U.S. patent application number 17/020715 was filed with the patent office on 2021-08-19 for systems and methods for matching oncology signatures.
The applicant listed for this patent is The Trustees of Columbia University in the City of New York. Invention is credited to Mariano Javier Alvarez, Andrea Califano.
Application Number | 20210257044 17/020715 |
Document ID | / |
Family ID | 1000005553431 |
Filed Date | 2021-08-19 |
United States Patent
Application |
20210257044 |
Kind Code |
A1 |
Califano; Andrea ; et
al. |
August 19, 2021 |
SYSTEMS AND METHODS FOR MATCHING ONCOLOGY SIGNATURES
Abstract
Techniques to profile a disease or a disorder (e.g., a tumor)
based on a protein activity signature are disclosed herein. An
example method can include measuring quantitatively protein
activity of a plurality of master regulator proteins in a sample
from a disease or disorder; and profiling the tumor from the
quantitative protein activity of the master regulator proteins.
Also disclosed are methods of identifying a compound or compounds
that treats diseases or disorders (e.g., inhibit tumor cell
growth).
Inventors: |
Califano; Andrea; (New York,
NY) ; Alvarez; Mariano Javier; (Cortlandt Manor,
NY) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
The Trustees of Columbia University in the City of New
York |
New York |
NY |
US |
|
|
Family ID: |
1000005553431 |
Appl. No.: |
17/020715 |
Filed: |
September 14, 2020 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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15249069 |
Aug 26, 2016 |
10777299 |
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17020715 |
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62211562 |
Aug 28, 2015 |
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62253342 |
Nov 10, 2015 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G01N 33/68 20130101;
G01N 33/57484 20130101; G01N 2500/20 20130101; G16B 5/00
20190201 |
International
Class: |
G16B 5/00 20060101
G16B005/00; G01N 33/574 20060101 G01N033/574; G01N 33/68 20060101
G01N033/68 |
Goverment Interests
GRANT INFORMATION
[0002] This invention was made with Government Support under Grant
Nos. CA121852 and CA168426 awarded by National Institutes of
Health. The Government has certain rights in the invention.
Claims
1-14. (canceled)
15. A processing method using a processor for screening for a
therapeutic compound or agent based on an individual subject's
sample, comprising: identifying a cell or tissue sample from a
subject having a disease or disorder; identifying a library
comprising a plurality of possible therapeutic compounds or agents;
quantifying a protein activity of each of a plurality of master
regulator proteins (MRPs) in the subject's cell or tissue sample to
provide a subject sample-specific MRP activity signature comprising
a plurality of activated and/or deactivated MRPs characteristic of
the disease or disorder, wherein quantifying the protein activity
of each MRP comprises obtaining a measured expression level for
each of a plurality of transcriptional targets (regulon) for each
MRP, and computationally inferring, using an algorithm processing
arrangement, each MRP activity based, at least in part, on the
measured regulon expression levels in the context of a
tissue-specific regulatory model; comparing, for each of the
plurality of possible therapeutic compounds or agents, the subject
sample-specific MRP activity signature to each of a corresponding
plurality of quantified compound-perturbed or agent-perturbed MRP
activity signatures of a cell line or an in vitro model that
reflects, prior to perturbation in each case, the subject
sample-specific MRP activity signature; computing, using the
algorithm processing arrangement, for each of the plurality of
possible therapeutic compounds or agents, a statistical enrichment
of the activated MRPs among the MRPs most deactivated by the
compound or agent, and/or a statistical enrichment of the
inactivated MRPs among the MRPs most induced by the compound or
agent; determining, using the algorithm processing arrangement, a
subject sample-specific ranking of the compounds or agents
according to the degree of enrichment, wherein the compounds or
agents inducing the greatest enrichment are deemed as having the
highest therapeutic value for the subject; and identifying, among
the plurality of possible therapeutic compounds or agents, a
therapeutic compound or agent for treating the subject's disease or
disorder based on the subject sample and the subject
sample-specific ranking.
16. The method of claim 15, wherein, with respect to the
sample-specific MRP activity signature, the compound or agent
induces global inversion of the protein activities of the activated
MRPs among the MRPs most inactivated by the compound or agent,
and/or of the inactivated MRPs among the MRPs most induced by the
compound or agent.
17. The method of claim 15, wherein said compound or agent is
selected from the group consisting of small molecule chemical
compounds, peptides, nucleic acids, oligonucleotides, antibodies,
aptamers, modifications thereof, and combinations thereof.
18. The method of claim 15, wherein the disease or disorder is a
tumor.
19. The method of claim 18, wherein the tumor is selected from the
group consisting of glioblastoma, meningioma, leukemia, lymphoma,
sarcoma, carcinoid, neuroendocrine, paraganglioma, melanoma,
prostate, pancreatic, bladder, stomach, colon, breast, head &
neck, kidney, gastric, small intestine, ovarian, hepatocellular,
uterine corpus, and lung carcinoma.
20-21. (canceled)
22. The method of claim 15, wherein computationally inferring the
protein activity of each MRP, comparing the subject sample-specific
MRP activity signature to each of a plurality of quantified
compound-perturbed or agent-perturbed MRP activity signatures, and
computing, for each compound or agent, a statistical enrichment of
the activated MRPs, and/or the statistical enrichment of the
inactivated MRPs, comprises use of virtual inference of protein
activity by enriched regulon analysis (VIPER).
23-25. (canceled)
26. The method of claim 15, wherein said sample from said disease
or disorder is derived from an in vivo source and/or derived from
an in vitro source.
27. The method of claim 26, wherein said in vitro source is an in
vitro model of the disease or disorder that has a similar master
regulator signature profile for said disease or disorder.
28. The method of claim 26, wherein said sample is selected from
the group consisting of tissue extracts, cells, tissues, organs,
blood, blood serum, body fluids, and combinations thereof.
29. The method of claim 26, wherein said sample from said disease
or disorder is at least one selected from the group consisting of a
cell line, cultured cells, cultured tissue, and cultured tumor.
30. The method of claim 15, wherein said regulons are inferred by
ARACNe.
31. The method of claim 15, further comprising validating the
efficacy of a ranked drug using an in vivo model.
32. The method of claim 31, wherein the in vivo model comprises a
xenograft model.
33. A system, comprising a processor, for computationally inferring
the protein activities of each of a plurality of MRP, comparing a
subject sample-specific MRP activity signature to each of a
plurality of quantified compound-perturbed or agent-perturbed MRP
activity signatures, and computing, for each compound or agent, a
statistical enrichment of the activated MRPs, and/or the
statistical enrichment of the inactivated MRPs.
34. The system of claim 33, comprising use of virtual inference of
protein activity by enriched regulon analysis (VIPER) algorithm.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application is a divisional of U.S. patent application
Ser. No. 15/249,069, filed Aug. 26, 2016, which claims priority to
U.S. Provisional Application No. 62/211,562, filed on Aug. 28,
2015, and U.S. Provisional Application No. 62/253,342, filed on
Nov. 10, 2015, the disclosures of which are incorporated herein by
reference in their entirety.
BACKGROUND
[0003] Certain efforts in precision cancer medicine are predicated
on the identification of "actionable oncogene mutations," under the
assumption that their pharmacological inhibition will elicit
oncogene addiction.sup.1. Despite integration of this methodology
into clinical cancer care, challenges remain.
[0004] First, stratification of cancer patients based on actionable
mutations.sup.2 has shown that certain adult malignancies lack
actionable alterations altogether or present with mutations in
undruggable oncogenes (e.g., RAS/MYC family proteins) or in genes
of uncharacterized therapeutic value.sup.3. Additionally, while
oncogene targeting can achieve initial responses, these can be
followed by rapid relapse due to emergence of
drug-resistance.sup.4,5. Also, analysis of hundreds of cell lines
and compounds shows that, with the exception of a handful of
well-characterized targets (e.g., ERBB2, EGFR, mTOR, ALK, MET, PI3K
and ESR1, among others), single-gene mutations can be poor overall
predictors of sensitivity to inhibitors of the
correspondingprotein.sup.6.
[0005] Drug sensitivity represents a multifactorial, polygenic
(i.e., complex) phenotype, highlighting the need for novel
approaches that complement and extend the actionable alteration
paradigm. Accordingly, there is a need for a novel approach that
complements and extends the actionable alteration paradigm.
SUMMARY
[0006] The presently disclosed subject matter provides systems and
methods to identify signatures representing aberrant activity of
specific proteins (e.g., Master Regulator ("MR") proteins) in a
tissue and to match said signatures to other tissue signatures,
including following treatment with specific small molecules or
biologics. As used herein, the term "Master Regulator (MRs)" refers
to aberrantly activated/inactivated proteins in a tissue including
these signatures, based on a predefined statistical threshold,
e.g., at a p-value of about 0.01 or less, corrected for multiple
hypothesis testing. These MR proteins can be necessary for tumor
viability, and thus represent a novel class of therapeutic target,
usually distinct from classical oncoproteins.
[0007] In accordance with certain embodiments of the presently
disclosed subject matter, the systems and methods can be used to
identify biological samples that represent diseases or disorders
(e.g., tumors) with similar drug sensitivity based on MR activity
signature similarity, to identify drugs and small molecule
compounds that revert MR activity in a specific tissue, and to
identify drugs that have complementary effect in reverting the
activity of MR proteins, thus representing candidate synergistic
drug-pairs.
[0008] The presently disclosed subject matter can be based on
identification and reversal of tumor checkpoint activity (e.g., of
the specific MR proteins driving the tumor cell state). For
example, tumors, models, and drug responses can be matched based on
the state and/or effect of the actual MR proteins regulating the
tumor cell phenotype.
[0009] The presently disclosed subject matter provides methods of
profiling a disease or a disorder. In certain embodiments, an
example method includes measuring quantitatively protein activity
of a plurality of MR proteins in a sample from the disease or
disorder, and profiling the disease or disorder from the
quantitative protein activity of the MR proteins. The sample can be
selected from the group consisting of tissue extracts, cells,
tissues, organs, blood, blood serum, body fluids and combinations
thereof.
[0010] In certain embodiments, the profiling assesses or identifies
MR proteins dysregulation status. In certain embodiments, the MR
proteins dysregulation status includes aberrantly activated MR
proteins and aberrantly inactivated MR proteins.
[0011] In certain embodiments, the profiling results in a MR
signature profile for the disease or disorder. The MR signature
profile for the disease or disorder subtype can be used in a method
of identifying a cell line or a model as an in vivo or in vitro
model for such disease or disorder. Such method can include
measuring quantitatively protein activity of the MR proteins in a
cell line or model, and profiling the cell line or model from the
quantitative protein activity of the MR proteins to obtain a MR
signature profile for the cell line or model. In certain
embodiments, the method includes assessing the similarity between
the MR signature profile for the cell line or model and the MR
signature profile for the disease or disorder. The method can
result in identification of a matched disease/disorder cell line or
model whose MR signature profile is substantially statistically
similar (p-value of about 1.times.10.sup.-5 or less) to the MR
signature profile for the disease or disorder. In certain
embodiments, the model is selected from patient derived tumor
xenograft models, mouse xenograft models and transgenic mouse
models.
[0012] The presently disclosed subject matter further provides
methods of identifying a compound that treats a disease or a
disorder. In certain embodiments, an example method includes
measuring quantitatively protein activity of a plurality of MR
proteins in a sample from the disease or disorder; exposing the
sample to the compound; measuring quantitatively protein activity
of the plurality of MR proteins in the compound-treated sample; and
assessing quantitatively inversion of protein activity of the
plurality of MR proteins in the compound-treated sample compared to
a sample from the disease or disorder without treatment with the
compound or a model exposed to a vehicle used to deliver the
compound. In certain embodiments, the vehicle can be Dimethyl
sulfoxide (DMSO). A compound that induces global inversion of
protein activity of the plurality of MR proteins indicates that the
compound inhibits tumor cell growth of the tumor.
