progenitor-like traits contribute to patient survival and ... · imaging, diagnosis, prognosis...

Imaging, Diagnosis, Prognosis

Progenitor-like Traits Contribute to Patient Survival andPrognosis in Oligodendroglial Tumors

Felicia Soo-Lee Ng1,2, Tan Boon Toh3, Esther Hui-Ling Ting3, Geraldene Rong-Hui Koh5, Edwin Sandanaraj1,Mark Phong2, Swee Seong Wong2, Siew Hong Leong6, Oi Lian Kon6, Greg Tucker-Kellogg2,4, Wai Hoe Ng7,8,Ivan Ng7,8, Carol Tang5,6,8, and Beng Ti Ang1,3,7,8

AbstractPurpose: Patient-derived glioma-propagating cells (GPC) contain karyotypic and gene expression

profiles that are found in the primary tumor. However, their clinical relevance is unclear. We ask whether

GPCs contribute to disease progression and survival outcome in patients with glioma by analyzing gene

expression profiles.

Experimental Design: We tapped into public sources of GPC gene expression data and derived a gene

signature distinguishing oligodendroglial from glioblastoma multiforme (GBM) GPCs. By adapting a

method in glioma biology, the Connectivity Map, we interrogated its strength of association in public

clinical databases. We validated the top-ranking signaling pathways Wnt, Notch, and TGFb, in GPCs and

primary tumor specimens.

Results:We observed that patients with better prognosis correlated with oligodendroglial GPC features

and lower tumor grade, and this was independent of the current clinical indicator, 1p/19q status. Patients

with better prognosis had proneural tumors whereas the poorly surviving cohort hadmesenchymal tumors.

In addition, oligodendroglial GPCs were more sensitive to Wnt and Notch inhibition whereas GBM GPCs

responded to TGFbR1 inhibition.

Conclusions: We provide evidence that GPCs are clinically relevant. In addition, the more favorable

prognosis of oligodendroglial tumors over GBM could be recapitulated transcriptomically at the GPC level,

underscoring the relevance of this cellular model. Our gene signature detects molecular heterogeneity in

oligodendroglial tumors that cannot be accounted for by the 1p/19q status alone, indicating that stem-like

traits contribute to clinical status. Collectively, these data highlight the limitation of morphology-based

histologic analyses in tumor classification, consequently impacting on treatment decisions.Clin Cancer Res;

18(15); 4122–35. �2012 AACR.

IntroductionGliomas are among the most prevalent of adult malig-

nant brain tumors and have a poor prognosis despiteadvanced surgical intervention and adjuvant radiotherapyand chemotherapy. In transgenicmousemodels, the gliomacell-of-origin has been proposed to be the neural stem cellor oligodendroglial precursor (1–3). Such cells have beenshown to be enriched in glioma-propagating cells (GPC)cultured in vitro (4); however, specific markers for the GPChave been controversial (5). This forces a reevaluation of thedefinition of the GPC to encompass the following charac-teristics (6): They must show long-term self-renewal, stem-ness and multipotentiality and the ability to form seriallytransplantable, orthotopic xenograft tumors that recapitu-late the patient’s original histopathology. Laks and collea-gues recently showed that the ability to formGPC spheres iscorrelated with clinical outcome (7). While the lengthyperiod of sphere culture precludes their use in clinicalapplications, these data nevertheless suggest that GPCscultured in vitro contribute to patient survival. Here, weasked the critical question of whether a gene expression

Authors' Affiliations: 1Singapore Institute for Clinical Sciences, Agencyfor Science, Technology and Research (A�STAR); 2Lilly Singapore Centrefor Drug Discovery, Eli Lilly and Company; 3Department of Physiology,Yong Loo Lin School of Medicine, 4Department of Biological Sciences,Faculty of Science, National University of Singapore; 5Department ofResearch, National Neuroscience Institute; 6Division of Medical Sciences,Humphrey Oei Institute of Cancer Research, National Cancer Centre;7Department of Neurosurgery, National Neuroscience Institute; and8Duke-National University of Singapore Graduate Medical School,Singapore

Note: Supplementary data for this article are available at Clinical CancerResearch Online (http://clincancerres.aacrjournals.org/).

F.S.-L. Ng and T.B. Toh contributed equally to the work.

Corresponding Authors: Carol Tang, Department of Research, NationalNeuroscience Institute, 11Jalan TanTockSeng,Singapore308433.Phone:65-6357-7634; Fax: 65-6256-9178; E-mail: [email protected]; BengTi Ang, [email protected]; and Felicia Soo-Lee Ng, SingaporeInstitute for Clinical Sciences, A*STAR, Brenner Centre for MolecularMedicine, 30 Medical Drive, Singapore 117609. Phone: 65-6407-0100;Fax: 65-6776-6840; E-mail: [email protected]

doi: 10.1158/1078-0432.CCR-11-3064

�2012 American Association for Cancer Research.

ClinicalCancer

Research

Clin Cancer Res; 18(15) August 1, 20124122

on December 6, 2020. © 2012 American Association for Cancer Research. clincancerres.aacrjournals.org Downloaded from

Published OnlineFirst June 6, 2012; DOI: 10.1158/1078-0432.CCR-11-3064

http://clincancerres.aacrjournals.org/

profile from GPCs is reflected in bulk tumor analysis andcontributes to patient survival and prognosis. These datawould be important because they: (i) provide evidence thatin vitro cultured, tumor-propagating stem-likeGPCs containmolecular information that contribute to patient survivaloutcome, thus directly linking these controversial GPCs tothe primary tumor and (ii) validate that the transcriptomicchanges driving the tumor phenotype reside in these GPCs,thus providing molecular evidence for their relevance as invitro culture systems. The cancer stem cell (CSC) hypothesishas been studied by many in several tumor types, withevidence suggesting that CSCs are themost likely culprits oftumor recurrence and regrowth (8). Consequently, thegeneral thought of the community is that effective treat-ments must target these CSC traits, often using survival asthe endpoint to monitor in animal models. Such studies donot reveal the clinical impact of CSCs in databases of actualpatient gliomas. As such, there is a need to show this directcorrelation between GPC properties and survival outcome.Several studies have illuminated the clinical contribution oftumor-propagating acute myeloid leukemia stem cells,breast CSCs, and colorectal CSCs to disease outcome andrelapse (9–12), thus highlighting that similar, importantconclusions might be made from analyzing GPCs frommajor glioma variants currently available through publicinformation.Recent works have shown that major glioma variants

glioblastoma multiforme (GBM) and oligodendroglialtumors are molecularly heterogeneous, with each subclassdistinguished by unique gene expression, genetic aberra-tions, and clinical profile (13–16). These findings highlightthat gene expression drives disease progression and survivaloutcome. We used gene expression analyses to explore the

clinical relevance of GPCs derived from GBM and oligo-dendroglial tumors, by tapping into publicly available GPCgene expression data fromour study and that of others (17–19) to enlarge the pool of cells and subsequently interro-gating their contribution in 2 large, clinical databases,REMBRANDT (20) and "Gravendeel" (15). Specifically, weasked whether GPCs derived from these 2 major gliomavariants show the uniqueness of each tumor subtype. Oli-godendroglial tumors tend to have a favorable prognosisand chemosensitivity is predicted by the only known clin-ical indicator, the 1p/19q codeletion status (21). It wouldtherefore be important to understand whether molecularheterogeneity exists that cannot be explained by currentclinical indicators alone. We adapted the Connectivity Mapmethod (22) to determine strengths of association betweenthe GPC gene signature and patient gene expression pro-files. The Connectivity Map was first used to define thestrengths of association of a biologic state (represented by agene signature) to the action of small-molecule therapeutics(a database of reference profiles). This method is advanta-geous in allowing us tomake connections between differentdata platforms and biologic information through the com-mon vocabulary of genome-wide expression profiling.Since then, the Connectivity Map has been successfullyused to determine the degree of oncogenic pathway activa-tion in gastric cancer (23). We also recently showed thatsuch amethod supported themechanistic role of Parkin as atumor suppressor in glioma (24). We show that this oligo-dendroglial gene signature is a positive prognostic factor ingliomas and detects heterogeneity in oligodendroglialtumor patient survival profiles that cannot be predicted bythe 1p/19q codeletion status alone. Moreover, we couldcorrelate our signature enrichment with tumor grade andthe "Phillips" molecular classification of gliomas (25).Collectively, our data establishes the direct relation betweenGPCs and patient primary tumors, emphasizing the clinicalrelevance of these cultured cells.

