U.S. patent application number 13/016951 was filed with the patent office on 2011-10-13 for prognostic and diagnostic method for cancer therapy.
Invention is credited to Gennadi V. Glinsky.
Application Number | 20110251087 13/016951 |
Document ID | / |
Family ID | 38564125 |
Filed Date | 2011-10-13 |
United States Patent
Application |
20110251087 |
Kind Code |
A1 |
Glinsky; Gennadi V. |
October 13, 2011 |
PROGNOSTIC AND DIAGNOSTIC METHOD FOR CANCER THERAPY
Abstract
The present invention relates to methods for the diagnosis and
prognosis of aggressive forms of cancer using a combination of gene
expression signatures.
Inventors: |
Glinsky; Gennadi V.;
(Loudonville, NY) |
Family ID: |
38564125 |
Appl. No.: |
13/016951 |
Filed: |
January 28, 2011 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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11732442 |
Apr 2, 2007 |
7890267 |
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13016951 |
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60823577 |
Aug 25, 2006 |
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60822705 |
Aug 17, 2006 |
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60787818 |
Mar 31, 2006 |
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60875061 |
Dec 15, 2006 |
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Current U.S.
Class: |
506/9 ; 435/6.1;
436/94 |
Current CPC
Class: |
C12Q 2600/106 20130101;
G01N 33/57415 20130101; C12Q 2600/178 20130101; G01N 33/57434
20130101; C12Q 2600/118 20130101; C12Q 1/6886 20130101; Y10T
436/143333 20150115; C12Q 2600/154 20130101 |
Class at
Publication: |
506/9 ; 436/94;
435/6.1 |
International
Class: |
C40B 30/04 20060101
C40B030/04; C12Q 1/68 20060101 C12Q001/68; G01N 33/48 20060101
G01N033/48 |
Goverment Interests
STATEMENT AS TO FEDERALLY SPONSORED RESEARCH
[0002] This invention was made using federal funds awarded by the
National Institutes of Health, National Cancer Institute under
contract number 1R01CA89827-01. The government has certain rights
to this invention.
Claims
1. A method for diagnosing a more aggressive form of prostate,
breast, lung, or ovarian cancer in a subject comprising determining
a cumulative cancer therapy outcome predictor (CTOP) score for six
gene expression signatures, wherein the six gene expression
signatures are TEZ, EED, SUZ12/POLII, SUZ12, NANOG/SOX2/OCT4, and
PCG-TF, wherein the CTOP score for each signature is determined by
measuring the mRNA expression of each gene comprising the signature
in a sample from the subject, and wherein a higher cumulative CTOP
score indicates a more aggressive form of the cancer.
2. The method of claim 1, further comprising determining the CTOP
score for one or more additional gene expression signatures
selected from the group consisting of BCD TF, ESC3, BMI-1 pathway,
PcG methylation, and histones H3/H2A.
3. The method of claim 1, wherein the cancer is breast cancer or
lung cancer and the one or more CTOP signatures comprises a
36-transcript TEZ CTOP signature comprising transcripts of the
following genes: GNG10, SOX9, BCI2, PURA, LIF, MN1, HOXD, BAI3,
GFRA2, SIX1, CACNAL, SIX2, SMARCA2, and SMARCA2.
4. The method of claim 1, wherein the cancer is breast cancer or
lung cancer and the one or more CTOP signatures comprises a
20-transcript EED CTOP signature consisting of transcripts of the
following genes: CCND2, YYL, SOX9, PDGFRA, EZH2, KIT, PAX6, DICERL,
LHX2, BMP6, HOXC6, PAX9, SOX5, WNT8B, ESRRB, FOXIL, EED, YYL,
CKAP2, and CBX8.
5. The method of claim 1, wherein the cancer is prostate cancer and
the one or more CTOP signatures comprises a 36-transcript EED CTOP
signature consisting of transcripts of the following genes: PAX8,
YYL, PCNA, IGF2, HPRTL, SOX9, PDGFRA, EZH2, FGFR3, HOXB7, BMPRLA,
TCF21, TALI, CRABP1, HOXB2, GATA4, FGF7, BMP6, HOXA4, CBX4, HOXC5,
HOXC6, DIX2, FOXHI, SOX21, NEUROG1, EED, GATA3, GATAL, MYST4, YYL,
HOXA2, PAX8, PDGFRA, TALL, and DKK2.
6. The method of claim 1, wherein the cancer is breast cancer or
lung cancer and the one or more CTOP signatures comprises a
20-transcript SUZ12/POLII CTOP signature consisting of transcripts
of the following genes: CXCR4, KIAA0368, PLD1, NID2, ATF3,
KIAA0427, DUSP4, SMTN, GADD45B, PRKCH, JUN, PCDH17, TIMP3, PLEC1,
LIF, SOX9 and CDH11.
7. The method of claim 1, wherein the cancer is prostate cancer and
the one or more CTOP signatures comprises a 22-transcript
SUZ12/POLII CTOP signature consisting of transcripts of the
following genes: PAK1, ING1, POLR3K, PLD1, BAIAP2, BDNF, EGFR,
FBN2, KIAA0427, CAST, GADD45B, PRKCH, CYP1B1, and JUN.
8. The method of claim 1, wherein the cancer is breast cancer or
lung cancer and the one or more CTOP signatures comprises a
25-transcript SUZ12 CTOP signature consisting of transcripts of the
following genes: SFRP1, COL15A1, DFNA5, THBD, PSMB9, MYO5A, MN1,
RASGRP1, SYNGR3, RELN, SALL1, MMP25, RASGRP2, GATA3, GATA6, PCDH7,
HLA-G, TFAP2B, FLJ10159, DLL3, GNA14, TGFB2, WNT5B, and BACH2.
9. The method of claim 1, wherein the cancer is breast cancer or
lung cancer and the one or more CTOP signatures comprises a
41-transcript NANOG/SOX2/OCT4 CTOP signature consisting of
transcripts of the following genes: RPS3A, TOP2A, BUB3, TALDO1,
USP7, SNRPN, ZFP36L1, GNG10, SFRP1, LAMA4, FEZ1, BUB1B, MTM1, TIF1,
NFE2L3, DTNA, CA4, TDGF1, 1F116, TLE3, SPAG9, ZIC3, FGFR2, 1F116,
STATS, PPAP2A, PPAP2A, FGFR2, PHF8, LARGE, RPS18, FUS, FLJ10769,
DUSP12, FLJ10652, FLJ11029, RAD54B, NANOG, FEZ1, FEZ1, and
MTM1.
10. The method of claim 1, wherein the cancer is prostate cancer
and the one or more CTOP signatures comprises a 28-transcript
NANOG/SOX2/OCT4 CTOP signature consisting of transcripts of the
following genes: JUP, BUB3, ICMT, GNG10, SET, PPAP2A, LARGE, FUS,
and NUCKS.
11. The method of claim 1, wherein the cancer is breast cancer or
lung cancer and the one or more CTOP signatures comprises a
30-transcript PcG TF CTOP signature consisting of transcripts of
the following genes: ATF3, SOX9, ZFHX1B, JUN, CEBPA, NPAS2, NPAS2,
DACH1, SIX1, EGR3, FOXF2, CART1, HOXC6, PAX9, DIX2, SIX6, ALX3,
HOXB3, POU4F3, POU3F3, NR2F2, MAFA, PAX1, GSH1, WT1, SOX18, LHX6,
TBR1, and EN1.
12. The method of claim 1, wherein the cancer is prostate cancer
and the one or more CTOP signatures comprises a 21-transcript PcG
TF CTOP signature consisting of transcripts of the following genes:
JUNB, HOXB2, NPAS2, IRF5, PAX6, SIX1, VVT1, HOXA4, HOXC6, NKX2B,
HOXD13, HOXB1, NEUROG1, FOXB1, GATA6, BARX2, LEF1, NHLH2, TALI, and
TBR1 BACH2.
13. The method of claim 2, wherein the cancer is breast cancer or
lung cancer and the one or more CTOP signatures comprises an
26-transcript BCD TF CTOP signature consisting of transcripts of
the following genes: DLX2, EN2, HOXA1, HOXA13, HOXA2, HOXA5, HOXB2,
HOXB3, HOXB6, HOXB8, HOXC13, HOXC5, HOXD3, IRX2, IRX4, LMO1, NR2F2,
PAX1, PAX2, SATB1, SHH, SIX2, TLX3, ZFHX1B, HOXA7, and TITF1.
14. The method of claim 2, wherein the cancer is prostate cancer
and the one or more CTOP signatures comprises a 31-transcript BCD
TF CTOP signature consisting of transcripts of the following genes:
SATB1, HOXD9, PAX6, LMO4, EBF1, FOXA1, FOXD3, HOXA, HOXB, HOXC,
HOXD, IRX4, MAF, MRG1, NKX2-2, and NKX6-2.
15. The method of claim 2, wherein the one or more CTOP signatures
comprises an 11-transcript BMI-1 CTOP signature consisting of
transcripts of the following genes: GBX2, MK167, CYCLIN B1, BUB1,
HEC, KIAAIDG3, HCFC1, RNF2, ANK3, FGFR2, and CES1. (OK--FIG. 10 of
507)
16. The method of claim 2, wherein the cancer is breast cancer or
lung cancer and the one or more CTOP signatures comprises a
35-transcript ESC3 CTOP signature consisting of transcripts of the
following genes: PTPRF, MARCKS, FLNA, TNFAIPL, P1S3, IRAK1, MARCKS,
PYCR1, TM4SF2, NDST1, DBN1, NSMAF, FZD7, AQP3, SSBP2, NKAT, CHC1I
FST, GSTT2, CDS1, CNTFR, GGT1, FST, GGT1, EPHX2, CDKN2A, CD24A,
EFS, BATT, TNNT1, IFITM3, PRDM4, SOX18, LEPREL, and MDFI.
17. A method for prognosis of prostate, breast, lung, or ovarian
cancer in a subject having prostate, breast, lung, or ovarian
cancer comprising determining a cumulative cancer therapy outcome
predictor (CTOP) score for six gene expression signatures, wherein
the six gene expression signatures are TEZ, EED, SUZ12/POLII,
SUZ12, NANOG/SOX2/OCT4, and PCG-TF, wherein the cumulative CTOP
score is the sum of the CTOP scores for each signature determined
by measuring the mRNA expression of each gene comprising the
signature in a sample from the subject, and wherein a higher
cumulative CTOP score indicates a more aggressive form of the
cancer and a poor prognosis.
Description
RELATED APPLICATIONS
[0001] This application is a continuation of U.S. Ser. No.
11/732,442, filed Apr. 2, 2007, now issued as U.S. Pat. No.
7,890,267, which claims priority to U.S. Provisional Application
Nos. 60/875,061, filed on Dec. 15, 2006; 60/823,577, filed on Aug.
25, 2006; 60/822,705, filed on Aug. 17, 2006; and 60/787,818, filed
on Mar. 31, 2006, each of which is incorporated herein by reference
in its entirety.
SEQUENCE LISTING
[0003] The instant application contains a sequence listing which
has been submitted via EFS-Web and is hereby incorporated by
reference in its entirety. The computer readable copy of the
sequence listing in ASCII format was created on Apr. 13, 2011 and
is entitled 26141.sub.--507C01US_SeqList_ST25. The file is 855
bytes in size.
FIELD OF THE INVENTION
[0004] The invention relates to diagnostic and prognostic methods
and kits for cancer therapy.
BACKGROUND
[0005] A wide variety of cancer treatment protocols have been
developed in recent years. Often, very aggressive cancer therapy is
reserved for late stage cancers due to unwanted side effects
produced by such therapy. However, even such aggressive therapy
commonly fails at such a late stage. The ability to identify
cancers responsive only to the most aggressive therapies at an
earlier stage could greatly improve the prognosis for patients
having such cancers.
[0006] Only very recently, however, have markers predictive of such
outcomes been identified. Glinsky, G. V. et al., J. Clin. Invest.
113: 913-923 (2004) teaches that gene expression profiling predicts
clinical outcomes of prostate cancer. van 't Veer et al., Nature
415: 530-536 (2002) teaches that gene expression profiling predicts
clinical outcomes of breast cancer. Glinsky et al., J. Clin.
Invest. 115: 1503-1521 (2005) teaches that altered expression of
the BMI1 oncogene is functionally linked with self-renewal state of
normal and leukemic stem cells as well as a poor prognosis profile
of an 11-gene death-from-cancer signature predicting therapy
failure in patients with multiple types of cancer. These studies
utilized the microarray gene expression analysis approach.
[0007] There is, therefore, a need for methods for early diagnosis
of cancer and for prognostic assays for cancer therapy that are
readily adaptable to the clinical setting. Such methods should
utilize technologies that can be readily carried out in clinical
laboratories, and should accurately predict the resistance of
various cancers to be applied to standard therapeutic regimens.
SUMMARY OF THE INVENTION
[0008] The present invention is directed to novel methods and kits
for diagnosing the presence of cancer within a patient, and for
determining whether a subject who has cancer is susceptible to
different types of treatment regimens. The cancers to be tested
include, but are not limited to, prostate, breast, lung, gastric,
ovarian, bladder, lymphoma, mesothelioma, medullablastoma, glioma,
and AML.
[0009] One embodiment of the present invention is directed to a
method for diagnosing cancer or predicting cancer-therapy outcome
by detecting the expression levels of multiple markers in the same
cell at the same time, and scoring their expression as being above
a certain threshold, wherein the markers are from a particular
pathway related to cancer, with the score being indicative or a
cancer diagnosis or a prognosis for cancer-therapy failure. This
method can be used to diagnose cancer or predict cancer-therapy
outcomes for a variety of cancers. The simultaneous co-expression
of at least two markers in the same cell from a subject is a
diagnostic for cancer and a predictor for the subject to be
resistant to standard cancer therapy. The markers can come from any
pathway involved in the regulation of cancer, including
specifically the PcG pathway and the "stemness" pathway. The
markers can be mRNA, DNA, or protein.
[0010] These and other embodiments of the present invention rely at
least in part upon the novel finding that the expression of
multiple markers above a threshold level in the same cell at the
same time, wherein the markers are found within pathways related to
cancer, can be used as an assay to diagnose cancer disorders and to
predict whether a patient already diagnosed with cancer will be
therapy-responsive or therapy-resistant. An element of the assay is
that two or more markers are detected simultaneously within the
same cell. Marker detection can be made through a variety of
detection means, including bar-coding through immunofluorescence.
The markers detected can be a variety of products, including mRNA,
DNA, and protein. For mRNA based markers, PCR can be used as a
detection means. Additionally, protein products or gene copy number
can be identified through detection means known in the art. The
markers detected can be from a variety of pathways related to
cancer. Suitable pathways for markers within the scope of the
present invention include any pathways related to oncogenesis and
metastasis, and more specifically include the Polycomb group (PcG)
chromatin silencing pathway and the "stemness" pathway.
[0011] In another embodiment, the invention is directed to a method
for diagnosing cancer or predicting cancer-therapy outcome in a
subject, said method comprising the steps of:
[0012] a) obtaining a sample from the subject,
[0013] b) selecting a marker from a pathway related to cancer,
[0014] c) screening for a simultaneous aberrant expression level of
two or more markers in the same cell from the sample, and
[0015] d) scoring their expression level as being aberrant when the
expression level detected is above or below a certain detection
threshold coefficient, wherein the detection threshold coefficient
is determined by comparing the expression levels of the samples
obtained from the subjects to values in a reference database of
sample phenotypes obtained from subjects with either a known
diagnosis or known clinical outcome after therapy, wherein the
presence of an aberrant expression level of two or more markers in
individual cells and presence of cells aberrantly expressing two or
more such markers is indicative of a cancer diagnosis or a
prognosis for cancer-therapy failure in the subject. The subset of
markers to be used within the methods of the present invention
include any markers associated with cancer pathways.
[0016] In preferred embodiments, the markers can be selected from
the genes identified in FIGS. 11-20. The markers can comprise
anywhere ranging from two markers listed within each table up to
the whole set of genes listed within each of these tables. The
markers can comprise any percentage of genes selected from each of
these tables, including 90%, 80%, 70%, 60%, or 50% of the genes
identified in FIGS. 11-20.
[0017] In this method, an aberrant co-expression level of the
markers can be indicative of the presence of cancer in the subject,
or predictive of cancer-therapy failure in the subject. The markers
can be selected from any suitable cancer pathway, including in
preferred embodiments markers from the Polycomb or "stemness"
pathway. These markers can be genes selected from the group
consisting of ADA, AMACR+p63, ANK3, BCL2L1, BIRC5, BMI-1, BUB1,
CCNB1, CCND1, CES1, CHAF1A, CRIP1, CRYAB, ESM1, EZH2, FGFR2, FOS,
Gbx2, HCFC1, IER3, ITPR1, JUNB, KLF6, KI67, KNTC2, MGC5466, Phc1,
RNF2, Suz12, TCF2, TRAP100, USP22, Wnt5A and ZFP36. In preferred
embodiments, the markers are selected from the group consisting of
BMI1, Ezh2, H2A, H3, transcription factors, and methylation
patterns. In one preferred embodiment, the aberrant co-expression
level detected is of BMI1 and Ezh2, and in another preferred
embodiment the aberrant co-expression level detected is of H2A and
H3. The markers being detected are in the form of either mRNA, DNA,
or protein.
[0018] In a preferred embodiment, the sample phenotypes are
selected from the group consisting of cancer, non-cancer,
recurrence, non-recurrence, relapse, non-relapse, invasiveness,
non-invasiveness, metastatic, non-metastatic, localized, tumor
size, tumor grade, Gleason score, survival prognosis, lymph node
status, tumor stage, degree of differentiation, age, hormone
receptor status, PSA level, histologic type, and disease free
survival.
[0019] The aberrant expression level of two or more markers can be
detected by any detection means known in the art, including, but
not limited to, subjecting the cells to an analysis selected from
the group consisting of multicolor quantitative immunofluorescence
co-localization analysis, fluorescence in situ hybridization, and
quantitative RT-PCR analysis.
[0020] In another embodiment, the present invention is directed to
a method of determining a detection threshold coefficient for
classifying a sample phenotype from a subject, the method
comprising the steps of:
[0021] a) obtaining a sample from the subject,
[0022] b) selecting two or more markers from a pathway related to
cancer,
[0023] c) screening for a simultaneous aberrant expression level of
the two or more markers in the same cell from the sample;
[0024] d) scoring the marker expression in the cells by comparing
the expression levels of the samples obtained from the subjects to
values in a reference database of samples obtained from subjects
with either a known diagnosis or known clinical outcome after
therapy, and
[0025] e) determining the detection threshold coefficient for the
sample classification accuracy at different detection thresholds
using reference database of samples from subjects with known
phenotypes.
[0026] Detection threshold coefficients which are indicative of a
cancer diagnosis or a prognosis for cancer-therapy failure have an
absolute value within the range of .gtoreq.0.5. to .gtoreq.0.999.
Preferred levels of detection threshold coefficients which are
indicative of a cancer diagnosis or a prognosis for cancer-therapy
failure have an absolute value of .gtoreq.0.5, .gtoreq.0.6,
.gtoreq.0.7, .gtoreq.0.8, .gtoreq.0.9, .gtoreq.0.95, .gtoreq.0.99,
.gtoreq.0.995, and .gtoreq.0.999.
[0027] In another embodiment, the method further comprises
determining the best performing magnitude of said detection
threshold and using said magnitude to assess the reliability of
said established detection threshold in classifying a sample
phenotype. In another embodiment, the method further comprises
using the best performing magnitude of said detection threshold to
score an unclassified sample and assign a sample phenotype to said
sample.
[0028] In another embodiment, the present invention is directed to
a method for simultaneously detecting an aberrant co-expression
level of two or more markers a single cell, said method comprising
the steps of:
[0029] a) obtaining a sample of tissue,
[0030] b) selecting a marker defined by a pathway,
[0031] c) screening for a simultaneous aberrant expression level of
the two or more markers, and
[0032] d) scoring their expression level as being aberrant when the
expression level detected is above or below a certain detection
threshold coefficient, wherein the detection threshold coefficient
is determined by comparing the expression levels of the samples
obtained from the subjects to values in a reference database of
sample phenotypes obtained from subjects with either a known
diagnosis or known clinical outcome after therapy.
[0033] The present invention is also directed to kits useful in
detecting the simultaneous aberrant co-expression levels of two or
more markers in a single cell.
BRIEF DESCRIPTION OF THE DRAWINGS
[0034] FIG. 1 shows EZH2 siRNA-mediated changes of the transcript
abundance levels of 11 genes comprising the BM-1-pathway
signature.
[0035] FIG. 2 shows siRNA-mediated changes of the transcript
abundance levels of 11 genes comprising BM-1-pathway signature. A.
BM-1 siRNA. B. EZH2 siRNA.
[0036] FIG. 3 shows expression profiles of 11 gene MM-1-signature
in distant metastatic lesions of the TRAMP transgenic mouse model
of prostate cancer and PNS neurospheres.
