 (e.g., 170-220, 190-220, 210-220). In some
embodiments, the acceptable range of cluster density or cluster PF
% is at least 280 (e.g., 280, 300, 450).
[0222] In some embodiments, %.gtoreq.Q30 is a parameter for
determining the quality of the sample run. In some embodiments, the
target %.gtoreq.Q30 is at least 85% (e.g., 85%, 90%, 95%). In some
embodiments, the acceptable %.gtoreq.Q30 is at least 75% (e.g.,
75%, 85%, 95%).
[0223] In some embodiments, error rate % is a parameter for
determining the quality of the sample run. In some embodiments, the
target error rate % is less than 0.7% (e.g., 0.6%, 0.5%, 0.4%). In
some embodiments, the acceptable error rate % is less than 1%
(e.g., 0.9%, 0.8%, 0.7%).
Whole Exome Sequencing (WES)
[0224] Whole exome sequencing (WES) is a genomic technique for
sequencing all of the protein-coding region of genes in a genome.
In some embodiments, WES is performed to identify genetic variants
that alter protein sequences. In some embodiments, WES is performed
to identify genetic variants that alter protein sequences at a cost
that is lower than the cost of whole genome sequencing.
[0225] In some embodiments, whole exome sequencing (WES) is
performed on a sample of DNA that has been extracted from a
biological sample. In some embodiments, a library of DNA fragments
is prepared from the sample of extracted DNA. In some embodiments,
any one of the methods described herein comprises performing whole
exome sequencing (WES) on a library of DNA fragments. Preparation
of DNA libraries from a sample of DNA for WES is described
above.
[0226] In some embodiments, libraries of DNA are quantified before
sequencing (e.g., using next-generation sequencing (NGS)). In some
embodiments, DNA libraries are pooled before sequencing. In some
embodiments, DNA libraries are amplified before sequencing. In some
embodiments, DNA libraries are indexed before sequencing to keep
track of the origin of a DNA fragment.
[0227] In some embodiments, WES comprises target-enrichment
allowing the selective capture of genomic regions of interest prior
to sequencing. In some embodiments, array-based capture is used
(e.g., using microarrays). In some embodiments, in-solution capture
is used.
[0228] Any high-throughput DNA sequencing platform and/or method
can be used in any one of the methods described herein. In some
embodiments, DNA sequencing can be conducted by using any suitable
platforms and/or methods. Non-limiting examples of high-throughput
sequencing methods include Single-molecule real-time sequencing,
Ion semiconductor (Ion Torrent sequencing), Pyrosequencing (i.e.,
454), Sequencing by synthesis (Illumina), Illumina (Solexa)
sequencing, Combinatorial probe anchor synthesis (cPAS-BGI/MGI),
Sequencing by ligation (SOLiD sequencing), Nanopore Sequencing
(e.g., using an instrument from Oxford Nanopore Technologies, Chain
termination (Sanger sequencing), massively parallel signature
sequencing (MPSS) pology sequencing, Heliscope single molecule
sequencing, and Single molecule real time (SMRT) sequencing (e.g.,
using an instrument from Pacific Biosciences). Other non-limiting
examples of high-throughput sequencing techniques include
Tunnelling currents DNA sequencing, sequencing by hybridization,
Sequencing with mass spectrometry, Microfluidic Sanger sequencing,
and RNAP sequencing.
[0229] In some embodiments, DNA sequencing yields paired-end reads.
Paired-end reads are reads of the same nucleic acid fragment and
are reads that start from either end of the fragment. In some
embodiments, DNA sequencing is performed with paired-end reads of
at least 2.times.25 (2.times.25, 2.times.50, 2.times.75,
2.times.100, 2.times.125, 2.times.150, 2.times.175, 2.times.200,
2.times.225, 2.times.250, 2.times.275, 2.times.300, 2.times.325, or
2.times.350) paired-end reads. In some embodiments, DNA sequencing
is performed with paired-end reads of at least 2.times.75
paired-end reads. DNA sequencing with 2.times.75 paired-end reads
means that on average each read, which is paired-end, reads 75 base
pairs. In some embodiments, DNA sequencing is performed with a
total of at least 20 million (e.g., at least 20 million, at least
30 million, at least 40 million, at least 50 million, at least 60
million, at least 70 million at least 80 million, at least 90
million, at least 100 million, at least 120 million, at least 140
million, at least 150 million, at least 160 million, at least 180
million, at least 200 million, at least 250 million, at least 300
million, at least 350 million, or at least 400 million) paired-end
reads. In some embodiments, DNA sequencing is performed with a
total of at least 50 million paired-end reads. In some embodiments,
DNA sequencing is performed with a total of at least 100 million
paired-end reads. In some embodiments, DNA sequencing is performed
so that at least a 20.times. (e.g., at least a 20.times., at least
a 30.times., at least a 40.times., at least a 50.times., at least a
60.times., at least a 70.times., at least a 80.times., at least a
90.times., at least a 1000.times., at least a 120.times., at least
a 125.times., at least a 150.times., at least a 175.times., at
least a 200.times., at least a 250.times., at least a 300.times.,
or at least a 400.times.) coverage is yielded. Coverage, which also
is referred to as depth, is the number of times, on average, a
single base pair in a sample of nucleic acid is read or sequenced.
In some embodiments, the portion of the genome that is targeted for
capture and sequencing is at least 10 Mb (e.g., at least 10 Mb, at
least 20 Mb, at least 30 Mb, at least 40 Mb, at least 50 Mb, at
least 60 Mb, at least 70 Mb, at least 80 Mb, at least 90 Mb, at
least 100 Mb, at least 120 Mb, at least 150 Mb, at least 200 Mb, at
least 250 Mb, at least 300 Mb, or at least 350 Mb). In some
embodiments, the portion of the genome that is targeted for capture
and sequencing is at least 48 Mb (e.g., after using the Agilent
Human All Exon V6 Capture system). In some embodiments, the portion
of the genome that is targeted for capture and sequencing is at
least 54 Mb (e.g., after using the Clinical Research Exome capture
system (Agilent)).
[0230] In some embodiments, quality control is performed for
whole-exome sequencing. In some embodiments, cluster density or
cluster PF % is a parameter for determining the quality of the
sample run. In some embodiments, the target range of cluster
density or cluster PF % is at least 170-220 (e.g., 170-220,
190-220, 210-220). In some embodiments, the acceptable range of
cluster density or cluster PF % is at least 280 (e.g., 280, 300,
450).
[0231] In some embodiments, actual yield is a parameter for
determining the quality of the sample run. In some embodiments, the
target actual yield is at least 15 Gbp (e.g., 15 Gbp, 20 Gbp, 30,
Gbp).
[0232] In some embodiments, %.gtoreq.Q30 is a parameter for
determining the quality of the sample run. In some embodiments, the
target %.gtoreq.Q30 is at least 85% (e.g., 85%, 90%, 95%). In some
embodiments, the acceptable %.gtoreq.Q30 is at least 75% (e.g.,
75%, 85%, 95%).
[0233] In some embodiments, error rate % is a parameter for
determining the quality of the sample run. In some embodiments, the
target error rate % is less than 0.7% (e.g., 0.6%, 0.5%, 0.4%). In
some embodiments, the acceptable error rate % is less than 1%
(e.g., 0.9%, 0.8%, 0.7%).
Reagents and Kits
[0234] Contemplated herein are reagents and kits comprising
reagents for performing any one of the methods described herein. In
some embodiments, a kit as provided herein comprises reagents
(e.g., buffers, preservatives, inhibitors, or enzymes) and/or
labware (e.g., pipettes, filters, tubes, storage containers such as
vacutainers, or dissection tools) for storing biological samples
obtained from a subject.
[0235] In some embodiments, a kit as provided herein comprises
reagents (e.g., buffers, preservatives, inhibitors or enzymes)
and/or labware (e.g., pipettes, filters, or tubes) for extracting
RNA and/or DNA from a biological sample or a sample derived from a
biological sample (e.g., a single-cells solution). In some
embodiments, a kit as provided herein comprises reagents (e.g.,
buffers, preservatives, inhibitors, enzymes or dyes) and/or labware
(e.g., pipettes, filters, tubes, storage containers, or
electrophoresis paper) for measuring the quality and quantity of
RNA and/or DNA extracted from a biological sample. In some
embodiments, a kit as provided herein comprises reagents (e.g.,
buffers, preservatives, inhibitors, enzymes or dyes) and/or labware
(e.g., pipettes, filters, tubes, storage containers, or
electrophoresis paper) for measuring the quality and quantity of
DNA libraries for sequencing (e.g., RNA-seq or WES).
[0236] In some embodiments, a kit as provided herein comprises
reagents (e.g., buffers, preservatives, inhibitors or enzymes)
and/or labware (e.g., pipettes, filters, tubes, storage containers
such as vacutainers, or dissection tools) for preparing a
single-cell solution from a biological sample.
[0237] In some embodiments, a kit as provided herein comprises
reagents (e.g., buffers, inhibitors, or enzymes such as reverse
transcriptase enzyme) and/or labware (e.g., pipettes, filters,
tubes, storage containers) for preparing DNA libraries for
sequencing.
[0238] In some embodiments, a kit as provided herein comprises
reagents (e.g., buffers, preservatives, inhibitors or enzymes)
and/or labware (e.g., pipettes, filters, tubes, storage containers
such as vacutainers, or dissection tools) for any combination of
two or more of the following: storing biological samples,
extracting RNA and/or DNA from a biological sample, testing the
quality and quantity of extracted RNA and/or DNA samples and/or DNA
libraries prepared therefrom, preparing single-cell solutions from
a biological sample, and preparing DNA libraries from extracted RNA
and/or DNA.
[0239] In some embodiments, any one of the kits described herein
comprises components for making a cell-dissociation cocktail. A
cell-dissociation cocktail may be enzymatic or non-enzymatic. In
some embodiments, a kit comprises one or more enzyme cocktails. In
some embodiments, a kit comprises any one or more of the following
components: media (e.g., L-15 media), antibacterials (e.g.,
penicillin and/or streptomycin), anti-fungals (e.g., amphoterecin),
collagenase (e.g., collagenase I, collagenase II, collagenase IV),
DNAse (e.g., DNAse I), elastase, hyaluronidase, and proteases
(e.g., protease XIV, trypsin, papain, or termolysin). In some
embodiments, any one of the kits described herein comprises one or
more of the following enzymes: collagenase I and Collagenase IV. In
some embodiments, these enzymes are comprised in separate
containers. In some embodiments, these enzymes are comprised in a
single container.
[0240] In some embodiments, a kit comprises a smaller equipment
such as a spectrophotometer. In some embodiments, a kit comprises
instructions for performing any one of, or a combination of any two
or more of the following: storing biological samples, extracting
RNA and/or DNA from a biological sample, testing the quality and
quantity of extracted RNA and/or DNA samples and/or DNA libraries
prepared therefrom, preparing single-cell solutions from a
biological sample, and preparing DNA libraries from extracted RNA
and/or DNA. In some embodiments, a kit comprises instructions for
performing any one of the methods described herein. In some
embodiments, a kit is fashioned or tailored for specific tissue
types, e.g., biopsies of a solid tumor, a liquid biopsy, a blood
sample, or urine.
Data Processing
[0241] Aspects of this disclosure relate to processing data
obtained from RNA sequencing. In some embodiments, a method to
process RNA expression data (e.g., data obtained from RNA
sequencing (also referred to herein as RNA-seq data)) comprises
aligning and annotating genes in RNA expression data with known
sequences of the human genome to obtain annotated RNA expression
data; removing non-coding transcripts from the annotated RNA
expression data; converting the annotated RNA expression data to
gene expression data in transcripts per kilobase million (TPM)
format; identifying at least one gene that introduces bias in the
gene expression data; and removing the at least one gene from the
gene expression data to obtain bias-corrected gene expression data.
In some embodiments, a method to process RNA expression data
comprises obtaining RNA expression data for a subject having or
suspected of having cancer.
[0242] In some embodiments, non-coding transcripts may comprise
genes that belong to groups selected from the list consisting of:
pseudogenes, polymorphic pseudogenes, processed pseudogenes,
transcribed processed pseudogenes, unitary pseudogenes, unprocessed
pseudogenes, transcribed unitary pseudogenes, constant chain
immunoglobulin (IG C) pseudogenes, joining chain immunoglobulin (IG
J) pseudogenes, variable chain immunoglobulin (IG V) pseudogenes,
transcribed unprocessed pseudogenes, translated unprocessed
pseudogenes, joining chain T cell receptor (TR J) pseudogenes,
variable chain T cell receptor (TR V) pseudogenes, small nuclear
RNAs (snRNA), small nucleolar RNAs (snoRNA), microRNAs (miRNA),
ribozymes, ribosomal RNA (rRNA), mitochondrial tRNAs (Mt tRNA),
mitochondrial rRNAs (Mt rRNA), small Cajal body-specific RNAs
(scaRNA), retained introns, sense intronic RNA, sense overlapping
RNA, nonsense-mediated decay RNA, non-stop decay RNA, antisense
RNA, long intervening noncoding RNAs (lincRNA), macro long
non-coding RNA (macro lncRNA), processed transcripts, 3prime
overlapping non-coding RNA (3prime overlapping ncrna), small RNAs
(sRNA), miscellaneous RNA (misc RNA), vault RNA (vaultRNA), and TEC
RNA.
[0243] In some embodiments, information (e.g., sequence
information) for one or more transcripts for one of more of these
types of transcripts can be obtained in a nucleic acid database
(e.g., a Gencode database, for example Gencode V23, Genbank
database, EMBL database, or other database).
[0244] In some embodiments, a method to process RNA expression data
(e.g., data obtained from RNA sequencing (also referred to herein
as RNA-seq data)) comprises identifying a cancer treatment (also
referred to herein as an anti-cancer therapy) for the subject using
the bias-corrected gene expression data. In some embodiments, any
one of the methods of processing RNA expression data is further
combined with administering to a subject one or more anti-cancer
therapy or cancer treatment. In some embodiments, any one of the
methods of processing RNA expression data is further combined with
directing or recommending the administering to a subject one or
more anti-cancer therapy or cancer treatment.
[0245] Obtaining RNA Expression Data
[0246] In some embodiments, a method to process RNA expression data
(e.g., data obtained from RNA sequencing (also referred to herein
as RNA-seq data)) comprises obtaining RNA expression data for a
subject (e.g., a subject who has or has been diagnosed with a
cancer). In some embodiments, obtaining RNA expression data
comprises obtaining a biological sample and processing it to
perform RNA sequencing using any one of the RNA sequencing methods
described herein. In some embodiments, RNA expression data is
obtained from a lab or center that has performed experiments to
obtain RNA expression data (e.g., a lab or center that has
performed RNA-seq). In some embodiments, a lab or center is a
medical lab or center.
[0247] In some embodiments, RNA expression data is obtained by
obtaining a computer storage medium (e.g., a data storage drive) on
which the data exists. In some embodiments, RNA expression data is
obtained via a secured server (e.g., a SFTP server, or Illumina
BaseSpace). In some embodiments, data is obtained in the form of a
text-based filed (e.g., a FASTQ file). In some embodiments, a file
in which sequencing data is stored also contains quality scores of
the sequencing data). In some embodiments, a file in which
sequencing data is stored also contains sequence identifier
information.
[0248] Alignment and Annotation
[0249] In some embodiments, a method to process RNA expression data
(e.g., data obtained from RNA sequencing (also referred to herein
as RNA-seq data)) comprises aligning and annotating genes in the
RNA expression data with known sequences of the human genome to
obtain annotated RNA expression data.
[0250] In some embodiments, alignment of RNA expression data
comprises aligning the data to a known assembled genome for a
particular species of subject (e.g., the genome of a human) or to a
transcriptome database. Various sequence alignment software are
available and can be used to align data to an assembled genome or a
transcriptome database. Non-limiting examples of alignment software
includes short (unspliced) aligners (e.g., BLAT; BFAST, Bowtie,
Burrows-Wheeler Aligner, Short Oligonucleotide Analysis package, or
Mosaik), spliced aligners, aligners based on known splice junctions
(e.g., Errange, IsoformEx, or Splice Seq), or de novo splice
aligner (e.g., ABMapper, BBMap, CRAC, or HiSAT). In some
embodiments, any suitable tool can be used for aligning and
annotating data. For example, Kallisto
(github.com/pachterlab/kallisto) is used to align and annotate
data. In some embodiments, a known genome is referred to as a
reference genome. A reference genome (also known as a reference
assembly) is a digital nucleic acid sequence database, assembled as
a representative example of a species' set of genes. In some
embodiments, human and mouse reference genomes used in any one of
the methods described herein are maintained and improved by the
Genome Reference Consortium (GRC). Non-limiting examples of human
reference releases are GRCh38, GRCh37, NCBI Build 36.1, NCBI Build
35, and NCBI Build 34. A non-limiting example of transcriptome
databased include Transcriptome Shotgun Assembly (TSA).
[0251] In some embodiments, annotating RNA expression data
comprises identifying the locations of genes and/or coding regions
in the data to be processed by comparing it to assembled genomes or
transcriptome databases. Non-limiting examples of data sources for
annotation include GENCODE (www.gencodegenes.org), RefSeq (see
e.g., www.ncbi.nlm.nih.gov/refseq/), and Ensembl. In some
embodiments, annotating genes in RNA expression data is based on a
GENCODE database (e.g., GENCODE V23 annotation;
www.gencodegenes.org).
[0252] Consea et al. (A survey of best practices for RNA-seq data
analysis; Genome Biology201617:13) provides best practices for
analyzing RNA-seq data, which are applicable to any one of the
methods described herein and is incorporated herein by reference in
its entirety. Pereira and Rueda
(bioinformatics-core-shared-training.github.io/cruk-bioinf-ssch-
ool/Day2/rnaSeq_align.pdf) also describe methods for analyzing RNA
sequencing data, which are applicable to any one of the methods
described herein, and is incorporated herein by reference in its
entirety.
[0253] Removing Non-Coding Transcripts
[0254] In some embodiments, a method to process RNA expression data
(e.g., data obtained from RNA sequencing (also referred to herein
as RNA-seq data)) comprises removing non-coding transcripts from
annotated RNA expression data. Aligning and annotating RNA
expression data allows identification of coding and non-coding
reads. In some embodiments, non-coding reads for transcripts are
removed so as to concentrate analysis effort on expression of
proteins (e.g., those that may be involved in pathology of cancer).
In some embodiments, removing reads for non-coding transcripts from
the data reduces the variance in the data, e.g., in replicates of
the same or similar sample (e.g., nucleic acid from the same cells
or cell-type). In some embodiments, non-limiting examples of
expression data that is removed include one or more non-coding
transcripts (e.g., 10-50, 50-100, 100-1,000, 1,000-2,500,
2,500-5,000 or more non-coding transcripts) that belong to one or
more gene groups selected from the list consisting of: pseudogenes,
polymorphic pseudogenes, processed pseudogenes, transcribed
processed pseudogenes, unitary pseudogenes, unprocessed
pseudogenes, transcribed unitary pseudogenes, constant chain
immunoglobulin (IG C) pseudogenes, joining chain immunoglobulin (IG
J) pseudogenes, variable chain immunoglobulin (IG V) pseudogenes,
transcribed unprocessed pseudogenes, translated unprocessed
pseudogenes, joining chain T cell receptor (TR J) pseudogenes,
variable chain T cell receptor (TR V) pseudogenes, small nuclear
RNAs (snRNA), small nucleolar RNAs (snoRNA), microRNAs (miRNA),
ribozymes, ribosomal RNA (rRNA), mitochondrial tRNAs (Mt tRNA),
mitochondrial rRNAs (Mt rRNA), small Cajal body-specific RNAs
(scaRNA), retained introns, sense intronic RNA, sense overlapping
RNA, nonsense-mediated decay RNA, non-stop decay RNA, antisense
RNA, long intervening noncoding RNAs (lincRNA), macro long
non-coding RNA (macro lncRNA), processed transcripts, 3prime
overlapping non-coding RNA (3prime overlapping ncrna), small RNAs
(sRNA), miscellaneous RNA (misc RNA), vault RNA (vaultRNA), and TEC
RNA.
[0255] In some embodiments, information (e.g., sequence
information) for one or more transcripts for one of more of these
types of transcripts can be obtained in a nucleic acid database
(e.g., a Gencode database, for example Gencode V23, Genbank
database, EMBL database, or other database). In some embodiments, a
fraction (e.g., 10%, 20% 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95%,
98%, 99%, or 99.5% or more) of the non-coding transcripts,
histone-encoding gene, mitochondrial genes, interleukin-encoding
genes, collagen-encoding genes, and/or T cell receptor-encoding
genes as described herein are removed from aligned and annotated
RNA expression data.
[0256] Conversion to TPM and Gene Aggregation
[0257] In some embodiments, a method to process RNA expression data
(e.g., data obtained from RNA sequencing (also referred to herein
as RNA-seq data)) comprises normalizing RNA expression data per
length of transcript (e.g., to transcripts per kilobase million
(TPM) format) that is read. In some embodiments, RNA expression
data that is normalized per length of transcript is first aligned
and annotated. Conversion of data to TPM allows presentation of
expression in the form of concentration, rather than counts, which
in turn allows comparison of samples with different total read
counts and/or length of reads.
[0258] In some embodiments, RNA expression data that is normalized
per length of transcript read is then analyzed to obtain gene
expression data (expression data per gene). This is also referred
to as gene aggregation. Gene aggregation comprises combining
expression data in reads for transcripts for all isoforms of a gene
to obtain expression data for that gene. In some embodiments, gene
aggregation to obtain gene expression data is performed after TPM
normalization but before identifying genes that introduce bias. In
some embodiments, gene aggregation is performed before conversion
of the data to TPM.
[0259] Wagner et al (Theory Biosci. (2012) 131:281-285) provides an
explanation of how TPM can be calculated and is incorporated herein
by reference in its entirety. In some embodiments, the following
formula is used to calculate TPM:
A 1 ( A ) 10 6 ##EQU00001## Where .times. .times. A = total .times.
.times. reads .times. .times. mapped .times. .times. to .times.
.times. gene 10 3 gene .times. .times. length .times. .times. in
.times. .times. bp ##EQU00001.2##
[0260] Removing Bias
[0261] Since conversion of RNA expression data to obtain expression
in TPM format requires dividing the number of reads for a given
transcript by the length of a transcript read, biases may be
introduced in the data for various reasons (as described below).
Accordingly, some embodiments of any one of the methods described
herein comprise identifying at least one gene that introduces bias
in the gene expression data. Some embodiments of any one of the
methods described herein comprise identifying at least one gene
that introduces bias in the gene expression data, and removing
expression data for the at least one gene from the gene expression
data to obtain bias-corrected gene expression data.
[0262] In some embodiments, removing data from a dataset may
involve deleting the data from the dataset, marking the data so
that it is not used in some or all subsequent processing of the
dataset, and/or doing any other suitable processing so that the
data is not used in some or all subsequent processing of the
dataset. For example, removing particular expression data (e.g.,
expression data for at least one gene introducing bias) from gene
expression data may involve deleting the particular expression data
from the gene expression data, marking the particular expression
data and/or doing any other suitable processing so that the
particular expression data is not used in some or all subsequent
processing of the gene expression data. As another example,
removing non-coding transcripts from the RNA expression data (as
described above) may involve deleting the non-coding transcripts,
marking the non-coding transcripts, and/or doing any other suitable
subsequent processing so that the non-coding transcripts are not
used in some or all subsequent processing of the RNA expression
data. As yet another example, removing sequence data, determined to
not pass one or more quality control checks during performance of
quality control techniques described herein, may involve deleting
the sequence data, marking the sequence data and/or doing any other
suitable processing so that the sequence data failing the quality
control check(s) is not used in some or all subsequent
processing.
[0263] In some embodiments, biases in expression data converted to
TPM format are attributed to transcripts of an average length that
is at least a threshold amount higher or lower than an average
length of transcript as read in the entire expression data set. For
example, a gene for which one or more transcript of one or more
isoforms has a length that is a threshold (e.g., at least 1
standard deviations, 2 standard deviations, 3 standard deviations,
4 standard deviations, 5 standard deviations, 6 standard
deviations, 7 standard deviations, 8 standard deviations, 9
standard deviations, 10 standard deviations, 11 standard
deviations, 12 standard deviations, 13 standard deviations 13
standard deviations, or 15 standard deviations or more) lower from
the mean or median transcript length in the entire expression data
set, the expression of the gene in TPM format will artificially
appear to be high. Conversely, if a gene for which one or more
reads of one or more isoforms is of a length that is a threshold
(e.g., at least 1 standard deviations, 2 standard deviations, 3
standard deviations, 4 standard deviations, 5 standard deviations,
6 standard deviations, 7 standard deviations, 8 standard
deviations, 9 standard deviations, 10 standard deviations, 11
standard deviations, 12 standard deviations, 13 standard deviations
13 standard deviations, or 15 standard deviations or more) higher
than the mean or median read length in the entire expression data
set, the expression of the gene in TPM format will artificially
appear to be low. In some embodiments, a threshold value is set in
terms of standard deviations (e.g., at least 1 standard deviations,
2 standard deviations, 3 standard deviations, 4 standard
deviations, 5 standard deviations, 6 standard deviations, 7
standard deviations, 8 standard deviations, 9 standard deviations,
10 standard deviations, 11 standard deviations, 12 standard
deviations, 13 standard deviations 13 standard deviations, or 15
standard deviations or more). In some embodiments, a threshold
value is set based on a length of transcript and/or length of read,
e.g., below 5 bp, below 10 bp, below 15 bp, below 20 bp, below
25bp, below 50bp, below 75bp, below 100bp, or below 150bp or
more.
[0264] In some embodiments, biases are attributed to the lengths of
polyA tail on a transcript. In some embodiments, RNA transcripts
having a polyA tail that is on average smaller or higher than the
average length of polyA tail for RNA transcripts in a sample are
enriched more or less than the average enrichment of all RNA
transcripts in a sample. Accordingly, a gene may be associated with
a polyA tail that is at least a threshold amount smaller in length
compared to an average length of polyA tails of genes from a sample
from which the RNA expression data was obtained. In some
embodiments, such expression data for such genes is also removed
from the gene expression data to obtain bias-corrected gene
expression data. Removing expression data associated with one or
more genes from a data set to reduce bias may be considered as a
type of filtering of the data. In some embodiments, "filtration"
may refer to any one or more of removing expression data for genes
that appear artificially high or low (e.g., because of the lengths
of transcripts, or the length of the polyA tails associated with
transcripts), and removing expression data of non-coding RNA from
data.
[0265] In some embodiments, identifying at least one gene that
introduces bias in the gene expression data comprises analyzing the
length of transcripts within the data set that is being analyzed.
In some embodiments, removing, from the gene expression data,
expression data for at least one gene that introduces bias
decreases variability and improves the overall accuracy of
subsequent gene expression-based analysis.
[0266] In some embodiments, identifying at least one gene that
introduces bias in the gene expression data comprises use of
knowledge gained from analyzing data outside of the expression data
set in questions, e.g., using reference data sets. The inventors
recognized that removing (expression data for) genes having polyA
tail length that is outside the average range of polyA tails in a
RNA expression data set effectively removes bias and/or outliers in
the gene expression data. For example, knowledge that a certain
family of genes introduces biases can be had a priori (from
previously performed experiments or previously performed processing
of data) to processing RNA expression data and can be used to
filter out data for that family of genes.
[0267] In some embodiments, a gene that introduces bias to an
expression data set may belongs to a family of genes having a polyA
tail that is on average smaller or higher compared to an average
length of polyA tails of genes from a sample from which the RNA
expression data was obtained (or another reference sample). In some
embodiments, "smaller or higher" may refer to a numerical value
that is smaller or higher relative to a known, average threshold
value of one or more genes.
[0268] In some embodiments, a gene that introduces bias to an
expression data set belongs to a family of genes selected from the
group consisting of: histone-encoding genes, mitochondrial genes,
interleukin-encoding genes, collagen-encoding genes, B cell
receptor encoding genes, and T cell receptor-encoding genes. In
some embodiments, a gene that introduces bias to an expression data
set can be any other gene that has a polyA tail that is on average
smaller or higher compared to an average length of polyA tails of
genes from a sample from which the RNA expression data was obtained
(or another reference sample).
[0269] In some embodiments, the histone-encoding genes,
mitochondrial genes, interleukin-encoding genes, collagen-encoding
genes, B cell receptor encoding genes, and/or T cell
receptor-encoding genes are genes in the human sample that comprise
a polyA tail that is on average smaller or higher compared to an
average length of polyA tails of genes from a sample from which the
RNA expression data was obtained. For example, histone-encoding
genes comprise a polyA tail that is on average smaller to an
average length of polyA tails of genes from a sample from which the
RNA expression data was obtained. In some embodiments,
histone-encoding genes do not comprise a polyA tail. In some
embodiments, a polyA tail is minimally or not detected in
histone-encoding genes.
[0270] In some embodiments, one or more gene or protein
abbreviations or acronyms are used in this application to refer to
the genes (or genes encoding the proteins) using their recognized
scientific nomenclature. Additional information about the genes
and/or encoded proteins can be found in one or more genetic
sequence databases, for example the NIH genetic sequence database
(GenBank, www.ncbi.nlm.nih.gov), the EMBL database (the European
Molecular Biology Laboratory nucleotide sequence database,
www.ebi.ac.uk/embl/index.html), the EMBL European Bioinformatics
Institute database (EMBL-EBI European Nucleotide Archive,
www.ebi.ac.uk/ena), the GENCODE database (www.gencodegenes.org), or
other suitable database, the contents of which are incorporated by
reference herein for the different types of genes and names of
genes referred to herein. In some embodiments, the gene or protein
abbreiations or acronyms are referring to the human genes (or human
genes encoding the proteins).
[0271] In some embodiments, a histone-encoding gene is HIST1H1A,
HIST1H1B, HIST1H1C, HIST1H1D, HIST1H1E, HIST1H1T, HIST1H2AA,
HIST1H2AB, HIST1H2AC, HIST1H2AD, HIST1H2AE, HIST1H2AG, HIST1H2AH,
HIST1H2AI, HIST1H2AJ, HIST1H2AK, HIST1H2AL, HIST1H2AM, HIST1H2BA,
HIST1H2BB, HIST1H2BC, HIST1H2BD, HIST1H2BE, HIST1H2BF, HIST1H2BG,
HIST1H2BH, HIST1H2BI, HIST1H2BJ, HIST1H2BK, HIST1H2BL, HIST1H2BM,
HIST1H2BN, HIST1H2BO, HIST1H3A, HIST1H3B, HIST1H3C, HIST1H3D,
HIST1H3E, HIST1H3F, HIST1H3G, HIST1H3H, HIST1H3I, HIST1H3J,
HIST1H4A, HIST1H4B, HIST1H4C, HIST1H4D, HIST1H4E, HIST1H4F,
HIST1H4G, HIST1H4H, HIST1H4I, HIST1H4J, HIST1H4K, HIST1H4L,
HIST2H2AA3, HIST2H2AA4, HIST2H2AB, HIST2H2AC, HIST2H2BE, HIST2H2BF,
HIST2H3A, HIST2H3C, HIST2H3D, HIST2H3PS2, HIST2H4A, HIST2H4B,
HIST3H2A, HIST3H2BB, HIST3H3, or HIST4H4. In some embodiments, a
mitochondrial gene is MT-ATP6, MT-ATPS, MT-CO1, MT-CO2, MT-CO3,
MT-CYB, MT-ND1, MT-ND2, MT-ND3, MT-ND4, MT-ND4L, MT-ND5, MT-ND6,
MT-RNR1, MT-RNR2, MT-TA, MT-TC, MT-TD, MT-TE, MT-TF, MT-TG, MT-TH,
MT-TI, MT-TK, MT-TL1, MT-TL2, MT-TM, MT-TN, MT-TP, MT-TQ, MT-TR,
MT-TS1, MT-TS2, MT-TT, MT-TV, MT-TW, MT-TY, MTRNR2L1, MTRNR2L10,
MTRNR2L11, MTRNR2L12, MTRNR2L13, MTRNR2L3, MTRNR2L4, MTRNR2L5,
MTRNR2L6, MTRNR2L7, or MTRNR2L8.
[0272] In some embodiments, removing expression data for at least
one gene that introduces bias in the gene expression data comprises
removing expression data for one or multiple (e.g., at least 2, at
least 5, at least 10, at least 15, at least 20, at least 30, at
least 40, at least 50, at least 60, at least 70, at least 80, at
least 90, at least 100, at least 150, at least 200, at least 250,
at least 300, at least 350, at least 400, at least 450, at least
500, between 2 and 1000, or any suitable number of genes in these
ranges) genes in each of one or multiple (2, 3, 4, 5, or all) gene
families including histone-encoding genes, mitochondrial genes,
interleukin-encoding genes, collagen-encoding genes, B cell
receptor-encoding genes, and T cell receptor-encoding genes). In
some embodiments, removing expression data for at least one gene
that introduces bias in the gene expression data comprises removing
expression date for any of one or more genes that have a polyA tail
that is on average smaller or higher compared to an average length
of polyA tails of genes from a sample from which the RNA expression
data was obtained (or a reference sample).
[0273] In some embodiments, after expression data for at least one
gene that introduces bias is removed from the gene expression data,
the remaining gene expression data may be normalized again
("renormalized") (e.g., to TPM or any other suitable unit such as
reads per kilobase million (RPKM) or fragments per kilobase million
(FPKM)) so that the normalized expression values are not biased by
the expression data of the biasing gene(s), which was removed. In
some embodiments, the remaining gene expression data may have
expression data for at least 1,000 genes, at least 5,000 genes, at
least 10,000 genes, between 500 and 5000 genes, between 1000 and
10,000 genes, between 5,000 and 15,000 genes or any suitable number
of genes within these ranges.
Post-Sequencing Nucleic Acid Data Quality Control
[0274] As provided in the present disclosure, quality control is
regularly performed during sample preparation processes. For
example, the purity of the extracted nucleic acids or the size
distribution of the DNA libraries) are detected. When one or more
of the quality control issues occurs and is not able to be remedied
in the laboratory, the provider (e.g., healthcare provider) of the
biological sample is notified before proceeding to the subsequence
steps. After the issues in connection to the quality are solved,
the processes of sample preparation are completed and
bioinformatics analysis (e.g., post-sequencing) is performed.
[0275] Aspects of methods and systems described herein provide for
quality control to be performed on gene expression data to improve
the accuracy and reliability of subsequent expression analysis
(e.g., to determine a diagnosis, prognosis, and/or treatment for
the patient or subject) and any resulting recommendation.
[0276] In some embodiments, bioinformatic quality control of
sequence data can be conducted as a standalone process (e.g., based
on nucleic acid data that is received from a healthcare provider)
or in connection with a prior sample preparation process (e.g., if
a patient sample is provided by the healthcare provider as opposed
to nucleic acid sequence data). As illustrated in FIG. 7, act 301
to act 310 illustrate non-limiting sample preparation processes as
described in the present disclosure, whereas act 311 to act 315
illustrate non-limiting quality control processes as described in
the present disclosure. In some embodiments, one or more of act 301
to act 310 can be performed independently (e.g., without one or
more of act 311 to act 315). In some instances, one or more of act
301 to act 310 can be skipped or delayed. Act 311 to act 315 can be
performed independently (e.g., without act 301 to act 310). In some
instances, one or more of act 311 to act 315 can be skipped or
delayed. In some instances, one or more sample preparation (act 301
to act 310) and quality control (act 311 to act 315) processes can
both be performed. In some instances, one or more of the sample
preparation processes and one or more of the quality control
processes can be performed.
[0277] In some embodiments, a process pipeline 300 is performed by
obtaining a first tumor sample from a subject having, suspected of
having, or at risk of having cancer at act 301, extracting RNA from
the first sample of the first tumor at act 302, enriching the
extracted RNA for coding RNA to obtain enriched RNA at act 303,
preparing a first library of cDNA fragments from the enriched RNA
for non-stranded RNA sequencing at act 304, obtaining RNA
expression data for a subject having, suspected of having, or at
risk of having cancer at act 305, aligning and annotating genes in
the RNA expression data with known sequences of the human genome to
obtain annotated RNA expression data at act 306, removing
non-coding transcripts from the annotated RNA expression data at
act 307, converting the annotated RNA expression data to gene
expression data in transcripts per kilobase million (TPM) at act
308, identifying at least one gene that introduces bias in the gene
expression data at act 309, removing at least one gene from the
gene expression data to obtain bias-corrected gene expression data
at act 310, obtaining sequence information and asserted information
at act 311, determining one or more features from sequence
information at act 312, determining whether one or more features
match asserted information at act 313, making at least one
additional determination of the features at act 314, identifying a
cancer treatment for the subject using the bias-corrected gene
expression data at act 315.
[0278] In some embodiments, act 305 may comprise obtaining the RNA
expression data by using a sequencing platform or by receiving from
a healthcare provider or laboratory. In some embodiments, act 306
may comprise converting the RNA expression data to gene expression
data. As described herein, the "known sequence of the human genome"
may refer to a reference. In some embodiments, act 307 may comprise
converting the RNA expression data to gene expression data. In some
embodiments, act 307 may comprise obtain filtered RNA expression
data. In some embodiments, act 308 may comprise normalizing the
filtered RNA expression data to obtain gene expression data in
transcripts per kilobase million (TPM). In some embodiments, the
asserted information act 311 may indicate an asserted source and/or
an asserted integrity of the sequence data. In some embodiments,
act 312 may comprise determining one or more disease features. In
some embodiments, act 312 may comprise processing the sequence
information or data to obtain determined information indicating a
determined source and/or a determined integrity of the sequence
information or data. In some embodiments, act 313 may comprise
determining whether the determined information matches the asserted
information. In some embodiments, the at least one additional
determination of the feature at process 314 may comprise
determining disease features or features that are not directly
related to diseases.
[0279] Aspects of methods and systems described herein provide an
approach for validating nucleic acid sequence data by obtaining
both the sequence data and asserted information related to one or
more features of the sequence data (e.g., source, type of nucleic
acid, expected integrity, etc.), determining one or more features
from the sequence data, and verifying that the one or more features
determined from the sequence data match the asserted information
about those features. In some embodiments, the asserted information
can be information about the patient, tissue type, tumor type,
nucleic acid type (RNA, DNA, WES, polyA, etc.), sequencing protocol
that was used, etc., or a combination thereof. In some embodiments,
the asserted information can be an expected and/or acceptable
(e.g., acceptable for a subsequent analysis of the sequence data)
integrity threshold for the sequence information, including for
example, an expected and/or acceptable level of GC content,
contamination, coverage (e.g., genome, exome, exon, protein
encoding, or other coverage) or other measure of integrity.
[0280] Nucleic acid sequencing, next generation sequencing (NGS) in
particular, allows for the generation of large amounts of
information for a given nucleic acid (DNA, RNA, genome, exome,
transcriptome, etc.). However, because of the many different
sequencing platforms that are available, the variety of sample
preparation and sequencing protocols and techniques that are used,
and the variability and inconsistency between platforms and
protocols, there is substantial variability in the content and
coverage of the resulting nucleic acid sequence information.
Moreover, when evaluating sequence information from several
sequencing runs, or large sets of sequence information from a
plurality of sequencing runs (e.g., including, for example,
historical data from different medical visits for one or more
patients) or from different studies (e.g., from studies to create
prognostic or diagnostic evaluations, or from studies to evaluate
the effect of a drug or treatment on the progression of a disease,
etc.) it can be can be challenging to combine sequence information
from different sources. In addition, in can be challenging to
detect incorrectly identified sequence data when large amounts of
information are being combined from different sources.
[0281] Currently, no robust methods exist to validate (e.g., raise
the confidence, reduce the uncertainty, correct for or omit low
quality sequence information, provide a signal to verify or retest
questionable sequence information or outliers, etc.) source and/or
integrity (e.g., also as may be referred to herein as quality) of
sequence information which may be the subject of further use (e.g.,
being used for analysis beyond the initial sequencing step), for
example for diagnostic, prognostic, and/or clinical
applications.
[0282] The disclosure recognizes the prevalence of next generation
sequencing techniques and platforms employed across a variety of
disciplines within the scientific community. The disclosure also
recognizes the variety of protocols and methodologies associated
with the different techniques and platforms employed. The variation
in the platforms, and protocols to use the various platforms,
creates variability within the data and sequence information
realized from the use thereof, which presents a significant hurdle
in using the sequence information for substantive analysis,
especially if such sequence information is to be used for analysis
beyond the initial data run by the original user of the sample
(e.g., by a secondary user, beyond the user who procured and
performed the initial sequencing, third parties to the sequencing,
etc.).
[0283] Accordingly, the disclosure presents a variety of methods
and processes to assess the quality of sequence information (e.g.,
for correct identification of the sequence information, sample
identification, subject identification, etc.), as well as to assess
the integrity of the sequence information (e.g., create checkpoints
to screen for various integrity issues, for example, contamination
or degradation). For example, in some embodiments, described herein
are methods for evaluating sequence information by obtaining
sequence information from a nucleic acid of a sample of a subject,
obtaining asserted information, determining a feature (e.g.,
source, identity, status, characteristic) of the sequence
information, and comparing the asserted information with the
determined information. The sequence information may be obtained
(e.g., acquired) from any source, or through any means known in the
art. Accordingly, the sequence information may be generated using
any suitable sequencing technology. Alternatively, the sequence
information may be obtained electronically from a third party that
generated the sequence information. In some embodiments, sequence
information (e.g., reference sequence information) is obtained from
an existing databank of sequences. In some embodiments, sequence
information is obtained from a company, a non-profit organization,
an academic institution, or a healthcare organization.
[0284] In some embodiments, a sample may be any specimen, biopsy,
or biological component obtained (e.g., procured, taken, received)
from a subject. For example, in some embodiments, the sample may be
a blood sample, hair sample, tissue sample, bodily fluid sample,
cell sample, blood component sample, or any other cell or tissue
sample from which a nucleic acid may be obtained for
sequencing.
[0285] In some embodiments, the subject may be any organism in need
of treatment or diagnosis using methods or systems of the
disclosure. For example, without limitation, subjects may include
mammals and non-mammals. As used herein, a "mammal," refers to any
animal constituting the class Mammalia (e.g., a human, mouse, rat,
cat, dog, sheep, rabbit, horse, cow, goat, pig, guinea pig,
hamster, chicken, turkey, or a non-human primate (e.g., Marmoset,
Macaque)). In some embodiments, the mammal is a human. In some
embodiments, the subject is a mammal. In some embodiments, the
subject is a human.
[0286] In some embodiments, a sample may be a biological sample
obtained from a subject, e.g., from a patient. In some embodiments,
a sample may be blood, serum, sputum, urine, or a tissue biopsy
(e.g., from any tissue, including but not limited to heart, liver,
pancreas, CNS, gastro-intestinal tract, mouth, colon, kidney, and
skin). In some embodiments, a sample may be suspected to be a
disease sample (e.g., a cancer sample). In some embodiments, a
sample may be a healthy sample (e.g., to be used as a
reference).
[0287] In some embodiments, sequence information is obtained from a
next generation sequencing platform (e.g., Illumina.TM., Roche.TM.,
Ion Torrent.TM., etc.), or any high-throughput or massively
parallel sequencing platform. In some embodiments, these methods
may be automated, in some embodiments, there may be manual
intervention. In some embodiments, the sequence information may be
the result of non-next generation sequencing (e.g., Sanger
sequencing). In some embodiments, the sample preparation may be
according to manufacturer's protocols. In some embodiments, the
sample preparation may be custom made protocols, or other protocols
which are for research, diagnostic, prognostic, and/or clinical
purposes. In some embodiments, the protocols may be experimental.
In some embodiments, the origin or preparation method of the
sequence information may be unknown.
