From: Subject: Microarray analysis identifies a death-from-cancer signature predicting therapy failure in patients with multiple types of cancer -- Glinsky et al. 115 (6): 1503 -- Journal of Clinical Investigation Date: Fri, 17 Nov 2006 16:36:44 +0100 MIME-Version: 1.0 Content-Type: multipart/related; type="text/html"; boundary="----=_NextPart_000_00ED_01C70A66.912017D0" X-MimeOLE: Produced By Microsoft MimeOLE V6.00.2900.2962 This is a multi-part message in MIME format. ------=_NextPart_000_00ED_01C70A66.912017D0 Content-Type: text/html; charset="iso-8859-1" Content-Transfer-Encoding: quoted-printable Content-Location: http://www.jci.org/cgi/content/full/115/6/1503?ijkey=84848e15a8e8ea0a94d19cc31537b1c6e80f1ce1 Microarray analysis identifies a death-from-cancer = signature predicting therapy failure in patients with multiple types of = cancer -- Glinsky et al. 115 (6): 1503 -- Journal of Clinical = Investigation
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J. Clin. Invest. 115:1503-1521 (2005). = doi:10.1172/JCI23412.
Copyright =A92005 by the American Society = for=20 Clinical Investigation

Research=20 Articles

Microarray analysis identifies a death-from-cancer signature=20 predicting therapy failure in patients with multiple types of = cancer=20

Gennadi V. = Glinsky,=20 Olga Berezovska and Anna B. Glinskii =

Sidney Kimmel Cancer Center, San Diego, = California, USA.=20

Address = correspondence to:=20 Gennadi V. Glinsky, Sidney Kimmel Cancer Center, 10835 Altman Row, = San=20 Diego, California 92121, USA. Phone: (858) 450-5990; Fax: (858) = 623-2740;=20 E-mail: gglinsky{at}skcc.org .

Received for publication September 21, = 2004, and accepted in revised form March 1, 2005.


  =20 Abstract
Top
3D"Abstract
Introduction
Results
Discussion
Methods
References
 
A= ctivation=20 in transformed cells of normal stem cells=92 = self-renewal=20 pathways might contribute to the survival life cycle of = cancer=20 stem cells and promote tumor progression. The = BMI-1=20 oncogene=96driven gene expression pathway is essential = for the=20 self-renewal of hematopoietic and neural stem cells. We = applied=20 a mouse/human comparative translational genomics = approach to=20 identify an 11-gene signature that consistently = displays a stem=20 cell=96resembling expression profile in distant = metastatic=20 lesions as revealed by the analysis of metastases and = primary=20 tumors from a transgenic mouse model of prostate cancer = and=20 cancer patients. To further validate these results, we = examined=20 the prognostic power of the 11-gene signature in = several=20 independent therapy-outcome sets of clinical samples = obtained=20 from 1,153 cancer patients diagnosed with 11 different = types of=20 cancer, including 5 epithelial malignancies (prostate, = breast,=20 lung, ovarian, and bladder cancers) and 5 nonepithelial=20 malignancies (lymphoma, mesothelioma, medulloblastoma, = glioma,=20 and acute myeloid leukemia). Kaplan-Meier analysis = demonstrated=20 that a stem cell=96like expression profile of the = 11-gene=20 signature in primary tumors is a consistent powerful = predictor=20 of a short interval to disease recurrence, distant = metastasis,=20 and death after therapy in cancer patients diagnosed with = 11=20 distinct types of cancer. These data suggest the presence = of=20 a conserved BMI-1=96driven pathway, which is similarly = engaged=20 in both normal stem cells and a highly malignant subset of=20 human cancers diagnosed in a wide range of organs and = uniformly=20 exhibiting a marked propensity toward metastatic = dissemination=20 as well as a high probability of unfavorable therapy=20 outcome.

See=20 the related Commentary beginning on page 1463


  =20 Introduction
Top
Abstract
3D"Introduction
Results
Discussion
Methods
References
 
R= ecent=20 studies indicate that the Polycomb group (PcG) gene=20 Bmi-1 determines the proliferative potential of = normal=20 and leukemic stem cells (1)=20 and is required for the self-renewal of hematopoietic = and=20 neural stem cells (1-3).=20 Self-renewal ability is an essential defining property = of a=20 pluripotent stem cell=96like phenotype distinguishing = stem cells=20 from other cell types (4).=20 An emerging concept of "tumor stem cells" argues that = the=20 presence of a rare stem cell=96resembling population of = cancer=20 cells among the heterogeneous mix of cells constituting = a tumor=20 is essential for tumor progression and metastasis of = epithelial=20 malignancies (5-7).=20 The concept of tumor stem cells implies that common genetic=20 pathways might define critical stem cell=96like = functions=20 in both normal and neoplastic stem cells (1,=20 5).=20 Bmi-1 oncogene is expressed in all primary = myeloid=20 leukemia and leukemic cell lines analyzed so far (1,=20 8),=20 and overexpression of Bmi-1 causes neoplastic=20 transformation of lymphocytes (9,=20 10).=20 Recently, BMI-1 expression was reported in human = non-small-cell lung cancer (11)=20 and breast cancer cell lines (12);=20 this suggests an oncogenic role for BMI-1 = activation in=20 epithelial malignancies.

Expression profiling of prostate tumor samples using=20 oligonucleotide or cDNA microarray technology rapidly = emerged=20 as a powerful tool to reveal multiple gene expression=20 signatures associated with human prostate cancer (13-27),=20 including potential prostate cancer prognosis markers = (14,=20 15,=20 18,=20 24-26).=20 However, one of the major limitations of these studies = was that=20 the same clinical data set was used for both signature=20 discovery and validation. Furthermore, microarray = analysis=20 typically identifies vast data sets of candidate = markers, and=20 usually only a single hit or a few hits were validated = using=20 independent methods and independent clinical data sets; = this=20 diminished the potential advantage of the use of a = panel of=20 markers over a single marker in diagnostic and/or = prognostic=20 applications. Recently we attempted to address some of = these=20 limitations by using microarray-based gene expression = profiling=20 to identify molecular signatures that comprise small = clusters=20 of coregulated transcripts and distinguish subgroups of = prostate cancer patients with differing outcomes after = therapy=20 (15).=20 In these experiments for signature discovery and validation, = we=20 used 2 independent cohorts of prostate cancer patients (15).=20 The proposed prostate cancer prognosis predictor algorithm = uses=20 a panel of 3 molecular signatures and appears to = demonstrate=20 high discrimination accuracy between subgroups of patients = with=20 distinct clinical outcome after therapy, providing = additional=20 predictive value over conventional markers of outcome (15).=20

Most recently, the global gene expression profiling = approach=20 was successfully used to identify molecular signatures=20 associated with activation of oncogenic pathways (28),=20 targeted genetic manipulations (29),=20 and cellular responses to physiological stimuli (30)=20 and to build robust transcriptional identifiers that = reliably=20 recognize the engagement of corresponding pathways = within the=20 highly complex patterns of gene expression in experimental = and=20 clinical samples.

We hypothesized that molecular signatures associated with=20 activation of a normal stem cell=92s self-renewal = program in=20 metastatic cancer cells might be detectable by = searching for=20 genes exhibiting concordant patterns of regulation in=20 metastatic lesions and normal stem cells in=20 Bmi-1+/+ versus Bmi-1=96/=96 = genetic=20 backgrounds. Here we report that the expression of = Bmi-1=20 is elevated and a stem cell=96like = BMI-1=96associated=20 gene expression pathway is activated in metastatic = prostate=20 tumors. We provide evidence that the stem = cell=96resembling=20 expression profile of the 11-gene signature in primary = prostate=20 tumors predicts therapy failure in prostate cancer = patients. We=20 show that expression of the 11-gene signature is a = powerful=20 predictor of a short interval to distant metastasis and = poor=20 survival after therapy in breast and lung cancer = patients=20 diagnosed with an early-stage disease. Finally, we = extend our=20 therapy-outcome analysis to include 1,153 cancer = patients=20 diagnosed with 11 different cancer types and = demonstrate that=20 prognostic power of the 11-gene signature is = informative in all=20 11 different types of human cancer diagnosed in = multiple=20 organs.


  =20 Results
Top
Abstract
Introduction
3D"Results
Discussion
Methods
References
 
<= FONT=20 size=3D-1>BMI-1 oncogene expression is elevated in = prostate=20 cancer.
Recent experimental observations = documented an=20 increased Bmi-1 expression in human = non-small-cell lung=20 cancer (11),=20 human breast carcinomas (31),=20 and established breast cancer cell lines (12),=20 suggesting that an oncogenic role of Bmi-1 activation = may=20 be extended beyond leukemia and, perhaps, may affect=20 progression of the epithelial malignancies as well. = Microarray=20 gene expression analysis of established cancer cell = lines=20 representing multiple experimental models of human = prostate=20 cancer (16)=20 revealed that BMI-1 expression seems to be = consistently=20 elevated in human prostate cancer cell lines compared = with the=20 primary cultures of normal human prostate epithelial = cells=20 (Figure 1,=20 A and B). To validate the results of the microarray=20 experiments, we confirmed these observations using = quantitative=20 RT-PCR (Q-RT-PCR) analysis of BMI-1 mRNA = expression=20 (Table 1;=20 and see Supplemental Figure 1; supplemental material = available=20 online with this article; doi:10.1172/JCI23412DS1). = Thus,=20 results of expression profiling experiments appear to = support=20 the notion that transcriptional activation of the = BMI-1=20 gene is frequently associated with human prostate = cancer.=20



View = larger=20 version (56K):
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  = 		Figure 1=20
Microarray = (A=96D) and=20 RT-PCR (E) analyses reveal increased expression = of=20 BMI-1 mRNA in multiple human prostate cancer cell = lines=20 established from metastatic tumors (PC-3, LNCap, = DuCap, VCap,=20 etc.) compared with normal human prostate epithelial = cells=20 (NPEC) (A and E); in xenograft-derived = human=20 prostate cancer cell line variants (PC-3M, PC-3MLN4,=20 PC-3MPro4) compared with the plastic-maintained = parental cells=20 (PC-3) (B); in highly metastatic human prostate = carcinoma xenografts (PC-3MLN4) compared with the less = metastatic parental counterparts (PC-3) growing = orthotopically=20 in nude mice (C); in lymph node metastases of = human=20 prostate cancer growing in the prostate of nude mice = (MET)=20 (C); and in invasive primary prostate tumors = and=20 distant metastatic lesions in the TRAMP transgenic = mouse model=20 of prostate cancer (D). Prostate tissues from=20 age-matched wild-type C57BL/6 mice served as control = samples=20 in Figure 1D.=20 The numbers 4, 5, and 7 indicate the age of TRAMP mice = (in=20 months). Each sample represents a pool of tissues from = 3=965=20 mice. P values were obtained using a 2-tailed t test. = LN3,=20 LNCapLN3; LN4, PC-3MLN4; PRO4, PC-3MPRO4; PRO5, = LNCapPRO5; SV,=20 seminal vesicles; X, human xenograft tumors in nude = mice. =

 = ;

View this=20 table:
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  = 		Table 1=20
Q-RT-PCR analysis of BMI-1 = mRNA=20 expression in human prostate carcinoma cell lines =

 = ;
Interestingly,=20 microarray analysis shows markedly higher BMI-1=20 expression levels in lymph node metastases and highly=20 metastatic orthotopic xenografts of human prostate = carcinoma in=20 nude mice compared with the less metastatic = counterparts=20 (Figure 1C),=20 implying that BMI-1 activation might be associated with=20 aggressive malignant behavior of prostate carcinoma = cells. To=20 test this hypothesis, we carried out expression = profiling=20 analysis of approximately 12,000 transcripts in a = transgenic=20 mouse model of metastatic prostate cancer (32).=20 Microarray experiments detected increased levels of=20 Bmi-1 mRNA expression in late-stage invasive = primary=20 tumors and multiple distant metastatic lesions in the = TRAMP=20 transgenic mouse model of prostate cancer (Figure 1D),=20 thus lending more credence to the idea that activation of = a=20 BMI-1=96associated pathway is linked with prostate = cancer=20 metastasis. It should be pointed out that despite the=20 apparently consistent pattern of increased Bmi-1 = expression in prostate cancer, there is considerable=20 variability in the degree of elevation of Bmi-1=20 expression at the distinct sites of malignant growth in = vivo=20 (Figure 1D).=20 We carried out the Q-RT-PCR analysis of Bmi-1 = mRNA=20 expression in 4 additional late-stage invasive primary = prostate=20 tumors of the 6- to 7-month old TRAMP mice and confirmed = the 2-=20 to 8-fold increase in Bmi-1 expression in all 4 tumors = (data not shown).