Materials and MethodsTissue collection and primary GPC culture

All GPCs except Pollard lines used in this study werecultured as spheroid structures in serum-free media supple-mented with basic fibroblast growth factor and EGF (17–19). Although Pollard lines were cultured on laminin (19),a recent molecular classification study showed that bothculture methods preserved the biologic and functionalsignaling pathways (26). This provides justification for oursubsequent analyses.

Graded brain tumor specimens were obtained withinformed consent, as part of a study protocol approved bythe Institutional ReviewBoard. NNI-1, 2, 3, 4, 5, 10, 11, and12 were derived from patients with GBM as previouslydescribed and serially propagated in nonobese diabetic/severe combined immunodeficient (NOD/SCID) gammamice (17).Only low passageGPCswere used for our studies(P1-10). We and others have shown previously that serialpropagation as orthotopic xenografts maintains GPC traits

Translational RelevanceGliomas predominate the spectrum of adult malig-

nant brain tumors and can arise from stem-like glioma-propagating cells (GPC). Patient-derived GPCs arecontroversial because their cell-of-origin cannot be iden-tified; however, they have been shown to contain kar-yotypic and gene expression profiles that mirror theprimary tumor. We sought to determine the GPC con-tribution to patient survival and prognosis by analyzinggene expression profiles. By adapting the ConnectivityMap, we tapped into public GPC data from multipleinvestigators and interrogated their contribution in pub-lic glioma clinical databases. We show that patients withbetter prognosis tend to exhibit oligodendroglial GPCprogenitor characteristics independently of the 1p/19qstatus, the current clinical indicator. In addition, stem-like signaling pathways such as Wnt, Notch, and TGFbdistinguish oligodendroglial from glioblastoma multi-forme (GBM) GPCs. Taken together, our data provide adirect link between GPCs and disease progression,highlighting the clinical relevance of these cells.

Stem-like Cells Contribute to Glioma Disease Outcome

www.aacrjournals.org Clin Cancer Res; 18(15) August 1, 2012 4123




(17, 27). NNI-7 and NNI-8 are newGPC lines derived frompatients with anaplastic oligoastrocytoma and character-ized according to previous methods (Supplementary Figs.S1 and S2; ref. 17). Accordingly, NNI-8 GPCs exhibitedpositivity for CD133 and Nestinmarkers, with low levels ofSSEA-1/CD15 and aldehyde dehydrogenase (ALDH; Sup-plementary Fig. S1B).GPC frequency andproliferationwerealsomaintained for up to 3passages in vitro (SupplementaryFig. S1C).NNI-8GPCs showedpresenceof stemnessmarkerexpression such as Musashi-1 (Msi-1), octamer-bindingtranscription factor 4 (Oct4) and were actively dividing asindicated by approximately 30% of Ki-67–positive cells(Supplementary Fig. S1D). The GPCs also exhibited multi-lineage potential, showing presence of aberrant cells cost-aining for neuron-specific class IIIb-tubulin (TuJ1) and glialfibrillary acidic protein (GFAP; Supplementary Fig. S1D).Finally, NNI-8 GPCs formed serially transplantable tumorxenografts in NOD/SCID gamma mice that recapitulatedthe patient’s original histopathology with maintenance ofstemness expression in both primary tumors and low-pas-sage GPCs (Supplementary Fig. S2). These data verify thatserial tumor propagation maintains the GPC phenotype.

"Gunther" lines: GS-1, 3, 4, 5, 6, 7, and 8 are GBM-propagating cells whereas GS-2 was derived from a high-grade tumor with oligodendroglial features as previouslydescribed (18).Cell lineswere cultured for up to 14passagesin vitro with preservation of transcriptomic profiles. "Pol-lard" lines: G144, 144ED, 166, 179, and GliNS2 are GBM-propagating cells whereas G174 was derived from a patientwith anaplastic oligoastrocytoma as previously described(19). Pollard lines could be cultured for 1 year (>20 pas-sages) with preservation of key stemness/differentiationexpression, karyotypic hallmarks, and tumor propagation.

Sphere assays, immunofluorescence analysis, andintracranial glioma mouse model

Sphere assays, immunofluorescence analysis, and theintracranial glioma mouse model were described in ourearlier work (17). Approval for in vivo analysis was soughtfrom the National Neuroscience Institute, InstitutionalAnimal Care and Use Committee. Antibodies used forimmunofluoresence included: (i) mouse monoclonalanti-Nestin (1:200, Chemicon, MAB5326); anti-Ki-67(1:200, Chemicon, MAB4190); anti-TuJ1 (1:200, Chemi-con, MAB1637), and anti-O4 (1:50, Chemicon, MAB345);(ii) rabbit polyclonal anti-Oct4 (H-134, 1:100, Santa Cruz,SC9081); anti-GFAP (1:3000, DAKO, ZO334), and anti-Musashi (1:100, Chemicon, AB5977). Antibodies used forprimary tumor or tumor xenograft paraffin sections includ-ed: (i) mouse monoclonal anti-Nestin (1:500, Chemicon,MAB5326); anti-ALDH (1:100, BD Biosciences, #611194),and anti-active b-catenin (1:300, Millipore, #05-665); and(ii) rabbit polyclonal anti-CD133 (1:500, Abcam,ab19898); anti-activated Notch (1:500, Abcam, ab8925),and anti-phospho-Smad2 (Ser465/467; 1:50, Millipore,AB3849). NOD/SCID gamma mice (NOD.Cg-Prkdcscid

Il2rgtm1Wjl/SzJ, The Jackson Laboratory) were stereotaxicallyinjected with NNI-8 or lentivirally-transduced GPCs using

pLKO.1-b-catenin knockdown constructs [clones: shbcat1(TRCN0000003843) and shbcat2 (TRCN0000003844)]from Open Biosystems or a nontargeting (NT) controlshRNA (SHC002, Sigma), packaged with pLenti-X packag-ing system according to the manufacturer’s instruction(Clontech). Animals were monitored for time to develop-ment of neurologic deficits. Kaplan–Meier survival analyseswere carried out using the log-rank test in GraphPad Prismsoftware.