[0037] FIG. 4 shows increased DNA copy numbers of the BM-1 and Ezh2
genes in human prostate carcinoma cells selected for high
metastatic potential.
[0038] FIG. 5 shows the quadruplicon of prostate cancer progression
in the LNCap progression model.
[0039] FIG. 6 shows the quadruplicon of prostate cancer progression
in the PC-3 progression model.
[0040] FIG. 7 shows the quadruplicon of prostate cancer progression
in the PC-3 bone metastasis progression model.
[0041] FIG. 8 shows that high expression levels of the BMI1 and
Ezh2 oncoproteins in human prostate carcinoma metastasis precursor
cells are associated with marked accumulation of a dual-positive
high BMI1/Ezh2-expressing cell population and increased DNA copy
number of the BMI1 and Ezh2 genes.
[0042] A quantitative reverse-transcription PCR (Q-RT-PCR) analysis
of DNA copy numbers of the BMI1 and Ezh2 genes in multiple
experimental models of human prostate cancer. Note marked increase
of the BMI1 and Ezh2 gene copy numbers in highly metastatic
variants compared to the low metastatic counterparts in the
multiple independently selected lineages. The results of one of two
independent experiments are shown.
[0043] FIG. 9 shows that targeted reduction of the BMI1 or Ezh2
expression increases sensitivity of human prostate carcinoma
metastasis precursor cells to anoikis. Anoikis-resistant PC-3-32
prostate carcinoma cells were treated with BMI1- or Ezh2-targeting
siRNAs and continuously monitored for expression levels of the
various mRNAs, BMI and Ezh2 oncoproteins, as well as cell growth
and viability under various culture conditions. PC-3-32 cells with
reduced expression of either BMI1 or Ezh2 oncoproteins acquired
sensitivity to anoikis as demonstrated by the loss of viability and
increased apoptosis compared to the control LUC siRNA-treated
cultures growing in detached conditions.
[0044] FIG. 10 shows that Increased BMI1 and Ezh2 expression is
associated with high likelihood of therapy failure and disease
relapse in prostate cancer patients after radical prostatectomy.
Kaplan-Meier survival analysis demonstrates that cancer patients
with more significant elevation of the BMI1 and Ezh2 expression
[having higher tumor (T) to adjacent normal tissue (N) ratio, T/N:
FIG. 10-1; or having tumors with higher levels of BMI1 or Ezh2
expression) are more likely to fail therapy and develop a disease
recurrence after radical prostatectomy. FIG. 10-3 shows the
Kaplan-Meier survival analysis of 79 prostate cancer patients
stratified into five sub-groups using eight-covariate cancer
therapy outcome (CTO) algorithm. CTO algorithm integrates
individual prognostic powers of BMI1 and Ezh2 expression values and
six clinico-pathological covariates (preoperative PSA, Gleason
score, surgical margins, extra-capsular invasion, seminal vesicle
invasion, and age).
[0045] FIG. 11 shows breast cancer CTOP signatures in Affymetrix
format, with predictive outcomes.
[0046] FIG. 12 shows breast cancer CTOP signatures in Agilent
Rosetta Chip format, with predictive outcomes.
[0047] FIG. 13 shows prostate cancer CTOP signatures in Affymetrix
format, with predictive outcomes.
[0048] FIG. 14 shows the parent methylation Signatures.
[0049] FIG. 15 shows the histones H3 and H2A CTOP signatures.
[0050] FIG. 16 shows the CTOP gene expression signatures for
prostate cancer.
[0051] FIG. 17 shows the CTOP gene expression signatures for breast
cancer.
[0052] FIG. 18 shows the CTOP gene expression signature and
survival data for lung cancer.
[0053] FIG. 19 shows the CTOP gene expression signature for ovarian
cancer.
[0054] FIG. 20 shows the CTOP gene expression signatures for breast
cancer.
[0055] FIG. 21 shows CTOP scores for lung cancer.
[0056] FIG. 22 shows Kaplan-Meier survival analysis of seventy-nine
prostate cancer patients stratified into sub-groups with distinct
expression profiles of the individual Polycomb pathway ESC
signatures (top six panels) or six ESC signatures algorithm (bottom
panel) in primary prostate tumors. In each individual signature
panel, patients were sorted in descending order based on the values
of the corresponding signature CTOP scores and divided into poor
prognosis (top 50% scores) and good prognosis (bottom 50% scores)
sub-groups. In the last panel, patients were sorted in descending
order based on the values of the cumulative CTOP scores and divided
into poor prognosis (top 50% scores) and good prognosis (bottom 50%
scores) sub-groups. The cumulative CTOP scores represent the sum of
the six individual CTOP scores calculated for each patient.
[0057] FIG. 23 shows Kaplan-Meier survival analysis of two-hundred
eighty-six early-stage LN negative breast cancer patients
stratified into sub-groups with distinct expression profiles of the
individual Polycomb pathway ESC signatures (top six panels) or six
ESC signatures algorithm (single middle panel) in primary breast
tumors. Bottom four panels show patients' classification
performance of the six ESC signatures algorithm in four different
breast cancer therapy outcome data sets. Patients' stratification
was performed using either individual CTOP scores (top six panels)
or cumulative CTOP scores (bottom five panels) as described in the
legend to the FIG. 22.
[0058] FIG. 24 shows bivalent chromatin domain-containing
transcription factors (BCD-TF) manifest "stemness" expression
profiles in therapy-resistant prostate and breast tumors.
1. Chromatin context identified by the presence of histones
harboring specific modifications of the histone tails defines
mutually exclusive transcriptionally active or silent states of
corresponding genetic loci in genomes of most cells. In ESC
multiple chromosomal regions were identified simultaneously
harboring both "silent" (H3K27met3) and "active" (H3K4) histone
marks and .about.100 transcription factor (TF) encoding genes are
residing within these bivalent chromatin domain-containing
chromosomal regions. Expression of selected TF encoding genes in
ESC, including bivalent chromatin domain-containing TF genes
(BCD-TF), maintenance of a "stemness" state, and transition to
differentiated phenotypes is regulated by the balance of the
"stemness" TFs (Nang, Sox2, Oct4) and Polycomb group (PcG) proteins
bound to the promoters of target genes. 2. Thirteen-gene BCD-TF
signature manifesting highly concordant (r=0.853; P<0.001) gene
expression profiles in breast and prostate tumors from patients
with therapy-resistant disease phenotypes. 3. Eight-gene BCD-TF
signature (derived from thirteen-gene BCD-TF signatures)
manifesting highly concordant expression profiles (r=0.716;
p<0.001) in ESC and therapy-resistant breast and prostate
tumors. Kaplan-Meier analysis demonstrates that prostate and breast
cancer patients with tumors harboring ESC-like expression profiles
of the eight-gene BCD-TF signature are more likely to fail therapy
(bottom two panels). Gene expression profiles of clinical samples
were independently generated for therapy-resistant breast and
prostate tumors using multivariate Cox regression analysis of
microarrays of tumor samples from 286 breast cancer and 79 prostate
cancer patients with known log-term clinical outcome after therapy.
Gene expression profiles of mouse ESC were derived by comparing
microarray analyses of pluripotent self-renewing ESC (control ESC
cultures treated with HP siRNA) versus ESC treated with Esrrb siRNA
(day 6). At this time point, Esrrb siRNA-treated ESC do not
manifest `stemness" phenotype and form colonies of differentiated
cells.
[0059] FIG. 25 shows Kaplan-Meier survival analysis of two-hundred
eighty-six early-stage LN negative breast cancer patients (top four
panels) and seventy-nine prostate cancer patients (bottom four
panels) stratified into sub-groups with distinct expression
profiles of the individual CTOP signatures [bivalent chromatin
domain transcription factors (BCD-TF) and ESC pattern 3
signatures], eight ESC signatures algorithm, and nine "stemness"
signatures algorithm in primary breast or prostate tumors.
Patients' stratification was performed using either individual CTOP
scores (for individual signatures) or cumulative CTOP scores (for
CTOP algorithms) as described in the legend to the FIG. 22.
[0060] FIG. 26 shows Kaplan-Meier survival analysis of seventy-nine
prostate cancer patients (top four panels) and ninety-seven
early-stage LN negative breast cancer patients (middle four panels)
stratified into sub-groups with distinct expression profiles of the
individual CTOP signatures [histones H3 and H2A signatures;
Polycomb (PcG) pathway methylation signature] and two signatures
PcG methylation/histones H3/H2A algorithm (bottom two panels) in
primary prostate and breast tumors. Patients' stratification was
performed using either individual CTOP scores (for individual
signatures) or cumulative CTOP scores (for CTOP algorithm) as
described in the legend to the FIG. 22.
[0061] FIG. 27 shows Kaplan-Meier survival analysis of two-hundred
eighty-six early-stage LN negative breast cancer patients (top left
panel), seventy-nine prostate cancer patients (top right panel),
ninety-one early-stage lung cancer patients (bottom left panel),
and one-hundred thirty-three ovarian cancer patients (bottom right
panel) stratified into sub-groups with distinct expression profiles
of the nine "stemness" signatures algorithm in primary breast,
prostate, lung, and ovarian tumors. Patients' stratification was
performed using cumulative CTOP scores of the nine "stemness`"
signatures as described in the legend to the FIG. 22. Patients were
sorted in descending order based on the values of the cumulative
CTOP scores and divided into five sub-groups at 20% increment of
the cumulative CTOP score values.
DETAILED DESCRIPTION
[0062] The present invention is directed to novel methods and kits
for diagnosing the presence of cancer within a patient, and for
determining whether a subject who has cancer is susceptible to
different types of treatment regimens. The cancers to be tested
include, but are not limited to, prostate, breast, lung, gastric,
ovarian, bladder, lymphoma, mesothelioma, medullablastoma, glioma,
mantle cell lymphoma, and AML.
[0063] In some embodiments, the kits and methods of the present
invention can be used to predict various different types of
clinical outcomes. For example, the invention can be used to
predict recurrence of disease state after therapy, non-recurrence
of a disease state after therapy, therapy failure, short interval
to disease recurrence (e.g., less than two years, or less than one
year, or less than six months), short interval to metastasis in
cancer (e.g., less than two years, or less than one year, or less
than six months), invasiveness, non-invasiveness, likelihood of
metastasis in cancer, likelihood of distant metastasis in cancer,
poor survival after therapy, death after therapy, disease free
survival and so forth.
[0064] One embodiment of the present invention is directed to a
method for diagnosing cancer or predicting cancer-therapy outcome
by detecting the expression levels of multiple markers in the same
cell at the same time, and scoring their expression as being above
a certain threshold, wherein the markers are from a particular
pathway related to cancer, with the score being indicative or a
cancer diagnosis or a prognosis for cancer-therapy failure. This
method can be used to diagnose cancer or predict cancer-therapy
outcomes for a variety of cancers. The simultaneous co-expression
of at least two markers in the same cell from a subject is a
diagnostic for cancer and a predictor for the subject to be
resistant to standard cancer therapy. The markers can come from any
pathway involved in the regulation of cancer, including
specifically the PcG pathway and the "stemness" pathway. The
markers can be mRNA (messenger RNA), DNA, microRNA, or protein.
[0065] The subset of markers to be used within the methods of the
present invention include any markers associated with cancer
pathways. In preferred embodiments, the markers can be selected
from the genes identified in FIGS. 11-20. The markers can comprise
anywhere ranging from two markers listed within each table up to
the whole set of genes listed within each of these tables. The
markers can comprise any percentage of genes selected from each of
these tables, including 90%, 80%, 70%, 60%, or 50% of the genes
identified in each of FIGS. 11-20.
[0066] These and other embodiments of the present invention rely at
least in part upon the novel finding that the expression of
multiple markers above a threshold level in the same cell at the
same time, wherein the markers are found within pathways related to
cancer, can be used as an assay to diagnose cancer disorders and to
predict whether a patient already diagnosed with cancer will be
therapy-responsive or therapy-resistant. An element of the assay is
that two or more markers are detected simultaneously within the
same cell.
[0067] Marker detection can be made through a variety of detection
means, including bar-coding through immunofluorescence. The markers
detected can be a variety of products, including mRNA, DNA,
microRNA, and protein. For mRNA or microRNA based markers, PCR can
be used as detection means. Additionally, protein products, gene
expression, or gene copy number can be identified through detection
means known in the art.
[0068] Detection means, in case of a nucleic acid probe, include
measuring the level of mRNA or cDNA to which a probe has been
engineered to bind, where the probe binds the intended species and
provides a distinguishable signal. In some embodiments, the probes
are affixed to a solid support, such as a microarray. In other
embodiments, the probes are primers for nucleic acid amplification
of a set of genes. Q-RT-PCR amplification can be used. Detecting
expression for measurement or determining protein expression levels
can also be accomplished by using a specific binding reagent, such
as an antibody. In general, expression levels of the markers can be
analyzed by any method now known or later developed to assess gene
expression, including but not limited to measurements relating to
the biological processes of nucleic acid amplification,
transcription, RNA splicing, and translation. Direct and indirect
measures of gene copy number (e.g., as by fluorescence in situ
hybridization or other type of quantitative hybridization
measurement, or by quantitative PCR), transcript concentration
(e.g., as by Northern blotting, expression array measurements,
quantitative RT-PCR, or comparative genomic hybridization) and
protein concentration (e.g., as by quantitative 2D gel
electrophoresis, mass spectrometry, Western blotting, ELISA, or
other method for determining protein concentration), can also be
used.
[0069] One of skill in the art would recognize that different
affinity reagents could be used with the present invention, such as
one or more antibodies (monoclonal or polyclonal) and the invention
can include using techniques, such as ELISA, for the analysis.
Thus, specific antibodies (specific to the markers to be detected)
can be used in a kit and in methods of the present invention. In a
kit of the present invention, the kit would include reagents and
instructions for use, where the reagents could be protein-specific
differentially-labeled fluorescent antibodies; protein-specific
antibodies from different species (mouse, rabbit, goat, chicken,
etc.) and differentially labeled species-specific antibodies; DNA
and RNA-based probes with different fluorescent dyes; bar-coded
nucleic acid- and protein-specific probes (each probes having a
unique combination of colors).
[0070] The markers detected can be from a variety of pathways
related to cancer. Suitable pathways for markers within the scope
of the present invention include any pathways related to
oncogenesis and metastasis, and more specifically include the
Polycomb group (PcG) chromatin silencing pathway and the "stemness"
pathway.
[0071] Representative cancer pathways within the context of the
present invention include but are not limited to, the Polycomb
pathway, the Polycomb pathway target genes, "stemness" pathways,
DNA methylation pathways, BMI1, Ezh2, Suz12, Suz12/PolII, EED,
PcG-TF, BCD-TF, TEZ, Nanog/Sox2/Oct4, Myc, He2/neu, CCND1, E2F3,
PI3K, beta-catenin, ras, src, PTEN, p53, Rb, p16/ARF, p21, Wnt, and
Hh pathways.
[0072] The Polycomb group (PcG) gene BMI1 is required for the
proliferation and self-renewal of normal and leukemic stem cells.
Over-expression of Bmi1 oncogene causes neoplastic transformation
of lymphocytes and plays an essential role in the pathogenesis of
myeloid leukemia. Another PcG protein, Ezh2, has been implicated in
metastatic prostate and breast cancers, suggesting that PcG pathway
activation is relevant for epithelial malignancies. Here it is
demonstrated that activation of the BMI1 oncogene-associated PcG
pathway plays an essential role in metastatic prostate cancer, thus
mechanistically linking the pathogenesis of leukemia, self-renewal
of stem cells, and prostate cancer metastasis.
[0073] In another aspect, the methods of the present invention
provide for the diagnosis, prognosis, and treatment strategy for a
patient with a disorder of the above mentioned types. Treatment
includes determining whether a patient has an expression pattern of
markers associated with cancer and administering to the patient a
therapeutic adapted to the treatment of the disorder. In one
embodiment, the method can include the identification of increased
BMI1 and Ezh2 expression and the formulation of a treatment plan
specific to this phenotype.
[0074] In another embodiment of the present invention, the
detection of appropriate or inappropriate activation of "stemness"
genetic pathways can be used to diagnose cancer and to predict the
likelihood of cancer therapy success or failure. Inappropriate
activation of "stemness" genes in cancer cells may be associated
with aggressive clinical behavior and increased likelihood of
therapy failure. A sub-set of human prostate tumors represents a
genetically distinct highly malignant sub-type of prostate
carcinoma with high propensity toward metastatic dissemination even
at the early stage of disease. Such a high propensity toward
metastatic dissemination of this type of prostate tumors is
associated with the early engagement of normal stem cells into
malignant process. Elucidation of such inappropriate activation of
"stemness" gene expression can help tailor cancer therapy to a
patient's individual needs.
[0075] The invention is directed to prognostic assays for cancer
therapy that can be used to diagnose cancer and to predict the
resistance of various cancers to standard therapeutic regimens. The
invention is directed to methods and compositions for predicting
the outcome of cancer therapy for individual patients. In one
embodiment, the method is used to predict whether a particular
cancer patient will be therapy-responsive or therapy-resistant. The
invention can be used with a variety of cancers, including but not
limited to, breast, prostate, ovarian, lung, glioma, and
lymphoma.
[0076] The invention is directed to personalized medicine for
cancer patients, and encompasses the selection of treatment options
with the highest likelihood of successful outcome for individual
cancer patients. The present invention is directed to the use of a
an assay to predict the outcome after therapy in patients with
early stage cancer and provide additional information at the time
of diagnosis with respect to likelihood of therapy failure.
[0077] In another embodiment of the present invention, the
detection of the state of transcription factors can be used to
diagnose the presence of cancer and to predict the likelihood of
cancer therapy success or failure. The determination of a common
pattern of the transcription factor expression can be used as a
profile to help determine clinical outcome. The invention is also
directed to a particular sub-set of BCD-TF genes defined here as
the eight gene BCD-TF signature that manifests "stemness"
expression profiles in therapy-resistant prostate and breast tumors
(FIG. 24).
[0078] In another embodiment of the present invention, the
detection of the methylation state of target genes can be used to
diagnose cancer and to predict the likelihood of cancer therapy
success or failure. More particularly, PcG target genes with
promoters frequently hypermethylated in cancer manifest distinct
expression profiles associated with therapy-resistant and
therapy-sensitive prostate and breast cancers (FIG. 25), implying
that differences in gene expression between tumors with distinct
outcome after therapy may be driven, in part, by the distinct
promoter hypermethylation patterns of the PcG target genes. These
differences can be exploited to generate highly informative gene
expression signatures of the PcG target genes hypermethylated in
cancer for stratification of prostate and breast cancer patients
into sub-groups with statistically distinct likelihood of therapy
failure (FIG. 25).
[0079] The invention involves both a method to classify patients
into sub-groups predicted to be either therapy-responsive or
therapy-resistant, and a method for determining alternate therapies
for patients who are classified as resistant to standard cancer
therapies. The method of the present invention is based on an
accurate classification of patients into subgroups with poor and
good prognosis reflecting a different probability of disease
recurrence and survival after standard therapy.
[0080] In one embodiment, the invention relates to a method for
diagnosing cancer or predicting cancer-therapy outcome in a
subject, said method comprising the steps of:
[0081] a) obtaining a sample from the subject,
[0082] b) selecting a marker from a pathway related to cancer,
[0083] c) screening for a simultaneous aberrant expression level of
two or more markers in the same cell from the sample, and
[0084] d) scoring their expression level as being aberrant when the
expression level detected is above or below a certain detection
threshold coefficient, wherein the detection threshold coefficient
is determined by comparing the expression levels of the samples
obtained from the subjects to values in a reference database of
samples obtained from subjects with either a known diagnosis or
known clinical outcome after therapy, wherein the presence of an
aberrant expression level of two or more markers in individual
cells and presence of cells aberrantly expressing two or more such
markers is indicative of a cancer diagnosis or a prognosis for
cancer-therapy failure in the subject.
[0085] An aberrant expression level is a level of expression that
can either be higher or lower than the expression level as compared
to reference samples. The reference samples can have a variety of
phenotypes, including both diseased phenotypes and non-diseased
phenotypes. The sample phenotypes within the scope of the present
invention include, but are not limited to, cancer, non-cancer,
recurrence, non-recurrence, relapse, non-relapse, invasiveness,
non-invasiveness, metastatic, non-metastatic, localized, tumor
size, tumor grade, Gleason score, survival prognosis, lymph node
status, tumor stage, degree of differentiation, age, hormone
receptor status, PSA level, histologic type, and disease free
survival.
[0086] A detection threshold coefficient within the context of the
present invention is a value above which or below which a patient
or sample can be classified as either being indicative of a cancer
diagnosis or a prognosis for cancer-therapy failure. The detection
threshold coefficients are defined by a plurality of measurements
of samples in the reference database; sorting the samples in
descending order of the values of measurements; assignment of the
probability of samples having a phenotype in sub-groups of samples
defined at different increments of the values of measurements
(e.g., samples comprising top 10%; 20%; 30%; 40%; 50%; 60%; 70%;
80%; 90% of the values); selecting the statistically
best-performing detection threshold coefficient defined as the
value of measurements segregating samples with the values below and
above the threshold into subgroups with statistically distinct
probability of having a phenotype (cancer vs non-cancer; therapy
failure vs cure; etc.), ideally, segregating patients into
subgroups with 100% probability of therapy failure and with 100%
probability of a cure or as close to this probability values as
practically possible.