[0013] The presently disclosed subject matter further provides
methods for identifying a pair of a first compound and a second
that synergistically treats a disease or a disease. In certain
embodiments, such method includes measuring quantitatively protein
activity of a plurality of MR proteins in a sample from the disease
or disorder; exposing a first sample from the disease or disorder
to a first compound; exposing a second sample from the disease or
disorder to a second compound; and assessing quantitatively
inversion of protein activity of the plurality of MR proteins in
the compound-treated first and second samples compared to a sample
from the disease or disorder without treatment with the first or
second compound or a model exposed to a vehicle used to deliver the
first or second compound. In certain embodiments, a pair is
identified as being capable of synergistically treating the disease
or disorder if one or more of the following criteria are met: (a)
if intersection of the MR proteins that the first and second
compounds activate or inactivate represents a more statistically
significant inversion of protein activity of the MR proteins; (b)
if union of the MR proteins that the first and second compounds
activate or inactivate represents a more statistically significant
inversion of protein activity of the MR proteins; and (c) if the
MRs that the first and second compounds individually invert have
been predicted to be synergistic regulators of tumor state.
[0014] Furthermore, the presently disclosed subject matter provides
methods of assessing in vivo therapeutic effect of a compound for
treating a disease or disorder. In certain embodiments, an example
method includes measuring quantitatively protein activity of a
plurality of MR proteins in a sample from the disease or disorder;
exposing the sample to the compound; measuring quantitatively
protein activity of the plurality of MR proteins in the
compound-treated sample; and assessing quantitatively inversion of
protein activity of the plurality of MR proteins in the
compound-treated sample compared to a sample from said disease or
disorder without treatment with the compound or a model exposed to
a vehicle used to deliver the compound. A compound that induces
global inversion of protein activity of the plurality of MR
proteins indicates that the compound will likely be effective for
treating the disease or disorder in vivo.
[0015] The compound can be selected from small molecule chemical
compounds, peptides, nucleic acids, oligonucleotides, antibodies,
aptamers, modifications thereof, and combinations thereof.
[0016] The disease or disorder can be a tumor or a tumor subtype.
The tumor can be selected from glioblastoma, meningioma, leukemia,
lymphoma, sarcoma, carcinoid, neuroendocrine, paraganglioma,
melanoma, prostate, pancreatic, bladder, stomach, colon, breast,
head & neck, kidney, gastric, small intestine, ovarian,
hepatocellular, uterine corpus, and lung carcinoma.
[0017] In any of the methods disclosed herein, measuring
quantitatively protein activity of the plurality of MR proteins can
be based directly or indirectly on expression of regulons of the MR
proteins, and/or be based directly or indirectly on enrichment of
regulons of the MR proteins. In certain embodiments, a regulon of a
specific protein (e.g., a MR protein) is differentially expressed
in a specific tissue, compared to a control tissue (e.g., the
average of all disease/disorder (e.g., tumor)-related samples,
normal samples, or untreated samples).
[0018] In any of the methods disclosed herein, measuring
quantitatively protein activity of the plurality of MR proteins can
include computationally inferring protein activity of the plurality
of MR proteins from gene expression profiles of regulons of the MR
proteins. In certain embodiments, the gene expression profiles are
derived from in vivo models. In certain embodiments the gene
expression profiles are derived from in vitro models. A regulon of
a MR protein can be inferred by the Algorithm for the
Reconstruction of Accurate Cellular Networks (ARACNe). The
computationally inferring protein activity of the plurality of MR
proteins can be performed by techniques such as the Master
Regulator Inference algorithm (MARINA), and Virtual Inference of
Protein-activity by Enriched Regulon analysis (VIPER).
BRIEF DESCRIPTION OF THE DRAWINGS
[0019] The patent or application file contains at least one drawing
executed in color. Copies of this patent or patent application
publication with color drawing(s) will be provided by the Office
upon request and payment of the necessary fee.
[0020] FIGS. 1A and 1B depict probability density for correlation
coefficient and relative rank position for mRNA (yellow), Reverse
Phase Protein Arrays (green) and VIPER-inferred protein activity
(cyan) signatures.
[0021] FIGS. 2A-2C depict heatmaps for gene expression (A and C)
and VIPER-inferred protein activity (B). Red indicates upregulated
genes or activated proteins, blue indicates downregulated genes or
inactivated proteins, gray indicates missing data.
[0022] FIG. 3 depicts validation of VIPER-inferred MYC inhibitors
in MCF7 cells. *p<0.05; **p<0.01; ***p<0.001.
[0023] FIGS. 4A and 4B. (A) Enrichment of NET-MET checkpoint MRs on
drug-response VIPER-inferred protein activity signatures. (B)
Effect of Entinostat (HDAC inhibitor identified by oncoMatch
approach), Belinostat (HDAC inhibitor not affecting NET-MET
checkpoint) and Tivantinib on H-STS xenograft growth.
[0024] FIGS. 5A and 5B. (A) Heatmap showing the synergistic score
(indicated as red color intensity), inferred as the increase in
enrichment of each drug pair combination MoA compared to the single
compounds MoA (indicated as blue color intensity in the first row
and column). (B) Receiver operating characteristic curve showing
the prediction of synergistic interaction for all combinations of
the 14 assessed compounds. Indicated are the 16 compound pairs
found by Bliss additivity to be synergistic (2012 DREAM challenge
dataset). 8/16 (50%) synergistic pairs were identified at a 10%
FPR.
[0025] FIGS. 6A-6E. (A) OncoMatch scores for 4 cell lines
indicating the extent to which they recapitulate the NET-MET
checkpoint of individual tumor metastasis. (B and C) Enrichment of
the NET-MET checkpoint for two patients on H-STS cell line
VIPER-inferred protein activity signature. (D) Heatmap indicating
the oncoMatch score for 55 cell lines (columns) as models for each
of 173 basal breast carcinoma samples (rows). Only matches at
p-value <10.sup.-10 are shown with orange color. (E) Selection
of 3 cell lines best covering the basal breast carcinoma tumor
space (173 tumors). Blue bars indicate cell line-specific coverage.
Red bars show the cumulative coverage.
[0026] FIGS. 7A and 7B depict EP-NET molecular subtypes. (A)
Unsupervised cluster analysis of 211 EP-NET samples based on their
gene expression profile. (B) Unsupervised cluster analysis based on
the VIPER-inferred protein activity for 5,578 regulatory
proteins.
[0027] FIGS. 8A and 8B depict master regulators for the metastatic
progression. (A) Heatmap showing the conservation of the top 50
most dysregulated proteins in association with liver metastasis
between each possible sample pair. (B) Heatmap showing relative
protein activity for the top 20 most dysregulated proteins from
each of the four clusters.
[0028] FIGS. 9A-9E depict conservation of metastasis Master
Regulators in NET cell lines and a xenograft model. (A) Enrichment
of the top 100 most dysregulated proteins from each metastasis on
each cell line and the H-STS xenograft model protein activity
signature. (B-E) Gene Set Enrichment Analysis for the top 50 most
activated and the top 50 most de-activated proteins in each
selected metastasis on the protein activity signature of the H-STS
cell line (B and C), and the H-STS xenograft model (D and E).
[0029] FIGS. 10A-10E depict small molecule compounds reverting the
metastasis regulatory check-point. (A) Enrichment of patient-0
metastasis checkpoint MRs on the protein activity signatures
induced by 6 selected compounds in the H-STS cells. (B and C)
Growth curves for the H-STS xenograft while treated by vehicle
control, and each of the 6 selected compounds. (D) Enrichment of
patient-0 metastasis checkpoint on the protein activity signatures
induced by 4 selected compounds in the H-STS xenograft. (E)
Enrichment of H-STS xenograft checkpoint on the protein activity
signatures induced by 4 selected compounds in the H-STS
xenograft.
[0030] FIG. 11 depicts interactome reliability as models for
EP-NET. Violin plot showing the probability density for the
absolute normalized enrichment score (INESI) and integrated Network
Score computed as the area over the |NES| cumulative probability
(See FIGS. 13A and 13B). NES was computed by VIPER for 211 EP-NET
samples and all the regulatory proteins represented in the 25
evaluated interactomes (see Table 2)
[0031] FIGS. 12A-12C depict unsupervised analysis of 211 EP-NET
samples. (A) Scatter-plots showing the first 5 principal
components, capturing 35% of the variance for 211 EP-NET expression
profiles. (B) 2D-tSNE projection for the expression data. (C)
2D-tSNE projection of the VIPER-inferred protein activity for 211
EP-NET samples.
[0032] FIGS. 13A-13G depict cluster reliability. (A) Probability
density plot for the cluster reliability estimated from the
expression profiles and VIPER-inferred protein activity profiles
for 211 EP-NET samples (see FIG. 13D). (B) Integrated reliability
score for the complete cluster structure computed as the area over
the cumulative probability curve. (C) Integrated reliability score
for different cluster structures (different number of clusters) for
the consensus cluster of 211 EP-NET expression (red) or
VIPER-inferred protein activity profiles (blue). (D) Cluster
reliability score for 211 EP-NET expression and VIPER-inferred
protein activity profiles after consensus clustering in 4 and 5
clusters, respectively. (E and F) Cluster reliability (E) and
silhouette score (F) for each sample from the 4 clusters structure
based on expression and the 5 clusters structure based on
VIPER-inferred protein activity data. (G) Cluster membership for
the H-STS xenograft model.
[0033] FIGS. 14A and 14B depict metastatic progression MRs. (A)
Conservation of the top 25 most activated and top 25 most
inactivated MRs between 66 NET liver metastasis. (B) Optimal number
of clusters based on the regulators of metastatic progression.
[0034] FIG. 15 depicts results of oncoTreat analysis. The heatmap
shows the enrichment of the conserved MRs of each tumor and the
H-STS xenograft model on the protein activity signature elicited by
each drug perturbation on the H-STS cells. Enrichment strength is
shown as -log.sub.10(p-value) and indicated by the numbers. Only
metastasis showing a significant similarity, at the MR level to the
H-STS xenograft model were included in this analysis. The
enrichment plot to the left shows the enrichment of the patient-0
MRs recapitulated by the xenograft model, on each drug perturbation
protein activity signature.
DETAILED DESCRIPTION
[0035] The presently disclosed subject matter provides methods to
match signatures, including protein activity signatures inferred
from gene expression profiling. The protein activity signatures can
be, for example, inferred by VIPER. The methods disclosed herein
can be used to identify: (a) biological samples that are similar
because of their protein activity profiles, with the special case
of matching models (cell lines, organoids, mouse models, etc.) to
patient-derived tissue samples (e.g., tumor) because they
recapitulates the activity of the key proteins that determine the
tissue cellular phenotype, (b) drugs and small molecule compounds
that as single agents revert the master regulators of cell state
and hence, specifically destabilize the cellular phenotype thus
abrogating tumor viability, and (c) drugs showing a synergistic
(i.e., more than additive) effect in reverting the master
regulators of cell state and hence, act synergistically in
destabilizing the cellular phenotype and in abrogating tumor
viability.
[0036] The key proteins, which are referred to as Master Regulators
(MRs), are those having the highest positive (aberrantly activated)
and highest negative (aberrantly inactivated) differential
activity, compared to a control tissue, based on a statistical
significance threshold (e.g., ap-value of about 0.01 or less
corrected for multiple hypothesis testing). Control tissues can
include the normal tissue from which a tumor is derived (e.g.,
normal breast epithelium for breast adenocarcinoma), the primary
tumor for a metastatic sample, or a drug-sensitive tumor for one
that is drug-resistant. The full set of MRs for a specific tumor is
called a tumor checkpoint.
[0037] In the case of tumor tissue, MR proteins have been shown to
constitute key determinant of tumor state and thus tumor specific
dependencies whose aberrant activity is necessary for tumor
viability. Thus, drugs that act as single agent or combinations
revert the specific set of MRs for a particular tumor (e.g., a
tumor checkpoint) represent potentially valuable therapeutic
options.
[0038] Certain methods to compare samples, tumors and models are
based on their similarity at their gene expression or protein
abundance levels, or conservation of genetic alterations. While the
first two can be reliable at the population level and be useful for
subtype discovery, they can be noisy (i.e., poorly reproducible) at
the single tumor and single cell level (see FIGS. 1A, 1B, and 2B).