Small-molecule inhibitors and reagentsThe small-molecule inhibitor of Wnt signaling, cercos-

porin (28) was purchased from Sigma. CCT036477 (29)was manufactured and synthesized by Laviania Corpora-tion according to the published chemical structure. Thesmall-molecule inhibitors of Notch signaling, g-secretaseinhibitor (30) and DAPT (N-[N-(3,5-difluorophenacetyl)-L-alanyl]-S-phenylglycine-t-butyl ester; ref. 31) were pur-chased from Sigma. TGFbR1 inhibitor, SB525334 (32) wasprocured from TOCRIS bioscience. GPCs were treated at 10mmol/L for g-secretase inhibitor, DAPT, and SB525334, andat IC50 concentrations for cercosporin and CCT036477(data not shown). TGFb1, used at 200 pmol/L, wasobtained from R&D.

Immunoblot analysisGPC cells were pelleted and lysed in buffer containing

0.5% sodium deoxycholate, 1% NP-40 detergent, 0.1%SDS, 0.15 mol/L NaCl, 10 mmol/L Tris-HCl (pH 7.4), withprotease and phosphatase inhibitors cocktail tablets(Roche). Equal amounts of protein lysate were resolved bySDS-PAGE and electrotransferred onto polyvinylidenedifluoride membranes. Membranes were processed accord-ing to standard procedures and proteins detected using theimaging system, SYNGENE G:Box, iChemiXT. The follow-ing antibodies were used: anti-active b-catenin (8E7,1:1,000; Millipore, #05-665), anti-b-catenin (1:1,000, BDTransduction Laboratories, #610153), anti-cleavedNotch 1(NICD; 1:1,000; Cell Signaling, #2421), anti-phospho-Smad2 (Ser465/467; 1:1,000; Cell Signaling, #3108),anti-Smad2 (1:1,000; Cell Signaling, #3122), anti-phos-pho-Smad3 (Ser423/425; 1:1,000; Millipore, #07-1389),anti-Smad3 (1:1,000; Cell Signaling, #9523), and anti-b-actin (AC-15, 1:10,000; Sigma, A5441).

Statistical analysisData are expressed as means � SEM of at least 3 inde-

pendent experiments. Student t or Mann–Whitney U testwas used where appropriate. P � 0.05 was accepted asstatistically significant.

Processing of microarray data, gene signaturegeneration, and pathway analysis

Affymetrix U133 Plus 2.0 CEL files were mas5 processedand quantile normalized in the R statistical software usingthe affy packages (33, 34). Probes with "absent" call in allsampleswere removed.Microarray datawere obtained fromtheGunther and colleagues (18) andPollard and colleagues

Ng et al.

Clin Cancer Res; 18(15) August 1, 2012 Clinical Cancer Research4124




(19) publication and were processed similarly. To under-stand the transcriptomic differences between oligodendrog-lial gliomas and GBM, a linear model was fitted with batchcorrection using the limma package (35). In addition, alinear model was applied to gene expression data ofNNI-8 GPC cells and its primary tumor. For both analyses,probe sets with adjusted P value � 0.05 were consideredsignificant and used as inputs for pathway analysis inMetaCore fromGeneGo, Inc. Significantly enriched processnetworks and canonical pathways were analyzed and topranking results were reported. All array platform gene anno-tation was derived from Biomart (36). Raw and processeddata are available on the GEO public database website(GSE31545).To further interrogate the oligodendroglial feature of

glioma, we defined an "oligodendroglial GPC signature"using a log ratio cutoff value of 0.8. Similarly, the "NNI-8GPC versus tumor" stemness signature was obtained bytaking the top ranking differentially expressed probe setsusing the log ratio cutoff value of 6. These signatures wereused in the Connectivity Map analysis.

Connectivity Map analysisWe adapted the Connectivity Map method (22) to score

glioma gene expression databases based on the extent ofpathway activation associated with our GPC gene signature.(i) First, we defined an "oligodendroglial GPC signature"—a set of genes exhibiting altered expression between 2 cellstates (oligodendroglialGPCvs.GBMGPC), (ii) Second,wegenerated databases of reference gene expression profilesfrom 2 glioma databases—REMBRANDT and "Gravendeel"(15, 20), (iii) Third, using a nonparametric, rank-basedpatternmatching procedure, wemapped the GPC signatureonto each patient gene expression profile and calculatedactivation scores based on the strength of association tothe GPC signature, and finally, (iv) The patients weresorted according to their pathway activation scores.Two patient classes were identified, (þ) and (�), where apositive activation score indicates that the patient geneexpression profile is positively associated to the gene sig-nature and vice versa. The 2-tailed test P values associatedwith each activation score were calculated as described inLamb and colleagues (22). P values � 0.1 were consideredsignificant.

Reference profile generation for Connectivity MapanalysisPublic GBM data sets with clinical data, in terms of

survival length, histology, grade, and age were obtainedfrom the REMBRANDT database (37) and the GEO data-base in the case of the Gravendeel data set (GSE16011). Togenerate the reference profiles, all raw files were processedseparately using the mas5. Expression values less than thethreshold value of 50 were replaced with the thresholdvalue. Next, these data were quantile normalized and geneexpression values were row-wise median centered. Mediancentering each probe set allows us to study the range of geneexpression values in a large data set.

Survival analysisKaplan–Meier andCox regression analysis of (þ) and (�)

groups were done in R using the survival package (38). Forthe REMBRANDT data set, only survival ranges were avail-able. Hence, the lower limit of the range was used in thisanalysis.

Prediction of Phillips Classification in REMBRANDTand Gravendeel data sets

To classify the REMBRANDT and Gravendeel samplesaccording to the Phillips and colleagues classification (25),Affymetrix U133A probes for the Phillips molecular sub-types were extracted from the publication. A shrunkencentroid model was trained and tested on the Phillips dataset (Supplementary Table S1; overall error rate 0.12) usingthe R package pamr (39). Next, classification of theREMBRANDT and Gravendeel data sets was conductedusing the trained model.

REMBRANDT SNP array processing and 1p19q LOHanalysis

CEL files from the Affymetrix 100K SNPArrays of patientswith oligodendroglioma and oligoastrocytomawere down-loaded from the REMBRANDT database and all sampleswere normalized in dChip (40, 41). Genotyping calls weregenerated in the Affymetrix Genotyping Console (Affyme-trix Inc.) software using the BRLMM algorithm. Chromo-some 1p and 19q loss-of-heterozygosity inference was car-ried out using an HMM algorithm in dChip with defaultparameters.

Gene set enrichment analysisThe gene signature was further evaluated in molecular

signature database using gene set enrichment approach.Gene set enrichment analysis (GSEA) tool was downloadedfrom Broad Institute portal (42). The significantly enrichedgene sets in molecular signature database (MSigDB) werefurther analyzed for phenotypic correlation in the referencedata sets.

ResultsAn oligodendroglial GPC gene signature is defined

For our purpose, we evaluated publicly available GPCgene expression data from 3 groups to enlarge our pool ofcells. We first determined differentially regulated genesbetween oligodendroglial GPCs (NNI-8, GS-2, G174) and17 GBM GPCs collectively obtained from our studies (thisstudy and ref. 17),Gunther and colleagues (18), andPollardand colleagues (ref. 19; Supplementary Fig. S3A,workflow).This differential gene list, "oligodendroglial GPC signa-ture," is shown in Supplementary Table S2. An analysis ofthe associated pathway networks using the GeneGo pro-gram revealed that the signature was enriched in the Wnt,Notch, and TGFb signaling pathways (Supplementary Fig.S3B). Interestingly, Notch (43, 44), TGFb (45, 46), and therecently published Wnt (47) signaling pathways have beenshown to be crucial in maintaining the growth of GBMGPCs.