[0288] In some embodiments, the size of the obtained RNA and/or DNA
sequence data comprises at least 5 kilobases (kb). In some
embodiments, the size of the obtained RNA and/or DNA sequence data
is at least 10 kb. In some embodiments, the size of the obtained
RNA and/or DNA sequence data is at least 100 kb. In some
embodiments, the size of the obtained RNA and/or DNA sequence data
is at least 500 kb. In some embodiments, the size of the obtained
RNA and/or DNA sequence data is at least 1 megabase (Mb). In some
embodiments, the size of the obtained RNA and/or DNA sequence data
is at least 10 Mb. In some embodiments, the size of the obtained
RNA and/or DNA sequence data is at least 100 Mb. In some
embodiments, the size of the obtained RNA and/or DNA sequence data
is at least 500 Mb. In some embodiments, the size of the obtained
RNA and/or DNA sequence data is at least 1 gigabase (Gb). In some
embodiments, the size of the obtained RNA and/or DNA sequence data
is at least 10 Gb. In some embodiments, the size of the obtained
RNA and/or DNA sequence data is at least 100 Gb. In some
embodiments, the size of the obtained RNA and/or DNA sequence data
is at least 500 Gb.
[0289] In some embodiments, the sequence information may be
generated using a nucleic acid from a sample from a subject. In
some embodiments, the sequence information may be a sequence data
indicating a nucleotide sequence of DNA and/or RNA from a
previously obtained biological sample of a subject having,
suspected of having, or at risk of having a disease. In some
embodiments, the nucleic acid is deoxyribonucleic acid (DNA). In
some embodiments, the nucleic acid is prepared such that the whole
genome is present in the nucleic acid. In some embodiments, the
nucleic acid is processed such that only the protein coding regions
of the genome remain (e.g., exomes). When nucleic acids are
prepared such that only the exomes are sequenced, it is referred to
as whole exome sequencing (WES). A variety of methods or known in
the art to isolate the exomes for sequencing, for example, solution
based isolation wherein tagged probes are used to hybridize the
targeted regions (e.g., exomes) which can then be further separated
from the other regions (e.g., unbound oligonucleotides). These
tagged fragments can then be prepared and sequenced.
[0290] In some embodiments, the nucleic acid is ribonucleic acid
(RNA). In some embodiments, sequenced RNA comprises both coding and
non-coding transcribed RNA found in a sample. When such RNA is used
for sequencing the sequencing is said to be generated from "total
RNA" and also can be referred to as whole transcriptome sequencing.
Alternatively, the nucleic acids can be prepared such that the
coding RNA (e.g., mRNA) is isolated and used for sequencing. This
can be done through any means known in the art, for example by
isolating or screening the RNA for polyadenylated sequences. This
is sometimes referred to as mRNA-Seq.
[0291] Sequence information can include the sequence data generated
by the nucleic acid sequencing protocol (e.g., the series of
nucleotides in a nucleic acid molecule identified by
next-generation sequencing, sanger sequencing, etc.) as well as
information contained therein (e.g., information indicative of
source, tissue type, etc.) which may also be considered information
that can be inferred or determined from the sequence data. For
example, in some embodiments RNA sequence information may be
analyzed to determine whether the nucleic acid was primarily
polyadenylated or not.
[0292] Asserted information can refer to information about the
sequence data, and by extension, the nucleic acid, the sample,
and/or the subject from which the sequence data was obtained. In
some embodiments, asserted information is provided along with the
sequence data and can be verified by analyzing the sequence data as
described herein. The asserted information may relate to a feature
of the nucleic acid, the sample, or the subject, and can be used to
evaluate the quality of the nucleic acid (e.g., the source or
integrity of the nucleic acid). Asserted information can refer to
an asserted source and/or an asserted integrity of the sequence
data or information.
[0293] In some embodiments, a third party may provide sequence data
as well as the related asserted information. In some embodiments,
the asserted information is obtained from the same entity that the
sequence data is obtained from. In some embodiments, the asserted
information and sequence data are obtained from different parties.
In some embodiments, the asserted information is obtained from a
database. In some embodiments, the asserted information is a
reference value or property. In some embodiments, the asserted
information may allege an identity of the sequence information, an
identity of the nucleic acid of the sequence information, an
identity of the sample from which the sequence information was
generated, an identity of the subject from which the sample was
obtained. In some embodiments, the asserted information may
identify the sequence data as obtained from polyadenylated RNA, as
originating from whole transcriptome sequencing, or as being from
WES. In some embodiments, the asserted information may identify a
cell or tissue type for the sample from which the nucleic acid was
obtained. In some embodiments, the asserted information may allege
a tumor type for the sample from which the nucleic acid was
obtained. In some embodiments, the asserted information may
identify an MHC profile (e.g., sequences for alleles of the MHCs of
the subject from which the nucleic acid was obtained) for the
subject from which the sample was obtained. In some embodiments,
the asserted information may identify an expected protein subunit
ratio for the sample. In some embodiments, the asserted information
may provide an expected complexity value for the sequence
information. In some embodiments, the asserted information may
provide an expected contamination value for the sequence
information. In some embodiments, the asserted information may
provide an expected coverage value for the sequence information. In
some embodiments, the asserted information may provide an expected
exon coverage value for the sequence information. In some
embodiments, the asserted information may provide an expected read
composition value for the sequence information. In some
embodiments, the asserted information may provide an expected Phred
score for the sequence information. In some embodiments, the
asserted information may provide an expected single nucleotide
polymorphism (SNP) value for the sequence information. In some
embodiments, the asserted information may relate to a GC content
value for the sequence information. In some embodiments, the
asserted information may comprise additional information. In some
embodiments, the asserted information may comprise information
relating to multiple or more than one feature of the sequence
information. In some embodiments, the asserted information is any
combination of the aforementioned features (e.g., determined
values, properties, characteristics, etc.).
[0294] As used herein, a "feature" may be a property or
characteristic, which is determined from analysis of the sequence
information which provides the user with information about the
sequence information, the sample from which it was taken, and/or
the subject from which the sample was taken, beyond the sequence of
the nucleotides of the sequence information. The sequence
information may be in connection with the gene expression data
obtained from a healthcare provider or a laboratory. For example, a
feature may be indicative of a source (e.g., patient, subject,
nucleic acid type), patient or subject identity, tissue type, tumor
type, polyadenylation status, MHC sequence, protein subunits,
complexity, contamination, coverage (e.g., total sequence, exon,
etc.), read composition, quality and/or Phred Score, single
nucleotide polymorphism (SNP) positions, and/or GC content. The
feature(s) of the sequence information can then be indicative of
whether the sequence information is potentially a match or mismatch
to the asserted information from a healthcare provider or a
laboratory.
[0295] When considering identity or source, it is important to
recognize that the term not only refers to identifying a specific
subject or patient as a particular individual, but also that one or
more features of sequence information for one sample can be
identified as being the same as the one or more features of
sequence information obtained from another sample. For example,
sequence information A may be compared to sequence information B
which is presented and asserted to be from the same nucleic acid,
subject or patient, tissue, or tumor. The identity can be
corroborated or questioned by the methods herein without knowing
the actual identity of the subject but can support the finding that
the identity is consistent with another given sequence information.
In some embodiments, the identity of the sequence information is
used to compare to asserted information for a given sample,
subject, tissue, or tumor. In some embodiments the identity of the
sequence information is used to compare the asserted information
for another nucleic acid or reference value.
[0296] In some embodiments, these determined features of the
sequence information are then evaluated (e.g., determined, matched,
aligned, measured, assessed) against the asserted information. This
evaluation can be done to increase the confidence that the sequence
information is of a particular origin (e.g., source), is identified
correctly, or has a particular or specific characteristic (e.g., is
from polyadenylated nucleic acids). In this respect, the methods
can be used to provide checkpoints and measures to highlight any
potential problems (e.g., non-matching values (e.g., for determined
features and asserted information), or determined values which fall
outside of accepted or established ranges). Such problems can
signify or signal problems with integrity (e.g., degraded or
contaminated) or source (e.g., misidentified, wrongly labeled,
etc.) of the sequence data. By using the methods and processes
herein, and matching determined features to asserted information
for given sequence information, it lessens the possibility of
incorrect or poor quality sequence data being used for analysis and
raises the confidence that the sequence data is of sufficient
quality to be used for diagnostic, prognostic, and/or clinical
analyses.
[0297] In some embodiments, evaluating whether determined
information matches asserted information involves determining
whether the determined information matches asserted information
exactly are within a specified threshold. More generally, in some
embodiments, evaluating whether two values "match" may involve
determining whether the two values match exactly or are within a
specified threshold. That threshold may be 0, requiring exact
matches in some embodiments. That threshold may be greater than 0
such that when numerical values are being compared, the numerical
values may be said to "match" if their values are within the
threshold of one another (e.g., when the absolute difference of the
numerical values is less than or equal to the threshold value). In
some embodiments, the threshold may be set as a function of a
standard deviation (or a multiple thereof), a quantile, a
percentile, or any other suitable statistical quantity. In some
embodiments, evaluating whether two values "match" may involve
determining, when there is a difference between the two values,
whether that difference is statistically significant. Such a
determination may be performed using a statistical hypothesis test,
a threshold, or any other suitable statistical or mathematical
technique, as aspects of the technology described herein are not
limited in this respect.
[0298] In some embodiments, one or more quality control parameters
are checked for the bioinformatics data. In some embodiments, tumor
purity can be checked. Tumor purity, as described herein, may refer
to the proportion of cancer cells in the admixture. In some
embodiments, the target tumor purity for the WES is .gtoreq.20%
(e.g., 20%, 40%, 60%). In some embodiments, the target tumor purity
for the RNA-seq is .gtoreq.20% (e.g., 20%, 40%, 60%).
[0299] In some embodiments, depth of coverage can be checked. In
some embodiments, the depth of coverage for the WES is
.gtoreq.150.times. average coverage of tumor sample (e.g.,
150.times., 180.times., 200.times.). In some embodiments, the
target depth of coverage for the RNA-seq is .gtoreq.100.times.
(e.g., 100.times., 150.times., 200.times.).
[0300] In some embodiments, alignment rate can be checked. In some
embodiments, the target alignment rate for the WES is more than 90%
(e.g., 91%, 95%, 99%). In some embodiments, the target alignment
rate for the RNA-seq is more than 90% (e.g., 91%, 95%, 99%).
[0301] In some embodiments, base call quality scores such as Phred
score can be checked. In some embodiments, the target Phred score
for the WES is more than 30 (e.g., 35, 40, 50). In some
embodiments, the target Phred score for the RNA-seq is more than 30
(e.g., 35, 40, 50).
[0302] In some embodiments, uniformity of coverage can be checked.
In some embodiments, the target uniformity of coverage for the WES
is 85% base pairs in target regions covered .gtoreq.20.times. for
tumor tissue (e.g., 85%, 95%, 99%). In some embodiments, the target
uniformity of coverage for the WES is 85% base pairs in target
regions covered .gtoreq.20.times. for normal tissue (e.g., 85%,
95%, 99%). In some embodiments, target regions for determining
uniformity of coverage may be ExonV7 target regions with the use of
the coding regions from CCDS (consensus coding sequence) genes.
[0303] In some embodiments, GC bias can be checked. In some
embodiments, the target GC bias for the WES is at least 50 (e.g.,
50, 60, 70). In some embodiments, the acceptable range of GC bias
for the WES is at least 45-65 (e.g., 45-65, 50-65, 55-65). In some
embodiments, the target GC bias for the RNA-seq is at least 50
(e.g., 50, 60, 70). In some embodiments, the acceptable range of GC
bias for the RNA-seq is at least 45-65 (e.g., 45-65, 50-65,
55-65).
[0304] In some embodiments, mapping quality can be checked. In some
embodiments, the mapping quality for the WES is .gtoreq.10 (e.g.,
10, 20, 30).
[0305] In some embodiments, duplication rate can be checked. In
some embodiments, the duplication rate for the WES is less than 30%
(e.g., 29.9%, 25%, 15%). In some embodiments, the duplication rate
for the RNA-seq is less than 85% (e.g., 84.99%, 80%, 70%).
[0306] In some embodiments, insert size can be checked. In some
embodiments, the acceptable median insert size for tumor tissue for
the WES is about 150 (e.g., 150, 280, 250). In some embodiments,
the target median insert size for tumor tissue for the WES is about
200 (e.g., 200, 250, 350). In some embodiments, the acceptable
median insert size for normal tissue for the WES is about 150
(e.g., 150, 280, 250). In some embodiments, the target median
insert size for normal tissue for the WES is about 200 (e.g., 200,
250, 350). In some embodiments, the acceptable median insert size
for tumor tissue for the RNA seq is about 150 (e.g., 150, 280,
250). In some embodiments, the target median insert size for tumor
tissue for the RNA seq is about 200 (e.g., 200, 250, 350).
[0307] In some embodiments, contamination can be checked. In some
embodiments, contamination acceptable for the WES is less than
0.05% (e.g., 0.04%, 0.03%, 0.01%). In some embodiments,
contamination acceptable for the RNA-seq is less than 0.05% (e.g.,
0.04%, 0.03%, 0.01%).
[0308] In some embodiments, SNP concordance of a pair of tumor
versus normal samples from the same patient can be checked. In some
embodiments, the target SNP concordance for the WES is more than
90% (e.g., 91%, 95%, 98%). In some embodiments, the acceptable SNP
concordance for the WES is more than 85% (e.g., 86%, 90%, 98%). In
some embodiments, the target SNP concordance for the RNA seq is
more than 90% (e.g., 91%, 95%, 98%). In some embodiments, the
acceptable SNP concordance for the RNA seq is more than 85% (e.g.,
86%, 90%, 98%).
[0309] In some embodiments, HLA allele concordance of a pair of
tumor versus normal samples from the same patient can be checked.
In some embodiments, the threshold for normal versus tumor tissue
for the WES is less than 5 (e.g., 4.5, 3, 2.5). In some
embodiments, the threshold for tumor RNA seq tissue versus normal
WES tissue for the RNA seq is less than 5 (e.g., 4.5, 3, 2.5).
[0310] In some embodiments, sequence information can be assessed
for genome contamination (e.g., non-human genome contamination). In
some embodiments, the samples or sequence information are assessed
to determine whether they are contaminated by determining whether
they contain sequences from other species or reference genomes such
as mouse, zebrafish, drosophila, celegans, saccharomyces,
arabidopsis, microbiome, mycoplasma, adapters, UniVec, and phiX
rRNA. In some embodiments, the target threshold for the ADA genomes
contamination for the WES is more than 60 (e.g., 65, 70, 80). In
some embodiments, the acceptable threshold for the ADA genomes
contamination for the WES is more than 40 (e.g., 45, 60, 80). In
some embodiments, the target threshold for the ADA genomes
contamination for the RNA seq is more than 40 (e.g., 50, 60, 80).
In some embodiments, the acceptable threshold for the ADA genomes
contamination for the RNA seq is more than 20 (e.g., 30, 50,
70).
[0311] In some embodiments, only one feature is evaluated against
an asserted information. In some embodiments, more than one feature
is evaluated against an asserted information. In some embodiments,
at least two or more features are evaluated against an asserted
information. In some embodiments, at least three or more features
are evaluated against an asserted information. In some embodiments,
at least four or more features are evaluated against an asserted
information. In some embodiments, at least five or more features
are evaluated against an asserted information. In some embodiments,
at least six or more features are evaluated against an asserted
information. In some embodiments, at least seven or more features
are evaluated against an asserted information. In some embodiments,
at least eight or more features are evaluated against an asserted
information. In some embodiments, at least nine or more features
are evaluated against an asserted information. In some embodiments,
at least ten or more features are evaluated against an asserted
information. In some embodiments, at least eleven or more features
are evaluated against an asserted information. In some embodiments,
at least twelve or more features are evaluated against an asserted
information. In some embodiments, at least thirteen or more
features are evaluated against an asserted information. In some
embodiments, at least fourteen or more features are evaluated
against an asserted information. In some embodiments, at least
fifteen or more features are evaluated against an asserted
information.
[0312] In some embodiments, if the features or determined values
are found to not meet or match the asserted information, additional
steps are performed. In some embodiments, if the features or
determined values are found to not meet or match the asserted
information, the sequence information is rejected (e.g., is not
used for subsequent analysis). In some embodiments, if the features
or determined values are found to not meet or match the asserted
information, the sequence information is retested, meaning that any
evaluation of features or determinations are performed for at least
another or second, or more (e.g., third, fourth, fifth, sixth,
etc.) time. In some embodiments, if the features or determined
values are found to not meet or match the asserted information,
another or second, or more (e.g., third, fourth, fifth, sixth,
etc.) sequence information is obtained and then tested, meaning
that any evaluation of features or determinations are performed for
at least one time, or second, or more (e.g., third, fourth, fifth,
sixth, etc.) time, independent of the initial determinations and
evaluations done on the first sequence information. In some
embodiments, if the features or determined values are found to not
meet or match the asserted information, the sequence information is
reported to a user as such. In some embodiments, any combination of
these steps may be performed in the event that features or
determined values are found to not meet or match the asserted
information.
[0313] In some embodiments, if the features or determined values
are found to not meet or match the asserted information, the
sequence information can still be evaluated for characteristics
related to disease (e.g., cancer), but information about the
quality (e.g., the extent and nature of the one or more features of
the determined sequence information that do not match the asserted
information) can be provided to a user (e.g., to a physician or
other medical practitioner). In some embodiments, the
characteristics relate to the type of cancer, its environment, its
stage, its location, its tissue of origin, its statistical
likelihood of responding to various treatments or therapies, or
other properties which may aid a practitioner in treating the
subject.
[0314] In some embodiments, if the features or determined values
are found to meet or match the asserted information (e.g., match,
exceed, or otherwise satisfy reference or threshold values), then
additional steps may be performed. In some embodiments, if the
features or determined values are found to meet or match the
asserted information (e.g., match, exceed, or otherwise satisfy
reference or threshold values), then additional steps may be
performed. In some embodiments, if the features or determined
values are found to meet or match the asserted information (e.g.,
match, exceed, or otherwise satisfy reference or threshold values),
the sequence information is evaluated for characteristics related
to cancer. In some embodiments, the characteristics relate to the
type of cancer, its environment, its stage, its location, its
tissue of origin, its statistical likelihood of responding to
various treatments or therapies, or other properties which may aid
a practitioner in treating the subject.
[0315] In some embodiments, after one or more quality control steps
are performed, a report is generated for the user with the results
of the quality control steps that were performed.
[0316] Accordingly, in one aspect, the disclosure relates to a
method of evaluating sequence information of at least one nucleic
acid, to determine at least one feature thereof. The at least one
feature can be used to evaluate the quality or integrity of the
sequence information, to interrogate the source of the sequence
information, or to allow for analyses of other sequence
information, which may or may not be from the same sequencing
platform, or from the same or a different sample preparation
protocol. Further the at least one feature may be used as a quality
control measure to ensure subsequent analyses of a threshold
quality and lower quality sequence information are omitted.
[0317] Accordingly, in one aspect, the disclosure relates to a
method of evaluating sequence information by (a) obtaining sequence
information which comprises: (1) sequence data from a first
ribonucleic acid (RNA); or (2) sequence data from a first whole
exome sequence (WES); and (b) determining one or more features of
the sequence data selected from the group consisting of: (i) the
identity of the subject from which the nucleic acid was obtained;
(ii) a tissue of origin from which the nucleic acid obtained; (iii)
a tumor type of from which the nucleic acid was obtained; (iv) a
quality measure of the first RNA sequence data; (v) whether the RNA
sequence data was obtained from polyadenylated (polyA) RNA or total
RNA; (vi) if the first sequence data set is first WES sequence
data, (vii) the sequencing platform that was used to generate the
first sequence data set; and (viii) a quality measure of the first
sequence data set.
[0318] In some embodiments, the method further comprises obtaining
additional sequence information if the one or more features of the
sequence information is below a quality control threshold suitable
for further analysis.
[0319] In some embodiments, the evaluated feature is the subject
identity. In some embodiments, the subject identity is determined
by performing one or more of evaluations from the group comprising:
a major histocompatibility complex evaluation and a SNP concordance
evaluation, wherein the results of the evaluations are compared to
an asserted value for the subject or a second sequence data set
from the subject.
[0320] In some embodiments, the evaluated feature is the tissue of
origin. In some embodiments, the tissue of origin is determined by
performing one or more of evaluations from the group comprising:
protein expression and biomarker analysis. In another aspect, the
disclosure relates to a method of evaluating a feature comprising
assigning a tissue of origin of the sample which generated the
sequence information. In some embodiments, the method comprises
evaluating the sequence information for markers or gene expression
indicative of a tissue type from which the sequence information
originated. In some embodiments, the method comprises evaluating
the markers or gene expression against a database of the same for
different tissue types. Different tissues throughout a subject
express different proteins which create a profile of such a tissue.
Accordingly, it is possible to evaluate the protein expression
profile and match it to a tissue type to identify the tissue from
which the sample, and by extension the sequence information, was
obtained. This can be done through a variety of methods known in
the art. For example, evaluating the number of a given messenger
RNA (mRNA) transcript (e.g., using it as a proxy for evaluating
protein expression) can be evaluated against a database of known
tissue markers (e.g., protein expression profiles), it can be
evaluated against a provided set of markers for a subject, or it
can be evaluated against a second sequence information or set of
tissue markers obtained from the subject. In some embodiments, a
tissue of origin is determined by evaluating the sequence
information for markers (e.g., protein expression) and matching the
markers with a database of tissues. In some embodiments, a tissue
of origin is determined by evaluating the sequence information for
markers (e.g., protein expression) and matching the markers with a
set of markers from a tissue of a subject. In some embodiments, a
tissue of origin is determined by evaluating the sequence
information for markers (e.g., protein expression) and matching the
markers with a second sequence information obtained from a subject
where the tissue of origin is known.
[0321] In some embodiments, the evaluated feature is a measure of
the integrity of the sequence information. In some embodiments, the
integrity measure of the first RNA sequence data is determined by
performing one or more of evaluations from the group comprising:
determining coverage of one or more genes in the RNA sequence data,
determining relative coverage of two or more exons for at least one
gene in the RNA sequence data, determining an expression ratio of
two known reference genes from the RNA sequence data, or other
feature or combinations of two or more thereof. In some
embodiments, the integrity measure of the DNA sequence data is
determined by performing one or more of evaluations from the group
comprising: total coverage and/or chromosomal coverage of the DNA
sequence data, or other feature or combinations of two or more
thereof.
[0322] In some embodiments, the RNA sequence data is analyzed to
determine whether it was obtained from polyA RNA or total RNA. In
some embodiments, the RNA sequence data is analyzed by evaluating
an expression level of one or more mitochondrial or histones genes
from the RNA sequence data, and/or other features that are
characteristic of polyA or total RNA.
[0323] In some embodiments, the feature being evaluated is the
sequencing platform that was used to generate the sequence. In some
embodiments, the sequencing platform used for generating the WES
sequence data is determined by performing one or more of
evaluations from the group comprising: determining % variance for
one or more reference genes in the WES sequence data, or other
property of sequencing data that is characteristic of the
sequencing platform that was used to generate the sequence
data.
[0324] In some embodiments, methods comprise evaluating at least
one of the features described herein. In some embodiments, a method
comprises evaluating at least two of the features described herein.
In some embodiments, a method comprises evaluating at least three
of the features described herein. In some embodiments, a method
comprises evaluating at least four of the features described
herein. In some embodiments, a method comprises evaluating at least
five of the features described herein. In some embodiments, a
method comprises evaluating at least six of the features described
herein. In some embodiments, a method comprises evaluating at least
seven of the features described herein.
[0325] In some embodiments, the quality (e.g., source or integrity)
of sequence information from one or more nucleic acid samples
(e.g., at least two nucleic acid samples) is evaluated by (a)
determining the sequence of two or more (e.g., 2, 3, 4, 5, 6 or
more) major histocompatibility complexes (MHCs), and (b)
determining whether the MHCs from the one or more samples match. In
some embodiments, if the MHCs don't match (e.g., if a calculated
agreement value is less than a statistically significant threshold)
the sequence information from each of the nucleic acids is deemed
likely from distinct sources, of insufficient quality, and is
removed, discarded, retested, and/or reported as such to a user. In
some embodiments, if the calculated agreement value (x) between WES
normal/tumor/RNAseq is 0<x.gtoreq.2(e.g., 1, 1.5, 2), it
represents acceptable and "warning." Warning means that the
calculated agreement value is within a range that is deemed
acceptable but is considered close to be not acceptable. In some
embodiments, if the calculated agreement value (x) between WES
normal/tumor/RNAseq is >5, it represents not acceptable or bad
quality. In some embodiments, if the calculated agreement value (x)
between WES normal/tumor/RNAseq is 0, it represents good quality.
In some embodiments, if the MHCs match (e.g., if the agreement
value is at or above the statistically significant threshold) the
sequence information from each of the nucleic acid samples is
deemed sufficiently likely from the same source, of sufficient
quality, and is retained for further analysis and/or reported as
such to a user.
[0326] In some embodiments, the quality of sequence information
from one or more (e.g., at least two) nucleic acid samples is
evaluated by determining a concordance value for single nucleotide
polymorphisms (SNPs) in the sequence information. In some
embodiments, the method further comprises evaluating the
concordance value. In some embodiments, if the concordance value is
less than 85%, less than 80%, or less than 75%, the sequence
information from each of the nucleic acid samples is deemed likely
from distinct sources, of insufficient quality, and is removed,
discarded, retested, and/or reported as such to a user. In some
embodiments, if the concordance value is less than 75%, the
sequence information is deemed not acceptable. In some embodiments,
if the concordance value is more than 80% and less than 95%, the
sequence information is deemed within the ranges that are close to
be not acceptable. In some embodiments, if the concordance value is
more than 95%, the sequence information is deemed acceptable. In
some embodiments, if the concordance value is at least 75%, at
least 80%, or at least 85%, the sequence information from each of
the nucleic acid samples is deemed sufficiently likely from the
same source, of sufficient quality, and is retained, and/or
reported as such to a user. In some embodiments, at least 5,000
SNPs can be evaluated for concordance values. In some embodiments,
at least 6,000 SNPs can be evaluated for concordance values. In
some embodiments, at least 7,000 SNPs can be evaluated for
concordance values. In some embodiments, at least 8,000 SNPs can be
evaluated for concordance values.
[0327] In some embodiments, the quality of sequence information
from one or more (e.g., at least two) nucleic acid samples is
evaluated by determining a contamination value for the sequence
information. In some embodiments, if the contamination value is
above a statistically significant threshold, the sequence
information is removed, discarded, retested, and/or reported as
such to a user. In some embodiments, if the contamination value is
more than 0.05% (e.g., 0.06%, 1%, 2%), the sequence information is
deemed to be close to not acceptable (e.g., warning). In some
embodiments, if the contamination value is more than 0.1% (e.g.,
0.1%, 0.5%, 1%), the sequence information is deemed to be not
acceptable for blood sample and fresh frozen tissue. In some
embodiments, if the contamination value is below the threshold, the
sequence information is retained, and/or reported as such to a
user.
[0328] In some embodiments, the quality of sequence information
from one or more (e.g., at least two) nucleic acid samples is
evaluated by analyzing sequence information from the one or more
nucleic acid samples against a set of tumor types, determining
predicted tumor type(s) from the sequence information, and
determining whether the predicted tumor type(s) matches the tumor
type(s) that were provided (e.g., asserted) for the one or more
nucleic acid samples. In some embodiments, determining predicted
tumor type(s) as a quality control step can be performed by using a
computerized system or process as described herein. In some
embodiments, determining predicted tumor type(s) as a quality
control step can be performed by determining a cancer grade from
sequence data by using machine learning techniques, as described
herein and in the U.S. Provisional Patent Application Ser. No.
62/943,976, titled "Machine Learning Techniques for Gene Expression
Analysis," filed on Dec. 5, 2019, which is incorporated by
reference herein in its entirety. In some embodiments, if there is
disagreement between the tumor type(s) (e.g., cancer grades)
obtained from the sequence evaluation and an asserted information,
the sequence information is identified as suspect, or insufficient
quality, and is removed, discarded, retested, and/or reported as
such to a user. In some embodiments, if there is agreement between
the predicted tumor type(s) and expected tumor type(s) for the one
or more nucleic acid samples, the sequence information is deemed of
sufficient quality, and is retained, and/or reported as such to a
user.
[0329] In some embodiments, matching the predicted tumor type(s) to
the tumor type(s) that were provided comprise using a set of
reference genes from a training data set containing a plurality of
signature genes that are up-regulated or down-regulated in a
certain tumor type, relative to a normal, healthy sample. For
instance, if the predicted tumor type is prostate cancer (e.g.,
asserted information), the sample will be checked against the known
reference genes of prostate cancer. In some embodiments, the
predicted tumor type is also evaluated against its tumor grade,
which can help determine the signature genes of the asserted cancer
grade at different stages of cancer.
[0330] As described above, in some embodiments, determining
predicted tumor type(s) as a quality control step may be performed
by determining a cancer grade from sequence information by using a
machine learning approach employing a statistical model trained
using training data.
[0331] For example, in some embodiments, a statistical model may be
used to predict characteristic(s) of a biological sample, using
gene expression data, based on an input ranking of genes, ranked
based on their respective expression levels, for a sequencing
platform. Using the input ranking(s), instead of the specific
values for the expression levels, allows for the same or similar
data processing pipeline to be used across different expression
data regardless of the specific manner in which the expression
levels were obtained (e.g., regardless of which sequencing
platform, sequencing conditions, sample preparation, data
processing to obtain expression levels, etc.). In some embodiments,
a statistical model may be used to predict cancer grade of the
biological sample. In some embodiments, a statistical model may be
used to predict tissue of origin of the biological sample, which
also may be used for performing quality control as described
herein.
[0332] For example, in some embodiments, rankings of genes based on
the gene expression levels (in a biological sample) as determined
by a sequencing platform may be provided as input to a statistical
model trained to predict tissue of origin for the biological
sample. The predicted tissue of origin may be compared against
asserted tissue of origin as part of the quality control techniques
described herein. As another example, in some embodiments, rankings
of genes based on the gene expression levels (in a biological
sample) as determined by a sequencing platform may be provided as
input to a statistical model trained to predict cancer grade for
the biological sample. The predicted cancer grade may be compared
against asserted cancer grade as part of the quality control
techniques described herein.
[0333] In some embodiments, the set of genes being ranked depends
on the particular biological characteristic of interest. For
example, one set of genes may be used for determining the tissue of
origin and another set of genes may be used for determining the
cancer grade.
[0334] In some embodiments, the expression data may be obtained for
cells in the biological sample, where the subject has, is suspected
of having or is at risk of having cancer. In the context where
tissue of origin is a characteristic being determined, the tissue
of origin is for the cells in the biological sample. The tissue of
origin may refer to a particular tissue type from which the cells
originate, such as lung, pancreas, stomach, colon, liver, bladder,
kidney, thyroid, lymph nodes, adrenal gland, skin, breast, ovary,
and prostate.
[0335] For example, some embodiments involve using a gene set for
predicting tissue of origin, which may include cell of origin, for
Diffuse Large B-Cell Lymphoma (DLBCL), such as germinal center
B-cell (GCB) and activated B-cell (ABC). Genes in the gene set may
be selected from the group consisting of: ITPKB, MYBL1, LMO2, BATF,
IRF4, LRMP, CCND2, SLA, SP140, PIM1, CSTB, BCL2, TCF4, P2RX5,
SPINK2, VCL, PTPN1, REL, FUT8, RPL21, PRKCB1, CSNK1E, GPR18, IGHM,
ACP1, SPIB, HLA-DQA1, KRT8, FAM3C, and HLA-DMB.
[0336] In the context where cancer grade is a characteristic being
determined, the cancer grade is for the cells in the biological
sample. The cancer grade may refer to proliferation and
differentiation characteristics of the cells in the biological
sample and refer to a numerical grade that is generally determined
by visual observation of cells using microscopy, such as Grade 1,
Grade 2, Grade 3, and Grade 4.
[0337] For example, some embodiments involve using a gene set for
predicting breast cancer grade. Genes in the gene set may be
selected from the group consisting of: UBE2C, MYBL2, PRAME, LMNB1,
CXCL9, KPNA2, TPX2, PLCH1, CCL18, CDK1, MELK, CCNB2, RRM2, CCNB1,
NUSAP1, SLC7A5, TYMS, GZMK, SQLE, Clorf106, CDC25B, ATAD2, QPRT,
CCNA2, NEK2, ID01, NDC80, ZWINT, ABCA12, TOP2A, TDO2, S100A8,
LAMP3, MMP1, GZMB, BIRC5, TRIP13, RACGAP1, ASPM, ESRP1, MAD2L1,
CENPF, CDC20, MCM4, MKI67, PBK, CKS2, KIF2C, MRPL13, TTK, BUB1,
TK1, FOXM1, CEP55, EZH2, ECT2, PRC1, CENPU, CCNE2, AURKA, HMGB3,
APOBEC3B, LAGE3, CDKN3, DTL, ATP6V1C1, KIAA0101, CD2, KIF11,
KIF20A, CDCA8, NCAPG, CENPN, MTFR1, MCM2, DSCC1, WDR19, SEMA3G,
KCND3, SETBP1, KIF13B, NR4A2, NAV3, PDZRN3, MAGI2, CACNA1D, STC2,
CHAD, PDGFD, ARMCX2, FRY, AGTR1, MARCH8, ANG, ABAT, THBD, RAI2,
HSPA2, ERBB4, ECHDC2, FST, EPHX2, FOSB, STARD13, ID4, FAM129A,
FCGBP, LAMA2, FGFR2, PTGER3, NME5, LRRC17, OSBPL1A, ADRA2A, LRP2,
Clorf115, COL4A5, DIXDC1, KIAA1324, HPN, KLF4, SCUBE2, FMO5,
SORBS2, CARD10, CITED2, MUC1, BCL2, RGS5, CYBRD1, OMD, IGFBP4,
LAMB2, DUSP4, PDLIM5, IRS2, and CX3CR1.
[0338] As another example, some embodiments involve using a gene
set for predicting kidney clear cell cancer grade. Genes in the
gene set may be selected from the group consisting of: PLTP, C1S,
LY96, TSKU, TPST2, SERPINF1, SRPX2, SAA1, CTHRC1, GFPT2, CKAP4,
SERPINA3, CFH, PLAU, BASP1, PTTG1, MOCOS, LEF1, SLPI, PRAME,
STEAP3, LGALS2, CD44, FLNC, UBE2C, CTSK, SULF2, TMEM45A, FCGR1A,
PLOD2, C19orf80, PDGFRL, IGF2BP3, SLC7A5, PRRX1, RARRES1, LHFPL2,
KDELR3, TRIB3, IL20RB, FBLN1, KMO, C1R, CYP1B1, KIF2A, PLAUR, CKS2,
CDCP1, SFRP4, HAMP, MMP9, SLC3A1, NAT8, FRMD3, NPR3, NAT8B, BBOX1,
SLC5A1, GBA3, EMCN, SLC47A1, AQP1, PCK1, UGT2A3, BHMT, FMO1, ACAA2,
SLC5A8, SLC16A9, TSPAN18, SLC17A3, STK32B, MAP7, MYLIP, SLC22A12,
LRP2, CD34, PODXL, ZBTB42, TEK, FBP1, and BCL2.
[0339] Aspects of using statistical models for predicting tissue of
origin, cancer grade, and/or any other characteristics of a
biological sample are described in the U.S. Provisional Patent
Application Ser. No. 62/943,976, titled "Machine Learning
Techniques for Gene Expression Analysis", filed on Dec. 5, 2019,
which is incorporated by reference herein in its entirety.
[0340] Returning to aspects of evaluating quality of sequence
information, in some embodiments, the quality of sequence
information from one or more (e.g., at least two) nucleic acid
samples is evaluated by determining the presence or absence of
polyadenylated RNA genes to predict whether the sequence
information was obtained from polyA RNA or not. In some
embodiments, if there is disagreement between the predicted and
expected (e.g., asserted) polyA status of one or more samples, the
sequence information for those samples is deemed as suspect, of
insufficient quality, and is removed, discarded, retested, and/or
reported as such to a user. In some embodiments, if there is
agreement between the predicted and expected polyA status for one
or more nucleic acid samples, the sequence information is deemed of
sufficient quality, and is retained, and/or reported as such to a
user.
[0341] In some embodiments, the quality of sequence information
from one or more (e.g., at least two) nucleic acid samples is
evaluated by determining a complexity value of the sequence
information. In some embodiments, determining a complexity value
comprises determining the number of duplications. In some
embodiments, the % duplications can be determined for a DNA or RNA
library. In some embodiments, if a large percentage of the library
is duplicated, either a library of low complexity or
over-amplification of the DNA or cDNA fragments is indicated. In
some instances, differences between libraries in the complexity or
amplification indicates that certain biases in the data are
introduced (e.g., differing % GC content). In some embodiments, if
the complexity value is less than 75%, or less than 80%, the
sequence information is deemed suspect, of insufficient quality,
and is removed, discarded, retested, and/or reported as such to a
user. In some embodiments, if the complexity value is at least 80%,
or at least 85%, the sequence information is deemed of sufficient
quality for further analysis, and is retained, and/or reported as
such to a user.
[0342] In some embodiments, the quality of sequence information
from one or more (e.g., at least two) nucleic acid samples is
evaluated by predicting a tissue source for the nucleic acid. In
some embodiments, if there is disagreement between a predicted and
an asserted tissue source for the nucleic acid, the sequence
information is deemed suspect, of insufficient quality, and is
removed, discarded, retested, or reported as such. In some
embodiments, if there is agreement between the predicted and
asserted tissue sources, the sequence information is deemed of
sufficient quality for further analysis, and is retained, and/or
reported as such to a user.
[0343] In some embodiments, the quality of sequence information
from one or more (e.g., at least two) nucleic acid samples is
evaluated by (a) determining a gene expression level for two
different subunits of a known protein; and (b) determining an
expression ratio for the two different subunits. In some
embodiments, if the determined expression ratio does not match the
expected expression ratio for the protein subunits, the sequence
information is identified as suspect, of insufficient quality, and
is removed, discarded, retested, and/or reported as such to a user.
In some embodiments, if the determined expression ratio matches an
expected expression ratio for the protein subunits, the sequence
information is deemed of sufficient quality for further analysis,
and is retained, and/or reported as such to a user.
[0344] In some embodiments, the quality of sequence information
from one or more (e.g., at least two) nucleic acid samples is
evaluated by determining a Phred Score for the sequence
information. In some embodiments, if the Phred Score is less than
27, the sequence information is deemed suspect, of insufficient
quality, and is removed, discarded, retested, and/or reported as
such to a user. In some embodiments, if the Phred Score is less
than 20, the sequence information is removed, discarded, retested,
and/or reported as such to a user. In some embodiments, if the
Phred Score is more than 20 and is less than 27, the sequence
information is deemed close to be removed, discarded, retested,
and/or reported as such to a user. In some embodiments, if the
Phred Score is at least 27, the sequence information is deemed of
sufficient quality for further analysis, and is retained, and/or
reported as such to a user.
[0345] In some embodiments, the quality of sequence information
from one or more (e.g., at least two) nucleic acid samples is
evaluated by determining a GC content for the sequence information.
In some embodiments, if the GC content is at least 30%, and less
than or equal to 55%, the sequence information is deemed of
sufficient information for further analysis, and is retained,
and/or reported as such to a user. In some embodiments, if the GC
content is in the range of 45-65%, the sequence information is
deemed of sufficient information for further analysis, and is
retained, and/or reported as such to a user (i.e., acceptable). In
some embodiments, GC content of at least 50% (e.g., 50%, 51%, 60%)
is the target value at least for human samples.
[0346] In some embodiments, at least two (e.g., 3, 4, 5, 6, 7, 8,
9, 10, or more) distinct methods for evaluating the quality (e.g.,
source and/or integrity) of sequence information are performed.
[0347] In some embodiments, the methods performed herein evaluate
sequence information from a mammal. In some embodiments, the mammal
is human.
[0348] In some embodiments, the subject from which the sample which
generated the sequence information has, is suspected of having, or
is at risk of having a disorder. In some embodiments, the disorder
is cancer.
[0349] In some embodiments, a report is generated comprising the
one or more features or results of the methods described herein. In
some embodiments, the report further comprises an analysis of the
results of the methods described herein.
[0350] In some embodiments, the methods or processes of the
disclosure may be carried out on a system or computer processor
(e.g., laptop, desktop, server, or other computerized machine). The
components of the system may reside in disparate places and
communicate over networks, such as local area networks or wide area
networks, or by internet protocols. The system may interface with
the user via a web-enable browsers and graphical user interfaces
(GUIs). In some embodiments, the system is under the control of the
user in one place. In some embodiments, the system is comprised of
components not in one place and which may not be under the direct
control of the user. In some embodiments, the information of the
system is stored locally.
[0351] As described herein, the term "process," "act," "step" or
the variations thereof that are used in computerized processes or
flow charts therein can be used interchangeably, unless indicated
otherwise.
[0352] As described herein, the term "patient," "subject," "human
subject" or the variations thereof can be used interchangeably,
unless indicated otherwise.
[0353] FIG. 6A is a flow chart showing illustrative computerized
Process 200 for performing non-stranded RNA sequencing with the
coding RNA enrichment. Process 200 begins at act 201, where a first
sample of a first tumor from a subject having, suspected of having,
or at risk of having cancer is obtained. Further aspects relating
to obtaining a first sample of a first tumor from a subject having,
suspected of having, or at risk of having cancer are provided in
section "Biological samples."
[0354] Next process 200 proceeds to act 202, wherein RNA from the
first sample of the first tumor is extracted. Aspects relating to
extracting RNA from the first sample of the first tumor are
described in the section called "Extraction of DNA and/or RNA."
[0355] Next the process 200 proceeds to act 203, where the
extracted RNA is enriched for coding RNA to obtain enriched RNA.
Aspects relating to enriching the extracted RNA for coding RNA to
obtain enriched RNA are described in the section called "RNA
enrichment."
[0356] Next process 200 proceeds to act 204, where a first library
of cDNA fragments from the enriched RNA for non-stranded RNA
sequencing is prepared. Aspects relating to preparing a first
library of DNA fragments from the enriched RNA for non-stranded RNA
sequencing are described in the section called "Library preparation
for RNA sequencing."
[0357] Next process 200 proceeds to act 205, where non-stranded RNA
sequencing is performed on the first library of cDNA fragments
prepared from the enriched RNA. Aspects relating to performing
non-stranded RNA sequencing on the first library of DNA fragments
prepared from the enriched RNA are described in the section called
"RNA sequencing." It should be appreciated that one or more acts of
process 200 may be optional.
[0358] FIG. 6B is a flow chart showing computerized process 210 for
identifying a cancer treatment by obtaining bias-corrected gene
expression data. Process 210 begins at act 211, where RNA
expression data for a subject having, suspected of having, or at
risk of having cancer is obtained. Aspects relating to obtaining
RNA expression data are described in the section called "Obtaining
RNA expression data."
[0359] Next, process 210 proceeds to act 212, where genes in the
RNA expression data are aligned to a reference and the RNA
expression data is annotated. Aspects relating to aligning and
annotating genes in the RNA expression data with known sequences of
the human genome to obtain annotated RNA expression data are
described in the section called "Alignment and annotation."
[0360] Next, process 210 proceeds to act 213, where non-coding
transcripts from the annotated RNA expression data are removed to
obtain filtered RNA expression data. Aspects relating to removing
non-coding transcripts from the annotated RNA expression data are
described in the section titled "Removing non-coding
transcripts."
[0361] Next, process 210 proceeds to act 214, where the filtered
RNA expression data is normalized to obtain gene expression data.
The gene expression data may be in transcripts per kilobase million
(TPM) format. Aspects of normalizing the filtered RNA expression
data to gene expression data in transcripts per kilobase million
(TPM) are described in the section called "Conversion to TPM and
gene aggregation."