Identification of a BMI-1=96pathway = signature with=20 concordant expression profiles in normal stem cells and distant = metastatic=20 lesions in a transgenic mouse model of prostate=20 cancer.
Recent experiments established that the = Bmi-1 gene is required for self-renewal of = hematopoietic=20 and neural stem cells (1-3)=20 and identified BMI-1=96regulated genes in neural stem = cells=20 that are presumably engaged in an execution of = self-renewal=20 programs in a state of both central nervous system (CNS) = and=20 peripheral nervous system (PNS) neurospheres (3).=20 We hypothesized that molecular signatures associated = with=20 activation of a normal-stem cell=92s self-renewal = program in=20 metastatic cancer cells might be detectable by looking = for=20 genes manifesting concordant patterns of regulation in=20 metastasis and normal stem cells in = Bmi-1+/+=20 versus Bmi-1=96/=96 genetic backgrounds. = Therefore,=20 we set out to determine whether expression profiles of=20 transcripts activated and suppressed in prostate cancer = metastases would recapitulate the expression profile of = the=20 BMI-1=96regulated genes in normal stem cells, by = comparing the=20 sets of differentially regulated genes in search of=20 intersection of lists for both up- and downregulated=20 transcripts (see Figures 2=20 and 3,=20 Methods, and supplemental material for description of a = signature discovery protocol). This analysis identified = genes=20 exhibiting highly concordant profiles of = transcript-abundance=20 behavior in prostate cancer metastases and=20 Bmi-1+/+ versus Bmi-1=96/=96 = PNS=20 neurospheres, suggesting the presence of a conserved=20 BMI-1=96regulated pathway(s) similarly engaged = in both=20 normal stem cells and distant metastatic lesions of = prostate=20 carcinoma (Figures 2=20 and 3).=20



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  = 		Figure 2=20
Distant metastatic lesions in = the=20 TRAMP transgenic mouse model of prostate cancer = exhibit stem=20 cell=96like expression signatures of the BMI-1 = pathway.=20 Transcripts differentially regulated in distant = metastatic=20 lesions of 6-month-old TRAMP mice (MTTS signature) = were=20 compared with the BMI-1=96regulated genes in neural = stem cells=20 (3)=20 in search of intersection of lists. (A) = Expression=20 profiles and the corresponding Pearson correlation = coefficient=20 for 199 genes (141 upregulated and 58 downregulated)=20 comprising concordant differentially regulated sets of = transcripts in metastatic TRAMP samples and PNS = neurospheres=20 are shown. Small gene expression signatures comprising = transcripts with a high level of expression = correlation in=20 metastatic cancer cells and stem cells (the selection=20 threshold for small signatures was arbitrarily set at = Pearson=20 correlation coefficients greater than 0.95) were = selected from=20 large concordant sets. The reduction in the signature=20 transcript number was terminated when further = elimination of a=20 transcript did not increase the value of the Pearson=20 correlation coefficient. Using this approach, a single = candidate prognostic gene expression signature was = selected=20 for each binary intersection of the MTTS signature and = parent=20 stem cell signatures (Figure 3).=20 Consecutive steps of selection from the 199-gene = concordant=20 set of a subset of 20 genes (A and B) = and a=20 small MTTS/PNS 11-gene signature (C and = D) are=20 shown. In D, r =3D 0.9897, P < 0.0001 = between gene=20 groups, and n =3D 11 per group. Complete lists of = genes and=20 corresponding concordant subsets are shown in = Supplemental=20 Table 2. See text, Figure 3,=20 and Table 3=20 for details. =

 = ;


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  = 		Figure 3=20
Sequential analytical steps = used for=20 identification, selection, and validation of the = 11-gene=20 death-from-cancer signature. The figure shows an = overview of=20 the approach used for the development and validation = of a=20 cancer survival predictor based on gene expression = monitoring. =

 = ;
The=20 metastatic TRAMP tumor sample (MTTS) signature is likely = to be=20 enriched for genes discriminative for the metastatic = phenotype.=20 It is reasonable to assume that many gene expression = patterns=20 wired into the MTTS signature would manifest the power to=20 discriminate the metastatic phenotype and would have no = relation to the transcriptional program of normal stem = cells.=20 We sought to use these features of the MTTS signature = for=20 identification of the gene expression components of a = stem cell=20 transcriptome that are coordinately expressed in = metastatic=20 cancer cells and might manifest discriminative = diagnostic power=20 for the malignant phenotype. Sets of differentially = regulated=20 transcripts were independently identified for distant=20 metastatic lesions and primary prostate tumors versus=20 age-matched control samples in a transgenic TRAMP mouse = model=20 of metastatic prostate cancer (MTTS signature) as well = as PNS=20 (PNS signature) and CNS (CNS signature) neurospheres in = Bmi-1+/+ versus = Bmi-1=96/=96=20 backgrounds. This analytical step defined 3 large parent=20 signatures (Figure 3):=20 MTTS signature comprising 868 upregulated and 477 = downregulated=20 transcripts; PNS signature comprising 885 upregulated = and 1,088=20 downregulated transcripts; and CNS signature comprising = 769=20 upregulated and 778 downregulated transcripts.

Next we intersected the MTSS signature with the stem cell=20 signatures in the state of PNS and CNS neurospheres to = identify=20 concordant sets of genes and define the stem cell = signatures=20 embedded into MTSS signature (Figures 2=20 and 3).=20 Subsets of transcripts exhibiting concordant expression = changes=20 in MTTS (MTTS signature) as well as PNS (PNS signature) = and CNS=20 (CNS signature) neurospheres in = Bmi-1+/+=20 versus Bmi-1=96/=96 backgrounds were = identified.=20 Thus, 2 concordant subsets of transcripts were identified=20 corresponding to each binary comparison of metastatic = TRAMP=20 tumors and neural stem cell samples in a state of PNS = and CNS=20 neurospheres (141 upregulated and 58 downregulated = transcripts=20 for PNS neurospheres [r =3D 0.7593; P = < 0.0001;=20 Figure 2A]=20 and 40 upregulated and 24 downregulated transcripts for = CNS=20 neurospheres [r =3D 0.7679; P < = 0.0001]). A=20 third concordant subset of 27 genes comprising 15 = upregulated=20 and 12 downregulated transcripts was selected for = intersection=20 common to all 3 signatures (r =3D 0.8002; P = <=20 0.0001).

This analysis also identified a stem cell=96like = expression=20 profile for transcripts coordinately expressed in = metastatic=20 cancer cells and normal stem cells, which can be used as a=20 consistent reference standard to interrogate = independent data=20 sets for possible presence of a stem cell=96like = expression=20 signature (Figure 2).=20 Practical considerations essential for future development = of=20 genetic diagnostic tests using an analytical platform most=20 compatible with the state-of-the-art clinical laboratory=20 practice prompted us to select from concordant gene = sets small=20 gene expression signatures comprising transcripts with = a high=20 level of expression correlation in metastatic cancer = cells and=20 stem cells (the selection threshold for small = signatures was=20 arbitrarily set at Pearson correlation coefficients = greater=20 than 0.95). The reduction in the signature transcript = number=20 was terminated when further elimination of a transcript = did not=20 increase the value of the Pearson correlation = coefficient.=20 Using this approach, a single candidate prognostic gene = expression signature was selected for each binary = intersection=20 of the MTTS signature and parent stem cell signatures = (Figure=20 3).=20 Then small signatures (1 11-gene signature for the PNS = set, 1=20 11-gene signature for the CNS set, and one 14-gene = signature=20 for the common PNS/CNS set) were tested for the power = to=20 discriminate the metastatic phenotype (using 1 mouse = prostate=20 cancer data set and 1 human prostate cancer data set = comprising=20 primary and metastatic tumors) and therapy-outcome=20 classification performance (using human prostate cancer = therapy=20 outcome set 1). Based on diagnostic and prognostic=20 classification performance, a single best-performing = 11-gene=20 MTTS/PNS signature was selected for further validation = analysis=20 (Figures 3=20 and 4).=20



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  = 		Figure 4=20
Selection of the = best-performing small=20 signature based on evaluation of the=20 metastatic-phenotype-discrimination performance and=20 therapy-outcome prediction power of candidate = prognostic=20 signatures. Expression profiles of the 3 small = signatures=20 (11-gene MTTS/PNS signature, A=96C; = 11-gene=20 MTTS/CNS signature, D=96F; and 14-gene=20 MTTS/PNS/CNS signature, G=96I) were = evaluated in=20 metastatic lesions at multiple distant target organs = and=20 primary prostate carcinomas in the TRAMP transgenic = mouse=20 model of prostate cancer (A, D, and = G)=20 and prostate cancer patients (B, E, and=20 H) for presence of a stem cell=96like = expression profile.=20 (B, E, and H) Data from the = analysis of 9=20 distant metastatic lesions and 23 primary human = prostate=20 carcinoma samples. (C, F, and I)=20 Kaplan-Meier analysis of the probability that patients = would=20 remain disease-free among 21 prostate cancer patients=20 constituting clinical outcome set 1, according to = whether they=20 had a good-prognosis or a poor-prognosis signature as = defined=20 by the expression profiles of the small prognostic = signatures.=20 The y axes in A, B, D, E,=20 G, and H show the SPAI values in = corresponding=20 metastatic and primary tumor samples (see Methods for = a=20 description of SPAI definition and calculation). CI,=20 confidence interval. =

 = ;
During=20 the malignant-phenotype classification performance tests=20 (Figure 4),=20 we asked whether individual metastatic lesions and = primary=20 prostate tumors would exhibit a stem cell=96like = expression=20 profile of the candidate prognostic signatures. We = selected for=20 this analysis 3 small signatures demonstrating the most = significant correlation (Figures 2=20 and 3)=20 of expression profiles in stem cells and prostate = cancer=20 metastasis. To assess a degree of similarity of the = signature=20 expression profiles in individual tumor samples and = normal stem=20 cells, we calculated a Pearson correlation coefficient = for each=20 sample by comparing signature expression profile in an=20 individual sample to the stem cell=96associated = expression=20 profile of the corresponding small signatures. Based on = expected similarity of the prognostic signatures in = stem cells=20 and prostate cancer metastasis, we named the = corresponding=20 Pearson correlation coefficients measured for = individual=20 samples the stem cell=96like phenotype association = indices=20 (SPAIs; see Methods for a description of definition and = measurement of SPAIs). As shown in Figure 4,=20 A, D, and G, 2 of 3 late-stage invasive primary tumors = and all=20 distant metastatic lesions in the TRAMP transgenic = mouse model=20 of prostate cancer have positive SPAIs, thus = manifesting a stem=20 cell=96like expression profile of the small = signatures.=20

Distant metastatic lesions and primary = prostate=20 tumors from cancer patients with differing therapy outcome display = distinct expression profiles of the 11-gene MTTS/PNS=20 signature.
To perform similar analysis for = human=20 tumors, we translated the murine small signatures into = a list=20 of human homologs using the NCBI UniGene database=20 (http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=3Dunigene) = and=20 retrieved the expression data for corresponding Affymetrix=20 probe sets (Tables 2=20 and 3=20 and Supplemental Table 2). We calculated the SPAIs for = each of=20 the 9 metastatic tumors and 23 primary prostate = carcinomas and=20 determined that 7 of 9 samples of distant metastatic = lesions=20 from prostate cancer patients exhibited a stem = cell=96like=20 expression profile of the 11-gene MTTS/PNS signature = (Figure 4B).=20 In contrast, a majority of primary prostate tumors = seemed to=20 display a distinct expression profile of the 11-gene = MTTS/PNS=20 signature as manifested in negative SPAI values (Figure = 4B).=20 Interestingly, a subset of samples of primary prostate=20 carcinomas manifested expression profiles of the 11-gene=20 MTTS/PNS signature similar to those of the metastatic = tumors,=20 as reflected in positive correlation coefficients = (positive=20 SPAI values in Figure 4B),=20 suggesting that primary prostate tumors with distinct=20 expression profiles of the PNS neurosphere=96derived = 11-gene=20 signature (e.g., positive and negative SPAI values) may = have=20 different biological features and distinct clinical = courses=20 of disease progression. Validation analysis using the CNS=20 neurosphere=96derived MTTS/CNS 11-gene signature and = MTTS/PNS/CNS=20 14-gene signature indicates that application of these=20 signatures is less informative in distinguishing = metastatic and=20 primary human prostate tumors (Figure 4,=20 E and H).