[0087] This value of markers measurements is defined as the best
performing magnitude of the detection threshold. The samples of
unknown phenotype are then placed into corresponding subgroups
based on the values of markers measurements and assigned the
corresponding probability of having a phenotype. To determine these
measurements, one skilled in the art can utilize different
statistical programs and approaches such as the univariate and
multivariate Cox regression analysis and Kaplan-Meier survival
analysis.
[0088] Detection threshold coefficients which are indicative of a
cancer diagnosis or a prognosis for cancer-therapy failure have an
absolute value within the range of .gtoreq.0.5. to .gtoreq.0.999.
Preferred levels of detection threshold coefficients which are
indicative of a cancer diagnosis or a prognosis for cancer-therapy
failure have an absolute value of .gtoreq.0.5, .gtoreq.0.6,
.gtoreq.0.7, .gtoreq.0.8, .gtoreq.0.9, .gtoreq.0.95, .gtoreq.0.99,
.gtoreq.0.995, and .gtoreq.0.999.
[0089] The present invention is also directed to a method of
determining detection threshold coefficients for classifying a
sample phenotype from a subject. This method comprises the steps of
selecting two or more markers from a pathway related to cancer,
screening for a simultaneous aberrant expression level of the two
or more markers in the same cell from the sample and scoring the
marker expression in the cells by comparing the expression levels
of the samples obtained from the subjects to values in a reference
database of samples obtained from subjects with either a known
diagnosis or known clinical outcome after therapy, and determining
the sample classification accuracy at different detection
thresholds using reference database of samples from subjects with
known phenotypes.
[0090] In another embodiment, the method of determining detection
threshold coefficients for classifying a sample phenotype from a
subject further comprises the additional step of determining the
best performing magnitude of said detection threshold and using
said magnitude to assess the reliability of said established
detection threshold in classifying a sample phenotype.
[0091] Selection of the statistically best-performing detection
threshold coefficient is defined as the value of measurements of
the segregating samples with the values below and above the
threshold, which are then split into subgroups with a statistically
distinct probability of having a phenotype (cancer vs non-cancer;
therapy failure vs cure, etc.). More preferably, patients or
samples can be segregated into subgroups with 100% probability of
therapy failure and with 100% probability of a cure, or as close to
this probability values as practically possible. This value of
markers measurements is defined as the best performing magnitude of
the detection threshold. Additionally, the best performing
magnitude of the detection threshold coefficient can be used to
score an unclassified sample and assign a sample phenotype to said
sample.
[0092] The present invention is also directed to a kit to detect
the presence of two or more markers from a pathway related to
cancer. The kit can contain as detection means protein-specific
differentially-labeled fluorescent antibodies; protein-specific
antibodies from different species (mouse, rabbit, goat, chicken,
etc.) and differentially labeled species-specific antibodies; DNA
and RNA-based probes with different fluorescent dyes; bar-coded
nucleic acid- and protein-specific probes (each probes having a
unique combination of colors), and any other detection means known
in the art. The kit can include a marker sample collection means
and a means for determining whether the sample expresses in the
same cell at the same time two or more markers from a pathway
related to cancer. Optionally, the kit contains a standard and/or
an algorithmic device for assessing the results and additional
reagents and components including for example DNA amplification
reagents, DNA polymerase, nucleic acid amplification reagents,
restrictive enzymes, buffers, a nucleic acid sampling device, DNA
purification device, deoxynucleotides, oligonucleotides (e.g.
probes and primers) etc.
[0093] The following non-standard abbreviations are used herein:
DFI, disease-free interval; FBS, fetal bovine serum; MSKCC,
Memorial Sloan-Kettering Cancer Center; NPEC, normal prostate
epithelial cells; PC, prostate cancer; PSA, prostate specific
antigen; Q-RT-PCR, quantitative reverse-transcription polymerase
chain reaction; RP, radical prostatectomy; SKCC, Sidney Kimmel
Cancer Center; AMACR, alpha-methylacyl-coenzyme A racemase; Ezh2,
enhancer of zeste homolog 2; FACS, fluorescence activated cell
sorting.
[0094] Human Genome Haplotype Map Leads to Identification of
Relevant Markers
[0095] The recent completion of the initial phase of a haplotype
map of the human genome provides an opportunity for integrative
analysis on a genome-wide scale of microarray-based gene expression
profiling and SNP variation patterns for discovery of
cancer-causing genes and genetic markers of therapy outcome. Here
the approach is used for analysis of SNPs of cancer-associated
genes, expression profiles of which predict the likelihood of
treatment failure and death after therapy in patients diagnosed
with multiple types of cancer. Unexpectedly, the analysis reveals a
common SNP pattern for a majority (60 of 74; 81%) of analyzed
cancer treatment outcome predictor (CTOP) genes.
[0096] The analysis suggests that heritable germ-line genetic
variations driven by a geographically localized form of natural
selection determining population differentiations may have a
significant impact on cancer treatment outcome by influencing the
individual's gene expression profile. A CTOP algorithm can be built
which combines the prognostic power of multiple gene
expression-based CTOP models. Application of a CTOP algorithm to
large databases of early-stage breast and prostate tumors
identifies cancer patients with 100% probability of a cure with
existing cancer therapies as well as patients with nearly 100%
likelihood of treatment failure, thus providing a clinically
feasible framework essential for the introduction of rational
evidence-based individualized therapy selection and prescription
protocols.
[0097] Relevant Genes for Cancer Diagnosis and Treatment
Prediction
[0098] Genes considered to be in an "elite" group for use in
predicting clinically relevant models are included in Table 1
below. These were generated by an analysis of the extensive
genome-wide database of SNPs generated after the completion of the
initial phase of the international HapMap project The initial
effort was focused on 1) an analysis of the BMI1 oncogene, altered
expression of which was functionally linked with the self-renewal
state of normal and leukemic stem cells, and 2) a poor prognosis
profile of an 11-gene death-from-cancer signature predicting
therapy failure in patients with multiple types of cancer. A
prominent feature of the BMI1-associated SNP pattern is YRI
population-specific profiles of genotype and allele frequencies of
multiple SNPs. Intriguingly, similar population-specific SNP
profiles are readily discernable for most of loci comprising the
11-gene CTOP signature. Furthermore, this common SNP pattern is
apparent for a majority of genetic loci expression profiles of
which are predictive of therapy failure in prostate cancer patients
after prostatectomy. Finally, 86% of genetic loci comprising a
proteomics-based 50-gene CTOP signature predicting therapy outcome
in patients diagnosed with multiple types of cancer show population
differentiation profiles of SNPs.
[0099] Based on this analysis it is concluded that CTOP genes
manifest a common feature of SNP patterns reflected in
population-specific profiles of SNP genotype and allele
frequencies. A majority of population-specific SNPs associated with
CTOP genes represented by YRI population-differentiation SNPs,
perhaps, reflecting a general trend of higher level of
low-frequency alleles in the YRI population compared to CEU, CHB,
and JPT populations due to bottlenecks in history of non-YRI
populations. During the survey of the population-specific SNPs
associated with CTOP genes, five non-synonymous coding SNPs were
identified that represented good candidates for follow-up
functional studies.
[0100] Oncogenes and tumor suppressor genes manifest
population-specific profiles of SNP genotype and allele
frequencies. Interestingly, in addition to CTOP genes,
population-specific SNP patterns are readily discernable for genes
with well-established causal role in cancer as oncogenes or tumor
suppressor genes, implying that the genes are targets for
geographically localized form of natural selection. Taken together,
the data suggests the presence of population
differentiation-associated cancer-related patterns of SNPs spanning
across multiple chromosomal loci and, perhaps, forming a
genome-scale cancer haplotype pattern. The data suggest that a
block-like structure and low haplotype diversity leading to
substantial correlations of SNPs with many of their neighbors may
span beyond small chromosomal regions and these "haplotype
principles" may be extended to include multiple chromosomal loci,
perhaps, on a genome-wide scale. Of note, gene expression
signatures associated with deregulation of corresponding oncogenic
pathways for most genes provide clinically relevant CTOP
models.
[0101] Genes considered to be in an "elite" group for use in
predicting clinically relevant CTOP models are included in Table 1
below.
TABLE-US-00001 TABLE 1 Elite set of genes and availability of
antibodies for detection of corresponding protein products selected
for development of diagnostic and prognostic applications. Gene
name UniGene Company Host Signature ADA Hs.407135 Santa Cruz
Biotechnology, Inc. rabbit IgG M AMACR + p63 Abcam mouse IgG2a PC
marker ANK3 Hs.440478 Santa Cruz Biotechnology, Inc. mouse
monoclonal DFC IgG1 BCL2L1 Hs.305890 Santa Cruz Biotechnology, Inc.
rabbit IgG M BIRC5 Hs.1578 Santa Cruz Biotechnology, Inc. mouse
IgG2a DFC BMI-1 NM_005180 Upstate mouse monoclonal DFC IgG1 BMI-1
NM_005180 Santa Cruz Biotechnology, Inc. rabbit polyclonal IgG DFC
BUB1 Hs.287472 Chemicon mouse monoclonal DFC CCNB1 Hs.23960 Santa
Cruz Biotechnology, Inc. mouse monoclonal DFC IgG1 CCND1 Hs.523852
Santa Cruz Biotechnology, Inc. rabbit IgG DFC CES1 Hs. 499222 Santa
Cruz Biotechnology, Inc. goat polyclonal DFC CHAF1A Hs.79018 Santa
Cruz Biotechnology, Inc. rabbit IgG polyclonal R CRIP1 Hs.70327 BD
Biosciences Pharmingen mouse monoclonal M CRYAB Hs.408767 Santa
Cruz Biotechnology, Inc. rabbit IgG M ESM1 Hs.410668 Santa Cruz
Biotechnology, Inc. goat IgG M EZH2 Hs.444082 Upstate rabbit
polyclonal DFC FGFR2 Hs.404081 Santa Cruz Biotechnology, Inc. mouse
IgG2b DFC FOS Hs.25647 Calbiochem rabbit polyclonal R Gbx2
Hs.184945 Chemicon rabbit polyclonal DFC HCFC1 Hs.83634 Santa Cruz
Biotechnology, Inc. goat polyclonal IgG DFC IER3 Hs.76095 Santa
Cruz Biotechnology, Inc. goat IgG polyclonal R ITPR1 Hs.149900
Abcam rabbit polyclonal R JUNB Hs.25292 Santa Cruz Biotechnology,
Inc. rabbit IgG R KLF6 Hs.285313 Santa Cruz Biotechnology, Inc.
rabbit IgG R KI67 Hs.80976 Santa Cruz Biotechnology, Inc. mouse
monoclonal DFC IgG1 KNTC2 Hs.414407 BD Biosciences Pharmingen mouse
monoclonal DFC IgG1 MGC5466 Hs.370367 Under development R RNF2
Hs.124186 Under development DFC Suz12 Hs.462732 Abcam rabbit
polyclonal IgG DFC TCF2 Hs.408093 Santa Cruz Biotechnology, Inc.
goat polyclonal R TRAP100 Hs.23106 Santa Cruz Biotechnology, Inc.
goat IgG polyclonal M USP22 Hs.462492 Under development DFC Wnt5A
Hs.152213 Santa Cruz Biotechnology, Inc. goat polyclonal R ZFP36
Hs.343586 Santa Cruz Biotechnology, Inc. rabbit polyclonal R
Legend: PC, prostate carcinoma; M, metastasis signature; R,
recurrence signature; DFC, death-from-cancer signature.
Differential expression of genes listed in the table was confirmed
by the Q-RT-PCR method using LCM dissected samples of malignant and
adjacent normal tissues from prostate tumor samples.
[0102] SNP-based gene expression signatures predict therapy outcome
in prostate and breast cancer patients. Our analysis demonstrates
that CTOP genes are distinguished by a common population specific
SNP pattern and potential utility as molecular predictors of cancer
treatment outcome based on distinct profiles of mRNA expression.
All gene expression models designed to predict cancer therapy
outcome were developed using phenotype-based signature discovery
protocols, e.g., genetic loci comprising the predictive models were
selected based on association of their expression profiles with
clinically relevant phenotype of interest. One of the implications
of our analysis is that heritable genetic variations driven by
geographically localized form of natural selection determining
population differentiations may have a significant impact on cancer
treatment outcome by influencing the individual's gene expression
profile. One of the predictions of this hypothesis is that genes,
expression levels of which are known to be regulated by SNP
variations, may provide good candidates for building gene
expression-based CTOP models.
[0103] Consistent with this idea, we found that loci with
genetically determined differences in mRNA expression levels among
normal individuals (demonstrated by linkage analysis and by allelic
associations of gene expression changes with SNP variations)
generate statistically significant therapy outcome prediction
models for breast and prostate cancer patients.
[0104] A hallmark feature of common SNP pattern of CTOP genes is
population-specific profiles of SNP allele and genotype
frequencies. Most CTOP genes have multiple SNPs with
population-specific genotype and allele frequencies, suggesting
that CTOP genes may be targets for geographically localized form of
natural selection contributing to population differentiation.
Consistent with this hypothesis, expression signatures of genes
containing high-differentiation non-synonymous SNPs provide CTOP
models for prostate and breast cancers. Similarly, expression
signatures of genes representing loci in which natural selection
most likely occurred appear highly informative in predicting
therapy outcome in breast and prostate cancer patients. To further
test the validity of this concept, we successfully used a common
SNP pattern of CTOP genes to define novel gene expression models of
cancer therapy outcome prediction without any input of mRNA
expression data in the initial gene screening and selection
process. Conversely, expression profiles of cancer-related genes
with established SNP-based associations with incidence and severity
of disease manifest therapy outcome prediction power (CYP3A4 for
prostate cancer and SULT1A1 for breast cancer). Important end-point
of this analysis with potential mechanistic implications is that
patients with low expression levels of genes regulating catabolism
of androgens (CYP3A4; prostate cancer), estrogens (SULT1A1; breast
cancer) and thyroid hormones (DIO3; breast cancer) have
significantly increased likelihood of therapy failure.
[0105] Microarray analysis identifies clinically relevant
cooperating oncogenic pathways associated with cancer therapy
outcome. Bild et al., Nature 439: 353-357 (2006) provides
compelling evidence of the power of microarray gene expression
analysis in identifying multiple clinically relevant oncogenic
pathways activated in human cancers. It provides mechanistic
explanation to mounting experimental data demonstrating that there
are multiple gene expression signatures predicting cancer therapy
outcome in a given set of patients diagnosed with a particular type
of cancer: presence of multiple CTOP models is most likely reflect
deregulation of multiple oncogenic pathways, perhaps, cooperating
in development of an oncogenic state.
[0106] We tested this hypothesis by comparing the cancer therapy
outcome prediction power of three gene expression signatures
derived from corresponding transgenic mouse models associated with
activation of oncogenic pathways driven by BMI1, Myc, and Her2/neu
oncogenes during the prostate and mammary carcinogenesis. To
evaluate the prognostic power of the BMI1-, Myc-, and
Her2/neu-pathway signatures, we made use of two previously
published gene expression datasets for prostate and breast cancers
(Glinsky, G. V. et al., J. Clin. Invest. 113: 913-923 (2004); van
't Veer et al., Nature 415: 530-536 (2002)). Applications of three
signatures clearly outperform individual signatures in patients'
stratification into statistically distinct sub-groups based on
likelihood of therapy failure. All cancer patients with evidence of
activation of three pathways (3 poor prognosis signatures) failed
therapy, whereas patients with no evidence of even single pathway
activation remained disease-free.
[0107] These data suggest that in a sub-group of prostate and
breast cancer patients with therapy-resistant disease phenotype
concomitant activation of pathways driven by BMI1, Myc, and
Her2/neu oncogenes may contribute to development of highly
malignant clinically lethal oncogenic state. Taken together with
data presented by Bild et al., supra, these results provides strong
rationale for translational application of microarray analysis in
assisting physicians and patients during rational evidence-based
selection of individualized target-tailored cancer therapies with
highest probability of cancer cure.
[0108] We tested a potential translational utility of this
genome-wide approach to SNP analysis and gene expression profiling
by building and retrospectively validating a CTOP algorithm
integrating therapy outcome prediction calls of multiple
phenotype-based and SNP-based molecular signatures of cancer
treatment outcome. This CTOP algorithm seems highly promising for
identification at diagnosis prostate and breast cancer patients
with 100% probability of a cure with existing therapy. It also
allows selection of patients who would most likely benefit from
more aggressive adjuvant systemic treatment protocols currently
prescribed for patients with advanced metastatic cancers or disease
relapse. If confirmed in prospective clinical validation studies,
this approach should enable the practical implementation of a
concept of individualized target-tailored cancer therapies allowing
for rational evidence-based justification of prescription of such
therapies for selected genetically defined group of patients at
diagnosis. Finally, our analysis provides a strong rationale for
development of genetic prognostic tests for prediction of cancer
therapy outcome based on SNP analysis and expression profiling of
individuals' normal cells such as blood cells.
[0109] In the human genome geographically localized form of natural
selection causing population differentiation is reflected in
population-specific signatures of a genome-wide SNP selection.
Population differentiation is a generally accepted as a clue to
past selection in one of the populations and 926 SNPs of this class
have been described in the recent release of the HapMap project.
Population-specific profiles of individual allele frequencies of
the SNPs associated with CTOP genes suggest that cancer therapy
outcome predictor genes can be found among genes carrying
SNP-signatures of a genome-wide geographically localized form of
natural selection causing population differentiation. Using these
principles, we identified genes with SNP pattern similar to known
CTO predictor genes among genetic loci with population
differentiation SNP variants. Importantly, mRNA expression profiles
of these genes generate statistically significant gene expression
models of cancer therapy outcome prediction. These models were
built without any input of mRNA expression data in the initial gene
screening and selection process.
[0110] Analysis of a haplotype map of human genome indicates that
vast majority of heterozygous sites in each person DNA will be
explained by a limited set of common SNPs now contained (or
captured through linkage disequilibrium, LD) in existing databases.
Therefore, it is reasonable to assume that individual subjects
within a population will likely carry unique combinations of
population-differentiation SNPs identified in this study (or SNPs
in LD with identified SNPs). We postulate that distinct patterns of
population-differentiation SNPs associated with cancer-causing,
cancer-associated, and CTOP genes would constitute important
germ-line determinants of susceptibility, incidence, and severity
of disease. Our analysis suggests that one of the main mechanisms
of translation the SNP pattern diversity in disease phenotypes
would be heritable SNP-driven variations in gene expression levels.
Our analysis adds further support to recent data that SNP-driven
effects on gene expression are seemingly spreading outside the
boundaries of individual chromosomes and, perhaps, reaching a
genome-wide scale. A majority of SNPs identified in this study is
represented by intronic SNPs, suggesting that intronic SNPs may
influence gene expression by yet unknown mechanism. Theoretically,
intronic SNPs may influence gene expression by affecting a variety
of processes such as chromatin silencing and remodeling,
alternative splicing, transcription of microRNA genes, processivity
of RNA polymerase, etc. Most likely mechanism of action would
entail effect on stability and affinity of interactions between DNA
molecule and corresponding multi-subunit complexes. Comparative
genomics analysis has shown that about 5% of the human sequence is
highly conserved across species, yet less than half of this
sequence spans known functional elements such as exons. It is
assumed that conserved non-genic sequences lack diversity because
of selective constraint due to purifying selection; alternatively,
such regions may be located in cold-spots for mutations. Most
recent evidence shows that conserved non-genic sequences are not
mutational cold-spots, and thus represent high interest for
functional study. It would be of interest to determine whether
population differentiation intronic SNPs overlap with such highly
evolutionary conserved non-genic sequences.
[0111] Our analysis provides a possible clue with regard to
mechanisms of genesis and evolution of disease-causing loci and
translation of SNP variations in disease phenotypes. Geographically
localized form of natural selection drives evolution of population
differentiation SNP profiles which is translated in phenotypic
diversity by determining individual gene expression variations.
Until recently, this selection-driven evolution in human population
was occurring within relatively restricted genetic pools due to
travel and migration limitations in the demographic context of
close alignment of populations' reproductive longevity and overall
lifespan. During last century rapid and dramatic socio-economic and
demographic changes (explosion in travel and migration; increasing
length of individual's reproductive period; widening gap between
reproductive longevity and life expectancy associated with a marked
extension of continuous in vivo exposure of proliferating tissues
to low levels of steroid hormones) altered the dynamic of these
relationships in human population enhancing probability of emerging
disease-enabling combinations of SNP profiles.