[0362] Next, process 210 proceeds to act 215, where at least one
gene that introduces bias in the gene expression data is
identified. Aspects of identifying at least one gene that
introduces bias in the gene expression data are described in the
section called "Removing bias."
[0363] Next, process 210 proceeds to act 216, where expression data
associated with the at least one gene that introduces bias is
removed from the gene expression data to obtain bias-corrected gene
expression data. Aspects of removing the expression data,
associated with the at least one gene that introduces bias into the
gene expression data, from the gene expression data to obtain
bias-corrected gene expression data are described in the section
called "Removing bias."
[0364] Next, process 210 proceeds to act 217, where a cancer
treatment for the subject using the bias-corrected gene expression
data is identified. Aspects relating to identifying a cancer
treatment for the subject using the bias-corrected gene expression
data are described in the section called "Identifying a cancer
treatment."
[0365] FIG. 6C is a flow chart showing computerized process 220 for
identifying a cancer treatment for the subject having, suspected of
having, or at risk of having cancer using the bias-corrected gene
expression data. Process 220 begins at act 221, where RNA for
coding RNA in a sample of extracted RNA from a first tumor sample
from a subject having, suspected of having, or at risk of having
cancer is enriched. Aspects relating to enriching RNA for coding
RNA in a sample of extracted RNA are described in the section
called "Extraction of DNA and/or RNA."
[0366] Next, process 220 proceeds to act 222, where non-stranded
RNA sequencing on a first library of cDNA fragments prepared from
the enriched RNA to obtain RNA expression data is performed.
Aspects relating to performing non-stranded RNA sequencing on a
first library of cDNA fragments prepared from the enriched RNA to
obtain RNA expression data are described in the section called "RNA
sequencing."
[0367] Next, process 220 proceeds to act 223, where the RNA
expression data is converted to gene expression data. Next, process
220 proceeds to act 224, where at least one gene that introduces
bias in the gene expression data is identified. Next, process 220
proceeds to act 225, where expression data associated with the at
least one gene that introduces bias is removed from the gene
expression data to obtain bias-corrected gene expression data.
Aspects relating to acts 223, 224, and 225 are described in the
section called "Removing bias."
[0368] Next, process 220 proceeds to act 226, where a cancer
treatment for the subject using the bias-corrected gene expression
data is identified. Aspects relating to identifying a cancer
treatment for the subject using the bias-corrected gene expression
data are described in the section called "Identifying a cancer
treatment."
[0369] FIG. 7 is an exemplary flow chart showing a computerized
process 300 for preparing patient samples for sequencing analysis
and performing bioinformatics quality control, so that a cancer
treatment suitable for the patient or subject, from which the
nucleic acids are extracted for sequencing analysis, can be
obtained.
[0370] In the illustrated embodiment, the process 300 comprises
obtaining a first sample of a first tumor from a subject having,
suspected of having, or at risk of having cancer at act 301,
extracting RNA from the first sample of the first tumor at act 302,
enriching the RNA for coding RNA to obtain enriched RNA at act 303,
preparing a first library of cDNA fragments from the enriched RNA
for non-stranded RNA sequencing at act 304, obtaining RNA
expression data for the subject at act 305, aligning and annotating
genes in the RNA expression data with known sequences of the human
genome to obtain annotated RNA expression data at act 306, removing
non-coding transcripts from the annotated RNA expression data at
act 307, converting the annotated RNA expression data to gene
expression data (e.g., in transcripts per kilobase million (TPM)
format) at act 308, identifying at least one gene that introduces
bias in the gene expression data at act 309, removing expression
data for the at least one gene that introduces bias from the gene
expression data to obtain bias-corrected gene expression data at
act 310, obtaining sequence information and asserted information at
act 311, determining one or more features from the sequence
information at act 312, determining whether one or more features
match asserted information at act 313, making at least one
additional determination of the features at act 314, and
identifying a cancer treatment for the subject using the
bias-corrected gene expression data at act 315.
[0371] It should be appreciated that one or more acts of process
300 may be optional. For example, in some embodiments, acts 301 and
303 may be performed and act 303 is optional. In some embodiments,
acts 301, 302, and 303 are all performed. In some embodiments, acts
301, 302, and 303 are all omitted, whereas the remaining acts are
performed. This may be useful when the extracted, enriched RNA from
the patient sample is already available prior to the start of
process 300. In some embodiments, the one or more features at act
312 comprises one or more of the following features: source,
patient, tissue type, tumor type, polyA status, MHC sequence,
protein subunit ratio, complexity, contamination, coverage, exon
coverage, read composition, Phred score, SNP concordance, and GC
content. In some embodiments, the one or more features at act 312
further comprise strandedness of RNA sequence analysis. In some
embodiments, any one or more of the features at act 312 can be
determined. In some embodiments, the additional determination of
the features at act 314 can include but are not limited to
concordance value of SNPs, contamination value, polyA status,
complexity value, Phred score, and GC content. In some embodiments,
the additional determination of any one or more of the features may
be performed at act 314. In some embodiments, any one or more of
acts 303, process 307, and process 314 may be omitted. In some
embodiments, all acts of the computerized process 300 may be
performed.
[0372] FIG. 8 illustrates a non-limiting process pipeline 800 FIG.
8 illustrates a non-limiting process pipeline 800 for processing
and validating sequence data and asserted information associated
with the sequence data for subsequent analysis (e.g., for
diagnostic, prognostic, therapeutic, and/or other clinical
applications). Act 801 is performed by obtaining nucleic acid data
comprising sequence data and asserted information indicating an
asserted source for the sequence data. In some embodiments, the
nucleic acid data is obtained from a biological sample that was
previously processed. In some embodiments, the biological sample
was previously obtained from a subject having, suspected of having,
or at risk of having cancer. In some embodiments, act 801 is
performed by obtaining nucleic acid data comprising an asserted
integrity of the sequence data. In some embodiments, act 801 can be
performed by obtaining nucleic acid data comprising sequence data
and asserted information indicating an asserted source and an
asserted integrity of the sequence data. In some embodiments, the
asserted information indicates the asserted integrity of the
sequence data. In some embodiments, the asserted information is
indicative of a subject from whom the nucleic acid was obtained.
For example, in some embodiments, asserted information comprises
MHC allele information and/or SNP information for one or more loci
of the subject. After act 801, process 800 proceeds to acts 802 and
803 where the nucleic acid data obtained at act 801 is validated.
The validation comprises processing the sequence data at act 802 to
obtain a determined integrity and/or a determined source and
determining whether the determined integrity and/or determined
source matches the asserted integrity and/or asserted source,
respectively, in act 803. The sequence data is processed in act 802
to obtain determined information indicating a determined source of
the sequence data in act 802a and/or determined information
indicating a determined integrity of the sequence data in act 802b.
In some embodiments, act 802a may comprise determining information
indicative of at least one, two, three of the MHC genotype of the
subject, whether the nucleic acid data is RNA data or DNA data, a
tissue type of the biological sample, a tumor type of the
biological sample, a sequencing platform used to generate the
sequence data, SNP concordance (e.g., determining whether one or
more SNPs in the sequence data match one or more SNPs in a
reference sequence), and/or a whether an RNA sample is polyA
enriched. In some embodiments, act 802b may comprise determining a
first level of a first nucleic acid encoding a first subunit of a
multimeric protein, determining a second level of a second nucleic
acid encoding a second subunit of a multimeric protein, and
determining whether a ratio between the first level and the second
level matches an expected ratio. In some embodiments the first
subunit and the second subunits are first and second CD3 subunits,
first and second CD8 subunits, or first and second CD79 subunits.
In some embodiments, the determined information indicative of the
determined integrity is indicative of at least one, two, three of
total sequence coverage, exon coverage, chromosomal coverage, a
ratio of nucleic acids encoding two or more subunits of a
multimeric protein, species contamination, complexity, and/or
guanine (G) and cytosine (C) percentage (%) of the sequence data.
In some embodiments, act 803 comprises determining one or more MHC
allele sequences from the sequence data and determining whether the
one or more MHC alleles sequences match the asserted MHC allele
information for the subject. In some embodiments, determining MHC
allele comprises determining sequences for six MHC loci from the
sequence data.
[0373] In act 803 the determined integrity and/or source is
evaluated by determining whether the determined source of the
sequence data matches the asserted source of the sequence data
and/or whether the determined integrity of the sequence data
matches the asserted integrity of the sequence data.
[0374] If the asserted and determined information match in act 803
(i.e., yes), process 800 proceeds to act 804 where the sequence
data is further evaluated to determine whether the sequence data is
indicative a diagnostic, prognostic, therapeutic, or other clinical
outcome.
[0375] For example, in some embodiments the sequence data is
further processed in act 804 to provide a recommendation for a
cancer treatment for a subject having, suspected of having, or at
risk of having cancer. In some embodiments, Act 804 is performed by
determining a therapy for the subject and the therapy is
subsequently administered to the subject.
[0376] In some embodiments, a process may further comprise
administering the therapy to the subject. In some embodiments, the
therapy is a cancer therapy.
[0377] In some embodiments, determining the therapy for the subject
may include determining a plurality of gene group expression levels
comprising a gene group expression level for each gene group in a
set of gene groups. In some embodiments, the set of gene groups
comprises at least one gene group associated with cancer
malignancy, and at least one gene group associated with cancer
microenvironment. The therapy for the subject is identified by
using the determined gene group expression levels.
[0378] If the asserted and determined information do not match in
act 803 (i.e., no), process 800 proceeds to 805, where one or more
remedial action(s) are performed. In some embodiments, a remedial
action comprises generating an indication that the determined
information does not match the asserted information, generating an
indication to not process the sequence data in a subsequent
analysis, and/or generating an indication to obtain additional
sequence data and/or other information about the biological sample
and/or the subject.
[0379] In some embodiments, a method comprises all acts illustrated
in FIG. 8. However, in some embodiments, a subset of the acts is
performed and any one or more of the acts may be omitted,
duplicated, and/or performed in a different order than illustrated
in FIG. 8. For example, either act 802a or act 802b is performed in
act 802. For example, act 803 can be performed twice to confirm the
decision. For example, one or more acts in process 800 can be
performed after the one or more remedial action(s) in act 805. In
some embodiments, one or more acts of FIG. 8 are implemented on a
computer.
[0380] In some embodiments, expression levels of one or more genes
in a sample are analyzed to evaluate the origin and/or quality of
the sample. For example, the expression of one or more genes that
are known to be expressed in a particular cell, tissue, or tumor
type is evaluated to determine whether it is at an expected
expression level based on the expected cell, tissue, or tumor that
is being analyzed. Similarly, the expression of one or more genes
that are known not to be expressed (or not highly expressed) in a
particular cell, tissue, or tumor type is evaluated to determine
whether it is at an expected expression level based on the expected
cell, tissue, or tumor that is being analyzed.
[0381] In some embodiments, expression levels of one or more genes
are analyzed for each of a plurality of samples (e.g., 2, 3, 4, 5,
4-10, 1-50, 50-500, or more samples). If the expression of one or
more genes is lower or higher than expected, this may be indicative
that the quality and/or source/origin of the data being analyzed is
not what was expected. In some embodiments, data from a sample that
has an unexpected level (e.g., a lower or higher than expected
level) of expression for one or more genes is excluded from further
analysis. In some embodiments, new sequence information is obtained
for a sample that has an unexpected level of expression for one or
more genes, for example to confirm whether the initial data was
correct. In some embodiments, a sample that has an unexpected level
of expression for one or more genes can be further analyzed, for
example to determine whether the sample was from a different source
than initially indicated.
[0382] In some embodiments, expression levels for one or more genes
were analyzed (e.g., using tSNE, PCA, or other technique) to
determine whether gene expression or patterns of gene expression
were similar or different in separate samples. In some embodiments,
if datasets comprising the same cell type or same tissue type did
not cluster within a group, or if one or more datasets were
identified as statistically different from other datasets
comprising the same cells or tissue, then the dataset(s) identified
as different could be excluded, further analyzed, or flagged as
potentially suspect. In some embodiments, additional sequence data
can be obtained for a sample identified as potentially suspect.
[0383] An illustrative implementation of a computer system 500 that
may be used in connection with any of the embodiments of the
technology described herein is shown in FIG. 9. The computer system
500 includes one or more processors 510 and one or more articles of
manufacture that comprise non-transitory computer-readable storage
media (e.g., memory 520 and one or more non-volatile storage media
530). The processor 510 may control writing data to and reading
data from the memory 520 and the non-volatile storage device 530 in
any suitable manner, as the aspects of the technology described
herein are not limited in this respect. To perform any of the
functionality described herein, the processor 510 may execute one
or more processor-executable instructions stored in one or more
non-transitory computer-readable storage media (e.g., the memory
520), which may serve as non-transitory computer-readable storage
media storing processor-executable instructions for execution by
the processor 510.
[0384] Computing device 500 may also include a network input/output
(I/O) interface 540 via which the computing device may communicate
with other computing devices (e.g., over a network), and may also
include one or more user I/O interfaces 550, via which the
computing device may provide output to and receive input from a
user. The user I/O interfaces may include devices such as a
keyboard, a mouse, a microphone, a display device (e.g., a monitor
or touch screen), speakers, a camera, and/or various other types of
I/O devices.
[0385] The embodiments described herein, can be implemented in any
of numerous ways. For example, the embodiments may be implemented
using hardware, software or a combination thereof. When implemented
in software, the software code can be executed on any suitable
processor (e.g., a microprocessor) or collection of processors,
whether provided in a single computing device or distributed among
multiple computing devices. It should be appreciated that any
component or collection of components that perform the functions
described above can be generically considered as one or more
controllers that control the above-described functions. The one or
more controllers can be implemented in numerous ways, such as with
dedicated hardware, or with general purpose hardware (e.g., one or
more processors) that is programmed using microcode or software to
perform the functions recited above.
[0386] In this respect, it should be appreciated that one
implementation of the embodiments described herein comprises at
least one computer-readable storage medium (e.g., RAM, ROM, EEPROM,
flash memory or other memory technology, CD-ROM, digital versatile
disks (DVD) or other optical disk storage, magnetic cassettes,
magnetic tape, magnetic disk storage or other magnetic storage
devices, or other tangible, non-transitory computer-readable
storage medium) encoded with a computer program (i.e., a plurality
of executable instructions) that, when executed on one or more
processors, performs the above-described functions of one or more
embodiments. The computer-readable medium may be transportable such
that the program stored thereon can be loaded onto any computing
device to implement aspects of the techniques described herein. In
addition, it should be appreciated that the reference to a computer
program which, when executed, performs any of the above-described
functions, is not limited to an application program running on a
host computer. Rather, the terms computer program and software are
used herein in a generic sense to reference any type of computer
code (e.g., application software, firmware, microcode, or any other
form of computer instruction) that can be employed to program one
or more processors to implement aspects of the techniques described
herein.
[0387] Aspects of the technology described herein provide computer
implemented methods for evaluating, generating, visualizing, and/or
classifying biological characteristic(s) of sequence information of
(e.g., cancer grade, tissue of origin) of subjects (e.g., cancer
patients) or those having, suspected of having, or at risk of
having a disorder (e.g., cancer).
[0388] In some embodiments, a software program may provide a user
with a visual representation of a subject (e.g., patient)'s
characteristic(s) and/or other information related to a subject
(e.g., patient)'s cancer using an interactive graphical user
interface (GUI). Such a software program may execute in any
suitable computing environment including, but not limited to, a
cloud-computing environment, a device co-located with a user (e.g.,
the user's laptop, desktop, smartphone, etc.), one or more devices
remote from the user (e.g., one or more servers), etc.
[0389] For example, in some embodiments, the techniques described
herein may be implemented in the illustrative environment 600 shown
in FIG. 10. As shown in FIG. 10, within illustrative environment
600, one or more biological samples of a subject 680 may be
provided to a laboratory 670. Laboratory 670 may process the
biological sample(s) to obtain expression data (e.g., DNA, RNA,
and/or protein expression data) and/or sequence information and
provide it, via network 610, to at least one database 660 that
stores information about subject (e.g., patient) 680.
[0390] Network 610 may be a wide area network (e.g., the Internet),
a local area network (e.g., a corporate Intranet), and/or any other
suitable type of network. Any of the devices shown in FIG. 10 may
connect to the network 610 using one or more wired links, one or
more wireless links, and/or any suitable combination thereof.
[0391] In the illustrated embodiment of FIG. 10, the at least one
database 620 may store expression data and or sequence information
for the subject (e.g., patient), medical history data for the
subject (e.g., patient), test result data for the subject (e.g.,
patient), and/or any other suitable information about the subject
680. Examples of stored test result data for the subject (e.g.,
patient) include biopsy test results, imaging test results (e.g.,
MRI results), and blood test results. The information stored in at
least one database 620 may be stored in any suitable format and/or
using any suitable data structure(s), as aspects of the technology
described herein are not limited in this respect. The at least one
database 620 may store data in any suitable way (e.g., one or more
databases, one or more files). The at least one database 620 may be
a single database or multiple databases.
[0392] As shown in FIG. 10, illustrative environment 600 includes
one or more external databases 620, which may store information for
patients other than patient 680. For example, external databases
660 may store expression data and/or sequence information (of any
suitable type) for one or more patients, medical history data for
one or more patients, test result data (e.g., imaging results,
biopsy results, blood test results) for one or more patients,
demographic and/or biographic information for one or more patients,
and/or any other suitable type of information. In some embodiments,
external database(s) 660 may store information available in one or
more publicly accessible databases such as TCGA (The Cancer Genome
Atlas), one or more databases of clinical trial information, and/or
one or more databases maintained by commercial sequencing
suppliers. The external database(s) 660 may store such information
in any suitable way using any suitable hardware, as aspects of the
technology described herein are not limited in this respect.
[0393] In some embodiments, the at least one database 620 and the
external database(s) 660 may be the same database, may be part of
the same database system, or may be physically co-located, as
aspects of the technology described herein are not limited in this
respect.
[0394] For example, in some embodiments, server(s) 640 may access
information stored in database(s) 620 and/or 660 and use this
information to perform processes described herein, described with
reference to FIG. 10, for determining one or more characteristics
of a biological sample and/or of the sequence information.
[0395] In some embodiments, server(s) 640 may include one or
multiple computing devices. When server(s) 640 include multiple
computing devices, the device(s) may be physically co-located
(e.g., in a single room) or distributed across multi-physical
locations. In some embodiments, server(s) 640 may be part of a
cloud computing infrastructure. In some embodiments, one or more
server(s) 640 may be co-located in a facility operated by an entity
(e.g., a hospital, research institution) with which doctor 650 is
affiliated. In such embodiments, it may be easier to allow
server(s) 640 to access private medical data for the patient
880.
[0396] As shown in FIG. 10, in some embodiments, the results of the
analysis performed by server(s) 640 may be provided to doctor 650
through a computing device 630 (which may be a portable computing
device, such as a laptop or smartphone, or a fixed computing device
such as a desktop computer). The results may be provided in a
written report, an e-mail, a graphical user interface, and/or any
other suitable way. It should be appreciated that although in the
embodiment of FIG. 10, the results are provided to a doctor 650, in
other embodiments, the results of the analysis may be provided to
patient 680 or a caretaker of patient 680, a healthcare provider
such as a nurse, or a person involved with a clinical trial.
[0397] In some embodiments, the results may be part of a graphical
user interface (GUI) presented to the doctor 650 via the computing
device 630. In some embodiments, the GUI may be presented to the
user as part of a webpage displayed by a web browser executing on
the computing device 630. In some embodiments, the GUI may be
presented to the user using an application program (different from
a web-browser) executing on the computing device 630. For example,
in some embodiments, the computing device 630 may be a mobile
device (e.g., a smartphone) and the GUI may be presented to the
user via an application program (e.g., "an app") executing on the
mobile device.
[0398] The GUI presented on computing device 630 may provide a wide
range of oncological data relating to both the patient and the
patient's cancer in a new way that is compact and highly
informative. Previously, oncological data was obtained from
multiple sources of data and at multiple times making the process
of obtaining such information costly from both a time and financial
perspective. Using the techniques and graphical user interfaces
illustrated herein, a user can access the same amount of
information at once with less demand on the user and with less
demand on the computing resources needed to provide such
information. Low demand on the user serves to reduce clinician
errors associated with searching various sources of information.
Low demand on the computing resources serves to reduce processor
power, network bandwidth, and memory needed to provide a wide range
of oncological data, which is an improvement in computing
technology. In some embodiments, the reports of the disclosure are
presented to a user by means of a system or by means of a GUI.
[0399] Accordingly, in an aspect, the disclosure relates to a
method of evaluating sequence information, to determine at least
one feature. The evaluation can take place on a computer or other
automated machine capable of carrying out programmable instructions
or can be performed manually by an evaluator. The features can be
used to generate a report for informing the evaluator of the at
least one feature of the sequence information. In some embodiments,
the feature is the sequence of the MHC alleles of the sequence
information.
[0400] The major histocompatibility complex (MHC) (referred to as
the Human Leukocyte Antigens (HLAs) in humans) is the mechanism by
which the immune system is able to differentiate between self and
nonself cells. It is a collection of glycoproteins (proteins with a
carbohydrate) that exist on the plasma membranes of nearly all body
cells. The (MHC) are highly polymorphic genes that are important in
the immune system of biological organisms and originate from 20
genes, with more than 50 variations per gene between individuals,
and allow for co-dominance between alleles. These glycoproteins are
part of a pathway which enables the immune system to identify self
and non-self cells by aberrations in the MHC displayed on the
plasma membrane.
[0401] Due to these properties, e.g., MHCs are highly polymorphic,
co-dominance, and that there are a large number of alleles that may
be present in a given species, the MHC profile of a subject is
highly specific and unique. Thus, it is extremely unlikely that two
people, except for identical twins, will possess cells with the
same set of MHC molecules. Accordingly, by evaluating the sequence
of the MHC profile of sequence information, it can be used to
corroborate, or disqualify, identifying information between the
sequence information, an asserted information, other sequence
information, or a combination thereof.
[0402] In some embodiments, one MHC allele is used for the
evaluation. In some embodiments, at least two MHC alleles are used
for the evaluation. In some embodiments, at least three MHC alleles
are used for the evaluation. In some embodiments, at least four MHC
alleles are used for the evaluation. In some embodiments, at least
five MHC alleles are used for the evaluation. In some embodiments,
at least six MHC alleles are used for the evaluation.
[0403] In some embodiments, the evaluated feature is a concordance
value of single nucleotide polymorphisms (SNPs). "SNP" or "Single
Nucleotide Polymorphism," as used herein, refers to a difference in
a nucleic acid sequence (e.g., genome, sequence data set) at a
single nucleotide (e.g., adenine (A), thymine (T), cytosine (C),
and/or guanine (G)) shared between subjects of a species or within
an individual subject on paired chromosomes. SNPs can be, or
represent: changed nucleotides (e.g., A changed to T, G changed to
A, etc.), known as a substitution; removed nucleotides, wherein the
nucleotide is absent from the sequence entirely, known as a
deletion; or added nucleotides, wherein an additional nucleotide is
added to the sequence. SNPs can lead to changes in an encoded
protein (e.g., nonsynonymous SNPs), or not (e.g., synonymous).
Further, when the SNP is nonsynonymous, it can cause a change in
the encoded amino acid (e.g., missense) or cause a premature stop
codon (e.g., nonsense). Synonymous SNPs can also alter the message
of the nucleic acid sequence by influencing or changing the splice
sites, transcription factor binding, and/or messenger RNA (mRNA)
binding. These mutations (e.g., changes to the protein encoding
abilities of the sequence) can cause a litany of effects including
differences in phenotypes as well as various disease types.
Moreover, SNPs occur in great numbers within a subject's genome,
with some estimates being that a typical genome differs from the
reference human genome at between 4 and 5 million sites, of which
more than 99.9% are SNPs.
[0404] Since SNPs are encoded in nucleic acids which are part of
the genome, they are passed from parent to progeny (both subjects,
and within a subject when nucleic acids replicate). Accordingly,
because of this stable inheritance, and because of the large number
thereof, SNPs can be used as a genetic marker of the relatedness of
subjects, and also as a measure of the identity of two nucleic acid
sequences as originating from the same subject. In some
embodiments, the SNP concordance value is determined between the
sequence information and a reference sequence. In some embodiments,
the SNP concordance value is determined between the sequence
information and an asserted value. In some embodiments, the SNP
concordance value must be equal to, or greater than a threshold
value to be acceptable (e.g., deemed of sufficient quality and
integrity) for use in further analyses. In some embodiments, the
threshold value is 80%. In some embodiments, a SNP concordance
value is determined between a sequence data set and a subject,
wherein if the SNP concordance value is at least 70% (e.g., at
least 71%, at least 72%, at least 73%, at least 74%, at least 75%,
at least 76%, at least 77%, at least 78%, at least 79%, at least
80%, at least 81%, at least 82%, at least 83%, at least 84%, at
least 85%, at least 86%, at least 87%, at least 88%, at least 89%,
at least 90%, at least 91%, at least 92%, at least 93%, at least
94%, at least 95%, at least 95.5%,at least 96%, at least 96.5%,at
least 97%, at least 97.5%,at least 98%, at least 98.5%,at least
99%, at least 99.5%, at least 99.6%, at least 99.7%, at least
99.8%, at least 99.9%, at least 99.95%, at least 99.99%, at least
99.999% or more), it is deemed to be sufficiently likely to be from
the subject and identified as being from the subject. As described
herein, in some embodiments, the determination of the concordance
value is as described in the present disclosure. SNP concordance
can be performed by any means available or known in the art, for
example SNP concordance is performed by a variety of online tools
such as Conpair (github.com/nygenome/Conpair) or GATK
GenotypeConcordance
(software.broadinstitute.org/gatk/documentation/tooldocs/3.8-0/org
broadinstitute gatk tools walkers variantutils
GenotypeConcordance.php) or may be calculated manually. In other
examples, SNP concordance can be performed by tools as described at
publicly available websites (genome.sph.umich.edu/wiki/VerifyBamID
or software.broadinstitute.org/cancer/cga/contest).
[0405] In some embodiments, the evaluated feature is a quality
score, for example a Phred score. A "Phred Score" (also may be
known or referred to herein as a "Phred Quality Score") as used
herein, refers to a measure of the quality for the identification
of nucleotides sequenced by nucleic acid sequencing systems or
platforms (e.g., NGS). Phred Scores are known in the art and are
often generated from the sequencing platform based upon several
parameters (e.g., peak shape, resolution, etc.) and a score (Q) is
assigned to each nucleotide base call (For a detailed review of the
calculation refer to Ewing B, Hillier L, Wendl M C, Green P.
Base-calling of automated sequencer traces using phred. I. Accuracy
assessment. Genome Res. 1998 March; 8(3):175-85. and Ewing B, Green
P. Base-calling of automated sequencer traces using phred. II.
Error probabilities. Genome Res. 1998 March; 8(3):186-94.). The
Phred Score of each base refers to the likelihood that a nucleotide
base call is incorrect (base-calling error probability (P)) and is
determined by the equation Q=-10 log.sub.10P. Thus, the score
(e.g., Q) indicates the base call accuracy, for example a Phred
Score of 10 indicates a 90% call accuracy for the base in question,
while a Phred Score of 40 indicates a 99.99% call accuracy for the
same base. In some embodiments, the Phred Score of the sequence
information is determined and compared to a reference value. In
some embodiments, the reference value is at least 27, at least 28,
at least 29, at least 30, or more than 30. In some embodiments, the
Phred Score is determined and compared to the Phred Score of other
sequence information. In some embodiments, the Phred Score is
determined and compared to an asserted score. In some embodiments,
the Phred Score is used as a base level determination of quality.
In some embodiments, it is used to compare a sequence with asserted
information to compare identity, as if the Phred Scores are
different it is unlikely they are the same sequence information or
from the same sample or subject.
[0406] In some embodiments, the evaluated feature is a tumor
type.
[0407] In some embodiments, the evaluated feature is a tissue
type.
[0408] In some embodiments, the evaluated feature is the
polyadenylation status of the sequence information.
"Polyadenylated" or "PolyA" as used herein, refers to the series of
multiple adenosine monophosphate nucleotides attached to the 3'-end
of messenger RNA (mRNA), this occurs after transcription and
cleavage of the 3'-end to of the transcript to free a hydroxyl. The
"polyA tail," as it is often referred, is a characteristic of fully
processed mRNA and assists in various cellular processes. For
example, the polyA tail is the binding site for a protein (e.g.,
polyA-binding protein) which promotes export from the nucleus of
the cell so that translation may occur, as well as effects
translation and stability of mRNA. If only transcripts of protein
coding mRNA are present, it is highly likely (e.g., indicative)
that the sample that generated the sequence information was
generated using mRNA-Seq. In some embodiments, the polyA status
indicates mRNA-Seq was not used (e.g., the whole transcriptome was
used). In some embodiments, the polyA status is evaluated against
an asserted information. In some embodiments, the polyA status is
evaluated against a reference sequence. In some embodiments, the
probability of the sequence information being generated using
either mRNA-Seq or the whole transcriptome must be above a
threshold. In some embodiments, the threshold is a reference value.
In some embodiments, the threshold level is 90%. In some
embodiments, the threshold value is an asserted information. In
some embodiments, the sequence information is from a sample which
contained primarily polyadenylated nucleic acids. In some
embodiments, the sequence information is from a sample which
contained both polyadenylated and non-polyadenylated nucleic
acids
[0409] In some embodiments, the evaluated feature is the GC content
of the sequence information. "G/C Content" or "guanine (G)-cytosine
(C) content," as used herein, refers to the percentage of
nucleotides in a nucleic acid sample which are either G or C. It
can be calculated by summing all of the G and C reads of given
sequence information and dividing by the total nucleotides
sequenced. In some embodiments, the sequence information is
evaluated and the GC content is calculated by summing the number of
base calls resulting in a G or C (e.g., G+C) in the sequence
information and dividing by the total number of base calls in the
sequence information (e.g., number of nucleotides in a sequence
data set), or (G+C)/(Number of nucleotides in a sequence data
set).
[0410] The GC content may also be used as a quality measure of the
sequence information. Many known genomes have been sequenced, along
with the respective exomes, transcriptomes, and various other
portions thereof (e.g., measure of specific RNA components).
Moreover, many of these sequences have been sequenced a great
number of times and averages and ranges for various components
thereof have been generated, for example, the GC content of the
human genome. The GC content of the human genome is known to vary
from approximately 35% to 60%, and has a mean value (e.g., average
value) of about 41%. Accordingly, as a quality measure if a
sequence information which is identified as a human genome were to
be evaluated to have a GC content of 75%, the quality of the
sequence information (or the sample from which it was derived)
would be in question. As a result, the evaluated GC content can be
compared to known ranges of GC contents expected for the sequence
information, to a value provided by the sequence information donor,
to a value from a database of such values, to additional sequence
information, or to a reference range for sequence information of a
given type to ascertain if they are consistent or if the GC content
indicates a problem, which may be due to degradation, residual
primers, contamination, or other complications with the sequence
information.
[0411] Accordingly, in some embodiments, the GC content feature is
used in a method to evaluate the integrity of the sequence
information by determining a GC content for each sequence data set
being evaluated; wherein, if the GC content is either: (i) less
than 30%; or greater than 55%, the nucleic acid samples are deemed
likely of insufficient quality, and are removed, discarded,
retested, or reported as of insufficient quality, and wherein, if
the GC content is: at least 30%, and less than or equal to 55%, the
nucleic acid samples are deemed of sufficient quality and are
retained. Additionally, in some embodiments, the GC content may be
calculated and compared to asserted information. The GC content, in
some embodiments, can be used to match the asserted information and
thus corroborate or question the identity of the sequence
information as being the same sequence information asserted, being
from a given sample, subject, or specific sample or subject.
[0412] In some embodiments, the evaluated feature is a ratio of
protein subunit expression (e.g., an expression ratio of nucleic
acids encoding different subunits of a protein). Protein expression
can be measured by any means known in the art, for example
expression can be determined (e.g., quantified) by counting the
number of reads that mapped to each locus in the transcriptome. By
evaluating the expression of protein subunits (e.g., proteins which
have multiple subunits expressed by different coding regions), and
then calculating a ratio thereof, it is possible to compare the
ratio with a known value, reference value, threshold value, other
sequence information, or an asserted information. In some
embodiments, the evaluated protein subunits are from proteins that
are present in a human sample, regardless of the presence or
absence of a certain type of cancer. Without wishing to be bound by
any theory, such protein is encoded by a housekeeping gene (e.g., a
positive or negative control of a human sample). In some
embodiments, the known value, reference value, or threshold value
is a fixed ratio. For example, subunit A and subunit B of a known
protein has a ratio of 1:1 or 2:1. In some embodiments, the
evaluated protein subunits are from proteins that are present in a
human sample having or suspected of having a certain type of
cancer.
[0413] In some embodiments, the ratio is compared to a known value.
In some embodiments, it is compared to an asserted information. In
some embodiments, it is compared to other sequence information. In
some embodiments, the protein, and subunits thereof are selected
for analysis due to their properties. For example, they may degrade
quickly and therefore serve as a proxy for the stability and/or
quality of the sample from which the sequence information was
generated. In some embodiments, they may be selected for their
variability between subjects, or samples, thereby allowing for
comparison to corroborate or disqualify the identity of the
sequence information.
[0414] In some embodiments, the evaluated feature is the coverage
value. "Coverage," as used herein, refers to the number of unique
reads of a given nucleotide in a reconstructed sequence. As the
nucleic acid is sequenced, it is not sequenced in one entire read
(e.g., start to finish in one pass), but rather is the result of
multiple reads of portions or segments of the nucleic acid (e.g.,
RNA, exome, genome) of which have an average length (L), wherein
the whole nucleic acid when reconstructed has an overall length of
(G). As the number of reads of a nucleic acid increase (N), the
coverage will also increase. The coverage can be calculated as
N.times.L/G. In some embodiments, the coverage value is compared to
an asserted information. In some embodiments, the coverage value is
compared to a threshold or reference value. In some embodiments,
the threshold or reference value is a statistically significant
value. In some embodiments, the coverage value is compared to other
sequence information. In some embodiments, the target value of the
coverage for tumor is more than 150.times. (e.g., 170.times.,
190.times.). In some embodiments, the target value of the coverage
for normal tissue is more than 100.times. (e.g., 110.times.,
120.times., 130.times.). Publicly available tools can be used for
determining the coverage value (github.com/brentp/mosdepth and
biodatageeks.org/sequila/).
[0415] The features and evaluations described herein can be
evaluated and used individually as well as in conjunction with one
another. In some embodiments, at least one feature is evaluated
(e.g., at least one, at least two, at least three, at least four,
or more). In some embodiments, at least two features are evaluated
(e.g., at least three, at least four, or more). In some
embodiments, at least four features are evaluated (e.g., at least
four, or more). In some embodiments, at least five features are
evaluated (e.g., at least five, or more). In some embodiments, at
least six features are evaluated (e.g., at least six, or more). In
some embodiments, at least seven features are evaluated (e.g., at
least seven, or more). In some embodiments, at least eight features
are evaluated (e.g., at least eight, or more). In some embodiments,
at least nine features are evaluated (e.g., at least nine, or
more). In some embodiments, at least ten features are evaluated
(e.g., at least ten, or more). In some embodiments, at least eleven
features are evaluated (e.g., at least eleven, or more). In some
embodiments, at least twelve features are evaluated (e.g., at least
twelve, or more). In some embodiments, at least thirteen features
are evaluated (e.g., at least thirteen, or more). In some
embodiments, at least fourteen features are evaluated (e.g., at
least fourteen, or more). In some embodiments, at least fifteen
features are evaluated (e.g., at least fifteen, or more).
[0416] The features described herein can be evaluated sequentially,
simultaneously, parallel, or a combination thereof. As it can be
envisioned, the evaluations required to evaluate some features, may
be useful in evaluating additional or other features. Accordingly,
where such information or evaluation results are useful for other
determinations, it is possible to perform evaluations of multiple
features at once (e.g., simultaneous), or to use the information
for a follow-on evaluations (e.g., sequential). Additionally, it
can be envisioned to run multiple evaluations at the same time of
different features (e.g., parallel). In some embodiments, features
are evaluated sequentially. In some embodiments, features are
evaluated simultaneously. In some embodiments, features are
evaluated in parallel. In some embodiments, features are evaluated
in a combination of methods (e.g., simultaneously as well as
sequentially).
Identifying a Cancer Treatment
[0417] A subject's sequencing data obtained using any one of the
methods described herein may be used for various clinical purposes
including, but not limited to, monitoring the progress of cancer in
a subject, assessing the efficacy of a treatment for cancer,
identifying subjects suitable for a particular treatment,
evaluating suitability of a patient for participating in a clinical
trial and/or predicting relapse in a subject. Accordingly,
described herein are diagnostic and prognostic methods for cancer
treatment based on sequencing data obtained using methods described
herein. In some embodiments, a method to process RNA expression
data as described herein comprises identifying a cancer treatment
(also referred to herein as an anti-cancer therapy) for the subject
using the bias-corrected gene expression data.
Molecular Functional Expression Signatures
[0418] In some embodiments, identifying a cancer treatment for a
subject comprises characterizing the cancer or tumor in the subject
using bias-corrected gene expression data. In some embodiments, a
cancer in a subject is characterized by determining a molecular
functional expression signature, which may include and/or reflect
information relating to the molecular characteristics of a tumor
including tumor genetics, pro-tumor microenvironment factors, and
anti-tumor immune response factors.
[0419] A "molecular functional expression signature (MFES)", as
described herein, refers to information relating to molecular and
cellular composition, and biological processes that are present
within and/or surrounding the tumor. In some embodiments, the MFES
of a patient includes gene express levels for each of one or more
groups of genes ("gene groups"). In some embodiments, the
information in the MFES may be generated using gene expression data
(e.g., bias corrected gene expression data) for the gene groups
obtained by sequencing normal and/or tumor tissue. Though other
types of gene expression data may be used to generate an MFES, it
should be appreciated that the inventors recognized that using
bias-corrected gene expression data to generate a molecular
functional expression signature allows the resulting MFES to more
accurately and faithfully represent the molecular functional
characteristics of the subject's tumor. In turn, applying an MFES
determined from bias-corrected gene expression data to identifying
a cancer therapy for the subject allows for the identification of
more effective therapies, improved ability to determine whether one
or more cancer therapies will be effective if administered to the
subject, improved ability to identify clinical trials in which the
subject may participate, and/or improvements to numerous other
prognostic, diagnostic, and clinical applications.
Gene Groups
[0420] A "gene group" refers to a group of genes associated with
molecular processes present within and/or surrounding a tumor.
Examples of gene groups and techniques for determining gene group
expression levels are described in International PCT Publication
WO2018/231771, published on Dec. 20, 2018, entitled "Systems and
Methods for Generating, Visualizing and Classifying Molecular
Functional Profiles," (being a publication of PCT Application No.:
PCT/US20/037017, filed Jun. 12, 2018), the entire contents of which
are incorporated herein by reference. A "gene group" may be
referred to herein as a "module".
[0421] Exemplary modules may include, but are not limited to, Major
histocompatibility complex I (MHC I) module, Major
histocompatibility complex II (MHC II) module, Coactivation
molecules module, Effector cells module, Effector T cell module;
Natural killer cells (NK cells) module, T cell traffic module, T
cells module, B cells module, B cell traffic module, Benign B cells
module, Malignant B cell marker module, M1 signatures module, Th1
signature module, Antitumor cytokines module, Checkpoint inhibition
(or checkpoint molecules) module, Follicular dendritic cells
module, Follicular B helper T cells module, Protumor cytokines
module, Regulatory T cells (Treg) module, Treg traffic module,
Myeloid-derived suppressor cells (MDSCs) module, MDSC and TAM
traffic module, Granulocytes module, Granulocytes traffic module,
Eosinophil signature model, Neutrophil signature model, Mast cell
signature module, M2 signature module, Th2 signature module, Th17
signature module, Protumor cytokines module, Complement inhibition
module, Fibroblastic reticular cells module, Cancer associated
fibroblasts (CAFs) module, Matrix formation (or Matrix) module,
Angiogenesis module, Endothelium module, Hypoxia factors module,
Coagulation module, Blood endothelium module, Lymphatic endothelium
module, Proliferation rate (or Tumor proliferation rate) module,
Oncogenes module, PI3K/AKT/mTOR signaling module, RAS/RAF/MEK
signaling module, Receptor tyrosine kinases expression module,
Growth Factors module, Tumor suppressors module, Metastasis
signature module, Antimetastatic factors module, and Mutation
status module.
[0422] In some embodiments, each of one or more gene groups in an
MFES may comprise at least two genes (e.g., at least two genes, at
least three genes, at least four genes, at least five genes, at
least six genes, at least seven genes, at least eight genes, at
least nine genes, at least ten genes, or more than ten genes as
shown in the following lists; in some embodiments all of the listed
genes are selected from each group; and in some embodiments the
numbers of genes in each selected group are not the same.
[0423] In some embodiments, the modules in a molecular functional
expression signature may comprise or consist of: Major
histocompatibility complex I (MHC I) module, Major
histocompatibility complex II (MHC II) module, Coactivation
molecules module, Effector cells (or Effector T cell) module,
Natural killer cells (NK cells) module, T cells module, B cells
module, M1 signatures module, Th 1 signature module, Antitumor
cytokines module, Checkpoint inhibition (or checkpoint molecules)
module, Regulatory T cells (Treg) module, Myeloid-derived
suppressor cells (MDSCs) module, Neutrophil signature model, M2
signature module, Th2 signature module, Protumor cytokines module,
Complement inhibition module, Cancer associated fibroblasts (CAFs)
module, Angiogenesis module, Endothelium module, Proliferation rate
(or Tumor proliferation rate) module, PI3K/AKT/mTOR signaling
module, RAS/RAF/MEK signaling module, Receptor tyrosine kinases
expression module, Growth Factors module, Tumor suppressors module,
Metastasis signature module, and Antimetastatic factors module. The
modules may additionally include: T cell traffic module, Antitumor
cytokines module, Treg traffic module, MDSC and TAM traffic module,
Granulocytes or Granulocyte traffic module, Eosinophil signature
model, Mast cell signature module, Th17 signature module, Matrix
formation (or Matrix) module, and Hypoxia factors module. Such an
MFES may be determined for a subject having a solid cancer (e.g., a
melanoma) and used, for example, to identify a therapy for treating
the solid cancer.
[0424] In some embodiments, the modules in a molecular functional
expression signature may comprise or consist of: Effector cells (or
Effector T cell) module, Natural killer cells (NK cells) module, T
cells module, Malignant B cell marker module, M1 signatures module,
Th 1 signature module, Checkpoint inhibition (or checkpoint
molecules) module, Follicular dendritic cells module, Follicular B
helper T cells module, Protumor cytokines module, Regulatory T
cells (Treg) module, Neutrophil signature model, M2 signature
module, Th2 signature module, Complement inhibition module,
Fibroblastic reticular cells module, Angiogenesis module, Blood
endothelium module, Proliferation rate (or Tumor proliferation
rate) module, Oncogenes module, and Tumor suppressors module. The
modules may additionally include: Major histocompatibility complex
I (MHC I) module, Major histocompatibility complex II (MHC II)
module, Coactivation molecules module, B cell traffic module,
Benign B cells module, Antitumor cytokines module, Treg traffic
module, Mast cell signature module, Th17 signature module, Matrix
formation (or Matrix) module, Hypoxia factors module, Coagulation
module, and Lymphatic endothelium module. Such an MFES may be
determined for a subject having follicular lymphoma and used, for
example, to identify a therapy for treating the follicular
lymphoma.