Functional validation of the Wnt, Notch, and TGFbpathways in GPCs

Although the Wnt, Notch, and TGFb pathways regulateGBM GPC survival, their relation to the major gliomavariants—oligodendroglial and GBM GPCs is unclear. Pre-viously, the oligodendroglial gene signature enrichedfor the Wnt, Notch, and TGFb signaling pathways (Supple-mentary Fig. S3B); however, their precise activation ordownregulation remains to be tested. To assess pathwayactivation inGPCs (NNI-4, 7, 8, 10, 11, 12),we carried out 2assays: (i) immunoblot analysis of key pathway compo-nents; and (ii) dependence on pathway by using well-established pharmacologic inhibitors.

NNI-7 and NNI-8 oligodendroglial GPCs showedincreased sensitivity to cercosporin (28) and CCT036477(29) compared with the other 2 of 3 GBM GPCs tested,consistent with the highest level of active b-catenin detected[nuclear-localized; Fig. 1A (i) and B (i)]. Sphere frequencywas significantly reduced upon pathway inhibition, indi-cating that GPCs were targeted [Fig. 1B (i)]. Moreover,lentiviral-mediated b-catenin knockdown abrogated glio-ma formation in an orthotopicmousemodel, consequentlyextending survival compared with the nontargeting vectorcontrol group (Fig. 2; P < 0.001).

Next, we assessed Notch pathway activation in our GPCs.Using g-secretase inhibitor (30) and DAPT (31), weobserved that NNI-7 and NNI-8 GPCs were most sensitiveto pathway inhibition than NNI-11, 12, and 4 [Fig. 1B (ii)].Again, these findings were consistent with the immunoblotanalysis showing highest level of Notch intracellulardomain (NICD) detected in NNI-8 GPCs [Fig. 1A (ii)].

Finally,we testedTGFb signalingbyusing SB525334 (32).Interestingly, all 3 GBM GPCs showed sensitivity toSB525334 with up to 80% inhibition in NNI-4 [Fig. 1B(iii)]. A less clear pattern of phospho-Smad2 and phospho-Smad3 levelswasobserveduponTGFb1 stimulation [Fig. 1A(iii)]. This may reflect the redundant roles of various Smadproteins inGPC regulation (46).Our data indicate that GPCfrequency was preferentially targeted in GBM GPCs.

Collectively, our data indicate, albeit a limited panel ofGPCs used, that Wnt and Notch pathways are upregulatedin NNI-7 and NNI-8 oligodendroglial GPCs, whereas TGFbpathway is active in all GBM GPCs tested.

The oligodendroglial GPC signature stratifies patientsurvival in gliomas

Next, we analyzed the strength of association of this genesignature with patient gene expression data fromREMBRANDT and Gravendeel (15, 20). We assigned pos-itive "(þ)" and negative "(�)" activation scores with sig-nificant P values (Supplementary Table S3) and observedthat the gene signature separated (þ) and (�) patientcohorts that make up 30% to 50% of all patients in eachdatabase (Table 1). Importantly, the signature stratifiedpatient survival (Fig. 3). Patients with better survival com-posed of (þ) association (more oligodendroglial GPCassociation) whereas poorly surviving patients tended tobe (�; i.e., more GBM GPC association; REMBRANDT P

value, 1.93E-05; Gravendeel P value, 0.0082). The (þ)activation score also contained more low-grade gliomas,especially enriched for oligodendrogliomas; whereas the(�) activation score enriched for high-grade gliomas withmainly GBMs. Cox regression analysis indicated that theGPC gene signature served as a significant prognostic indi-cator and the positive score patients (oligodendroglial GPC-like) in REMBRANDT had 54% lower risk of death; the HR(95% confidence interval, CI) was 0.462 (0.322–0.664) in aunivariate model (P¼ 2.90E-05). Consistently, the positivescore patients in Gravendeel were associated with 47%lower risk of death and the HR (95% CI) was 0.535(0.334–0.856) in a univariate model (P ¼ 0.009). Thisassociation remains significant in REMBRANDT afteradjusting for other clinical factors such as age and tumorgrade (P ¼ 2.22E-05). Although we did not detect a signif-icant multivariate analysis P value in the Gravendeel dataset, this does not mean the absence of GPC transcriptomecontribution to patient survival as shown in theREMBRANDT data set. First, most glioma databases areretrospectively generated and therefore, this limits ourability to assess the true predictive value of the gene signa-ture. Second, a significant P value was observed in theunivariate analysis, highlighting the relevance of the genesignature as an alternative prognostic tool. Collectively,these results suggest that distinct GPCs likely drive tumorformation and give rise to differences in response totherapy.

The oligodendroglial GPC signature correlates with"Phillips" molecular classification of gliomas

We next attempted to strengthen our findings basedon the Connectivity Map (CMAP) by asking whether ourGPC-derived gene signature could predict glioma survivaloutcome similar to other existing molecular-based classifi-cation schemes. This would be important to further validatethe significance of the GPC-derived signature in relation todisease progression. We applied as an independent geneexpression–based approach, the "Phillips" classification ofgliomas (25)whichmolecularly categorizes the tumors into3 subclasses: proneural, proliferative, and mesenchymal.The (þ) activation score enriched for the proneural sub-class, whereas the (�) activation score tended to be prolif-erative or mesenchymal (Fig. 3; Supplementary Table S4).Proneurals are typically lower grade gliomas with oligoden-droglial features, frequently associated with better progno-sis; in contrast, the mesenchymal subclass characterizeshighly aggressive, recurrent gliomas such as GBM. Interest-ingly, recent work has suggested that oligodendrogliomasare more chemosensitive because their cells-of-origin areoligodendrocyte precursor cells (OPC), compared with themore resistant neural stem cells and astrocytes inGBM (48).Although this conclusion arose from transgenic mousemodels, we find it intriguing that all cultured patient-derived GPCs from multiple studies are transcriptomicallyconsistent with this hypothesis; however, we cannot defin-itively pinpoint the identity of GPCs due to its humanorigin. Furthermore, to eliminate the possibility that we

Ng et al.





Figure 1. Functional validation of theWnt, Notch, and TGFb signaling pathways in GPCs. Immunoblot analyses to evaluate key signaling components and useof pharmacologic agents to assess pathwaydependenceover 21days (to detect slow-growingGPCs)were carried out for A (i), B (i),Wnt; A (ii), B (ii), Notch; andA (iii), B (iii), TGFb pathways. �, P < 0.05; ��, P < 0.01; ��, P < 0.001. DMSO, dimethyl sulfoxide.






were biasing the gene signature selection toward bettersurviving oligodendroglial tumors by our filtering proce-dure, we in addition derived a "stemness" gene signature bycomparing NNI-8 GPCs to its primary tumor (Supplemen-tary Tables S5–S7). This, we rationalized, would allow anassessment of the GPC traits within the bulk tumor mass.This gene signature similarly stratified patient survival, withthe (þ) class enriched for lower grade tumors of proneuralclassification, whereas the (�) class enriched for highergrade tumors with mesenchymal features (SupplementaryFig. S4). Collectively, our data support that human oligo-dendroglial GPCs contribute to favorable prognosis, likelymediated by more chemosensitive OPC-like properties.