Markers from Polycomb Group (PcG) Pathway
[0112] Preferred markers within the context of the present
invention include the double positive BMI1/Ezh2 from the PcG
pathway. The Polycomb group (PcG) gene BMI1 is required for the
proliferation and self-renewal of normal and leukemic stem cells.
Over-expression of Bmi1 oncogene causes neoplastic transformation
of lymphocytes and plays essential role in pathogenesis of myeloid
leukemia. Another PcG protein, Ezh2, was implicated in metastatic
prostate and breast cancers, suggesting that PcG pathway activation
is relevant for epithelial malignancies. Whether an oncogenic role
of the BMI1 and PcG pathway activation may be extended beyond the
leukemia and may affect progression of solid tumors has previously
remained unknown. Here it is demonstrated that activation of the
BMI1 oncogene-associated PcG pathway plays an essential role in
metastatic prostate cancer, thus mechanistically linking the
pathogenesis of leukemia, self-renewal of stem cells, and prostate
cancer metastasis.
[0113] To characterize the functional status of the PcG pathway in
metastatic prostate cancer, advanced cell- and whole animal-imaging
technologies, gene and protein expression profiling, stable
siRNA-gene targeting, and tissue microarray (TMA) analysis in
relevant experimental and clinical settings were utilized.
[0114] It was also demonstrated that in multiple experimental
models of metastatic prostate cancer both BMI1 and Ezh2 genes are
amplified and gene amplification is associated with increased
expression of corresponding mRNAs and proteins. Images of human
prostate carcinoma metastasis precursor cells isolated from blood
were provided and shown to over-express both BMI1 and Ezh2
oncoproteins. Consistent with the PcG pathway activation
hypothesis, increased BMI1 and Ezh2 expression in metastatic cancer
cells is associated with elevated levels of H2AubiK119 and H3metK27
histones.
[0115] Quantitative immunofluorescence co-localization analysis and
expression profiling experiments documented increased BMI1 and Ezh2
expression in clinical prostate carcinoma samples and demonstrated
that high levels of BMI1 and Ezh2 expression are associated with
markedly increased likelihood of therapy failure and disease
relapse after radical prostatectomy. Gene-silencing analysis
reveals that activation of the PcG pathway is mechanistically
linked with highly malignant behavior of human prostate carcinoma
cells and is essential for in vivo growth and metastasis of human
prostate cancer. It is concluded that the results of experimental
and clinical analyses indicate the important biological role of the
PcG pathway activation in metastatic prostate cancer. It is
suggested that the PcG pathway activation is a common oncogenic
event in pathogenesis of metastatic solid tumors and provides the
basis for development of small molecule inhibitors of the PcG
chromatin silencing pathway as a novel therapeutic modality for
treatment of metastatic prostate cancer.
Activation of PcG Protein Chromatin Silencing Pathway in Human
Prostate Carcinoma Metastasis Precursor Cells.
[0116] The PcG pathway activation hypothesis implies that
individual cells with activated chromatin silencing pathway would
exhibit a concomitant nuclear expression of both BMI1 and Ezh2
proteins. Furthermore, cells with activated PcG pathway would
manifest the increased expression levels of protein substrates
targeted by the activation of corresponding enzymes to catalyze the
H2A-K119 ubiquitination (BMI1-containing PRC1 complex) and H3-K27
methylation (Ezh2-containing PRC2 complex). Observations that
increased BMI1 expression is associated with metastatic prostate
cancer suggest that the PcG pathway might be activated in
metastatic human prostate carcinoma cells. Consistent with this
idea, previous independent studies documented an association of the
increased Ezh2 expression with metastatic disease in prostate
cancer patients. Therefore, immunofluorescence analysis was applied
to measure the expression of protein markers of the PcG pathway
activation in prostate cancer metastasis precursor cells isolated
from blood of nude mice bearing orthotopic human prostate carcinoma
xenografts.
[0117] Immunofluorescence analysis reveals that expression of all
four individual protein markers of PcG pathway activation is
elevated in blood-borne human prostate carcinoma metastasis
precursor cells compared to the parental cells comprising a bulk of
primary tumors. In order to document the PcG pathway activation in
individual cells, the quantitative immunofluorescence
co-localization analysis allowing for a simultaneous detection and
quantification of several markers in a single cell was carried out.
The quantitative immunofluorescence co-localization analysis
demonstrates a marked enrichment of the population of blood-borne
human prostate carcinoma metastasis precursor cells with the dual
positive high BMI1/Ezh2-expressing cells.
[0118] These results were confirmed using two different
mouse/rabbit primary antibody combinations for BMI1 and Ezh2
protein detection as well as different secondary fluorescent
antibodies. Similar enrichment for the PcG pathway activated cells
in a pool of circulating metastasis precursor cells is evident for
other two-marker combination panels as well. In contrast to the
protein markers of the PcG pathway activation, a significantly
smaller fraction of cells expressing concomitantly high levels of
the cytoplasmic AMACR/nuclear p63 proteins was detected in human
prostate carcinoma metastasis precursor cells compared to the
parental cell population. Therefore, the results of a quantitative
immunofluorescence co-localization analysis indicate that
measurements of several two-marker combinations demonstrate a
significant enrichment of the population of prostate carcinoma
metastasis precursor cells with the cells expressing high levels of
the PcG pathway activation markers. Increased BMI1 and Ezh2 mRNA
expression is associated with metastatic prostate cancer. Taken
together these data support the hypothesis that PcG chromatin
silencing pathway is activated in blood-borne human prostate
carcinoma metastasis precursor cells and might contribute to the
ability of metastatic cancer cells to survive and grow at distant
sites.
Amplification of the BMI1 and Ezh2 Genes in Multiple Experimental
Models of Human Prostate Cancer.
[0119] Increased expression of oncogenes is often associated with
gene amplification. In agreement with proposed oncogenic role of
the BMI1 and Ezh2 over-expression in human prostate carcinoma
cells, it was documented that a significant amplification of both
BMI1 and Ezh2 genes in human prostate carcinoma cell lines
representing multiple experimental models of metastatic prostate
cancer (FIG. 8). Notably, the level of gene amplification as
determined by the measurement of DNA copy number for both BMI1 and
Ezh2 genes is higher in metastatic cancer cell variants compared to
the non-metastatic or less malignant counterparts, suggesting that
gene amplification may play a casual role in elevation of the BMI1
and Ezh2 oncoprotein expression levels and high
BMI1/Ezh2-expressing cells may acquire a competitive survival
advantage during tumor progression.
PcG Pathway Activation Renders Circulating Human Prostate Carcinoma
Metastasis Precursor Cells Resistant to Anoikis.
[0120] To ascertain the biological role of the PcG pathway
activation in prostate cancer metastasis, human prostate carcinoma
metastasis precursor cells were isolated from the blood of nude
mice bearing orthotopic human prostate carcinoma xenografts,
transfected with BMI1, Ezh2, or control siRNAs, and continuously
monitored for mRNA and protein expression levels of BMI1, Ezh2, and
a set of additional genes and protein markers using
immunofluorescence analysis, RT-PCR, and Q-RT-PCR methods. Q-RT-PCR
and RT-PCR analyses showed that siRNA-mediated BMI1-silencing
caused .about.90% inhibition of the endogenous BMI1 mRNA
expression. The effect of siRNA-mediated BMI1 silencing was
validated at the protein expression level using immunofluorescence
analysis (FIG. 9). The BMI1 silencing was specific since the
expression levels of nine un-related transcripts were not altered
(FIG. 9). Consistent with the hypothesis that expression of genes
comprising the 11-gene death-from-cancer signature is associated
with the expression of the BMI1 gene product, mRNA abundance levels
of 8 of 11 interrogated BMI1-pathway target genes were altered in
the human prostate carcinoma cells with siRNA-silenced BMI1 gene.
For biological analysis we adopted the silencing protocol resulting
in 80-100% reduction of the level of dual-positive BMI1/Ezh2
high-expressing metastasis precursor cells, thus yielding the cell
population more closely resembling non-treated parental cells and
markedly distinct from metastasis precursor cells treated with
control siRNA (FIG. 9).
[0121] Reduction of the BMI1 mRNA and protein expression in human
prostate carcinoma metastasis precursor cells did not alter
significantly the viability of adherent cultures grown at the
optimal growth condition and in serum starvation experiments. siRNA
treatment had only modest inhibitory effect on proliferation
causing .about.25% reduction in the number of cells. However, the
ability of human prostate carcinoma cells to survive in
non-adherent state was severely affected after siRNA-mediated
reduction of the BMI1 expression (FIG. 9-1). FACS analysis revealed
.about.3-fold increase of apoptosis in the BMI1 siRNA-treated human
prostate carcinoma cells cultured in non-adherent conditions (FIG.
9-2). These data suggest that human prostate carcinoma cells
expressing high level of the BMI1 protein are more resistance to
apoptosis induced in cells of epithelial origin in response to
attachment deprivation (anoikis). It is likely that these
anoikis-resistant cancer cells would survive better in blood or
lymph during metastatic dissemination thus forming a pool of
circulatory stress-surviving metastasis precursor cells. Similar
results were obtained when Ezh2 silencing experiments were
performed (FIG. 9-3), suggesting that targeting of either PRC1 or
PRC2 complexes is sufficient for interference with the PcG pathway
activity and inhibition of anoikis-resistance mechanisms in
metastatic prostate carcinoma cells.
Targeted Depletion of Human Prostate Carcinoma Cells with Activated
PcG Pathway Creates Population of Cancer Cells with Dramatically
Diminished Malignant Potential In Vivo.
[0122] Results of the experiments demonstrate that a population of
highly metastatic prostate carcinoma cells is markedly enriched for
cancer cells expressing increased levels of multiple markers of the
PcG pathway activation. These data suggest that carcinoma cells
with activated PcG pathway may manifest a highly malignant behavior
in vivo characteristic of cancer cell variants selected for
increased metastatic potential. To test this hypothesis,
blood-borne human prostate carcinoma metastasis precursor cells
were treated with chemically modified stable siRNA targeting either
BMI1 or Ezh2 mRNAs to generate a cancer cell population with
diminished levels of dual positive high BMI1/Ezh2-expressing
carcinoma cells. Stable siRNA-treated prostate carcinoma cells
continue to grow in adherent culture in vitro for several weeks
allowing for expansion of siRNA-treated cultures in quantities
sufficient for in vivo analysis.
[0123] These observations also indicate that the treatment protocol
was well-tolerated and was not detrimental for the general growth
properties of a cancer cell population. Quantitative
immunofluorescence co-localization analysis demonstrated that
carcinoma cells after treatment with the BMI1- or Ezh2-targeting
stable siRNA continue to express significantly lower levels of
targeted proteins for extended period of time (.about.30-50%
reduction at the 11 days post-treatment time point) compared to the
cells treated with the control LUC siRNA. Importantly, the
siRNA-treated human prostate carcinoma cell populations were
essentially depleted for dual positive high BMI1/Ezh2-expressing
carcinoma cells thus setting up the stage for critical in vivo
analysis using a fluorescent orthotopic model of human prostate
cancer metastasis in nude mice.
[0124] Remarkably, highly malignant human prostate carcinoma cell
populations depleted for dual positive high BMI1/Ezh2-expressing
cells demonstrated markedly diminished tumorigenic and metastatic
potential in vivo. Within 3 weeks after inoculation of the
1.5.times.10.sup.6 of tumor cells, 100% of control animals
developed rapidly growing highly invasive and metastatic carcinomas
in the mouse prostate and all animal died within 50 days of the
experiment. In contrast, only 20% of animals in both BMI1- and
Ezh2-targeting therapy groups developed seemingly less malignant
tumors causing death of hosts 78-87 days after tumor cell
inoculation. Significantly, 150 days after tumor cell inoculation
83% and 67% of animals remain alive and disease-free in the therapy
groups targeting the BMI1 and Ezh2 proteins, respectively
(p=0.0007, Log rank test).
Increased Levels of Dual Positive High BMI/Ezh2-Expressing Cells
Indicate Activation of the PcG Pathway in a Majority of Human
Prostate Adenocarcinomas.
[0125] To validate the significance of our findings for human
disease, the quantitative immunofluorescence co-localization
analysis was applied for measurements of the expression of BMI1 and
Ezh2 proteins and detection of dual positive high
BMI/Ezh2-expressing carcinoma cells in clinical samples obtained
from patients diagnosed with prostate adenocarcinomas. The results
of this analysis demonstrate that a majority (79%-91% in different
cohorts of patients) of human prostate tumors contains dual
positive high BMI1/Ezh2-expressing carcinoma cells exceeding the
threshold expression level in prostate samples from normal
individuals. Interestingly, a panel of adenocarcinoma samples
appears quite heterogeneous with respect to the relative levels of
dual positive high BMI1/Ezh2-expressing cells. While in 50%-74% of
prostate tumors the level of high BMI1-, high Ezh2-, or dual
positive high BMI1/Ezh2-expressing cells was only slightly elevated
(<15% of positive cells), a significant fraction (17%-29%) of
prostate adenocarcinomas demonstrates a marked enrichment for dual
positive high BMI1/Ezh2-expressing cells (>15% of positive
cells).
[0126] Increased BMI1 and Ezh2 Expression is Associated with High
Likelihood of Therapy Failure in Prostate Cancer Patients after
Radical Prostatectomy.
[0127] Microarray analysis demonstrates that cancer patients with
high levels of BMI1 and Ezh2 mRNA expression in prostate tumors
have a significantly worst relapse-free survival after radical
prostatectomy (RP) compared with the patients having low levels of
BMI1 and Ezh2 expression (FIG. 10), suggesting that more profound
alterations of the PcG protein chromatin silencing pathway in
carcinoma cells are associated with therapy resistant clinically
lethal prostate cancer phenotype. FIG. 10-3 shows the Kaplan-Meier
survival analysis of 79 prostate cancer patients stratified into
five sub-groups using eight-covariate cancer therapy outcome (CTO)
algorithm (Table 2, below).
TABLE-US-00002 TABLE 2 8-covariate prostate cancer recurrence
predictor model Signifi- Confidence Confidence cance, interval,
interval, Covariate Coefficient SE P low 95% high 95% BMI1 4.7732
1.5179 0.0017 1.798 7.7483 Ezh2 0.4345 0.8215 0.5969 -1.1756 2.0446
PRE RP 0.0236 0.023 0.3054 -0.0215 0.0686 PSA RP GLSN 0.2809 0.1955
0.1508 -0.1023 0.6642 SUM Capsular 1.4752 0.7593 0.052 -0.0131
2.9634 Inv SM 0.7786 0.4641 0.0934 -0.1311 1.6883 Sem Ves 0.5876
0.4419 0.1836 -0.2785 1.4538 Inv AGE 0.041 0.0335 0.2214 -0.0247
0.1066 RP, radical prostatectomy; PSA, prostate-specific antigen;
GLSN SUM, Gleason sum; SM, surgical margins; Sem Ves Inv, seminal
vesicle invasion; Capsular Inv, capsular invasion. Overall model
fit: Chi Square = 40.1250; df = 8; p < 0.0001.
[0128] The multivariate Cox proportional hazards survival analysis
were carried out to ascertain the prognostic power of measurements
of BMI1 and Ezh2 expression in combination with known clinical and
pathological markers of prostate cancer therapy outcome such as
Gleason score, surgical margins, extra-capsular invasion, seminal
vesicle invasion, serum PSA levels, and age. Of note, BMI1
expression level remains a statistically significant prognostic
marker in the multivariate analysis (Table 3). Application of the
8-covariate prostate cancer recurrence model combining the
incremental statistical power of individual prognostic markers
appears highly informative in stratification of prostate cancer
patients into sub-groups with differing likelihood of therapy
failure and disease relapse after radical prostatectomy (FIG. 10).
One of the distinctive features of this model is that it identifies
a sub-group of prostate cancer patients comprising bottom 20% of
recurrence predictor score and manifesting no clinical or
biochemical evidence of disease relapse (FIG. 10). In contrast, 80%
of patients in a sub-group comprising top 20% of recurrence
predictor score failed therapy within five year period after
radical prostatectomy.
[0129] Increasing experimental evidence suggest that an oncogenic
role of the BMI1 activation may be extended beyond the leukemia
and, perhaps, play a key role in progression of the epithelial
malignancies and other solid tumors as well. One of the compelling
examples revealing an association of the activated BMI1
oncoprotein-driven pathway(s) with clinically lethal
therapy-resistant malignant phenotype in patients diagnosed with
multiple types of cancer is identification of a death-from-cancer
gene expression signature. An 11-gene signature distinguishes stem
cells with normal self-renewal function versus stem cells with
drastically diminished self-renewal ability due to the loss of the
BMI-1 oncogene and similarly expressed in metastatic prostate
tumors. To date, the prognostic power of the 11-gene signature was
validated in multiple independent therapy outcome sets of clinical
samples obtained from more than 2,500 cancer patients diagnosed
with 12 different types of cancer, including six epithelial
(prostate; breast; lung; ovarian; gastric; and bladder cancers) and
five non-epithelial (lymphoma; mesothelioma; medulloblastoma;
glioma; and acute myeloid leukemia, AML) malignancies.
[0130] These data suggest the presence of a conserved BMI1
oncogene-driven pathway, which is similarly activated in both
normal stem cells and a highly malignant subset of human cancers
diagnosed in a wide range of organs and uniformly exhibiting a
marked propensity toward metastatic dissemination as well as a
therapy resistance phenotype. Taken together with the results of
the present study these data support the hypothesis that activation
of the PcG chromatin silencing pathway is one of the key regulatory
factors determining a cellular phenotype captured by the expression
of a death-from-cancer signature in therapy-resistant clinically
lethal malignancies.
[0131] Cancer cells with activated PcG pathway would be expected to
exhibit a concomitantly high expression of both BMI1 and Ezh2
proteins. Furthermore, cells with activated PcG pathway would
manifest the increased expression levels of protein substrates
targeted by the activation of corresponding enzymes to catalyze the
H2A-K119 ubiquitination (BMI1-containing PRC1 complex) and H3-K27
methylation (Ezh2-containing PRC2 complex). In this study it was
experimentally tested that the relevance of this concept for
metastatic prostate cancer. A quantitative co-localization
immunofluorescence analysis was applied to measure the expression
of four distinct protein markers of the PcG pathway activation and
demonstrated a concomitantly increased expression of all four
markers in a sub-population of human prostate carcinoma metastasis
precursor cells isolated from the blood of nude mice bearing
orthotopic metastatic human prostate carcinoma xenografts. Presence
of dual positive high BMI1/Ezh2-expressing cells appears essential
for maintenance of tumorigenic and metastatic potential of human
prostate carcinoma cells in vivo, since targeted depletion of dual
positive high BMI1/Ezh2-expressing cells from a population of
highly metastatic human prostate carcinoma cells treated with
stable siRNAs generates a cancer cell population with dramatically
diminished malignant potential in vivo.
[0132] Histone Markers within PcG Pathway
[0133] The BMI1 and Ezh2 proteins are members of the Polycomb group
protein (PcG) chromatin silencing complexes conferring genome scale
transcriptional repression via covalent modification of histones.
The BMI1 PcG protein is a component hPRC1L complex (human Polycomb
repressive complex 1-like) which was recently identified as the E3
ubiquitin ligase complex that is specific for histone H2A and plays
a key role in Polycomb silencing. Ubiquitination/deubiquitination
cycle of histones H2A and H2B is important in regulating chromatin
dynamics and transcription mediated, in part, via `cross-talk`
between histone ubiquitination and methylation. Importantly, one of
the up-regulated genes in the 11-gene death-from-cancer signature
profile (Rnf2) plays a central role in the PRC1 complex formation
and function thus complementing the BMI-1 function in the PRC1
complex. Rnf2 expression plays a crucial non-redundant role in
development during a transient contact formation between PRC1 and
PRC2 complexes via Rnf2 as described for Drosophila.
[0134] The Ezh2 protein is a member of the Polycomb PRC2 and PRC3
complexes with a histone lysine methyltransferase (HKMT) activity
that is associated with transcriptional repression due to chromatin
silencing. The HKMT-Ezh2 activity targets lysine residues on
histones H1 and H3 (H3-K27 or H1-K26). H3-K27 methylation conferred
by an active HKMT-Ezh2-containing complex is one of the key
molecular events essential for chromatin silencing in vivo.
Collectively, these data imply that in vivo Polycomb chromatin
silencing pathway in distinct cell types would require a coordinate
activation of multiple distinct PRC complexes. For example, Ezh2
associates with different EED isoforms thereby determining the
specificity of histone methyltransferase activity toward histone
H3-K27 or histone H1-K26. Collectively, these results suggest that
coherent function of the PcG chromatin silencing pathway would
require a concomitant coordinated activation of multiple protein
components of PRC1, PRC2, and PRC3 complexes implying a coordinate
regulation of expression of their essential components such as BMI1
and Ezh2 oncoproteins. It follows that dual positive high
BMI1/Ezh2-expressing carcinoma cells with elevated expression of
the H2AubiK119 and H3metK27 histones should be regarded as cells
with activated PcG protein chromatin silencing pathway.