FIG. 1A illustrates the probability density for the correlation
coefficient computed between samples from the same B-cell subtype
based on expression (yellow) and VIPER-inferred protein activity
(cyan). FIG. 1B illustrates the probability density for the
relative rank position of the most over-expressed gene (mRNA,
yellow), abundant protein (RPPA, green) or activated protein
(VIPER, cyan) from one basal breast carcinoma tumor on the other
basal breast carcinoma tumor profiled by TCGA. On the other hand,
conservation of genetic alterations can be poorly reproducible,
given the high number of possible combinations of genetic
alterations and the poor correlate between such alterations and
tumor subtypes. An unsupervised cluster analysis of single cells
isolated from a single glioblastoma tumor was performed based on
gene expression or VIPER-inferred protein activity, and the results
are shown in FIGS. 2A (for gene expression) and 2B-2C (for
VIPER-inferred protein activity). While no clear stratification can
be detected based on gene expression (see FIG. 2A), the analysis
that involved VIPER-inferred protein activity showed a strong
separation of the cells in two sub-populations, which are defined
by the differential protein activity of previously characterized
regulators of the proneural and mesenchymal subtypes (see FIG. 2B).
FIG. 2C shows the same arrangement of cells (columns) and genes
(rows) as in FIG. 2B, indicating that the sub-populations and
associated genes cannot be identified directly from the gene
expression profile data.
[0039] An exemplary disclosed method that involves protein
activity-signatures inferred from gene expression profiling (e.g.,
VIPER-inferred protein activity-signatures), and in particular
tumor checkpoints, can be robust when compared to gene expression
and protein abundance (RPPA) (see FIGS. 1A and 1B). This can
involve two key properties of the protein activity inferred from
gene expression profiling (e.g., VIPER-inferred protein activity):
(1) protein activity is inferred by integrating the expression of
tens to hundreds of genes (e.g., VIPER-inferred protein activity),
which constitute an endogenous multiplexed reporter assay for the
activity of the assessed protein (its regulon), while RNA
expression and RPPA rely on the noisy measurement of a single
species; and (2) only gene expression patterns produced by
transcriptional regulatory programs can be captured (e.g., by
VIPER), and hence patterns produced by technical artifacts,
including batch effects, are efficiently removed (e.g., by
VIPER).
[0040] Exemplary disclosed methods (e.g., OncoMatch and OncoTreat
methodologies discussed below) can involve conservation of tumor
checkpoints (e.g., on proteins driving tumor cell state), and thus
can match tumors, models and drug response based on the state and
effect of the actual proteins regulating the tumor cell
phenotype.
[0041] As used herein, the term "about" or "approximately" means
within an acceptable error range for the particular value as
determined by one of ordinary skill in the art, which will depend
in part on how the value is measured or determined, i.e., the
limitations of the measurement system. For example, "about" can
mean within 3 or more than 3 standard deviations, per the practice
in the art. Alternatively, "about" can mean a range of up to 20%,
preferably up to 10%, more preferably up to 5%, and more preferably
still up to 1.sup.% of a given value. Alternatively, particularly
with respect to biological systems or processes, the term can mean
within an order of magnitude, preferably within 5-fold, and more
preferably within 2-fold, of a value.
1. Master Regulator Proteins and Tumor Checkpoints
[0042] In accordance with the presently disclosed subject matter,
master regulator (MR) proteins include proteins whose activity is
statistically significantly dysregulated (including both activated
and inactivated proteins)--whose transcriptional targets (regulon)
are differentially expressed in a disease or disorder (e.g., a
tumor), at a specific statistical significance threshold (e.g., a
p-value of about 0.01 or less).
[0043] As used herein, the term "tumor checkpoint" refers to a
pivotal regulatory module comprising a plurality of MR proteins
(e.g., MR proteins whose coordinated activity is necessary for
maintaining tumor viability) for a specific tumor. Coordinated
aberrant activity of MR proteins in a tumor checkpoint is necessary
to maintain a tumor cell state and thus to ensure tumor
viability.
[0044] The reasons for calling these modules tumor checkpoints
because--much as a highway checkpoint--they channel and integrate
the signaling traffic originating from a wide and diverse range of
upstream mutations and aberrant signals.
[0045] Genetic and/or pharmacological MR protein inhibition can
lead to tumor checkpoint collapse and loss of tumor viability both
in vitro and in vivo, e.g., as shown in lymphoma.sup.7,8,
glioblastoma.sup.9, prostate.sup.10,11 and breast cancer.sup.12.
Further extension of this concept to drug-resistance has led to
identification of MR proteins whose pharmacologic inhibition
rescues drug sensitivity, including in leukemia.sup.13 and breast
cancer.sup.12. Stat3, CEBP-beta, and CEBP-delta were identified as
the tumor checkpoint for glioblastoma; FOXM1, and CENPF were
identified as the tumor checkpoint for prostate cancer; Notch-1,
RUNX1, TLX1 and TLX3 were identified as the tumor checkpoint for
leukemia; Myc, BCL6, and BCL2 were identified as the tumor
checkpoint for lymphoma; AKT1 was identified as the tumor
checkpoint for T-cell acute lymphoblastic leukemia (T-ALL)
resistant to glucocorticoid therapy; and MYCN and TEAD4 were
identified as the tumor checkpoint for neuroblastoma.
[0046] MRs in tumor checkpoints are rarely mutated or even
differentially expressed.sup.9,10; rather they implement tightly
autoregulated modules that integrate the effect of a large and
diverse repertoire of genetic and epigenetic alterations in
upstream pathways.sup.7,14.
[0047] MR proteins elicit tumor essentiality.sup.7 and synthetic
lethality.sup.8-10, 12, thus representing classic non-oncogene
dependencies.sup.15,16 and can suggest a novel class of
pharmacological targets. MR proteins can be efficiently and
systematically prioritized by interrogating genome-wide regulatory
networks with tumor-related gene expression signatures representing
either an entire tumor subtype or individual tumor samples using
the Master Regulator Inference algorithm (MARINa).sup.9,17 and its
single sample equivalent Virtual Inference of Protein-activity by
Enriched Regulon analysis (VIPER).sup.18. Functional and
biochemical evaluation of MARINa/VIPER inferred MR proteins has
yielded validation rates in the 70% to 80% range.sup.8-10, 12.
2. Methods of Profiling a Disease or a Disorder
[0048] The presently disclosed method quantifies the extent of
conservation, at the level of protein activity, between a tissue,
cell culture or single cell sample, or a specific perturbation, and
a cellular state of interest, characterized by its master regulator
(MR) proteins of cell state, or tumor checkpoint in the case of
tumor. The analysis can be performed by inferring the MR proteins
of cell state for the phenotype of interest, and then computing the
enrichment of such master regulators on the full regulatory protein
activity signature of the second tissue or cell, or obtained in
response to chemical perturbations. The enrichment can be computed
by the analytic Rank Enrichment Analysis algorithm, part of
VIPER.
[0049] In certain embodiments, the method of profiling a disease or
a disorder (e.g., a tumor) includes measuring quantitatively
protein activity of a plurality of MR proteins in a sample from the
disease or disorder; and profiling the disease or disorder from the
quantitative protein activity of the MR proteins.
[0050] The method results in determination of a Master Regulator
(MR) signature profile for a disease or a disorder, e.g., a tumor.
As used herein, a "Master Regulator (MR) signature profile for a
disease or a disorder" refers to a protein activity profile of
Master Regulators (MRs) which is characteristic of the disease or
disorder. Such a MR signature profile is the result of a
quantitative determination of protein activity of a plurality of MR
proteins in a sample from the disease or disorder compared to the
protein activity of such MR proteins in an adequate control or
reference (e.g., healthy individuals, different types of the
disease or disorder, or different stages of the disease or
disorder), thereby identifying which combination of MR proteins
allows for differentiation of the disease, type or stage of disease
or disorder over the control or reference.
[0051] The signature profile obtained from the presently disclosed
method allows for diagnosis of a general disease or disease (e.g.,
tumor) condition, distinction between different types (subtypes) of
the disease or disorder (e.g., tumor), distinction between
different stages (e.g., metastatic progression) of the disease or
disorder (e.g., tumor), predictive diagnosis of further evolution
of the disease or disorder (e.g., tumor), and identification of
responsiveness to a specific therapy. The profiling methods can be
used to identify a cancer type, including, but not limited to, a
malignant tumor, a benign tumor, a primary tumor, a secondary
tumor, an aggressive tumor, and a non-aggressive tumor.
[0052] Profiling the disease or disorder (e.g., tumor) can assess
or identify MR proteins dysregulation status. In certain
embodiments, the MR proteins dysregulation status includes
aberrantly activated MR proteins and aberrantly inactivated MR
proteins.
[0053] In certain embodiments, the ability to identify MR proteins
depends on the availability of accurate models of tissue-specific
regulation, representing both direct targets of transcription
factors (TFs) and least-indirect targets of signaling proteins
(SPs). TFs and SPs can be effectively inferred by analyzing large,
tumor-specific gene expression profile datasets using the Algorithm
for the Accurate Reconstruction of Cellular Networks
(ARACNe).sup.19-20, as supported by extensive experimental
validation assays.sup.9, 10, 17, 21. ARACNe analysis of
tumor-specific gene expression profile can produce a tumor-specific
regulatory network (interactome), which can be used both to assess
protein activity on an individual sample basis, for optimal cluster
analysis, as well as to elucidate novel MRs.
[0054] Protein activity of the MR proteins can be based directly or
indirectly on expression of regulons of the MR proteins.
Additionally or alternatively, protein activity can be based
directly or indirectly on enrichment of regulons of the MR
proteins. As used herein, the term "regulon" refers to the
transcriptional targets of a protein, e.g., a MR protein. Regulon
of a specific protein (e.g., a MR protein) can be differentially
expressed in a specific tissue, compared to a control tissue (e.g.,
the average of all tumor-related samples, normal samples, or
untreated samples). A regulon of a specific protein (e.g., a MR
protein) can be inferred by the Algorithm for the Reconstruction of
Accurate Cellular Networks (ARACNe).
[0055] In certain embodiments, measuring quantitatively protein
activity of the MR proteins include computationally inferring
protein activity of the MR proteins from gene expression profiles
of regulons of the MR proteins. The gene expression profiles can be
derived from in vivo models. Additionally or alternatively, the
gene expression profiles can be derived from in vitro models.
Computational inference of protein activity of MR proteins can be
performed by a suitable data analysis system, e.g., MARINA and/or
VIPER techniques. In certain embodiments, the technique is
VIPER.
[0056] VIPER allows computational inference of protein activity, on
an individual sample basis, from gene expression profile data.
VIPER infers protein activity by systematically analyzing
expression of the protein's regulon.sup.18. VIPER uses the
expression of genes that are most directly regulated by a given
protein, such as the targets of a TF, as an accurate reporter of
its activity. Analysis of TF targets inferred by ARACNe.sup.19,20,
using MARINA.sup.17, can be effective in identifying drivers of
specific cellular phenotypes, which can be experimentally
validated.sup.9,17. While VIPER exploits the same principle as
MARINA, it implements a dedicated algorithm specially formulated to
estimate regulon activity, which takes into account the regulator
mode of action, the regulator-target gene interaction confidence
and the pleiotropic nature of each target gene regulation. In
addition, VIPER can effectively extend to signal transduction
proteins. The VIPER technique is described in Alvarez et al., Nat.
Genet. (2016), 48(8):838:847, U.S. Patent Provisional Application
No. 62/211,373, and U.S. patent application Ser. No. 15/248,975
entitled "Virtual Inference Of Protein Activity By Regulon
Enrichment Analysis" filed contemporaneously herewith, which are
incorporated by reference in their entireties.
[0057] Protein activity, inferred from single-sample transcriptome
readouts using VIPER, can be a more robust descriptor of cell state
than gene expression.sup.18. The reason is three-fold. First,
VIPER-inferred protein activity represents a more direct and
mechanistic determinant of cell state, compared to gene expression;
second, while individual gene expression measurements are quite
noisy (i.e., poorly reproducible) and poorly reproducible, VIPER
infers protein activity from the expression of a large number (tens
to hundreds) of its transcriptional targets (e.g., the protein's
regulon), thus resulting in much higher accuracy and
reproducibility.sup.18; third, bias and technical noise that is
inconsistent with the regulatory model is effectively filtered out,
thus effectively removing a major source of confounding data. VIPER
can effectively segregate samples according to tissue of
origin.