The oligodendroglial GPC signature is enriched in theWnt, Notch, and TGFb pathways in patient gliomadatabases

Our previous findings indicate that the oligodendroglialGPC signature is enriched in the Wnt, Notch, and TGFbsignaling pathways (Supplementary Fig. S3B); however,their activation or downregulation is unclear. On the basisof our in vitro data in a limited but unique GPC collection(Fig. 1), we suggested that oligodendroglial GPCs weremore sensitive to Wnt and Notch inhibition, whereas GBMGPCs tended to be responsive to TGFbR1 inhibition. Inrecognizing the limitations posed by a small GPC panel, aswith any such studies to-date, we sought to understandwhether our GPC-derived conclusions bore similar signif-icance in 2 of the largest patient glioma databases. Werationalized that our hypothesis would suggest the similar

regulation of signaling pathways as predicted by our GPCsin Fig. 1 and the sheer number of patients in REMBRANDT(N ¼ 298) and Gravendeel (N ¼ 276) would provide firmevidence. In addition, we analyzed a panel of primarytumors by immunohistochemical staining and observedsimilar pathway regulation (Fig. 4); that is, GBM tumorsexhibited elevated p-Smad2 expression (P¼0.0122)where-as oligodendroglial tumors displayed elevated NICDexpression (P¼ 0.0331) and a trend toward elevated activeb-catenin (3 of 4 tumors). Accordingly, usingGSEA (49),weobserved the following (Table 2): (i) The (�) activationscore patients defined by our Connectivity Map, whichcorrelate inversely with the oligodendroglial gene signature(i.e., more GBM GPC-like) in both databases, showedupregulated TGFb1 response pathways upon closer analysisof the gene modules, further supported by downregulationof this pathway in Gravendeel (þ) cohort. This is consistentwith our in vitrodatawhich suggest thatGBMGPCs respondmore strongly to TGFbR1 inhibition than oligodendroglialGPCs [Fig. 1A (iii) and B (iii)]. Furthermore, Gravendeel(�) patients showed upregulation of the Nutt_GBM versusAO (anaplastic oligodendroglioma) gene module, provid-ing an independent verification that our GBM versus oli-godendroglial GPCs mirror their primary tumor transcrip-tomic profile; (ii) The (þ) patient cohort in Gravendeelshowed upregulation of Wnt signaling pathway, againconsistent with our in vitro data where NNI-7 and NNI-8oligodendroglial GPCs were more sensitive to Wnt inhibi-tion [Fig. 1A (i) and B (i)]; and (iii) The REMBRANDT (�)patients showed upregulation of Notch signaling. Upon

Table 1. Summary of results from Connectivity Maps, log-rank, and Cox regression analysis for all patientsamples

Connectivity Maps analysisLog-rank

Multivariate Cox Univariate Cox

Data set # of probes # of samples (þ) (�) Total (þ)(�) % (þ)(�) P HR P HR P

REMBRANDT 95 298 86 61 147 49.33 1.93E-05 0.440 (0.301–0.643) 2.22E-05 0.462 (0.322–0.664) 2.90E-05

Gravendeel 95 276 58 34 92 33.33 0.0082 0.851 (0.504–1.436) 0.546 0.535 (0.334–0.856) 0.009

NOTE: (þ) represents patients with concordance to oligodendroglial GPC signature; (�) represents patients with inverse geneexpression relationship to oligodendroglial GPC signature.

Figure 2. Lentiviral-mediatedb-catenin knockdown abrogatesGPC tumorigenicity. NOD/SCIDgamma mice were implanted withGPCs lentivirally transduced withnontargeting control (NT), shbcat1,or shbcat2 vectors. The time todevelopment of neurologic deficitswas monitored (A). ��, P < 0.001.Representative images of mousebrains are shown in B. Scale bardenotes 0.2 cm.

Ng et al.





closer analysis, this upregulation comprised the Notchinhibitor, Numbl homolog, which acts to inhibit Notchsignaling. This is thus consistent with Fig. 1A (ii) and B(ii) findings where NNI-7 and NNI-8 oligodendroglialGPCs were more sensitive to Notch pathway inhibitors. Inaddition, we analyzed the enrichment of core stem cellprograms (embryonic, hematopoietic, and neural stemcell) in the patient cohorts (12). CMAPþ patients displayan enrichment of progenitor-like behavior with lowertumor grade, whereas CMAP� patients resemble the

CD34þ leukemia-initiating and -propagating cells (Table2). These data suggest that core stem cell programs docontribute to the survival-correlated CMAP patientcohorts. Collectively, by interrogating independent, pub-lic glioma databases, we show that predictions made byour oligodendroglial GPC signature produced congruentdata in GPCs, primary tumors, and patient databases. Thisthus supports our hypothesis that GPCs mirror theirprimary tumors and contribute to disease progressionand survival outcome.

Figure 3. Oligodendroglial GPCgene signature stratifies patientsurvival. Patient survival is shown inall glioma patients in A,REMBRANDT; and B, Gravendeeldatabases. Tumor grade ("Grade")and molecular classification("Phillips") distributioncorresponding to (þ) and (�) classesare shown below the activation scoregraphs.






The oligodendroglial GPC signature defines molecularheterogeneity within oligodendroglial tumors

Next, we interrogated this GPC gene signature inpatients with oligodendroglial tumors. The (þ) classenriched for lower tumor grades associated with the1p/19q codeletion (Fig. 5). Interestingly, patients withoutloss-of-heterozygosity at 1p/19q (yellow) were spreadthroughout both classes, indicating that our stemness genesignature detected molecular heterogeneity and survivalprofiles that cannot be accounted for by the 1p/19q statusalone. Although these retrospective data cannot determinewhether the gene signature is an independent predictor ofsurvival; furthermore, the 1p/19q status is specificallyrelated to PCV chemotherapy (procarbazine, CCNU andvincristine; ref. 21); nevertheless, these data do suggest thatthe signature is a positive prognostic factor for humanglioma patients.

DiscussionGPCsmirror the phenotypic andmolecular fingerprint of

their primary tumors (27). Consequently, they serve as auseful in vitro platform to carry out further investigations.However, much less is known about their direct contribu-tion to disease progression and survival outcome. Weattempted to address this gap in knowledge by: (i) tappinginto publicly available GPC gene expression from severalinvestigators and determining the differential gene listbetween 2 major variants, oligodendroglial GPCs versusGBM GPCs for which distinct patient survival patterns areseen in the primary tumors; (ii) using a rank-based, pattern-matching approach, the Connectivity Map (CMAP), tointerrogate the strength of association between the oligo-dendroglial gene signature and individual patient geneexpression profiles, as gene expression drives gliomadiseaseoutcome (16); (iii) drawing connections between (þ) or(�) patients, tumor grade, and primary tumor molecularclassification.

We found that oligodendroglial GPCs could be distin-guished from GBM GPCs by Wnt, Notch, and TGFb regu-lation. Although these findings are not entirely novel in thatthese pathways were previously implicated in GBM GPCs,their relation between the 2major variants—oligodendrog-lial versus GBM GPCs is unclear. Our in vitro analysisshowed that Wnt and Notch pathways were upregulatedin NNI-7 and NNI-8 oligodendroglial GPCs, whereas TGFbsignaling was upregulated in GBM GPCs. Moreover, thesepathways were similarly detected in primary tumors. Inter-estingly, Lottaz and colleagues showed that mesenchymalGPCs map into the mesenchymal class of primary tumorsand exhibit upregulated TGFb signaling pathway (26). Inrecognizing that a limited number of patient specimenswere available for our in vitro and primary tumor analyses,we sought to tap into major patient glioma gene expressionand molecular signature databases to substantiate ourhypothesis that GPCs contribute to disease outcome.Indeed, using our oligodendroglial gene signature, ourGSEA study indicated that patients with GBM (CMAP�)are enriched in the TGFb signaling module, whereaspatients with oligodendroglial tumors (CMAPþ) areenriched in the Wnt and Notch pathways. Moreover,CMAPþ patients display a progenitor-like transcriptomicprogram that correlates with lower tumor grade, consistentwith the idea previously established in a transgenic mousemodel of oligodendroglioma that identified the more line-age-committed oligodendrocyte progenitor cell as thetumor cell-of-origin (48). Furthermore, these cells are moresensitive to standard chemotherapeutic drugs than neuralstem cells. Our study is important because it providesclinical evidence, using large databases, that GPCs containsignaling pathways that dictate primary tumor progression,consequently impacting on survival outcome. These find-ings emphasize the relevance of in vitro cultured GPCs asinvestigational tools. Furthermore, our oligodendroglialgene signature stratified survival of even oligodendroglialtumor patients without 1p/19q LOH, suggesting that this

Figure 4. Analysis ofWnt, Notch, and TGFb signaling pathways in primarytumors. Active b-catenin (A); NICD (B); and phospho-Smad2 (C) wereimmunohistochemically detected in patient tumors of GBM andoligodendroglial (oligo) features.