View this=20 table:
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  = 		Table 2=20
Cancer types and number of = cancer=20 patients in the therapy-outcome sets analyzed in this = study =

 = ;

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  = 		Table 3=20
The 11-gene signature = associated with=20 poor prognosis of cancer patients diagnosed with = multiple=20 types of cancer =

 = ;
To=20 evaluate the potential biological significance and clinical=20 utility of the 11-gene MTTS/PNS signature expression in = human=20 prostate cancer, we set out to examine whether the = detection=20 of a stem cell=96like expression profile in primary = prostate=20 tumors of individual cancer patients would help in = patients=92=20 stratification at the time of diagnosis into subgroups = with=20 distinct courses of disease progression based on differing=20 therapy outcome after radical prostatectomy (RP). We = assessed=20 the prognostic power of the 11-gene signature based on = ability=20 to segregate the patients with recurrent and = nonrecurrent=20 course of disease progression after RP into distinct = subgroups.=20 We calculated a Pearson correlation coefficient for = each of 21=20 tumor samples of outcome set 1 by comparing the 11-gene = signature expression profiles of individual samples = with the=20 stem cell=96like expression profile of the 11-gene = BMI-1=96pathway=20 signature in PNS neurospheres (Figure 2).=20 To determine the prognostic power of the 11-gene = signature, we=20 performed the Kaplan-Meier survival analysis using, as = a=20 clinical endpoint, the disease-free interval after = therapy in=20 prostate cancer patients with positive and negative = SPAIs.=20

The Kaplan-Meier survival curves showed a highly = significant=20 difference in the probability that prostate cancer = patients=20 would remain disease-free after therapy between the groups = with=20 positive and negative SPAIs defined by the 11-gene = MTTS/PNS=20 signature (Figure 4C),=20 suggesting that patients with positive SPAIs exhibit a = poor=20 outcome signature whereas patients with negative SPAIs = manifest=20 a good outcome signature. The estimated hazard ratio = for=20 disease recurrence after therapy in the group of = patients with=20 positive SPAIs as compared with the group of patients = with=20 negative SPAIs defined by the 11-gene MTTS/PNS = signature=20 (Figure 4C)=20 was 9.259 (95% confidence interval of ratio, = 1.545=9626.07;=20 P =3D 0.0104). Fifty-eight percent of patients = with=20 positive SPAIs had a disease recurrence within 3 years = after=20 therapy, whereas 90% of patients with negative SPAIs = remained=20 relapse-free (Figure 4C).=20 Five years after therapy, 69% of patients with positive = SPAIs=20 had a disease recurrence, whereas 90% of patients with = negative=20 SPAIs remained relapse-free (Figure 4C).=20 In contrast to the PNS neurosphere=96derived signature, = the CNS=20 neurosphere=96derived signature failed to stratify the = prostate=20 cancer patients into prognostic subgroups with distinct = probability of disease relapse after therapy (P = =3D=20 0.6501; Figure 4F).=20 Similarly, the 14-gene MTTS/PNS/CNS signature failed in = both=20 classification-performance tests using human cancer = specimens=20 (P =3D 0.4916; Figure 4,=20 H and I). Based on this analysis, we identified the = 11-gene=20 MTTS/PNS signature as a best-performing = malignant-phenotype=20 classifier and proposed to identify the group of = prostate=20 cancer patients with positive values of the PNS=20 neurosphere=96derived 11-gene signature as a = poor-prognosis group=20 and the group of prostate cancer patients with negative = values=20 of the 11-gene signature as a good-prognosis = group.

The identified signature genes were defined based on a = strong=20 correlative behavior in multiple independent sets of=20 experimental and clinical samples obtained from 2 = species (mice=20 and humans). To test by independent methods the = suspected=20 association of the expression of BMI-1=96pathway target = genes=20 with the expression of the BMI-1 gene product in the = context of=20 human cancer cells, we subjected human prostate = carcinoma cells=20 to small interfering RNA=96mediated (siRNA-mediated)=20 silencing of expression of the endogenous BMI-1 = gene.=20 The PC-3-32 human prostate carcinoma cells were = transfected=20 with BMI-1 or control siRNAs and continuously = monitored=20 for mRNA expression levels of BMI-1 and a = selected set=20 of genes using RT-PCR and Q-RT-PCR methods (data not = shown).=20 Q-RT-PCR and RT-PCR analyses showed that the = siRNA-mediated=20 BMI-1=96silencing protocol allowed for = approximately 90%=20 inhibition of the endogenous BMI-1 mRNA = expression. We=20 validated the effect of siRNA-mediated BMI-1 = silencing=20 at the BMI-1 protein expression level using immunofluorescent = analysis. The BMI-1 silencing was specific, since the = expression levels of 9 unrelated transcripts (such as=20 GAPDH, EZH2, and several other genes) = were not=20 altered (data not shown). Consistent with the = hypothesis that=20 expression of genes comprising the BMI-1=96pathway = signature is=20 associated with the expression of the BMI-1 gene = product, mRNA=20 abundance levels of 8 of 11 interrogated = BMI-1=96pathway target=20 genes were altered in the human prostate carcinoma = cells with=20 approximately 90% silenced BMI-1 gene. =

Reduction of the BMI-1 mRNA and protein expression in = human=20 prostate carcinoma metastasis precursor cells did not = alter=20 significantly the viability of adherent cultures grown at = the=20 optimal growth condition and in serum-starvation = experiments=20 (data not shown) and had only modest inhibitory effect on=20 proliferation (an approximately 25=9630% reduction in = the number=20 of cells during the 3-day silencing protocol). However, = the=20 ability of human prostate carcinoma cells to survive in = a=20 nonadherent state was severely affected after = siRNA-mediated=20 reduction of the BMI-1 expression.=20 Fluorescence-activated cell sorting (FACS) analysis = revealed an=20 approximately 3-fold increase of apoptosis in the = BMI-1=20 siRNA=96treated human prostate carcinoma cells cultured = in=20 nonadherent conditions. These data suggest that human = prostate=20 carcinoma cells expressing a high level of the BMI-1 = protein=20 are more resistant to apoptosis induced in cells of = epithelial=20 origin in response to attachment deprivation (anoikis) = and,=20 perhaps, would survive better in blood during metastatic=20 dissemination, thus forming a pool of metastasis precursor=20 cells that can survive circulatory stress. Thorough = follow-up=20 experiments would be required to establish to a full = extent the=20 biological and functional role of BMI-1 overexpression = and=20 BMI-1=96pathway activation in the various epithelial=20 cancers.

Expression of the 11-gene MTTS/PNS = signature in=20 primary prostate tumors is a predictor of a therapy failure in = prostate=20 cancer patients.
To validate a survival = prediction=20 model based on the 11-gene MTTS/PNS signature, we = tested the=20 prognostic performance of the model in multiple = independent=20 therapy-outcome data sets representing 5 epithelial and = 5=20 nonepithelial cancers (Table 3).=20 We divided patients within individual cohorts into a training = set, which was used to select the cutoff threshold and to = test=20 the model, and a test set, which was used to evaluate the=20 reproducibility of the classification performance. = Using the=20 training set of samples, we selected the=20 prognosis-discrimination cutoff value for a signature = based on=20 the highest level of statistical significance in = patients=92=20 stratification into poor- and good-prognosis groups as=20 determined by the log-rank test (lowest P value and=20 highest hazard ratio in the training set). Clinical = samples=20 having the Pearson correlation coefficient at or higher = than=20 the cutoff value were identified as having the = poor-prognosis=20 signature. Clinical samples with the Pearson correlation=20 coefficient lower than the cutoff value were identified = as=20 having the good-prognosis signature. The same = discrimination=20 cutoff value was then applied to evaluate the = reproducibility=20 of the prognostic performance in the test set of = patients.=20 Lastly, we applied the model to the entire outcome set = using=20 the same cutoff threshold to confirm the classification = performance. The training and test sets were balanced = with=20 respect to the total number of patients, negative and = positive=20 therapy outcomes, and the length of survival. At this = stage of=20 the analysis, we did not carry out additional model = training,=20 development, or optimization steps, except for = selection of a=20 prognostic cutoff threshold in the training set. = Throughout the=20 study, we consistently used the same MTTS/PNS = expression=20 profile as a reference standard to quantify the Pearson=20 correlation coefficients of the individual samples. =

In addition to this analysis, we confirmed the model = performance=20 using various sample-stratification approaches, such as = terrain=20 (TRN) clustering (Figure 5),=20 support vector machine (SVM) classification = (Supplemental Table=20 4), and weighted survival score algorithm (Figure 6E=20 and Figure 7D).=20 Finally, we evaluated the therapy outcome=96predictive = power of=20 the 11-gene model in a prostate cancer setting using a=20 prognostic test based on an independent method of gene=20 expression analysis, namely the Q-RT-PCR method (Figure = 6F).=20



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  = 		Figure 5=20
TRN analysis within the mRNA = abundance=20 space of genes constituting the 11-gene MTTS/PNS = signature=20 reveals clustering patterns, among prostate cancer = (A=20 and B) and breast cancer (C and = D)=20 patients, that are associated with distinct = frequencies of=20 therapy failure (A and B) and differing=20 probability of disease-free survival after therapy = (C=20 and D). A TRN clustering algorithm was applied = to the=20 79 samples (A) constituting prostate cancer = therapy=20 outcome set 2 and the 97 samples (C) = constituting the=20 breast cancer therapy outcome set. Kaplan-Meier = analysis=20 (B and D) was applied to subgroups of = patients=20 defined by the TRN clustering algorithm as shown in = A=20 and C. =

 = ;


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  = 		Figure 6=20
Classification of prostate = cancer=20 patients into subgroups with distinct therapy outcome = based on=20 expression profile of the 11-gene MTTS/PNS signature.=20 (A=96C) Kaplan-Meier analysis of the = probability=20 that patients would remain disease-free among 79 = prostate=20 cancer patients constituting clinical outcome set 2, = according=20 to whether they had a good-prognosis or a = poor-prognosis=20 signature as defined by the expression profiles of the = 11-gene=20 MTTS/PNS signature. The patients=92 stratification = cutoff value=20 of 0.4 was defined in the training set of 40 patients = (19 poor=20 prognosis and 21 good prognosis; A), validated = in a=20 test set of 39 patients (18 poor prognosis and 21 good = prognosis; B) and confirmed in an entire cohort = of 79=20 patients (C). (D) Kaplan-Meier survival = curves=20 for distinct subgroups of prostate cancer patients = diagnosed=20 with early-stage disease (stages 1C and 2A). = (E)=20 Kaplan-Meier survival curves for 79 prostate cancer = patients=20 stratified into distinct subgroups using a weighted = survival=20 predictor score algorithm. (F) Kaplan-Meier = survival=20 curves for 20 prostate cancer patients stratified into = distinct subgroups using Q-RT-PCR assay of the 11-gene = signature. =

 = ;


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  = 		Figure 7=20
Classification of patients = diagnosed=20 with 4 different types of epithelial cancer into = subgroups=20 with distinct therapy outcome based on expression = profile of=20 the 11-gene MTTS/PNS signa-ture. Kaplan-Meier analysis = of the=20 probability that patients would remain metastasis-free = (for=20 the breast cancer group) or survive after therapy (for = the=20 other groups) among 97 early-stage breast cancer = patients=20 (A=96D), 125 lung adenocarcinoma = patients of all=20 stages (E=96G), 35 lung adenocarcinoma = patients=20 diagnosed with stage 1A disease (H), 37 ovarian = cancer=20 patients of all stages (I=96K), and 31 = bladder=20 cancer patients (L=96N), according to = whe-ther=20 they had a good-prognosis or a poor-prognosis = signature as=20 defined by the expression profiles of the 11-gene = MTTS/PNS=20 signature. For each type of cancer, the patient=92s=20 stratification cutoff value was defined in the = training set,=20 validated in a test set, and confirmed in an entire = cohort.=20 D and I=96K show the Kaplan-Meier = survival=20 curves for 97 breast cancer patients and 37 ovarian = cancer=20 patients, respectively, stratified into distinct = subgroups=20 using a weighted survival predictor score algorithm. =