[0058] VIPER analysis of gene expression signatures representing
cellular responses to compound perturbations in vitro or in vivo
can identify MRs representing physical compound targets (e.g.,
enzymes for which the compound represents a high-affinity
substrate) and effectors (e.g., proteins not directly bound by the
drug but necessary for it to perform its pharmacological activity),
also known as the compound Mechanism of Action (MoA). VIPER can
outperform gene expression analysis in the elucidation of compound
MoA. This can be because small molecules generally act
post-translationally to affect the activity (rather than
expression) of their targets/effectors which affects the expression
their transcriptional targets. In fact, this analysis can be used
to identify agents effectively targeting the MR protein activity
(see FIG. 3). FIG. 3 shows the results of TERT-promoter-luciferase
based reporter assay activity in response to 4 serial dilutions at
1/2 starting from each compound IC20 at 24 h, to ensure operation
at sub-lethal regime. Seven of the top 10 compounds predicted by
VIPER to inhibit MYC protein activity showed a dose-dependent
inhibition of its activity on the TERT-promoter-based reporter
assay.
[0059] In certain embodiments, the disease or disorder is a tumor
or a tumor subtype. As used herein, the term "tumor subtype" refers
to a collection of tumors with similar molecular characteristics.
Non-limiting examples of samples include tissue extracts, cells,
tissues, organs, blood, blood serum, body fluids and combinations
thereof. Non-limiting examples of tumors include glioblastoma,
meningioma, leukemia, lymphoma, sarcoma, carcinoid, neuroendocrine,
paraganglioma, melanoma, prostate, pancreatic, bladder, stomach,
colon, breast, head & neck, kidney, gastric, small intestine,
ovarian, hepatocellular, uterine corpus, and lung carcinoma. Other
diseases or disorders include, but are not limited to,
neurogenerative disorders (e.g., amyotrophic lateral sclerosis,
Parkinson's disease, and Alzheimer disease etc.), diabetes,
obesity, and other metabolic diseases.
3. Methods of Identifying Compounds that Treats a Disease or a
Disorder
[0060] When activity of an essential MR or MRs (e.g., a pair of
MRs) is abrogated, the entire MR activity pattern can collapse.
This is because MRs generally operate as tight (i.e.,
highly-interconnected) regulatory modules, acting as regulatory
switches to maintain cell state, normal or tumor-related. As a
result, a compound that inhibits an essential MR or MRs can be
screened or identified by measuring the global protein activity
change of VIPER-inferred tumor-MRs, following treatment in
representative cells (see FIG. 4A). Compounds with greatest effect
in inverting the activity of the full repertoire of tumor
checkpoint, can do so by targeting one or more essential MRs or
MR-pairs, and can thus abrogate tumorigenesis in vivo.
[0061] The presently disclosed method relates to prioritization of
compounds (e.g., small molecules) as MR inhibitors to induce
drug-mediated tumor checkpoint collapse and regression in vivo,
including on an individual patient basis. Candidate MR proteins
have been individually validated to identify essential MRs.sup.7,12
or synthetic lethal MR pairs.sup.8-10. This process can be slow,
costly, and inefficient for prioritizing patient treatment in a
precision cancer medicine context. Yet, since inhibition of
essential MRs or MR-pairs can induce global tumor checkpoint
collapse (i.e., global inversion of the activity of all MRs in the
module), there is a strong rational to using the patient-specific
tumor checkpoint activity (i.e., the signature of the entire MR
proteins signature) as a gene reporter assay to identify compounds
capable of inducing tumor checkpoint collapse and consequent loss
of tumor viability in vivo, without requiring extensive and time
consuming MR validation.
[0062] The presently disclosed subject matter provides for a method
of identifying a compound that treats a disease or a disorder
(e.g., inhibits tumor cell growth). In certain embodiments, the
disease or disorder is a tumor or a tumor subtype. In certain
embodiments, the method includes: measuring quantitatively protein
activity of a plurality of MR proteins in a sample from the disease
or disorder (e.g., tumor); exposing the sample to the compound;
measuring quantitatively protein activity of the plurality of MR
proteins in the compound-treated sample; and assessing
quantitatively inversion of protein activity of the plurality of MR
proteins in the compound-treated sample compared to a sample from
the disease or disorder (e.g., tumor) without treatment with the
compound or a model exposed to a vehicle that is used to deliver
the compound, e.g., DMSO. A compound that induces global inversion
of protein activity of the plurality of MR proteins indicates that
the compound treats the disease or disorder (e.g., tumor).
[0063] Global inversion of protein activity f a plurality of MR
proteins following treatment with compound(s) can be assessed based
on the statistical significance of enrichment of MR proteins that
are inactivated following compound treatment in MR proteins that
are aberrantly activated in the tumor, and/or enrichment of MR
proteins that are activated following compound treatment in MR
proteins that are aberrantly inactivated in the tumor. The aREA
technique can be used to measure the statistical significance of
protein enrichment. The statistical significance of protein
enrichment can be measured by any suitable enrichment analysis,
including, but not limited to, Gene Set Enrichment Analysis or
related methodologies at a pre-defined p-value threshold (e.g., a
p-value of about 0.01 or less, e.g., 1.times.10.sup.-5, corrected
for multiple hypothesis testing).
[0064] Non-limiting examples of compounds include small molecule
chemical compounds, peptides, nucleic acids, oligonucleotides,
antibodies, aptamers, modifications thereof, and combinations
thereof.
[0065] By utilizing a presently disclosed screening method,
entinostat was identified as the most potent agent for reverting
the rectal neuroendocrine tumor metastasis (NET-MET) tumor
checkpoint (see FIG. 4A). Drug MoA information was inferred using a
NET liver metastasis-derived cell line (H-STS), which recapitulated
the tumor checkpoint inferred from patient's samples (FIG. 6A).
When tested in xenograft models, entinostat abrogated completely
tumor growth (FIG. 4B).
[0066] The presently disclosed subject matter further provides a
method of identifying a pair of compounds (a first compound and a
second compound) that synergistically treats a disease or a
disorder (e.g., inhibits tumor cell growth). In certain
embodiments, such method includes: measuring quantitatively protein
activity of a plurality of MR proteins in a sample from the disease
or disorder (e.g., tumor); exposing a first sample from the disease
or disorder to a first compound; exposing a second sample from the
disease or disorder (e.g., tumor) to a second compound; and
assessing quantitatively inversion of protein activity of the
plurality of MR proteins in the first and second compound-treated
samples to a sample from the disease or disorder (e.g., tumor)
without treatment with the compound or a model exposed to a vehicle
that is used to deliver the compound, e.g., DMSO.
[0067] Assessment on whether a pair of the first and second
compounds is synergistic can be based on one or more of the
following criteria: (a) if intersection of the MR proteins that the
first and second compounds activate or inactivate represents a more
statistically significant inversion of protein activity of the MR
proteins; (b) if union of the MR proteins that the first and second
compounds activate or inactivate represents a more statistically
significant inversion of protein activity of the MR proteins; and
(c) if the MRs that the first and second compounds individually
invert have been predicted to be synergistic regulators of
disease/disorder (e.g., tumor) state. More statistically
significant in this context is defined by the difference in the
statistical significance obtained by the combination of compounds
and the most significant individual compound. Such difference can
be calculated at the normalized enrichment score level.
[0068] Synergistic interaction between compounds is obtained when
the effect of the combination is higher than the additive effect of
the individual agents. This is critical because with synergistic
compound combinations, it is possible to achieve the therapeutic
effect while using doses lower than the ones that would be required
if the compounds are used in isolation, decreasing in this way
compound-related toxicity and unwanted secondary effects. Following
a similar reasoning to the one used to match individual compounds
to tumor checkpoints (see FIG. 4A), compounds that affect
complementary subsets of the tumor checkpoint MRs can synergize in
inducing loss of cell viability.
4. Methods of Identifying Cell Lines and Models for Diseases or
Disorders
[0069] By directly matching the dysregulated protein activity for
the MRs that constitute the tumor checkpoint, a presently disclosed
method can be used to identify a cell line or a model (e.g., a
genetically engineered mouse model or a patient derived xenograft
(PDX) model) that represents the best surrogate model to study a
patient-specific disease or disorder (e.g., a tumor) because it
recapitulates the key MRs in the tumor checkpoint. The quality of
the match can be assessed based on the statistical significance of
the enrichment of activated and inactivated MR proteins in proteins
that are most activated or inactivated in the cell line or model,
as computed by gene set enrichment analysis methods such as GSEA or
aREA.
[0070] Thus, the presently disclosed subject matter provides for a
method of identifying a cell line or a model as an in vitro or in
vivo model for a patient-specific disease or disorder, e.g., to
increase the confidence that drugs that can abrogate viability in
these models may work in the patient(s). In certain embodiments,
the disease or disorder is a tumor or a tumor subtype.
[0071] Such method can include measuring quantitatively protein
activity of the MR proteins in the cell line or model, and
profiling the cell line or model from the quantitative protein
activity of the MR proteins to obtain a MR signature profile for
the cell line or model. Additionally, the method can include
assessing the similarity between the MR signature profile for the
cell line or model and the MR signature profile for the disease or
disorder (e.g., tumor or tumor subtype) to identify a matched
disease/disorder (e.g., tumor or tumor subtype) cell line or model
whose MR signature profile is substantially statistically similar
(p-value of 1.times.10.sup.-5 or less) to the MR signature profile
for the disease or disorder (e.g., tumor or tumor subtype).
Non-limiting examples of models include PDX models, mouse xenograft
models, and transgenic mouse models.
[0072] This analysis was performed to select H-STS as the NET cell
line recapitulating the tumor checkpoint of several rectal NET-MET
(see FIGS. 6A and 6B), to prioritize a set of 3 cell lines
recapitulating the checkpoint of 95% of TCGA basal breast carcinoma
tumors (see FIGS. 6D and 6E), and to select the most appropriate
genetically engineered mouse model of aggressive prostate
carcinoma.
5. Methods of Assessing In Vivo Therapeutic Effects of Compounds
for Treatments
[0073] The presently disclosed method can be used to assess the
extent at which the predicted effect of a compound or a pair of
compounds in vitro, is recapitulated in vivo in preclinical models
before its therapeutic application in patients with diseases or
disorders (e.g., tumors). This can be performed by computing the
enrichment of the tumor checkpoint MRs on compound(s)-induced
protein activity signature obtained by VIPER-analysis of in vivo
models-derived expression profile data.
[0074] The presently disclosed subject matter provides for methods
of assessing in vivo therapeutic effect of a compound for treating
a disease or a disorder. In certain embodiments, the disease or
disorder is a tumor. Such method can include measuring
quantitatively protein activity of a plurality of MR proteins in a
sample from the disease or disorder (e.g., tumor); exposing the
sample to the compound; measuring quantitatively protein activity
of the plurality of MR proteins in the compound-treated sample; and
assessing quantitatively inversion of protein activity of the
plurality of master regulator proteins in the compound-treated
sample compared to a sample from the disease or disorder (e.g.,
tumor) without treatment with the compound or a model exposed to a
vehicle used to deliver the compound (e.g., DMSO). A compound that
induces global inversion of protein activity of the plurality of MR
proteins indicates that the compound will likely be effective for
treating the disease or disorder (e.g., tumor) in vivo (see FIGS.
10D and 10E).
EXAMPLES
[0075] The following examples are merely illustrative of the
presently disclosed subject matter and should not be considered as
a limitation in any way.
Example 1--Validation of Myc Inhibitors Predicted By VIPER
[0076] 17-AAG, allantoin, amoxapine, chlorthalidone, clemastine,
dilazep, etoposide, fulvestrant, furazolidone, and ionomycin were
predicted by VIPER to be Myc inhibitors. TERT-promoter-luciferase
based reporter assay was performed on these compounds to assess
their Myc inhibitory activity. As shown in FIG. 3, seven of these
10 compounds predicted by VIPER to inhibit Myc protein activity
showed a dose-dependent inhibition of its activity on the
TERT-promoter-based reporter assay.