Ng et al.





Table 2. Summary of gene set enrichment analysis

Gene sets Description SizeNormalizedenrichment Score FDR Q value CMAP class association

ST_WNT_BETA_

CATENIN_PATHWAY

Wnt/beta-catenin pathway

(N ¼ 31)

28 1.45 1 Wnt pathway is upregulated, whereas

TGFb1 signaling is downregulated in

Gravendeel (þ) patients

JAZAG_TGFB1_SIGNALING_

VIA_SMAD4_DN

Genesdownregulated in PANC-1-

S4KD cells (pancreatic cancer;

SMAD4knockeddownbyRNAi)

after stimulation by TGFB1 for 2

hours (N ¼ 64)

51 1.4 0.978

VERRECCHIA_RESPONSE_

TO_TGFB1_C1

ECM-related genes upregulated

in dermal fibroblasts within 30

minutes after TGFb1 addition

and kept increasing with time

(N ¼ 18)

16 �1.58 0.704 TGFb1 signaling and Nutt GBM vs.

anaplastic oligodendroglioma (AO) are

upregulated in Gravendeel (�) patients

VERRECCHIA_EARLY_

RESPONSE_TO_TGFB1


early (within 30 min) in dermal

fibroblasts after addition of

TGFb1 (N ¼ 51)

47 �1.53 0.73

NUTT_GBM_VS_

AO_GLIOMA_UP

Top 50 marker genes for GBM, a

class of high-grade glioma

(N ¼ 47)

42 �1.52 0.746

KEGG_NOTCH_

SIGNALING_PATHWAY

Notch signaling pathway (N ¼ 47) 40 �1.66 1 Notch signaling is downregulated (due to

Numbl) whereas TGFb1 pathway is

upregulated inREMBRANDT (�) patients

VERRECCHIA_EARLY_

RESPONSE_TO_TGFB1


early (within 30 min) in dermal

fibroblasts after addition of

TGFb1 (N ¼ 51)

47 �1.53 0.972


TO_TGFB1_C5





(N ¼ 22)

18 �1.47 0.63


TO_TGFB1_C1





(N ¼ 18)

16 �1.43 0.559

BENPORATH_ES_

WITH_H3K27ME3

Genes possessing the

trimethylated H3K27

(H3K27me3) mark in their

promoters in human embryonic

stem cells, associated with

lower grade tumor expression

and poor stemness nature

(N ¼ 1,117)

8 1.38 0.16 Stem cell core programs enriched in

REMBRANDT (þ) patients

BENPORATH_EED_TARGETS Eed targets genes identified by

ChIP on chip as targets of the

Polycombprotein EED in human

embryonic stemcell, associated

with lower grade tumor

expression and poor stemness

nature (N ¼ 1,062)

6 1.34 0.13

DIAZ_CHRONIC_

MEYLOGENOUS_

LEUKEMIA_UP

Genes upregulated in CD34þ cells

isolated from bone marrow of

CML (chronic myelogenous

leukemia) patients, compared

with those from normal donors

(N ¼ 1,398)

5 �1.07 0.52 Stem cell core programs enriched in

REMBRANDT (�) patients

(Continued on the following page)






previously "untreatable" class can now be further subdi-vided into drug-sensitive and -resistant patients. Thisindicates that our gene signature detects molecular hetero-geneity in patients with oligodendroglial tumors that can-not be accounted for by the 1p/19q status alone and furtherhighlights the limitation of morphology-based histologicanalyses to diagnose and subsequently treat patients.Although oligodendroglial tumors are traditionally morechemosensitive than GBM tumors and would seeminglyrender our findings not unexpected, our study is importantbecause we provide a direct clinical link between thesecontroversial GPCs and their primary tumor. Essentially,we show that GPCs of different histologies not only mirrorthe phenotype and molecular fingerprint of their primarytumor, but also contain transcriptomic profiles that reflectthe different survival outcomes. It is therefore of interestwhat signaling pathways are enriched in these transcrip-tomic profiles. The use of, for example, Wnt and Notchpathway inhibitors, should therefore be prioritized to treatnot only oligodendroglial tumor patients but more impor-tantly the sensitive group from 1p/19q LOH-negativepatients. Our study provides strong evidence that long-termself-renewingGPCs are targeted by these pathway inhibitorsand should thus prioritize their development for an effec-

tive cure, as opposed to inhibitors that abrogate bulk tumorand transient-amplifying cells.

In our study, we focused on defining the GPC by its long-term self-renewing potential, reflected in its ability at serialtumor propagation (6).We have avoided the use ofmarkersas conflicting data have been obtained (5); furthermore,marker expression has been shown to be an artefactualconsequence of experimental conditions (50). The impor-tance of this trait was highlighted in several key studieslinking CSCs of the breast and acute myeloid leukemia tosurvival outcome (9, 10), where only serially transplantablestem-like cells (and not marker-based) contributed to dis-ease progression and patient survival, consequently empha-sizing the importance of the self-renewing, tumor-initiat-ing, and sustaining property. Indeed, recent workshighlighted the contribution stem cell core programs tochemoresistance and survival outcome in other tumorsystems (9, 10, 12).

Althoughour gene signature came froma limitednumberof GPC lines as with all studies to-date, we improved on itsrobustness and performance by 2 approaches: (i) We col-lated gene expression data from GPCs sourced from mul-tiple investigators/laboratories that have published com-plete functional evidence of serial tumor-propagating

Table 2. Summary of gene set enrichment analysis (Cont'd )

Gene sets Description SizeNormalizedenrichment Score FDR Q value CMAP class association

RIGGI_EWING_

SARCOMA_PROGENITOR_UP

Genes upregulated in

mesenchymal stem cells (MSC)

engineered to express EWS-

FLI1 fusion protein (N ¼ 421)

5 �0.71 0.89

BENPORATH_ES_

WITH_H3K27ME3

Genes possessing the

trimethylated H3K27

(H3K27me3) mark in their

promoters in human embryonic

stem cells, associated with

lower grade tumor expression

and poor stemness nature

(N ¼ 1,117)

8 1.12 1 Stem cell core programs enriched in

Gravendeel (þ) patients

BENPORATH_EED_TARGETS Eed targets genes identified by

ChIP on chip as targets of the

Polycombprotein EED in human

embryonic stemcell, associated

with lower grade tumor

expression and poor stemness

nature (N ¼ 1,062)

6 1.01 0.93

DIAZ_CHRONIC_

MEYLOGENOUS_

LEUKEMIA_UP

Genes upregulated in CD34þ cells

isolated from bone marrow of

CML (chronic myelogenous

leukemia) patients, compared

with those from normal donors

(N ¼ 1,398)

5 �1.09 0.76 Stem cell core programs enriched in

Gravendeel (�) patients

BENPORATH_NANOG_

TARGETS

Genes upregulated and identified

by ChIP on chip as Nanog

transcription factor targets in

human embryonic stem cells

(N ¼ 988)

5 �1.01 0.48

Ng et al.





activity; and (ii) We cross-compared the robustness of oursignature performance in 2 large, independent gliomadatabases, REMBRANDT andGravendeel, with gene expres-sion generated from bulk tumor samples. In this manner,we leveraged the unavoidable small sample size with val-idation in much larger data sets. Thus, clinically amenablemolecular tests may be developed by profiling unsortedbulk tumor cells because disease progression is in part, amanifest of the activation of stemness-related pathways.Taken together, we provide evidence that human-derivedGPCs are clinically relevant.