[0425] In some embodiments, the gene groups in an MFES may comprise
at least two genes (e.g., at least two genes, at least three genes,
at least four genes, at least five genes, at least six genes, at
least seven genes, at least eight genes, at least nine genes, at
least ten genes, or more than ten genes as shown in the following
lists; in some embodiments all of the listed genes are selected
from each group; and in some embodiments the numbers of genes in
each selected group are not the same): Major histocompatibility
complex I (MHC I) module: HLA-A, HLA-B, HLA-C, B2M, TAP1, and TAP2;
Major histocompatibility complex II (MHC II) module: HLA-DRA,
HLA-DRB1, HLA-DOB, HLA-DPB2, HLA-DMA, HLA-DOA, HLA-DPA1, HLA-DPB1,
HLA-DMB, HLA-DQB1, HLA-DQA1, HLA-DRB5, HLA-DQA2, HLA-DQB2, and
HLA-DRB6; Coactivation molecules module: CD80, CD86, CD40, CD83,
TNFRSF4, ICOSLG, CD28; Effector cells module: IFNG, GZMA, GZMB,
PRF1, LCK, GZMK, ZAP70, GNLY, FASLG, TBX21, EOMES, CD8A, and CD8B;
Effector T cell module: IFNG, GZMA, GZMB, PRF1, LCK, GZMK, ZAP70,
GNLY, FASLG, TBX21, EOMES, CD8A, and CD8B; Natural killer cells (NK
cells) module: NKG7, CD160, CD244, NCR1, KLRC2, KLRK1, CD226, GZMH,
GNLY, IFNG, KIR2DL4, KIR2DS1, KIR2DS2, KIR2DS3, KIR2DS4, KIR2DS5,
EOMES, CLIC3, FGFBP2, KLRF1, and SH2D1B; T cell traffic module:
CXCL9, CXCL10, CXCR3, CX3CL1, CCR7, CXCL11, CCL21, CCL2, CCL3,
CCL4, and CCL5; T cells module: EOMES, TBX21, ITK, CD3D, CD3E,
CD3G, TRAC, TRBC1, TRBC2, LCK, UBASH3A, TRAT1, CD5, and CD28; B
cells module: CD19, MS4A1, TNFRSF13C, CD27, CD24, CR2, TNFRSF17,
TNFRSF13B, CD22, CD79A, CD79B, BLK, FCRL5, PAX5, and STAP1; B cell
traffic module: CXCL13 and CXCR5; Benign B cells module: CD19,
MS4A1, TNFRSF13C, CD27, CD24, CR2, TNFRSF17, TNFRSF13B, CD22,
CD79A, CD79B, and BLK; Malignant B cell marker module: MME, CD70,
CD20, CD22, and PAX5; M1 signatures module: NOS2, IL12A, IL12B,
IL23A, TNF, IL1B, and SOCS3; Th1 signature module: IFNG, IL2,
CD40LG, IL15, CD27, TBX21, LTA, and IL21; Antitumor cytokines
module: HMGB1, TNF, IFNB1, IFNA2, CCL3, TNFSF10, and FASLG;
Checkpoint inhibition (or checkpoint molecules) module: PDCD1,
CD274, CTLA4, LAG3, PDCD1LG2, BTLA, HAVCR2, and VSIR; Follicular
dendritic cells module: CR1, FCGR2A, FCGR2B, FCGR2C, CR2, FCER2,
CXCL13, MADCAM1, ICAM1, VCAM1, BST1, LTBR, and TNFRSF1A; Follicular
B helper T cells module: CXCR5, B3GAT1, ICOS, CD40LG, CD84, IL21,
BCL6, MAF, and SAP; Protumor cytokines module: IL10, TGFB1, TGFB2,
TGFB3, IL22, MIF, TNFSF13B, IL6, and IL7; Regulatory T cells (Treg)
module: TGFB1, TGFB2, TGFB3, FOXP3, CTLA4, IL10, TNFRSF18, TNFR2,
and TNFRSF1B; Treg traffic module: CCL17, CXCL12, CXCR4, CCR4,
CCL22, CCL1, CCL2, CCL5, CXCL13, and CCL28; Myeloid-derived
suppressor cells (MDSCs) module: ID01, ARG1, IL4R, IL10, TGFB1,
TGFB2, TGFB3, NOS2, CYBB, CXCR4, and CD33; MDSC and TAM traffic
module: CXCL1, CXCL5, CCL2, CCL4, CCL8, CCR2, CCL3, CCL5, CSF1, and
CXCL8; Granulocytes module: CXCL8, CXCL2, CXCL1, CCL11, CCL24,
KITLG, CCL5, CXCL5, CCR3, CCL26, PRG2, EPX, RNASE2, RNASE3, IL5RA,
GATA1, SIGLEC8, PRG3, MPO, ELANE, PRTN3, CTSG, FCGR3B, CXCR1,
CXCR2, CD177, PI3, FFAR2, PGLYRP1, CMA1, TPSAB1, MS4A2, CPA3, IL4,
IL5, IL13, and SIGLEC8; Granulocyte traffic module: CXCL8, CXCL2,
CXCL1, CCL11, CCL24, KITLG, CCL5, CXCL5, CCR3, and CCL26;
Eosinophil signature model: PRG2, EPX, RNASE2, RNASE3, IL5RA,
GATA1, SIGLEC8, and PRG3; Neutrophil signature model: MPO, ELANE,
PRTN3, CTSG, FCGR3B, CXCR1, CXCR2, CD177, PI3, FFAR2, and PGLYRP1;
Mast cell signature module: CMA1, TPSAB1, MS4A2, CPA3, IL4, IL5,
IL13, and SIGLEC8; M2 signature module: IL10, VEGFA, TGFB1, IDO1,
PTGES, MRC1, CSF1, LRP1, ARG1, PTGS1, MSR1, CD163, and CSF1R; Th2
signature module: IL4, IL5, IL13, IL10, IL25, and GATA3; Th17
signature module: IL17A, IL22, IL26, IL17F, IL21, and RORC;
Protumor cytokines module: IL10, TGFB1, TGFB2, TGFB3, IL22, and
MIF; Complement inhibition module: CFD, CFI, CD55, CD46, CR1, and
CD59; Fibroblastic reticular cells module: DES, VIM, PDGFRA, PDPN,
NT5E, THY1, ENG, ACTA2, LTBR, TNFRSF1A, VCAM1, ICAM1, and BST1;
Cancer associated fibroblasts (CAFs) module: COL1A1, COL1A2,
COL4A1, COL5A1, TGFB1, TGFB2, TGFB3, ACTA2, FGF2, FAP, LRP1, CD248,
COL6A1, COL6A2, COL6A3, FBLN1, LUM, MFAP5, LGALS1, and PRELP;
Matrix formation (or Matrix) module: MMP9, FN1, COL1A1, COL1A2,
COL3A1, COL4A1, CA9, VTN, LGALS7, TIMP1, MMP2, MMP1, MMP3, MMP12,
LGALS9, MMPI, and COL5A1; Angiogenesis module: VEGFA, VEGFB, VEGFC,
PDGFC, CXCL8, CXCR2, FLT1, PIGF, CXCL5, KDR, ANGPT1, ANGPT2, TEK,
VWF, CDH5, NOS3, VCAM1, MMRN1, LDHA, HIF1A, EPAS1, CA9, SPP1, LOX,
SLC2A1, and LAMP3; Endothelium module: VEGFA, NOS3, KDR, FLT1,
VCAM1, VWF, CDH5, MMRN1, CLEC14A, MMRN2, and ECSCR; Hypoxia factors
module: LDHA, HIF1A, EPAS1, CA9, SPP1, LOX, SLC2A1, and LAMP3;
Coagulation module: HPSE, SERPINE1, SERPINB2, F3, and ANXA2; Blood
endothelium module: VEGFA, NOS3, KDR, FLT1, VCAM1, VWF, CDH5, and
MMRN1; Lymphatic endothelium module: CCL21 and CXCL12;
Proliferation rate (or Tumor proliferation rate) module: MKI67,
ESCO2, CETN3, CDK2, CCND1, CCNE1, AURKA, AURKB, E2F1, MYBL2, BUB1,
PLK1, PRC1, CCNB1, MCM2, MCM6, CDK4, and CDK6; Oncogenes module:
MDM2, MYC, AKT1, BCL2, MME, and SYK; PI3K/AKT/mTOR signaling
module: PIK3CA, PIK3CB, PIK3CG, PIK3CD, AKT1, MTOR, PTEN, PRKCA,
AKT2, and AKT3; RAS/RAF/MEK signaling module: BRAF, FNTA, FNTB,
MAP2K1, MAP2K2, MKNK1, and MKNK2; Receptor tyrosine kinases
expression module: ALK, AXL, KIT, EGFR, ERBB2, FLT3, MET, NTRK1,
FGFR1, FGFR2, FGFR3, ERBB4, ERBB3, BCR-ABL, PDGFRA, PDGFRB, and
ABL1; Growth Factors module: NGF, CSF3, CSF2, FGF7, IGF1, IGF2,
IL7, and FGF2; Tumor suppressors module: TP53, MLL2, CREBBP, EP300,
ARID1A, HIST1H1, EBF1, IRF4, IKZF3, KLHL6, PRDM1, CDKN2A, RB1,
EPHA7, TNFAIP3, TNFRSF14, FAS, SHP1, SOCS1, SIK1, PTEN, DCN, MTAP,
AIM2, and MITF; Metastasis signature module: ESRP1, HOXA1, SMARCA4,
TWIST1, NEDD9, PAPPA, CTSL, SNAI2, and HPSE; Antimetastatic factors
module: NCAM1, CDH1, KISS1, BRMS1, ADGRG1, TCF21, PCDH10, and MITF;
and Mutation status module: APC, ARID1A, ATM, ATRX, BAP1, BRAF,
BRCA2, CDH1, CDKN2A, CTCF, CTNNB1, DNMT3A, EGFR, FBXW7, FLT3,
GATA3, HRAS, IDH1, KRAS, MAP3K1, MTOR, NAV3, NCOR1, NF1, NOTCH1,
NPM1, NRAS, PBRM1, PIK3CA, PIK3R1, PTEN, RB1, RUNX1, SETD2, STAG2,
TAF1, TP53, and VHL. In certain embodiments, two or more genes from
any combination of the listed modules may be used to generate a
molecular functional expression signature (or a visualization
thereof, termed an "MF PORTRAIT" herein) for a subject.
[0426] In some embodiments, the gene groups in an MFES may comprise
at least two genes (e.g., at least two genes, at least three genes,
at least four genes, at least five genes, at least six genes, at
least seven genes, at least eight genes, at least nine genes, at
least ten genes, or more than ten genes as shown in the following
lists; in some embodiments all of the listed genes are selected
from each group; and in some embodiments the numbers of genes in
each selected group are not the same): Major histocompatibility
complex I (MHC I) module: HLA-A, HLA-B, HLA-C, B2M, TAP1, and TAP2;
Major histocompatibility complex II (MHC II) module; HLA-DRA,
HLA-DRB1, HLA-DOB, HLA-DPB2, HLA-DMA, HLA-DOA, HLA-DPA1, HLA-DPB1,
HLA-DMB, HLA-DQB1, HLA-DQA1, HLA-DRB5, HLA-DQA2, HLA-DQB2, and
HLA-DRB6; Coactivation molecules module: CD80, CD86, CD40, CD83,
TNFRSF4, ICOSLG, CD28; Effector cells (or Effector T cell) module:
IFNG, GZMA, GZMB, PRF1, LCK, GZMK, ZAP70, GNLY, FASLG, TBX21,
EOMES, CD8A, and CD8B; Natural killer cells (NK cells) module:
NKG7, CD160, CD244, NCR1, KLRC2, KLRK1, CD226, GNLY, KIR2DL4,
KIR2DS1, KIR2DS2, KIR2DS3, KIR2DS4, KIR2DS5, EOMES, CLIC3, FGFBP2,
KLRF1, and SH2D1B; T cells module: TBX21, ITK, CD3D, CD3E, CD3G,
TRAC, TRBC1, TRBC2, LCK, UBASH3A, TRAT1, CD5, and CD28; B cells
module: CD19, MS4A1, TNFRSF13C, CD27, CD24, CR2, TNFRSF17,
TNFRSF13B, CD22, CD79A, CD79B, BLK, FCRL5, PAX5, and STAP1; M1
signatures module: NOS2, IL12A, IL12B, IL23A, TNF, IL1B, and SOCS3;
Th1 signature module: IFNG, IL2, CD40LG, IL15, CD27, TBX21, LTA,
and IL21; Checkpoint inhibition (or checkpoint molecules) module:
PDCD1, CD274, CTLA4, LAG3, PDCD1LG2, BTLA, HAVCR2, and VSIR;
Regulatory T cells (Treg) module: TGFB1, TGFB2, TGFB3, FOXP3,
CTLA4, IL10, and TNFRSF1B; Myeloid-derived suppressor cells (MDSCs)
module: IDO1, ARG1, IL4R, IL10, TGFB1, TGFB2, TGFB3, NOS2, CYBB,
CXCR4, and CD33; Neutrophil signature model: MPO, ELANE, PRTN3,
CTSG, FCGR3B, CXCR1, CXCR2, CD177, PI3, FFAR2, and PGLYRP1; M2
signature module: IL10, VEGFA, TGFB1, IDO1, PTGES, MRC1, CSF1,
LRP1, ARG1, PTGS1, MSR1, CD163, and CSF1R; Th2 signature module:
IL4, IL5, IL13, IL10, IL25, and GATA3; Protumor cytokines module:
IL10, TGFB1, TGFB2, TGFB3, IL22, and MIF; Complement inhibition
module: CFD, CFI, CD55, CD46, and CR1; Cancer associated
fibroblasts (CAFs) module: COL1A1, COL1A2, COL4A1, COL5A1, TGFB1,
TGFB2, TGFB3, ACTA2, FGF2, FAP, LRP1, CD248, COL6A1, COL6A2,
COL6A3, FBLN1, LUM, MFAP5, and PRELP; Angiogenesis module: VEGFA,
VEGFB, VEGFC, PDGFC, CXCL8, CXCR2, FLT1, PIGF, CXCL5, KDR, ANGPT1,
ANGPT2, TEK, VWF, CDH5, NOS3, VCAM1, and MMRN1; Endothelium module:
VEGFA, NOS3, KDR, FLT1, VCAM1, VWF, CDH5, MMRN1, CLEC14A, MMRN2,
and ECSCR; Proliferation rate (or Tumor proliferation rate) module:
MKI67, ESCO2, CETN3, CDK2, CCND1, CCNE1, AURKA, AURKB, E2F1, MYBL2,
BUB1, PLK1, CCNB1, MCM2, MCM6, CDK4, and CDK6; PI3K/AKT/mTOR
signaling module: PIK3CA, PIK3CB, PIK3CG, PIK3CD, AKT1, MTOR, PTEN,
PRKCA, AKT2, and AKT3; RAS/RAF/MEK signaling module: BRAF, FNTA,
FNTB, MAP2K1, MAP2K2, MKNK1, and MKNK2; Receptor tyrosine kinases
expression module: ALK, AXL, KIT, EGFR, ERBB2, FLT3, MET, NTRK1,
FGFR1, FGFR2, FGFR3, ERBB4, ERBB3, BCR-ABL, PDGFRA, PDGFRB, and
ABL1; Growth Factors module: NGF, CSF3, CSF2, FGF7, IGF1, IGF2,
IL7, and FGF2; Tumor suppressors module: TP53, SIK1, PTEN, DCN,
MTAP, AIM2, RB1, and MITF; Metastasis signature module: ESRP1,
HOXA1, SMARCA4, TWIST1, NEDD9, PAPPA, and HPSE; and Antimetastatic
factors module: NCAM1, CDH1, KISS1, and BRMS1. In some embodiments,
the gene groups may further comprise at least two genes (e.g., at
least two genes, at least three genes, at least four genes, at
least five genes, at least six genes, at least seven genes, at
least eight genes, at least nine genes, at least ten genes, or more
than ten genes as shown in the following lists; in some embodiments
all of the listed genes are selected from each group; and in some
embodiments the numbers of genes in each selected group are not the
same): T cell traffic module: CXCL9, CXCL10, CXCR3, CX3CL1, CCR7,
CXCL11, CCL21, CCL2, CCL3, CCL4, and CCL5; Antitumor cytokines
module: HMGB1, TNF, IFNB1, IFNA2, CCL3, TNFSF10, and FASLG; Treg
traffic module: CCL17, CXCL12, CXCR4, CCR4, CCL22, CCL1, CCL2,
CCL5, CXCL13, and CCL28; MDSC and TAM traffic module: CXCL1, CXCL5,
CCL2, CCL4, CCL8, CCR2, CCL3, CCL5, CSF1, and CXCL8; Granulocyte
traffic module: CXCL8, CXCL2, CXCL1, CCL11, CCL24, KITLG, CCL5,
CXCL5, CCR3, and CCL26; Eosinophil signature model: PRG2, EPX,
RNASE2, RNASE3, IL5RA, GATA1, SIGLEC8, and PRG3; Mast cell
signature module: CMA1, TPSAB1, MS4A2, CPA3, IL4, IL5, IL13, and
SIGLEC8; Th17 signature module: IL17A, IL22, IL26, IL17F, IL21, and
RORC; Matrix formation (or Matrix) module: FN1, CA9, MMP1, MMP3,
MMP12, LGALS9, MMPI, MMP9, COL1A1, COL1A2, COL4A1, and COL5A1; and
Hypoxia factors module: LDHA, HIF1A, EPAS1, CA9, SPP1, LOX, SLC2A1,
and LAMP3. In certain embodiments, two or more genes from each of
the listed modules are included. Any of the foregoing sets of
modules may be used to generate an MFES (or a visualization
thereof) for a subject with a solid cancer (e.g., melanoma).
[0427] In some embodiments, the gene groups may comprise at least
two genes (e.g., at least two genes, at least three genes, at least
four genes, at least five genes, at least six genes, at least seven
genes, at least eight genes, at least nine genes, at least ten
genes, or more than ten genes as shown in the following lists; in
some embodiments all of the listed genes are selected from each
group; and in some embodiments the numbers of genes in each
selected group are not the same): Effector T cell module: IFNG,
GZMA, GZMB, PRF1, LCK, GZMK, ZAP70, GNLY, FASLG, TBX21, EOMES,
CD8A, and CD8B; Natural killer cells (NK cells) module: NKG7,
CD160, CD244, NCR1, KLRC2, KLRK1, CD226, GZMH, GNLY, IFNG, KIR2DL4,
KIR2DS1, KIR2DS2, KIR2DS3, KIR2DS4, and KIR2DS5; T cells module:
EOMES, TBX21, ITK, CD3D, CD3E, CD3G, TRAC, TRBC1, TRBC2, LCK,
UBASH3A, and TRAT1; Benign B cells module: CD19, MS4A1, TNFRSF13C,
CD27, CD24, CR2, TNFRSF17, TNFRSF13B, CD22, CD79A, CD79B, and BLK;
Malignant B cell marker module: MME, CD70, CD20, CD22, and PAX5; M1
signatures module: NOS2, IL12A, IL12B, IL23A, TNF, IL1B, and SOCS3;
Th1 signature module: IFNG, IL2, CD40LG, IL15, CD27, TBX21, LTA,
and IL21; Checkpoint inhibition (or checkpoint molecules) module:
PDCD1, CD274, CTLA4, LAG3, PDCD1LG2, BTLA, and HAVCR2; Follicular
dendritic cells module: CR1, FCGR2A, FCGR2B, FCGR2C, CR2, FCER2,
CXCL13, MADCAM1, ICAM1, VCAM1, BST1, LTBR, and TNFRSF1A; Follicular
B helper T cells module: CXCR5, B3GAT1, ICOS, CD40LG, CD84, IL21,
BCL6, MAF, and SAP; Protumor cytokines module: IL10, TGFB1, TGFB2,
TGFB3, IL22, MIF, TNFSF13B, IL6, and IL7; Regulatory T cells (Treg)
module: TGFB1, TGFB2, TGFB3, FOXP3, CTLA4, IL10, TNFRSF18, and
TNFR2; Neutrophil signature model: MPO, ELANE, PRTN3, and CTSG; M2
signature module: IL10, VEGFA, TGFB1, ID01, PTGES, MRC1, CSF1,
LRP1, ARG1, PTGS1, MSR1, CD163, and CSF1R; Th2 signature module:
IL4, IL5, IL13, IL10, IL25, and GATA3; Complement inhibition
module: CFD, CFI, CD55, CD46, CR1, and CD59; Fibroblastic reticular
cells module: DES, VIM, PDGFRA, PDPN, NT5E, THY1, ENG, ACTA2, LTBR,
TNFRSF1A, VCAM1, ICAM1, and BST1; Angiogenesis module: VEGFA,
VEGFB, VEGFC, PDGFC, CXCL8, CXCR2, FLT1, PIGF, CXCL5, KDR, ANGPT1,
ANGPT2, TEK, VWF, and CDH5; Blood endothelium module: VEGFA, NOS3,
KDR, FLT1, VCAM1, VWF, CDH5, and MMRN1; Proliferation rate (or
Tumor proliferation rate) module: MKI67, ESCO2, CETN3, CDK2, CCND1,
CCNE1, AURKA, AURKB, E2F1, MYBL2, BUB1, PLK1, CCNB1, MCM2, and
MCM6; Oncogenes module: MDM2, MYC, AKT1, BCL2, MME, and SYK; and
Tumor suppressors module: TP53, MLL2, CREBBP, EP300, ARID1A,
HIST1H1, EBF1, IRF4, IKZF3, KLHL6, PRDM1, CDKN2A, RB1, EPHA7,
TNFAIP3, TNFRSF14, FAS, SHP1, and SOCS1. In some embodiments, the
gene groups of the modules may further comprise at least two genes
(e.g., at least two genes, at least three genes, at least four
genes, at least five genes, at least six genes, at least seven
genes, at least eight genes, at least nine genes, at least ten
genes, or more than ten genes as shown in the following lists; in
some embodiments all of the listed genes are selected from each
group; and in some embodiments the numbers of genes in each
selected group are not the same): Coactivation molecules module:
TNFRSF4 and CD28; B cell traffic module: CXCL13 and CXCR5;
Antitumor cytokines module: HMGB1, TNF, IFNB1, IFNA2, CCL3,
TNFSF10, FASLG; Treg traffic module: CCL17, CCR4, CCL22, and
CXCL13; Eosinophil signature model: PRG2, EPX, RNASE2, RNASE3,
IL5RA, GATA1, SIGLEC8, and PRG3; Mast cell signature module: CMA1,
TPSAB1, MS4A2, CPA3, IL4, IL5, IL13, and SIGLEC8; Th17 signature
module: IL17A, IL22, IL26, IL17F, IL21, and RORC; Matrix formation
(or Matrix) module: MMP9, FN1, COL1A1, COL1A2, COL3A1, COL4A1, CA9,
VTN, LGALS7, TIMP1, and MMP2; Hypoxia factors module: LDHA, HIF1A,
EPAS1, CA9, SPP1, LOX, SLC2A1, and LAMP3; Coagulation module: HPSE,
SERPINE1, SERPINB2, F3, and ANXA2; and Lymphatic endothelium
module: CCL21 and CXCL12. In certain embodiments, two or more genes
from each of the listed modules are included. Any of the foregoing
sets of modules may be used to generate an MFES (or a visualization
thereof) for a subject with a follicular lymphoma.
[0428] In some embodiments, an MFES may include one or more gene
groups associated with cancer malignancy and one or more gene
groups associated with the cancer microenvironment. In some
embodiments, the gene group(s) associated with cancer malignancy
include the tumor properties gene group. In some embodiments, the
gene group(s) associated with cancer microenvironment include the
tumor-promoting immune microenvironment gene group, the anti-tumor
immune microenvironment gene group, the gene angiogenesis group,
and the gene fibroblasts group.
[0429] In some embodiments, the gene groups associated with cancer
malignancy comprises at least three genes from the following group
(e.g., at least three genes, at least four genes, at least five
genes, at least six genes, at least seven genes, at least eight
genes, at least nine genes, at least ten genes, or more than ten
genes are selected from each group; in some embodiments all of the
listed genes are selected from each group): the tumor properties
group: MKI67, ESCO2, CETN3, CDK2, CCND1, CCNE1, AURKA, AURKB, CDK4,
CDK6, PRC1, E2F1, MYBL2, BUB1, PLK1, CCNB1, MCM2, MCM6, PIK3CA,
PIK3CB, PIK3CG, PIK3CD, AKT1, MTOR, PTEN, PRKCA, AKT2, AKT3, BRAF,
FNTA, FNTB, MAP2K1, MAP2K2, MKNK1, MKNK2, ALK, AXL, KIT, EGFR,
ERBB2, FLT3, MET, NTRK1, FGFR1, FGFR2, FGFR3, ERBB4, ERBB3,
BCR-ABL, PDGFRA, PDGFRB, NGF, CSF3, CSF2, FGF7, IGF1, IGF2, IL7,
FGF2, TP53, SIK1, PTEN, DCN, MTAP, AIM2, RB1, ESRP1, CTSL, HOXA1,
SMARCA4, SNAI2, TWIST1, NEDD9, PAPPA, HPSE, KISS1, ADGRG1, BRMS1,
TCF21, CDH1, PCDH10, NCAM1, MITF, APC, ARID1A, ATM, ATRX, BAP1,
BRAF, BRCA2, CDH1, CDKN2A, CTCF, CTNNB1, DNMT3A, EGFR, FBXW7, FLT3,
GATA3, HRAS, IDH1, KRAS, MAP3K1, MTOR, NAV3, NCOR1, NF1, NOTCH1,
NPM1, NRAS, PBRM1, PIK3CA, PIK3R1, PTEN, RB1, RUNX1, SETD2, STAG2,
TAF1, TP53, and VHL.
[0430] In some embodiments, the gene groups associated with cancer
microenvironment includes at least three genes from each of the
following groups (e.g., at least three genes, at least four genes,
at least five genes, at least six genes, at least seven genes, at
least eight genes, at least nine genes, at least ten genes, or more
than ten genes are selected from each group; in some embodiments
all of the listed genes are selected from each group): the
anti-tumor immune microenvironment group: HLA-A, HLA-B, HLA-C, B2M,
TAP1, TAP2, HLA-DRA, HLA-DRB1, HLA-DOB, HLA-DPB2, HLA-DMA, HLA-DOA,
HLA-DPA1, HLA-DPB1, HLA-DMB, HLA-DQB1, HLA-DQA1, HLA-DRB5,
HLA-DQA2, HLA-DQB2, HLA-DRB6, CD80, CD86, CD40, CD83, TNFRSF4,
ICOSLG, CD28, IFNG, GZMA, GZMB, PRF1, LCK, GZMK, ZAP70, GNLY,
FASLG, TBX21, EOMES, CD8A, CD8B, NKG7, CD160, CD244, NCR1, KLRC2,
KLRK1, CD226, GZMH, GNLY, IFNG, KIR2DL4, KIR2DS1, KIR2DS2, KIR2DS3,
KIR2DS4, KIR2DS5, CXCL9, CXCL10, CXCR3, CX3CL1, CCR7, CXCL11,
CCL21, CCL2, CCL3, CCL4, CCL5, EOMES, TBX21, ITK, CD3D, CD3E, CD3G,
TRAC, TRBC1, TRBC2, LCK, UBASH3A, TRAT1, CD19, MS4A1, TNFRSF13C,
CD27, CD24, CR2, TNFRSF17, TNFRSF13B, CD22, CD79A, CD79B, BLK,
NOS2, IL12A, IL12B, IL23A, TNF, IL1B, SOCS3, IFNG, IL2, CD40LG,
IL15, CD27, TBX21, LTA, IL21, HMGB1, TNF, IFNB1, IFNA2, CCL3,
TNFSF10, and FASLG; the tumor-promoting immune microenvironment
group: PDCD1, CD274, CTLA4, LAG3, PDCD1LG2, BTLA, HAVCR2, VSIR,
CXCL12, TGFB1, TGFB2, TGFB3, FOXP3, CTLA4, IL10, TNFRSF1B, CCL17,
CXCR4, CCR4, CCL22, CCL1, CCL2, CCL5, CXCL13, CCL28, IDO1, ARG1,
IL4R, IL10, TGFB1, TGFB2, TGFB3, NOS2, CYBB, CXCR4, CD33, CXCL1,
CXCL5, CCL2, CCL4, CCL8, CCR2, CCL3, CCL5, CSF1, CXCL8, CXCL8,
CXCL2, CXCL1, CCL11, CCL24, KITLG, CCL5, CXCL5, CCR3, CCL26, PRG2,
EPX, RNASE2, RNASE3, IL5RA, GATA1, SIGLEC8, PRG3, CMA1, TPSAB1,
MS4A2, CPA3, IL4, IL5, IL13, SIGLEC8, MPO, ELANE, PRTN3, CTSG,
IL10, VEGFA, TGFB1, ID01, PTGES, MRC1, CSF1, LRP1, ARG1, PTGS1,
MSR1, CD163, CSF1R, IL4, IL5, IL13, IL10, IL25, GATA3, IL10, TGFB1,
TGFB2, TGFB3, IL22, MIF, CFD, CFI, CD55, CD46, and CR1; the
fibroblasts group: LGALS1, COL1A1, COL1A2, COL4A1, COL5A1, TGFB1,
TGFB2, TGFB3, ACTA2, FGF2, FAP, LRP1, CD248, COL6A1, COL6A2, and
COL6A3; and the angiogenesis group: VEGFA, VEGFB, VEGFC, PDGFC,
CXCL8, CXCR2, FLT1, PIGF, CXCL5, KDR, ANGPT1, ANGPT2, TEK, VWF,
CDH5, NOS3, KDR, VCAM1, MMRN1, LDHA, HIF1A, EPAS1, CA9, SPP1, LOX,
SLC2A1, and LAMP3. In some embodiments, an unequal number of genes
may be selected from each of the listed groups for use. In specific
embodiments, all or almost all of the listed genes are used.
[0431] In some embodiments, gene groups associated with cancer
malignancy are: the proliferation rate group, the PI3K/AKT/mTOR
signaling group, the RAS/RAF/MEK signaling group, the receptor
tyrosine kinases expression group, the tumor suppressors group, the
metastasis signature group, the anti-metastatic factors group, and
the mutation status group. In some embodiments, the gene groups
associated with cancer microenvironment are: the cancer associated
fibroblasts group, the angiogenesis group, the antigen presentation
group, the cytotoxic T and NK cells group, the B cells group, the
anti-tumor microenvironment group, the checkpoint inhibition group,
the Treg group, the MDSC group, the granulocytes group, and the
tumor-promotive immune group.
[0432] In some embodiments, the gene groups associated with cancer
malignancy comprises at least three genes from each of the
following groups (e.g., at least three genes, at least four genes,
at least five genes, at least six genes, at least seven genes, at
least eight genes, at least nine genes, at least ten genes, or more
than ten genes are selected from each group): the proliferation
rate group: MKI67, ESCO2, CETN3, CDK2, CCND1, CCNE1, AURKA, AURKB,
CDK4, CDK6, PRC1, E2F1, MYBL2, BUB1, PLK1, CCNB1, MCM2, and MCM6;
the PI3K/AKT/mTOR signaling group: PIK3CA, PIK3CB, PIK3CG, PIK3CD,
AKT1, MTOR, PTEN, PRKCA, AKT2, and AKT3; the RAS/RAF/MEK signaling
group: BRAF, FNTA, FNTB, MAP2K1, MAP2K2, MKNK1, and MKNK2; the
receptor tyrosine kinases expression group: ALK, AXL, KIT, EGFR,
ERBB2, FLT3, MET, NTRK1, FGFR1, FGFR2, FGFR3, ERBB4, ERBB3,
BCR-ABL, PDGFRA, and PDGFRB; the tumor suppressors group: TP53,
SIK1, PTEN, DCN, MTAP, AIM2, and RB1; the metastasis signature
group: ESRP1, CTSL, HOXA1, SMARCA4, SNAI2, TWIST1, NEDD9, PAPPA,
and HPSE; the anti-metastatic factors group: KISS1, ADGRG1, BRMS1,
TCF21, CDH1, PCDH10, NCAM1, and MITF; and the mutation status
group: APC, ARID1A, ATM, ATRX, BAP1, BRAF, BRCA2, CDH1, CDKN2A,
CTCF, CTNNB1, DNMT3A, EGFR, FBXW7, FLT3, GATA3, HRAS, IDH1, KRAS,
MAP3K1, MTOR, NAV3, NCOR1, NF1, NOTCH1, NPM1, NRAS, PBRM1, PIK3CA,
PIK3R1, PTEN, RB1, RUNX1, SETD2, STAG2, TAF1, TP53, and VHL.
[0433] In some embodiments, the gene groups associated with cancer
microenvironment comprises at least three genes from each of the
following groups (e.g., at least three genes, at least four genes,
at least five genes, at least six genes, at least seven genes, at
least eight genes, at least nine genes, at least ten genes, or more
than ten genes are selected from each group): the cancer associated
fibroblasts group: LGALS1, COL1A1, COL1A2, COL4A1, COL5A1, TGFB1,
TGFB2, TGFB3, ACTA2, FGF2, FAP, LRP1, CD248, COL6A1, COL6A2, and
COL6A3; the angiogenesis group: VEGFA, VEGFB, VEGFC, PDGFC, CXCL8,
CXCR2, FLT1, PIGF, CXCL5, KDR, ANGPT1, ANGPT2, TEK, VWF, CDH5,
NOS3, KDR, VCAM1, MMRN1, LDHA, HIF1A, EPAS1, CA9, SPP1, LOX,
SLC2A1, and LAMP3; the antigen presentation group: HLA-A, HLA-B,
HLA-C, B2M, TAP1, TAP2, HLA-DRA, HLA-DRB1, HLA-DOB, HLA-DPB2,
HLA-DMA, HLA-DOA, HLA-DPA1, HLA-DPB1, HLA-DMB, HLA-DQB1, HLA-DQA1,
HLA-DRB5, HLA-DQA2, HLA-DQB2, HLA-DRB6, CD80, CD86, CD40, CD83,
TNFRSF4, ICOSLG, and CD28; the cytotoxic T and NK cells group:
IFNG, GZMA, GZMB, PRF1, LCK, GZMK, ZAP70, GNLY, FASLG, TBX21,
EOMES, CD8A, CD8B, NKG7, CD160, CD244, NCR1, KLRC2, KLRK1, CD226,
GZMH, GNLY, IFNG, KIR2DL4, KIR2DS1, KIR2DS2, KIR2DS3, KIR2DS4,
KIR2DS5, CXCL9, CXCL10, CXCR3, CX3CL1, CCR7, CXCL11, CCL21, CCL2,
CCL3, CCL4, CCL5, EOMES, TBX21, ITK, CD3D, CD3E, CD3G, TRAC, TRBC1,
TRBC2, LCK, UBASH3A, and TRAT1; the B cells group: CD19, MS4A1,
TNFRSF13C, CD27, CD24, CR2, TNFRSF17, TNFRSF13B, CD22, CD79A,
CD79B, and BLK; the anti-tumor microenvironment group: NOS2, IL12A,
IL12B, IL23A, TNF, IL1B, SOCS3, IFNG, IL2, CD40LG, IL15, CD27,
TBX21, LTA, IL21, HMGB1, TNF, IFNB1, IFNA2, CCL3, TNFSF10, and
FASLG; the checkpoint inhibition group: PDCD1, CD274, CTLA4, LAG3,
PDCD1LG2, BTLA, HAVCR2, and VSIR; the Treg group: CXCL12, TGFB1,
TGFB2, TGFB3, FOXP3, CTLA4, IL10, TNFRSF1B, CCL17, CXCR4, CCR4,
CCL22, CCL1, CCL2, CCL5, CXCL13, and CCL28; the MDSC group: IDO1,
ARG1, IL4R, IL10, TGFB1, TGFB2, TGFB3, NOS2, CYBB, CXCR4, CD33,
CXCL1, CXCL5, CCL2, CCL4, CCL8, CCR2, CCL3, CCL5, CSF1, and CXCL8;
the granulocytes group: CXCL8, CXCL2, CXCL1, CCL11, CCL24, KITLG,
CCL5, CXCL5, CCR3, CCL26, PRG2, EPX, RNASE2, RNASE3, IL5RA, GATA1,
SIGLEC8, PRG3, CMA1, TPSAB1, MS4A2, CPA3, IL4, IL5, IL13, SIGLEC8,
MPO, ELANE, PRTN3, and CTSG; the tumor-promotive immune group:
IL10, VEGFA, TGFB1, ID01, PTGES, MRC1, CSF1, LRP1, ARG1, PTGS1,
MSR1, CD163, CSF1R, IL4, IL5, IL13, IL10, IL25, GATA3, IL10, TGFB1,
TGFB2, TGFB3, IL22, MIF, CFD, CFI, CD55, CD46, and CR1. In some
embodiments, an unequal number of genes may be selected from each
of the listed groups for use. In specific embodiments, all or
almost all of the listed genes are used.
[0434] In some embodiments, the gene groups associated with cancer
malignancy are: the proliferation rate group, the PI3K/AKT/mTOR
signaling group, the RAS/RAF/MEK signaling group, the receptor
tyrosine kinases expression group, the growth factors group, the
tumor suppressors group, the metastasis signature group, the
anti-metastatic factors group, and the mutation status group. In
some embodiments, the plurality of gene groups associated with
cancer microenvironment are: the cancer associated fibroblasts
group, the angiogenesis group, the MHCI group, the MHCII group, the
coactivation molecules group, the effector cells group, the NK
cells group, the T cell traffic group, the T cells group, the B
cells group, the M1 signatures group, the Th1 signature group, the
antitumor cytokines group, the checkpoint inhibition group, the
Treg group, the MDSC group, the granulocytes group, the M2
signature group, the Th2 signature group, the protumor cytokines
group, and the complement inhibition group.
[0435] In some embodiments, the gene groups associated with cancer
malignancy comprises at least three genes from each of the
following groups (e.g., at least three genes, at least four genes,
at least five genes, at least six genes, at least seven genes, at
least eight genes, at least nine genes, at least ten genes, or more
than ten genes are selected from each group): the proliferation
rate group: MKI67, ESCO2, CETN3, CDK2, CCND1, CCNE1, AURKA, AURKB,
CDK4, CDK6, PRC1, E2F1, MYBL2, BUB1, PLK1, CCNB1, MCM2, and MCM6;
the PI3K/AKT/mTOR signaling group: PIK3CA, PIK3CB, PIK3CG, PIK3CD,
AKT1, MTOR, PTEN, PRKCA, AKT2, and AKT3; the RAS/RAF/MEK signaling
group: BRAF, FNTA, FNTB, MAP2K1, MAP2K2, MKNK1, and MKNK2; the
receptor tyrosine kinases expression group: ALK, AXL, KIT, EGFR,
ERBB2, FLT3, MET, NTRK1, FGFR1, FGFR2, FGFR3, ERBB4, ERBB3,
BCR-ABL, PDGFRA, and PDGFRB; the growth factors group: NGF, CSF3,
CSF2, FGF7, IGF1, IGF2, IL7, and FGF2; the tumor suppressors group:
TP53, SIK1, PTEN, DCN, MTAP, AIM2, and RB1; the metastasis
signature group: ESRP1, CTSL, HOXA1, SMARCA4, SNAI2, TWIST1, NEDD9,
PAPPA, and HPSE; the anti-metastatic factors group: KISS1, ADGRG1,
BRMS1, TCF21, CDH1, PCDH10, NCAM1, and MITF; and the mutation
status group: APC, ARID1A, ATM, ATRX, BAP1, BRAF, BRCA2, CDH1,
CDKN2A, CTCF, CTNNB1, DNMT3A, EGFR, FBXW7, FLT3, GATA3, HRAS, IDH1,
KRAS, MAP3K1, MTOR, NAV3, NCOR1, NF1, NOTCH1, NPM1, NRAS, PBRM1,
PIK3CA, PIK3R1, PTEN, RB1, RUNX1, SETD2, STAG2, TAF1, TP53, and
VHL. In some embodiments, the plurality of gene groups associated
with cancer microenvironment comprises at least three genes from
each of the following groups: the cancer associated fibroblasts
group: LGALS1, COL1A1, COL1A2, COL4A1, COL5A1, TGFB1, TGFB2, TGFB3,
ACTA2, FGF2, FAP, LRP1, CD248, COL6A1, COL6A2, and COL6A3; the
angiogenesis group: VEGFA, VEGFB, VEGFC, PDGFC, CXCL8, CXCR2, FLT1,
PIGF, CXCL5, KDR, ANGPT1, ANGPT2, TEK, VWF, CDH5, NOS3, KDR, VCAM1,
MMRN1, LDHA, HIF1A, EPAS1, CA9, SPP1, LOX, SLC2A1, and LAMP3; the
MHCI group: HLA-A, HLA-B, HLA-C, B2M, TAP1, and TAP2; the MHCII
group: HLA-DRA, HLA-DRB1, HLA-DOB, HLA-DPB2, HLA-DMA, HLA-DOA,
HLA-DPA1, HLA-DPB1, HLA-DMB, HLA-DQB1, HLA-DQA1, HLA-DRB5,
HLA-DQA2, HLA-DQB2, and HLA-DRB6; the coactivation molecules group:
CD80, CD86, CD40, CD83, TNFRSF4, ICOSLG, and CD28; the effector
cells group: IFNG, GZMA, GZMB, PRF1, LCK, GZMK, ZAP70, GNLY, FASLG,
TBX21, EOMES, CD8A, and CD8B; the NK cells group: NKG7, CD160,
CD244, NCR1, KLRC2, KLRK1, CD226, GZMH, GNLY, IFNG, KIR2DL4,
KIR2DS1, KIR2DS2, KIR2DS3, KIR2DS4, and KIR2DS5; the T cell traffic
group: CXCL9, CXCL10, CXCR3, CX3CL1, CCR7, CXCL11, CCL21, CCL2,
CCL3, CCL4, and CCL5; the T cells group: EOMES, TBX21, ITK, CD3D,
CD3E, CD3G, TRAC, TRBC1, TRBC2, LCK, UBASH3A, and TRAT1; the B
cells group: CD19, MS4A1, TNFRSF13C, CD27, CD24, CR2, TNFRSF17,
TNFRSF13B, CD22, CD79A, CD79B, and BLK; the M1 signatures group:
NOS2, IL12A, IL12B, IL23A, TNF, IL1B, and SOCS3; the Th1 signature
group: IFNG, IL2, CD40LG, IL15, CD27, TBX21, LTA, and IL21; the
antitumor cytokines group: HMGB1, TNF, IFNB1, IFNA2, CCL3, TNFSF10,
and FASLG; the checkpoint inhibition group: PDCD1, CD274, CTLA4,
LAG3, PDCD1LG2, BTLA, HAVCR2, and VSIR; the Treg group: CXCL12,
TGFB1, TGFB2, TGFB3, FOXP3, CTLA4, IL10, TNFRSF1B, CCL17, CXCR4,
CCR4, CCL22, CCL1, CCL2, CCL5, CXCL13, and CCL28; the MDSC group:
IDO1, ARG1, IL4R, IL10, TGFB1, TGFB2, TGFB3, NOS2, CYBB, CXCR4,
CD33, CXCL1, CXCL5, CCL2, CCL4, CCL8, CCR2, CCL3, CCL5, CSF1, and
CXCL8; the granulocytes group: CXCL8, CXCL2, CXCL1, CCL11, CCL24,
KITLG, CCL5, CXCL5, CCR3, CCL26, PRG2, EPX, RNASE2, RNASE3, IL5RA,
GATA1, SIGLEC8, PRG3, CMA1, TPSAB1, MS4A2, CPA3, IL4, IL5, IL13,
SIGLEC8, MPO, ELANE, PRTN3, and CTSG; the M2 signature group: IL10,
VEGFA, TGFB1, IDO1, PTGES, MRC1, CSF1, LRP1, ARG1, PTGS1, MSR1,
CD163, and CSF1R; the Th2 signature group: IL4, IL5, IL13, IL10,
IL25, and GATA3; the protumor cytokines group: IL10, TGFB1, TGFB2,
TGFB3, IL22, and MIF; and the complement inhibition group: CFD,
CFI, CD55, CD46, and CR1. In some embodiments, an unequal number of
genes may be selected from each of the listed groups for use. In
specific embodiments, all or almost all of the listed genes are
used.