Example 2--Identification of Compound that Synergistically
Reverting Tumor Checkpoints
[0077] Gene expression profiles were generated following OCI-LY3
DLBCL cell perturbation with 14 distinct compound at three time
points (6 h, 12 h, and 24 h) and 2 concentrations (IC20 at 24 h and
1/10th of that), in triplicate. These data were used to predict
compound synergy. A second dataset of CellTiterGlo cell viability
assays at 60 h, following treatment with each of 91 unique
compound-pairs, using a 4.times.4 dilution matrix starting at each
compound IC50, was generated to evaluate the disclosed approach and
31 additional submissions to the DREAM/NCI drug sensitivity
challenge. Synergy was experimentally assessed by using the second
set to compute the Excess Over Bliss (EOB); that is, whether the
combined effect of two compounds is significantly greater or
smaller than the sum of their individual effects (Bliss
Independence). Statistical significance was assessed by comparing
the difference in the mean of multiple assessment compared to the
standard deviation of these measurements. Compound pairs were thus
ranked from most synergistic to most antagonistic using the EOB.
The disclosed approach was not developed to predict antagonism and
was thus evaluated only on synergy. In this context, it
outperformed all other 31 methods, essentially doubling the
sensitivity of the next best technique. Indeed, of the top 10% most
significant predictions, .about.60% were experimentally validated
as synergistic (see FIGS. 5A and 5B).
Example 3--Identification of Entinostat as the Most Potent Agent
for Reverting Rectal Neuroendocrine Tumor Metastasis (NET-MET)
Tumor Checkpoint
[0078] Drug-induced VIPER-inferred protein activity signatures were
obtained for a few drugs including entinostat. As shown in FIG. 4A,
among all the tested drugs, entinostat was the most potent agent
for reverting the rectal neuroendocrine tumor metastasis (NET-MET)
tumor checkpoint. Drug MoA information was inferred using a NET
liver metastasis-derived cell line (H-STS), which recapitulated the
tumor checkpoint inferred from patient's samples, as shown in FIG.
6A. When tested in xenograft models (H-STS xenograft models),
entinostat abrogated completely tumor growth, while belinostat (an
HDAC inhibitor not affecting NET-MET checkpoint) did not abrogate
tumor growth, as shown in FIG. 4B.
Example 4--Selection of Cell Lines Recapitulating Tumor
Checkpoint
[0079] By directly matching the dysregulated protein activity for
the MRs that constitute the tumor checkpoint, H-STS was selected as
the NET cell line recapitulating the rectal NET-MET tumor
checkpoint (see FIGS. 6A and 6B, and a set of 3 cell lines were
prioritized as breast carcinoma cell lines recapitulating the
checkpoint for 95% of TCGA basal breast carcinoma tumors (see FIGS.
6D and 6E).
Example 5--Systematic Pharmacological Targeting of Master Regulator
Proteins in Neuroendocrine Tumors: A Novel StrateY for Precision
Cancer Medicine Applications
[0080] Summary
[0081] This example is directed to a novel precision cancer
medicine approach ("OncoTreat"; also referred to as "OncoMatch")
using the systematic identification and pharmacological inhibition
of master regulator (MR) proteins, whose concerted aberrant
activity represents a critical dependency of cancer cells.
FDA-approved and investigational compounds were prioritized based
on their ability to abrogate MR activity based on analysis of
large-scale perturbational assays. OncoTreat was applied to a
cohort of 211 enteropancreatic neuroendocrine tumors (EP-NET)
originating from pancreatic (PAN-NET), small intestine (SI-NET) and
colorectal (RE-NET) primaries. RNASeq profiles were first used to
assemble an EP-NET-specific regulatory model, whose interrogation
identified MR proteins necessary for maintenance of metastatic
tumor state. Analysis of RNASeq profiles representing EP-NET cell
perturbation with 108 compounds prioritized them based on their
ability to abrogate the MR activity patterns of individual
patients. In vivo validation confirmed that the compound inducing
the most profound checkpoint MR activity inversion elicited
dramatic response in vivo, suggesting that the approach can extend
and complement precision cancer medicine approaches based on
oncogene addiction.
INTRODUCTION
[0082] Emerging efforts in precision cancer medicine are almost
invariably predicated on the identification of "actionable oncogene
mutations," under the assumption that their pharmacological
inhibition will elicit oncogene addiction.sup.1. Despite remarkable
initial successes, which have led to rapid integration of this
methodology into clinical cancer care, significant challenges are
emerging. First, stratification of cancer patients based on
actionable mutations has shown that a majority of adult
malignancies lack actionable alterations altogether or present with
mutations in undruggable oncogenes (e.g., RAS/MYC family proteins)
or in genes of uncharacterized therapeutic value.sup.3.
Additionally, while oncogene targeting can achieve initial
responses that are at times remarkable, these are frequently
followed by rapid relapse due to emergence of
drug-resistance.sup.4,5. Finally, analysis of hundreds of cell
lines and compounds shows that, with the exception of a handful of
well-characterized targets (e.g., ERBB2, EGFR, mTOR, ALK, MET, P3K
and ESR1, among others), single-gene mutations are poor overall
predictors of sensitivity to inhibitors of the corresponding
protein.sup.6. This is not entirely surprising, as drug sensitivity
clearly represents a multifactorial, polygenic (i.e., complex)
phenotype, thus further highlighting the urgent need for novel
approaches that complement and extend the actionable alteration
paradigm.
[0083] OncoTreat or OncoMatch explored systematic strategies for
the prioritization of small molecule compounds as MR inhibitors to
induce drug-mediated tumor checkpoint collapse and regression in
vivo, including on an individual patient basis. Candidate MR
proteins have been individually validated to identify essential
MRs.sup.7,12 or synthetic lethal MR pairs.sup.8-10. This process
can be slow, costly, and inefficient for prioritizing patient
treatment in a precision cancer medicine context. Yet, since
inhibition of essential MRs or MR-pairs has been shown to induce
global tumor checkpoint collapse (i.e., global inversion of the
activity of all MRs in the module), there is a strong rational to
using the patient-specific tumor checkpoint activity (i.e., the
signature of the entire MR proteins signature) as a gene reporter
assay to identify compounds capable of inducing tumor checkpoint
collapse and consequent loss of tumor viability in vivo, without
requiring extensive and time consuming MR validation.
[0084] OncoTreat or OncoMatch was tested on a rare class of
enteropancreatic neuroendocrine tumors (EP-NET), representing
pancreatic, small-bowel, and rectal NETs. Once they undergo
metastatic progression, these tumors are essentially incurable and
have poor prognosis. Using a cohort of 211 fresh frozen EP-NET
patient samples collected at 17 institutions, MR proteins
responsible for metastatic progression can be prioritized on an
individual tumor basis and then prioritized a set of 108 compounds
with differential EP-NET cell sensitivity, based on their ability
to globally invert the activity pattern of these MRs rather than on
the basis of viability assays. Validation in tumor xenografts
selected to specifically match the MR-activity profile of
individual patients confirmed the utility of the OncoTreat or
OncoMatch program and support that OncoTreat or OncoMatch program
can provide a valuable complement to genetic based strategies in
precision cancer medicine.
[0085] Method
[0086] Agent Efficacy Evaluation: All test agents were formulated
according to manufacturer's specifications. Beginning Day 0, tumor
dimensions were measured twice weekly by digital caliper and data,
including individual and mean estimated tumor volumes (Mean
TV.+-.SEM), were recorded for each group. Tumor volume was
calculated using the formula:
TV=width.sup.2.times.length.times..pi./2.
[0087] Tumor Growth Inhibition and RECIST: At study completion,
percent tumor growth inhibition (% TGI) values were calculated and
reported for each treatment group (T) versus control (C) using
initial (i) and final (f) tumor measurements by the formula: %
TGI[-(T.sub.f-T.sub.i)/(C.sub.f-C.sub.i)].times.100. Individual
mice reporting a tumor volume >120% of the Day 0 measurement
were considered to have progressive disease (PD). Individual mice
with neither sufficient shrinkage nor sufficient tumor volume
increases are considered to have stable disease (SD). Individual
mice reporting a tumor volume .ltoreq.70% of the Day 0 measurement
for two consecutive measurements over a seven day period were
considered partial responders (PR). If the PR persisted until study
completion, percent tumor regression (% TR) was determined using
the formula: % TR=(1-T.sub.f/T.sub.i).times.100; a mean value was
calculated for the entire treatment group. Individual mice lacking
palpable tumors for two consecutive measurements over a seven day
period were classified as complete responders (CR). All data
collected in this study were managed electronically and stored on a
redundant server system.
[0088] Results
[0089] Assembling and characterizing an EP-NET tumor cohort: To
identify and pharmacologically target MR proteins presiding over
metastatic EP-NET cell state, a large collection of 211
fresh-frozen samples assembled at 17 distinct institutions across
North America, Europe, and Asia (i.e., The International NET
Consortium, iNETCon) can be leveraged. The collection includes both
primary and metastatic samples from pancreatic (PanNET: 83 and 30
respectively), small intestine (SI-NET: 44 and 37, respectively),
and colorectal (RE-NET: 3 and 15, respectively) EP-NETs. Total RNA
was isolated and sequenced by Illumina TruSeq profiling, at an
average depth of 30M SE reads (Table 1).