Disclosure of Potential Conflicts of InterestW.H. Ng is an employee for Duke-NUS GMS. No potential conflicts of

interest were disclosed by the other authors.

Authors' ContributionsConception and design: F.S.L. Ng, C. Tang, B.T. AngDevelopment of methodology: F.S.L. Ng, M. Phong, G. Tucker-Kellogg, C.Tang, B.T. AngAcquisitionofdata (provided animals, acquired andmanagedpatients,provided facilities, etc.): T.B. Toh, E. Ting, G.R.H. Koh, S.H. Leong, W.H.Ng, C. Tang, B.T. Ang

Analysis and interpretation of data (e.g., statistical analysis, biosta-tistics, computational analysis): F.S.L. Ng, T.B. Toh, E. Ting, E. Sandanaraj,M. Phong, S.S. Wong, S.H. Leong, G. Tucker-Kellogg, I. Ng, C. Tang, B.T. AngWriting, review, and/or revision of the manuscript: F.S.L. Ng, T.B. Toh,M. Phong, S.S. Wong, O.L. Kon, I. Ng, C. Tang, B.T. AngAdministrative, technical, or material support (i.e., reporting or orga-nizing data, constructing databases): F.S.L. Ng, C. Tang, B.T. AngStudy supervision: M. Phong, C. Tang, B.T. Ang

AcknowledgmentsThe authors thank Dr. Khoon Leong Chuah of Tan Tock Seng Hospital

(Department of Pathology) for pathologic analyses and Selamat Wata forexcellent technical assistance. They also thank laboratory members andJonathon D. Sedgwick of Lilly Singapore Centre for Drug Discovery forcritical review of the manuscript.

Grant SupportThe authors acknowledge financial support from competitive grants

awarded by the Biomedical Research Council, A�STAR to C. Tang (07/1/33/19/530, 09/1/33/19/611) and institutional grant funding to B.T. Ang atthe Singapore Institute for Clinical Sciences, A�STAR.

The costs of publication of this article were defrayed in part by thepayment of page charges. This article must therefore be hereby markedadvertisement in accordance with 18 U.S.C. Section 1734 solely to indicatethis fact.

ReceivedDecember 1, 2011; revisedMay 23, 2012; acceptedMay 25, 2012;published OnlineFirst June 6, 2012.

References1. Alcantara Llaguno S, Chen J, Kwon CH, Jackson EL, Li Y, Burns DK,

et al. Malignant astrocytomas originate from neural stem/progenitorcells in a somatic tumor suppressor mouse model. Cancer Cell2009;15:45–56.

2. Liu C, Sage JC, Miller MR, Verhaak RG, Hippenmeyer S, Vogel H, et al.Mosaic analysis with double markers reveals tumor cell of origin inglioma. Cell 2011;146:209–21.

Figure 5. Oligodendroglial GPCgene signature is associated withlower tumor grade and 1p/19qcodeletion. Tumor grade ("Grade")and 1p/19q codeletion distributioncorresponding to (þ) and (�) classesare shown below the activation scoregraphs in A, REMBRANDT; and B,Gravendeel databases.






3. Zheng H, Ying H, Yan H, Kimmelman AC, Hiller DJ, Chen AJ, et al. p53and Pten control neural and glioma stem/progenitor cell renewal anddifferentiation. Nature 2008;455:1129–33.

4. Galli R, Binda E, Orfanelli U, Cipelletti B, Gritti A, De Vitis S, et al.Isolation and characterization of tumorigenic, stem-like neural pre-cursors from human glioblastoma. Cancer Res 2004;64:7011–21.

5. Beier D, Hau P, Proescholdt M, Lohmeier A, Wischhusen J, Oefner PJ,et al. CD133(þ) and CD133(-) glioblastoma-derived cancer stem cellsshowdifferential growth characteristics andmolecular profiles.CancerRes 2007;67:4010–5.

6. Rich JN, Eyler CE. Cancer stem cells in brain tumor biology. ColdSpring Harb Symp Quant Biol 2009;73:411–20.

7. Laks DR, Masterman-Smith M, Visnyei K, Angenieux B, Orozco NM,Foran I, et al. Neurosphere formation is an independent predictor ofclinical outcome in malignant glioma. Stem Cells 2009;27:980–7.

8. Visvader JE. Cells of origin in cancer. Nature 2011;469:314–22.9. Eppert K, Takenaka K, Lechman ER, Waldron L, Nilsson B, van Galen

P, et al. Stemcell geneexpressionprograms influenceclinical outcomein human leukemia. Nat Med 2011;17:1086–93.

10. Liu R, Wang X, Chen GY, Dalerba P, Gurney A, Hoey T, et al. Theprognostic role of a gene signature from tumorigenic breast-cancercells. N Engl J Med 2007;356:217–26.

11. Merlos-Suarez A, Barriga FM, Jung P, Iglesias M, Cespedes MV,Rossell D, et al. The intestinal stem cell signature identifies colorectalcancer stem cells and predicts disease relapse. Cell Stem Cell2011;8:511–24.

12. Shats I,GatzaML,Chang JT,Mori S,WangJ, Rich J, et al. Using astemcell-based signature to guide therapeutic selection in cancer. CancerRes 2011;71:1772–80.

13. Atlas TCG. Comprehensive genomic characterization defines humanglioblastoma genes and core pathways. Nature 2008;455:1061–8.

14. French PJ, Swagemakers SM, Nagel JH, Kouwenhoven MC,Brouwer E, van der Spek P, et al. Gene expression profiles asso-ciated with treatment response in oligodendrogliomas. Cancer Res2005;65:11335–44.

15. Gravendeel LA, Kouwenhoven MC, Gevaert O, de Rooi JJ, StubbsAP, Duijm JE, et al. Intrinsic gene expression profiles of gliomas area better predictor of survival than histology. Cancer Res 2009;69:9065–72.

16. VerhaakRG,HoadleyKA,PurdomE,WangV,Qi Y,WilkersonMD, et al.Integrated genomic analysis identifies clinically relevant subtypes ofglioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR,and NF1. Cancer Cell 2010;17:98–110.

17. Chong YK, Toh TB, Zaiden N, Poonepalli A, Leong SH, Ong CE, et al.Cryopreservation of neurospheres derived from human glioblastomamultiforme. Stem Cells 2009;27:29–39.

18. Gunther HS, Schmidt NO, Phillips HS, Kemming D, Kharbanda S,Soriano R, et al. Glioblastoma-derived stem cell-enriched culturesform distinct subgroups according to molecular and phenotypic cri-teria. Oncogene 2008;27:2897–909.