 = ;
To=20 further validate the potential clinical utility of the = 11-gene=20 MTTS/PNS signature, we evaluated the prognostic power of = the=20 11-gene signature applied to an independent set of 79 = clinical=20 samples (prostate cancer outcome set 2) obtained from 37=20 prostate cancer patients who developed recurrence after = the=20 therapy and 42 patients who remained disease-free. In = this=20 cohort of patients, the Kaplan-Meier survival analysis=20 demonstrated a highly significant difference in the = probability=20 that prostate cancer patients would remain disease-free = after=20 therapy between the groups with positive and negative = SPAIs=20 defined by the 11-gene BMI-1=96pathway signature = (Figure 6,=20 A=96C). The estimated hazard ratio for disease = recurrence after=20 therapy in the group of patients with positive SPAIs as = compared with the group of patients with negative SPAIs = defined=20 by the 11-gene MTTS/PNS signature (Figure 6C)=20 was 3.74 (95% confidence interval of ratio, 3.010=9625.83;=20 P < 0.0001). Sixty-seven percent of patients with=20 positive SPAIs had a disease recurrence within 3 years = after=20 therapy, whereas 70% of patients with negative SPAIs = remained=20 relapse-free (Figure 6C).=20 Five years after therapy, 83% of patients with positive = SPAIs=20 had a disease recurrence, whereas 64% of patients with = negative=20 SPAIs remained relapse-free (Figure 6C).=20

The standard Kaplan-Meier log-rank statistic assesses the=20 difference in the survival curves. However, it does not = test=20 for multiple hypotheses or account for random = co-occurrence;=20 this represents an inherent problem of the gene = expression=20 profiling experiments. We attempted to partly mitigate = this=20 problem by using an alternative biological endpoint to = the=20 patients=92 survival during the signature selection = process and=20 by applying the survival analysis to a single = signature, thus=20 eliminating the multiple comparisons from the survival = model=20 building protocol. The MTTS signature is likely to = carry many=20 gene expression patterns displaying the power to = discriminate=20 the metastatic phenotype that have no relation to the=20 transcriptional program of normal stem cells. One of = our main=20 goals was to identify the stem cell signature that is=20 associated with the pluripotency self-renewal phenotype = and is=20 embedded into MTTS signature. This approach implies that = a=20 candidate marker signature would have a defined stem = cell=96like=20 expression profile that can be used in the subsequent = follow-up=20 validation analyses as a reference standard to look for=20 expression of a stem cell=96like signature in clinical = samples.=20 To further assess the statistical validity of the = 11-gene=20 stem cell=96like profile, we performed 1,000 random=20 permutations of the 11-gene stem cell profiles randomly = selected from the 1,973-gene PNS signature. For each = random=20 11-gene stem cell profile, we assessed its metastatic=20 phenotype=96discriminative performance in the TRAMP = transgenic=20 mouse model at the discriminative confidence levels of = the=20 11-gene BMI-1-pathway MTTS/PNS signature. Only 1 random = 11-gene=20 stem cell profile of the 1,000 permutations = demonstrated=20 classification power matching the metastatic = phenotype=96discriminative=20 performance of the 11-gene MTTS/PNS signature. We = performed=20 10,000 permutations to test the likelihood that small = 11-gene=20 signatures derived from the large MTTS signature would = display=20 high discrimination power to assess the significance at = the=20 0.1% level. We carried out 10,000 permutations of small = 11-gene=20 signatures derived from the large 1,345-gene MTTS = signature=20 and compared their sample-stratification power with that = of=20 the 11-gene MTTS/PNS signature. The = classification-performance=20 cutoff P values were established by application of a=20 2-tailed Student=92s t test to the 11-gene = MTTS/PNS=20 signature (P =3D 0.0005 for metastasis versus = primary=20 prostate cancer data set and P =3D 0.026 for = recurrent=20 versus nonrecurrent prostate cancer data set). We found = that=20 10,000 permutations generated 7 random 11-gene = signatures=20 performing at the sample-classification level of the = 11-gene=20 MTTS/PNS signature.

Cox proportional hazard survival = regression=20 analysis.
To ascertain the incremental = statistical=20 power of the individual covariates as predictors of = therapy=20 outcome and unfavorable prognosis, we performed both = univariate=20 and multivariate Cox proportional hazard survival = analyses=20 (Table 4).=20 Several individual gene members of the 11-gene = signature, such=20 as KI67 and Cyclin B1, have been = described=20 previously as significant predictors of prognosis and = may=20 reflect correlation between proliferative fraction and = poor=20 therapy outcome as has been shown recently for the = lymphoma=20 survival predictor signature. However, our analysis = appears to=20 indicate that the 11-gene signature is a more uniform=20 therapy-outcome predictor across the multiple data sets = compared with the individual genes (see below) and, perhaps, = is=20 a better "integrator" and "sensor" of the biological = diversity=20 across the spectrum of human cancers. We performed both=20 univariate and multivariate Cox proportional hazard = survival=20 analyses to compare the prognostic performance of the = entire=20 11-gene signature and individual genes (Table 4=20 and Supplemental Table 3). In the univariate analysis,=20 prognostic performance of KI67 expression as a = predictor=20 of therapy outcome varied in different outcome data = sets. It=20 was highly significant in the prostate cancer therapy = outcome=20 set 2 (Memorial Sloan-Kettering Cancer Center [MSKCC] = data=20 set); however, it showed only a trend toward statistical=20 significance in the prostate cancer outcome set 1 (P = =3D=20 0.1; Harvard data set) and the breast cancer outcome = data set=20 (P =3D 0.0533). In prostate cancer, the = significant=20 prognosis predictors in univariate Cox regression = analysis were=20 KI67, ANK3, FGFR2, CES, and = the=20 11-gene MTTS/PNS signature. In breast cancer, the = significant=20 prognosis predictors in univariate analysis were = Cyclin=20 B1, BUB1, HEC, and the 11-gene signature. Thus, = our=20 analysis seems to indicate that individual genes = demonstrate a=20 variable performance across multiple outcome data sets, = and we=20 were unable to identify a single gene uniformly = predictive of=20 the poor therapy outcome.


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  = 		Table 4=20
Cox proportional hazard = survival=20 regression analysis =

 = ;
In=20 the multivariate analysis (Table 5),=20 the most significant prostate cancer recurrence = predictor was=20 the model that included 11 covariates (11-gene = signature; 4=20 individual genes [KI67, ANK3, = FGFR2, and=20 CES1]; and 6 clinico-pathological features = [pre-RP PSA,=20 RP Gleason sum, surgical margins, seminal vesicle = invasion,=20 age, and extracapsular extension]). Interestingly, = several=20 covariates, such as the 11-gene signature, KI67, = CES1,=20 pre-RP prostate-specific antigen (PSA) level, surgical = margins,=20 and extracapsular extension, remained statistically = significant=20 prognostic markers in the multivariate analysis (Table 5).=20 Thus, while prognostic performance of individual gene = members=20 of the 11-gene signature varied greatly in different = outcome=20 data sets, the identified 11-gene signature seems to = perform as=20 the most consistent predictor of poor therapy outcome = across=20 multiple independent outcome data sets comprising over = 1,000=20 clinical samples and representing 11 distinct types of = human=20 cancer (see below). Yet the statistically = best-performing=20 multivariate cancer type=96specific model seems to = require a=20 combination of calls based on expression levels of = individual=20 genes, a gene expression signature, and = clinico-pathological=20 covariates (Tables 4=20 and 5).=20


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  = 		Table 5=20
11-Covariate prostate cancer=20 recurrence predictor model =

 = ;
We=20 sought to use an alternative statistical metric to further=20 evaluate the prognostic power of the genes constituting = the=20 11-gene signature. We implemented the weighted survival = score=20 analysis to reflect the incremental statistical power of = the=20 individual covariates as predictors of therapy outcome = based=20 on a multicomponent prognostic model (Figure 6E).=20 Final survival predictor score comprises a sum of = scores for=20 individual genes and reflects the relative contribution = of each=20 of the 11 genes in the multivariate analysis. The = negative=20 weighting values imply that higher expression = correlates with=20 longer survival and favorable prognosis, whereas the = positive=20 score values indicate that higher expression correlates = with=20 poor outcome and shorter survival. Application of the = weighted=20 survival predictor model based on a cumulative score of = the=20 weighted expression values of 11 genes confirmed the = prognostic=20 power of the identified 11-gene signature in = stratification of=20 prostate cancer patients into subgroups with = statistically=20 distinct probability of relapse-free survival after RP = (Figure=20 6E).=20

Expression of the 11-gene MTTS/PNS = signature is a=20 predictor of a short relapse-free survival after therapy in = prostate=20 cancer patients with an early-stage=20 disease.
Identification of patients with high=20 likelihood of poor outcome after therapy would be = particularly=20 desirable in a cohort of patients diagnosed with a = seemingly=20 localized early-stage prostate cancer. Next we = determined=20 whether the 11-gene MTTS/PNS signature would be useful = in=20 defining subgroups of patients diagnosed with an = early-stage=20 prostate cancer and having a statistically significant=20 difference in the likelihood of disease relapse after = therapy.=20 In the group of patients diagnosed with stage 1C or 2A = prostate=20 cancer (Figure 6D),=20 the median relapse-free survival after therapy in the=20 poor-prognosis subgroup defined by the 11-gene = BMI-1=96pathway=20 signature was 27 months. In contrast, the median = relapse-free=20 survival after therapy in the good-prognosis group was = 82.4=20 months. Eighty-eight percent of patients in the = poor-prognosis=20 subgroup had a disease recurrence within 5 years after = therapy.=20 Conversely, 64% of patients in the good-prognosis = subgroup=20 remained relapse-free (Figure 6D).=20 The estimated hazard ratio for disease recurrence after = therapy=20 in the poor-prognosis subgroup as compared with the=20 good-prognosis subgroup of patients defined by the = 11-gene=20 signature was 3.907 (95% confidence interval of ratio,=20 2.687=9634.84; P =3D 0.0005).

Validation of the prognostic = performance of the=20 11-gene BMI-1=96pathway signature using a Q-RT-PCR=96based=20 assay.
Routine clinical use of prognostic tests = based=20 on microarray-derived gene expression signatures would = require=20 prospective validation study of the utility of = identified=20 markers in an experimental setting highly compatible = with=20 state-of-the-art clinical laboratory practice. Since=20 microarray-based assay format is not readily available = for=20 application in the clinical laboratory, we considered = the=20 Q-RT-PCR=96based test as an alternative clinically compatible = analytical platform suitable for measurements of mRNA=20 expression level of marker genes. Expression of mRNA = for 11=20 genes (Supplemental Table 1) and an endogenous control = gene=20 (GAPDH) was measured by real-time PCR in 20 = specimens of=20 primary prostate cancer obtained from patients with = documented=20 PSA recurrence within 5 years after RP and patients who = remained disease-free for at least 5 years after RP (10 = patients in each group). As shown in Figure 6F,=20 a prostate cancer therapy outcome test based on = measurements of=20 mRNA expression levels of 11 genes using Q-RT-PCR = discriminates=20 prostate cancer patients into subgroups with statistically=20 distinct probability of relapse-free survival after RP. =

The Kaplan-Meier survival analysis demonstrated that = application=20 of the 11-gene Q-RT-PCR=96based prostate cancer therapy = outcome test segregates prostate cancer patients into = subgroups=20 with statistically significant difference in the = probability=20 of remaining relapse-free after the therapy (Figure 6F).=20 The estimated hazard ratio for disease recurrence after = therapy in the poor-prognosis group of patients as = compared=20 with the good-prognosis group defined by the test was = 21.3 (95%=20 confidence interval of ratio, 5.741=9698.39; P = <=20 0.0001). One hundred percent of patients in the = poor-prognosis=20 group had a disease recurrence within 4 years after RP, = whereas=20 91% of patients in the good-prognosis group remained=20 relapse-free (Figure 6F).=20