[0135] In human cells the BMI1-containing PcG complex forms a
unique discrete nuclear structure that was termed the PcG bodies,
the size and number of which in nuclei significantly varied in
different cell types. Of note, the nuclei of dual positive high
BMI1/Ezh2-expressing cells almost uniformly contain six prominent
discrete PcG bodies, perhaps, reflecting the high level of the BMI1
expression and indicating the active state of the PcG protein
chromatin silencing pathway. It has been shown recently that in
cancer cells expressing high level of the Ezh2 protein the new type
of the PcG chromatin silencing complex is formed containing the
Sirt1 protein. This suggests that in high Ezh2-expressing carcinoma
cells a distinct set of genetic loci could be repressed due to
activation of the Ezh2/Sirt1-containing PcG chromatin silencing
complex.
[0136] One of the notable features of dual positive high
BMI1/Ezh2-expressing carcinoma cells is a prominent cytosolic
expression of the Ezh2 oncoprotein (FIG. 8). Recent evidence
revealed the existence of the cytosolic Ezh2-containing
methyltransferase complex regulating actin polymerization and
extra-nuclear signaling processes in various cell types. It is
possible that both nuclear and extra-nuclear functions of the
Ezh2-containing methyltransferase complex may play an important
role in determining the malignant behavior of metastatic human
prostate carcinoma cells. Recent observations directly demonstrated
that the PcG repressive complexes PRC1 and PRC2 co-occupied a large
set of genes in human and murine genomes, many of which are
transcriptional developmental regulators. This suggests that
repression of multiple developmental and differentiation pathways
by Polycomb complexes may be required for maintaining stem cell
pluripotency and add further support to the idea that repression of
critical developmental regulators by PcG proteins may play a
crucial role in tumor progression and metastasis.
[0137] The results of our experiments indicate that PcG pathway is
frequently activated in human prostate tumors and is
mechanistically linked to the highly malignant behavior of human
prostate carcinoma cells in a xenograft model of prostate cancer
metastasis. It remains to be elucidated whether similarly to the
xenograft model of human prostate cancer metastasis in nude mice
the PcG pathway activation is mechanistically associated with
metastatic disease in prostate cancer patients as well. Whether the
level of enrichment of primary prostate tumors with dual positive
high BMI1/Ezh2-expressing cancer cells would correlate with a
degree of PcG pathway activation and would be informative in
predicting the clinical behavior of prostate cancer in patients.
Follow-up studies are expected to determine whether human prostate
tumors manifesting markedly increased levels of dual positive high
BMI1/Ezh2-expressing cells represent a therapy resistant clinically
lethal type of prostate adenocarcinomas. This technology provides
the basis for development of small molecule inhibitors of the PcG
protein chromatin silencing pathway as a novel therapeutic modality
for treatment of metastatic prostate cancer.
[0138] Stemness Pathway
[0139] Another pathway implicated in cancer progression is the
"stemness" pathway. A cancer stem cell hypothesis proposes that the
presence of rare stem cell-resembling tumor cells among the
heterogeneous mix of cells comprising a tumor is essential for
tumor progression and metastasis of epithelial malignancies. One of
the implications of a cancer stem cell hypothesis is that similar
genetic regulatory pathways might define critical stem cell-like
functions in both normal and tumor stem cells.
[0140] Recent experimental and clinical observations identified the
BMI1 oncogene-driven pathway(s) as one of the key regulatory
mechanisms of "stemness" functions in both normal and cancer stem
cells. The Polycomb group (PcG) gene BMI1 influences the
proliferative potential of normal and leukemic stem cells and is
required for the self-renewal of hematopoietic and neural stem
cells. Self-renewal ability is one of the essential defining
properties of a pluripotent stem cell phenotype. BMI1 oncogene is
expressed in all primary myeloid leukemia and leukemic cell lines
analyzed so far and over-expression of BMI1 causes neoplastic
transformation of lymphocytes. Recent experimental observations
documented an increased BMI1 expression in human non-small-cell
lung cancer, human breast carcinomas and breast cancer cell lines,
human medulloblastomas, prostate carcinomas, and gastrointestinal
cancers, supporting the idea that an oncogenic role of the BMI1
activation may affect progression of the epithelial malignancies
and other solid tumors as well.
[0141] Recent clinical genomics data provide a powerful evidence
supporting a cancer stem cell hypothesis and suggest that gene
expression signatures associated with the "stemness" state of a
cell (defined as phenotypes of self-renewal, asymmetrical division,
and pluripotency) might be informative as molecular predictors of
cancer therapy outcome. A mouse/human comparative cross-species
translational genomics approach was utilized to identify an 11-gene
signature that distinguishes stem cells with normal self-renewal
function from stem cells with drastically diminished self-renewal
ability due to the loss of the BMI1 oncogene as well as
consistently displays a normal stem cell-like expression profile in
distant metastatic lesions as revealed by the analysis of
metastases and primary tumors in both a transgenic mouse model of
prostate cancer and cancer patients.
[0142] Kaplan-Meier analysis confirmed that a stem cell-like
expression profile of the 11-gene signature in primary tumors is a
consistent powerful predictor of a short interval to disease
recurrence, distant metastasis, and death after therapy in cancer
patients diagnosed with twelve distinct types of cancer. These data
suggest the presence of a conserved BMI1 oncogene-driven pathway,
which is similarly activated in both normal stem cells and a
clinically lethal therapy-resistant subset of human tumors
diagnosed in a wide range of organs and uniformly exhibiting a
marked propensity toward metastatic dissemination. Consistent with
this idea, the essential role of the BMI1 oncogene activation in
prostate cancer metastasis as well as in the maintenance of a
self-renewal ability and high malignant potential of human breast
cancer stem cells has been demonstrated. Cancer stem cells may
indeed constitute metastasis precursor cells since most of the
early disseminated carcinoma cells detected in the bone marrow of
breast cancer patients manifest a breast cancer stem cell
phenotype.
[0143] Recent genome-scale chromatin immunoprecipitation (ChIP)
experiments and RNA interference analysis identified multiple
critical pathways comprising an essential genetic regulatory
circuitry of mouse and human embryonic stem cells (ESC). Similarly
to the BMI1 knockout studies, in these experiments the self-renewal
and proliferation functions of the normal stem cells appeared
successfully uncoupled, thus allowing to dissect the critical
regulatory pathways essential for maintenance of the self-renewal
state of ESC and providing reliable models to study the relevance
of the ESC-defined "stemness"/differentiation pathways to human
cancer.
[0144] These advances were used to identify gene expression
signatures of embryonic stem cells (ESC) during transition from
self-renewing, pluripotent state to differentiated phenotypes in
several experimental models of differentiation of human and mouse
ESC. This analysis reveals multiple gene expression signatures of
the ESC regulatory circuitry which appear highly informative in
stratification of the early-stage breast, lung, and prostate cancer
patients into sub-groups with dramatically distinct likelihood of
therapy failure.
Genetic Signatures of Regulatory Circuitry of Embryonic Stem Cells
(ESC) Identify Therapy-Resistant Phenotypes in Cancer Patients
Diagnosed with Multiple Types of Epithelial Malignancies.
[0145] Recent discovery of death-from-cancer signature genes
implies that genetic signatures associated with a "stemness" state
(defined as phenotypes of asymmetrical division, pluripotency, and
self-renewal) might be informative as molecular predictors of
cancer therapy outcome (Glinsky et al., J. Clin. Invest. 115:
1503-1521 (2005)). The validity of this concept was tested while
exploring the results of genome-wide microarray and chromatin
immunoprecipitation analyses of several experimental models of
differentiation of human and mouse ESC (Boyer et al, Cell 122
947-956 (2005; Lee et al., Cell 125: 301-313 (2006); Bernstein et
al., Cell 125: 315-326 (2006); Boyer et al., Nature 441: 349-353
(2006).
[0146] Applying signature discovery principles to analysis of gene
expression profiles during transition of ESC from self-renewing,
pluripotent state to differentiated phenotypes, it was identified
that seven gene expression signatures associated with a "stemness"
epigenetic program of ESC that appear highly informative in
stratification of the early-stage breast, prostate, and lung cancer
patients into sub-groups with dramatically distinct likelihood of
therapy failure. Cancer therapy outcome predictor (CTOP) algorithm
employing a panel of "stemness` signatures [signatures of
Nanog/Sox2/Oct4-, EED-, and Suz12-pathways; transposon exclusion
zones (TEZ) and bivalent chromatin domains (BCD) signatures] and a
Myc-driven "wound signature" demonstrates nearly 100% specificity
and sensitivity of CTOP power in retrospective analysis of large
independent cohorts of breast, prostate, lung, and ovarian cancer
patients. To date, the retrospective analysis of the prognostic
power of individual "stemness" signatures is being extended to more
than 3,100 patients diagnosed with 12 distinct types of cancer
(Table 3)
TABLE-US-00003 TABLE 3 Cancer types and number of cancer patients
in clinical cohorts utilized for analysis of therapy outcome
correlations with distinct expression profiles of the 11-gene
BMI1-pathway signature Number of patients in the outcome Cancer
Type sets References Prostate Cancer 220 J. Clin. Invest., 113: 913
(2004); Cancer Cell, 1: 203 (2002); PNAS, 101: 614 (2004); PNAS,
101: 811 (2004); JCO, 22: 2790 (2004); J. Clin. Invest., 115: 1503
(2005) Breast Cancer 1171 Nature, 415: 530 (2002); NEJM, 347: 1999
(2002); PNAS, 100: 10393 (2003); Cancer Cell, 5: 507 (2004); PNAS,
100: 8419 (2003); Lancet, 381: 1590 (2003); Lancet, 365: 571
(2005); JCI, 115: 44 (2005); Nature, 439: 353 (2006) Lung Cancer
340 PNAS, 98: 13790 (2001); Nature Medicine, 8: 815 (2002); Nature,
439: 353 (2006) Gastric Cancer 39 PNAS, 99: 15203 (2002) Ovarian
Cancer 216 Clin. Cancer Res. 10: 3291 (2004); J. Soc. Gynecol.
Investig. 11: 51 (2004); Bladder Cancer 31 Nature Genetics, 33: 90
(2003) Follicular 191 NEJM, 351: 2159 (2004) Lymphoma Lymphoma 298
NEJM, 346: 1937 (2002); Nature (DLBCL) Medicine, 8: 68 (2002)
Mesothelloma 17 J. National Cancer Inst., 96: 698 (2003)
Medulloblastoma 60 Nature, 415: 436 (2002) Glioma 50 Cancer Res.,
53: 1602 (2003) Lymphoma 92 Cancer Cell, 3: 185 (2003) (MCL) AML
401 NEJM, 350: 1605 (2004); NEJM, 350: 1617 (2004) Total 3176
[0147] The analysis demonstrates that therapy-resistant and
therapy-responsive cancer phenotypes manifest distinct patterns of
association with "stemness"/differentiation pathways, suggesting
that therapy-resistant and therapy-responsive tumors develop within
genetically distinct "stemness"/differentiation programs. These
differences can be exploited for development of prognostic and
therapy selection genetic tests utilizing microarray-based CTOP
algorithm. One of the major regulatory pathways manifesting
distinct patterns of association with therapy-resistant and
therapy-responsive cancer phenotypes is the Polycomb group (PcG)
proteins chromatin silencing pathway. RNAi-mediated targeting of
the critical regulatory components of the PcG pathway in metastatic
cancer cells eradicates disease in 67-83% of animals in a
fluorescent orthotopic model of human prostate cancer metastasis in
nude mice. To further validate the clinical relevance of these
findings, the quantitative co-localization immunofluorescence
analysis of the selected PcG proteins was carried out using TMA of
more than 300 prostate tumors obtained from patients with known
long-term clinical outcome after therapy. The analysis demonstrates
that "stemness" pattern of the PcG pathway activation in prostate
tumors is associated with the increased likelihood of therapy
failure. Genetic signatures of "stemness" state identify
therapy-resistant phenotypes in cancer patients diagnosed with
multiple types of epithelial malignancies. These results provide
powerful clinical evidence supporting the validity of the concept
of cancer stem cells for human solid tumors.
Multiple Gene Expression Signatures of the ESC Regulatory Circuitry
Predict Therapy Failure in Prostate Cancer Patients
[0148] Translational genomics data suggest that gene expression
signatures associated with the "stemness" state of a cell might be
informative as molecular predictors of cancer therapy outcome.
Recent ChIP and RNA interference experiments identified multiple
genetic pathways comprising an essential genetic regulatory
circuitry of mouse and human embryonic stem cells. Similarly to the
BMI1 knockout studies, in these experiments the self-renewal and
proliferation functions of the normal stem cells were successfully
uncoupled, thus providing reliable model systems dissecting the
critical regulatory pathways essential for maintenance of the
self-renewal state of ESC. These advances were used to study the
relevance to human cancer of the multiple ESC-associated
"stemness"/differentiation pathways defined in several experimental
models of differentiation of human and mouse ESC.
[0149] Six large parent gene sets representing major genetic
pathways associated with the essential regulatory circuitry of
mouse and human ESC were selected for the initial analysis (Table
4).
TABLE-US-00004 TABLE 4 Classification performance of individual
Polycomb pathway "stemness" signatures and CTOP "stemness"
algorithms in predicting clinical outcome of prostate cancer Number
of Number of Log-rank Affymetrix Transcripts Transcripts test
Parent Gene Microarray Platform Parent Gene Prostate Detection of
Chi Hazard Sets CTOP "stemness" signatures Sets Cancer failures, %
P value square Ratio 95% CI of ratio Data Source TEZ 236 32 33/37
(89%) <0.0001 54.03 16.12 6.925 to 28.29 FIG. 17 EED-pathway 117
36 33/37 (89%) <0.0001 52.73 15.7 6.691 to 27.28 Suz12/POLII 79
22 33/37 (89%) <0.0001 52.44 15.86 6.559 to 26.49 FIG. 21 Suz12
142 26 35/37 (95%) <0.0001 66.58 34.87 9.343 to 38.38 FIG. 21
Nanog/Sox2/Oct4 164 28 33/37 (89%) <0.0001 54.37 16.04 7.052 to
29.01 FIG. 17 PcG-TF 176 21 33/37 (89%) <0.0001 48.49 14.96
5.787 to 22.89 FIG. 16 BCD-TF 73 31 33/37 (89%) <0.0001 50.53
15.4 6.180 to 24.73 FIG. 15 ESC pattern 3 158 37 35/37 (95%)
<0.0001 72.9 37.19 11.30 to 47.95 BMI1 pathway 199 11 28/37
(76%) <0.0001 18.81 4.454 2.240 to 8.471 FIG. 14 PcG methylation
98 35 33/37 (89%) <0.0001 55.71 16.57 7.275 to 29.90 FIG. 16
Histone H3 20 20 29/37 (78%) <0.0001 26.7 5.903 3.036 to 11.80
This work Histone H2A 24 24 32/37 (86%) <0.0001 41.44 11.08
4.767 to 18.71 This work Histones H3/H2A 44 27 34/37 (92%)
<0.0001 59.97 21.97 8.103 to 33.46 This work Six ESC signatures
914 165 37/37 (100%) <0.0001 83.12 Und Undefined This work Eight
ESC signatures 1145 233 37/37 (100%) <0.0001 83.12 Und Undefined
This work Nine "stemness" signatures 1344 244 37/37 (100%)
<0.0001 83.12 Und Undefined This work Ten "stemness" signatures
1442 279 37/37 (100%) <0.0001 81.18 Und Undefined This work
Eleven "stemness" signatures 1486 306 37/37 (100%) <0.0001 81.18
Und Undefined This work Legend: Seventy-nine prostate cancer
patients, thirty-seven of which failed therapy within five years
after radical prostatectomy and forty-two remain disease-free for
at least five years, were stratified into poor prognosis (top 50%
scores) and good prognosis (bottom 50% scores) groups based on the
values of either individual CTOP scores (determined using weighted
algorithm scores of the corresponding "stemness" signatures) or
cumulative CTOP scores comprising the sum of the multiple
individual signatures: Six ESC signatures (TEZ; EED; Suz12/POLII;
Suz12; Nanog/Sox2/Oct4; PcG-TF signatures); Eight ESC signatures
(six ESC signatures plus BCD-TF and ESC pattern3 signatures); Nine
"stemness" signatures (eight ESC signatures plus BMI-pathway
signature); Ten "stemness" signatures (nine "stemness" signatures
plus PcG methylation signature); Eleven "stemness" signatures (ten
"stemness" signatures plus Histones H3/H2A signature). Detection of
failures (the number and percentage) was calculated as the number
of cases that actually failed therapy and were classified by the
CTOP algorithm into poor prognosis groups (top 50% scores) with
relation to the total number of therapy failure cases in the data
set. Microarray data sets and associated clinical information were
reported elsewhere (5). Und, undefined due to the 100% cure rate in
the good prognosis group.
[0150] These pathways were independently defined by different
groups using distinct experimental approaches and protocols. Using
multivariate Cox regression analysis, the prognostic power of these
gene sets were interrogated and it was found that all six gene sets
provide highly informative signatures for stratification of
prostate cancer patients into sub-groups with distinct likelihood
of therapy failure (FIG. 22 and Table 4). To assess the comparative
prognostic performance of the signatures, we evaluated the
individual Kaplan-Meier survival curves using the same 50% cut-off
level in dividing the patients into poor prognosis (top 50% scores)
and good prognosis (bottom 50% scores) sub-groups. It was found
that all six signatures perform with similar accuracy in
stratification of prostate cancer patients into sub-groups with
statistically distinct probability of relapse after radical
prostatectomy (FIG. 22). When the prognostic powers of the
ESC-derived signatures were combined into six-signature cancer
therapy outcome predictor (CTOP) algorithm by adding the values of
individual CTOP scores, the resulting prognostic performance
appears significantly improved reaching nearly 100% accuracy (FIG.
22 and Table 4).
Gene Expression Signatures of the ESC Regulatory Circuitry Predict
Therapy Failure in Multiple Independent Data Sets of Breast Cancer
Patients.
[0151] At the next step of the analysis it was sought to determine
whether this approach would be applicable for evaluation of therapy
outcome in breast cancer patients as well. Similarly to the
prostate cancer data set, all six gene sets of the ESC regulatory
circuitry generate gene expression-based predictors of the
likelihood of treatment failure in breast cancer patients (FIG. 23
and Table 5).
TABLE-US-00005 TABLE 5 Classification performance of individual
Polycomb pathway "stemness" signatures and CTOP "stemness"
algorithms in predicting clinical outcome of the early-stage LN
negative breast cancer (Affymetrix Microarray Platform) Affymetrix
Microarray Number of Number of Platform Transcripts Transcripts
Log-rank Parent Gene CTOP "stemness" Parent Gene Breast Detection
of Chi test Sets signatures Sets Cancer failures, % P value square
Hazard Ratio 95% CI of ratio Data Source TEZ 236 36 85/107 (79%)
<0.0001 60.1 5.191 3.131 to 6.778 FIG. 17 EED-pathway 117 20
79/107 (74%) <0.0001 41.46 3.704 2.413 to 5.217 Suz12/POLII 79
20 82/107 (77%) <0.0001 51.63 4.427 2.800 to 6.064 FIG. 21 Suz12
142 25 81/107 (76%) <0.0001 46.63 4.092 2.603 to 5.623 FIG. 21
Nanog/Sox2/Oct4 164 41 87/107 (81%) <0.0001 73.64 6.282 3.724 to
8.110 FIG. 17 PcG-TF 176 30 81/107 (76%) <0.0001 48.47 4.182
2.680 to 5.804 FIG. 20 BCD-TF 73 26 82/107 (77%) <0.0001 51.42
4.413 2.793 to 6.048 FIG. 17 ESC pattern 3 158 35 87/107 (81%)
<0.0001 72.67 6.218 3.679 to 8.009 BMI1 pathway 199 11 67/107
(63%) 0.0005 12.11 1.972 1.345 to 2.886 FIG. 14 PcG methylation 98
22 87/107 (81%) <0.0001 73.94 6.301 3.737 to 8.139 FIG. 16
Histone H3 20 13 72/107 (67%) <0.0001 22.23 2.54 1.713 to 3.687
This work Histone H2A 24 24 70/107 (65%) <0.0001 19.53 2.378
1.618 to 3.482 This work Histones H3/H2A 44 44 76/107 (71%)
<0.0001 31.98 3.113 2.063 to 4.447 This work Six ESC signatures
914 172 94/107 (88%) <0.0001 107.4 11.09 5.381 to 11.79 This
work Eight ESC signatures 1145 233 95/107 (89%) <0.0001 112.3
12.17 5.651 to 12.40 This work Nine "stemness" 1344 244 97/107
(91%) <0.0001 124.3 15.25 6.351 to 13.98 This work signatures
Ten "stemness" 1442 266 98/107 (92%) <0.0001 127.7 17.01 6.538
to 14.37 This work signatures Eleven "stemness" 1486 310 99/107
(93%) <0.0001 132.1 19.31 6.793 to 14.93 This work signatures
Legend: Two-hundred-eighty-six early-stage LN-negative breast
cancer patients, one-hundred-seven of which failed therapy within
five years after surgery and one-hundred-seventy-nine remain
disease-free for at least five years, were stratified into poor
prognosis (top 50% scores) and good prognosis (bottom 50% scores)
groups based on the values of either individual CTOP scores
(determined using weighted algorithm scores of the corresponding
"stemness" signatures) or cumulative CTOP scores comprising the sum
of the multiple individual signatures: Six ESC signatures (TEZ;
EED; Suz12/POLII; Suz12; Nanog/Sox2/Oct4; PcG-TF signatures); Eight
ESC signatures (six ESC signatures plus BCD-TF and ESC pattern3
signatures); Nine "stemness" signatures (eight ESC signatures plus
BMI-pathway signature); Ten "stemness" signatures (nine "stemness"
signatures plus PcG methylation signature); Eleven "stemness"
signatures (ten "stemness" signatures plus Histones H3/H2A
signature). Detection of failures (the number and percentage) was
calculated as the number of cases that actually failed therapy and
were classified by the CTOP algorithm into poor prognosis groups
(top 50% scores) with relation to the total number of therapy
failure cases in the data set. Microarray data sets and associated
clinical information were reported elsewhere.