TABLE-US-00001 TABLE 1 EP-NET profiled samples. Mapper reads in
millions. SampleID Type Tissue Origin Mapped Reads SampleID Type
Tissue Origin Mapped Reads AC47 liver met rectum 351.3 AC213
primary pancreas 29.5 AC452 liver met rectum 170.8 AC214 primary
pancreas 25.9 AC455 liver met rectum 136.2 AC215 primary pancreas
27.8 AC508 liver met rectum 110.6 AC216 primary pancreas 38.8 AC509
liver met rectum 148.2 AC218 primary pancreas 23.7 AC510 liver met
rectum 141.9 AC219 primary pancreas 31.2 AC241 liver met rectum
250.5 AC221 primary pancreas 27.3 AC242 liver met rectum 206.7
AC224 primary pancreas 33.4 AC243 liver met rectum 245.3 AC226
primary pancreas 28.5 AC246 liver met rectum 172.6 AC227 primary
pancreas 25.6 AC261 liver met small intestine 160.9 AC231 primary
pancreas 27.4 AC274 liver met small intestine 179.6 AC233 primary
pancreas 28.2 AC534 primary rectum 183.8 AC234 primary pancreas
30.8 AC535 primary rectum 202.7 AC235 liver met pancreas 28.5 AC100
primary pancreas 32.1 AC236 primary small intestine 30.1 AC103
primary pancreas 28.8 AC249 liver met pancreas 28.7 AC105 primary
pancreas 30.4 AC255 liver met pancreas 34.8 AC106 primary pancreas
28 AC270 liver met small intestine 30.2 AC108 primary pancreas 28.6
AC271 liver met small intestine 40.3 AC110 primary pancreas 32.9
AC273 liver met small intestine 32.6 AC111 primary small intestine
25.7 AC276 liver met pancreas 18.9 AC113 primary small intestine
38.6 AC277 liver met small intestine 1.3 AC114 primary pancreas
32.6 AC279 liver met pancreas 23.7 AC115 primary pancreas 30.9
AC281 liver met small intestine 30.6 AC116 primary pancreas 34.8
AC282 liver met pancreas 29.5 AC121 primary pancreas 26.1 AC283
liver met small intestine 29.4 AC123 primary pancreas 31.6 AC286
liver met small intestine 32.3 AC125 primary pancreas 34.5 AC288
liver met small intestine 32.8 AC126 primary pancreas 33.3 AC291
liver met pancreas 35.2 AC127 primary small intestine 30.3 AC292
liver met pancreas 34.3 AC130 primary pancreas 31 AC300 liver met
small intestine 29 AC133 primary small intestine 25.3 AC301 liver
met small intestine 23.4 AC137 primary pancreas 33.8 AC302 liver
met pancreas 32.3 AC139 primary pancreas 32.7 AC305 liver met
pancreas 29.5 AC141 primary small intestine 36.3 AC309 liver met
pancreas 37.5 AC143 primary small intestine 30.4 AC310 liver met
pancreas 30.6 AC146 primary small intestine 24.5 AC312 liver met
pancreas 25.2 AC147 primary small intestine 28.7 AC314 liver met
small intestine 25.8 AC153 primary pancreas 35.4 AC315 liver met
small intestine 30.9 AC157 primary small intestine 35.1 AC316 liver
met small intestine 34.3 AC158 primary small intestine 33.5 AC318
liver met pancreas 20.1 AC162 primary small intestine 43.8 AC319
liver met small intestine 30.9 AC188 primary small intestine 23.9
AC322 primary pancreas 23.2 AC196 primary small intestine 15.6
AC323 primary pancreas 25.9 AC199 primary small intestine 26.4
AC325 primary pancreas 24.5 AC203 primary small intestine 35.5
AC326 primary pancreas 24 AC205 primary small intestine 26.8 AC328
primary pancreas 29.2 AC206 primary small intestine 28 AC329
primary pancreas 28.9 AC208 primary small intestine 25.8 AC333
primary pancreas 35.6 AC209 primary pancreas 18.6 AC337 primary
pancreas 30.3 AC210 primary pancreas 23.9 AC339 primary pancreas 25
AC211 primary pancreas 27.7 AC341 primary pancreas 33.2 AC212
primary pancreas 28.4 AC343 primary pancreas 30.7 AC346 primary
pancreas 25.5 AC417 primary small intestine 21.9 AC347 primary
pancreas 28.8 AC422 primary pancreas 32.5 AC348 primary pancreas
31.9 AC425 primary pancreas 20.8 AC350 primary pancreas 23.2 AC429
primary pancreas 30.5 AC351 primary pancreas 26.7 AC430 primary
small intestine 32.9 AC355 primary pancreas 34.2 AC431 liver met
pancreas 28.4 AC361 primary pancreas 27.7 AC462 primary pancreas
27.6 AC363 primary pancreas 32.5 AC468 primary small intestine 26.6
AC365 primary pancreas 31.2 AC472 primary pancreas 30.9 AC380
primary pancreas 29.4 AC473 primary pancreas 30 AC383 primary small
intestine 30.7 AC474 primary pancreas 28.8 AC384 liver met small
intestine 27.3 AC480 primary small intestine 21.3 AC385 liver met
pancreas 26.8 AC483 primary pancreas 20.8 AC58 liver met pancreas
50.7 AC484 primary pancreas 22 AC60 primary small intestine 38.8
AC485 primary pancreas 22.4 AC61 liver met small intestine 43 AC486
primary pancreas 26.7 AC71 liver met pancreas 32.1 AC487 primary
pancreas 30.1 AC73 liver met pancreas 32 AC488 primary pancreas
33.3 AC75 liver met small intestine 31.1 AC489 primary pancreas
28.1 AC79 liver met small intestine 25.5 AC490 primary small
intestine 27.2 AC87 liver met pancreas 32.6 AC494 lymphnode m small
intestine 27.5 AC89 liver met pancreas 32.8 AC495 lymphnode m small
intestine 27.1 AC99 primary small intestine 27.8 AC496 primary
small intestine 28.7 AC244 liver met small intestine 33.6 AC497
primary small intestine 30.4 AC248 liver met small intestine 30.3
AC498 lymphnode m small intestine 28.3 AC252 liver met small
intestine 31.3 AC499 liver met pancreas 31.7 AC254 liver met
pancreas 30.2 AC500 liver met pancreas 31.9 AC256 liver met
pancreas 28.4 AC539 primary small intestine 28.8 AC257 liver met
pancreas 40.2 AC540 primary small intestine 35.2 AC258 liver met
pancreas 30.9 AC541 primary small intestine 27 AC259 liver met
small intestine 29 AC542 primary small intestine 26.6 AC262 liver
met small intestine 29.2 AC543 primary small intestine 29 AC263
liver met small intestine 30 AC603 primary small intestine 31.3
AC267 liver met small intestine 28.3 AC604 primary pancreas 30.9
AC268 liver met small intestine 32.3 AC606 primary small intestine
21.1 AC269 liver met pancreas 29.7 AC607 primary pancreas 30.7
AC388 primary small intestine 27.9 AC608 primary pancreas 15.4
AC389 primary small intestine 36.8 AC609 mesenteric m small
intestine 28.6 AC391 primary pancreas 24 AC610 lymphnode m small
intestine 27.7 AC392 primary small intestine 29.5 AC611 lymphnode m
small intestine 32.3 AC393 primary pancreas 34.7 AC612 primary
small intestine 22.7 AC395 primary small intestine 30 AC577 primary
rectum 158.2 AC397 primary pancreas 43.1 AC578 lymphnode m rectum
156.2 AC398 primary pancreas 19 AC579 lymphnode m rectum 178.1
AC399 primary pancreas 19.5 AC576 lymphnode m small intestine 18.7
AC400 primary pancreas 25.8 AC630 primary small intestine 23.1
AC403 primary pancreas 23.3 AC631 primary small intestine 18.6
AC405 primary pancreas 25.9 AC632 lymphnode m small intestine 19.4
AC408 primary pancreas 26.8 AC633 lymphnode m small intestine 13.9
AC409 liver met pancreas 26.3 AC636 liver met rectum 94.2 AC410
liver met pancreas 19.8 AC646 primary small intestine 32.3 AC411
primary pancreas 18.1 AC652 lymphnode m rectum 52.1 AC412 primary
pancreas 34.2
[0090] Assembling an EP-NET specific regulatory model: The ability
to identify MR proteins depends on the availability of accurate
models of tissue-specific regulation, representing both direct
targets of transcription factors (TF) and least-indirect targets of
signaling proteins (SP). It has been shown that both can be
effectively inferred by analyzing large, tumor-specific gene
expression profile datasets using the Algorithm for the Accurate
Reconstruction of Cellular Networks (ARACNe).sup.19, 20, as
supported by extensive experimental validation assays.sup.9, 10,
17, 21.
[0091] ARACNe analysis of the 211 EP-NET RNASeq profiles produced a
tumor-specific regulatory network (interactome) comprising 571,499
regulatory interactions between 5,631 regulatory proteins over
20,136 target genes. Regulator proteins include 1,785 TFs and 3,846
SPs. This network was then used both to assess protein activity on
an individual sample basis, for optimal cluster analysis, as well
as to elucidate novel master regulators (MRs) of tumor
progression.
[0092] Additional evaluation of the ARACNe inferred interactome
confirmed that it is highly relevant for the analysis of EP-NET
specific samples and that it is substantially distinct from other
interactomes previously generated and validated, which would not
have been appropriate for the analyses discussed below (see FIG.
12). When the network model is not representative of
tissue-specific regulation, the master regulator analysis produces
very few and barely significant results. Here, the EP-NET
interactome produced the strongest enrichment for 211 EP-NET
signatures when compared to 24 additional interactomes (Table 2 and
FIG. 11), indicating that EP-NET is the best interactome, among all
25 tested ones, as a model for EP-NET context-specific
transcriptional regulation.
TABLE-US-00002 TABLE 2 Interactomes Acronym Tumor Type Samples
Regulators Targets Interactions BRCA Breast carcinoma 1,100 6,054
19,359 331,919 UCEC Uterine corpus endometrial carcinoma 546 6,055
19,716 469,845 KIRC Kidney renal clear cell carcinoma 534 6,054
19,843 350,478 HNSC Head and neck carcinoma 522 6,055 19,772
423,104 LUAD Lung adenocarcinoma 517 6,055 19,742 399,513 THCA
Thyroid carcinoma 509 6,053 19,861 317,582 LUSC Lung squamous cell
carcinoma 501 6,054 19,741 455,032 PRAD Prostate adenocarcinoma 498
6,053 19,820 330,922 COAD Colon adenocarcinoma 459 6,056 19,820
413,789 BLCA Bladder urothelial carcinoma 408 6,054 19,785 489,101
LIHC Liver hepatocellular carcinoma 373 6,056 19,829 469,922 CESC
Cervical carcinoma 306 6,056 19,839 583,961 OV Ovarian carcinoma
299 6,007 19,140 647,358 KIRP Kidney renal papillary cell carcinoma
291 6,055 19,858 452,653 NET Neuroendocrine tumor 211 5,631 20,136
571,499 STAD Stomach adenocarcinoma 274 6,056 21,663 561,858 SARC
Sarcoma 263 6,112 20,479 526,591 ESCA Esophageal carcinoma 185
5,951 18,679 529,286 PCPG Pheochromocytoma and paraganglioma 184
6,056 19,861 603,617 LAML Acute myeloid leukemia 179 6,007 19,269
531,535 PAAD Pancreatic adenocarcinoma 179 6,056 19,858 520,756
READ Rectum adenocarcinoma 167 6,056 19,856 557,911 GBM
Glioblastoma multiforme 166 6,056 19,858 563,850 TGCT Testicular
germ cell tumor 156 6,056 19,860 432,621 THYM Thymoma 120 6,056
19,862 387,923
[0093] Identification of EP-NET molecular subtypes: Unsupervised
analysis of EP-NET profiles suggests a strong tissue-of-origin
component present in the transcriptome. Specifically, analysis of
the first 5 principal components, based on singular value
decomposition (SVD) analysis of the transcriptional data, captured
33% of the total sample variance and partially clustered with
primary tumor site, regardless of whether samples represented
primary, lymph node, or liver metastases (FIG. 12A). This
observation was further confirmed based on a t-Distributed
Stochastic Neighbor Embedding (t-SNE) projection of EP-NET
transcriptomes in two dimensions (FIG. 12B). FIG. 12A depicts
scatter-plots showing the first 5 principal components, capturing
35% of the variance for 211 EP-NET expression profiles. The tissue
of origin is indicated by different colors. Primary tumors are
shown with circles, while METs are shown with triangles. FIG. 12B
depicts 2D-tSNE projection for the expression data. Different
colors indicate the different tissue of origin. FIG. 12C depicts
2D-tSNE projection of the VIPER-inferred protein activity for 211
EP-NET samples. The color of the symbols indicates tissue of
origin, their shape indicates their status as primary tumor
(circles) or METs (triangles). The color of the clouds indicate the
cluster membership according to FIG. 7B.
[0094] Consistently, Partitioning Around Medoids (PAM)-based
consensus clustering, followed by cluster reliability analysis,
suggested an optimal partitioning of the samples in four clusters
that also partially co-segregated with primary tumor site (FIG. 13C
and FIG. 7A). Specifically, clusters 1-3 were highly enriched in
SI-NET, Pan-NET and Rec-NET samples, respectively, while clusters 4
included samples from SI-NET and Pan-NET (FIG. 7A).
[0095] FIG. 13A depicts the probability density plot for the
cluster reliability estimated from the expression profiles and
VIPER-inferred protein activity profiles for 211 EP-NET samples
(see FIG. 13D). FIG. 13B depicts integrated reliability score for
the complete cluster structure computed as the area over the
cumulative probability curve. FIG. 13C depicts integrated
reliability score for different cluster structures (different
number of clusters) for the consensus cluster of 211 EP-NET
expression (red) or VIPER-inferred protein activity profiles
(blue). FIG. 13D depicts cluster reliability score for 211 EP-NET
expression and VIPER-inferred protein activity profiles after
consensus clustering in 4 and 5 clusters, respectively. The
horizontal black line indicates the threshold for FDR<0.01.
FIGS. 13E and 13F depict cluster reliability (E) and silhouette
score (F) for each sample from the 4 clusters structure based on
expression and the 5 clusters structure based on VIPER-inferred
protein activity data. FIG. 13G depicts cluster membership for the
H-STS xenograft model. Shown is the enrichment of the samples from
each of the 5 clusters on the distance to the xenograft model based
on the correlation between protein activity signatures. Enrichment
significance is shown as
-log.sub.10 (p-value) by the bar-plot.
[0096] Protein activity, inferred from single-sample transcriptome
readouts using VIPER, can be a more robust descriptor of cell state
than gene expression.sup.18. The reason is three-fold. First,
VIPER-inferred protein activity represents a more direct and
mechanistic determinant of cell state, compared to gene expression;
second, while individual gene expression measurements are quite
noisy and poorly reproducible, VIPER infers protein activity from
the expression of a large number (tens to hundreds) of its
transcriptional targets (i.e., the protein's regidon), thus
resulting in much higher accuracy and reproducibilityl.sup.18;
third, bias and technical noise that is inconsistent with the
regulatory model is effectively filtered out, thus effectively
removing a major source of confounding data. The EP-NET interactome
can be used to transform the 211 individual transcriptional
profiles into equivalent protein-activity profiles for 5,578 of the
regulator proteins represented in the network, using
VIPER.sup.18.