[0436] A molecular functional expression signature may include any
suitable number of gene groups. In some embodiments, an MFES
comprises at least 2, at least 3, at least 4, at least 5, at least
6, at least 7, at least 8, at least 9, 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, or at least 28 modules. In some embodiments, an MFES
comprises up to 2, up to 3, up to 4, up to 5, up to 6, up to 7, up
to 8, up to 9, up to 10, up to 11, up to 12, up to 13, up to 14, up
to 15, up to 16, up to 17, up to 18, up to 19, up to 20, up to 21,
up to 22, up to 23, up to 24, up to 25, up to 26, up to 27, or up
to 28 gene groups.
Tumor Microenvironment Types
[0437] The inventors have recognized that a molecular functional
expression signature for a subject having, suspected of having, or
at risk of having cancer may provide valuable information about the
microenvironment of the subject's cancer. The inventors have
recognized that a subject's MFES may be used to classify the
subject's microenvironment as being one of multiple types. For
example, in some embodiments, the MFES may be used to classify the
subject's microenvironment as being one of four different types of
microenvironment (e.g., "1st MF profile" or "type A"
microenvironment, "2nd MF profile" or "type B" microenvironment,
"3rd MF profile" or "type C" microenvironment, "4th MF profile" or
"type D" microenvironment, which are described in International PCT
Publication WO2018/231771, which is incorporated by reference
herein in its entirety). In turn, the identified microenvironment
type be used to identify a cancer therapy and/or determine the
effectiveness (or lack thereof) for one or more cancer therapies.
Examples of identifying cancer therapies based on a type of cancer
microenvironment (e.g., determined from gene group expression data,
for example, part of a molecular functional expression signature or
a molecular functional profile) are described in International PCT
Publication WO2018/231771.
[0438] First MF profile cancers may also be described as
"inflamed/vascularized" and/or "inflamed/fibroblast-enriched";
Second MF profile cancers may also be described as
"inflamed/non-vascularized" and/or
"inflamed/non-fibroblast-enriched"; Third MF profile cancers may
also be described as "non-inflamed/vascularized" and/or
"non-inflamed/fibroblast-enriched"; and Fourth MF profile cancers
may also be described as "non-inflamed/non-vascularized" and/or
"non-inflamed/non-fibroblast-enriched" and/or "immune desert."
[0439] In some embodiments, "inflamed" refers to the level of
compositions and processes related to inflammation in a cancer
(e.g., a tumor). In some embodiments, inflamed cancers (e.g.,
tumors) are highly infiltrated by immune cells, and are highly
active with regard to antigen presentation and T-cell activation.
In some embodiments, "vascularized" refers to the formation of
blood vessels in a cancer (e.g., a tumor). In some embodiments,
vascularized cancers (e.g., tumors) comprise high levels of
cellular compositions and process related to blood vessel
formation. In some embodiments, "fibroblast enriched" refers to the
level or amount of fibroblasts in a cancer (e.g., a tumor). In some
embodiments, fibroblast enriched tumors comprise high levels of
fibroblast cells.
Predicting Therapy Response
[0440] In some embodiments, sequencing data obtained using systems
and methods described herein (e.g., bias-corrected gene expression
data, data processed using the quality control techniques described
herein, etc.) may be used for identifying subjects suitable for a
particular treatment, and/or predicting likelihood of a patient's
response or lack thereof to a particular treatment and/or
predicting whether a patient may or may not have one or more
adverse reactions to a particular therapy as described in
International PCT Publication WO2018/231771, published on Dec. 20,
2018, entitled "Systems and Methods for Generating, Visualizing and
Classifying Molecular Functional Profiles," (being a publication of
PCT Application No.: PCT/US2018/037017, filed Jun. 12, 2018), the
entire contents of which are incorporated herein by reference.
[0441] In some embodiments, sequencing data obtained as described
herein (e.g., bias-corrected gene expression data, data processed
using the quality control techniques described herein, etc.) is
useful for identifying a subject suitable for a particular
treatment. In some embodiments, sequencing data (e.g.,
bias-corrected gene expression data, data processed using the
quality control techniques described herein, etc.) obtained as
described herein is useful for predicting likelihood of a patient's
response or lack thereof to a particular treatment. In some
embodiments, sequencing data obtained as described herein (e.g.,
bias-corrected gene expression data, data processed using the
quality control techniques described herein, etc.) is useful for
predicting whether a patient may or may not have one or more
adverse reactions to a particular therapy.
[0442] In some embodiments, predicted efficacy of an immune
checkpoint blockade therapy may be determined using sequencing data
obtained as described herein (e.g., bias-corrected gene expression
data, data processed using the quality control techniques described
herein, etc.) as described in International PCT Publication
WO2018/231772, published on Dec. 20, 2018, entitled "Systems and
Methods for Identifying Responders and Non-Responders to Immune
Checkpoint Blockade Therapy" (being a publication of International
patent application number PCT/US2018/037018, filed Jun. 12, 2018),
the entire contents of which are incorporated herein by
reference.
[0443] In some embodiments, sequencing data obtained as described
herein (e.g., bias-corrected gene expression data, data processed
using the quality control techniques described herein, etc.) is
useful for determining a biomarker, a biomarker score, a normalized
biomarker score, a therapy score, and/or an impact score as
described in International PCT Publication WO2018/231762, published
on Dec. 20, 2018, entitled "Systems and Methods for Identifying
Cancer Treatments from Normalized Biomarker Scores" (being a
publication of International patent application number
PCT/US2018/037008, filed Jun. 12, 2018), the entire contents of
which are incorporated herein by reference.
Methods of Treatment
[0444] In certain methods described herein, an effective amount of
anti-cancer therapy described herein may be administered or
recommended for administration to a subject (e.g., a human) in need
of the treatment via a suitable route (e.g., intravenous
administration).
[0445] The subject to be treated by the methods described herein
may be a human patient having, suspected of having, or at risk for
a cancer. Examples of a cancer include, but are not limited to,
melanoma, lung cancer, brain cancer, breast cancer, colorectal
cancer, pancreatic cancer, liver cancer, prostate cancer, skin
cancer, kidney cancer, bladder cancer, or prostate cancer. The
subject to be treated by the methods described herein may be a
mammal (e.g., may be a human). Mammals include but are not limited
to: farm animals (e.g., livestock), sport animals, laboratory
animals, pets, primates, horses, dogs, cats, mice, and rats.
[0446] A subject having a cancer may be identified by routine
medical examination, e.g., laboratory tests, biopsy, PET scans, CT
scans, or ultrasounds. A subject suspected of having a cancer might
show one or more symptoms of the disorder, e.g., unexplained weight
loss, fever, fatigue, cough, pain, skin changes, unusual bleeding
or discharge, and/or thickening or lumps in parts of the body. A
subject at risk for a cancer may be a subject having one or more of
the risk factors for that disorder. For example, risk factors
associated with cancer include, but are not limited to, (a) viral
infection (e.g., herpes virus infection), (b) age, (c) family
history, (d) heavy alcohol consumption, (e) obesity, (f) genetics,
and (g) chemical or toxin exposure, and (h) tobacco use.
[0447] "An effective amount" as used herein refers to the amount of
each active agent required to confer therapeutic effect on the
subject, either alone or in combination with one or more other
active agents. Effective amounts vary, as recognized by those
skilled in the art, depending on the particular condition being
treated, the severity of the condition, the individual patient
parameters including age, physical condition, size, gender and
weight, the duration of the treatment, the nature of concurrent
therapy (if any), the specific route of administration and like
factors within the knowledge and expertise of the health
practitioner. These factors are well known to those of ordinary
skill in the art and can be addressed with no more than routine
experimentation. It is generally preferred that a maximum dose of
the individual components or combinations thereof be used, that is,
the highest safe dose according to sound medical judgment. It will
be understood by those of ordinary skill in the art, however, that
a patient may insist upon a lower dose or tolerable dose for
medical reasons, psychological reasons, or for virtually any other
reasons.
[0448] Empirical considerations, such as the half-life of a
therapeutic compound, generally contribute to the determination of
the dosage. For example, antibodies that are compatible with the
human immune system, such as humanized antibodies or fully human
antibodies, may be used to prolong half-life of the antibody and to
prevent the antibody being attacked by the host's immune system.
Frequency of administration may be determined and adjusted over the
course of therapy, and is generally (but not necessarily) based on
treatment, and/or suppression, and/or amelioration, and/or delay of
a cancer. Alternatively, sustained continuous release formulations
of an anti-cancer therapeutic agent may be appropriate. Various
formulations and devices for achieving sustained release are known
in the art.
[0449] In some embodiments, dosages for an anti-cancer therapeutic
agent as described herein may be determined empirically in
individuals who have been administered one or more doses of the
anti-cancer therapeutic agent. Individuals may be administered
incremental dosages of the anti-cancer therapeutic agent. To assess
efficacy of an administered anti-cancer therapeutic agent, one or
more aspects of a cancer (e.g., tumor formation, tumor growth,
tumor type, MF expression signature) may be analyzed.
[0450] Generally, for administration of any of the anti-cancer
antibodies described herein, an initial candidate dosage may be
about 2 mg/kg. For the purpose of the present disclosure, a typical
daily dosage might range from about any of 0.1 .mu.g/kg to 3
.mu.g/kg to 30 .mu.g/kg to 300 .mu.g/kg to 3 mg/kg, to 30 mg/kg to
100 mg/kg or more, depending on the factors mentioned above. For
repeated administrations over several days or longer, depending on
the condition, the treatment is sustained until a desired
suppression or amelioration of symptoms occurs or until sufficient
therapeutic levels are achieved to alleviate a cancer, or one or
more symptoms thereof. An exemplary dosing regimen comprises
administering an initial dose of about 2 mg/kg, followed by a
weekly maintenance dose of about 1 mg/kg of the antibody, or
followed by a maintenance dose of about 1 mg/kg every other week.
However, other dosage regimens may be useful, depending on the
pattern of pharmacokinetic decay that the practitioner (e.g., a
medical doctor) wishes to achieve. For example, dosing from
one-four times a week is contemplated. In some embodiments, dosing
ranging from about 3.mu.g/mg to about 2 mg/kg (such as about
3.mu.g/mg, about 10 .mu.g/mg, about 30 .mu.g/mg, about 100
.mu.g/mg, about 300 .mu.g/mg, about 1 mg/kg, and about 2 mg/kg) may
be used. In some embodiments, dosing frequency is once every week,
every 2 weeks, every 4 weeks, every 5 weeks, every 6 weeks, every 7
weeks, every 8 weeks, every 9 weeks, or every 10 weeks; or once
every month, every 2 months, or every 3 months, or longer. The
progress of this therapy may be monitored by conventional
techniques and assays and/or by monitoring cancer Types A-D as
described herein. The dosing regimen (including the therapeutic
used) may vary over time.
[0451] When the anti-cancer therapeutic agent is not an antibody,
it may be administered at the rate of about 0.1 to 300 mg/kg of the
weight of the patient divided into one to three doses, or as
described herein. In some embodiments, for an adult patient of
normal weight, doses ranging from about 0.3 to 5.00 mg/kg may be
administered. The particular dosage regimen, e.g., dose, timing,
and/or repetition, will depend on the particular subject and that
individual's medical history, as well as the properties of the
individual agents (such as the half-life of the agent, and other
considerations well known in the art).
[0452] For the purpose of the present disclosure, the appropriate
dosage of an anti-cancer therapeutic agent will depend on the
specific anti-cancer therapeutic agent(s) (or compositions thereof)
employed, the type and severity of cancer, whether the anti-cancer
therapeutic agent is administered for preventive or therapeutic
purposes, previous therapy, the patient's clinical history and
response to the anti-cancer therapeutic agent, and the discretion
of the attending physician. Typically the clinician will administer
an anti-cancer therapeutic agent, such as an antibody, until a
dosage is reached that achieves the desired result.
[0453] Administration of an anti-cancer therapeutic agent can be
continuous or intermittent, depending, for example, upon the
recipient's physiological condition, whether the purpose of the
administration is therapeutic or prophylactic, and other factors
known to skilled practitioners. The administration of an
anti-cancer therapeutic agent (e.g., an anti-cancer antibody) may
be essentially continuous over a preselected period of time or may
be in a series of spaced dose, e.g., either before, during, or
after developing cancer.
[0454] As used herein, the term "treating" refers to the
application or administration of a composition including one or
more active agents to a subject, who has a cancer, a symptom of a
cancer, or a predisposition toward a cancer, with the purpose to
cure, heal, alleviate, relieve, alter, remedy, ameliorate, improve,
or affect the cancer or one or more symptoms of the cancer, or the
predisposition toward a cancer.
[0455] Alleviating a cancer includes delaying the development or
progression of the disease, or reducing disease severity.
Alleviating the disease does not necessarily require curative
results. As used therein, "delaying" the development of a disease
(e.g., a cancer) means to defer, hinder, slow, retard, stabilize,
and/or postpone progression of the disease. This delay can be of
varying lengths of time, depending on the history of the disease
and/or individuals being treated. A method that "delays" or
alleviates the development of a disease, or delays the onset of the
disease, is a method that reduces probability of developing one or
more symptoms of the disease in a given time frame and/or reduces
extent of the symptoms in a given time frame, when compared to not
using the method. Such comparisons are typically based on clinical
studies, using a number of subjects sufficient to give a
statistically significant result.
[0456] "Development" or "progression" of a disease means initial
manifestations and/or ensuing progression of the disease.
Development of the disease can be detected and assessed using
clinical techniques known in the art. Alternatively or in addition
to the clinical techniques known in the art, development of the
disease may be detectable and assessed based on the cancer types
described herein. However, development also refers to progression
that may be undetectable. For purpose of this disclosure,
development or progression refers to the biological course of the
symptoms. "Development" includes occurrence, recurrence, and onset.
As used herein "onset" or "occurrence" of a cancer includes initial
onset and/or recurrence.
[0457] In some embodiments, the anti-cancer therapeutic agent
(e.g., an antibody) described herein is administered to a subject
in need of the treatment at an amount sufficient to reduce cancer
(e.g., tumor) growth by at least 10% (e.g., 20%, 30%, 40%, 50%,
60%, 70%, 80%, 90% or greater). In some embodiments, the
anti-cancer therapeutic agent (e.g., an antibody) described herein
is administered to a subject in need of the treatment at an amount
sufficient to reduce cancer cell number or tumor size by at least
10% (e.g., 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90% or more). In
other embodiments, the anti-cancer therapeutic agent is
administered in an amount effective in altering cancer type.
Alternatively, the anti-cancer therapeutic agent is administered in
an amount effective in reducing tumor formation or metastasis.
[0458] Conventional methods, known to those of ordinary skill in
the art of medicine, may be used to administer the anti-cancer
therapeutic agent to the subject, depending upon the type of
disease to be treated or the site of the disease. The anti-cancer
therapeutic agent can also be administered via other conventional
routes, e.g., administered orally, parenterally, by inhalation
spray, topically, rectally, nasally, buccally, vaginally or via an
implanted reservoir. The term "parenteral" as used herein includes
subcutaneous, intracutaneous, intravenous, intramuscular,
intraarticular, intraarterial, intrasynovial, intrasternal,
intrathecal, intralesional, and intracranial injection or infusion
techniques. In addition, an anti-cancer therapeutic agent may be
administered to the subject via injectable depot routes of
administration such as using 1-, 3-, or 6-month depot injectable or
biodegradable materials and methods.
[0459] Injectable compositions may contain various carriers such as
vegetable oils, dimethylactamide, dimethyformamide, ethyl lactate,
ethyl carbonate, isopropyl myristate, ethanol, and polyols (e.g.,
glycerol, propylene glycol, liquid polyethylene glycol, and the
like). For intravenous injection, water soluble anti-cancer
therapeutic agents can be administered by the drip method, whereby
a pharmaceutical formulation containing the antibody and a
physiologically acceptable excipients is infused. Physiologically
acceptable excipients may include, for example, 5% dextrose, 0.9%
saline, Ringer's solution, and/or other suitable excipients.
Intramuscular preparations, e.g., a sterile formulation of a
suitable soluble salt form of the anti-cancer therapeutic agent,
can be dissolved and administered in a pharmaceutical excipient
such as Water-for-Injection, 0.9% saline, and/or 5% glucose
solution.
[0460] In one embodiment, an anti-cancer therapeutic agent is
administered via site-specific or targeted local delivery
techniques. Examples of site-specific or targeted local delivery
techniques include various implantable depot sources of the agent
or local delivery catheters, such as infusion catheters, an
indwelling catheter, or a needle catheter, synthetic grafts,
adventitial wraps, shunts and stents or other implantable devices,
site specific carriers, direct injection, or direct application.
See, e.g., PCT Publication No. WO 00/53211 and U.S. Pat. No.
5,981,568, the contents of each of which are incorporated by
reference herein for this purpose.
[0461] Targeted delivery of therapeutic compositions containing an
antisense polynucleotide, expression vector, or subgenomic
polynucleotides can also be used. Receptor-mediated DNA delivery
techniques are described in, for example, Findeis et al., Trends
Biotechnol. (1993) 11:202; Chiou et al., Gene Therapeutics: Methods
And Applications Of Direct Gene Transfer (J. A. Wolff, ed.) (1994);
Wu et al., J. Biol. Chem. (1988) 263:621; Wu et al., J. Biol. Chem.
(1994) 269:542; Zenke et al., Proc. Natl. Acad. Sci. USA (1990)
87:3655; Wu et al., J. Biol. Chem. (1991) 266:338. The contents of
each of the foregoing are incorporated by reference herein for this
purpose.
[0462] Therapeutic compositions containing a polynucleotide may be
administered in a range of about 100 ng to about 200 mg of DNA for
local administration in a gene therapy protocol. In some
embodiments, concentration ranges of about 500 ng to about 50 mg,
about 1 .mu.g to about 2 mg, about 5 .mu.g to about 500 .mu.g, and
about 20 .mu.g to about 100 .mu.g of DNA or more can also be used
during a gene therapy protocol.
[0463] Therapeutic polynucleotides and polypeptides can be
delivered using gene delivery vehicles. The gene delivery vehicle
can be of viral or non-viral origin (e.g., Jolly, Cancer Gene
Therapy (1994) 1:51; Kimura, Human Gene Therapy (1994) 5:845;
Connelly, Human Gene Therapy (1995) 1:185; and Kaplitt, Nature
Genetics (1994) 6:148). The contents of each of the foregoing are
incorporated by reference herein for this purpose. Expression of
such coding sequences can be induced using endogenous mammalian or
heterologous promoters and/or enhancers. Expression of the coding
sequence can be either constitutive or regulated.
[0464] Viral-based vectors for delivery of a desired polynucleotide
and expression in a desired cell are well known in the art.
Exemplary viral-based vehicles include, but are not limited to,
recombinant retroviruses (see, e.g., PCT Publication Nos. WO
90/07936; WO 94/03622; WO 93/25698; WO 93/25234; WO 93/11230; WO
93/10218; WO 91/02805; U.S. Pat. Nos. 5,219,740 and 4,777,127; GB
Patent No. 2,200,651; and EP Patent No. 0 345 242),
alphavirus-based vectors (e.g., Sindbis virus vectors, Semliki
forest virus (ATCC VR-67; ATCC VR-1247), Ross River virus (ATCC
VR-373; ATCC VR-1246) and Venezuelan equine encephalitis virus
(ATCC VR-923; ATCC VR-1250; ATCC VR 1249; ATCC VR-532)), and
adeno-associated virus (AAV) vectors (see, e.g., PCT Publication
Nos. WO 94/12649, WO 93/03769; WO 93/19191; WO 94/28938; WO
95/11984 and WO 95/00655). Administration of DNA linked to killed
adenovirus as described in Curiel, Hum. Gene Ther. (1992) 3:147 can
also be employed. The contents of each of the foregoing are
incorporated by reference herein for this purpose.
[0465] Non-viral delivery vehicles and methods can also be
employed, including, but not limited to, polycationic condensed DNA
linked or unlinked to killed adenovirus alone (see, e.g., Curiel,
Hum. Gene Ther. (1992) 3:147); ligand-linked DNA (see, e.g., Wu, J.
Biol. Chem. (1989) 264:16985); eukaryotic cell delivery vehicles
cells (see, e.g., U.S. Pat. No. 5,814,482; PCT Publication Nos. WO
95/07994; WO 96/17072; WO 95/30763; and WO 97/42338) and nucleic
charge neutralization or fusion with cell membranes. Naked DNA can
also be employed. Exemplary naked DNA introduction methods are
described in PCT Publication No. WO 90/11092 and U.S. Pat. No.
5,580,859. Liposomes that can act as gene delivery vehicles are
described in U.S. Pat. No. 5,422,120; PCT Publication Nos. WO
95/13796; WO 94/23697; WO 91/14445; and EP Patent No. 0524968.
Additional approaches are described in Philip, Mol. Cell. Biol.
(1994) 14:2411, and in Woffendin, Proc. Natl. Acad. Sci. (1994)
91:1581. The contents of each of the foregoing are incorporated by
reference herein for this purpose.
[0466] It is also apparent that an expression vector can be used to
direct expression of any of the protein-based anti-cancer
therapeutic agents (e.g., anti-cancer antibody). For example,
peptide inhibitors that are capable of blocking (from partial to
complete blocking) a cancer causing biological activity are known
in the art.
[0467] In some embodiments, more than one anti-cancer therapeutic
agent, such as an antibody and a small molecule inhibitory
compound, may be administered to a subject in need of the
treatment. The agents may be of the same type or different types
from each other. At least one, at least two, at least three, at
least four, or at least five different agents may be
co-administered. Generally anti-cancer agents for administration
have complementary activities that do not adversely affect each
other. Anti-cancer therapeutic agents may also be used in
conjunction with other agents that serve to enhance and/or
complement the effectiveness of the agents.
[0468] Treatment efficacy can be assessed by methods well-known in
the art, e.g., monitoring tumor growth or formation in a patient
subjected to the treatment. Alternatively or in addition to,
treatment efficacy can be assessed by monitoring tumor type over
the course of treatment (e.g., before, during, and after
treatment).
Combination Therapy
[0469] Compared to monotherapies, combinations of treatment
approaches showed higher efficacy in many studies, but the choice
of remedies to be combined and designing the combination therapy
regimen remain speculative. Given that the number of possible
combinations is now extremely high, there is great need for a tool
that would help to select drugs and combinations of remedies based
on objective information about a particular patient. Use of patient
specific information (e.g., a patient's sequencing data) for
designing or electing a specific combination therapy establishes a
scientific basis for choosing the optimal combination of
preparations.
[0470] Also provided herein are methods of treating a cancer or
recommending treating a cancer using any combination of anti-cancer
therapeutic agents or one or more anti-cancer therapeutic agents
and one or more additional therapies (e.g., surgery and/or
radiotherapy). The term combination therapy, as used herein,
embraces administration of more than one treatment (e.g., an
antibody and a small molecule or an antibody and radiotherapy) in a
sequential manner, that is, wherein each therapeutic agent is
administered at a different time, as well as administration of
these therapeutic agents, or at least two of the agents or
therapies, in a substantially simultaneous manner.
[0471] Sequential or substantially simultaneous administration of
each agent or therapy can be affected by any appropriate route
including, but not limited to, oral routes, intravenous routes,
intramuscular, subcutaneous routes, and direct absorption through
mucous membrane tissues. The agents or therapies can be
administered by the same route or by different routes. For example,
a first agent (e.g., a small molecule) can be administered orally,
and a second agent (e.g., an antibody) can be administered
intravenously.
[0472] As used herein, the term "sequential" means, unless
otherwise specified, characterized by a regular sequence or order,
e.g., if a dosage regimen includes the administration of an
antibody and a small molecule, a sequential dosage regimen could
include administration of the antibody before, simultaneously,
substantially simultaneously, or after administration of the small
molecule, but both agents will be administered in a regular
sequence or order. The term "separate" means, unless otherwise
specified, to keep apart one from the other. The term
"simultaneously" means, unless otherwise specified, happening or
done at the same time, i.e., the agents of the invention are
administered at the same time. The term "substantially
simultaneously" means that the agents are administered within
minutes of each other (e.g., within 10 minutes of each other) and
intends to embrace joint administration as well as consecutive
administration, but if the administration is consecutive it is
separated in time for only a short period (e.g., the time it would
take a medical practitioner to administer two agents separately).
As used herein, concurrent administration and substantially
simultaneous administration are used interchangeably. Sequential
administration refers to temporally separated administration of the
agents or therapies described herein.
[0473] Combination therapy can also embrace the administration of
the anti-cancer therapeutic agent (e.g., an antibody) in further
combination with other biologically active ingredients (e.g., a
vitamin) and non-drug therapies (e.g., surgery or
radiotherapy).
[0474] It should be appreciated that any combination of anti-cancer
therapeutic agents may be used in any sequence for treating a
cancer. The combinations described herein may be selected on the
basis of a number of factors, which include but are not limited to
the effectiveness of altering identified tumor type, reducing tumor
formation or tumor growth, and/or alleviating at least one symptom
associated with the cancer, or the effectiveness for mitigating the
side effects of another agent of the combination. For example, a
combined therapy as provided herein may reduce any of the side
effects associated with each individual members of the combination,
for example, a side effect associated with an administered
anti-cancer agent.
[0475] In some embodiments, an anti-cancer therapeutic agent is an
antibody, an immunotherapy, a radiation therapy, a surgical
therapy, and/or a chemotherapy.
[0476] Examples of the antibody anti-cancer agents include, but are
not limited to, alemtuzumab (Campath), trastuzumab (Herceptin),
Ibritumomab tiuxetan (Zevalin), Brentuximab vedotin (Adcetris),
Ado-trastuzumab emtansine (Kadcyla), blinatumomab (Blincyto),
Bevacizumab (Avastin), Cetuximab (Erbitux), ipilimumab (Yervoy),
nivolumab (Opdivo), pembrolizumab (Keytruda), atezolizumab
(Tecentriq), avelumab (Bavencio), durvalumab (Imfinzi), and
panitumumab (Vectibix).
[0477] Examples of an immunotherapy include, but are not limited
to, a PD-1 inhibitor or a PD-L1 inhibitor, a CTLA-4 inhibitor,
adoptive cell transfer therapy, therapeutic cancer vaccines,
oncolytic virus therapy, T-cell therapy, and immune checkpoint
inhibitors. In some embodiments, an immunotherapy may include a
chimeric antigen receptor (CAR) T-cell therapy. A CAR is designed
for a T-cell and is a chimera of a signaling domain of the T-cell
receptor (TcR) complex and an antigen-recognizing domain (e.g., a
single chain fragment (scFv) of an antibody) (Enblad et al., Human
Gene Therapy. 2015; 26(8):498-505). In some embodiments, an antigen
binding receptor is a chimeric antigen receptor (CAR). A T cell
that expressed a CAR is referred to as a "CAR T cell." A CAR T cell
receptor, in some embodiments, comprises a signaling domain of the
T-cell receptor (TcR) complex and an antigen-recognizing domain
(e.g., a single chain fragment (scFv) of an antibody) (Enblad et
al., Human Gene Therapy. 2015; 26(8):498-505).
[0478] Examples of radiation therapy include, but are not limited
to, ionizing radiation, gamma-radiation, neutron beam radiotherapy,
electron beam radiotherapy, proton therapy, brachytherapy, systemic
radioactive isotopes, and radiosensitizers.
[0479] Examples of a surgical therapy include, but are not limited
to, a curative surgery (e.g., tumor removal surgery), a preventive
surgery, a laparoscopic surgery, and a laser surgery.
[0480] Examples of the chemotherapeutic agents include, but are not
limited to, Carboplatin or Cisplatin, Docetaxel, Gemcitabine,
Nab-Paclitaxel, Paclitaxel, Pemetrexed, and Vinorelbine.
[0481] Additional examples of chemotherapy include, but are not
limited to, Platinating agents, such as Carboplatin, Oxaliplatin,
Cisplatin, Nedaplatin, Satraplatin, Lobaplatin, Triplatin,
Tetranitrate, Picoplatin, Prolindac, Aroplatin and other
derivatives; Topoisomerase I inhibitors, such as Camptothecin,
Topotecan, irinotecan/SN38, rubitecan, Belotecan, and other
derivatives; Topoisomerase II inhibitors, such as Etoposide
(VP-16), Daunorubicin, a doxorubicin agent (e.g., doxorubicin,
doxorubicin hydrochloride, doxorubicin analogs, or doxorubicin and
salts or analogs thereof in liposomes), Mitoxantrone, Aclarubicin,
Epirubicin, Idarubicin, Amrubicin, Amsacrine, Pirarubicin,
Valrubicin, Zorubicin, Teniposide and other derivatives;
Antimetabolites, such as Folic family (Methotrexate, Pemetrexed,
Raltitrexed, Aminopterin, and relatives or derivatives thereof);
Purine antagonists (Thioguanine, Fludarabine, Cladribine,
6-Mercaptopurine, Pentostatin, clofarabine, and relatives or
derivatives thereof) and Pyrimidine antagonists (Cytarabine,
Floxuridine, Azacitidine, Tegafur, Carmofur, Capacitabine,
Gemcitabine, hydroxyurea, 5-Fluorouracil (5FU), and relatives or
derivatives thereof); Alkylating agents, such as Nitrogen mustards
(e.g., Cyclophosphamide, Melphalan, Chlorambucil, mechlorethamine,
Ifosfamide, mechlorethamine, Trofosfamide, Prednimustine,
Bendamustine, Uramustine, Estramustine, and relatives or
derivatives thereof); nitrosoureas (e.g., Carmustine, Lomustine,
Semustine, Fotemustine, Nimustine, Ranimustine, Streptozocin, and
relatives or derivatives thereof); Triazenes (e.g., Dacarbazine,
Altretamine, Temozolomide, and relatives or derivatives thereof);
Alkyl sulphonates (e.g., Busulfan, Mannosulfan, Treosulfan, and
relatives or derivatives thereof); Procarbazine; Mitobronitol, and
Aziridines (e.g., Carboquone, Triaziquone, ThioTEPA,
triethylenemalamine, and relatives or derivatives thereof);
Antibiotics, such as Hydroxyurea, Anthracyclines (e.g., doxorubicin
agent, daunorubicin, epirubicin and relatives or derivatives
thereof); Anthracenediones (e.g., Mitoxantrone and relatives or
derivatives thereof); Streptomyces family antibiotics (e.g.,
Bleomycin, Mitomycin C, Actinomycin, and Plicamycin); and
ultraviolet light.
EXAMPLES
[0482] In order that the invention described herein may be more
fully understood, the following examples are set forth. The
examples described in this application are offered to illustrate
the methods, compositions, and systems provided herein and are not
to be construed in any way as limiting their scope.
Example 1
Workflow for WES and RNA Sequencing
[0483] Provided below is an example of specimen collection from a
subject having or suspected of having cancer, DNA and/or RNA
extraction therefrom, DNA library preparation (cDNA in the case of
library preparation from RNA), and data processing.
Specimen Collection
[0484] Prior to collection of biological samples from a subject
having or suspected of having a cancer, sufficient quantities of
sterilized instruments, consumables, and reagents (e.g., digest
buffer) were verified.
[0485] For tumor tissue (bulk), 30 mg of tumor tissue was collected
from a subject and put into a 2 ml cryogenic tube with RNA-later,
the contents of which were then snap frozen. The specimens were
shipped on dry ice as needed.
[0486] For blood samples (which are considered "normal tissue" (or
non-cancerous)), 0.5-1 mL of whole blood was collected in an EDTA
vacutainer collection tube (plastic preferred) labeled with at
least the sample ID and date/time collected. The vacutainer tube
was then placed into a sealed biohazard bag with absorbent
materials. Whole blood in EDTA was frozen on dry ice as needed and
sent to a laboratory with other specimen(s) as needed. FIG. 1B
illustrates an embodiment of a process that includes the sample
collection process.
Creation of Single Cell Suspension for CYTOF and RNA-Seq (SCS,
Optional, Validation)
[0487] The following steps were used to create single-cells
suspensions (SCS) from tumor samples that were collected in 50 mL
of cold L-15 medium (1.times.). [0488] 1) Transfer the container
with tumor sample from an operating room on ice to a biological
safety hood for dissection, wherein it takes approximately 60-90
min from surgical resection to the bench. [0489] 2) Transfer the
tumor sample into a 100.times.15 mm petri dish containing fresh
L-15 medium. Using a curved scissor, dissect the tumor into
fragments of 1-2 mm.sup.3 on a sterile petri dish with L-15 to keep
the tissue moist. To 50 mL conical tube containing 25 mL of enzyme
cocktail add 0.5 gm of tumor tissue. [0490] 3) Place the tube on a
shaker at a speed of 85 rpm for 45 min at 37.degree. C. [0491] 4)
After 45 min, vigorously pipette the contents using a 10 mL
pipette. Incubate for another 45 min under the same conditions.
[0492] 5) After the incubation, filter the sample through a 70
.mu.m cell strainer into a new 50 mL conical tube. Using the back
of a 3 mL syringe, gently apply pressure on the cell strainer to
disaggregate any remaining tissue. [0493] 6) Add 25 mL of warm
(37.degree. C.) L-15 media containing 10% FBS through the cell
strainer, into the 50 mL conical tube. [0494] 7) Centrifuge at 300
g at room temperature for 5 min. Decant the supernatant. [0495] 8)
Add 10 mL of warm (37.degree. C.) 1.times. eBioscience multi
species RBC lysis buffer. Incubate in the dark for 5 min at room
temperature. [0496] 9) After the incubation add 40 mL of cold
1.times.PBS to the tube. Centrifuge at 300 g for 5 min at 4.degree.
C. Decant supernatant. Add 10 mL of cold DMEM with 10% FBS, and
resuspend the pellet gently. [0497] 10) Centrifuge at 300 g for 5
min at 4.degree. C. Decant the supernatant. Resuspend cells in 1 mL
of cold L-15 with 10% FBS. [0498] 11) Filter the sample through a
70 um cell strainer into a new 50 mL conical tube. [0499] 12) Count
cells using Trypan blue. Also assess viability using MoxiFlow (4
.mu.L cells+196 .mu.l MoxiFlow Viability Reagent, use 75 .mu.L to
test). [0500] 2,000,000 cells were aliquoted into a 15 mL conical
tube. Once the cells have been pelleted (more then 2*10.sup.6),
each lysate was resuspended in 500-750 .mu.l of RNAlater/RNA
Protect in a 1.5 ml microcentrifuge tube. The 1.5 ml tube was
placed into a 50 mL conical tube with tissue paper/paper towels on
top to secure the 1.5 mLtubes. The 50 ml tube can then be shipped
with the tumor specimen(s) on dry ice. DNA and RNA Extraction from
Bulk Biopsy
[0501] Extraction of normal DNA and RNA. DNA from biopsy specimens
was extracted using DSP DNA Midi Kit (Qiagen.RTM.) using an
automated process on the QIAsymphony
(www.qiagen.com/us/shop/automated-solutions/sample-preparation/qiasymphon-
y-spas-instruments/).
[0502] A minimum of 1000-2000 ng of total DNA mass in at least 100
volumes (e.g., 100-200 ng/ul in 100 minimum) for each DNA sample
was collected. Moreover, the extracted DNA solutions had 260/280
ratios of .about.1.8.
[0503] A minimum of 1000-6000 ng of total RNA mass was collected.
RNA Integrity Number (RIN) scores obtained via Agilent's
BioAnalyzer or Tape Station were of at least 7.
[0504] Extraction of tumor DNA and tumor RNA. DNA and RNA were
extracted from 30 mg of tissue using AllPrep DNA/RNA Mini Kit by
Qiagen.RTM. (using the manual process described by the
manufacturer).
DNA/RNA Extraction and CYTOF for SCSs
[0505] Extraction: RNeasy Micro Plus Kit by Qiagen.RTM. (manual
process) was used. A minimum of 2,000,000 cells were used for
extraction. Table 1 below shows that the RNA concentration, yield,
and quality drops substantially if RNA is extracted from a total of
less than 2 million cells. It was found that 2 million cells
provided at least 1.8 .mu.g of RNA, which is sufficient for good
quality RNAseq data (i.e., less noise and better correlation
between RNA expression within different isoforms of the same
protein coding RNA). It is recommended to have more than lug of RNA
for better quality.
TABLE-US-00001 TABLE 1 RNA quantity and quality as a function of
the number of cells from which RNA is extracted. Number RNA Total
Sample of Concentration Volume Yield ID Cells (ng/uL) (uL) (ng) RIN
BG002 2 million 70.2 26 1825 8.5 BG020 1 million 8.0 57.5 460 8.3
BG005 0.5 million 8.2 26 213 8.7 BG008 0.5 million 8.0 26 208
8.4
[0506] CyTOF: Resuspend cells that are not going to be used for
RNAseq (minimum 5 million), in cold cell staining buffer (CSB) and
place on ice in preparation for antibody labeling.
[0507] Library Preparation, RNA Sequencing, and WES
[0508] Illumina libraries were made and subjected to quality
control (e.g., using Tapestation D1000 High Sensitivity DNA screen
tape) to evaluate their integrity and peak size. The analysis
consumed up to 1 ng library in 2 .mu.L.
[0509] Whole Exome Sequencing (WES) on DNA samples (tumor tissue
and germline blood) was performed using Agilent Human All Exon V6
Capture (48.2 Mb) or Clinical Research Exome (54.6 Mb). WES
Illumina deep sequencing was performed with standard NextSeq
RNA-seq configuration, Paired-End 100 bp Reads with an estimated
coverage >100.times..
[0510] RNA Sequence on RNA samples (tumor tissue and SCS) was
performed using Ilumina TruSeq RNA Library Prep PCR enrichment of
captured DNA (Poly-A mRNA-seq), non-stranded (to compared data with
that of The Cancer Genome Atlas (TCGA)) paired-end 100 bp Reads
(75+75) with an estimated coverage >50 million paired-end
reads.
PolyA Enrichment
[0511] Different RNA enrichment methods provide various enrichment
of RNA transcripts. riboRNA depletion retains 10-50% of non-coding
transcripts (e.g., rRNA, miRNA, long non-coding RNA (LncRNA)) in
the library. So, the percentages of protein-coding reads strongly
vary depending on the method of RNA enrichment. In clinical
settings the focus was on expression of protein coding transcripts.
PolyA enrichment, compared to rRNA depletion, provided more stable
and controllable percent of protein coding transcripts (FIG.
2).
[0512] Further, because PolyA enrichment was used, and it was known
that protein-coding RNA was enriched, RNA sequencing was performed
on non-stranded RNA. FIG. 2B demonstrates that differences in RNA
expression levels of IL24, ICAM4, and GAPDH RNA seen when either
stranded or non-stranded RNA is used for sequencing.
FASTQ Files Processing, and RNA Expression Assessment
[0513] The raw data in the NextSeq BCL file format was converted to
the standard Illumina FASTQ format. As described herein, any type
of format that is suitable for further analysis can be used. In
this example, the FASTQ data was subjected to quality control using
standard quality control algorithms (e.g., FastQ Screen
(www.bioinformatics.babraham.ac.uk/projects/fastqc/), RSeQC
(rseqc.sourceforge.net/), and then processed to obtain expression
per gene in TPM with no or minimal batch effects across samples.
Data in the form of FASTQ files was delivered via a secure SFTP
server or Illumina BaseSpace.
Quality Control Steps Assuring Quality of FASTQ Files
[0514] The following are steps involved in assuring quality control
of the data in FASTQ files: [0515] (1) Remove low-quality reads.
This can be performed by using any suitable software or tool to
evaluate and/or remove reads that are deemed of low-quality such as
based on positional information. In some embodiments, low-quality
reads can be removed by using FILTERBYTILE (e.g.,
www.filterbytile.sh (from BBmap)). In some embodiments, low-quality
reads (e.g., bad tiles) are removed from sequence files (e.g.,
FASTQ files). In some embodiments, the data analysis pipeline may
be stopped if the quality of the reads is too low for further
analysis with sufficient confidence. For example, in some
embodiments, if bad tiles represent greater than a threshold
percentage (e.g., 50%) of the sample, the analysis pipeline is
terminated. [0516] (2) Assure quality control based on various
parameters. This can be performed by using any suitable software or
tool to evaluate the confidence of the quality control. In some
embodiments, quality control can be assured by using FastQC (e.g.,
www.bioinformatics.babraham.ac.uk/projects/fastqc/). In some
embodiments, quality control can be assured by reviewing read
counts as a measure of the complexity of the library. In some
embodiments, quality control can be assured by reviewing per base
Phred quality score as a measure of sequencing quality of the
platform. In some embodiments, quality control can be assured by
reviewing per tile quality score. In some embodiments, quality
control can be assured by reviewing per sequence GC content to
identify contamination. In some embodiments, quality control can be
assured by reviewing per base sequencing content to identify
adapter and other contamination. In some embodiments, quality
control can be assured by reviewing sequence duplication levels as
a measure of a quality of RNA/DNA selection and PCR. In some
embodiments, quality control can be assured by reviewing adapter
content.
[0517] In some embodiments, the data analysis pipeline may be
stopped if the quality control cannot be assured for further
analysis with sufficient confidence. For example, in some
embodiments, if read counts represent greater than a threshold
value (e.g., >20 min) or Phred score represent greater than a
threshold percentage (e.g., >50% green zone), the analysis
pipeline is terminated. [0518] (3) Determine cross-species
contamination. This can be performed by using any suitable software
or tool to evaluate the cross-species contamination. In some
embodiments, cross-species contamination can be determined by using
Fastq Screen (e.g.,
www.bioinformatics.babraham.ac.uk/projects/fastq_screen/_build/html/index-
.html). In some embodiments, cross-species contamination can
comprise contamination from various species such as mouse,
zebrafish, drosophila, C. elegans, Saccharomyces, Arabidopsis,
microbiome, adapters, vectors, and phiX. In some embodiments, the
data analysis pipeline may be stopped if the cross-species
contamination is too severe for further analysis with sufficient
confidence. For example, in some embodiments, if contamination
represents greater than a threshold percentage (e.g., >20%), the
analysis pipeline is terminated. [0519] (4) Assure quality of the
data based on various parameters. This can be performed by using
any suitable software or tool to evaluate the quality. In some
embodiments, quality control can be assured by using Mosdepth
(e.g., github.com/brentp/mosdepth). In some embodiments, quality
can be assured by determining per chromosome coverage distribution
(as a sex prediction algorithm). In some embodiments, quality can
be assured by determining
[0520] specific regions coverage distribution (e.g., Collaborative
Consensus Coding Sequence (CCDS), exons, etc.). In some
embodiments, the data analysis pipeline may be stopped if the
quality of the data is too low for further analysis with sufficient
confidence. For example, in some embodiments, if the confirmation
of coverage of clinically important genome regions is failed, the
analysis pipeline is terminated. [0521] (5) Assure the presence and
the quality of certain characteristics of the data. This can be
performed by using any suitable software or tool. In some
embodiments, the presence or quality of certain characteristics of
the data can be assured by using Picard
(broadinstitute.github.io/picard/). In some embodiments, certain
characteristics can be the number of the percentage of the
duplicities. In some embodiments, certain characteristics can be
mapped regions. In some embodiments, certain characteristics can be
properly paired regions. [0522] (6) Assure quality control based on
various parameters. This can be performed by using any suitable
software or tool to evaluate the confidence of the quality control.