The individual predictors perform with similar prognostic
classification accuracy and six-signature CTOP algorithm
demonstrates significantly improved patients' stratification
performance compared to the individual signatures (FIG. 23 and
Table 5). To validate the findings, the analysis is extended by
using four additional breast cancer therapy outcome data sets which
were previously developed and analyzed in three independent
institutions. As shown in FIG. 23, this analysis confirmed that
ESC-based CTOP algorithm is informative in multiple independent
breast cancer therapy outcome data sets comprising altogether more
than 900 breast cancer patients (FIG. 23 and Tables 5-7).
TABLE-US-00006 TABLE 6 Classification performance of individual
Polycomb pathway "stemness" signatures and CTOP "stemness"
algorithms in predicting clinical outcome of the early-stage LN
negative breast cancer (Agilent Microarray Platform; clinical
end-point: metastasis-free survival) Agilent Microarray Platform
Number of "Stemness" CTOP Transcripts Detection of Log-rank test
signatures Breast Cancer failures, % P values Chi square Hazard
Ratio 95% CI of ratio TEZ signature 17 37/46 (80%) <0.0001 37
6.797 3.580 to 12.04 EED-pathway 22 36/46 (78%) <0.0001 33.98
6.045 3.313 to 11.15 Suz12/POLII 21 39/46 (85%) <0.0001 47.16
9.493 4.631 to 15.76 Suz12 27 37/46 (80%) <0.0001 36.59 6.724
3.545 to 11.93 Nanog/Sox2/Oct4 38 39/46 (85%) <0.0001 52.78
10.36 5.378 to 18.64 PcG-TF signature 28 33/46 (72%) <0.0001
16.55 3.445 1.888 to 6.161 BCD-TF 26 39/46 (85%) <0.0001 52.6
10.37 5.338 to 18.45 BMI1 pathway 11 31/36 (67%) 0.0003 13.23 2.946
1.660 to 5.428 PcG methylation 29 43/46 (93%) <0.0001 73.54
26.55 8.258 to 28.85 Histone H3 14 31/46 (67%) 0.0002 14.15 3.041
1.728 to 5.681 Histone H2A 15 33/46 (72%) <0.0001 15.72 3.357
1.827 to 5.935 Histones H3/H2A 29 36/46 (78%) <0.0001 29.23
5.451 2.865 to 9.484 Six ESC signatures 153 43/46 (93%) <0.0001
75.11 27.11 8.547 to 29.95 Ten "stemness" 248 44/46 (96%)
<0.0001 88.05 44.81 11.18 to 40.00 signatures Legend:
Ninety-seven early-stage LN-negative breast cancer patients,
forty-six of which failed therapy within five years after surgery
and fifty-one remain disease-free for at least five years, were
stratified into poor prognosis (top 50% scores) and good prognosis
(bottom 50% scores) groups based on the values of either individual
CTOP scores (determined using weighted algorithm scores of the
corresponding "stemness" signatures) or cumulative CTOP scores
comprising the sum of the multiple individual signatures: Six ESC
signatures (TEZ; EED; Suz12/POLII; Suz12; Nanog/Sox2/Oct4; PcG-TF
signatures); Ten "stemness" signatures (six ESC signatures plus
BCD-TF, BMI1-pathway, PcG methylation, and Histones H3/H2A
signatures). Detection of failures (the number and percentage) was
calculated as the number of cases that actually failed therapy and
were classified by the CTOP algorithm into poor prognosis groups
(top 50% scores) with relation to the total number of therapy
failure cases in the data set. Microarray data sets and associated
clinical information were reported elsewhere.
TABLE-US-00007 TABLE 7 Classification performance of individual
Polycomb pathway "stemness" signatures and CTOP "stemness"
algorithms in predicting clinical outcome of breast cancer (Agilent
Microarray Platform; clinical end-point: death after therapy)
Agilent Microarray Platform Number of "Stemness" CTOP Transcripts
Detection of Log-rank test signatures Breast Cancer failures, % P
values Chi square Hazard Ratio 95% CI of ratio TEZ signature 17
63/79 (80%) <0.0001 42.45 5.116 2.819 to 6.876 EED-pathway 22
66/79 (84%) <0.0001 50.08 6.419 3.202 to 7.810 Suz12/POLII 21
62/79 (78%) <0.0001 34.11 4.321 2.404 to 5.829 Suz12 27 63/79
(80%) <0.0001 41.40 5.021 2.768 to 6.753 Nanog/Sox2/Oct4 38
66/79 (84%) <0.0001 57.62 7.071 3.654 to 9.007 PcG-TF signature
28 62/79 (78%) <0.0001 38.07 4.621 2.603 to 6.343 BCD-TF 26
57/79 (72%) <0.0001 23.00 3.122 1.901 to 4.620 BMI1 pathway 11
60/79 (76%) <0.0001 30.95 3.877 2.264 to 5.505 PcG methylation
29 65/79 (82%) <0.0001 42.31 5.483 2.793 to 6.775 Histone H3 14
51/79 (65%) 0.0008 11.18 2.148 1.369 to 3.328 Histone H2A 15 60/79
(76%) <0.0001 32.50 3.984 2.341 to 5.709 Histones H3/H2A 9 61/79
(77%) <0.0001 36.30 4.348 2.529 to 6.186 Six ESC signatures 153
72/79 (91%) <0.0001 80.42 14.33 5.010 to 12.34 Nine "stemness"
219 72/79 (91%) <0.0001 80.05 14.26 4.987 to 12.29 signatures
Ten "stemness" 238 73/79 (92%) <0.0001 85.38 17.07 5.347 to
13.19 signatures Legend: Two-hundred-ninety-five breast cancer
patients, seventy-nine of which died within five years after
therapy and two-hundred-sixteen remain alive for at least five
years, were stratified into poor prognosis (top 50% scores) and
good prognosis (bottom 50% scores) groups based on the values of
either individual CTOP scores (determined using weighted algorithm
scores of the corresponding "stemness" signatures) or cumulative
CTOP scores comprising the sum of the multiple individual
signatures: Six ESC signatures (TEZ; EED; Suz12/POLII; Suz12;
Nanog/Sox2/Oct4; PcG-TF signatures); Nine "stemness" signatures
(six ESC signatures plus BCD-TF, BMI1-pathway, and PcG methylation
signatures). Ten "stemness" signatures (six ESC signatures plus
BCD-TF, BMI1-pathway, PcG methylation, and Histones H3/H2A
signatures). Detection of failures (the number and percentage) was
calculated as the number of cases that actually failed therapy and
were classified by the CTOP algorithm into poor prognosis groups
(top 50% scores) with relation to the total number of therapy
failure cases in the data set. Microarray data sets and associated
clinical information were reported elsewhere.
Transcription Factors as Markers within Oncogenic Pathways
[0152] The present invention can also be used to analyze the level
of transcription factors as either an indicator of the presence of
cancer or as a predictor of cancer therapy outcome. Details of
transcription factor analysis are below.
Distinct Gene Expression Profiles of the Bivalent Chromatin Domain
Transcription Factor Genes (BCD-TF) are Associated with
Therapy-Resistant and Therapy-Sensitive Phenotypes of Human
Prostate and Breast Cancers.
[0153] In genomes of somatic cells nucleosomal compositions of
histones harboring specific modifications of the histone tails
defines mutually exclusive transcriptionally active or silent
states of the chromatin. Transcriptional status of corresponding
genetic loci in genomes of most cells is governed by the
nucleosome-defined chromatin patterns and strictly follows
activation/repression rules. In contrast to somatic cells, in ESC
multiple chromosomal regions were identified simultaneously
harboring both "silent" (H3K27met3) and "active" (H3K4) histone
marks and .about.100 transcription factor (TF) encoding genes are
residing within these bivalent chromatin domain-containing
chromosomal regions. Many of the bivalent chromatin domain
(BCD)--containing genes were previously identified as the Polycomb
Group (PcG) protein-target genes in both human and mouse ESC and
are repressed or transcribed at low levels in ESC.
[0154] These observations form the basis for a hypothesis that
transcriptional repression of BCD genes is essential for
maintenance of the "stemness" state of ESC and the unique BCD
status of these genes make them poised for rapid transcriptional
activation during transition from pluripotent self-renewing state
of ESC to differentiated phenotypes.
[0155] Consistent with this idea, in differentiated cells the BCD
pattern of these genes is resolved in either transcriptionally
active or repressed chromatin domains and activated or repressed
transcription of corresponding genes. It is noted that many BCD
genes were also identified earlier as members of the core
transcriptional regulatory circuitry of ESC manifesting the
co-occupancy of their promoters by major "stemness" transcription
factors. Furthermore, careful review of the available gene
expression data sets of ESC in pluripotent self-renewing state
reveals that several BCD-TF genes of this category are maintained
in a transcriptionally active state.
[0156] This analysis suggests that expression of selected TF
encoding genes in ESC, including bivalent chromatin
domain-containing TF genes (BCD-TF), maintenance of a "stemness"
state, and transition to differentiated phenotypes may be regulated
by the balance of the "stemness" TFs such as Nanog, Sox2, Oct4, and
PcG proteins bound to the promoters of target genes. If this is
true, the "stemness" state of ESC should be associated with the
unique profile of the BCD-TF expression comprising both up- and
down-regulated transcripts that may be defined as the "stemness"
BCD-TF signature (FIG. 24). It would be of interest to determine
whether human tumors manifest a common pattern of the BCD-TF
expression resembling a "stemness" profile of the BCD-TF
signature.
[0157] Gene expression profiles of BCD-TF in clinical samples were
independently generated for therapy-resistant breast and prostate
tumors using multivariate Cox regression analysis of microarrays of
tumor samples from 286 breast cancer and 79 prostate cancer
patients with known long-term clinical outcome after therapy and
tested for concordant pattern. This analysis identified the
thirteen-gene BCD-TF signature manifesting highly concordant gene
expression profiles (r=0.853; P<0.001; FIG. 24) in breast and
prostate tumors from patients with therapy-resistant disease
phenotypes. Next, "stemness" gene expression profiles of BCD-TF in
mouse ESC were derived by comparing microarray analyses of
pluripotent self-renewing ESC (control ESC cultures treated with HP
siRNA) versus ESC treated with Esrrb siRNA (day 6). At this time
point, Esrrb siRNA-treated ESC does not manifest "stemness"
phenotype and form colonies of differentiated cells. Mouse genes
comprising the "stemness" BCD-TF signature were translated into set
of human orthologs and BCD-TF gene expression profiles of
therapy-resistant clinical samples and ESC were tested for
concordant pattern. This analysis identifies the eight-gene BCD-TF
signature manifesting highly concordant expression profiles
(r=0.716; p<0.001; FIG. 24) in ESC and therapy-resistant breast
and prostate tumors. Kaplan-Meier analysis demonstrates that
prostate and breast cancer patients with tumors harboring ESC-like
expression profiles of the eight-gene BCD-TF signature are more
likely to fail therapy (bottom two panels), suggesting that a
sub-set of BCD-TF genes defined here as the eight gene BCD-TF
signature manifests "stemness" expression profiles in
therapy-resistant prostate and breast tumors (FIG. 24).
Therapy-Resistant and Therapy-Sensitive Tumors Manifest Distinct
Gene Expression Profiles of the ESC "Stemness"/Differentiation
Program.
[0158] The analysis suggests that therapy-resistant and
therapy-sensitive tumors manifest distinct pattern of association
with "stemness"/differentiation pathways engaged in ESC during
transition from pluripotent self-renewing state to differentiated
phenotypes. One of the major implications of this hypothesis is the
prediction that therapy-resistant and therapy-sensitive tumors
develop within genetically distinct "stemness"/differentiation
programs. This prediction was tested by interrogating the
prognostic power of genes comprising the ESC pattern 3
"stemness"/differentiation program recently identified by a
combination of the RNA interference and gene expression analyses.
It was found that similarly to the BCD-TF signatures the gene set
comprising the ESC pattern 3 "stemness"/differentiation pathway
generates gene expression signatures discriminating
therapy-resistant and therapy-sensitive prostate and breast tumors
(FIG. 25). These results support the hypothesis that
therapy-resistant and therapy-sensitive cancers may develop within
genetically distinct "stemness"/differentiation programs triggered
by the altered balance of "stemness" TF and immediate down-stream
changes in expression of the BCD-TF genes.
DNA Promoter Methylation Patterns as Markers within Oncogenic
Pathways
[0159] The present invention can also be used to analyze the DNA
promoter methylation patterns of genes within oncogenic pathways as
either an indicator of the presence of cancer or as a predictor of
cancer therapy outcome. Details of the analysis of DNA promoter
methylation patterns of genes within oncogenic pathways are
below.
Is Therapy-Resistant Phenotype of Human Epithelial Malignancies
Associated with Distinct Methylation Patterns of the Polycomb
Target Genes?
[0160] Recent experimental observations indicate that promoters of
genes identified as the PcG targets in ESC are preferentially
targeted for cancer-associated DNA hypermethylation and stable
transcriptional repression in multiple types of human cancers. DNA
promoter methylation patterns of the PcG target genes appear
significantly distinct in different types of tumors, suggesting the
presence of cancer type-specific profiles of DNA promoter
hypermethylation, transcriptional repression, and mRNA expression
of the PcG target genes. To determine whether gene expression
profiles of the PcG target genes promoters of which are
hypermethylated in human cancers would be associated with distinct
likelihood of therapy failure in prostate and breast cancer
patients was analyzed. The analysis utilized a set of 88 PcG target
genes previously reported to be hypermethylated in cancer (FIG.
14). Multivariate Cox regression analysis demonstrates that PcG
target genes with promoters frequently hypermethylated in cancer
manifest distinct expression profiles associated with
therapy-resistant and therapy-sensitive prostate and breast cancers
(FIG. 26), implying that differences in gene expression between
tumors with distinct outcome after therapy may be driven, in part,
by the distinct promoter hypermethylation patterns of the PcG
target genes. These differences can be exploited to generate highly
informative gene expression signatures of the PcG target genes
hypermethylated in cancer for stratification of prostate and breast
cancer patients into sub-groups with statistically distinct
likelihood of therapy failure (FIG. 26). This analysis suggests
that therapy-resistant and therapy-sensitive tumors are likely to
manifest different profiles of the promoter hypermethylation of PcG
target genes and these differences can be utilized for development
of DNA-based diagnostic, prognostic, and individualized therapy
selection tests.
[0161] Post-translational modifications of the histones H3 and H2A,
in particular, trimethylation of the lysine 27 residue (H3met3K27)
by the Ezh2-containing PRC2 complex and ubiquitination of the
histone H2A by the BMI1-containing PRC1 complex, are consistently
linked to the transcriptional silencing mediated by the PcG
proteins and a cross-talk between Polycomb targeting and DNA
promoter hypermethylation. It was therefore tested whether
therapy-resistant and therapy-sensitive tumors would manifest
distinct expression profiles of the histones H3 and H2A variants.
Multivariate Cox regression analysis demonstrates that activation
and inhibition of expression of distinct variants of the H3 and H2A
histones are associated with tumors manifesting different outcome
after therapy. Strikingly, gene expression signatures capturing
expression profiles of the limited number of variants of a single
protein (either histone H3 or histone H2A) appear informative in
distinguishing prostate and breast cancer patients with
statistically distinct probabilities of therapy failure (FIG. 26).
Interestingly, cumulative CTOP scores comprising a sum of the
individual CTOP scores of the H3, H2A, and PcG methylation
signatures demonstrate improved patients' stratification
performance compared to individual signatures (FIG. 26).
"Stemness" CTOP Algorithm Identifies Therapy-Resistant Phenotypes
and Predicts the Likelihood of Treatment Failure in Prostate,
Breast, Ovarian, and Lung Cancer Patients.
[0162] The analysis indicates that genetic components of the PcG
chromatin silencing complexes as well as genes identified as either
direct or immediate down-stream targets of the Polycomb pathway in
ESC manifest distinct patterns of association with
therapy-resistant and therapy-sensitive phenotypes of human
prostate and breast cancers. To investigate the status of the
Polycomb pathway in human tumors with distinct clinical outcome
after therapy, we divided PcG pathway-associated genes into several
functionally and/or structurally linked groups (Tables 4-8) and
interrogated each gene set for gene expression pattern association
with therapy-resistant phenotypes using multivariate Cox regression
analysis.
TABLE-US-00008 TABLE 8 Classification performance of the CTOP
algorithm comprising six Polycomb pathway ESC "stemness" signatures
in predicting clinical outcome of breast cancer in multiple
independent cohorts of patients Affymetrix and Agilent Microarray
Platform Breast cancer Log-rank test Data Sets Number of patients
Detection of failures, % P values Chi square Hazard Ratio 95% CI of
ratio Netherlands-286 286 (107) 94/107 (88%) <0.0001 107.4 11.09
5.381 to 11.79 MSKCC-95 95 (33) 31/33 (94%) <0.0001 48.22 25.64
6.450 to 27.94 DUKE-169 169 (52) 47/52 (90%) <0.0001 55.42 14.01
4.775 to 14.60 Netherlands-97 97 (46) 43/46 (93%) <0.0001 75.11
27.11 8.547 to 29.95 Netherlands-295 295 (79) 65/79 (82%)
<0.0001 51.20 6.242 3.279 to 8.034 Netherlands-295 295 (79)
72/79 (91%) <0.0001 80.42 14.33 5.010 to 12.34 Legend: The
Affymetrix-based CTOP algorithms were developed using the
Netherlaqnds-286 data set and tested using the MSKCC-95 and
Duke-169 data sets. The Agilent-based CTOP algorithms were
developed using the Netherlads-97 data set and tested using the
Netherlands-295 data set. The CTOP algorithms based on the
cancer-specific death after therapy were developed using the
Netherlands-295 data set (last row). In the Duke-169, MSKCC-95, and
Netherlands-295 data sets the end-points are the overall survival
and cancer-specific death. In the Netherlands-286 data set the
end-points are the relapse-free survival. In the Netherlands-97
data set the end-points are metastasis-free survival.
This approach generates multiple gene expression signatures that
are highly informative in stratification of cancer patients into
sub-groups with statistically distinct likelihood of therapy
failure (FIGS. 22-26). However, all of the signatures appear
informative as therapy outcome predictors only for a fraction of
patients and none of the signatures seems sufficiently accurate and
robust to serve as a prototype for diagnostic, prognostic, or
therapy-selection applications. Therefore, whether CTOP algorithm
combining the prognostic power of individual gene expression
signatures would be more informative as a molecular predictor
cancer treatment outcome (FIGS. 25 and 26). For each patient a
cumulative CTOP score was calculated comprising a sum of nine
individual CTOP scores derived from analysis of nine gene
expression signatures (Tables 4-7). Next, the patients were ranked
within data set in descending order based on the values of the
cumulative CTOP scores, divided each data set into five sub-groups
at 20% increment of the cumulative CTOP score values, and carried
out the Kaplan-Meier survival analysis (FIG. 27). This approach
generates highly informative CTOP algorithm stratifying cancer
patients into five sub-groups with statistically distinct
probabilities of therapy failure (FIG. 27). One of the striking
features revealed by our analysis is the apparent applicability of
this approach for development of gene expression-based CTOP
algorithms for lung and ovarian cancer patients as well (FIG.
27).