[0097] VIPER-inferred protein activity was effective in segregating
samples according to tissue of origin. Both, unsupervised PAM-based
consensus cluster analysis, and t-SNE projection of the
protein-activity data into the two-dimensional space, identified 5
strongly distinct clusters representing molecularly distinct EP-NET
subtypes (FIGS. 7B and 12C). These included a SI-NET specific
cluster (C1: yellow), a Pan-NET specific cluster (C3, blue), a
Rec-NET cluster (C4: red), and two heterogeneous clusters including
mainly Pan-NET and SI-NET samples (C2: green and C5: purple), see
FIGS. 7B and 12C. Same color scheme was used to represent samples
belonging to these clusters in the t-SNE projection, thus
highlighting an essentially equivalent clustering structure by both
unsupervised analysis approaches (FIGS. 7B and 12C). FIG. 7A shows
the results of an unsupervised cluster analysis of 211 EP-NET
samples based on their gene expression profile. The heatmap shows
the weighted Pearson's correlation coefficient. Samples were
partitioned in 4 clusters and sorted according to their silhouette
score (indicated by the color bars on the right of the heatmap).
Each cluster average silhouette score is indicated by numbers. The
tissue of origin is indicated in the top horizontal bar: rectum
(red), small intestine (green) and pancreas (blue). The expression
level (RPKM) for gastrin, glucagon, insulin, somatostatin and VIP
is indicated by the bottom heatmap. FIG. 7B shows the results of an
unsupervised cluster analysis based on the VIPER-inferred protein
activity for 5,578 regulatory proteins. The heatmap shows the
scaled similarity score computed by the aREA technique.
[0098] Cluster reliability analysis confirmed that protein-activity
based clusters significantly outperformed the noisier gene
expression based clustering (FIGS. 13C-13F; p<10.sup.-15,
U-test). Besides the more reliable clusters obtained from protein
activity, both clusters structures were remarkably similar
(Adjusted Rand index: 0.57, p<10.sup.-5 by permutation test).
Interestingly, Pan-NET tumors were divided across three distinct
clusters, consistent with potential cell of origin, including
gastrinoma, insulinoma (green), glucagonoma (blue), and
non-secretory pancreas-NETs (purple) (FIG. 7). These results
clearly support a strong tissue-of-origin epigenetic memory in
EP-NETs, independent of tumor stage.
[0099] Inference of MR proteins of metastatic progression: To
identify Master Regulator proteins responsible for the metastatic
progression (MET) phenotype, the EP-NET interactome can be
interrogated with Gene Expression Signatures representing the cell
state transition between primary tumors and hepatic metastases
(MET-GES). Clinically, metastatic progression to the liver
determines a transition to an intractable form of the disease,
associated with poor prognosis. As previously shown, some of these
MR proteins would represent critical tumor dependencies associated
with the metastatic form of the disease, which can be targeted
pharmacologically.
[0100] To directly account for the potential heterogeneity of tumor
progression mechanisms, as well as to support the proposed
patient-specific approach to elucidating MR dependencies and
associated small molecule inhibitors, 69 metastatic samples were
analyzed on an individual basis. Specifically, individual MET-GES
signatures were generated by differential expression analysis of
each hepatic metastasis sample in a cluster (i.e., C1-C5) against
the average of all primary samples in that cluster (FIG. 7B). 3 of
the 211 samples cannot be reliably clustered (cluster reliability
FDR>0.01), including 1 pancreas and 2 small intestine primary
tumors. These samples were not considered for further analysis.
Individual MET-GES where then analyzed using VIPER, against the
EP-NET interactome, to identify MR proteins responsible for
directly regulating the change in gene expression repertoire during
metastatic progression.
[0101] Metastatic progression MRs were remarkably conserved both
within and across the C1-C5 molecular clusters. Indeed, the top 25
most activated and 25 most inactivated MRs, as identified from each
metastatic progression signature, were highly enriched in the
overall ranking of VIPER-inferred protein activity from other
MET-GES signatures. Specifically, 1,416 of the 2,346 possible
metastatic sample pairs showed significant MR overlap (FDR<0.01)
(FIGS. 8A and 12A).
[0102] Unsupervised consensus cluster analysis supports the
presence of four distinct clusters (MC1-MC4) representing highly
conserved, yet distinct mechanisms of metastatic progression (FIG.
8A), each one sharing a large subset of common MRs (FIG. 8B). FIG.
8A depicts heatmap showing the conservation of the top 50 most
dysregulated proteins in association with liver metastasis between
each possible sample pair. Samples were partitioned in 4 clusters
based on metastasis drivers conservation and sorted according to
the silhouette score. The tissue of origin is indicated by the
first color bar: rectum (red), small intestine (green) and pancreas
(blue). The clusters corresponding to FIG. 7B are indicated with
the same colors in the second color bar. FIG. 8B depicts heatmap
showing relative protein activity for the top 20 most dysregulated
proteins from each of the four clusters. Color bars on the right
indicate tissue of origin and correspondence to the five clusters
depicted in FIG. 7B. Single sample silhouette score and cluster
average are indicated to the right of the plot.
[0103] Interestingly, when comparing the 5 molecular subtype
clusters with the four tumor progression clusters, there was a very
weak association between them, with most of the samples from C1
clustering in MC5, which was enriched in SI-NETs and most of
samples from C4 falling in MCI, which was enriched in Rec-NETs.
However, all three MC-clusters were composed of samples from
different subtypes, supporting that the mechanisms of metastatic
progression are largely decoupled from primary tumor site and
subtype identity.
[0104] Selection of appropriate in vitro models for MR validation
and drug profiling: Experimental MR validation on an individual
sample basis requires availability of appropriate in vitro and in
vivo models. This is especially relevant to assess whether analysis
of patient-derived samples can elucidate small molecule compounds
that can abrogate tumor viability in vivo by inducing MR activity
inversion (i.e., tumor checkpoint collapse). EP-NETs were
characterized by the paucity of available high-fidelity models,
including both cell lines and xenografts. Five cell lines derived
from EP-NET patients that were previously characterized in the
literature were considered and shown to present certain features,
including expression of chromogranin A and somatostatin receptor
II, representing the hallmark of these tumors. Specifically, three
isogenic cell lines isolated from a single SI-NET patient including
from the primary tumor (P-STS) were considered, from a lymph node
metastasis (L-STS) and from a hepatic metastasis (H-STS).sup.22, an
additional cell line isolated from a distinct SI-NET patient
(KRJ-I).sup.23, and a cell line from a poorly differentiated
adenocarcinoma of the caecum with neuroendocrine features
(NCI-H716).
[0105] To assess the value of these cell lines as in vitro models
for the individual patient metastases represented in the dataset,
cell line-specific MET MRs analysis was performed by generating a
MET-GES progression signature for each metastatic cell line against
the P-STS cell line, as a representative of a primary tumor. A
computation can be performed to determine whether each patient top
100 MRs were enriched in each cell line MRs activity signature by
the aREA technique.sup.18.
[0106] MRs for 20 of the 69 metastatic patient-derived samples were
significantly recapitulated by the available EP-NET cell lines.
H-STS cell line is of particular interest because it is derived
from a metastatic lesion while the isogenic line P-STS was
established form the primary NET tumor. H-STS recapitulated the MRs
of 17 tumors (Bonferroni's adjusted p<10.sup.-10, FIG. 9A). In
particular, it recapitulated the MRs of a substantial majority of
RE-NET samples ( 8/11, 73%), and of a few SI-NET ( 2/28, 7.1%) and
Pan-NET ( 7/30, 23%) samples, including the MRs of one Pan-NET
patient of interest (P0) on which the oncoTreat analysis is based
(FIGS. 9A and 9B). One patient whose MRs were not properly
recapitulated by the H-STS cell line was selected for comparison
purposes (P1, FIG. 9C). FIG. 9A depicts enrichment of the top 100
most dysregulated proteins from each metastasis on each cell line
and the H-STS xenograft model protein activity signature. The color
bar on top of the plot indicates the tissue of origin for the
primary tumor. The blue triangles indicate two Pan-NET metastasis
(patient-0 and patient-1) for which a detailed plot of this
analysis is shown in panels B through E. FIGS. 9B-9E depict the
results of Gene Set Enrichment Analysis for the top 50 most
activated and the top 50 most de-activated proteins in each
selected metastasis on the protein activity signature of the H-STS
cell line (B and C), and the H-STS xenograft model (D and E).
Enrichment score for the de-activated (blue) and activate (red)
proteins in the metastasis is shown by the curves. The top 50 most
dysregulated proteins in the metastasis are indicated by vertical
lines as projected on the H-STS and the xenograft protein activity
signatures, which are indicated by the color scale on the bottom of
the plot.
[0107] Transcriptome analysis of an H-STS xenograft model indicated
a clear similarity to the molecular cluster C5 (purple, FIG. 13G).
Interestingly, this xenograft model recapitulated the metastasis
MRs of 32 of the 69 metastatic tumors, including 73% of the RE-NET
(8 tumors), 32% of the SI-NET (9 tumors) and 50% of the Pan-NET (15
tumors) (FIGS. 9A, 9D and 9E).
[0108] Systematic inference of MR activity inhibitors: To identify
candidate small molecule compounds capable of abrogating the MR
activity signature driving metastatic progression, a library of 504
compounds previously screened at the Broad Institute, Cambridge,
Mass., were interrogated for differential activity against a panel
of 242 genomically characterized cancer cell lines (CCL), of which
354 had been previously published.sup.6. All 504 compounds were
re-screened in the available neuroendocrine tumor cell lines,
including H-STS, L-STS, P-STS, KRJ-I, and NCI-H716. The top 108
most differentially active compounds in NET-related cells compared
to the other 242 CCL were selected, based on their differential
activity on cell viability, as measured by the area under the dose
response curve (AUC). Dose response curves for these compounds were
repeated in the HTS facility at Columbia University and compared to
those generated at the Broad. Overall, these studies presented
remarkable overlap with an AUC Spearman correlation of 0.71
(p=1.times.10.sup.-10).
[0109] To assess the ability of these compounds to induce Tumor
Checkpoint collapse (i.e., global inversion of patient-derived MR
activity pattern), gene expression profiles were generated at 6 h
and 24 h following perturbation of H-STS cells with two sub-lethal
compound concentrations, the 72 h IC.sub.20 and 1/10.sup.th of that
concentration in duplicate. The 24 h time point was considered more
informative for long term response. These were produced by 30M SE
read Illumina TruSeq profiling of RNA purified from treated cells
as well as from cells treated with control media (DMSO). This
ensured that the highest compound concentration can be tested that
would not induce cell death processes and would thus faithfully
recapitulate the compound mechanism of action (MoA) rather than the
mechanisms and programs associated with cell demise. While in vivo
endpoint phenotypes (e.g., tumor viability) are not effectively
recapitulated in 2D cultures in vitro, compound MoA is reasonably
well-recapitulated in both contexts. One aim can be to identify
compounds capable of inverting MR activity signature in vitro in a
relatively faithful model of the tumor regulatory context, to
assess whether these compounds would have activity in vivo.
[0110] Drug signatures were analyzed with VIPER to assess the
change in protein activity before and after the perturbation.
Specifically, RX-GES were generated by differential expression of
compound-treated vs. control-vehicle-treated cells at all time
points and concentrations and analyzed with VIPER against the
EP-NET interactome. This ranked all 5,602 regulatory proteins
represented in the interactome from the one whose activity was most
inhibited to the one whose activity was most increased following
drug perturbation. An aREA analysis of patient samples that were
well represented by the H-STS xenograft model was performed to
assess enrichment of metastatic progression MRs in proteins whose
activity was most inverted following drug perturbation. Since
validation of these predictions was performed in H-STS mouse
xenograft models, the analysis was limited to each NET-MET MRs that
were recapitulated in the H-STS xenograft. This does not compromise
the generality of the methodology. Rather they allow optimal tuning
of the results to available in vitro and in vivo models for optimal
design of validation assays. Thus, the OncoTreat methodology uses
the ability to prioritize small molecule compounds that optimally
reverse a patient-specific MR activity signature.