Expression of the 11-gene MTTS/PNS = signature=20 predicts metastatic recurrence and poor survival after therapy in = breast=20 cancer and lung adenocarcinoma patients diagnosed with an = early-stage=20 disease.
BMI-1 expression was previously = implicated in=20 human breast and lung cancers (11,=20 12,=20 31),=20 which suggests that activation of BMI-1=96associated = pathway(s)=20 might be relevant to these types of carcinomas as well. = We=20 therefore sought to investigate whether measurements of = expression of the 11-gene MTTS/PNS signature would be=20 informative in the prediction of the patients=92 = prognosis in a=20 group of 97 young women diagnosed with sporadic lymph=20 node=96negative early-stage breast cancer who were = analyzed in a=20 recent expression profiling study of early-stage breast = cancer=20 (33).=20 (This group comprises 46 patients who developed distant = metastases within 5 years and 51 patients who continued = to be=20 disease-free at least 5 years after therapy; they constitute=20 clinically defined poor-prognosis and good-prognosis = groups,=20 correspondingly.) Kaplan-Meier analysis indicates that = breast=20 cancer patients with tumors displaying a stem = cell=96like=20 expression profile of the 11-gene signature have a=20 significantly higher probability of developing distant=20 metastases within 5 years after therapy and therefore = can be=20 identified as a poor-prognosis subgroup (Figure 7,=20 A=96D). Median metastasis-free survival after therapy = in the=20 poor-prognosis subgroup of breast cancer patients = defined by=20 the 11-gene signature was 26 months. Eighty-four = percent of=20 patients in the poor-prognosis subgroup were diagnosed = with=20 distant metastasis within 5 years after therapy (Figure = 7C).=20 In contrast, 62% of patients in the good-prognosis subgroup=20 remained metastasis-free (Figure 7C).=20 The estimated hazard ratio for metastasis-free survival = after=20 therapy in the poor-prognosis subgroup as compared with = the=20 good-prognosis subgroup of patients defined by the = 11-gene=20 signature was 3.762 (95% confidence interval of ratio,=20 3.421=9620.27; P < 0.0001). Thus, the expression=20 pattern of the 11-gene MTTS/PNS signature is strongly=20 predictive of a short postdiagnosis and post-treatment = interval=20 to distant metastases in early-stage breast cancer=20 patients.

Next we asked whether expression analysis of the 11-gene = signature=20 would be informative in patients=92 stratification into = subgroups with distinct survival probability after therapy = in=20 a group of 125 patients diagnosed with lung adenocarcinoma = (34).=20 Similarly to the prostate and breast cancer patients, the=20 Kaplan-Meier analysis shows that patients with tumors=20 displaying a stem cell=96like expression profile of the = 11-gene=20 signature have significantly higher risk of death after = therapy=20 and therefore can be defined as a poor-prognosis = subgroup=20 (Figure 7,=20 E=96H). Median survival after therapy in the = poor-prognosis=20 subgroup of lung adenocarcinoma patients defined by the = 11-gene=20 BMI-1=96pathway signature was 15.2 months (Figure 7G).=20 In contrast, the median survival after therapy in the=20 good-prognosis subgroup was 48.8 months. One hundred = percent of=20 patients in the poor-prognosis subgroup died within 3 = years=20 after therapy. Conversely, 58% of patients in the=20 good-prognosis subgroup remained alive (Figure 7G).=20 The estimated hazard ratio for death after therapy in = the=20 poor-prognosis subgroup as compared with the = good-prognosis=20 subgroup of patients defined by the 11-gene signature = was 3.589=20 (95% confidence interval of ratio, 2.910=9646.67; = P =3D=20 0.0005).

Next we examined whether the 11-gene MTTS/PNS signature = would=20 be useful in defining subgroups of patients diagnosed with = an=20 early-stage lung adenocarcinoma and having a statistically=20 significant difference in survival probability after = therapy.=20 In the group of patients diagnosed with stage 1A lung=20 adenocarcinoma (Figure 7H),=20 the median survival after therapy in the poor-prognosis=20 subgroup defined by the 11-gene signature was 49.6 months.=20 Fifty-three percent of patients in the poor-prognosis = subgroup=20 died within 5 years after therapy. In contrast, 92% of = patients=20 remained alive in the good-prognosis subgroup (Figure = 7H).=20 The estimated hazard ratio for death after therapy in = the=20 poor-prognosis subgroup as compared with the = good-prognosis=20 subgroup of patients defined by the 11-gene signature = was 8.909=20 (95% confidence interval of ratio, 1.418=9613.12; = P =3D=20 0.01).

Based on this analysis, we concluded that detection of a = stem=20 cell=96like expression profile of the 11-gene MTTS/PNS = signature=20 in primary tumors from patients diagnosed with early-stage=20 prostate, breast, and lung carcinomas is associated = with a high=20 propensity toward metastatic dissemination and = significantly=20 higher risk of poor therapy outcome. Interestingly, = therapy=20 outcome in cancer patients diagnosed with other types = of=20 epithelial cancers, such as ovarian and bladder = cancers, seems=20 to manifest similar association with distinct patterns = of=20 expression of the 11-gene signature (Figure 7,=20 I=96N).

Expression of the 11-gene signature = predicts=20 therapy outcome in patients diagnosed with nonepithelial=20 malignancies.
Altered BMI-1 expression was = implicated=20 recently in several nonepithelial malignancies, such as = B cell=20 non-Hodgkin lymphoma (35)=20 and pediatric brain tumors (36).=20 We therefore sought to analyze whether the 11-gene = MTTS/PNS=20 signature would be useful in defining subgroups of = patients=20 diagnosed with nonepithelial cancers and having a = statistically=20 significant difference in survival probability after = therapy.=20 Using Kaplan-Meier method, we analyzed the prognostic = power of=20 the 11-gene signature in patients diagnosed with = diffuse large=20 B cell lymphoma, mantle cell lymphoma, acute myeloid = leukemia,=20 mesothelioma, medulloblastoma, and glioma (Table 2).=20 Kaplan-Meier analysis demonstrates that a stem = cell=96like=20 expression profile of the 11-gene signature in primary = tumors=20 is a consistent powerful predictor of a therapy failure = and=20 short survival in cancer patients diagnosed with 5 = distinct=20 types of nonepithelial cancers (Figure 8,=20 A=96F). Consistent with our findings, an increased = BMI-1=20 expression in human medulloblastomas was demonstrated = in a=20 recent study (37).=20 Taken together, these data seem to imply the presence = of a=20 conserved BMI-1=96associated pathway(s) similarly engaged = in both=20 neural stem cells and a highly malignant subset of human=20 cancers diagnosed in a wide range of organs and uniformly=20 exhibiting a marked propensity toward metastatic = dissemination=20 as well as a high probability of unfavorable therapy=20 outcome.



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  = 		Figure 8=20
Classification of cancer = patients=20 diagnosed with different types of nonepithelial = malignancies=20 into subgroups with distinct therapy outcome based on=20 expression profile of the 11-gene MTTS/PNS signature.=20 Kaplan-Meier survival analysis of the probability of a = therapy=20 failure in cancer patients diagnosed with different = types of=20 nonepithelial cancers and having distinct expression = profiles=20 of the 11-gene MTTS/PNS signature is shown. Data from = lymphoma=20 patients (A), malignant glioma patients = (B),=20 mesothelioma patients (C), medulloblastoma = patients=20 (D), mantle cell lymphoma patients (E), = and=20 acute myeloid leukemia patients (F) are shown. =

 = ;

  =20 Discussion
Top
Abstract
Introduction
Results
3D"Discussion
Methods
References
 
A= =20 growing number of expression profiling studies provide = experimental=20 evidence indicating the presence of a transcriptionally=20 distinct subtype of human solid tumors manifesting a = marked=20 propensity toward metastatic dissemination, highly = malignant=20 clinical behavior, and a high probability of poor = therapy=20 outcome in cancer patients diagnosed with early-stage=20 carcinomas of various origins (refs. 15,=20 38-40;=20 this study). These results are consistent with the idea = that,=20 at least in a subset of human solid tumors, the acquisition = of=20 full metastatic potential, including an emergence and seeding = of potent metastasis precursor cells, is a relatively = early=20 event in tumor progression. Collectively, these data = suggest=20 an early involvement, in development of this = transcriptionally=20 defined subtype of human carcinomas, of a highly malignant=20 combination of mutant alleles conferring the proclivity = to=20 metastasize (40)=20 and/or an engagement of unique unconventional cellular = targets=20 such as stem cells and/or early progenitor cells in=20 transformation and tumor progression.

One of the hallmark biological features of normal stem = cells=20 is the ability to fuse spontaneously in vitro and in vivo = with=20 other cell types, leading to formation of reprogrammed = viable=20 somatic cell hybrids (41-44).=20 Accumulation of normal stem cells in experimental = tumors in=20 vivo has been demonstrated in several studies (45,=20 46).=20 Furthermore, most recent studies demonstrated that = committed=20 myelomonocytic cells such as macrophages can produce = functional=20 epithelial cells by in vivo fusion (47),=20 thus extending the number of cell types that might serve = as=20 hypothetically "eligible" fusion partners for tumor cells. = It=20 would be of interest to study how cancer cells co-opt stem=20 cell=96like transcriptome into progression pathways and = whether=20 some human carcinomas could attract stem cells by = mimicking a=20 stem cell "niche" microenvironment, thus directly = engaging=20 normal stem cells into malignant process via cell = fusion. One=20 interesting endpoint of our analysis is that a = relatively small=20 set of coregulated transcripts appears to predict = clinical=20 outcome in a large number of human tumors representing = 10=20 distinct types of cancer. Perhaps inclusion of a = relevant=20 biological model in the signature discovery protocol = was an=20 essential component of the successful hit selection, = since=20 recent metaanalysis of cancer microarray data that was = based=20 solely on statistical approaches did not identify an outcome=20 signature common to multiple cancer types (48).=20

It has been suggested that sets of coordinately expressed = genes=20 defined as cancer-associated gene expression signatures = might=20 reflect the cell of origin of cancer (49).=20 Unlike stem cells in the state of CNS neurospheres that = are=20 recovered from the CNS, stem cells in the state of PNS=20 neurospheres might be present in many (if not all) = peripheral=20 tissues and therefore are more likely and readily = accessible=20 cellular targets for direct involvement into malignant = process.=20 It remains to be elucidated whether the precision of = analytical=20 protocols used in this study was sufficient to identify = the=20 broadly applicable gene expression markers of the=20 BMI-1-pathway activation and normal stem cell = engagement=20 in malignant progression of human cancers. Protein = products of=20 2 genes upregulated in the MTTS/PNS signature profile = (BUB1 and=20 HEC1) are known to play an important role in the spindle=20 assembly mitotic checkpoint. A recent study suggested a = novel=20 mechanism leading to development of frequent aneuploidy in=20 human cancer due to aberrant expression of Mad2 = protein,=20 inappropriate activation of the spindle checkpoint, = and,=20 eventually, aneuploidy (50).=20 Both BUB1 and HEC1 proteins play a key role in the assembly = of=20 checkpoint proteins, being required for Mad2 recruitment = to the=20 kinetochores (51,=20 52);=20 this suggests that aberrant BUB1 and HEC1 expression = might=20 contribute to and/or reflect the altered function of = the=20 mitotic checkpoint in metastatic cancer cells.

In conclusion, using a mouse/human comparative = translational=20 genomics approach, we identified an 11-gene signature that=20 consistently displays a stem cell=96like expression = pattern in=20 metastatic lesions of prostate carcinomas recovered = from=20 multiple distant target organs. Our results indicate = that a=20 stem cell=96resembling expression profile of the = 11-gene=20 signature is associated with a highly malignant = clinical course=20 of disease progression and predicts high likelihood of = therapy=20 failure in multiple types of human cancer. = Statistically=20 significant negative prognostic value of a stem = cell=96like=20 expression of the 11-gene signature in early-stage = primary=20 solid tumors of diverse origin suggests the presence of = a=20 genetically distinct subtype of human carcinomas with = high=20 propensity toward metastatic dissemination even at the = early=20 stage of disease progression. Further elucidation of = possible=20 causal relationships between activation of a stem=20 cell=96resembling gene expression program and malignant = behavior of human carcinoma cells should have considerable=20 theoretical and practical implications.