TABLE-US-00009 TABLE 9 Classification performance of the CTOP
algorithm comprising nine "stemness" signatures in predicting
clinical outcome in prostate, breast, lung, and ovarian cancer
patients Affymetrix and Agilent Microarray Breast cancer Log-rank
Platform Number of Detection of test Chi Hazard 95% CI of Data Sets
patients failures, % P values square Ratio ratio Breast Cancer 286
(107) 97/107 (91%) <0.0001 124.3 15.25 6.351 to 13.98 Prostate
Cancer 79 (37) 37/37 (100%) <0.0001 83.12 Und Und Lung Cancer 91
(45) 41/45 (91%) <0.0001 84.64 22.92 11.69 to 44.23 Ovarian
Cancer 133 (72) 56/72 (78%) <0.0001 78.47 7.592 6.272 to 17.81
Legend: The Affymetrix-based CTOP algorithms were developed
separately for breast cancer and prostate cancer data sets. CTOP
algorithm identified using breast cancer data set was applied to
the lung cancer data set and ovarian cancer data set. In the
ovarian cancer and lung cancer data sets the end-points are the
overall survival and cancer-specific death. In the breast cancer
data set the end-points are the disease-free survival. In the
prostate cancer data set the end point is the relapse-free
survival. In all data sets poor prognosis groups include patients
with top 50% values of the cumulative CTOP scores in a given data
set. Und, undefined due to the 100% cure rate in the good prognosis
group. See text for details.
TABLE-US-00010 TABLE 10 Classification performance of the CTOP
algorithm comprising nine "stemness" signatures in predicting
clinical outcome in prostate, breast, lung, and ovarian cancer
patients Affymetrix and Agilent Microarray Breast cancer Log-rank
Platform Number of Detection of test Chi Hazard 95% CI of Data Sets
patients failures, % P values square Ratio ratio Breast Cancer 286
(107) 104/107 (97%) <0.0001 96.59 34.31 4.663 to 10.04 Prostate
Cancer 79 (37) 37/37 (100%) <0.0001 43.72 Und Und Lung Cancer 91
(45) 44/45 (98%) <0.0001 65.05 62.87 6.910 to 23.90 Ovarian
Cancer 133 (72) 71/72 (99%) <0.0001 28.19 29.19 2.436 to 6.904
Legend: The Affymetrix-based CTOP algorithms were developed
separately for breast cancer and prostate cancer data sets. CTOP
algorithm identified using breast cancer data set was applied to
the lung cancer data set and ovarian cancer data set. In the
ovarian cancer and lung cancer data sets the end-points are the
overall survival and cancer-specific death. In the breast cancer
data set the end-points are the disease-free survival. In the
prostate cancer data set the end point is the relapse-free
survival. In all data sets, except ovarian cancer, poor prognosis
groups include patients with top 60% values of the cumulative CTOP
scores in a given data set. In ovarian cancer data set the poor
prognosis group includes patients with top 80% cumulative CTOP
score values. Und, undefined due to the 100% cure rate in the good
prognosis group. See text for details.
TABLE-US-00011 TABLE 11 Classification performance of the CTOP
algorithm comprising nine "stemness" signatures in predicting
clinical outcome in prostate, breast, lung, and ovarian cancer
patients Affymetrix and Agilent Microarray Breast cancer Log-rank
Platform Number of Detection of test Chi Hazard 95% CI of Data Sets
patients failures, % P values square Ratio ratio Breast Cancer 286
(107) 104/107 (97%) <0.0001 96.59 34.31 4.663 to 10.04 Prostate
Cancer 79 (37) 37/37 (100%) <0.0001 43.72 Und Und Lung Cancer 91
(45) 44/45 (98%) <0.0001 65.05 62.87 6.910 to 23.90 Ovarian
Cancer 133 (72) 62/72 (86%) <0.0001 57.15 7.890 4.040 to 10.74
Legend: The Affymetrix-based CTOP algorithms were developed
separately for breast cancer and prostate cancer data sets. CTOP
algorithm identified using breast cancer data set was applied to
the lung cancer data set and ovarian cancer data set. In the
ovarian cancer and lung cancer data sets the end-points are the
overall survival and cancer-specific death. In the breast cancer
data set the end-points are the disease-free survival. In the
prostate cancer data set the end point is the relapse-free
survival. In all data sets poor prognosis groups include patients
with top 60% values of the cumulative CTOP scores in a given data
set. Und, undefined due to the 100% cure rate in the good prognosis
group. See text for details.
Validation of the PcG proteins Chromatin Silencing Pathway
Involvement in Development of Therapy-Resistant Prostate
Cancer.
[0163] The association of the PcG protein chromatin silencing
pathway activation with therapy-resistant cancer using alternative
analytical approaches were investigated. Consistent with this idea,
a quantitative immunofluorescent co-localization analysis
demonstrates that a cancer stem cell-like CD44+/CD34- population
isolated by sterile FACS sorting from the blood-borne PC3-32 human
prostate carcinoma metastasis precursor cells is markedly enriched
for dual-positive BMI1/Ezh2 high expressing cancer cells compared
to the CD44+/CD24- population isolated from the maintained in
culture parental PC3 cell line. Furthermore, a multi-color FISH
analysis reveals that blood-borne human prostate carcinoma
metastasis precursor cell population contains a large proportion of
cancer cells with the high level co-amplification of both BMI1 and
Ezh2 genes (Table 12), suggesting that increased co-expression in
these cells of the BMI1 and Ezh2 oncoproteins is driven by the
co-amplification of two oncogenes, BMI1 and Ezh2.
TABLE-US-00012 TABLE 12 FISH analysis of DNA copy numbers of the
Polycomb Group BMI1 and Ezh2 genes in human prostate carcinoma cell
lines (parental PC-3 cells and blood-borne PC-3-32 metastasis
precursor cells) and diploid hTERT-immortalized human fibroblasts.
Dual-positive, Dual-positive, N BMI1-Cy3 Ezh2-Cy5 N (%) N BMI1-Cy5
Ezh2-Cy3 N (%) BJ-1 52 Average 2.333333 2 0 45 2.386364 2.533333 0
STDEV 0.905388 0.709768 1.125103 1.013545 PC-3 74 Average 2.125
4.125 1 (1.4%) 59 2.192308 4.482143 2 (3%) STDEV 1.090475 1.470492
1.00738 2.071031 T-test* 0.941271 5.13E-13 0.393451 1.38E-08
PC-3-32 99 Average 3.597561 5.185185 33 (33%) 102 3.540816 5.490196
34 (33%) STDEV 1.638481 1.743298 1.486492 1.733451 T-test**
8.43E-09 7.24E-31 1.49E-06 2.55E-25 T-test*** 7.49E-09 3.19E-08
6.38E-10 0.002259 Dual-positive, N (%); nuclei with 5 or more
copies of the Ezh2 gene and 4 or more copies of the BMI1 gene
T-test*, BJ-1 vs PC-3 T-test**, BJ-1 vs PC-3-32 T-test***, PC-3 vs
PC-3-32
[0164] Finally, a multi-color quantitative immunofluorescent
co-localization TMA analysis of 71 prostate carcinomas indicates
that patients with tumors having increased levels (>1%) of
dual-positive BMI1/Ezh2 high expressing cells manifest clinically
aggressive disease phenotypes and significantly more likely to
relapse and develop disease recurrence after radical prostatectomy.
Taken together with the previously reported experimental evidence
of the essential role of PcG pathway activation in metastatic
prostate cancer, these data strongly support the hypothesis of the
causal association of the Polycomb pathway activation and
manifestation of the clinically lethal therapy-resistant prostate
cancer phenotypes.
[0165] The analysis generated a "stemness" cancer therapy outcome
predictor (CTOP) algorithm comprising a combination of nine
signatures [signatures of BMI1-, Nanog/Sox2/Oct4-, EED-, and
Suz12-pathways; transposon exclusion zones (TEZ) and ESC pattern 3
signatures; signatures of polycomb-bound transcription factors
(PcG-TF) and bivalent chromatin domain transcription factors
(BCD-TF)]. A "stemness" CTOP algorithm demonstrates nearly 100%
prognostic accuracy for a majority of patients in retrospective
analysis of large cohorts of breast, prostate, lung, and ovarian
cancer patients, suggesting that therapy-resistant and
therapy-sensitive tumors develop within genetically distinct
"stemness"/differentiation programs driven by engagement of the PcG
proteins chromatin silencing pathway. The signatures of the PcG
pathway appear highly informative in stratification of the
early-stage breast, lung, and prostate cancer patients into
sub-groups with dramatically distinct likelihood of therapy
failure. The findings and conclusions were validated by applying
alternatives analytical techniques and methodologies of the PcG
pathway analysis in cell culture experiments, animal models of
cancer metastasis, and clinical tumor samples, including a variety
of protein expression assays using combinations of
immunofluorescence, FACS, and tissue microarray techniques. Taking
together, the analysis indicates that epigenetic landscape of
therapy-resistant human cancers is defined to a significant extent
by the activation of the PcG protein chromatin silencing pathway
and heritable imprinting of a stem cell-like epigenetic program via
cross-talk between PcG pathway and DNA promoter
hypermethylation.
[0166] Clinical genomics data suggest that gene expression
signatures associated with the "stemness" state of a cell might be
informative as molecular predictors of cancer therapy outcome. This
hypothesis was tested by applying the signature discovery
principles to genomic analysis of human and mouse ESC during
transition from self-renewing, pluripotent state to differentiated
phenotypes in several experimental models of ESC differentiation.
Collectively, the data suggest that therapy-resistant and
therapy-sensitive tumors develop within genetically distinct
"stemness"/differentiation programs. To date, the retrospective
analysis of the prognostic power of individual "stemness"
signatures is being extended to more than 3,100 patients diagnosed
with 13 distinct types of cancer supporting the conclusion that
therapy-resistant and therapy-responsive cancer phenotypes manifest
distinct patterns of association with "stemness"/differentiation
pathways.
[0167] Taken together, the analysis further supports the existence
of transcriptionally discernable type of human cancer detectable in
a sub-group of early-stage cancer patients diagnosed with distinct
epithelial malignancies appearing in multiple organs. These
early-stage carcinomas of seemingly various origins appear to
exhibit a poor therapy outcome gene expression profile, which is
uniformly associated with increased propensity to develop
metastasis, high likelihood of treatment failure, and increased
probability of death from cancer after therapy. Cancer patients who
fit this transcriptional profile might represent a genetically,
biologically, and clinically distinct type of cancer exhibiting
highly malignant clinical behavior and therapy resistance phenotype
even at the early stage of tumor progression. It has been suggested
that one of the characteristic features of this early-stage,
therapy-resistant metastatic cancer is the transcriptional (and,
perhaps, biological) resemblance to the normal stem cells. A stem
cell cancer hypothesis has been proposed to explain a possible
mechanistic contribution of the normal stem cells to the
pathogenesis of this type of human cancer. According to this
hypothesis, a genetically defined sub-set of transformed cells
(perhaps, arising with higher probability in a genetically defined
human sub-population) form tumors with high tropism toward normal
stem cells (NSCs) mediated by molecules collectively defined as
"presence of wound" and/or "hypoxia" signals. Enrichment of primary
tumors with NSCs increases likelihood of horizontal genomic
transfer (large-scale transfer of DNA and chromatin) between NSCs
and tumor cells via cell fusion and/or uptake of apoptotic bodies.
Reprogrammed somatic hybrids of tumor cells and NSCs acquire
transformed phenotype and epigenetic self-renewal program.
Postulated progeny of hybrid cells contains a sub-population of
self-renewing cancer stem cells with epigenetic and transcriptional
markers of NSCs and high propensity toward metastatic
dissemination. Recent experimental observations demonstrate direct
involvement of the bone marrow-derived cells in development of
breast and colon cancers in transgenic mouse cancer models
suggesting that cancer stem cells can originate from the bone
marrow-derived cells.
[0168] The analysis highlights the significant challenges
associated with a prospect of practical implementation of the
concept of personalized medicine in clinical oncology settings.
Many of these challenges are based on a fundamental reality of a
biological context defined by the multigenic nature of human
cancers and its implications for diagnostic, prognostic
(inter-patients and intra-tumor heterogeneities; requirements for
multi-signatures diagnostic, prognostic, and therapy selection
algorithms), and therapeutic applications (the eventual necessity
for highly individualized combinations of cancer therapeutics for
simultaneous targeting of relevant oncogenic and stemness pathways
to alleviate the probability of selection of therapy-resistant
phenotypes). One of such non-anticipated near-term health care
management and regulatory implications for successful clinical
implementation of the concept of personalized cancer therapies
revealed by the analysis is the unrestricted physicians' ability to
prescribe and exercise in a routine clinical setting an off-label
use of the FDA approved drugs.
[0169] One of the important end-points of our work is development
of a concise catalog of gene expression changes comprising
.about.300 human genes divided into nine signatures and reflecting
a transcriptional pathology of "stemness`/differentiation pathways
associated with therapy-resistant phenotypes of human solid tumors.
One of the significant advantages of having such a "stemness"
catalog available is the potential to exploit this information for
a therapeutic gain in the effort to target clinically lethal states
of malignant phenotypes. Therefore, evaluating a potential
therapeutic utility of the association of "stemness" and
therapy-resistant cancer phenotypes was attempted by exploring the
connectivity map (CMAP) of "stemness" pathways in human solid
tumors with distinct clinical outcome after therapy. CMAP-based
search for cancer therapeutics targeting "stemness" pathways in
solid tumors reveals drug combinations causing transcriptional
reversal of "stemness" signatures associated with therapy-resistant
phenotypes of epithelial cancers. CMAP analysis demonstrates that a
combination of the PI3K pathway inhibitor, estrogen receptor (ER)
antagonist, and mTOR inhibitor causes transcriptional reversal of
"stemness" signatures in 35 of 37 (95%) patients diagnosed with
therapy-resistant prostate cancer. CMAP-based design of
target-tailored individualized breast cancer therapies reveals drug
combinations causing transcriptional reversal of "stemness`
signatures in 91 of 107 (85%) of the early-stage breast cancer
patients with therapy-resistant disease phenotypes. A combination
of PI3K pathway inhibitor, ER antagonist, and HDAC inhibitor causes
transcriptional reversal of "stemness" pathways in 53 of 107
(49.5%) patients diagnosed with the early-stage therapy-resistant
breast cancer. Similarly, CMAP-based analysis of target-tailored
individualized therapies for lung cancer reveals drug combinations
causing transcriptional reversal of "stemness` signatures in 39 of
45 (87%) of the early-stage lung cancer patients with
therapy-resistant tumor phenotypes. Outlined in this work the
connectivity map-based approach to discovery of small molecule
drugs targeting clinical phenotype-associated gene expression
signatures may be useful for multiple therapeutic applications
beyond therapy-resistant human malignancies.
[0170] The analysis seems to indicate that several individual drugs
and/or their analogs which are already either FDA approved for
clinical use or in the late-stage clinical trials may have a
promising therapeutic potential against therapy-resistant
clinically lethal forms of human cancers. Therefore, the findings
may have a significant near-term impact on design and conduct of
clinical trials for evaluation of the efficacy of novel
personalized target-tailored combinations of cancer therapeutics
designed to target therapy-resistant phenotypes of human solid
tumors by applying the evidence-based rational selection principles
during the design stage of drug combinations. These findings will
likely have a near-term impact on protocols of design and execution
of the clinical trials for novel cancer therapeutics, including the
regulatory guidelines for patients' eligibility requirements at the
enrollment stage. It should allow the execution of such protocols
in most cost-efficient way and with the maximum potential benefits
for patients by facilitating the selection for a trial the
populations at the high-risk of failure of existing therapy.
Another conclusion from our analysis with major health care
management and regulatory implications is that a near-term progress
in practical implementation of the concept of personalized cancer
therapies would depend on physicians' ability to select, prescribe,
and exercise in a routine clinical setting an off-label use of the
FDA approved drugs. In this context the issues of timely delivery
to the practicing physicians of relevant scientific information and
the dynamic evolution of the supporting regulatory environment
adherent to the state of the art scientific evidence would be of
paramount importance.
[0171] The following examples are intended to further illustrate
certain embodiments of the invention and are not intended to limit
the scope of the invention.
Example 1
Preparation of Clinical Samples
[0172] Two clinical outcome sets comprising 21 (outcome set 1) and
79 (outcome set 2) samples were utilized for analysis of the
association of the therapy outcome with expression levels of the
BMI1 and Ezh2 genes and other clinico-pathological parameters.
Expression profiling data of primary tumor samples obtained from
1243 microarray analyses of eight independent therapy outcome
cohorts of cancer patients diagnosed with four types of human
cancer were analyzed in this study. Microarray analysis and
associated clinical information for clinical samples analyzed in
this work were previously published and are publicly available.
[0173] Prostate tumor tissues comprising clinical outcome data set
were obtained from 79 prostate cancer patients undergoing
therapeutic or diagnostic procedures performed as part of routine
clinical management at the Memorial Sloan-Kettering Cancer Center
(MSKCC). Clinical and pathological features of 79 prostate cancer
cases comprising validation outcome set are presented elsewhere.
Median follow-up after therapy in this cohort of patients was 70
months. Samples were snap-frozen in liquid nitrogen and stored at
-80.degree. C. Each sample was examined histologically using
H&E-stained cryostat sections. Care was taken to remove
nonneoplastic tissues from tumor samples. Cells of interest were
manually dissected from the frozen block, trimming away other
tissues. Overall, 146 human prostate tissue samples were analyzed
in this study, including forty-six samples in a tissue microarray
(TMA) format. TMA samples analyzed in this study were exempt
according to the NIH guidelines.
[0174] In addition, we carried out the analysis of gene expression
profiling data from 942 microarray experiments derived from five
different breast cancer therapy outcome data sets. Expression
profiling data for tumor samples obtained from 91 lung
adenocarcinoma patients, 169 breast cancer patients, and 133
ovarian cancer patients were analyzed in this study. The original
microarray analyses as well as associated clinical information for
these samples were reported elsewhere. Primary gene expression data
files of clinical samples as well as associated clinical
information can be found in corresponding papers. To date the
cancer therapy outcome database includes 3,176 therapy outcome
samples from patients diagnosed with thirteen distinct types of
cancers (Table 3): prostate cancer (220 patients); breast cancer
(1171 patients); lung adenocarcinoma (340 patients); ovarian cancer
(216 patients); gastric cancer (89 patients); bladder cancer (31
patients); follicular lymphoma (191 patients); diffuse large B-cell
lymphoma (DLBCL, 298 patients); mantle cell lymphoma (MCL, 92
patients); mesothelioma (17 patients); medulloblastoma (60
patients); glioma (50 patients); acute myeloid leukemia (AML, 401
patients).
Example 2
Cell Culture
[0175] Cell lines used in this study were previously described in
Glinsky et al., Cancer Lett., 201: 67-77 (2003). The LNCap- and
PC-3-derived cell lines were developed by consecutive serial
orthotopic implantation, either from metastases to the lymph node
(for the LN series), or reimplanted from the prostate (Pro series).
This procedure generated cell variants with differing
tumorigenicity, frequency and latency of regional lymph node
metastasis. Except where noted, cell lines were grown in RPMI1640
supplemented with 10% FBS and gentamycin (Gibco BRL) to 70-80%
confluence and subjected to serum starvation as described, or
maintained in fresh complete media, supplemented with 10% FBS.
Growth inhibitory experiments were carried out in the 96-well
format based on Hoechst staining for the estimate of live cell
counts using high-through put robotics of the Target and Drug
Discovery Facility (TDDF) of the Ordway Research Institute Cancer
Center. Chemicals, reagents, and drugs were purchased from Sigma,
except were indicated otherwise.
Example 3
Anoikis Assay
[0176] Cells were harvested by 5-min digestion with 0.25%
trypsin/0.02% EDTA (Irvine Scientific, Santa Ana, Calif., USA),
washed and resuspended in serum free medium. Cells at concentration
1.7.times.10.sup.5 cells/well in 1 ml of serum free medium were
plated in 24-well ultra low attachment polystyrene plates (Corning
Inc., Corning, N.Y., USA) and incubated at 37.degree. C. and 5%
CO.sub.2 overnight. Viability of cell cultures subjected to anoikis
assays were >95% in Trypan blue dye exclusion test.
Example 4
Apoptosis Assay
[0177] Apoptotic cells were identified and quantified using the
Annexin V-FITC kit (BD Biosciences Pharmingen) per manufacturer
instructions. The following controls were used to set up
compensation and quadrants: 1) Unstained cells; 2) Cells stained
with Annexin V-FITC (no PI); 3) Cells stained with PI (no Annexin
V-FITC). Each measurements were carried out in quadruplicate and
each experiments were repeated at least twice Annexin V-FITC
positive cells were scored as early apoptotic cells; both Annexin
V-FITC and PI positive cells were scored as late apoptotic cells;
unstained Annexin V-FITC and PI negative cells were scored as
viable or surviving cells. In selected experiments apoptotic cell
death was documented using the TUNEL assay.
Example 5
Flow Cytometry
[0178] Cells were washed in cold PBS phosphate-buffered saline and
stained according to manufacturer's instructions using the Annexin
V-FITC Apoptosis Detection Kit (BD Biosciences, San Jose, Calif.,
USA) or appropriate antibodies for cell surface markers. Flow
analysis was performed by a FACS Calibur instrument (BD
Biosciences, San Jose, Calif., USA). Cell Quest Software was used
for data acquisition and analysis. All measurements were performed
under the same instrument setting, analyzing 10.sup.3-10.sup.4
cells per sample.