In some embodiments, quality control can be assured by using RseQC
(e.g., rseqc.sourceforge.net/). In some embodiments, quality
control can be assured by reviewing strandedness analysis to prove
stranded or non-stranded RNA-seq protocol. In some embodiments,
quality control can be assured by reviewing gene body coverage to
detect coverage bias due to the extraction protocol (polyA/total
RNA-seq) and RIN. In some embodiments, quality control can be
assured by reviewing read distribution of exons, introns,
transcription end sites (TES), and transcription start sites (TSS).
In some embodiments, the data analysis pipeline may be stopped if
the quality control cannot be assured for further analysis with
sufficient confidence. For example, in some embodiments, if
duplicates represent greater than a threshold percentage (e.g.,
<60%) for RNA or less than a threshold percentage (e.g.,
<20%) for adapter contamination, the analysis pipeline is
terminated. [0523] (7) Check cross-individual contamination by
determining concordance of a pair of samples (e.g., tumor/normal
from the same patient). This can be performed by using any suitable
software or tool. In some embodiments, cross-individual
contamination can be determined by using Conpair (e.g.,
github.com/nygenome/Conpair). In some embodiments, the data
analysis pipeline may be stopped if the cross-individual
contamination is too severe for further analysis with sufficient
confidence. For example, in some embodiments, if normal DNA does
not match tumor DNA, the analysis pipeline is terminated. In some
embodiments, if large cross-individual contamination is detected,
the analysis pipeline is terminated. [0524] (8) Run a tumor type
classifier. This can be performed by using any suitable software or
tools. In some embodiments, a gene expression-based classifier can
be used. For example, a gene expression-based classifier trained on
RNAseq of previously sequenced tumors of different tissue types can
be used to classify tumor type. Examples of such classifiers are
described herein and in U.S. Provisional Patent Application Ser.
No. 62/943,976, titled "Machine Learning Techniques for Gene
Expression Analysis," filed on Dec. 5, 2019, which is incorporated
by reference herein in its entirety. In some embodiments, this
allows the prediction of the tumor type from RNA-seq data on the
basis of the gene expression data. In some embodiments, the data
analysis pipeline may be stopped if the tumor type is a mismatch
for further analysis with sufficient confidence. For example, in
some embodiments, if the asserted tumor type from clinicians does
not match the determined tumor type, the analysis pipeline is
terminated. [0525] (9) Predict library type. This can be performed
by using any suitable software or tools. In some embodiments,
RNA-seq type classifier can be used. In some embodiments, the
RNA-seq type classifier can be a gene expression-based classifier
on XGboost (e.g., xgboost.readthedocs.io/en/latest/) trained model.
In some embodiments, the prediction of library type is based on
expression of specific genes from the RNA-seq data. In some
embodiments, the data analysis pipeline may be stopped if the
library type is a mismatch for further analysis with sufficient
confidence. For example, in some embodiments, if the asserted
library type does not match the determined library type (e.g.,
total RNA-seq, or polyA-RNA-seq), the analysis pipeline is
terminated. [0526] (10) Check concordance of HLA allele. This can
be performed by using any suitable software or tools. In some
embodiments, MHC allele composition can be determined. In some
embodiments, the data analysis pipeline may be stopped if the HLA
allele is a mismatch for further analysis with sufficient
confidence. For example, in some embodiments, if the HLA allele
from a sample does not confirm the source of the samples, the
analysis pipeline is terminated. [0527] (11) Perform distribution
analysis of expression for different transcripts types. This can be
performed by using any suitable software or tools. In some
embodiments, the transcripts type can be Mt rRNA, Mt tRNA, lincRNA,
miRNA, misc RNA, protein coding, rRNA, snRNA, snoRNA, ribozyme, Ig,
processed, NMD, or retained intron. In some embodiments, one or
more transcripts type can be determined. In some embodiments, the
data analysis pipeline may be stopped if the transcript type is not
suitable for further analysis with sufficient confidence. For
example, in some embodiments, if the transcripts represent a
greater threshold percentage (e.g., >70% transcripts are
protein-coding transcripts), the analysis pipeline is
terminated.
Alignment
[0528] Alignment can be performed by using any suitable software or
tools. For example, a program for quantifying transcripts, for
example from bulk and single-cell RNA-Seq data, using
high-throughput sequencing reads (e.g., Kalliso available from
Github, www.github.com, for example as described in Nicolas L Bray,
Harold Pimentel, Pall Melsted and Lior Pachter, Near-optimal
probabilistic RNA-seq quantification, Nature Biotechnology 34,
525-527 (2016), doi:10.1038/nbt.3519) was performed with input
FASTQ files. Kalisto indexing was performed based on:
[0529] a. GRCh38 genome assembly (no alt analysis) with overlapping
genes from the PAR locus removed.
[0530] b. Gene annotation based on GENCODE V23 comprehensive
annotation (regions ALL) (www.gencodegenes.org)
[0531] Files with transcript expression in TPM (Transcripts Per
Kilobase Million) were obtained thereafter.
Removing of Non-Coding Transcripts for the Data and Other
Biases
[0532] The Transcripts Per Million (TPM) expression allows
presentation of gene expression in the format of concentration (in
1 million of transcripts). This allows comparison of samples with
different coverage and RNA sequencing depth.
[0533] TPM uses correction of the read counts by the length of each
gene in bases, so it can create a great bias in the samples with
uneven distribution of non-coding transcripts after TPM calculation
because some non-coding transcripts (miRNA, snRNA, snoRNA) have
very small transcript length. FIG. 3 shows the biases that are
created upon TPM calculation.
[0534] To remove batches based on uneven distribution of non-coding
transcripts in RNA library, non-coding transcripts were removed
from the data before further RNA expression quantification. [0535]
Excluded types included: [0536] {pseudogene,
polymorphic_pseudogene, processed_pseudogene,
transcribed_processed_pseudogene, unitary_pseudogene,
unprocessed_pseudogene, transcribed_unitary_pseudogene,
IG_C_pseudogene, IG_J_pseudogene, IG_V_pseudogene,
transcribed_unprocessed_pseudogene,
translated_unprocessed_pseudogene [0537] TR_J_pseudogene,
TR_V_pseudogene [0538] snRNA, snoRNA, miRNA [0539] Ribozyme, rRNA,
Mt_tRNA, Mt_rRNA, scaRNA [0540] retained_intron, sense_intronic,
sense_overlapping [0541] nonsense_mediated_decay, non_stop_decay
[0542] Antisense, lincRNA, macro_lncRNA [0543]
processed_transcript, 3prime_overlapping_ncrna [0544] sRNA,
misc_RNA, vaultRNA, TEC} [0545] Retained types: [0546]
{protein_coding, [0547] Ig (IG_C_gene, IG_D_gene, IG_J_gene,
IG_V_gene) [0548] TCR (TR_C_gene, TR_D_gene, TR_J_gene,
TR_V_gene)}
[0549] In addition to removing non-coding transcripts, genes that
were found to have the highest variance between PolyA RNA
sequencing and total RNA sequencing were also removed. Such genes
included (1) histone-encoding genes, and (2) mitochondria-related
genes, having very long or very short PolyA tails, which result in
uneven enrichment of the transcripts.
[0550] FIG. 4A shows the variation in the length of PolyA tails for
different histone-encoding genes. FIG. 4B shows a comparison of
expression of histone coding and mitochondrial genes within samples
in which RNA was enriched by either polyA enrichment or by ribo-RNA
depletion (total RNA). Genes that are excluded are described in the
present disclosure (e.g., transcripts from protein non-coding
regions, histone-encoding genes, and mitochondria-related
genes).
Gene Aggregation and TPM Normalization
[0551] Expression per gene was calculated as a sum of the
expression of the transcripts for the gene. Gene expression data
was normalized to the total number of transcripts (in the million).
This procedure allows correction for major batch effects associated
with library preparation, uneven RNA transcript distribution
between samples, and correction for RNA enrichment method (FIG.
5).
Example 2
DNA and RNA Extraction from Peripheral Blood Mononuclear Cells
(PBMC) or Cell Suspensions
[0552] To prepare nucleic acid materials for downstream sequencing
analysis, DNA and/or RNA was extracted from a single PBMC cell
pellet or suitable cell suspensions. In brief, AllPrep DNA/RNA
assay kits (Qiagen.RTM.) were used for purifying genomic DNA and
total RNA simultaneously from a single biological sample.
Biological samples were first lysed and homogenized in a highly
denaturing guanidine-isothiocyanate-containing buffer, which
immediately inactivated DNases and RNases to ensure isolation of
intact DNA and RNA. The lysate was then passed through an AllPrep
DNA spin column. This column, in combination with the high-salt
buffer, allowed selective and efficient binding of genomic DNA. The
column was washed and the DNA was then eluted. Alternatively, the
lysate that passed through the AllPrep DNA spin column went through
an RNeasy spin column to selectively isolate RNA.
[0553] In some circumstances, for further improving the quality of
the starting RNA, ethanol was added to the flow-through from the
AllPrep DNA spin column to provide appropriate binding conditions
for RNA. The sample was then applied to an RNeasy spin column,
where total RNA was bound to the membrane and contaminants were
washed away. High-quality RNA was then eluted in water. Some of the
steps and/or the entire procedure can be managed and conducted by
lab personnel. In the event that quality control related issues
arise, lab personnel will notify the provider (e.g., healthcare
provider) of the cells or tissue (e.g., PMBCs or cell
suspensions).
Preparation of Reagents
[0554] Reagents for the extraction of DNA and/or RNA from a sample
were prepared according to the manufacturer's instruction,
including AllPrep DNA/RNA Mini handbook and AllPrep DNA/RNA Micro
handbook, the contents of which are incorporated by reference
herein. Some of the processes may be customized based on the
requirements of the nucleic acids of a given sequencing platform.
In general, B-mercaptoethanol (.beta.-ME) was added to Buffer RLT
Plus before use. 10 .mu.L .beta.-ME per 1 mL Buffer RLT Plus was
added. The lab personnel who conducted the preparation of the
reagents wore appropriate Personal Protective Equipment (PPE) and
the reagents were dispensed in a fume hood. Buffer RLT Plus was
generally stable at room temperature for 1 month after addition of
.beta.-ME. The date of the addition of .beta.-ME and the 1-month
expiration date were marked on the bottle.
[0555] Buffer RPE, Buffer AW1, and Buffer AW2 were each supplied as
a concentrate by the manufacturer. Before using for the first time,
appropriate volume of 100% ethanol was added, as indicated on the
bottle, to obtain a working solution. The solutions were
appropriately labeled per the Solutions and Reagent Labeling
Standard Operating Procedure (SOP) as described herein. Buffer RLT
Plus may form a precipitate during storage. If necessary, the
precipitates formed in Buffer RLT Plus were dissolved by warming in
a 37.degree. C. water bath until precipitates were dissolved. The
Buffer RLT Plus without precipitates was then place at room
temperature. Prolonged incubation in the water bath was not
recommended. It was noted that Buffer RLT Plus, Buffer RW1, and
Buffer AW1 contained a guanidine salt.
Preparation of Material for Extraction
[0556] Before the start of extraction, tubes and columns were
labeled with a specimen ID for each sample being processed. Frozen
cell pellets were thawed slightly, so they were dislodged by
flicking the tube. Cell lysates were incubated at 37.degree. C. in
a water bath until completely thawed. Prolonged incubation was
discouraged, due to its potential compromise on RNA integrity. For
pelleted cells, the cell pellet was loosened thoroughly by flicking
the tube. This was an important step for properly preparing the
nucleic acid materials because incomplete loosening of the cell
pellet may lead to inefficient lysis and reduced nucleic acid
yields. Appropriate volume of Buffer RLT Plus was added, followed
by vortexing or pipetting to mix. In general, for
<5.times.10.sup.5 cells, 350 .mu.L Buffer RLT Plus was added.
For 5.times.10.sup.5-1.times.10.sup.7cells, 600 .mu.L Buffer RLT
Plus was added.
[0557] The lysate was homogenized by using QIAshredder. In brief,
the lysate was pipetted directly into a QIAshredder spin column
placed in a 2 mL collection tube. The lysate was then pipetted and
centrifuged for 2 minutes at maximum speed (18,565.times.g). The
homogenized lysate was transferred to an AllPrep DNA spin column
placed in a 2 mL collection tube. The lid was closed gently, and
the spin column was centrifuged for 30 s at .gtoreq.8000.times.g.
After centrifugation, any remaining liquid on the column membrane
was checked and removed. If necessary, the centrifugation step was
repeated until all liquid was passed through the membrane. The
AllPrep DNA spin column was placed in a new 2 ml collection tube
and was stored at room temperature or at 4.degree. C. (not in the
freezer) for later DNA purification. The flow-through for RNA
purification was used.
Total RNA Purification
[0558] For purifying RNA, 600 .mu.L of 70% ethanol was added to the
flow through from the previous step, and was mixed well by
pipetting. Up to 700 .mu.L of the sample was immediately
transferred, including any precipitate that was formed which might
be visible, to an RNeasy spin column placed in a 2 ml collection
tube. The lid of the collection tube was closed gently and was
centrifuged for 15 s at .gtoreq.8000.times.g. The flow-through was
discarded. In the event that the sample volume exceeded 700 .mu.L,
successive aliquots were centrifuged in the same RNeasy spin
column. The flow-through was discarded after each centrifugation.
The collection tube was re-used in the following step.
[0559] 700 .mu.L Buffer RW1 was added to the RNeasy spin column.
The lid was closed gently, and was centrifuged for 15 s at
.gtoreq.8000.times.g to wash the spin column membrane. The
flow-through was discarded. The collection tube was re-used in the
following step. 500 .mu.L Buffer RPE was added to the RNeasy spin
column. The lid was closed gently, and was centrifuged for 15 s at
.gtoreq.8000.times.g to wash the spin column membrane. The
flow-through was discarded.
[0560] In general, if <5.times.10.sup.5 cells were processed,
500 .mu.L of 80% ethanol was added to the RNeasy MinElute spin
column. The lid was closed gently, and the spin column was
centrifuged for 2 min at .gtoreq.8000.times.g to wash the spin
column membrane. The collection tube with the flow-through was
discarded. The RNeasy MinElute spin column was placed in a new 2 mL
collection tube. The lid of the spin column was opened, and the
spin column was centrifuged at full speed (18,565.times.g) for 5
minutes. The collection tube with the flow-through was discarded.
The RNeasy MinElute spin column was placed in a new 1.5 mL
collection tube. 14 .mu.L RNase-free water was directly added to
the center of the spin column membrane. The lid was closed gently,
and was centrifuged for 1 min at full speed (18,565.times.g) to
elute the RNA. The spin column was discarded, and the 1.5 mL tube
was stored with extracted RNA at -80.degree. C. until further
processing.
[0561] If >5.times.10.sup.5 cells were processed, 500 .mu.L
Buffer RPE was added to the RNeasy spin column. The lid was closed
gently, and was centrifuged for 2 min at .gtoreq.8000.times.g to
wash the spin column membrane. The RNeasy spin column was placed in
a new 2 mL collection tube. The old collection tube with the
flow-through was discarded. The collection tube was then
centrifuged at full speed (18,565.times.g) for 1 min. The RNeasy
spin column was placed in a new 1.5 mL collection tube. 30-500
.mu.L of RNase-free water was added directly to the spin column
membrane. The lid was closed gently, and was centrifuge for 1 min
at .gtoreq.8000.times.g to elute the RNA.
Genomic DNA Purification
[0562] 500 .mu.L Buffer AW1 was added to the AllPrep DNA spin
column (previously placed in a new 2 ml collection tube and stored
at room temperature or at 4.degree. C.). The lid was closed gently,
and the spin column was centrifuged for 15 s at
.gtoreq.8000.times.g. The flow-through was discarded. The spin
column was re-used in the following step. 500 .mu.L Buffer AW2 was
added to the AllPrep DNA spin column. The lid was closed gently,
and was centrifuged for 2 min at full speed (18,565.times.g) to
wash the spin column membrane. After centrifugation, the AllPrep
DNA spin column was carefully removed from the collection tube. If
the column contacted the flow-through, the collection tube was
emptied and the spin column was centrifuged again for 1 min at full
speed.
[0563] If <5.times.10.sup.5 cells were processed, the AllPrep
DNA spin column was placed in a new 1.5 mL collection tube. 50
.mu.L Buffer EB was added (preheated to 70.degree. C.) directly to
the spin column membrane and the lid was closed and was incubated
at room temperature for 2 min. The spin column was centrifuged for
1 min at .gtoreq.8000.times.g to elute the DNA. Repeat addition of
Buffer EB was conducted and centrifugation to elute further DNA. A
new 1.5 mL collection tube was used to collect the second DNA
eluate, and then was combined with the first eluate. The spin
column was discarded, and was stored in the 1.5 mL tube with
extracted DNA at 4.degree. C. until further processing.
[0564] If >5.times.10.sup.5 cells were processed, the AllPrep
DNA spin column was placed in a new 1.5 mL collection tube. 50
.mu.L Buffer EB was added directly to the spin column membrane and
the lid was closed. The spin column was incubated at room
temperature for 1 min was centrifuged for 1 min at
.gtoreq.8000.times.g to elute the DNA. Repeat addition of Buffer EB
was conducted and centrifugation to elute further DNA. A new 1.5 mL
collection tube was used to collect the second DNA eluate, and then
was combined with the first eluate. The spin column was discarded,
and was stored in the 1.5 mL tube with extracted DNA at 4.degree.
C. until further processing.
[0565] Troubleshooting processes included, but were not limited to
the following in Table 3.
TABLE-US-00002 TABLE 3 Troubleshooting Steps for AllPrep DNA/RNA
Procedure Incident Troubleshooting steps Clogged AllPrep DNA and
Clogged column can be caused by the following: RNeasy spin column
a) Inefficient disruption and/or homogenization 1. Increase g-force
and centrifugation time if necessary 2. In subsequent preparations,
reduce the amount of starting material and/or increase the
homogenization time. b) Too much starting material 1. Reduce the
amount of starting material. It is essential to use the correct
amount. c) Centrifugation temperature too low 1. The centrifugation
temperature should be 20-25.degree. C. Some centrifuges may cool
Low nucleic acid yield Low nucleic acid yield can be caused by the
following: a) Inefficient disruption and/or homogenization 1. In
subsequent preparations, reduce the amount of starting material
and/or increase the homogenization time. b) Too much starting
material 1. Reduce the amount of starting material. It is essential
to use the correct amount. c) RNA still bound RNeasy spin column
membrane 1. Repeat RNA elution, but incubate the RNeasy spin column
on the benchtop for 10 min with RNase-free water before
centrifuging d) DNA still bound to AllPrep DNA spin column membrane
1. Repeat DNA elution, but incubate the AllPrep DNA spin column on
the benchtop for 10 minutes with Buffer EB before centrifuging. e)
Ethanol carryover 1. During the second wash with Buffer RPE, be
sure to centrifuge at .gtoreq.8000 .times. g for 2 min at
20-25.degree. C. to dry the RNeasy spin column membrane. 2. Perform
the optional centrifugation to dry the RNeasy spin column membrane
if any flow-through is present on the outside of the column. DNA
contaminated with RNA This can be caused by the following: a)
Lysate applied to the AllPrep DNA spin column contains ethanol 1.
Add ethanol to the lysate after passing the lysate through the
AllPrep DNA spin column. b) Sample is affecting pH of homogenate 1.
The final homogenate should have a pH of 7. Make sure that the
sample is not highly acidic or basic. Contamination of RNA with
This can be caused by the following: DNA affects downstream a) Cell
number too high applications 1. For some cell types, the efficiency
of DNA binding to the AllPrep DNA spin column may be reduced when
processing very high cell numbers. b) Tissue has high DNA content
1. For certain tissues with extremely high DNA content (e.g.,
thymus), some DNA will pass through the AllPrep DNA spin column.
Try using smaller samples. Alternatively, perform DNase digestion
on the RNeasy spin column membrane, or perform DNase digestion of
the eluted RNA followed by RNA cleanup. Low A.sub.260/A.sub.280
value in RNA Use 10 mM Tris HCl, pH 7.5, not RNase-free water, to
dilute the eluate sample before measuring purity. RNA degraded RNA
degradation can be caused by the following: a) Inappropriate
handling of starting material 1. Ensure that tissue samples are
properly stabilized and stored in RNAlater RNA Stabilization
Reagent. 2. Ensure that frozen tissue was flash-frozen immediately
in liquid nitrogen and properly stored at -70.degree. C. Perform
the AllPrep DNA/RNA procedure quickly, especially the first few
steps. b) RNase contamination 1. Although all AllPrep buffers have
been tested and are guaranteed RNase-free, RNases can be introduced
during use. Be certain not to introduce any RNases during the
AllPrep DNA/RNA procedure or later handling. DNA fragmented This
can happen when homogenization is too vigorous. The length of
purified DNA depends strongly on the homogenization conditions. If
longer DNA fragments are required, keep the homogenization time to
a minimum or use a gentler homogenization method if possible.
Nucleic acid concentration too The elution volume may be too high.
Elute nucleic acids in a low smaller volume. Do not use less than
50 .mu.L Buffer EB for the AllPrep DNA spin column, or less than 1
.times. 30 .mu.L of water for the RNeasy spin column. Although
eluting in smaller volumes results in increased nucleic acid
concentrations, yields may be reduced. Nucleic acids do not perform
a) Salt carryover during elution well in downstream 1. Ensure that
buffers are at 20-30.degree. C. experiments 2. Ensure that the
correct buffer is used for each step of the procedure. 3. When
reusing collection tubes between washing steps, remove residual
flow-through from the rim by blotting on clean paper towels. b)
Ethanol carryover 1. During the second wash with Buffer RPE, be
sure to centrifuge at .gtoreq.8000 .times. g for 2 min at
20-25.degree. C. to dry the RNeasy spin column membranes. After
centrifugation, carefully remove the column from the collection
tube so that the column does not contact the flow-through.
Otherwise, carryover of ethanol will occur. 2. Perform the optional
centrifugation to dry the RNeasy spin column membrane if any
flow-through is present on the outside of the column.
Example 3
The Constructions of DNA Libraries for Sequencing
[0566] DNA libraries were prepared before performing the downstream
sequencing. In brief, library Construction (LC) consisted of
shearing extracted genomic DNA to a pre-determined size (e.g., 200
base pairs), and then prepared the libraries for Hybrid Capture.
Fragmented DNA was repaired, and unique molecular barcodes were
added to each DNA sample, so that each DNA sample could be
identified during sequencing. DNA samples were purified before
amplifying the barcoded libraries with Polymerase Chain Reaction
(PCR). The DNA samples were then purified again before the amount
and quality of each library was assessed using Quality Control (QC)
steps described according to manufacturer instructions.
[0567] In general, library Construction consisted of four main
steps. First, genomic DNA was sheared to about 200 base pairs using
the SureSelect XT HS Enzymatic Fragmentation Kit. The shearing
resulted in DNA fragments that needed to undergo blunt end repair.
The second step was the repairing and dA-tailing of the DNA ends.
This step added an "A" base to the 3' end of a blunt phosphorylated
DNA fragment. This treatment created compatible overhangs for the
next step of DNA sample preparation. In the third step, specific
molecular-barcoded adaptors were ligated to each sample using the
"A" base overhang created in the last step. Adapters were
platform-specific sequences for fragment recognition by the
sequencer: for example, the P5 and P7 sequences enabled library
fragments to bind to the flow cells of Illumina platforms. The
molecular barcode was unique to each sample being run and allowed
multiple samples to be subsequently mixed together, with the
barcode used to identify each sample at sequencing. The samples
were then purified using AMPure XP beads. In the final step, the
adaptor-ligated libraries were amplified with PCR, and then
purified a second time using AMPure XP beads. Some or all of the
procedures were managed and conducted by lab personnel. In the
event that quality control related issues arise, lab personnel will
notify the provider (e.g., healthcare provider) of the biopsy
sample or the extracted DNA.
Normalization of Samples for Library Construction
[0568] Samples were normalized to 10-200 ng in 7 .mu.L, using low
TE. The maximum amount of DNA available was used for each sample,
within the range provided. The lab personnel then navigated to the
normalization spreadsheet, which was located in the Clinical Lab
Documents folder in the shared Google Drive. The tab labeled "LC
Normalization" was selected. The Sample ID was entered into column
A. The measured concentration was entered into column B. The
spreadsheet automatically calculated the volumes of sample and low
TE required for normalization in columns G and H. If the
concentration of a sample was on the lower side, the spreadsheet
calculated a volume of sample >7 .mu.L and a volume of low TE
<0 .mu.L. If this occurred, only 7 .mu.L of sample was used and
was not diluted. The volumes calculated in the spreadsheet were
used for normalizing the appropriate volumes into a 96 well
semi-skirted PCR plate.
Enzymatic DNA Shearing
[0569] In some embodiments, DNA is fragmented using an endonuclease
(e.g., using an enzymatic fragmentation kit from SureSelect). In
some embodiments, a SureSelect Fragmentation Buffer and Enzyme were
thawed on ice. Fragmentation Buffer was vortexed and spun down
before use. A 3 .mu.L Fragmentation master mix for each sample was
prepared using 2 .mu.L of 5.times.SureSelect Fragmentation Buffer
mixed with 1 .mu.L SureSelect Fragmentation Enzyme. In some
embodiments, larger volumes can be prepared for multiple reactions
(e.g., 18 .mu.L of 5.times.SureSelect Fragmentation Buffer mixed
with 9 .mu.L SureSelect Fragmentation Enzyme for 8 reactions
including excess).
[0570] 3 .mu.L Fragmentation master mix was added to each sample
well and was mixed by pipetting up and down 20 times. The plate was
immediately placed on the thermal cycler on the Enzymatic
Fragmentation program (step 1: 37.degree. C. for 15 minutes; step
2: 65.degree. C. for 5 minutes and step 3: 4.degree. C. on
hold).
Repair and dA-Tail the Fragmented DNA Ends
[0571] In some embodiments, fragmented DNA is repaired and
dA-tailed, for example using a kit from SureSelect. In some
embodiments, reagents were first thawed on ice (e.g., from
-20.degree. C. storage) and Agencourt AMPure XP beads were
equilibrated to room temperature for at least 30 minutes. End
Repair A-Tailing Buffer, Ligation Buffer, End Repair A-Tailing
Enzyme Mix, T4 DNA Ligase, and Adaptor Oligo Mix (all from
SureSelect XT HS Library Preparation Kit for ILM) were mixed by
vortexing.
[0572] In some embodiments, a ligation master mix was prepared. The
thawed vial of
[0573] Ligation Buffer was vortexed for 15 seconds at high speed to
ensure homogeneity. The Ligation Buffer used in this step was
viscous and was mixed thoroughly by vortexing at high speed for 15
seconds before removing an aliquot for use. When combined with
other reagents, the Ligation Buffer was mixed well by pipetting up
and down 15-20 times using a pipette set to at least 80% of the
mixture volume or by vortexing at high speed for 10-20 seconds. A
flat-top vortex mixer was used when vortexing strip tubes or plates
throughout the protocol. When reagents were mixed by vortexing, the
occurrence of adequate mixing was visually verified.
[0574] In some embodiments, an appropriate volume of Ligation
master mix was prepared by combining reagents as follows: a 25
.mu.L reaction volume for 1 reaction containing 23 .mu.L of
Ligation Buffer and 2 .mu.L of T4 DNA Ligase, a 225 .mu.L reaction
volume for 8 reactions (including excess) containing 207 .mu.L of
Ligation Buffer and 18 .mu.L of T4 DNA Ligase, a 625 .mu.L reaction
volume for 24 reactions (including excess) containing 575 .mu.L of
Ligation Buffer and 50 .mu.L of T4 DNA Ligase.
[0575] The Ligation Buffer was slowly pipetted into a 1.5 mL
Eppendorf tube, ensuring that the full volume was dispensed. The T4
DNA Ligase was slowly added, rinsing the enzyme tip with buffer
solution after addition and was mix well by slowly pipetting up and
down 15-20 times or sealed the tube and vortexed at high speed for
10-20 seconds. The liquid was spun briefly to collect the liquid,
which was kept at room temperature for a minimum of 30 minutes, but
not more than 45 minutes before use.
[0576] The thawed vial of End Repair-A Tailing Buffer was thawed
for 15 seconds at high speed to ensure homogeneity. The solution
was visually inspected. If any solids were observed, vortexing was
continued until all solids were dissolved. The appropriate volume
of End Repair/dA-Tailing master mix was prepared by combining the
following reagents: a 20 .mu.L reaction volume for 1 reaction
containing 16 .mu.L of End Repair A-Tailing Buffer and 4 .mu.L of
End Repair A-Tailing Enzyme Mix, a 180 .mu.L reaction volume for 8
reactions (including excess) containing 144 .mu.L of End Repair
A-Tailing Buffer and 36 .mu.L of End Repair A-Tailing Enzyme Mix, a
500 .mu.L reaction volume for 24 reactions (including excess)
containing 400 .mu.L of End Repair A-Tailing Buffer and 100 .mu.L
of End Repair A-Tailing Enzyme Mix.
[0577] The End Repair-A Tailing Buffer was slowly pipetted into a
1.5 mL Eppendorf tube, ensuring that the full volume was dispensed.
The End Repair-A Tailing Enzyme Mix was slowly added, rinsing the
enzyme tip with buffer solution after addition and was mixed well
by pipetting up and down 15-20 times or sealed the tube and
vortexed at high speed for 5-10 seconds. The liquid was spun
briefly to collect and was kept on ice. 20 .mu.L of the End
Repair/dA-Tailing master mix was added to each sample well
containing approximately 50 of fragmented DNA and was mixed by
pipetting up and down 15-20 times using a pipette set to 60 .mu.L
or capped the wells and vortexed at high speed for 5-10 seconds.
The samples were briefly spun and then the plate or strip tube was
immediately placed in the Thermal cycler and started the End
Repair/dA-Tailing program (step 1: 20.degree. C. for 15 minutes;
step 2: 72.degree. C. for 15 minutes and step 3: 4.degree. C. on
hold).
Ligate the Molecular-Barcoded Adaptor
[0578] Once the thermal cycler reached the 4.degree. C. Hold step,
the samples were transferred to ice while setting up this step. To
each end-repaired/dA-tailed DNA sample (approximately 70 .mu.L), 25
.mu.L of the Ligation master mix that was prepared previously was
added, kept at room temperature, and was mixed by pipetting up and
down at least 10 times using a pipette set to 85 .mu.L or capped
the wells and vortexed at high speed for 5-10 seconds. The samples
were briefly spun. 5 .mu.L of Adaptor Oligo Mix (white capped tube)
was added to each sample and was mixed by pipetting up and down
15-20 times using a pipette set to 85 .mu.L or capped the wells and
vortexed at high speed for 5-10 seconds. The Ligation master mix
and the Adaptor Oligo Mix were added to the samples in separate
addition steps as directed in the steps above, mixing after each
addition. The samples were briefly spun and the plate or strip tube
was then immediately placed in the thermal cycler and bean the
Ligation program (step 1: 20.degree. C. for 30 minutes; step 2:
4.degree. C. on hold). The sample wells were sealed and were stored
overnight at either 4.degree. C. or -20.degree. C. if next steps
were not continued.
Purify the Samples Using AMPure XP Beads
[0579] The AMPure XP beads were verified and held at room
temperature for at least 30 minutes before use. The beads were not
frozen at any time. 400 .mu.L of 70% ethanol per sample was
prepared, plus excess, for use in the following steps. The
freshly-prepared 70% ethanol may be used for subsequent
purification steps run on the same day. The complete Library
Preparation protocol required 0.8 ml of fresh 70% ethanol per
sample. The AMPure XP bead suspension was mixed well so that the
reagent appeared homogeneous and consistent in color. 80 .mu.L of
homogeneous AMPure XP beads were added to each DNA sample
(approximately 100 .mu.L) in the PCR plate or strip tube and were
pipetted up and down 15-20 times or capped the wells and vortexed
at high speed for 5-10 seconds to mix. Samples were incubated for 5
minutes at room temperature. The plate or strip tube was put into a
magnetic separation device (DynaMag -96 Side Magnet) and was waited
for the solution to clear (approximately 5 to 10 minutes). The
plate or strip tube was placed in the magnetic stand. The cleared
solution from each well was carefully removed and discarded. The
beads were not touched while removing the solution. The plate or
strip tube was continued to keep in the magnetic stand while 200
.mu.L of freshly-prepared 70% ethanol in each sample well was
dispensed. Any disturbed beads were allowed to settle after 1
minute and the ethanol was removed. The plate or strip tube was
placed in the magnetic stand while you dispense another 200 .mu.L
of freshly-prepared 70% ethanol in each sample well. Any disturbed
beads were allowed to settle after 1 minute and the ethanol was
removed. The wells were sealed with strip caps, and the samples
were then briefly spun to collect the residual ethanol. The plate
or strip tube was returned to the magnetic stand for 30 seconds.
The residual ethanol was removed with a P20 pipette. The samples
were air dried for 5 minutes. The bead pellet was not dried to the
point that the pellet appeared cracked during any of the bead
drying steps in the protocol. Elution efficiency was significantly
decreased when the bead pellet was excessively dried. 35 .mu.L
nuclease-free water was added to each sample well. The wells were
sealed with strip caps, then were mixed well on a vortex mixer and
the plate or strip tube was briefly spun to collect the liquid and
was incubated for 2 minutes at room temperature. The plate or strip
tube was put in the magnetic stand and was left for approximately 5
minutes, until the solution was clear. The cleared supernatant
(approximately 34.5 .mu.L) was removed to a fresh PCR plate or
strip tube sample well and was kept on ice. The beads could be
discarded at this time. It was noted that it may not be possible to
recover the entire 34.5 supernatant volume at this step. The
maximum possible amount of supernatant was transferred for further
processing. To maximize recovery, the cleared supernatant was
transferred to a fresh well in two rounds of pipetting, using a P20
pipette set at 17.25 .mu.L.
Amplify the Adaptor-Ligated Library
[0580] The following PCR reagents from the SureSelect XT HS Library
Preparation Kit for ILM (PrePCR) were thawed, mixed and kept on
ice. Herculase II Fusion DNA Polymerase was mixed by pipetting up
and down 15-20 times. 5.times.Herculase II Reaction Buffer was
mixed by vortexing. 100 Mm dNTP Mix was mixed by vortexing. Forward
Primer and SureSelect XT HS Index Primers A01 through H04 were
separately mixed by vortexing. The appropriate index assignments
for each sample were determined. The SureSelect XT HS Index Primers
were provided in single-use aliquots. To avoid cross-contamination
of libraries, each vial was discarded after use in one library
preparation reaction. Residual volume was not re-used or retained
for subsequent experiments.
[0581] Appropriate volume of pre-capture PCR reaction mix was
prepared as described below on ice and then was mixed well on a
vortex mixer. For example, a 13.50 .mu.L reaction volume for 1
reaction contained 10 .mu.L of 5x Herculase II Reaction Buffer, 0.5
.mu.L of 100 mM dNTP Mix, 2 .mu.L of Forward Primer, and 1 .mu.L of
5.times. Herculase II Fusion DNA, a 121 .mu.L reaction volume for 8
reactions (including excess) contained 90 .mu.L of 5.times.
Herculase II Reaction Buffer, 4.5 .mu.L of 100 mM dNTP Mix, 18
.mu.L of Forward Primer, and 9 .mu.L of 5.times. Herculase II
Fusion DNA, or a 337 .mu.L reaction volume for 24 reactions
(including excess) contained 250 .mu.L of 5.times. Herculase II
Reaction Buffer, 12.5 .mu.L of 100 mM dNTP Mix, 50 .mu.L of Forward
Primer, and 25 .mu.L of 5.times. Herculase II Fusion DNA.
[0582] 13.5 .mu.L of the PCR reaction mixture was added to each
purified DNA library sample (34.5 .mu.L) in the PCR plate wells. 2
.mu.L of the appropriate SureSelect XT HS Index Primer was added to
each reaction. The wells were capped and were then vortex at high
speed for 5 seconds. The plate or strip tube was spun briefly to
collect the liquid and any bubbles were released. Before adding the
samples to the thermal cycler, the Pre-Capture PCR program was
started according to the conditions below to bring the temperature
of the thermal block to 98.degree. C. Once the thermal cycler
reached 98.degree. C., the sample plate or strip tube was
immediately placed in the thermal block and the following
temperature cycling protocol was performed.
TABLE-US-00003 Segment Number of Cycles Temperature Time 1 1
98.degree. C. 2 minutes 2 8 98.degree. C. 30 seconds 60.degree. C.
30 seconds 72.degree. C. 1 minute 3 1 72.degree. C. 5 minutes 4 1
4.degree. C. Hold
Purify the Amplified Library with AMPure XP Beads
[0583] The AMPure XP beads were verified to be held at room
temperature for at least 30 minutes before use. 400 .mu.L of 70%
ethanol per sample was prepared, plus excess. The AMPure XP bead
suspension was mixed well, so that the reagent appeared homogeneous
and consistent in color. 50 .mu.L of homogenous AMPure XP beads
were added to each amplification reaction in the PCR plate or strip
tube and was pipetted up and down 15-20 times to mix. Samples were
incubated for 5 minutes at room temperature. The plate was out into
a magnetic separation device (DynaMag -96 Side Magnet) and was
waited up to 5 minutes for the solution to clear. The plate or
strip tube was put on the magnetic stand and the cleared solution
from each well was carefully removed and discarded. The beads were
touched while removing the solution. The plate or strip tube was
continued to be kept in the magnetic stand while dispensing 200
.mu.L of freshly-prepared 70% ethanol into each sample well.
Disturbed beads were allowed to settle after the wait for 1 minute,
then removed the ethanol. The ethanol wash was repeated once. The
wells were sealed with strip caps, then the samples were briefly
spun to collect the residual ethanol. The plate or strip tube was
returned to the magnetic stand for 30 seconds. The residual ethanol
was removed with a P20 pipette. The samples were dried by keeping
the unsealed plate or strip tube at room temperature for up to 5
minutes, until the residual ethanol was just evaporated. 15 .mu.L
nuclease-free water was added to each sample well. The wells were
sealed with strip caps, then were mixed well on a vortex mixer and
the plate or strip tube was briefly spun to collect the liquid and
was incubate for 2 minutes at room temperature. The plate or strip
tube was put in a magnetic stand and was left for 3 minutes, until
the solution was clear. 15 .mu.L of the cleared supernatant was
removed to a fresh PCR plate or strip tube sample well and was kept
on ice. The new PCR plate was sealed containing libraries. The
beads were discarded. The quality of sample libraries was checked
using the an electrophoresis device, for example an automated
electrophoresis device (e.g., a TapeStation System available from
Agilent, www.agilent.com) and a spectrophotometer, for example a
small volume full-spectrum, UV-visible spectrophotometer (e.g.,
Nanodrop spectrophotometer available from ThermoFisher Scientific,
www.thermofisher.com), or the plate was stored at -20.degree.
C.
[0584] Resources from the manufacturers, including Agilent
SureSelect XT HS Target Enrichment System for Illumina Paired-End
Multiplexed Sequencing Library protocol and Agilent SureSelect XT
HS and XT Low Input Enzymatic Fragmentation Kit protocol, were
incorporated by reference herein.
Example 4
Hybridization-Capture and Target Enrichment of DNA Libraries
[0585] Hybridization-Capture based target enrichment was used
directly after Library Construction described in Example 3. This
protocol described the steps to hybridize the prepared gDNA
libraries with a target-specific capture probes. Target enrichment
worked by mixing target-specific biotinylated probes with the DNA
Library. The probes were bound to the targets which were then
isolated by streptavidin coated magnetic bead pulldown, leaving
uncaptured DNA (the areas of the genome that we do not want)
behind. The steps to hybridize the prepared DNA libraries with a
target-specific capture library were provided. After library
preparation, the libraries were denatured and biotin-labeled probes
specific to targeted regions were used for hybridization. The pool
was enriched for regions of interest by adding streptavidin-coated
beads that were bound to the biotinylated probes. DNA fragments
bound to the streptavidin-coated beads via biotinylated probes were
magnetically pulled down from the solution. The enriched fragments
were then eluted from the beads. Each DNA library sample must be
hybridized and captured individually. Some or all of the procedures
were managed and conducted by lab personnel. In the event that
quality control related issues arise, lab personnel will notify the
provider (e.g., healthcare provider) of the biopsy sample or the
extracted DNA. As a general work procedure, before beginning the
procedure, work surfaces and pipettes were thoroughly disinfected
by wiping down with 10% bleach, followed by 70% ethanol. The same
cleaning process was followed after completion of work
procedure.
Normalization of Samples for Hybrid Capture
[0586] 12 .mu.L nuclease-free water was used to normalize samples
to 500-1000 ng. The maximum amount of DNA available was used for
each sample, within the range provided. The lab personnel then
navigated to the normalization spreadsheet, located in the Clinical
Lab. Documents folder in the shared Google Drive. The tab labeled
"HC Normalization" was selected. The Sample ID was entered into
column A and the measured concentration was entered into column B.
The spreadsheet automatically calculated the volumes of sample and
low TE required for normalization in columns G and H. If the
concentration of a sample was on the lower side, the spreadsheet
calculated a volume of sample >12 .mu.L and a volume of
nuclease-free water <0 .mu.L. If this occurred, only 12 .mu.L of
sample was used, and the sample was not diluted. Using the volumes
calculated in the spreadsheet, appropriate volumes were normalized
into a 96 well semi-skirted PCR plate.
Hybridize DNA Samples to the Capture Library
[0587] In some embodiments, the component reagents for
hybridization using a SureSelect kit were thawed, according to the
thawing conditions described below. Each reagent was vortexed to
mix, then tubes were spun briefly to collect the liquid.
[0588] To each DNA library sample well, 5 .mu.l SureSelect XT HS
and XT Low Input Blocker Mix (previously thawed on ice) were added.
The wells were capped and then vortexed at high speed for 5
seconds. The plate was spun briefly to collect the liquid and any
bubbles were released. The sealed sample plates were transferred to
the thermal cycler and the Hybridization program was started. The
thermal cycler was programmed to pause during Segment 3 of the
Hybridization program to allow additional reagents to be added to
the Hybridization wells, as described in the next sections. During
Segments 1 and 2 of the thermal cycling program, the additional
reagents were prepared as described in the next section. If needed,
these steps could be finished after the thermal cycler program
pauses in Segment 3. A 25% solution of SureSelect RNase Block
(e.g., previously thawed on ice) was prepared and was mixed well by
vortexing, and the mix was briefly centrifuged and then kept on
ice.
[0589] Further, a Capture Library Hybridization Mix was prepared as
follow for one or more reactions. For example, a 13 .mu.L reaction
volume for 1 reaction contained 2 .mu.L of 25% RNase Block
solution, 5 .mu.L of Capture Library .gtoreq.3 Mb (e.g., previously
thawed on ice), and 6 .mu.L of SureSelect Fast Hybridization Buffer
(e.g., previously thawed and kept at room temperature), a 117 .mu.L
reaction volume for 8 reactions (including excess) contained 18
.mu.L of 25% RNase Block solution, 45 .mu.L of Capture Library
.gtoreq.3 Mb, and 54 .mu.L of SureSelect Fast Hybridization Buffer,
or a 325 .mu.L reaction volume for 24 reactions (including excess)
contained 50 .mu.L of 25% RNase Block solution, 125 .mu.L of
Capture Library .gtoreq.3 Mb, and 150 .mu.L of SureSelect Fast
Hybridization Buffer.
[0590] The listed reagents were combined at room temperature, mix
well by vortexing at high speed for 5 seconds, and then were spun
down briefly. The mixture was just prepared before pausing the
thermal cycler in Segment 3. The mixture was kept at room
temperature briefly until the mixture was added to the DNA samples
on the cycler. Solutions containing the Capture Library were not
kept at room temperature for extended periods.