  =20 Methods
Top
Abstract
Introduction
Results
Discussion
3D"Methods
References
 
<= FONT=20 size=3D-1>Clinical samples.
Expression = profiling=20 data of primary tumor samples obtained from 1,122 = cancer=20 patients representing therapy-outcome cohorts for 10 = types of=20 human cancer (Table 2)=20 were analyzed in this study. Microarray analysis and = associated=20 clinical information for 32 clinical samples (23 = primary=20 prostate tumors and 9 distant metastatic lesions) used = to=20 delineate the expression profiles of human prostate = cancer=20 metastases were reported previously (13).=20 Two clinical outcome sets comprising 21 (outcome set 1) = and 79=20 (outcome set 2) samples were used for analysis of the=20 association of the therapy outcome with distinct = expression=20 profiles of the 11-gene signature. Original gene = expression=20 profiles of the 21 clinical samples analyzed in this study = were=20 reported elsewhere (14).=20 Primary gene expression data files of clinical samples = as well=20 as associated clinical information can be found at=20 http://www-genome.wi.mit.edu/cancer/.

The prostate tumor tissues constituting the second = clinical=20 outcome set were obtained from 79 prostate cancer patients=20 undergoing therapeutic or diagnostic procedures = performed as=20 part of routine clinical management at the MSKCC (New = York, New=20 York, USA). Clinical and pathological features of 79 = prostate=20 cancer cases constituting the validation outcome set = are=20 presented elsewhere (15).=20 Median follow-up after therapy in this cohort of patients = was=20 70 months. Samples were snap-frozen in liquid nitrogen and=20 stored at =9680=B0C. Each sample was examined = histologically=20 using H&E-stained cryostat sections. Care was taken to=20 remove nonneoplastic tissues from tumor samples. Cells = of=20 interest were manually dissected from the frozen block = and=20 other tissues trimmed away. All of the studies were = approved by=20 the MSKCC Institutional Review Board.

Expression analysis data for tumor samples obtained from = 125=20 lung adenocarcinoma patients as well as associated = clinical=20 information were reported elsewhere (34).=20 Original work describing gene expression profiles of = the set of=20 97 clinical samples of early-stage breast cancer was = reported=20 elsewhere (33).=20 Primary gene expression data files of clinical samples = as well=20 as associated clinical information have been previously = described (33).=20 To date, our analysis includes 1,153 therapy-outcome = samples=20 from patients diagnosed with 11 distinct types of = cancer (Table=20 2):=20 prostate cancer (100 patients); breast cancer (97 = patients);=20 lung adenocarcinoma (211 patients); ovarian cancer (50=20 patients); bladder cancer (31 patients); diffuse large = B cell=20 lymphoma (298 patients); mantle cell lymphoma (MCL, 92=20 patients); mesothelioma (17 patients); medulloblastoma = (60=20 patients); glioma (50 patients); and acute myeloid = leukemia=20 (116 patients).

Cell culture.
Cell = lines used=20 in this study were previously described (16).=20 The LNCap- and PC-3=96derived cell lines were developed = by=20 consecutive serial orthotopic implantation, either from = metastases=20 to the lymph node (for the LN series) or reimplanted from = the=20 prostate (Pro series). This procedure generated cell = variants=20 with differing tumorigenicity, frequency, and latency of=20 regional lymph node metastasis (16).=20 Except where noted, cell lines were grown in RPMI1640=20 supplemented with 10% FBS and gentamycin (GIBCO; = Invitrogen=20 Corp.) to 70=9680% confluence and subjected to serum = starvation=20 as described previously (16),=20 or maintained in fresh complete media, supplemented = with 10%=20 FBS.

Anoikis assay.
Cells = were=20 harvested by 5-minute digestion with 0.25% trypsin/0.02% = EDTA=20 (Irvine Scientific), washed, and resuspended in serum-free=20 medium. Cells at a concentration of 1.7 x 105 cells per well = in=20 1 ml of serum-free medium were plated in 24-well Ultra Low=20 Attachment polystyrene plates (Corning Inc.) and incubated = at=20 37=B0C and 5% CO2 overnight. Viability of cell = cultures=20 subjected to anoikis assays was greater than 95% in a = trypan=20 blue dye exclusion test.

Apoptosis = assay.
Apoptotic=20 cells were identified and quantified using the Annexin = V-FITC=20 kit (BD Biosciences =97 Pharmingen) according to the=20 manufacturer=92s instructions. The following controls = were used=20 to set up compensation and quadrants: (a) unstained = cells; (b)=20 cells stained with Annexin V-FITC (no propidium iodine); = and=20 (c) cells stained with propidium iodine (no Annexin V-FITC).=20 Each measurement was carried out in quadruplicate, and = each=20 experiment was repeated at least twice. Annexin = V-FITC=96positive=20 cells were scored as early apoptotic cells; both Annexin=20 V-FITC=96 and propidium iodine=96positive cells were = scored as=20 late apoptotic cells; unstained Annexin V-FITC=96 and=20 propidium iodine=96negative cells were scored as viable = or=20 surviving cells. In selected experiments, apoptotic = cell death=20 was documented using the TUNEL assay.

Flow = cytometry.
Cells were=20 washed in cold PBS and stained using the Annexin V-FITC = Apoptosis Detection Kit (BD Biosciences) according to = the=20 manufacturer=92s instructions. Flow analysis was performed = with a=20 FACSCalibur instrument (BD Biosciences). CellQuest software = (BD=20 Biosciences) was used for data acquisition and analysis. = All=20 measurements were performed under the same instrument = setting,=20 analyzing 103=96104 cells per = sample.=20

Orthotopic=20 xenografts.
Orthotopic xenografts of human = prostate=20 PC-3 cells and sublines used in this study were = developed by=20 surgical orthotopic implantation as previously = described (15,=20 16).=20 Briefly, 2 x 106=20 cultured PC-3 cells or cells of the PC-3M or PC-3MLN4 = subline=20 were injected s.c. into male athymic mice, and allowed = to=20 develop into firm palpable and visible tumors over the = course=20 of 2=964 weeks. Intact tissue was harvested from a = single s.c.=20 tumor and surgically implanted in the ventral lateral = lobes of=20 the prostate gland in a series of 6 athymic mice per = cell line=20 subtype as described earlier (16).=20

Transgenic mouse model of prostate=20 cancer.
A breeding colony of TRAMP (transgenic=20 adenocarcinoma of the mouse prostate) mice is = maintained on=20 C57BL/6 background in the Animal Care Facility at the = Sidney=20 Kimmel Cancer Center (53).=20 The TRAMP mouse colony is based on a breeding pair of = TRAMP=20 mice kindly provided by Norman Greenberg (Baylor College = of=20 Medicine, Houston, Texas, USA). Standard PCR assay was = carried=20 out to monitor the presence of the SV-40 large T antigen = in=20 new litters. Twenty-one PCR-confirmed male TRAMP mice were=20 defined for microarray analysis carried out in this = study.=20 Animals were killed at different ages according to the=20 established time course of the disease progression (32),=20 and prostates as well as primary and metastatic tumors = were=20 immediately removed and snap-frozen in liquid nitrogen. = Prostate tissues from age-matched wild-type C57BL/6 = mice served=20 as control samples in our microarray analysis of the = TRAMP=20 model of prostate cancer. Necropsies with gross = microscopic=20 examination were carried out. All procedures were = approved by=20 the Sidney Kimmel Cancer Center Institutional Animal = Care and=20 Use Committee and followed Sidney Kimmel Cancer Center = Standard=20 Operating Procedures in accordance with the NIH Guide = for the=20 Care and Use of Laboratory Animals.

Tissue processing for mRNA and RNA=20 isolation.
Fresh-frozen orthotopic and = transgenic=20 primary tumors, metastases, and mouse prostates were = examined=20 by use of H&E-stained frozen sections. Orthotopic = tumors of=20 all sublines exhibited similar morphology consisting of = sheets=20 of monotonous closely packed tumor cells with little = evidence=20 of differentiation, interrupted by only occasional = zones of=20 largely stromal components, vascular lakes, or = lymphocytic=20 infiltrates. Fragments of tumor judged free of these=20 nonepithelial clusters were used for mRNA preparation. = Frozen=20 tissue (1=963 mm x 1=963 mm) = was=20 submerged in liquid nitrogen in a ceramic mortar and = ground=20 to powder. The frozen tissue powder was dissolved and=20 immediately processed for mRNA isolation using a = FastTrack kit=20 for mRNA extraction (Invitrogen Corp.) according to the = manufacturer=92s instructions.

RNA and mRNA = extraction.
For=20 gene expression analysis, cells were harvested in lysis = buffer=20 2 hours after the last media change at 70=9680% confluence, = and=20 total RNA or mRNA was extracted using the RNeasy (QIAGEN) = or=20 FastTrack kit (Invitrogen Corp.). Cell lines were not split=20 more than 5 times before RNA extraction, except where = noted.=20

Affymetrix = arrays.
The=20 protocol for mRNA quality control and gene expression = analysis=20 was that recommended by Affymetrix. In brief, = approximately=20 1 =B5g of mRNA was reverse-transcribed with an oligo-dT = primer that has a T7 RNA polymerase promoter at the 5' = end.=20 Second-strand synthesis was followed by complementary RNA=20 (cRNA) production incorporating a biotinylated base.=20 Hybridization to Affymetrix U95Av2 arrays representing = 12,625=20 transcripts overnight for 16 hours was followed by = washing and=20 labeling using a fluorescently labeled antibody. The = arrays=20 were read and data processed using Affymetrix equipment = and=20 software as reported previously (13,=20 15,=20 16).=20

Data = analysis.
Detailed=20 protocols for data analysis and documentation of the=20 sensitivity, reproducibility, and other aspects of the=20 quantitative statistical microarray analysis using = Affymetrix=20 technology have been reported previously (15,=20 16).=20 Forty to fifty percent of the surveyed genes were = called=20 present by Affymetrix Microarray Suite version 5.0 = software in=20 these experiments. The concordance analysis of = differential=20 gene expression across the data sets was performed = using=20 Affymetrix MicroDB version 3.0 and DMT version 3.0 = software as=20 described previously (13,=20 15,=20 16).=20 We processed the microarray data using the Affymetrix=20 Microarray Suite version 5.0 software and performed = statistical=20 analysis of the expression data set using the = Affymetrix=20 MicroDB and Affymetrix DMT software. The Pearson = correlation=20 coefficient for individual test samples and the = appropriate=20 reference standard were determined using Microsoft = Excel=20 version 2002 (Microsoft Corp.) and GraphPad Prism = version 4.00=20 software (GraphPad Software). We calculated the = significance of=20 the overlap between the lists of stem cell=96associated = and=20 prostate cancer=96associated genes by using the = hypergeometric=20 distribution test (54).=20 Analytical protocol of identification and validation of = the=20 11-gene BMI-1=96pathway signature is described below = and=20 presented in Figure 3.=20 We used MultiExperiment Viewer (MEV) software version = 3.0.3 of=20 the Institute for Genomic Research for SVM = classification and=20 TRN clustering algorithm data analysis and = visualization.=20

Protocol of discovery and validation of = the=20 11-gene BMI-1=96pathway signature.
We = hypothesized that=20 molecular signatures associated with activation of a = normal=20 stem cell=92s self-renewal and/or survival program in = metastatic=20 cancer cells might be detectable by looking for genes=20 manifesting concordant patterns of regulation in distant=20 metastatic lesions and stem cells in = Bmi-1+/+=20 versus Bmi-1=96/=96 genetic = backgrounds.=20 Therefore, we sought to determine whether expression = profiles=20 of transcripts activated and suppressed in prostate = cancer=20 metastases would recapitulate the expression profile of = the=20 BMI-1=96regulated genes in neural stem cells, by = comparing the=20 sets of differentially regulated genes in search of=20 intersection of lists for both up- and downregulated = transcripts.=20 Thus, according to this model the primary criterion for=20 transcript selection should be the concordance of = changes in=20 expression rather than a magnitude of changes (e.g., = fold=20 change). One of the predictions of this model is that=20 transcripts of interest would be expected to have a = tightly=20 controlled "rank order" of expression within a cluster = of=20 coregulated genes, reflecting a balance of up- and=20 downregulated mRNAs as a desired regulatory endpoint in = a cell.=20 A degree of resemblance of the transcript-abundance = rank order=20 within a gene cluster between a test sample and reference=20 standard is measured by a Pearson correlation coefficient = and=20 designated as a phenotype association index (PAI). Samples = with=20 stem cell=96resembling expression profiles of PAI = (SPAIs)=20 are expected to have positive values of Pearson = correlation=20 coefficients. Detailed prognostic signature identification = and=20 validation protocol are described below and shown in = Figure=20 3.=20

Step 1.
Sets of = differentially=20 regulated transcripts were independently identified for = distant=20 metastatic lesions and primary prostate tumors versus=20 age-matched control samples in a transgenic TRAMP mouse = model=20 of metastatic prostate cancer (MTTS signature) as well = as PNS=20 (PNS signature) and CNS (CNS signature) neurospheres in = BMI-1+/+ versus BMI-1=96/=96 = backgrounds=20 using the Affymetrix microarray processing and = statistical=20 analysis software package (Affymetrix Microarray Suite = version=20 5.0, MicroDB version 3.0, and DMT version 3.0) as = described=20 above and in previous publications (15,=20 16).=20 Transcripts with negative signal-intensity values in = both=20 experimental and control sets were eliminated from = further=20 consideration. At least 2-fold changes of the mRNA = abundance=20 levels in experimental versus control samples for both=20 upregulated and downregulated genes were required for = inclusion=20 in the lists of differentially regulated transcripts. Fold=20 expression changes of the mRNA abundance levels for = each=20 transcript were calculated as ratios of the average = intensity=20 values for a given transcript in experimental versus = control=20 samples for both upregulated and downregulated genes = and=20 log10-transformed for further analysis. = Thus, this=20 analytical step defined 3 large parent signatures (see = Figure=20 3):=20 MTTS signature comprising 868 upregulated and 477 = downregulated=20 transcripts; PNS signature comprising 885 upregulated = and 1,088=20 downregulated transcripts; and CNS signature comprising = 769=20 upregulated and 778 downregulated transcripts.