Example 6
Tissue Processing for mRNA and RNA Isolation
[0179] Fresh frozen orthotopic and transgenic primary tumors,
metastases, and mouse prostates were examined by use of hematoxylin
and eosin stained frozen sections as described previously.
Orthotopic tumors of all sublines exhibited similar morphology
consisting of sheets of monotonous closely packed tumor cells with
little evidence of differentiation interrupted by only occasional
zones of largely stromal components, vascular lakes, or lymphocytic
infiltrates. Fragments of tumor judged free of these non-epithelial
clusters were used for mRNA preparation. Frozen tissue (1-3
mm.times.1-3 mm) was submerged in liquid nitrogen in a ceramic
mortar and ground to powder. The frozen tissue powder was dissolved
and immediately processed for mRNA isolation using a Fast Tract kit
for mRNA extraction (Invitrogen, Carlsbad, Calif., see above)
according to the manufacturers instructions.
[0180] RNA and mRNA extraction: For gene expression analysis, cells
were harvested in lysis buffer 2 hrs after the last media change at
70-80% confluence and total RNA or mRNA was extracted using the
RNeasy (Qiagen, Chatsworth, Calif.) or FastTract kits (Invitrogen,
Carlsbad, Calif.). Cell lines were not split more than 5 times
prior to RNA extraction, except where noted. Detailed protocols
were described elsewhere.
[0181] Affymetrix arrays: The protocol for mRNA quality control and
gene expression analysis was that recommended by Affymetrix. In
brief, approximately one microgram of mRNA was reverse transcribed
with an oligo(dT) primer that has a T7 RNA polymerase promoter at
the 5' end. Second strand synthesis was followed by cRNA production
incorporating a biotinylated base. Hybridization to Affymetrix
U95Av2 arrays representing 12,625 transcripts overnight for 16 h
was followed by washing and labeling using a fluorescently labeled
antibody. The arrays were read and data processed using Affymetrix
equipment and software as reported previously.
[0182] Data analysis: Detailed protocols for data analysis and
documentation of the sensitivity, reproducibility and other aspects
of the quantitative statistical microarray analysis using
Affymetrix technology have been reported. 40-50% of the surveyed
genes were called present by the Affymetrix Microarray Suite 5.0
software in these experiments. The concordance analysis of
differential gene expression across the data sets was performed
using Affymetrix MicroDB v. 3.0 and DMT v.3.0 software as described
earlier. The microarray data was processed using the Affymetrix
Microarray Suite v.5.0 software and performed statistical analysis
of expression data set using the Affymetrix MicroDB and Affymetrix
DMT software. The Pearson correlation coefficient for individual
test samples and appropriate reference standard was determined
using the Microsoft Excel and the GraphPad Prism version 4.00
software. The significance of the overlap between the lists of stem
cell-associated and prostate cancer-associated genes was calculated
by using the hypergeometric distribution test. The Multiple
Experiments Viewer (MEV) software version 3.0.3 of the Institute
for Genomic Research (TIGR) was used for clustering algorithm data
analysis and visualization.
[0183] Polycomb pathway "stemness" signatures: The initial analysis
was performed using two cancer therapy outcome data sets:
79-patients prostate cancer data set and 286-patients breast cancer
data set. For each parent signature (Table 4), the multivariate Cox
regression analysis was carried out. Consistent with the concept
that therapy resistant and therapy sensitive tumors develop within
distinct Polycomb-driven "stemness"/differentiation programs, all
signatures generate statistically significant models of cancer
therapy outcome were found. The number of predictors in each
signature, we removed from further analysis all probe sets with low
independent predictive values were removed from further analysis to
eliminate redundancy (typically, with the p>0.1 in multivariate
Cox regression analysis). These steps generate nine cancer therapy
outcome signatures listed in the Table 4 all of which provide
statistically significant therapy outcome models in multivariate
Cox regression analysis in multiple cancer therapy outcome data
sets. For each patient, the expression values of all genes
comprising a signature into a single numerical value were
calculated using either Pearson correlation coefficient approach or
weighted coefficient method as scribed previously. These numerical
values provide the cancer therapy outcome predictor (CTOP) scores
for each signature for every individual patient. The log 10
transformed fold change expression values or individual weighted
coefficients obtained from the multivariate Cox regression analysis
were used as multidimensional numerical vectors in Pearson and
weighted methods, respectively. The Kaplan-Meier survival analysis
was performed to assess the patients' stratification performance of
each signature. Patients were sorted in descending order based on
the numerical values of the CTOP scores and survival curves were
generated by designating the patients with top 50% scores and
bottom 50% scores into poor prognosis and good prognosis groups,
respectively. These analytical protocols were independently carried
out for 79-pateints prostate cancer data set and 286-patients
breast cancer data set. Gene expression signatures generated using
286-patients breast cancer data set were utilized in subsequent
analyses of four additional independent breast cancer data sets as
well as lung cancer and ovarian cancer data sets (Table 3).
Example 7
Random Co-Occurrence Test
[0184] 10,000 permutations test were performed to check how likely
small gene signatures derived from the large signature would
display high discrimination power to assess the significance at the
0.1% level as described earlier. It was found that 10,000
permutations generated 7 random 11-gene signatures performing at
sample classification level of the 11-gene MTTS/PNS signature.
Example 8
Weighted Survival Predictor Score Algorithm
[0185] The weighted survival score analysis was implemented to
reflect the incremental statistical power of the individual
covariates as predictors of therapy outcome based on a
multi-component prognostic model. The microarray-based or
Q-RT-PCR-derived gene expression values were normalized and
log-transformed on a base 10 scale. The log-transformed normalized
expression values for each data set were analyzed in a multivariate
Cox proportional hazards regression model, with overall survival or
event-free survival as the dependent variable. To calculate the
survival/prognosis predictor score for each patient, the
log-transformed normalized gene expression value measured for each
gene by a coefficient derived from the multivariate Cox
proportional hazard regression analysis was multiplied. Final
survival predictor score comprises a sum of scores for individual
genes and reflects the relative contribution of each of the eleven
genes in the multivariate analysis. The negative weighting values
indicate that higher expression correlates with longer survival and
favorable prognosis, whereas the positive score values indicate
that higher expression correlates with poor outcome and shorter
survival. Thus, the weighted survival predictor model is based on a
cumulative score of the weighted expression values of eleven genes.
For example, the following equation is describing the relapse-free
survival predictor score for prostate cancer patients (Table 4):
CTOP
score=(-0.403.times.Gbx2)+(1.2494.times.KI67)+(-0.3105.times.Cyclin
B1)+(-0.1226.times.BUB1)+(0.0077.times.HEC)+(0.0369.times.KIAA1063)+(-1.7-
493.times.HCFC1)+(-1.1853.times.RNF2)+(1.5242.times.ANK3)+(-0.5628.times.F-
GFR2)+(-0.4333.times.CES1).
Example 9
Immunofluorescence Microscopy
[0186] Cells fixed with 3.7% paraformaldehyde in phosphate-buffered
saline (PFA/PBS) for 15 minutes were permeabilized with 0.5%
Triton-X100 (Sigma, St. Louis, Mo., USA)/PBS for 5 min. After
washing in PBS, cells were incubated in PBS containing 100 mM
glycine for 10 min. Primary antibodies were diluted in 0.5%
BSA/0.05% gelatin cold water fish skin/PBS, and cells were
incubated in this buffer for 10 min before antibodies were applied
for 16 hrs at room temperature. After washing in PBS buffer, cells
were incubated with secondary antibodies at 1:500 dilution.
Coverslips were mounted in Prolong (Molecular Probes, Inc.). Images
were collected on an inverted microscope (OlympusIX70) equipped
with a DeltaVision imaging system using a .times.40 objective.
Images were processed by softWoRx v.2.5 software (Applied Precision
Inc., Issaquah, Wash.) and images were quantified with using ImageJ
1.29.times. software.
[0187] Quantitative immunofluorescence analysis of the PcG protein
expression was performed using human prostate cancer tissue
microarrays (TMAs) representing 46 prostate tissue samples
(thirty-nine cases of prostate cancer and seven cases of normal
prostate). Analysis was carried-out on the prostate cancer TMAs
from Chemicon (Temecula, Calif.; TMA # 3202-4; four cancer cases
and two cases of normal tissue; and TMA # 1202-4; twenty five cases
of cancer and five cases of normal tissue) and TMA of 10 cases of
prostate cancer from the SKCC tumor bank (San Diego, Calif.). TMAs
contain two 2.0 mm cores of each case and haematoxylin-and-eosin
(H&E) sections which were used for visual selection of the
pathological tissues, histological diagnosis, and grading by the
pathologists of TMA providers.
[0188] Four- or five-micrometer paraffin-embedded sections were
baked at 56.degree. C. for 1 hour, allowed to cool for about 5
minutes, dewaxed in xylene, and rehydrated in a series of graded
alcohols. Antigen retrieval was achieved by boiling slides in 10 mM
sodium citrate buffer, 0.05% Tween 20, pH 6.0 in a water bath for
30 minutes. The sections were washed with PBS, incubated in 100 mM
glycine/PBS for 10 minutes, blocked in 0.5% BSA/0.05% gelatine cold
water fish skin/PBS and incubated with primary antibody
overnight.
[0189] Primary antibodies were EZH2 rabbit polyclonal antibody
(1:50), BMI1 mouse monoclonal IgG1 antibody (1:50), ubiH2A mouse
IgM (1:100), 3metK27 rabbit polyclonal antibody (1:100) (Upstate,
Lake Placid, N.Y.). Suz12 rabbit (1:50), AMACR rabbit (1:50)
antibodies and Dicer mouse IgG1 (1:20) were purchased from Abcam
(Cambridge, Mass.). BMI1 rabbit (1:50) and TRAP100 (1:50) goat
antibodies were from Santa Cruz Biotechnology (Santa Cruz, Calif.).
Cyclin D1 rabbit polyclonal antibody (1:50) were from Biocare
Medical (Concord, Calif.). EZH2 mouse monoclonal antibodies were
kindly provided by Dr. A. P. Otte.
[0190] The primary antibodies were rinsed off with PBS and slides
were incubated with secondary antibodies at 1:300 dilutions for 1
hour at room temperature. Secondary antibodies (chicken antirabbit
Alexa 594, goat antimouse Alexa 488, goat antimouse IgG1 Alexa 350,
and donkey antigoat Alexa 488 conjugates) were from Molecular
Probes (Eugene, Oreg.). The slides were washed four times in PBS
for five minutes each wash, rinsed in distilled water and the
specimen were coverslipped with Prolong Gold Antifade Reagent
(Molecular Probes, Eugene, Oreg.) containing DAPI. For negative
controls, the primary antibodies were omitted. Three samples were
excluded from analysis because one of the following reasons: core
loss, unrepresentative sample, or sub-optimal DNA and antigen
preservation.
[0191] Images were collected on an inverted fluorescent microscope
(LEICA DMIRE 2 or Olympus IX70) using an .times.40 objective.
Images were processed by Leica FW4000 software and images were
quantified with using ImageJ 1.29.times. software
(http://rsb.info.nih.gov/ij). Expression values were measured in at
least 200 nuclei from two microscopic fields for each case.
[0192] The measurements were carried out in the nuclei of
individual cells defined by DAPI staining both in experimental and
clinical samples. For experimental samples, the comparison
thresholds for each marker combination were defined at the 90-95%
exclusion levels for dual positive cells in corresponding control
samples (parental low metastatic cells). For clinical samples, the
comparison thresholds for each marker combination were defined at
the 99% or greater exclusion levels for dual positive cells in
corresponding control samples (normal epithelial cells in TMA
experiments). All individual immunofluorescent assay experiments
(defined as the experiments in which the corresponding comparisons
were made) were carried out simultaneously using the same reagents
and included all experimental samples and controls utilized for a
quantitative analysis. Statistical significance of the measurements
was ascertained and consistency of the findings was confirmed in
multiple independent experiments, including several independent
sources of the prostate cancer TMA samples.
Example 10
Orthotopic Xenografts
[0193] Orthotopic xenografts of human prostate PC-3 cells and
prostate cancer metastasis precursor sublines used in this study
were developed by surgical orthotopic implantation as previously
described in Glinsky et al (2003), supra. Briefly, 2.times.10.sup.6
cultured PC-3 cells or sublines were injected subcutaneously into
male athymic mice, and allowed to develop into firm palpable and
visible tumors over the course of 2-4 weeks. Intact tissue was
harvested from a single subcutaneous tumor and surgically implanted
in the ventral lateral lobes of the prostate gland in a series of
ten athymic mice per cell line subtype as described in Glinsky et
al (2003), supra. During orthotopic cell inoculation experiments, a
single-cell suspension of 1.5.times.10.sup.6 cells was injected
into mouse prostate gland in a series of ten athymic mice per
therapy group.
Example 11
Fluorescence In Situ Hybridization (FISH)
[0194] PC3 human prostate adenocarcinoma cell line, derived subline
PC3-32 and diploid human fibroblast BJ1-hTERT cells were used for
the assessment of gene amplification status. The cyanine-3 or
cyanine-5 labeled BAC clone RP11-28C14 was used for the EZH2 locus
(7q35-q36), the BAC clone RP11-232K21 was used for the BMI1 locus
(10p11.23), the BAC clone RP11-440N18 was used for the Myc locus
(8q24.12-q24.13), the BAC clone RP-11-1112H21 was used for the LPL
locus (8p22). FISH analysis was done accordingly protocol as
described previously.
[0195] Methanol/glacial acetic acid cell fixation: Cell cultures
were synchronized with 4 ug/ml aphidicolin (Sigma Chemical Co.) for
17 hour at 37.degree. C. Synchronized cells were subjected to
hypotonic treatment in 0.56% KCl for 20 minute at 37.degree. C.,
followed by fixation in Carnoy's fixative (3:1 methanol:glacial
acetic acid). Cell suspension was dropped onto glass slides, air
dried. The slides are treated for 30 minutes with 0.005% pepsin in
0.01N HCl at room temperature and then are dehydrated through a
series washes in 70%, 85%, and 100% ethanol. Denaturation of DNA is
performed by plunging the slide in a coplin jar containing 70%
formamide/2.times.SSC (pH 7.0) for 30 min at 75.degree. C. The
slide immediately are plunged into ice-cold 2.times.SSC and then
dehydrated as earlier.
[0196] Fluorescence in situ hybridization (FISH): All BAC clones
were obtained from the Rosewell Park Cancer Institute (RPCI,
Buffalo, N.Y.). The BAC DNA was labeled with Cy3-dCTP or Cy5-dCTP
(Perkin Elmer Life Sciences, Inc.) using BioPrime DNA Labeling
System (Invitrogen). The resultant probes are purified with
QIAquick PCR Purification Kit (Qiagen). DNA recovery and the amount
of incorporated Cy3 or Cy5 are verified by Nanodrop
spectrophotometry.
[0197] Prior to hybridization the probe is precipitated with 20 ug
competitor human Cot-1 DNA (per 18.times.18 mm coverslip) and
washed in 70% ethanol. The dried pellet is thoroughly resuspended
in 10 ul hybridization buffer (2.times.SSC, 20% dextran sulfate, 1
mg/ml BSA; NEB Inc.). The denaturated probe solution is deposited
onto cells on slide. Hybridization was carried out overnight at
42.degree. C. in a dark humidified chamber. After three washes in
50% formamide/2.times.SSC (adjusted to pH 7.0) and three washes in
2.times.SSC at 42.degree. C., slides were counterstained and
mounted in Prolong Gold Antifade Reagent with
4',6-diamino-2-phenylindole (Invitrogen). Slides were examined
using a Leica DMIRE2 fluorescence microscope (Leica, Deerfield,
Ill.). Gene amplification status was determined by scoring 60-100
nuclei.
Example 12
siRNA Experiments
[0198] The target siRNA SMART pools and chemically modified
degradation-resistant variants of the siRNAs (stable siRNAs) for
BMI1, Ezh2, and control luciferase siRNAs were purchased from
Dharmacon Research, Inc. siRNAs were transfected into human
prostate carcinoma cells according to the manufacturer's protocols.
Cell cultures were continuously monitored for growth and viability
and assayed for mRNA expression levels of BMI1, Ezh2, and selected
set of genes using RT-PCR and Q-RT-PCR methods. Eight individual
siRNA sequences comprising the SMART pools (four sequences for each
gene, BMI1 and Ezh2) were tested and a single most effective siRNA
sequence was selected for synthesis in the chemically modified
stable siRNA form for each gene. The siRNA treatment protocol [two
consecutive treatments of cells in adherent cultures with 100 nM
(final concentration) of Dharmacon degradation-resistant siRNAs at
day 1 and 4 after plating], as designed, caused only moderate
reduction in the average BMI1 and Ezh2 protein expression levels
(20-50% maximal effect) and having no or only marginal effect on
cell proliferation in the adherent cultures (at most .about.25%
reduction in cell proliferation).
Example 13
Quantitative RT-PCR Analysis
[0199] The real time PCR methods measures the accumulation of PCR
products by a fluorescence detector system and allows for
quantification of the amount of amplified PCR products in the log
phase of the reaction. Total RNA was extracted using RNeasy
mini-kit (Qiagen, Valencia, Calif., USA) following the
manufacturer's instructions. A measure of 1 .mu.g (tumor samples),
or 2 .mu.g and 4 .mu.g (independent preparations of reference cDNA
and DNA samples from cell culture experiments) of total RNA was
used then as a template for cDNA synthesis with SuperScript II
(Invitrogen, Carlsbad, Calif., USA). cDNA synthesis step was
omitted in the DNA copy number analysis (32). Q-PCR primer
sequences were selected for each cDNA and DNA with the aid of
Primer Express.TM. software (Applied Biosystems, Foster City,
Calif., USA). PCR amplification was performed with the
gene-specific primers.
[0200] Q-PCR reactions and measurements were performed with the
SYBR-Green and ROX as a passive reference, using the ABI 7900 HT
Sequence Detection System (Applied Biosystems, Foster City, Calif.,
USA). Conditions for the PCR were as follows: one cycle of 10 min
at 95.degree. C.; 40 cycles of 0.20 min at 94.degree. C.; 0.20 min
at 60.degree. C. and 0.30 min at 72.degree. C. The results were
normalized to the relative amount of expression of an endogenous
control gene GAPDH.
[0201] Expression of messenger RNA (mRNA) and DNA copy number for
target genes and an endogenous control gene (GAPDH) were measured
by real-time PCR method on an ABI PRISM 7900 HT Sequence Detection
System (Applied Biosystems). For each gene at least two sets of
primers were tested and the set-up with highest amplification
efficiency was selected for the assay used in this study.
Specificity of the assay for mRNA measurements was confirmed by the
absence of the expected PCR products when genomic DNA was used as a
template. Glyceraldehyde-3-phosphate dehydrogenase (GAPDH:
5'-CCCTCAACGACCACTTTGTCA-3' [SEQ ID NO.:1] and
5'-TTCCTCTTGTGCTCTTGCTGG-3' [SEQ ID NO.:2]) was used as the
endogenous RNA and cDNA quantity normalization control. For
calibration and generation of standard curves, several reference
cDNAs were prepared: cDNA prepared from primary in vitro cultures
of normal human prostate epithelial cells, cDNA derived from the
PC-3M human prostate carcinoma cell line, and cDNA prepared from
normal human prostate. For DNA copy number analysis, human
placental DNA was used as a normalization control. Expression and
DNA copy number analysis of all genes was assessed at least in two
independent experiments using reference cDNAs to control for
variations among different Q-RT-PCR experiments. Prior to
statistical analysis, the normalized gene expression values were
log-transformed (on a base 10 scale) similarly to the
transformation of the array-based gene expression data.
Example 14
Survival Analysis
[0202] The Kaplan-Meier survival analysis was carried out using the
GraphPad Prism version 4.00 software (GraphPad Software, San Diego,
Calif.). The end point for survival analysis in prostate cancer was
the biochemical recurrence defined by the serum PSA increase after
therapy. Disease-free interval (DFI) was defined as the time period
between the date of radical prostatectomy (RP) and the date of PSA
relapse (recurrence group) or date of last follow-up
(non-recurrence group). Statistical significance of the difference
between the survival curves for different groups of patients was
assessed using Chi square and Log-rank tests. To evaluate the
incremental statistical power of the individual covariates as
predictors of therapy outcome and unfavorable prognosis, both
univariate and multivariate Cox proportional hazard survival
analyses were performed. Clinico-pathological covariates included
in this analysis were preoperative PSA, Gleason score, surgical
margins, extra-capsular invasion, seminal vesicle invasion, and
age.
Sequence CWU 1
1
2121DNAArtificial SequenceChemically Synthesized 1ccctcaacga
ccactttgtc a 21221DNAArtificial SequenceChemically Synthesized
2ttcctcttgt gctcttgctg g 21
* * * * *
References