[0591] The thermal cycler was pauses at Segment 3 of the
Hybridization program (1 minute at 65.degree. C.). With the cycler
paused, and while keeping the DNA+Blocker samples in the cycler, 13
.mu.l of the room-temperature Capture Library Hybridization Mix was
transferred to each sample well and was mixed well by pipetting up
and down slowly 10 times. The wells were sealed with fresh domed
strip caps and that all wells were made sure to completely sealed.
A compression pad was placed on the plate to prevent evaporation
during hybridization. The Play button was pushed to resume the
thermal cycling program to allow hybridization of the prepared DNA
samples to the Capture Library. Wells were adequately sealed to
minimize evaporation to prevent results from being negatively
impacted.
Prepare Streptavidin-Coated Magnetic Beads
[0592] In some embodiments, the bead preparation steps began
approximately one hour after starting hybridization. Reagents for
capture from the SureSelect XT HS Target Enrichment Kit ILM Hyb
Moedule included the SureSelect Binding Buffer, the SureSelect Wash
Buffers 1 and 2 (e.g., all kept at room temperature), and Dynabead
MyOne Streptavidin T1 (e.g., stored at 2.degree. C. to 8.degree.
C.). Dynabeads MyOne Streptavidin T1 magnetic beads were brought to
room temperature for at least 30 minutes. The Dynabeads MyOne
Streptavidin T1 magnetic beads were vigorously resuspend on a
vortex mixer. The magnetic beads settled during storage. For each
hybridization sample, 50 .mu.l of the resuspended beads was added
to wells of a fresh PCR plate. The beads were washed by adding 200
.mu.l of SureSelect Binding Buffer, mixing by pipetting up and down
20 times or capping the wells and vortexing at high speed for 5-10
seconds. The plate was put into a magnetic separator device and
waited for the solution to clear, approximately 5 minutes. The
supernatant was removed and discarded. The wash steps were repeated
two more times, for a total of three washed. The beads were
resuspended in 200 .mu.l of SureSelect Binding Buffer.
Capture the Hybridized DNA Using Streptavidin-Coated Beads
[0593] After the hybridization step was complete on the thermal
cycler, the samples were transferred to room temperature. The
entire volume (approximately 30 .mu.l) was immediately transferred
of each hybridization mixture to the wells containing 200 .mu.l of
washed streptavidin beads using a multichannel pipette. The mixture
was pipetted up and down 5-8 times to mix and then the wells were
sealed with fresh caps. The capture plate was incubated on a
96-well plate mixer and was mixed at 1500 rpm for 30 minutes at
room temperature. The samples were properly mixed in the wells.
During the 30-minute incubation for capture, SureSelect Wash Buffer
2 was pre-warmed in the thermal cycler at 70.degree. C. by placing
200 .mu.L aliquots of Wash Buffer 2 in wells of a fresh 96-well
plate and aliquot 6 wells of buffer for each DNA sample in the
run.
[0594] The wells were capped and then incubated in the thermal
cycler, with heated lid ON, held at 70.degree. C. until time for
use. When the 30-minute sample incubation period was complete, the
samples were briefly spun to collect the liquid. The plate was put
in a magnetic separator to collect the beads and was waited until
the solution was clear, then the supernatant was removed and
discarded. The beads were resuspended in 200 .mu.l of SureSelect
Wash Buffer 1 and were mixed by pipetting up and down 15-20 times,
until beads were fully resuspended. The plate was put in the
magnetic separator and was waited for the solution to clear
(approximately 1 minute), and then the supernatant was removed and
discarded. The plate was removed from the magnetic separator and
was transferred to room temperature. The beads were washed with
Wash Buffer 2, using the steps below: 1) resuspend the beads in 200
.mu.l of 70.degree. C. pre-warmed Wash Buffer 2; 2) pipetted up and
down 15-20 times, until beads were fully resuspended; 3) incubated
the samples for 5 minutes at 70.degree. C. on the thermal cycler
with the heated lid on; 4) After the 5 minute incubation, the plate
was put in the magnetic separator at room temperature; 5) the
solution was waited to clear (approximately 1 minute), then the
supernatant was removed and discarded; and 6) the wash steps were
repeated five more times for a total of 6 washes.
[0595] After verifying that all wash buffer was removed, 25 .mu.l
of nuclease-free water was added to each sample well and then
pipetted up and down 8 times to resuspend the beads. The plate was
sealed and the samples were kept on ice until they were used later.
Captured DNA was retained on the streptavidin beads during the
post-capture amplification step.
Amplify the Captured Libraries
[0596] In some embodiments, reagents for post-capture PCR
amplification were thawed and kept on ice, and included a Herculase
II Fusion DNA Polymerase (mixed by pipetting up and down), a
5.times. Herculase II Reaction Buffer, 100 mM dNTP Mix, and
SureSelect Post-Capture Primer Mix (e.g., all mixed by
vortexing).
[0597] The Post-Capture PCR thermal cycler program was started to
preheat the cycler. Appropriate volumes of PCR reaction mix were
prepared, on ice, and mixed well on a vortex mixer. For example, a
25 .mu.L reaction volume for 1 reaction contained 12.5 .mu.L of
nuclease-free water, 10 .mu.L of 5.times. Herculase II Reaction
Buffer, 1 .mu.L of Herculase II Fusion DNA Polymerase, 0.5 .mu.L of
100 mM dNTP Mix, and 1 .mu.L of SureSelect Post-Capture Primer
Mix.
[0598] For each reaction, 25 .mu.l of the PCR reaction mix was
added to each sample well containing bead-bound target-enriched
DNA. The PCR reactions were mixed well by pipetting up and down
until the bead suspension was homogeneous. Splashing samples onto
well walls was avoided and the samples were not spun at this step.
The plate was sealed well. The plate was placed in the thermal
cycler and compression pad was placed on the plate to prevent
evaporation. The Play button was pressed to resume the Post-Capture
PCR thermal cycler program. When the PCR amplification program was
complete, the plate was spun briefly. The streptavidin-coated beads
were removed by placing the plate on the magnetic stand at room
temperature. The solution was waited to clear (approximately 2
minutes), and then each supernatant (approximately 50 .mu.l) was
transferred to wells of a fresh plate. The beads could be discarded
at this time.
Purify the Amplified Capture Libraries using AMPure XP Beads
[0599] In brief, the AMPure XP beads were come to room temperature
for at least 30 minutes. The beads were not frozen at any time. 400
.mu.l of fresh 70% ethanol per sample was prepared for later use in
step as described herein. The AMPure XP bead suspension was mixed
well so that the suspension appeared homogeneous and consistent in
color. 50 .mu.l of the homogeneous AMPure XP bead suspension was
added to each amplified DNA sample (approximately 50 .mu.l) in the
PCR plate and was mixed well by pipetting up and down 15-20 times,
or the wells were capped and vortexed at high speed for 5-10
seconds. The beads were made sure to be in a homogeneous suspension
in the sample wells. Each well had a uniform color with no layers
of beads or clear liquid present. The samples were then incubated
for 5 minutes at room temperature. The plate was put on the
magnetic stand at room temperature and was waited for the solution
to clear (approximately 3 to 5 minutes). While keeping the plate on
the magnetic stand, the cleared solution from each well was
carefully removed and discarded. The beads were not disturbed while
removing the solution. The plate was continued to be placed on the
magnetic stand while dispensing 200 .mu.l of freshly-prepared 70%
ethanol in each sample well and waited for 1 minute to allow any
disturbed beads to settle, then the ethanol was removed.
[0600] The ethanol wash was repeated once for a total of two
washes. All of the ethanol at each wash step was carefully removed.
The wells with then sealed with strip caps, and then were briefly
spin to collect the residual ethanol. The plate was returned to the
magnetic stand for 30 seconds. The residual ethanol was removed
with a P20 pipette. Next, the samples were dried by keeping them at
room temperature until the wells were dry (about 5-10 minutes). The
bead pellet was ensured to not start to crack, as this was a sign
of over drying. 25 .mu.l of nuclease-free water was then added to
each sample well. The sample wells were sealed, mixed well on a
vortex mixer and then briefly spun to collect the liquid without
pelleting the beads. The wells were incubated for 2 minutes at room
temperature. The plate was put on the magnetic stand and left until
the solution was clear. A new PCR plate was labeled with the Run
ID. The cleared supernatant (approximately 25 .mu.l) was
transferred to the fresh plate. The beads could be discarded at
this time. Then, the quality of captured libraries was checked by
qPCR methods by using the Roche LightCycler SOP, or stored at
-20.degree. C.
Example 5
The Constructions of RNA Libraries for Sequencing
[0601] RNA libraries were prepared before performing the downstream
sequencing. In brief, this protocol explained how to convert cDNA
was synthesized from mRNA in a total RNA sample, into a library of
DNA for hybridization capture prior to sequencing. The reagents
provided in an Illumina TruSeq Stranded mRNA library prep workflow
were used.
[0602] The process involved the adenylation of the 3' ends of blunt
ended fragments by the addition of one adenine nucleotide. This
prevented them from ligating to each other during adapter ligation
reaction. One corresponding thymine nucleotide on the 3' end of the
adapter provided a complementary overhang for ligating the adapter
to the fragment. This strategy ensured a low rate of chimera
(concatenated template) formation. In the next step, multiple
indexing adapters were ligated to the ends of the ds cDNA
fragments, which prepared them for hybridization onto a flow cell.
Fragments with no adapters were not hybridized to surface-bound
primers on the flow cell. Fragments with an adapter on one end can
hybridize to surface bound primers, but did not form clusters. The
DNA fragment enrichment process used PCR to selectively enrich
those DNA fragments that had adapter molecules on both ends and to
amplify the amount of DNA in the library. PCR was performed with a
PCR Primer Cocktail that annealed to the ends of the adapters. RNA
Library Construction consisted of three steps as described herein.
Introduction above followed by a library clean up and library
quantitation by qPCR per the protocol of using a nucleic acid
amplification device (e.g., a PCR system), for example a real-time
PCR system (e.g., a LightCycler Instrument 480 available from
Roche, www.lifescience.roche.com)by Roche Life Science. Accurate
quantification achieved by qPCR allowed to create optimum cluster
densities across all four lanes of the flow cell.
[0603] Some or all of the procedures were managed and conducted by
lab personnel. In the event that quality control related issues
arise, lab personnel will notify the provider (e.g., healthcare
provider) of the biopsy sample or the extracted RNA.
Adenylate 3' Ends
[0604] The reagents were prepared according to the conditions
below. In brief, 2.5 .mu.L Resuspension buffer was added to each
well containing sample (Resuspension Buffer is typically stored at
-25.degree. C. to -15.degree. C. and let stand for 30 minutes to
bring to room temperature before use). 12.5 .mu.L A-Tailing Mix was
added to each well, and then was mixed thoroughly by pipetting up
and down 10 times (A-Tailing Mix is typically stored at -25.degree.
C. to -15.degree. C. and thawed at room temperature). The plate was
sealed and centrifuged at 280.times.g for 1 minute. The plate was
incubated on the ATAIL70 program of the thermal cycler. The ATAIL70
program was as the following steps: 1) preheat lid: 100.degree. C.
hold time, 2) step 1: 37.degree. C. for 30 minutes, 3) step 2:
70.degree. C. for 5 minutes, and 4) step 3: 4.degree. C. hold time.
The plate was then centrifuged at 280.times.g for 1 minute.
Ligate Adapters
[0605] The reagents were prepared according to the conditions
below. In brief, the RNA Adapter tubes were centrifuged at
600.times.g for 5 seconds. Ligation Mix was removed from
-25.degree. C. to -15.degree. C. storage. The following reagents
were added in the order listed to each well: 1) Resuspension
Buffer, 2) 2.5 .mu.L Ligation Mix, and 3) 2.5 .mu.L RNA Adapter
Indexes. The mixed reagents were then mixed thoroughly by pipetting
up and down 10 times and were centrifuged at 280.times.g for 1
minute. The plate was placed on the thermal cycler and the LIG
program was run. The LIG program was as the following: 1) preheat
lid: 100.degree. C. hold time, 2) step 1: 30.degree. C. for 10
minutes, and 3) step 2: 4.degree. C. hold time. The Stop Ligation
Buffer was centrifuged at 600.times.g for 5 seconds. Once the LIG
program stopped, the plate was removed from thermal cycler and 5
.mu.L Stop Ligation Buffer was added to each well, and was mixed
thoroughly by pipetting up and down. The plate was then centrifuged
at 280.times.g for 1 minute. Ligation Mix from storage was not
removed until instructed to do so in the procedure. RNA Adapter
Indexes are typically stored at -25.degree. C. to -15.degree. C.
and thawed at room temperature for 10 minutes prior to use.
Resuspension Buffer and AMPure XP Beads are typically stored at
2.degree. C. to 8.degree. C. and let stand for 30 minutes to bring
to room temperature before use. Stop Ligation Buffer is typically
stored at -25.degree. C. to -15.degree. C. and thawed at room
temperature before use.
Clean Up Ligated Fragments
[0606] In brief, 42 .mu.L AMPure XP beads were added to each well
and mixed thoroughly by pipetting up and down before incubated at
room temperature for 15 minutes. After incubation, the mix was
centrifuged at 280.times.g for 1 minute. The wells were then placed
on a magnetic stand and waited until the liquid is clear (about 2-5
minutes). While waiting for the liquid to clear, fresh 80% EtOH was
made for use in the two washes step above. After the liquid was
cleared, all supernatant was removed and discarded from each well,
and was wash two times as the following: 1) added 200 .mu.L fresh
80% EtOH to each well, 2) incubated on the magnetic stand for 30
seconds, and 3) removed and discarded all supernatant from each
well. 20 .mu.L pipette was used to remove residual EtOH from each
well.
[0607] The magnetic stand was air-dried for 5 minutes. The bead
pellet did not start to crack, as this was a sign of over drying.
The magnetic stand was then removed. 52.5 .mu.L Resuspension buffer
was added to each well and mixed thoroughly by pipetting up and
down before incubating at room temperature for 2 minutes. The mixed
buffer was centrifuged at 280.times.g for 1 minute. A magnetic
stand was placed and waited until the liquid was clear (about 2-5
minutes). 50 .mu.L supernatant was transferred to the corresponding
well of a newly labeled PCR plate. 50 .mu.L AMPure XP beads were
added to the plate and mixed thoroughly by pipetting up and down
before incubating at room temperature for 15 minutes. The plate was
centrifuged at 280.times.g for 1 minute. A magnetic stand was
placed and waited until the liquid was clear (2-5 minutes). All
supernatant was removed and discarded from each well. The well was
washed two times as following: 1) added 200 .mu.L fresh 80% EtOH to
each well, 2) incubated on the magnetic stand for 30 seconds, and
3) removed and discarded all supernatant from each well.
[0608] After that, 20 .mu.L pipette was used to remove residual
EtOH from each well. The magnetic stand was air-dried for 5
minutes. The bead pellet was ensured to not starting to crack, as
this would be a sign of over drying. The bead pellet was then
removed from the magnetic stand. 22.5 .mu.L Resuspension Buffer was
added to each well and mixed thoroughly by pipetting up and down
before incubating at room temperature for 2 minutes. The wells then
were centrifuged at 280.times.g for 1 minute. A magnetic stand was
placed and waited until the liquid was clear (2-5 minutes). 20
.mu.L supernatant was transferred to the corresponding well of a
newly labeled PCR plate. The beads were not disturbed during the
process. Alternatively, this step was a safe stopping point. The
plate could be sealed and stored at -25.degree. C. to -15.degree.
C. for up to 7 days.
Enrich DNA Fragments
[0609] The reagents were prepared according to the conditions
below. In brief, the PCR plate was placed on ice and 5 .mu.L PCR
primer cocktail was added to each well. 25 .mu.L PCR Master Mix was
added to each well, and then mixed thoroughly by pipetting up and
down 10 times. The sample wells were sealed and centrifuged at
280.times.g for 1 minute. The sample wells were placed on the
thermal cycler and the mRNA PCR program was performed. The mRNA PCR
program was as the following: 1) preheat lid: 100.degree. C. hold
time, 2) step 1: 98.degree. C. for 30 seconds, 3) step 2 (15
cycles): 98.degree. C. for 10 seconds, 60.degree. C. for 30
seconds, and 72.degree. C. for 30 seconds, 4) step 3: 72.degree. C.
for 5 minutes, and step 4) 4.degree. C. hold time.
[0610] Once the program was complete and the plate was centrifuged
at 280.times.g for 1 minute. AMPure XP beads were mixed by thorough
vortexing and 50 .mu.L was added to each well and mixed thoroughly
by pipetting up and down 10 times before incubating at room
temperature for 15 minutes. The sample wells were centrifuged at
280.times.g for 1 minute. A magnetic stand was placed and waited
until the liquid was clear (2-5 minutes). All supernatant was
removed and discarded from each well. The wells were washed two
times as the following: 1) added 200 .mu.L fresh 80% EtOH to each
well, 2) incubated on the magnetic stand for 30 seconds, and 3)
removed and discarded all supernatant from each well. A 20 .mu.L
pipette was used to remove residual EtOH from each well. The
magnetic stand was air-dried for 5 minutes. The bead pellet was
ensured to not start to crack, which was a sign of over drying. The
magnetic stand was removed next. 32.5 .mu.L Resuspension buffer was
added to each well, and was mixed thoroughly by pipetting up and
down 10 times before incubating at room temperature for 2 minutes.
The wells were centrifuged at 280.times.g for 1 minute. A magnetic
stand was placed and waited until the liquid was clear (2-5
minutes). 30 .mu.L supernatant was suspended to the corresponding
well of a newly labeled PCR plate. The lab personnel then proceeded
with library QC using the a nucleic acid amplification device
(e.g., a PCR system), for example a real-time PCR system (e.g., a
LightCycler Instrument 480 available from Roche,
www.lifescience.roche.com), or the plate was sealed and stored at
-20.degree. C. for up to 7 days. PCR Primer Cocktail is typically
stored at -25.degree. C. to -15.degree. C. and thawed at room
temperature before use. PCR Master Mix is typically stored at
-25.degree. C. to -15.degree. C. and thawed on ice before use.
Resuspension Buffer and AMPure XP Beads are typically stored at
2.degree. C. to 8.degree. C. and let stand for 30 minutes to bring
to room temperature before use.
[0611] Resource from the manufacturers, including TruSeq Stranded
mRNA Reference Guide, is incorporated by reference herein.
Example 6
Quality Control Concerning the DNA/RNA Library Preparation Process
Based on DNA and RNA from Fresh Frozen Tissue Library
Sequencing
[0612] For library preparation, extracts of DNA and RNA from the
tissue were obtained by using the AllPrep DNA/RNA Mini Kit. Any
suitable extraction kit known in the art could also be used.
Library construction from purified DNA was carried out with Agilent
SureSelect XT HS and Agilent SureSelect Human All Exon V7 exome
kits. Library construction from purified RNA was carried out with
Illumina TruSeq mRNA stranded kit. Quality control (QC) metrics
were carried out after each stage of library preparation. All QC
metrics were prepared with a spectrophotometer, for example a small
volume full-spectrum, UV-visible spectrophotometer (e.g., Nanodrop
spectrophotometer available from ThermoFisher Scientific,
www.thermofisher.com), a fluorometer, for example for
quantification of DNA or RNA (e.g., a Qubit Flex fluorometer
available from ThermoFisher Scientific, www.thermofisher.com), a
nucleic acid amplification device (e.g., a PCR system), for example
a real-time PCR system (e.g., a LightCycler Instrument 480 II
available from Roche, www.lifescience.roche.com)and an
electrophoresis device, for example an automated electrophoresis
device (e.g., a, Agilent TapeStation System 4150 available from
Agilent, www.agilent.com). All measurement content carried purity,
concentrations and size of DNA/RNA fragments.
[0613] QC metrics during the next generation sequencing experiment
were a set of individual parameters that evaluated the overall
quality of the data set. The following metrics were evaluated:
cluster density, percentage of clusters passing filters that were
assigned to an index, quality score of 30 (Q30) and error rate. The
next stage was to estimate quality metrics used in the
bioinformatics pipeline (Bioinformatics QC). It was divided into
two processes: WES (DNA sequencing) and RNA sequencing (RNA-seq).
The following metrics were taken into account: tumor purity, depth
of coverage, alignment rate, base call quality scores or Phred
score, uniformity of coverage, GC content, mapping quality,
duplication rate, insert size, contamination, SNP concordance, HLA
allele concordance and ADA genomes contamination.
[0614] In general, the protocol described in the present example
provides the metrics for QC methods used in the library preparation
stages and in the bioinformatic pipeline of whole-exome sequencing
(WES) and RNA-seq analysis. Bioinformatic pipeline was divided into
two components: QC from sequencing platform and Bioinformatics QC.
On a sequencer platform and in Bioinformatics QC, a table with
estimated metrics for WES and RNA-seq data was provided. For some
metrics within a targeted range, which were the most appreciable
value and acceptable range, the sample data could be used. If the
values of the obtained metrics fell outside of the acceptable
range, the corresponding sample was considered having poor
quality.
[0615] In any given experiment or project, one or more of the
quality control processes can be used. In some experiment or
project, all of the quality control processes can be used. Some or
all of the procedures were managed and conducted by lab personnel.
In the event that quality control related issues arise, lab
personnel will notify the provider (e.g., healthcare provider) of
the biopsy sample or the extracted DNA/RNA.
Quality Control Steps in the Process of DNA and RNA Library
Preparation
[0616] Table 4-Table 6 describe embodiments of DNA and RNA library
preparation including one or more quality control steps at each
phase: extraction, library construction, and hybridization and
capture. Measuring the concentrations of extracts, primary
libraries and libraries after hybridization and capture, the
quality of products from the samples were identified. Based on the
determination of the quality of DNA or RNA in the tested sample, a
decision was made to either go forward to the next step or to
repeat the processes. A spectrophotometer, for example a small
volume full-spectrum, UV-visible spectrophotometer (e.g., Nanodrop
spectrophotometer available from ThermoFisher Scientific,
www.thermofisher.com), a fluorometer, for example for
quantification of DNA or RNA (e.g., a Qubit fluorometer available
from ThermoFisher Scientific, www.thermofisher.com), an
electrophoresis device, for example an automated electrophoresis
device (e.g., a TapeStation System available from Agilent,
www.agilent.com) and a nucleic acid amplification device (e.g., a
PCR system), for example a real-time PCR system (e.g., a
LightCycler Instrument available from Roche,
www.lifescience.roche.com) were used for measuring purity,
concentrations and size of DNA/RNA fragments. The acceptable and
the targeted ranges of DNA and RNA for the respective devices for
each phase are indicated in the tables below. The results of the
quality control can be confirmed by performing electrophoresis. The
results of the quality control can be confirmed by the
determination of the size distribution of the nucleic acids.
[0617] The present Example provides troubleshooting protocols. For
example, if an additional peak at 150 bp at the electropherogram
from the TapeStation on the LC or HC stage was observed. An
additional step of washing with AMPure beads (library to beads 1:
0.8 volume ratio) were made.
TABLE-US-00004 TABLE 4 Quality Control: Extraction of DNA or RNA
for Library Preparation Extraction Device DNA (Acceptable|Target)
RNA (Acceptable|Target) Comments Total amount for next step Total
amount for next step 20-200 ng | 30-200 ng 100-1000 ng |
>200-1000 ng Nanodrop concentration >4 ng/ul | concentration
>3 ng/ul | If the metrics are out of range >5.5 ng/ul >5
ng/ul you have to repeat extraction 260/280 >1.5 | 1.8-2.0
260/280 >1.5 | 1.8-2.0 process 260/230 >1.5| 2.0-2.2 260/230
>1.5 | 2.0-2.2 Qubit Concentration >3 ng/ul | Concentration
>2 ng/ul | >4.5 ng/ul >4 ng/ul Tapestation Concentration
>2.5 ng/ul | Concentration >1.5 ng/ul | >4 ng/ul >3
ng/ul RIN >5 | >8
TABLE-US-00005 TABLE 5 Quality Control: DNA or RNA for Library
Construction Library construction Device DNA (Acceptable|Target)
RNA (Acceptable|Target) Comments Total amount for next step Total
amount for next step 200-1000 ng | 500-1000 ng 0.5-4 nmol/l | 0.5-4
nmol/l Qubit Concentration >17 ng/ul | Concentration >0.1
ng/ul | If the metrics are out of range, >42 ng/ul >0.1 ng/ul
extraction process can be repeated. Tapestation Concentration
>15 ng/ul | Concentration >0.1 ng/ul | If an electropherogram
with >40 ng/ul >0.1 ng/ul an additional peak at 150 bp is
Concentration >0.5 nmol/l | observed, the troubleshooting
>0.5 nmol/l as described in the present Average 370-440 |
370-440 Example would provide guidance. LightCycler Not required
Concentration >0.5 nmol/l | >0.5 nmol/l
TABLE-US-00006 TABLE 6 Quality Control: DNA or RNA for Library
after Hybridization and Capture Device DNA (Acceptable|Target)
Comments Final concentration for pooling 0.5-4 nmol/l | 0.5-4
nmol/l Qubit Concentration >0.1 ng/ul | If the metrics are out
of >0.1 ng/ul range, extraction process can be repeated.
Tapestation Concentration >0.1 ng/ul | If you see an >0.1
ng/ul electropherogram with an Concentration >0.5 nmol/l |
additional peak at 150 bp is >0.5 nmol/l observed, look in the
Average 380-440 | 380-440 troubleshooting list (#1)as described in
the present LightCycler Concentration >0.5 nmol/l | Example
would provide >0.5 nmol/l guidance.
Quality Control Steps After DNA and RNA Library Preparation
[0618] The main quality control metrics for the sequencing process
occurred on the Illumina NextSeq.RTM. 500/550 sequencer. Table 7
shows the QC parameters of the sample run for whole-genome
sequencing (WES) and RNA-sequencing.
TABLE-US-00007 TABLE 7 Quality Control: Sequencing Processes Sample
run Targeted values RNA- QC parameter Targeted values WES seq
Comments Cluster density Targeted range 170-220 Targeted range
170-220 Cluster density and CLUSTER PF (%) Acceptable range <280
Acceptable range <280 Alignment rate should be monitored in
every run. Actual Yield >15 Gbp The yield that's been received %
.gtoreq.Q30 Targeted range >85% Targeted range >85% The
percentage of bases Acceptable range >75% Acceptable range
>75% with a quality score of 30 or higher Quality scores and
quality of signal/noise ratio should be monitored in every run.
ERROR RATE % Targeted <0.7% Targeted <0.7% Refers to the
percentage of Acceptable <1% Acceptable <1% bases called
incorrectly at any one cycle. It is calculated from the reads that
are aligned to Illumina's PhiX control. If this was not used then %
Q30 would be the best tool to check base quality. Error rate
increases along the length of the read.
Bioinformatics QC
[0619] After the performance of sequencing (e.g., RNA seq), quality
control can be performed for bioinformatics pipeline. In brief, the
software was created and generated by the following parameters for
measurements: single nucleotide variants (SNVs; somatic plus
germline variants), small indels, copy number alterations (CNAs)
(plus loss of heterozygosity (LOH)), focal
amplifications/deletions, gene fusions rearrangement (mRNA
expressed), fusion protein expression, RNA expression (for
biomarker proteins), and tumor mutational burden (TMB).
[0620] In any given experiment or project, one or more paramaters
could be used for quality control. For example, only SNV detection
was performed for certain bioinformatic analysis. Only in/del
detection was performed for certain bioinformatic analysis. Only
CNA detection was performed for certain bioinformatic analysis.
Only fusion detection was performed for certain bioinformatic
analysis. Only RNA expression measurement detection was performed
for certain bioinformatic analysis. Only TMB measurement detection
was performed for certain bioinformatic analysis. In other
examples, SNV detection and CAN detection can be performed for
certain bioinformatic analysis.
[0621] Table 8 provides a lists of quality control parameters for
bioinformatics analysis.
TABLE-US-00008 TABLE 8 Quality Control: Bioinformatics
Bioinformatics Targeted values Targeted values QC parameter WES
RNA-seq Comments Tumor purity >=20% >=20% In case of
failure-inform the physician if to proceed with sample below LOD
Depth of coverage >=150.times. average >50 mln pair-end In
case of failure-resequence the coverage-tumor reads sample sample
>=100.times. average coverage of normal tissue Alignment rate
>90% >90% Base call quality scores Phred score >30 Phred
score >30 Quality scores and quality of signal/noise ratio
should be monitored in every run. Low-quality scores can lead to
increased false-positive variant calls; thus, results must be
interpreted with caution and repeat testing may be indicated.
Uniformity of coverage 85% of base pairs Not applicable -- in
target regions covered >=20.times. for tumor tissue 85% of base
pairs in target regions covered >=20.times. for normal tissue GC
bias Targeted value 50 Targeted value 50 GC bias should be
monitored with Acceptable range: Acceptable range: 45-65 every run
to detect changes in test 45-65 performance or sample quality
issues. In the last assembly of the reference human genome, the GC
composition for the entire genome is ~40%, 48.9% for the RNA
encoding. We use ExonV7 kit, the mean GC content for the targets
there is 47.9%. Mapping quality MapQ >=10 Not applicable The
proportion of reads that do not map to target regions must be
monitored during each run. Poor mapping quality may be a result of
non-specific amplification, capture of off target DNA, or
contamination. Reads with MapQ = 0 are obtained with simultaneous
alignment to several regions. Duplication rate <30% <85% The
duplication rate should be monitored in every run and for each
sample independently to monitor library diversity. Insert size
Median insert size Median insert size~ Insert size is the length of
the for tumor tissue~ [150; 200] sequencing DNA (or RNA) that is
[150; 200] "inserted" between the adapters. Median insert size for
normal tissue ~[150; 200] Contamination <0.05% <0.05%
Contamination-Percentage of sequence segments of foreign origin in
sample. A contaminated sequence is one that does not faithfully
represent the genetic information from the biological source SNP
concordance of a Targeted >90% Targeted >90% In case of
failure, investigate where the pair of samples Acceptable
Acceptable >85% mix-up could have happened. Stop the
tumor/normal from the >85% bioinformatics analysis until the
same patient troubleshooting and sample concordance HLA allele
concordance Threshold for Threshold for tumor In case of
failure-Investigate where of a pair of samples normal vs tumor
RNAseq vs normal the mix-up could have happened. Stop tumor/normal
from the tissue <5 WES tissue <5 the bioinformatics analysis
until the same patient troubleshooting and sample concordance ADA
genomes Targeted Targeted threshold For One hit one genome,
contamination threshold >60 >40 GRCh38.d1.vd1 Acceptable
Acceptable threshold In case of failure-report to lab threshold
>40 >20 (with WARNING personnel (with WARNING sign) sign)
[0622] The present Example provides troubleshooting protocols. For
example, if the quality control of HLA allele concordance of
tumor/normal pair failed, it was an indicator that the tissues from
different patients were potentially mixed up. The lab personnel
should proceed to confirm the potential mix up and investigate the
reason of the potential mix up. The lab personnel should reach out
to the physician if the mix up was not due to the internal errors
in the laboratory.
Example 7
Determining the Sequence of Major Histocompatibility Complexes
(MHC) can be used to Assess Sequence Data Identity and/or
Integrity
[0623] MHC genes are highly polymorphic, with large numbers of
alleles for the genes of each class (e.g., Class I, II, and III) of
MHC (e.g., human leukocyte antigens (HLAs) in humans). The
combination of the number of potential alleles in a population with
the number of genes in each individual result in a large number of
unique MHC profiles. These can be used to assess the likelihood
that sequence data is from a given source or subject (or if
sequence data from multiple samples are from the same subject).
Sequences corresponding to one or more MHC loci can be used to
determine an MHC allele combination for a particular nucleic acid
sample.
[0624] The result of the sequencing one or more MHC loci can be
evaluated against asserted information (e.g., an asserted HLA
allele combination) which is expected to be consistent with the
sequence data. If the determined MHC combination matches the
asserted information, the sequence data is consistent. If the
determined MHC combination does not match the asserted information,
the sequence data is inconsistent and this can indicate a problem
with the sample and/or sequence data. For example, the sample
and/or sequence data may have been contaminated, misidentified,
degraded, or otherwise corrupted. This can prompt investigation
into the origin of the inconsistency. Such investigation may entail
determining the sequence of the sequence data at the MHC loci at
least one additional time, obtaining a second sequence data from
the sample and determining the sequences of the sequence data at
the MHC loci at least one additional time, reporting the sequence
data as inconsistent, and/or a combination thereof. FIG. 11
illustrates an example of MHC data validation. In FIG. 11, six HLA
alleles are determined from each of three sequence data sets
(RNA-Seq data, WES tumor data, and WES normal data) from two
subjects (e.g., 103 and 105). As can be seen from FIG. 11, all
three samples share all six alleles for subject 105, indicating
they are consistent and likely from the same subject. In the case
of subject 103 however, there is consistency for two sequence data
sets (between whole exome sequence data from a tumor sample (WES
Tumor) and whole exome sequence data from a normal sample (WES
Normal)), but an inconsistency for a third sequence data set (RNA
sequence data allegedly from the same subject 103).
[0625] In some embodiments, the sequence of at least one MHC locus
is determined and verified against at least one MHC sequence of the
same locus from a reference sequence data set (e.g., from a sample
asserted to be from the same subject). In some embodiments, two or
more MHC alleles loci are sequenced (e.g., at least three, four, or
five MHC loci are sequenced). In some embodiments, six MHC loci are
sequenced. In some embodiments, more than six MHC loci are
sequenced
[0626] In some embodiments, the sequence data is from a human
subject. In some embodiments, the MHC is human leukocyte antigens
(HLA). Accordingly, in some embodiments two or more HLA loci are
sequenced (e.g., three, four, five, or more HLA loci). In some
embodiments, six HLA loci are sequenced. In some embodiments, more
than six HLA loci are sequenced.
[0627] In some embodiments, the results are displayed to a user in
a report (e.g., via a GUI).
Example 8
Predicted Tumor Type can be Used to Assess Sequence Data Identity
and/or Integrity
[0628] Various techniques can be used to predict, based on nucleic
acid sequence data, the type of tumor from which a sample was taken
(e.g., breast, colon, prostate, bladder, kidney, rectal, lung,
lymphoma, melanoma, oral, oropharyngeal, pancreatic, thyroid,
uterine, eye, gastrointestinal, etc.). Many existing tools rely on
large data sets of known samples with evaluated biomarkers, thereby
allowing for the comparison of biomarkers from a sequence data set
to be evaluated against an existing known data set. Other methods
of prediction utilize neural networks and deep learning systems to
analyze data sets and perform the data analysis. The sequence data
can be trained against an existing network or data set to predict
the type of tumor from which the sequence data was obtained.
[0629] The result of a tumor type prediction (e.g., determined
information) can then be evaluated against asserted information
(e.g., a tumor type) which is believed to be consistent with the
sequence data. If the determined information matches the asserted
information, the sequence data is consistent and believed to be
identified correctly. If the determined information matches the
asserted information, the sequence data can be processed to
determine whether the sequence data is indicative of one or more
disease features. If the determined information does not match the
asserted information, the sequence data is inconsistent and may
indicate a problem with the sample or sequence data. For example,
the sample and/or sequence data may have been contaminated,
misidentified, degraded, or otherwise corrupted. This can prompt
investigation into the origin of the inconsistency. Such
investigation may entail predicting the tumor type from the
sequence data at least one additional time, obtaining a second
sequence data set from the sample and performing the prediction at
least one additional time, reporting the sequence data as
inconsistent, and/or a combination thereof.
[0630] As can be seen in FIG. 12, a tumor type can be predicted
(e.g., BRCA associated breast cancer) and evaluated in the context
of an asserted tumor type or in the context of at least one
additional sequence data set (e.g., reference sequence data, or
sequence data from the same subject and/or the same tumor sample).
If the asserted information matches the determined information, the
data is consistent. If they do not match, it signals a possible
inconsistency which may be evaluated and/or reported to the user.
Further, when the determined value is evaluated in the context of
additional sequence data, it can be used to evaluate whether the
sequence data are from the same subject or source, or from
different sources.
[0631] Accordingly, in some embodiments a predicted tumor type is
determined from the sequence information and evaluated against an
asserted tumor type. In some embodiments, the results are displayed
to a user in a report (e.g., via a GUI).
Example 9
Ratio of Protein Subunits can be Used to Assess Sequence Data
Identity and/or Quality
[0632] Multi-subunit proteins encoded by the nucleic acid can be
used to evaluate the sequence data. The expression levels of
different subunits of a protein can be evaluated by determining the
expression of each subunit (e.g., by determining DNA or RNA levels
encoding each subunit) and determining a ratio of the subunits
(e.g., by determining a ratio of DNA or RNA levels encoding
different protein subunits in a nucleic acid sample). This ratio
(determined information) can then be validated against either
asserted information (e.g., an expected ratio) or additional
sequence data. If the ratio matches an expected ratio (e.g., a
ratio either believed to be accurate based on other sequence data
obtained from the subject, or a known ratio for the protein and its
constituent subunits), the sequence information can be validated.
If the determined ratio does not match the expected ratio, the
sequence data is inconsistent and may indicate a problem with the
sample or sequence data. For example, the sample and/or sequence
data may have been contaminated, misidentified, degraded, or
otherwise corrupted. This can prompt an investigation into the
origin of the inconsistency. Such investigation may entail
determining a new ratio from the sequence data at least one
additional time, obtaining a second sequence data set from the
sample and determining the ratio at least one additional time,
reporting the sequence data as inconsistent, and/or a combination
thereof.
[0633] FIG. 13A shows a graph representing expression levels of
subunits which agree with a predicted or known value for the
subunits being evaluated or are within an acceptable or determined
threshold for such ratio. FIG. 13B shows a graph representing
expression levels of subunits which disagree with the predicted or
known value for the subunits being evaluated or are outside an
acceptable or determined threshold for such ratio.
[0634] As can be seen in FIG. 13A, nucleic acids encoding protein
subunits can be evaluated against a known ratio (e.g., an existing
measured value, or a theoretical value based on sequences known in
the art) or can be evaluated against measured data from known
samples (e.g., fit to a line as shown). When the ratio falls within
accepted or established thresholds for variability and deviation,
it is identified as consistent. As can be seen in FIG. 13B, nucleic
acids encoding protein subunits can evaluated against a known ratio
(e.g., existing measured value, or theoretical value based on
sequences known in the art), or can be evaluated against measured
data from known samples (e.g., fit against a line as shown). When
the ratio falls outside accepted or established thresholds for
variability and deviation, it can be identified as
inconsistent.
[0635] In some embodiments, at least one ratio is determined. In
some embodiments, nucleic acids encoding a second protein and/or
its subunits are evaluated to determine a second ratio. In some
embodiments, nucleic acids encoding a third protein and/or its
subunits are evaluated to determine a third ratio. In some
embodiments, nucleic acids encoding a fourth protein and/or its
subunits are evaluated to determine a fourth ratio. In some
embodiments, nucleic acids encoding at least one additional protein
and/or its subunits are used to determine at least one additional
ratio.
[0636] In some embodiments, the subunits used to determine the
ratio are CD3 subunits CD3D and CD3G. In some embodiments, the
subunits used to determine the ratio are CD3 subunits CD3E and
CD3D. In some embodiments, the subunits used to determine the ratio
are CD3 subunits CD3G and CD3E. In some embodiments, the subunits
used to determine the ratio are CD8 subunits CD8B and CD8A. In some
embodiments, the subunits used to determine the ratio are the CD79
subunits CD79A and CD79B. In some embodiments, the results are
displayed to a user in a report (e.g., via a GUI).
Example 10
Polyadenylation Status can be Used to Assess Sequence Data Identity
and/or Integrity
[0637] PolyA status can be used to evaluate the sequence data. The
sequence data can evaluated to determine whether different genes
are polyadenylated are present or not (e.g., histone genes,
mitochondrial genes). This analysis can be used to evaluate and or
assess the likelihood that an asserted sample preparation protocol
is correct (e.g., to validate whether an RNA sample is a polyA or a
total RNA sample). If the determined polyA status matches the
asserted polyA status, the sequence data is validated as
consistent. If the determined polyA status does not match the
asserted polyA status, the sequence data is identified as
inconsistent and may indicate a problem with the sample or sequence
data. Additionally, in the instance where ambiguous results are
returned for the polyA status (e.g., where polyadenylated genes are
found, but others are not, or where unanticipated expression is
found, or where less than expected expression is found (e.g.,
partial expression)), it may indicate problems with the sample
preparation, degradation of the sample from which the sequence data
was prepared, or other quality issues. For example, the sample
and/or sequence data may have been contaminated, misidentified,
degraded, or otherwise corrupted. This can prompt an investigation
into the origin of the inconsistency. Such investigation may entail
determining a polyA status from the sequence data at least one
additional time, obtaining a second sequence data from the sample
and determining the polyA status at least one additional time,
reporting the sequence data as inconsistent, and/or a combination
thereof.
[0638] FIGS. 14A-14B show examples of bar graphs representing the
probability that sequence information was obtained from samples
that contained only polyadenylated RNA or from samples that
contained total or all RNA (total RNA). FIG. 14A shows positive
results (indicating sequences which appear uniform) from the
analysis of two different sequences. The left set of bars (bars
1-20, as read left to right) show results from a sequence which has
a high probability of being from samples which contained primarily
polyadenylated RNA. The right set of bars (bars 21-40, as read left
to right) show results from a sequence which has a high probability
of being from samples which contained primarily total RNA. FIG. 14B
shows poor results (indicating, for example, possible contamination
or degradation) from the analysis of two different sequences. The
outlined box, tagged "Bad," shows probability of the sequences as
being from polyadenylated RNA about 50%, indicating it is
indeterminate that the sequences are uniform.
[0639] As can be seen in FIG. 14A, the sequence data can be
evaluated and a polyA status can be determined as either
polyadenylated or total RNA in some embodiments. FIG. 14B shows an
example where the determination falls below a threshold of either
polyadenylated or total RNA (e.g., 50% polyA, 50% total RNA). In
this case, the sequence data can be identified as inconsistent
and/or of poor quality and may signal a problem with the nucleic
acid sample.
[0640] Accordingly, in some embodiments the sequence data is
identified as polyadenylated sequence data. In some embodiments,
the sequence data is identified as total RNA sequence data.
[0641] In some embodiments, the threshold for identifying a sample
as polyA is when the percent polyA RNA in a sample is above 50%. In
some embodiments, the threshold is 60%. In some embodiments, the
threshold is 70%. In some embodiments, the threshold is 80%. In
some embodiments, the threshold is 90%. In some embodiments, the
threshold is 95%. In some embodiments, the threshold is 96%. In
some embodiments, the threshold is 97%. In some embodiments, the
threshold is 98%. In some embodiments, the threshold is 99%. In
some embodiments, the results are displayed to a user in a report
(e.g., via a GUI).
Example 11
Exon Coverage can be Used to Assess Sequence Data Identity and/or
Integrity
[0642] Various techniques can be used for evaluating the
consistency of data and/or to group data points for analysis (e.g.,
in some embodiments principal component analysis (PCA) can be
used). Such techniques can be useful in evaluating sequence data
for identity and/or integrity. For instance, exon coverage can be
determined from the sequence information and evaluated to determine
whether there is a consistent level of coverage when compared to
other sequence information or to an asserted (e.g., expected)
coverage level. An inconsistency in the coverage (e.g., higher or
lower coverage than expected) could indicate that the sequence data
is from a different source than expected (e.g., than asserted), or
that there is a problem with the sequence data or the sample from
which it was obtained.
[0643] Exon coverage can be determined for different batches of
sequence data reads from a given subject and plotted against
sequence data from other subjects.
[0644] In some embodiments, the results of the evaluation are
presented to a user in a report (e.g., via a GUI).