Step 2.
Subsets of = transcripts=20 exhibiting concordant expression changes in metastatic = TRAMP=20 tumor samples (MTTS signature) as well as PNS (PNS = signature)=20 and CNS (CNS signature) neurospheres in=20 BMI-1+/+ versus = BMI-1=96/=96=20 backgrounds were identified. Concordant lists of = transcripts=20 were obtained by intersecting the 2 lists each of = upregulated=20 and downregulated genes. Thus, 2 concordant subsets of=20 transcripts were identified corresponding to each = binary=20 comparison of metastatic TRAMP tumors and neural stem = cell=20 samples in a state of PNS and CNS neurospheres (141 = upregulated=20 and 58 downregulated transcripts for PNS neurospheres = [r=20 =3D 0.7593; P < 0.0001; Figure 2A]=20 and 40 upregulated and 24 downregulated transcripts for = CNS=20 neurospheres [r =3D 0.7679; P < = 0.0001]). A=20 third concordant subset of 27 genes comprising 15 = upregulated=20 and 12 downregulated transcripts was selected for = intersection=20 common to all 3 signatures (r =3D 0.8002; P = <=20 0.0001).

Step 3.
Selection of = small=20 gene clusters was performed from subsets of genes = exhibiting=20 concordant changes of transcript-abundance behavior in=20 metastatic TRAMP tumor samples and PNS and CNS neurospheres = in=20 BMI-1+/+ versus BMI-1=96/=96 = backgrounds.=20 Expression profiles were presented as log10 = average=20 fold changes for each transcript and processed for=20 visualization and Pearson correlation analysis using = Microsoft=20 Excel software (Microsoft Corp.). For the concordant=20 differentially expressed genes, vectors of log10=20 average fold change were determined for both experimental=20 settings, and the correlation between 2 vectors was = determined.=20 Practical considerations essential for future = development of=20 genetic diagnostic tests prompted us to select from = concordant=20 gene sets small gene expression signatures comprising=20 transcripts with a high level of expression correlation = in=20 metastatic cancer cells and stem cells. The concordant = list of=20 differentially expressed genes was reduced by removing = those=20 genes whose removal led to the largest increase in the=20 correlation coefficient. The reduction in the signature = transcript number was terminated when further = elimination of a=20 transcript did not increase the value of the Pearson=20 correlation coefficient. The cutoff criterion for = signature=20 reduction was arbitrarily set to exceed a Pearson = correlation=20 coefficient of 0.95 (P < 0.0001). Using this=20 approach, a single candidate prognostic gene expression=20 signature was selected for each intersection of the = MTTS=20 signature and parent stem cell signatures (Figure 3).=20 Thus, 3 highly concordant small signatures were = identified=20 corresponding to 3 concordant subsets of genes defined = in step=20 2 (a set of 11 genes comprising 8 upregulated and 3=20 downregulated transcripts for PNS neurospheres, i.e., = the=20 11-gene MTTS/PNS signature; a set of 11 genes comprising = 7=20 upregulated and 4 downregulated transcripts for CNS = neurospheres,=20 i.e., the 11-gene MTTS/CNS signature; and a set of 14 = genes=20 comprising 8 upregulated and 6 downregulated transcripts, = i.e.,=20 the MTTS/PNS/CNS signature).

Step 4.
The small = signatures=20 identified in step 3 (one 11-gene signature for the PNS = set,=20 one 11-gene signature for the CNS set, and one 14-gene=20 signature for the common PNS/CNS set) were tested for = the power=20 to discriminate the metastatic phenotype (using 1 mouse = prostate cancer data set and 1 human prostate cancer = data set=20 comprising primary and metastatic tumors) and therapy-outcome = classification performance (using human prostate cancer = therapy=20 outcome set 1). Three identified small signatures were=20 evaluated for their ability to discriminate metastatic = and=20 primary prostate tumors in a TRAMP mouse model of = prostate=20 cancer, and clinical samples of 9 metastatic versus 23 = primary=20 prostate tumors as well as primary prostate tumors from = 21=20 patients with distinct outcome after the therapy (8 = recurrent=20 and 13 nonrecurrent samples). To assess a potential = diagnostic=20 and prognostic relevance of small signatures, we = calculated a=20 Pearson correlation coefficient for each individual = tumor=20 sample by comparing the expression profiles of = individual=20 samples with the reference expression profile in either = PNS or=20 CNS neurospheres in BMI-1+/+ versus=20 BMI-1=96/=96 backgrounds. Fold expression = changes=20 in individual clinical samples were calculated for each = gene=20 as a ratio of the expression value in a given sample to = the=20 "average" expression value of the gene across the = entire data=20 set of clinical samples. For each data set, the vector=20 (X) of average gene expression was determined, = and then=20 the relative expression vector (R) was = determined for=20 each sample (R =3D X/X). The relative = expression=20 vectors were log10-transformed and correlated = with the=20 fixed vectors of gene expression determined in step 3. = Negative=20 expression values were treated as missing data. Based = on the=20 expected correlation of expression profiles of = identified gene=20 clusters with stem cell=96like expression profiles, we = named the=20 corresponding correlation coefficients calculated for=20 individual samples the SPAIs. We evaluated the = prognostic power=20 of identified small signatures based on their ability = to=20 discriminate metastatic versus primary tumors = (criterion 1) and=20 to segregate the patients with recurrent and = nonrecurrent=20 prostate tumors into distinct subgroups (criterion 2) = and=20 selected a single best-performing small signature for=20 subsequent validation analysis (Figures 3=20 and 4).=20 Based on diagnostic and prognostic classification = performance,=20 a single best-performing 11-gene MTTS/PNS signature was = selected for further validation analysis (Figures 3=20 and 4).=20 The fixed numerical vector describing the stem cell=96like=20 signature in 11 genes is shown in Supplemental Tables 2, = 3,=20 and 5.

Step 5.
To assess = the=20 incremental statistical power of the individual genetic = and=20 clinical covariates as predictors of therapy outcome = and=20 unfavorable prognosis in prostate cancer patients, we = performed=20 both univariate and multivariate Cox proportional hazard=20 survival analyses (Table 4).=20

Step 6.
To validate = a survival=20 prediction model based on the 11-gene MTTS/PNS = signature, we=20 tested the prognostic performance of the model in the = multiple=20 independent therapy-outcome data sets representing 5 = epithelial=20 and 5 nonepithelial cancers. We divided the patients = within=20 individual cohorts into a training set, which was used = to=20 select the cutoff threshold and to test the model, and = a test=20 set, which was used to evaluate the reproducibility of = the=20 classification performance. We used the training set to = select=20 the prognosis-discrimination cutoff value for a signature = based=20 on the highest level of statistical significance in = patients=92=20 stratification into poor- and good-prognosis groups as=20 determined by the log-rank test (lowest P value = and=20 highest hazard ratio in the training set). Clinical = samples=20 having the Pearson correlation coefficient at or higher = than=20 the cutoff value were identified as having the = poor-prognosis=20 signature. Clinical samples with the Pearson = correlation=20 coefficient below the cutoff value were identified as = having=20 the good-prognosis signature. Each training set was = used to=20 estimate a threshold of the correlation coefficients = before a=20 survival analysis was performed. These thresholds are = shown in=20 the legends to Figures 6=20 and 7.=20 The same discrimination cutoff value was then applied = to=20 evaluate the reproducibility of the prognostic = performance in=20 the test set of patients. Lastly, we applied the model = to the=20 entire outcome set using the same cutoff threshold to = confirm=20 the classification performance. The average gene = expression=20 vectors were determined for each gene and applied = separately on=20 the training, test, and combined data sets. The = training and=20 test sets were balanced with respect to the total = number of=20 patients, negative and positive therapy outcomes, and = the=20 length of survival. For the breast cancer data set, we=20 maintained the patients=92 distribution among training = and test=20 data sets described in the original publication (33).=20 At this stage of the analysis, we did not carry out = additional=20 model training, development, or optimization steps, except = for=20 selection of a prognostic cutoff threshold in the training = set.=20 The same MTTS/PNS expression profile was consistently used=20 throughout the study as a reference standard to = quantify the=20 Pearson correlation coefficients of the individual = samples. The=20 distribution of the patients in the training and = validation=20 sets as well as the threshold values that separate the = samples=20 into good- and poor-prognostic groups are shown in the = legends=20 to Figures 6=20 and 7.=20

The cohort of 97 breast cancer patients (shown together in = Figure=20 7C)=20 was divided into a training set (78 patients; 34 poor = prognosis=20 and 44 good prognosis) (Figure 7A)=20 and a test set (19 patients; 12 poor prognosis and 7 = good=20 prognosis) (Figure 7B)=20 as described in the original publication (33).=20 One hundred twenty-five lung cancer patients (shown = together in=20 Figure 7G)=20 were divided into a training set (63 patients; 36 poor=20 prognosis and 27 good prognosis) (Figure 7E)=20 and a test set (62 patients; 35 poor prognosis and 27 = good=20 prognosis) (Figure 7F).=20 Thirty-seven ovarian cancer patients (shown together in = Figure=20 7K)=20 were divided into a training set (19 patients; 8 poor = prognosis=20 and 11 good prognosis) (Figure 7I)=20 and a test set (18 patients; 7 poor prognosis and 11 good=20 prognosis) (Figure 7J).=20 Thirty-one bladder cancer patients (shown together in = Figure 7N)=20 were divided into a training set (16 patients; 11 poor=20 prognosis and 5 good prognosis) (Figure 7L)=20 and a test set (15 patients; 10 poor prognosis and 5 good=20 prognosis) (Figure 7M).=20 The cutoff values used for classification were 0.56, = 0.75,=20 1.16, and 0.35 for breast, lung, ovarian, and bladder = cancer=20 data sets, respectively.

Step 7.
We tested = the model=20 performance using various sample-stratification = approaches,=20 such as TRN clustering (Figure 5),=20 SVM classification (Supplemental Table 4), and weighted = survival score algorithm (Figures 6E=20 and 7D).=20 We evaluated the therapy outcome=96predictive power of = the=20 11-gene model in a prostate cancer setting using a = prognostic=20 test based on an independent method of gene expression=20 analysis, namely Q-RT-PCR (Figure 6F).=20 In order to facilitate the evaluation of the 11-gene = model, the=20 average expression vector for different cancer types = and the=20 thresholds that separate the samples into good- and=20 poor-prognostic groups are shown in Supplemental Table = 6.=20

SPAI.
Definition of = the=20 Pearson correlation coefficient as a phenotype = association=20 index is based on highly concordant behavior of the = 11-gene=20 signature between neural stem cells in the state of PNS = neurospheres and prostate cancer metastasis (Figure 2,=20 C and D; r =3D 0.9897; P < 0.0001). Values = for a=20 standard PNS neurosphere and for TRAMP metastasis were=20 established as described in the signature discovery = protocol=20 and shown in Figure 2D.=20 They were used consistently throughout the study as = uniform=20 reference standards for measurements of Pearson = correlation=20 coefficients for clinical samples. A degree of resemblance = of=20 the transcript-abundance rank order within a gene cluster=20 between a test sample and reference standard is = measured by a=20 Pearson correlation coefficient and designated as a = PAI.=20 Samples with stem cell=96resembling expression profiles = of PAI=20 (SPAIs) are expected to have positive values of Pearson = correlation coefficients.