[0111] Three drugs were identified that significantly reverted the
selected patient (P0, see FIG. 6A) and H-STS xenograft specific
MET-MR activity (Bonferroni's adjusted p<10.sup.-10), including
the HDAC1/3 inhibitor (entinostat), the protein bromodomain
inhibitor (I-BET151), and the NF-.kappa.B inhibitor (bardoxolone
methyl). Among them, entinostat showed the most significant
reversal in both, the patient 0 MET-MR program recapitulated by the
H-STS xenograft model, and the MRs of the xenograft model (FIGS.
10A-10C). FIG. 15 shows the oncoTreat (or oncoMatch) results for 46
selected compounds on patient 0, H-STS xenograft, and 31 additional
tumors whose MRs were shown to be recapitulated by the H-STS
xenograft model (see FIG. 9A). FIG. 15 depicts the enrichment of
the top 50 most activated (shown in red in the enrichment plots)
and the top 50 most deactivated (shown in blue) proteins in patient
0 on the protein activity signature induced by each compound
perturbation in the H-STS cell line. The heatmap shows the
statistical significance for MR reversal, expressed as
-log.sub.10(p-value), as quantified by the aREA technique by
measuring the enrichment of each of 32 tumor samples and one
xenograft sample MRs on the protein activity signature elicited by
compound perturbation of H-STS cells. The colored bar indicates the
tissue of origin for each of the evaluated tumors.
[0112] Drug validation in vivo: H-STS cells effectively engraft in
nude mice and RNASeq of resulting xenograft tumors showed
remarkable overlap with patient-derived MRs (FIGS. 9A and 9D). Six
compounds were selected for in vivo validation (FIG. 10A),
including two compounds significantly abrogating the activity of
patient-0 and xenograft MRs: Entinostat (the top prioritized
compound), and I-BET151, a bromodomain inhibitor; one compound
reverting the patient-0 MRs but not the xenograft MRs: Bardoxolone
methyl, an oxidative stress activator/NF.kappa.B inhibitor; one
compound reverting the xenograft but not patient-0 MRs: Tivantinib,
a c-Met inhibitor with complementary activity as a microtubule
inhibitor; and one compound showing no significant reversal of
either patient-0 or xenograft MRs: PDX101 (Belinostat), a pan-HDAC
inhibitor. The latter compound was selected among the ones showing
no activity because, from a pharmacological perspective, it should
have effects similar to entinostat and yet the two compounds were
predicted to be at the opposite end of the MR-signature reversal
activity.
[0113] In vivo validation in NOD-SCIDS mice xenografts established
by subcutaneous injection of H-STS cells was first conducted at
Champions Oncology and then independently confirmed in the mouse
hospital facility at Columbia University. Mice were enrolled in
treatment arms when tumor size reached 250 mm- and were treated for
25 days. Tumor size was measured twice weekly by digital caliper,
see methods. While mild tumor growth inhibition (TGI) was seen with
high levels of Tivantinib (43% TG at 200 mg/kg/dose and 28% TGI at
100 mg/kgdose), the tumor still progressed, albeit at a slower rate
than the controls. Tumors treated with Belinostat showed minimal
TGI, with only an 8% TGI at the 20 mg/kg/dose level. In stark
contrast, treatment with Entinostat showed high levels of tumor
regression (TR), with 68% TR and 112% TGI at 25 mg/kg/dose and 58%
TR and 110% TGI at 50 mg/kg/dose. Treatment with Entinostat was
toxic at 100 mg/kgdose, however the single surviving animal from
that group showed tumor regression of 49%. These results are
summarized in Table 3 and FIG. 10B.
TABLE-US-00003 TABLE 3 Tumor Volume and Agent Activity Data %
RECIST % Group TGI PD/SD/PR/CR* TR Control 3/0/0/0 n/a ARQ197 200
mg 43% 3/0/0/0 n/a ARQ197 100 mg 28% 3/0/0/0 n/a ARQ197 50 mg -46%
3/0/0/0 n/a PDX101 20 mg 8% 3/0/0/0 n/a PDX101 40 mg -55% 3/0/0/0
n/a MS-27-275 25 mg 112% 0/0/3/0 68 MS-27-275 50 mg 110% 0/0/3/0 58
MS-27-275 100 mg** n/a 0/0/1/0 49 *PD--Progressive Disease;
SD--Stable Disease; PR--Partial Response; CR--Complete Response
**Four of five animals died one week into the test, likely as a
result of drug toxicity; results are representative of the single
surviving animal
[0114] Follow-up studies at Columbia confirmed the original
observation for Entinostat, with complete tumor growth abrogation
(FIG. 10C). A weak reduction in tumor growth for Bardoxolone methyl
was observed only for the last two time points evaluated, and no
significant difference when compared to vehicle control for
I-BET151 and Bortezomib (FIG. 10C). In agreement with compound
perturbation effect in vitro (FIG. 10A), analysis of xenograft
transcriptome 3 hours after 3.sup.rd drug administration indicated
a strong inhibition of patient-0 checkpoint protein activity by
Entinostat, I-BET151 and Bardoxolone methyl, and no effect of
Bortezomib (FIG. 10D). Similarly, the same analysis showed a
significant inhibition of the H-STS xenograft checkpoint only by
Entinostat. This is in line with the poor effect of Bardoxolone
methyl, I-BET151 and Bortezomib on xenograft tumor growth, and the
strong abrogation elicited by Entinostat (FIGS. 10B and 10C). In
summary, while the effect of the Entinostat, I-BET151 and
Bardoxolone methyl on the reversal of patient-0 checkpoint activity
inferred from the in vitro H-STS perturbation assay was confirmed
in the xenograft model, only Entinostat reverted the activity of
the H-STS xenograft checkpoints and abrogated tumor growth (FIG.
10).
[0115] FIG. 10A depicts enrichment of patient-0 metastasis
checkpoint MRs on the protein activity signatures induced by 6
selected compounds in the H-STS cells. FIGS. 10B and 10C depict
growth curves for the H-STS xenograft while treated by vehicle
control, and each of the 6 selected compounds. Curves show tumor
volume for individual animals (FIG. 10B) or the mean.+-.SEM of 8
animals (FIG. 10C). Figure OD depicts enrichment of patient-0
metastasis checkpoint on the protein activity signatures induced by
4 selected compounds in the H-STS xenograft. FIG. 10E depicts
enrichment of H-STS xenograft checkpoint on the protein activity
signatures induced by 4 selected compounds in the H-STS
xenograft.
DISCUSSION
[0116] Despite success, the oncogene addiction paradigm.sup.1 has
shown increasing challenges including a diminishing number of
novel, high-penetrance actionable targets identifiable by genetic
alterations in tumor sequences, lack of actionable mutations in the
majority of cancer patients, and high frequency of relapse
following targeted therapy. Indeed, only 5% to 11% of patients
experience progression free survival increase when treated with
targeted inhibitors based on tumor genetics (Mardis personal
communication).
[0117] Certain results have revealed the existence of a new class
of proteins (master regulators) responsible for mechanistically
implementing the transcriptional signature of a specific tumor. MR
proteins can be efficiently identified by regulatory network based
analysis, even on an individual patient basis.sup.18, despite the
fact that they are rarely mutated or differentially expressed. This
example supports unbiased assessment of FDA approved drugs and
investigational compounds in terms of their ability to reverse
patient-specific MR activity signatures, using the OncoTreat
analysis, is effective in prioritizing compounds that can abrogate
tumor viability in vivo.
[0118] The OncoTreat methodology was tested in a rare tumor type
(EP-NETs), which notoriously lack targetable alterations and are
poorly characterized in the literature. This choice was deliberate
to show that the proposed approach can be efficiently applied in
unbiased fashion even to tumors for which little information is
available at the molecular level. Indeed, the more complex
component of the the analyses presented in this example was the
collection and profiling of a large number of EP-NET tumors from 17
collaborating centers to provide adequate data for assembling the
regulatory model and for interrogating it with signatures of
metastatic progression. The OncoTreat methodology was however,
completely generalizable and is tested in a much broader study that
covers 14 rare and otherwise untreatable malignancies.
[0119] Validation assays confirmed that drugs predicted to have
high, medium, and no activity on MR-signature reversal produced
tumor regression, tumor growth reduction, and no effect,
respectively, thus substantially validating the approach.
Remarkably, all of these compounds had been prioritized based on
their high differential toxicity in EP-NET cell lines, thus
confirming that in vitro toxicity is not a good predictor of in
vivo activity, even when the same cell line is used in both assays.
It is also important to note the top drugs prioritized by
VIPER-based perturbational profile analysis induced profound
reversal of virtually all top 50 master regulator proteins (i.e.,
of the entire tumor checkpoint module). Since it is unlikely that
these compounds can represent specific inhibitors and activators of
such large and unique protein sets, this support that tumor
checkpoints represent tightly auto-regulated modules that can be
switched globally off by pharmacological intervention. This had
been previously reported, for instance by RNAi mediated silencing
of synergistic MR-pairs in glioma.sup.9 and prostate cancer.sup.10,
which caused collapse of the entire MR module. Thus, these analyses
presented in this Example further confirm the critical role of
tumor checkpoint modules as regulatory switches responsible for
maintaining the stability of tumor state.
[0120] Since the OncoTreat methodology prioritizes compound
activity based on patient-specific MR signatures, prioritized drugs
are naturally coupled with MR-based biomarkers for the selection of
responders vs. non responder cohorts. Interestingly, as shown for
EP-NET tumors, patients clustered within a handful of subtypes,
each presenting a virtually identical MR activity profile. This
support a potential for more universal therapies, despite tumor
heterogeneity at the genetic level. As a result, the OncoTreat
methodology can be suited to the efficient generation of basket
study designs, where patients can be assigned to different
treatment arms depending on their specific MR signature.
[0121] If a patient that responded to targeted therapy effectively
clusters within a relatively small number of distinct MR
signatures, this supports that once a sufficient number of PDX
model have been tested for each subtype, treatment for additional
patients can be determined on the basis of previous response in PDX
models that represent a close match for the patient MR activity
signature. Additionally, the ability to screen compound in vitro
can lead to assessing effective compound activity in reversing MR
activity signatures but at concentrations that are not
physiologically achievable. This can be addressed for instance by
studying compound PD in vivo at maximum tolerated doses, by
analyzing the gene expression patterns of the top prioritized
compounds following in vivo perturbation of tumor xenografts. This
would also address potential issues related to differential
compound activity in vitro and in vivo, even though compound
mechanism of action, as opposed to phenotypic endpoint, is
relatively well-conserved in these contexts.
CONCLUSION
[0122] As shown in this Example, the OncoTreat or OncoMatch
methodology is a highly innovative and broadly applicable RNA-based
approach to precision cancer medicine. It provides a comprehensive
and experimentally validated framework for prioritizing therapeutic
strategies on an individual patient basis. Specifically,
therapeutic strategies are prioritized by simultaneously
identifying critical tumor dependencies and the drugs that are
optimally suited to abrogate their activity, via context specific
regulatory network analysis. This methodology has been tested in a
rare tumor context--enteropancreatic neuroendocrine tumors--with
full in vivo validation of therapeutic strategies.
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[0146] In addition to the various embodiments depicted and claimed,
the disclosed subject matter is also directed to other embodiments
having other combinations of the features disclosed and claimed
herein. As such, the particular features presented herein can be
combined with each other in other manners within the scope of the
disclosed subject matter such that the disclosed subject matter
includes any suitable combination of the features disclosed herein.
The foregoing description of specific embodiments of the disclosed
subject matter has been presented for purposes of illustration and
description. It is not intended to be exhaustive or to limit the
disclosed subject matter to those embodiments disclosed.
[0147] It will be apparent to those skilled in the art that various
modifications and variations can be made in the compositions and
methods of the disclosed subject matter without departing from the
spirit or scope of the disclosed subject matter. Thus, it is
intended that the disclosed subject matter include modifications
and variations that are within the scope of the appended claims and
their equivalents.
[0148] All patents and publications in this specification are
herein incorporated by reference to the same extent as if each
independent patent and publication and sequence was specifically
and individually indicated to be incorporated by reference.
* * * * *