Example 12
RNAseq Read Distribution and Composition can be Used to Assess
Sequence Data Identity and/or Integrity
[0645] In some embodiments, read composition can be evaluated in
the context of the number of reads of a given component (e.g.,
protein coding sequence) of the sequence data in terms of either
total number of reads for that component and/or as a relative
percentage of that component calculated against the total number of
reads). These can be compared against a threshold established for
each parameter (e.g., total number of reads, and/or reads of a
component relative to the total number of reads).
[0646] In some embodiments, a threshold is 20 million total reads
per protein coding region. In some embodiments, a threshold for the
relative number of reads of a protein coding region compared to the
total number of reads in a sample is 50% or more. In some
embodiments, the results are displayed to a user in a report (e.g.,
via a GUI).
Example 13
Biomarkers can be Used to Assess Sequence Identity and/or
Integrity
[0647] Biomarkers can also be assessed to evaluate the quality
and/or identity of the sequence data. As shown in FIG. 15, PCA can
be performed to evaluate the expression of biomarkers. The results
can be compared or trained against existing data sets of similar
cohorts. The evaluation can be used to help validate an asserted
information and/or one or more additional sequence data sets. In
some embodiments, this will be useful to determine with increased
likelihood that the sequence information is from a given source or
subject. In contrast, inconsistency (e.g., if the evaluation does
not match the asserted information and/or one or more additional
sequence data sets) may indicate that there is a potential quality
issue related to the data that should be further investigated to
identify the source of the inconsistency (and/or that that the data
should not be used for further analysis).
[0648] In some embodiments, the biomarker is for follicular
lymphoma. In some embodiments, the results are displayed to a user
in a report (e.g., via a GUI).
Example 14
Non-Limiting Examples of Quality Control Metrics for Assessing
Sequence Data Identity and/or Integrity
[0649] In some embodiments, the disclosure relates to a method
wherein at least one of the following additional features is
determined: (1) mean quality score; (2) contamination value; (3) GC
content; (4) duplication level; (5) gene body coverage; and (6) per
chromosome coverage.
[0650] One or more of these determinations can be used to further
assess the source or integrity of the sequence data by comparison
with a reference, or by comparison with at least one additional
sequence data set.
[0651] In some embodiments, at least one additional feature is
determined. In some embodiments, at least two additional features
are determined. In some embodiments, at least three additional
features are determined. In some embodiments, at least four
additional features are determined. In some embodiments, at least
five additional features are determined. In some embodiments, at
least six additional features are determined.
[0652] In some embodiments, evaluation of a concordance value of
single nucleotide polymorphisms (SNPs) comprises: (a) determining a
concordance value of single nucleotide polymorphisms (SNPs) from
the sequence data; and (b) determining whether the concordance
value of the sequence data matches or exceeds a reference
concordance value. In some embodiments, the reference concordance
value is 80%.
[0653] In some embodiments, evaluation of a contamination value
comprises; (a) determining a contamination value of the sequence
data; and (b) determining whether the contamination value is less
than a reference contamination value. In some embodiments, the
reference contamination value is 10%.
[0654] In some embodiments, evaluation of a complexity value
comprises: (a) determining a complexity value of the sequence data;
and (b) determining whether the complexity value matches a
reference complexity value.
[0655] In some embodiments, evaluation of a Phred Score comprises:
(a) determining a Phred Score of the sequence data; and (b)
determining whether the Phred Score matches or exceeds a reference
Phred Score.
[0656] In some embodiments, evaluation of a GC content comprises:
(a) determining a GC content of the sequence data; and (b)
determining if the GC content matches a reference GC content.
[0657] In some embodiments, the methods further comprise generating
a report to display the results of the at least one additional
determination to a user (e.g., via a GUI).
Example 15
Non-Limiting Protocol for Assessing Sequencing Data Quality
Control
[0658] In some embodiments, a quality protocol for sequence data
(e.g., for WES and/or RNAseq data) comprises one or more of the
following steps: [0659] i) In some embodiments, low-quality reads
(for example, based on positional information) are removed. In some
embodiments, low-quality sequences (e.g., reads from low-quality
areas of a sequencing flow cell) are removed from sequence data
(e.g., from a FASTQ file). In some embodiments, if a significant
fraction of the sequence reads are of low-quality (e.g., if bad
tiles represent more than 30%, more than 40%, or more than 50% of a
sequence data file). [0660] ii) In some embodiments, quality
control tool for sequence data (e.g., FastQC as an example) is used
to evaluate one or more of library complexity (e.g., read counts);
quality of the sequencing platform (e.g., based on a per base Phred
quality score); per tile quality score; per sequence GC content
(for example to detect contamination based on an unexpected GC
content), per base sequencing content (e.g., to detect adapter or
other contamination); sequence duplication levels (e.g., to
evaluate the quality of RNA/DNA selection and/or PCR
amplification); and/or adapter content. In some embodiments, a
quality threshold for further analysis includes greater than 10
million read counts (e.g., greater than 20 million read counts),
and/or a Phred score of greater than 25 (e.g., 28 or greater than
28) in more than 30% of reads (e.g., in 50% of reads or more than
50% reads). In some embodiments, a quality control pipeline is
stopped if the quality threshold is not met. [0661] iii) In some
embodiments, sequence data is screened against a library of
sequences (e.g., using FastQ Screen, from Babraham Bioinformatics),
for example to detect cross-species contamination (e.g., from other
sources such as mouse, zebrafish, drosophila, C. elegans,
Saccharomyces, Arabidopsis, microbiome, adapters, vectors, phiX, or
other source). In some embodiments, a quality control threshold for
further analysis based on cross-species contamination is set at
around 10%, around 20%, around 30%, or higher. For example, in some
embodiments, a quality control pipeline is stopped if sequence data
comprises 30% or greater than 30% contamination (e.g., with
bacterial sequence). [0662] iv) In some embodiments, per chromosome
coverage distribution and/or coverage distribution is determined
for one or more specific regions (e.g., one or more CCDS protein
coding regions, exons, etc.) using an analytical tool (e.g.,
Mosdepth). In some embodiments, a quality control threshold for
further analysis involves confirming that sequence data covers
clinically important genomic regions. In some embodiments, a
quality control pipeline is stopped if sequence coverage does not
include one or more target genomic regions of interest. [0663] v)
In some embodiments, an analytical tool (e.g., Picard) is used to
evaluate one or more sequence data parameters such as insert size,
duplicates, mapping, pairing, or other parameter(s). [0664] vi) In
some embodiments, an analytical tool for evaluating RNA sequence
data (e.g., RseQC as an example) is used, for example, to determine
insert size (e.g., inner distance between paired RNA reads),
strandedness (e.g., to determine or confirm whether a stranded or
non-stranded RNA sequence protocol was used), and/or gene body
coverage (e.g., to determine coverage bias, for example associated
with an RNA extraction protocol, for example to distinguish polyA
versus total RNA sequence data). [0665] vii) In some embodiments, a
quality threshold for RNA analysis comprises determining the
percentage duplicates and/or adapter contamination and proceeding
with further analysis for RNA sequence data that has less than 70%
(for example less than 60%, or less than 50%) duplicates and/or
less than 25% (for example less than 20%, less than 15%, or less
than 10%) adapter contamination. Accordingly, an analysis protocol
is terminated, in some embodiments, when RNA sequence data that has
more than 50% (e.g., 60% or more than 60%, or more than 70%)
duplicates and/or more than 10% (e.g., more than 15%, 20% or more
than 20%, or more than 30%) adapter contamination. [0666] viii) In
some embodiments, cross-individual contamination is evaluated
(e.g., using a concordance and/or contamination estimator such as
Conpair), for example to determine the concordance of a pair of
samples (e.g., tumor and normal) obtained from the same patient. In
some embodiments, further analysis is performed if the normal and
tumor samples (e.g., normal and tumor DNA) are identified as being
from the same subject. [0667] ix) In some embodiments, a tumor-type
classifier is used to predict a tumor type from gene expression
data of a sample, and the predicted tumor type is compared to the
asserted tumor type (e.g., the tumor type provided along with the
nucleic acid data). In some embodiments, further analysis is
performed if the predicted and asserted tumor types match. [0668]
x) In some embodiments, an RNA sequence type classifier is used to
predict the library type from RNA sequence data (e.g., based on
specific gene expression levels or patterns). In some embodiments,
further analysis is performed if the predicted library type matches
an asserted library type for the sample being analyzed. [0669] xi)
In some embodiments, an MHC allele composition is determined for
two or more samples (e.g., from tumor and/or normal tissue) from
the same subject. In some embodiments, further analysis is
performed if the MHC allele compositions for the two or more
samples match.
[0670] In some embodiments, one or more of the steps described
above are performed. If sequence data (e.g., RNA and/or DNA
sequence data) fails to satisfy one or more of these quality
control steps, the sequence data can be excluded from further
analysis. In some embodiments, additional sequence data can be
obtained for a subject for which an initial set of sequence data
did not satisfy one or more quality control criteria.
Example Embodiments
[0671] Some embodiments provide for a method comprising: obtaining
a first sample of a first tumor from a subject having, suspected of
having, or at risk of having cancer; extracting RNA from the first
sample of the first tumor; enriching the RNA for coding RNA to
obtain enriched RNA; preparing a first library of DNA fragments
from the enriched RNA for non-stranded RNA sequencing; and
performing non-stranded RNA sequencing on the first library of DNA
fragments prepared from the enriched RNA.
[0672] In some embodiments, the method further comprises extracting
DNA from the first sample of the tumor; preparing a second library
of DNA fragments from the extracted DNA; and
[0673] performing whole exome sequencing (WES) on the second
library of DNA fragments.
[0674] In some embodiments, the method further comprises: obtaining
a first sample of blood from the subject; extracting DNA from the
first sample of blood; preparing a third library of DNA fragments
from the DNA extracted from the first sample of blood; and
performing whole exome sequencing (WES) on the third library of
DNA.
[0675] In some embodiments, the method further comprises obtaining
a second sample of a second tumor from the subject. In some
embodiments, the first tumor and the second tumor are a same tumor.
In some embodiments, the first and second tumors are different
tumors.
[0676] In some embodiments, the method further comprises combining
the first and second tumor samples to form a combined tumor sample,
and extracting the RNA comprises extracting the RNA from the
combined tumor sample.
[0677] In some embodiments, the method further comprises:
extracting RNA from the second sample; and combining the RNA
extracted from the second sample with the RNA extracted from the
first sample to form combined extracted RNA, and wherein enriching
the RNA for coding RNA comprises enriching the combined extracted
RNA for coding RNA. In some embodiments, the method further
comprises: extracting DNA from the second tumor sample; and
[0678] combining the DNA extracted from the second tumor sample
with the DNA extracted from the first tumor sample to form combined
extracted DNA, and preparing a second library of DNA fragments from
the extracted DNA comprises preparing a library of DNA fragment
from the combined extracted DNA.
[0679] In some embodiments, the method further comprises placing
the first sample in a first cryogenic tube, the first cryogenic
tube comprising a composition that is able to penetrate the sample
and protect DNA and/or RNA therein from degradation. In some
embodiments, the method further comprises snap freezing the
contents of the first cryogenic tube.
[0680] In some embodiments, the method further comprises placing
the first sample of blood in a vacutainer comprising an
anticoagulant. In some embodiments, the method further comprises
snap freezing the contents of the vacutainer. In some embodiments,
the snap-frozen contents of the cryogenic tube and/or vacutainer
are stored for up to 7 months at -65.degree. C. to -80.degree.
C.
[0681] In some embodiments, the first tumor sample is at least 20
mg in weight, consist of at least 2.times.10.sup.6 cells, or
provides at least 1 .mu.g of RNA upon RNA extraction.
[0682] In some embodiments, the method further comprises: forming a
single-cell suspension of cells from the first sample of tumor; and
performing mass cytometry on at least a first part of the
single-cell suspension, the at least the first part of the
single-cell suspension comprising at least 5.times.10.sup.6
cells.
[0683] In some embodiments, the method further comprises forming a
lysate from at least a second part of the single-cell suspension,
the at least the second part of the single-cell suspension
comprising at least 2.times.10.sup.6 cells; extracting RNA from the
lysate; performing RNA sequencing on the extracted RNA to obtain
RNA expression data; and/or determining whether the first tumor is
heterogeneous based on the RNA expression data.
[0684] In some embodiments, forming the single-cell suspension of
cells comprises: dissecting the first tumor sample to obtain tumor
sample fragments; incubating the tumor sample fragments in an
enzyme cocktail, the enzyme cocktail comprising penicillin and/or
streptomycin, collagenase I, and collagenase IV; and filtering the
enzyme cocktail through a 70 .mu.m cell strainer.
[0685] In some embodiments, the first sample of blood is at least
0.5-1.0 ml in volume.
[0686] In some embodiments, the RNA extracted from either the first
sample or the second sample is at least 1000-6000 ng in total mass,
has a purity corresponding to a ratio of absorbance at 260 nm to
absorbance at 280 nm of at least 2.0.
[0687] In some embodiments, the DNA extracted from the first sample
is at least 1000-2000 ng in total mass, in at least 10 .mu.l of
solution, of a concentration of 100-200 ng/.mu.l, and has a purity
corresponding to a ratio of absorbance at 260 nm to absorbance at
280 nm of at least 1.8.
[0688] In some embodiments, enriching the RNA for coding RNA
comprises performing polyA enrichment.
[0689] In some embodiments, the WES performed on the second library
of DNA fragments, and the WES performed on the third library of DNA
fragments have at least 100 bp paired-end reads, and an estimated
coverage of at least 100.times..
[0690] In some embodiments, the WES has an estimated coverage of at
least 150.times..
[0691] In some embodiments, the RNA sequencing on the first library
of DNA fragments has at least 100 bp paired-end reads, and an
estimated total number of reads of at least 50 million paired-end
reads.
[0692] In some embodiments, the RNA sequencing on the first library
of DNA fragments at least 100 bp paired-end reads, and an estimated
total number of reads of at least 100 million paired-end reads.
[0693] In some embodiments, the method further comprises subjecting
a sample of any one of the prepared libraries of DNA fragments to
quality control tests to evaluate their integrity and/or peak size,
wherein each sample of the prepared libraries comprises up to 1 ng
of a library.
[0694] In some embodiments, the subject is human.
[0695] Some embodiments provide for a kit, comprising: a
composition that is able to penetrate tissue and protect DNA and/or
RNA therein from degradation; at least one tool for dissecting a
sample of tumor and preparing a single-cell suspension therefrom;
at least one reagent for snap-freezing of biological samples; an
anticoagulant; at least one vacutainer; at least one reagent for
extracting DNA and RNA from tissue samples and blood; and at least
one reagent for preparing DNA libraries from DNA and/or RNA
samples.
[0696] Some embodiments provide for a kit for use in a method
according to any of the preceding examples.
[0697] Some embodiments provide for a system comprising at least
one computer hardware processor and at least one non-transitory
computer-readable storage medium storing processor-executable
instructions that, when executed by the at least one computer
hardware processor, cause the at least one computer hardware
processor to perform a method for processing RNA expression data.
The method comprises using at least one computer hardware processor
to perform: obtaining RNA expression data for a subject having,
suspected of having, or at risk of having cancer; aligning and
annotating genes in the RNA expression data with known sequences of
the human genome to obtain annotated RNA expression data; removing
non-coding transcripts from the annotated RNA expression data;
converting the annotated RNA expression data to gene expression
data in transcripts per kilobase million (TPM); identifying at
least one gene that introduces bias in the gene expression data;
removing the at least one gene from the gene expression data to
obtain bias-corrected gene expression data; and identifying a
cancer treatment for the subject using the bias-corrected gene
expression data.
[0698] Some embodiments provide for at least one non-transitory
computer-readable storage medium storing processor-executable
instructions that, when executed by at least one computer hardware
processor, cause the at least one computer hardware processor to
perform a method for processing RNA expression data. The method
comprises using at least one computer hardware processor to
perform: obtaining RNA expression data for a subject having,
suspected of having, or at risk of having cancer; aligning and
annotating genes in the RNA expression data with known sequences of
the human genome to obtain annotated RNA expression data; removing
non-coding transcripts from the annotated RNA expression data;
converting the annotated RNA expression data to gene expression
data in transcripts per kilobase million (TPM); identifying at
least one gene that introduces bias in the gene expression data;
removing the at least one gene from the gene expression data to
obtain bias-corrected gene expression data; and identifying a
cancer treatment for the subject using the bias-corrected gene
expression data.
[0699] Some embodiments provide for a method for processing RNA
expression data, the method comprising using at least one computer
hardware processor to perform: obtaining RNA expression data for a
subject having, suspected of having, or at risk of having cancer;
aligning and annotating genes in the RNA expression data with known
sequences of the human genome to obtain annotated RNA expression
data; removing non-coding transcripts from the annotated RNA
expression data; converting the annotated RNA expression data to
gene expression data in transcripts per kilobase million (TPM);
identifying at least one gene that introduces bias in the gene
expression data; removing the at least one gene from the gene
expression data to obtain bias-corrected gene expression data; and
identifying a cancer treatment for the subject using the
bias-corrected gene expression data.
[0700] In some embodiments, identifying the at least one gene from
the gene expression data comprises identifying at least one gene
having an average transcript length at least a threshold amount
higher or lower than an average length of transcripts in the gene
expression data.
[0701] In some embodiments, identifying the at least one gene from
the gene expression data comprises identifying at least one gene
having at least a threshold variation in average transcript
expression level based on transcript expression levels in reference
samples.
[0702] In some embodiments, identifying the at least one gene from
gene expression data comprises identifying one or more genes having
a polyA tail that is at least a threshold amount smaller in length
compared to an average length of polyA tails of genes from a sample
from which the RNA expression data was obtained.
[0703] In some embodiments, the at least one gene belongs to a
family of genes selected from the group consisting of:
histone-encoding genes, mitochondrial genes, interleukin-encoding
genes, collagen-encoding genes, B-cell receptor-encoding genes, and
T cell receptor-encoding genes.
[0704] In some embodiments, the at least one gene comprises at
least one histone-encoding gene selected from the group consisting
of: HIST1H1A, HIST1H1B, HIST1H1C, HIST1H1D, HIST1H1E, HIST1H1T,
HIST1H2AA, HIST1H2AB, HIST1H2AC, HIST1H2AD, HIST1H2AE, HIST1H2AG,
HIST1H2AH, HIST1H2AI, HIST1H2AJ, HIST1H2AK, HIST1H2AL, HIST1H2AM,
HIST1H2BA, HIST1H2BB, HIST1H2BC, HIST1H2BD, HIST1H2BE, HIST1H2BF,
HIST1H2BG, HIST1H2BH, HIST1H2BI, HIST1H2BJ, HIST1H2BK, HIST1H2BL,
HIST1H2BM, HIST1H2BN, HIST1H2BO, HIST1H3A, HIST1H3B, HIST1H3C,
HIST1H3D, HIST1H3E, HIST1H3F, HIST1H3G, HIST1H3H, HIST1H3I,
HIST1H3J, HIST1H4A, HIST1H4B, HIST1H4C, HIST1H4D, HIST1H4E,
HIST1H4F, HIST1H4G, HIST1H4H, HIST1H4I, HIST1H4J, HIST1H4K,
HIST1H4L, HIST2H2AA3, HIST2H2AA4, HIST2H2AB, HIST2H2AC, HIST2H2BE,
HIST2H2BF, HIST2H3A, HIST2H3C, HIST2H3D, HIST2H3PS2, HIST2H4A,
HIST2H4B, HIST3H2A, HIST3H2BB, HIST3H3, and HIST4H4.
[0705] In some embodiments, the at least one gene comprises at
least one mitochondrial gene selected from the group consisting of:
MT-ATP6, MT-ATPS, MT-CO1, MT-CO2, MT-CO3, MT-CYB, MT-ND1, MT-ND2,
MT-ND3, MT-ND4, MT-ND4L, MT-ND5, MT-ND6, MT-RNR1, MT-RNR2, MT-TA,
MT-TC, MT-TD, MT-TE, MT-TF, MT-TG, MT-TH, MT-TI, MT-TK, MT-TL1,
MT-TL2, MT-TM, MT-TN, MT-TP, MT-TQ, MT-TR, MT-TS1, MT-TS2, MT-TT,
MT-TV, MT-TW, MT-TY, MTRNR2L1, MTRNR2L10, MTRNR2L11, MTRNR2L12,
MTRNR2L13, MTRNR2L3, MTRNR2L4, MTRNR2L5, MTRNR2L6, MTRNR2L7, and
MTRNR2L8.
[0706] In some embodiments, the RNA expression data is
characterized by at least 100 bp paired-end reads, and an estimated
coverage of at least 50 million paired-end reads.
[0707] In some embodiments, the RNA expression data is
characterized by at least 100 bp paired-end reads, and an estimated
total number of reads of at least 100 million paired-end reads.
[0708] In some embodiments, aligning genes in the RNA expression
data is performed using a GRCh38 genome assembly.
[0709] In some embodiments, annotating the genes in the RNA
expression data is based on GENCODE V23 comprehensive annotation
(www.gencodegenes.org).
[0710] In some embodiments, the removed non-coding transcripts
belong to groups selected from the list consisting of: pseudogenes,
polymorphic pseudogenes, processed pseudogenes, transcribed
processed pseudogenes, unitary pseudogenes, unprocessed
pseudogenes, transcribed unitary pseudogenes, constant chain
immunoglobulin (IG C) pseudogenes, joining chain immunoglobulin (IG
J) pseudogenes, variable chain immunoglobulin (IG V) pseudogenes,
transcribed unprocessed pseudogenes, translated unprocessed
pseudogenes, joining chain T cell receptor (TR J) pseudogenes,
variable chain T cell receptor (TR V) pseudogenes, small nuclear
RNAs (snRNA), small nucleolar RNAs (snoRNA), microRNAs (miRNA),
ribozymes, ribosomal RNA (rRNA), mitochondrial tRNAs (Mt tRNA),
mitochondrial rRNAs (Mt rRNA), small Cajal body-specific RNAs
(scaRNA), retained introns, sense intronic RNA, sense overlapping
RNA, nonsense-mediated decay RNA, non-stop decay RNA, antisense
RNA, long intervening noncoding RNAs (lincRNA), macro long
non-coding RNA (macro lncRNA), processed transcripts, 3prime
overlapping non-coding RNA (3prime overlapping ncrna), small RNAs
(sRNA), miscellaneous RNA (misc RNA), vault RNA (vaultRNA), and TEC
RNA.
[0711] In some embodiments, the RNA expression data has been
obtained by performing RNA sequencing on one or more samples of a
subject's tumor.
[0712] In some embodiments, identifying the cancer treatment for
the subject using the bias-corrected gene expression data
comprises: determining, using the bias-corrected gene expression
data, a gene group expression level for each gene group in a set of
gene groups, wherein the set of gene group comprises at least one
gene group associated with cancer malignancy, and at least one gene
group associated with cancer microenvironment; and identifying the
cancer treatment using the determined gene group expression levels.
In some embodiments, the method further comprises: administering
the cancer treatment to the subject.
[0713] Some embodiments provide for a method comprising: enriching
RNA for coding RNA in a sample of extracted RNA from a first tumor
sample from a subject having, suspected of having, or at risk of
having cancer; performing non-stranded RNA sequencing on a first
library of cDNA fragments prepared from the enriched RNA to obtain
RNA expression data; converting the RNA expression data to gene
expression data in transcripts per kilobase million (TPM);
identifying at least one gene that introduces bias in the gene
expression data; removing, from the gene expression data,
expression data associated with the at least one gene to obtain
bias-corrected gene expression data; and identifying a therapy for
the subject using the bias-corrected gene expression data. In some
embodiments, the method further comprises administering to the
subject the identified therapy.
[0714] In some embodiments, identifying the therapy for the subject
using the bias-corrected gene expression data comprises:
determining, using the bias-corrected gene expression data, a
plurality of gene group expression levels comprising a gene group
expression level for each gene group in a set of gene groups,
wherein the set of gene groups comprises at least one gene group
associated with cancer malignancy, and at least one gene group
associated with cancer microenvironment; and identifying the
therapy using the determined plurality of gene group expression
levels.
[0715] In some embodiments, identifying the at least one gene that
introduces bias in the gene expression data comprises identifying
at least one gene having an average transcript length at least a
threshold amount higher or lower than an average length of
transcripts in the gene expression data.
[0716] In some embodiments, identifying the at least one gene that
introduces bias in the gene expression data comprises identifying
at least one gene having at least a threshold variation in average
transcript expression level based on transcript expression levels
in reference samples.
[0717] In some embodiments, identifying the at least one gene
comprises identifying one or more genes having a polyA tail that is
at least a threshold amount smaller in length compared to an
average length of polyA tails of genes from a sample from which the
RNA expression data was obtained.
[0718] In some embodiments, the at least one gene belongs to a
family of genes selected from the group consisting of:
histone-encoding genes, mitochondrial genes, interleukin-encoding
genes, collagen-encoding genes, B-cell receptor-encoding genes, and
T cell receptor-encoding genes.
[0719] In some embodiments, the at least one gene comprises at
least one histone-encoding gene selected from the group consisting
of: HIST1H1A, HIST1H1B, HIST1H1C, HIST1H1D, HIST1H1E, HIST1H1T,
HIST1H2AA, HIST1H2AB, HIST1H2AC, HIST1H2AD, HIST1H2AE, HIST1H2AG,
HIST1H2AH, HIST1H2AI, HIST1H2AJ, HIST1H2AK, HIST1H2AL, HIST1H2AM,
HIST1H2BA, HIST1H2BB, HIST1H2BC, HIST1H2BD, HIST1H2BE, HIST1H2BF,
HIST1H2BG, HIST1H2BH, HIST1H2BI, HIST1H2BJ, HIST1H2BK, HIST1H2BL,
HIST1H2BM, HIST1H2BN, HIST1H2BO, HIST1H3A, HIST1H3B, HIST1H3C,
HIST1H3D, HIST1H3E, HIST1H3F, HIST1H3G, HIST1H3H, HIST1H3I,
HIST1H3J, HIST1H4A, HIST1H4B, HIST1H4C, HIST1H4D, HIST1H4E,
HIST1H4F, HIST1H4G, HIST1H4H, HIST1H4I, HIST1H4J, HIST1H4K,
HIST1H4L, HIST2H2AA3, HIST2H2AA4, HIST2H2AB, HIST2H2AC, HIST2H2BE,
HIST2H2BF, HIST2H3A, HIST2H3C, HIST2H3D, HIST2H3PS2, HIST2H4A,
HIST2H4B, HIST3H2A, HIST3H2BB, HIST3H3, and HIST4H4.
[0720] In some embodiments, the at least one gene comprises at
least one mitochondrial gene selected from the group consisting of:
MT-ATP6, MT-ATPS, MT-CO1, MT-CO2, MT-CO3, MT-CYB, MT-ND1, MT-ND2,
MT-ND3, MT-ND4, MT-ND4L, MT-ND5, MT-ND6, MT-RNR1, MT-RNR2, MT-TA,
MT-TC, MT-TD, MT-TE, MT-TF, MT-TG, MT-TH, MT-TI, MT-TK, MT-TL1,
MT-TL2, MT-TM, MT-TN, MT-TP, MT-TQ, MT-TR, MT-TS1, MT-TS2, MT-TT,
MT-TV, MT-TW, MT-TY, MTRNR2L1, MTRNR2L10, MTRNR2L11, MTRNR2L12,
MTRNR2L13, MTRNR2L3, MTRNR2L4, MTRNR2L5, MTRNR2L6, MTRNR2L7, and
MTRNR2L8.
[0721] In some embodiments, the RNA expression data is
characterized by at least 100 bp paired-end reads, and an estimated
read depth of at least 50 million paired-end reads.
[0722] In some embodiments, the method further comprises: aligning
and annotating genes in the RNA expression data with known
sequences of the human genome to obtain annotated RNA expression
data before identifying at least one gene that introduces bias in
the gene expression data, aligning genes in the RNA expression data
is performed using a GRCh38 genome assembly, and annotating the
genes in the RNA expression data is performed using a GENCODE V23
comprehensive annotation (www.gencodegenes.org).
[0723] In some embodiments, the method further comprises: removing
non-coding transcripts from the RNA expression data, wherein the
removed non-coding transcripts belong to groups selected from the
list consisting of: pseudogenes, polymorphic pseudogenes, processed
pseudogenes, transcribed processed pseudogenes, unitary
pseudogenes, unprocessed pseudogenes, transcribed unitary
pseudogenes, constant chain immunoglobulin (IG C) pseudogenes,
joining chain immunoglobulin (IG J) pseudogenes, variable chain
immunoglobulin (IG V) pseudogenes, transcribed unprocessed
pseudogenes, translated unprocessed pseudogenes, joining chain T
cell receptor (TR J) pseudogenes, variable chain T cell receptor
(TR V) pseudogenes, small nuclear RNAs (snRNA), small nucleolar
RNAs (snoRNA), microRNAs (miRNA), ribozymes, ribosomal RNA (rRNA),
mitochondrial tRNAs (Mt tRNA), mitochondrial rRNAs (Mt rRNA), small
Cajal body-specific RNAs (scaRNA), retained introns, sense intronic
RNA, sense overlapping RNA, nonsense-mediated decay RNA, non-stop
decay RNA, antisense RNA, long intervening noncoding RNAs
(lincRNA), macro long non-coding RNA (macro lncRNA), processed
transcripts, 3prime overlapping non-coding RNA (3prime overlapping
ncrna), small RNAs (sRNA), miscellaneous RNA (misc RNA), vault RNA
(vaultRNA), and TEC RNA.
[0724] In some embodiments, the method further comprises: obtaining
a first sample of a first tumor from a subject having or suspected
of having cancer, and extracting RNA from the first sample of the
first tumor to obtain the sample of extracted RNA; before enriching
the RNA for coding RNA. In some embodiments, the method further
comprises obtaining a second sample of a second tumor from the
subject.
[0725] In some embodiments, the method further comprises: combining
the first and second samples to form a combined tumor sample, and
extracting the RNA comprises extracting the RNA from the combined
tumor sample.
[0726] In some embodiments, the method further comprises:
extracting RNA from the second sample; combining the RNA extracted
from the second sample with the RNA extracted from the first sample
to form combined extracted RNA, and enriching the RNA for coding
RNA comprises enriching the combined extracted RNA for coding
RNA.
[0727] In some embodiments, the sample of extracted RNA comprises
at least 1 .mu.g of RNA upon RNA extraction.
[0728] In some embodiments, the extracted RNA is at least 1000-6000
ng in total mass, has a purity corresponding to a ratio of
absorbance at 260 nm to absorbance at 280 nm of at least 2.0.
[0729] In some embodiments, enriching the RNA for coding RNA
comprises performing polyA enrichment.
[0730] Some embodiments provide for a system, comprising: at least
one computer hardware processor; and at least one non-transitory
computer-readable storage medium storing processor-executable
instructions that, when executed by the at least one computer
hardware processor, cause the at least one computer hardware
processor to perform: a method, comprising: (a) obtaining nucleic
acid data comprising: (i) sequence data comprising at least 5
kilobases (kb) of DNA and/or RNA, the sequence data obtained by
sequencing a biological sample of a subject having, suspected of
having, or at risk of having a disease; and (ii) asserted
information indicating an asserted source and/or an asserted
integrity of the sequence data; and (b) validating the nucleic acid
data by: (i) processing the sequence data to obtain determined
information indicating a determined source and/or a determined
integrity of the sequence data; and (ii) determining whether the
determined information matches the asserted information.
[0731] Some embodiments provide for at least one non-transitory
computer-readable storage medium storing processor-executable
instructions that, when executed by at least one computer hardware
processor, cause the at least one computer hardware processor to
perform: a method, comprising: (a) obtaining nucleic acid data
comprising: (i) sequence data comprising at least 5 kilobases (kb)
of DNA and/or RNA, the sequence data obtained by sequencing a
biological sample of a subject having, suspected of having, or at
risk of having a disease; and (ii) asserted information indicating
an asserted source and/or an asserted integrity of the sequence
data; and (b) validating the nucleic acid data by: (i) processing
the sequence data to obtain determined information indicating a
determined source and/or a determined integrity of the sequence
data; and (ii) determining whether the determined information
matches the asserted information.
[0732] Some embodiments provide for a method, comprising: (a)
obtaining nucleic acid data comprising: (i)sequence data comprising
at least 5 kilobases (kb) of DNA and/or RNA, the sequence data
obtained by sequencing a biological sample of a subject having,
suspected of having, or at risk of having a disease; and (ii)
asserted information indicating an asserted source and/or an
asserted integrity of the sequence data; and (b) validating the
nucleic acid data by:
[0733] (i) processing the sequence data to obtain determined
information indicating a determined source and/or a determined
integrity of the sequence data; and (ii) determining whether the
determined information matches the asserted information.
[0734] In some embodiments, when it is determined that the asserted
information matches the determined information: (i) accessing a
database of disease features; and (ii) processing the sequence data
to determine whether it is indicative of one or more of the disease
features; and
[0735] (d) when it is determined that the asserted information does
not match the determined information: (i) indicating to a user that
the determined and asserted information do not match; (ii)
excluding the sequence data from further analysis; and/or (iii)
obtaining additional sequence data and/or other information about
the biological sample and/or the subject.
[0736] In some embodiments, the asserted information for the
sequence data is based on (one, at least two, at least three,
between 2 and 10, between 5 and 10 pieces of) information selected
from the group consisting of: MHC allele sequence information;
nucleic acid type; subject identity; sample identity; tissue type
from which the sample was obtained; tumor type from which the
sample was obtained; sequencing platform used to generate the
sequence data; sequence integrity; polyA status of an RNA sample
(e.g., indicating whether the RNA sample was polyA enriched); total
sequence coverage; exon coverage; chromosomal coverage; ratio of
expression levels of nucleic acids encoding two or more subunits of
the same protein; contamination; single nucleotide polymorphisms
(SNPs); complexity; and/or guanine (G) and cytosine (C) percentage
(%).
[0737] In some embodiments, the determined information for the
sequence data is based on (one, at least two, at least three,
between 2 and 10, between 5 and 10 pieces of) information selected
from the group consisting of: MHC allele sequence information;
nucleic acid type; subject identity; sample identity; tissue type
from which the sample was obtained; tumor type from which the
sample was obtained; sequencing platform used to generate the
sequence data; sequence integrity; polyA status of an RNA sample
(e.g., indicating whether the RNA sample was polyA enriched); total
sequence coverage; exon coverage; chromosomal coverage; ratio of
expression levels of nucleic acids encoding two or more subunits of
the same protein; contamination; single nucleotide polymorphisms
(SNPs); complexity; and/or guanine (G) and cytosine (C) percentage
(%).
[0738] In some embodiments, the disease is cancer. In some
embodiments, the subject is human.
[0739] In some embodiments, the source of the sequence data is a
subject, a tissue type, a tumor type, an RNA sequence type, or a
DNA sequence type.
[0740] In some embodiments, the subject from which the sequence
data is obtained is evaluated by determining one or more MHC
sequences, for example, by determining MHC sequences for six MHC
loci.
[0741] In some embodiments, the source of one or more nucleic acid
sequence data sets is evaluated by determining a SNP concordance
for the nucleic acid sequence data sets.
[0742] In some embodiments, the integrity of the sequence data is
evaluated by determining exon coverage, one or more ratios of
protein subunit encoding nucleic acids, and/or gene coverage of the
sequence data.
[0743] In some embodiments, the integrity of RNA sequence data is
evaluated by determining coverage of one or more genes in the RNA
sequence data.
[0744] In some embodiments, the integrity of RNA sequence data is
evaluated by determining a relative coverage of two or more exons
for at least one gene in the RNA sequence data.
[0745] In some embodiments, the integrity of RNA sequence data is
evaluated by determining an expression ratio of two known reference
genes in the RNA sequence data.
[0746] In some embodiments, the method further comprises
determining a level of nucleic acid degradation, contamination,
and/or GC content.
[0747] In some embodiments, determining whether RNA sequence data
is polyA RNA sequence data or total RNA sequence data comprises
determining the expression level of one or more mitochondrial
and/or histone genes in the RNA sequence data.
[0748] In some embodiments, the sequencing platform that was used
for generating WES sequence data is identified by determining a
percent (%) variance for one or more reference genes in the WES
sequence data.
[0749] In some embodiments, the method further comprises generating
a report that indicates an extent of a match between one or more
features that are determined from the sequence data and one or more
corresponding asserted features in the asserted information.
Equivalents and Scope
[0750] All of the features described in this specification may be
combined in any combination. Each feature described in this
specification may be replaced by an alternative feature serving the
same, equivalent, or similar purpose. Thus, unless expressly stated
otherwise, each feature described is only an example of a generic
series of equivalent or similar features.
[0751] All of the features described in this specification may be
combined in any combination. Each feature described in this
specification may be replaced by an alternative feature serving the
same, equivalent, or similar purpose. Thus, unless expressly stated
otherwise, each feature described is only an example of a generic
series of equivalent or similar features.
[0752] From the above description, one skilled in the art can
easily ascertain the essential characteristics of the present
disclosure, and without departing from the spirit and scope
thereof, can make various changes and modifications of the
disclosure to adapt it to various usages and conditions. Thus,
other embodiments are also within the claims.
[0753] The terms "program" or "software" are used herein in a
generic sense to refer to any type of computer code or set of
processor-executable instructions that can be employed to program a
computer or other processor (physical or virtual) to implement
various aspects of embodiments as described above. Additionally,
according to one aspect, one or more computer programs that when
executed perform methods of the technology described herein need
not reside on a single computer or processor, but may be
distributed in a modular fashion among different computers or
processors to implement various aspects of the technology described
herein.
[0754] Processor-executable instructions may be in many forms, such
as program modules, executed by one or more computers or other
devices. Generally, program modules include routines, programs,
objects, components, data structures, etc. that performs particular
tasks or implement particular abstract data types. Typically, the
functionality of the program modules may be combined or
distributed.
[0755] Also, data structures may be stored in one or more
non-transitory computer-readable storage media in any suitable
form. For simplicity of illustration, data structures may be shown
to have fields that are related through location in the data
structure. Such relationships may likewise be achieved by assigning
storage for the fields with locations in a non-transitory
computer-readable medium that convey relationship between the
fields. However, any suitable mechanism may be used to establish
relationships among information in fields of a data structure,
including through the use of pointers, tags or other mechanisms
that establish relationships among data elements.
[0756] Various inventive concepts may be embodied as one or more
processes, of which examples have been provided. The acts performed
as part of each process may be ordered in any suitable way. Thus,
embodiments may be constructed in which acts are performed in an
order different than illustrated, which may include performing some
acts simultaneously, even though shown as sequential acts in
illustrative embodiments.
[0757] As used herein in the specification and in the claims, the
phrase "at least one," in reference to a list of one or more
elements, should be understood to mean at least one element
selected from any one or more of the elements in the list of
elements, but not necessarily including at least one of each and
every element specifically listed within the list of elements and
not excluding any combinations of elements in the list of elements.
This definition also allows that elements may optionally be present
other than the elements specifically identified within the list of
elements to which the phrase "at least one" refers, whether related
or unrelated to those elements specifically identified. Thus, for
example, "at least one of A and B" (or, equivalently, "at least one
of A or B," or, equivalently "at least one of A and/or B") can
refer, in one embodiment, to at least one, optionally including
more than one, A, with no B present (and optionally including
elements other than B); in another embodiment, to at least one,
optionally including more than one, B, with no A present (and
optionally including elements other than A); in yet another
embodiment, to at least one, optionally including more than one, A,
and at least one, optionally including more than one, B (and
optionally including other elements); etc.
[0758] The phrase "and/or," as used herein in the specification and
in the claims, should be understood to mean "either or both" of the
elements so conjoined, i.e., elements that are conjunctively
present in some cases and disjunctively present in other cases.
Multiple elements listed with "and/or" should be construed in the
same fashion, i.e., "one or more" of the elements so conjoined.
Other elements may optionally be present other than the elements
specifically identified by the "and/or" clause, whether related or
unrelated to those elements specifically identified. Thus, as an
example, a reference to "A and/or B", when used in conjunction with
open-ended language such as "comprising" can refer, in one
embodiment, to A only (optionally including elements other than B);
in another embodiment, to B only (optionally including elements
other than A); in yet another embodiment, to both A and B
(optionally including other elements); etc.
[0759] In the claims articles such as "a," "an," and "the" may mean
one or more than one unless indicated to the contrary or otherwise
evident from the context. Claims or descriptions that include "or"
between one or more members of a group are considered satisfied if
one, more than one, or all of the group members are present in,
employed in, or otherwise relevant to a given product or process
unless indicated to the contrary or otherwise evident from the
context. The disclosure includes embodiments in which exactly one
member of the group is present in, employed in, or otherwise
relevant to a given product or process. The disclosure includes
embodiments in which more than one, or all of the group members are
present in, employed in, or otherwise relevant to a given product
or process.
[0760] Furthermore, the described methods and systems encompass all
variations, combinations, and permutations in which one or more
limitations, elements, clauses, and descriptive terms from one or
more of the listed claims are introduced into another claim. For
example, any claim that is dependent on another claim can be
modified to include one or more limitations found in any other
claim that is dependent on the same base claim. Where elements are
presented as lists, e.g., in Markush group format, each subgroup of
the elements is also described, and any element(s) can be removed
from the group. It should it be understood that, in general, where
the systems and methods described herein (or aspects thereof) are
referred to as comprising particular elements and/or features,
certain embodiments of the systems and methods or aspects of the
same consist, or consist essentially of, such elements and/or
features. For purposes of simplicity, those embodiments have not
been specifically set forth in haec verba herein.
[0761] It is also noted that the terms "including," "comprising,"
"having," "containing", "involving", are intended to be open and
permits the inclusion of additional elements or steps.
[0762] Where ranges are given, endpoints are included. Furthermore,
unless otherwise indicated or otherwise evident from the context
and understanding of one of ordinary skill in the art, values that
are expressed as ranges can assume any specific value or sub-range
within the stated ranges in different embodiments of the described
systems and methods, to the tenth of the unit of the lower limit of
the range, unless the context clearly dictates otherwise.
[0763] Use of ordinal terms such as "first," "second," "third,"
etc., in the claims to modify a claim element does not by itself
connote any priority, precedence, or order of one claim element
over another or the temporal order in which acts of a method are
performed. Such terms are used merely as labels to distinguish one
claim element having a certain name from another element having a
same name (but for use of the ordinal term).
[0764] Additionally, as used herein the terms "patient" and
"subject" may be used interchangeably. Such terms may include, but
are not limited to, human subjects or patients. Such terms may also
include non-human primates or other animals.
[0765] The terms "approximately", "substantially," and "about" may
be used to mean within .+-.20% of a target value in some
embodiments, within .+-.10% of a target value in some embodiments,
within .+-.5% of a target value in some embodiments, and within
.+-.2% of a target value in some embodiments. The terms
"approximately" and "about" may include the target value.
[0766] This application refers to various issued patents, published
patent applications, journal articles, and other publications, all
of which are incorporated herein by reference. If there is a
conflict between any of the incorporated references and the instant
specification, the specification shall control. In addition, any
particular embodiment of the present disclosure that fall within
the prior art may be explicitly excluded from any one or more of
the claims. Because such embodiments are deemed to be known to one
of ordinary skill in the art, they may be excluded even if the
exclusion is not set forth explicitly herein. Any particular
embodiment of the systems and methods described herein can be
excluded from any claim, for any reason, whether or not related to
the existence of prior art.
[0767] Those skilled in the art will recognize or be able to
ascertain using no more than routine experimentation many
equivalents to the specific embodiments described herein. The scope
of the present embodiments described herein is not intended to be
limited to the above Description, but rather is as set forth in the
appended claims. Those of ordinary skill in the art will appreciate
that various changes and modifications to this description may be
made without departing from the spirit or scope of the present
disclosure, as defined in the following claims.
* * * * *
References