Random co-occurrence = test.
We=20 performed 10,000 permutations to test the likelihood that = small=20 11-gene signatures derived from the large MTTS signature = would=20 display high discrimination power to assess the significance = at=20 the 0.1% level. We carried out 10,000 permutations of small=20 11-gene signatures derived from the large 1,345-gene MTTS=20 signature and compared their sample-stratification = power with=20 that of the 11-gene MTTS/PNS signature. The=20 classification-performance cutoff P values were=20 established by application of a 2-tailed Student=92s = t=20 test to the 11-gene MTTS/PNS signature (P =3D = 0.0005 for=20 metastasis versus primary prostate cancer data set and = P=20 =3D 0.026 for recurrent versus nonrecurrent prostate = cancer data=20 set). Random concordant gene sets comprising approximately = 200=20 transcripts were generated using a mouse transcriptome data = set=20 representing expression profiling data of approximately = 12,000=20 transcripts across 38 normal tissues (55).=20 Inter- and intraspecies probe set match between = different array=20 types was performed at 95% or greater identity level = using the=20 Affymetrix database (www.affymetrix.com). To assess=20 discrimination of random 11-gene signatures derived = from the=20 1,345-gene MTTS signature, a 2-tailed Student=92s = t test=20 was carried out for metastatic versus primary prostate = cancer=20 data set (32 samples) and recurrent versus nonrecurrent = prostate cancer data set (21 samples). The signatures = were=20 ranked based on P values, and ranking metrics of = each=20 random 11-gene signature were compared with the 11-gene=20 MTTS/PNS signature P values. We found that 10,000=20 permutations generated 7 random 11-gene signatures = performing=20 at the sample-classification level of the 11-gene = MTTS/PNS=20 signature.

Weighted survival predictor score=20 algorithm.
We implemented the weighted survival = score=20 analysis to reflect the incremental statistical power = of the=20 individual covariates as predictors of therapy outcome = based on=20 a multicomponent prognostic model. The microarray-based = or=20 Q-RT-PCR=96derived gene expression values were = normalized and=20 log-transformed on a base 10 scale. The log-transformed = normalized expression values for each data set were = analyzed in=20 a multivariate Cox proportional hazard regression = model, with=20 overall survival or event-free survival as the = dependent=20 variable. To calculate the survival/prognosis predictor = score=20 for each patient, we multiplied the log-transformed = normalized=20 gene expression value measured for each gene by a = coefficient=20 derived from the multivariate Cox proportional hazard=20 regression analysis. Final survival predictor score = comprises=20 a sum of scores for individual genes and reflects the = relative=20 contribution of each of the 11 genes in the multivariate=20 analysis. The negative weighting values indicate that = higher=20 expression correlates with longer survival and = favorable=20 prognosis, whereas the positive score values indicate = that=20 higher expression correlates with poor outcome and = shorter=20 survival. Thus, the weighted survival predictor model = is based=20 on a cumulative score of the weighted expression values = of 11=20 genes. For example, the following equation describes = the=20 relapse-free survival predictor score for prostate = cancer=20 patients (see Table 5):=20 relapse-free survival score =3D (=960.403 x Gbx2) + (1.2494 x KI67) + (=960.3105 x cyclin B1) + = (=960.1226=20 x BUB1) + (0.0077 x HEC) + (0.0369 x KIAA1063) + = (=961.7493 x HCFC1) + (=961.1853 x RNF2) + (1.5242 = x ANK3) + (=960.5628 x FGFR2) + (=960.4333 x CES1).

BMI-1 siRNA = experiments.
The=20 target siRNA SMART pools for BMI-1 and control = luciferase=20 siRNAs were purchased from Dharmacon Research Inc. They = were=20 transfected into PC-3-32 human prostate carcinoma cells=20 according to the manufacturer=92s protocols. Cell = cultures were=20 continuously monitored for growth and viability and = assayed for=20 mRNA expression levels of BMI-1 and selected = sets of=20 genes (Table 2=20 and Figure 7)=20 using RT-PCR and Q-RT-PCR methods.

Q-RT-PCR = analysis.
The=20 real-time PCR method measures the accumulation of PCR = products=20 with a fluorescence detector system and allows for=20 quantification of the amount of amplified PCR products = in the=20 log phase of the reaction. Total RNA was extracted = using RNeasy=20 Mini Kit (QIAGEN) according to the manufacturer=92s=20 instructions. A measure of 1 =B5g (tumor samples), or 2 = =B5g and=20 4 =B5g (independent preparations of reference cDNA = samples),=20 of total RNA was used then as a template for cDNA = synthesis=20 with SuperScript II (Invitrogen Corp.). Q-RT-PCR primer=20 sequences were selected for each cDNA with the aid of = Primer=20 Express software (Applied Biosystems). PCR = amplification was=20 performed with the gene-specific primers listed in = Supplemental=20 Table 1.

Q-RT-PCR reactions and measurements were performed with = SYBR=20 Green and ROX (Applied Biosystems) as a passive reference,=20 using the ABI 7900HT Sequence Detection System (Applied = Biosystems). Conditions for the PCR were as follows: 1 = cycle of=20 10 minutes at 95=B0C; and 40 cycles of 0.20 minutes at = 94=B0C,=20 0.20 minutes at 60=B0C, and 0.30 minutes at 72=B0C. The = results were normalized to the relative amount of = expression of=20 an endogenous control gene, GAPDH.

Expression of mRNA for 11 genes (Supplemental Table 1) and = an=20 endogenous control gene (GAPDH) was measured by = real-time=20 PCR on an ABI PRISM 7900HT Sequence Detection System = (Applied=20 Biosystems) in 20 specimens of primary prostate cancer = obtained=20 from patients with documented PSA recurrence within 5 = years=20 after RP and patients who remained disease-free for at = least 5=20 years after RP (10 patients in each group). For each = gene, at=20 least 2 sets of primers were tested, and the set-up = with=20 highest amplification efficiency was selected for the = assay=20 used in this study. Specificity of the assay for mRNA=20 measurements was confirmed by the absence of the = expected PCR=20 products when genomic DNA was used as a template. = GAPDH=20 (5'-CCCTCAACGACCACTTTGTCA-3' and 5'-TTCCTCTTGTGCTCTTGCTGG- = 3')=20 was used as the endogenous RNA and cDNA quantity = normalization=20 control. For calibration and generation of standard = curves,=20 we used several reference cDNAs: cDNA prepared from = primary=20 in vitro cultures of normal human prostate epithelial = cells=20 (15,=20 16),=20 cDNA derived from the PC-3M human prostate carcinoma = cell line=20 (15,=20 16),=20 and cDNA prepared from normal human prostate (15,=20 16).=20 Expression analysis of all genes was assessed in 2 = independent=20 experiments using reference cDNAs to control for = variations=20 among different Q-RT-PCR experiments. Before statistical=20 analysis, the normalized gene expression values were=20 log-transformed (on a base 10 scale) similarly to the=20 transformation of the array-based gene expression = data.=20

Survival = analysis.
The=20 Kaplan-Meier survival analysis was carried out using GraphPad = Prism version 4.00 software (GraphPad Software). The = endpoint=20 for survival analysis in prostate cancer was the = biochemical=20 recurrence defined by the serum PSA increase after = therapy.=20 Disease-free interval was defined as the time period = between=20 the date of RP and the date of PSA relapse (for the = recurrence=20 group) or the date of last follow-up (for the = nonrecurrence=20 group). Statistical significance of the difference between = the=20 survival curves for different groups of patients was = assessed=20 using 3D{chi}2 and log-rank tests. To evaluate the = incremental=20 statistical power of the individual covariates as = predictors of=20 therapy outcome and unfavorable prognosis, we performed = both=20 univariate and multivariate Cox proportional hazard = survival=20 analyses.


  =20 Acknowledgments =
 
We thank C.=20 Pettaway (M.D. Anderson Cancer Center, Houston, Texas, = USA) for=20 providing human prostate cancer cell lines and W. = Sellers=20 (Dana-Farber Cancer Institute, Boston, Massachusetts, = USA) and=20 W. Gerald (MSKCC, New York, New York, USA) for providing = the=20 Affymetrix CEL files of human prostate tumors and associated=20 clinical information. We thank M. McClelland and J. Welsh = for=20 critical comments and technical and material assistance. = We=20 thank D. Mercola and A. Krones-Herzig for technical and=20 logistical help with tissue acquisition and RNA = extraction, and=20 A. Sawyers and Y. Ivanova for excellent technical = assistance.=20 We thank W. Gerald (MSKCC, New York, New York, USA) and = R. Bast=20 (M.D. Anderson Cancer Center, Houston, Texas, USA) for=20 providing the original microarray data files and = associated=20 clinical information. This work was supported in part = by=20 NIH/National Cancer Institute grant 5RO1 CA89827 (to = G.V.=20 Glinsky). This work was greatly facilitated by the use = of=20 previously published and publicly accessible research=20 data.


  =20 Footnotes
 
Nonstandard abbreviations used: MSKCC, = Memorial Sloan-Kettering Cancer Center; MTTS, = metastatic TRAMP=20 tumor sample; PAI, phenotype association index; PNS, = peripheral=20 nervous system; PSA, prostate-specific antigen; = Q-RT-PCR,=20 quantitative RT-PCR; RP, radical prostatectomy; siRNA, = small=20 interfering RNA; SPAI, stem cell=96like PAI; SVM, = support vector=20 machine; TRN, terrain.

Conflict of interest: Gennadi V. Glinsky is a named=20 inventor on a related patent application and serves as = a=20 consultant to Stratagene Inc. (La Jolla, California, = USA) in=20 the field of cancer biomarker development.


  =20 References
Top
Abstract
Introduction
Results
Discussion
Methods
3D"References
&nb= sp;

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M. S. Wicha, S. Liu, and G. = Dontu
Cancer Stem=20 Cells: An Old Idea--A Paradigm Shift
Cancer = Res.,=20 February 15, 2006; 66(4): 1883 - 1890.
[Abstract]=20 [Fu= ll=20 Text] [PDF]=20
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 3D""=20 3D"Cancer3D"Home
R. P. Hill
Identifying Cancer Stem = Cells in Solid=20 Tumors: Case Not Proven
Cancer Res.,=20 February 15, 2006; 66(4): 1891 - 1896.
[Abstract]=20 [Fu= ll=20 Text] [PDF]=20
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 3D""=20 3D"Nucleic3D"Home
K. Nowak, K. Kerl, D. Fehr, C. Kramps, C. Gessner, = K.=20 Killmer, B. Samans, B. Berwanger, H. Christiansen, and W.=20 Lutz
BMI1 is a target gene of E2F-1 and is = strongly=20 expressed in primary neuroblastomas.
Nucleic = Acids Res.,=20 January 1, 2006; 34(6): 1745 - 1754.
[Ab= stract]=20 [Full=20 Text] [PDF]=20
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 3D""=20 3DCarcinogenesis=203D"Home
M. Mimeault and S. K. Batra
Recent = advances on=20 multiple tumorigenic cascades involved in prostatic cancer=20 progression and targeting = therapies
Carcinogenesis,=20 January 1, 2006; 27(1): 1 - 22.
[Ab= stract]=20 [Full=20 Text] [PDF]=20
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W. Debinski
Personalized, Multivalent, = and More=20 Affordable: The Globalization of Vaccines
Clin. = Cancer=20 Res., August 15, 2005; 11(16): 5663 - 5664.
[Full=20 Text] [PD= F]=20
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J. P. Lahad, G. B. Mills, and K. R. = Coombes
Stem=20 cell-ness: a "magic marker" for cancer
J. Clin. = Invest.,=20 June 1, 2005; 115(6): 1463 - 1467.
[Abstract]=20 [Full=20 Text] [PDF] =
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