19. Pollard SM, Yoshikawa K, Clarke ID, Danovi D, Stricker S, Russell R,et al. Glioma stem cell lines expanded in adherent culture have tumor-specificphenotypes andare suitable for chemical andgenetic screens.Cell Stem Cell 2009;4:568–80.

20. Madhavan S, Zenklusen JC, Kotliarov Y, Sahni H, Fine HA, Buetow K.Rembrandt: helping personalized medicine become a reality throughintegrative translational research. Mol Cancer Res 2009;7:157–67.

21. Cairncross JG, Ueki K, Zlatescu MC, Lisle DK, Finkelstein DM, Ham-mond RR, et al. Specific genetic predictors of chemotherapeuticresponse and survival in patients with anaplastic oligodendrogliomas.J Natl Cancer Inst 1998;90:1473–9.

22. LambJ,CrawfordED,PeckD,Modell JW,Blat IC,WrobelMJ, et al. TheConnectivity Map: using gene-expression signatures to connect smallmolecules, genes, and disease. Science 2006;313:1929–35.

23. Ooi CH, Ivanova T, Wu J, Lee M, Tan IB, Tao J, et al. Oncogenicpathway combinations predict clinical prognosis in gastric cancer.PLoS Genet 2009;5:e1000676.

24. Yeo CW, Ng FS, Chai C, Tan JM, Koh GR, Chong YK, et al. Parkinpathway activation mitigates glioma cell proliferation and predictspatient survival. Cancer Res 2012;72:2543–53.

25. Phillips HS, Kharbanda S, Chen R, Forrest WF, Soriano RH, Wu TD,et al. Molecular subclasses of high-grade glioma predict prognosis,delineate a pattern of disease progression, and resemble stages inneurogenesis. Cancer Cell 2006;9:157–73.

26. Lottaz C, Beier D, Meyer K, Kumar P, Hermann A, Schwarz J, et al.Transcriptional profiles of CD133þ and CD133- glioblastoma-derivedcancer stem cell lines suggest different cells of origin. Cancer Res2010;70:2030–40.

27. Lee J, Kotliarova S, Kotliarov Y, Li A, Su Q, Donin NM, et al. Tumorstem cells derived from glioblastomas cultured in bFGF and EGFmore closely mirror the phenotype and genotype of primarytumors than do serum-cultured cell lines. Cancer Cell 2006;9:391–403.

28. Lepourcelet M, Chen YN, France DS, Wang H, Crews P, Petersen F,et al. Small-molecule antagonists of the oncogenic Tcf/beta-cateninprotein complex. Cancer Cell 2004;5:91–102.

29. Ewan K, Pajak B, Stubbs M, Todd H, Barbeau O, Quevedo C, et al. Auseful approach to identify novel small-molecule inhibitors of Wnt-dependent transcription. Cancer Res 2010;70:5963–73.

30. Wolfe MS, Citron M, Diehl TS, Xia W, Donkor IO, Selkoe DJ. Asubstrate-based difluoro ketone selectively inhibits Alzheimer's gam-ma-secretase activity. J Med Chem 1998;41:6–9.

31. Hovinga KE, Shimizu F, Wang R, Panagiotakos G, Van Der Heijden M,Moayedpardazi H, et al. Inhibition of notch signaling in glioblastomatargets cancer stem cells via an endothelial cell intermediate. StemCells 2010;28:1019–29.

32. Grygielko ET, Martin WM, Tweed C, Thornton P, Harling J, Brooks DP,et al. Inhibition of gene markers of fibrosis with a novel inhibitor oftransforming growth factor-beta type I receptor kinase in puromycin-induced nephritis. J Pharmacol Exp Ther 2005;313:943–51.

33. Team RDC. R: A language and environment for statistical computing.Version 2.10.1. Vienna, Austria: R Foundation for Statistical Comput-ing; 2009. ISBN 3-900051-07-0 [cited 2011 Dec 15]. Available from:http://www.R-project.org/.

34. Gautier L, Cope L, Bolstad BM, Irizarry RA. affy–analysis of Affy-metrix GeneChip data at the probe level. Bioinformatics 2004;20:307–15.

35. Smyth GK. LIMMA: linear models for microarray data. In:Gentleman R,Carey V, Dudoit S, Irizarry R, Huber W, editors. Bioinformatics andcomputational biology solutions using R and bioconductor. New York,NY: Springer; 2005. p. 397-420.

36. BioMart [cited 2011 Sept 10]. Available from: http://www.biomart.org/.37. REMBRANDT. Bethesda, MD: National Cancer Institute [cited 2011

Aug 31]. Available from: https://caintegrator.nci.nih.gov/rembrandt/.38. Therneau T. survival: Survival analysis, including penalised likelihood.

R package version 2.36-9 [cited 2011 Nov 3]. Available from: http://CRAN.R-project.org/package¼survival. Terry Therneau and originalSplus->R port by Thomas Lumley.

39. Tibshirani R, Hastie T, Narasimhan B, Chu G. Diagnosis of multiplecancer types by shrunken centroids of gene expression. Proc NatlAcad Sci U S A 2002;99:6567–72.

40. Li C, Wong WH. Model-based analysis of oligonucleotide arrays:expression index computation and outlier detection. Proc Natl AcadSci U S A 2001;98:31–6.

41. Lin M, Wei LJ, Sellers WR, Lieberfarb M, Wong WH, Li C. dChipSNP:significance curve and clustering of SNP-array-based loss-of-hetero-zygosity data. Bioinformatics 2004;20:1233–40.

42. Gene Set Enrichment Analysis. Cambridge, MA: Broad Institute of MITand Harvard. Available from: http://www.broadinstitute.org/gsea/index.jsp.

43. Fan X, Khaki L, Zhu TS, Soules ME, Talsma CE, Gul N, et al. NOTCHpathway blockade depletes CD133-positive glioblastoma cells andinhibits growth of tumor neurospheres and xenografts. Stem Cells2010;28:5–16.

44. ZhuT,CostelloMA, TalsmaCE, FlackCG,Crowley JG,HammLL, et al.Endothelial cells create a stem cell niche in glioblastoma by providingNotch ligands that nurture self-renewal of cancer stem-like cells.Cancer Res 2011;71:6061–72.

45. Anido J, Saez-Borderias A, Gonzalez-Junca A, Rodon L, Folch G,CarmonaMA, et al. TGF-beta receptor inhibitors target theCD44(high)/

Ng et al.





Id1(high) glioma-initiating cell population in human glioblastoma. Can-cer Cell 2010;18:655–68.

46. Penuelas S, Anido J, Prieto-Sanchez RM, Folch G, Barba I,Cuartas I, et al. TGF-beta increases glioma-initiating cell self-renewalthrough the induction of LIF in human glioblastoma. Cancer Cell2009;15:315–27.

47. Zheng H, Ying H, Wiedemeyer R, Yan H, Quayle SN, Ivanova EV,et al. PLAGL2 regulates Wnt signaling to impede differentia-tion in neural stem cells and gliomas. Cancer Cell 2010;17:497–509.

48. Persson AI, Petritsch C, Swartling FJ, Itsara M, Sim FJ, Auvergne R,et al. Non-stem cell origin for oligodendroglioma. Cancer Cell 2010;18:669–82.

49. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL,Gillette MA, et al. Gene set enrichment analysis: a knowledge-basedapproach for interpreting genome-wide expression profiles. ProcNatl Acad Sci U S A 2005;102:15545–50.

50. Quintana E, Shackleton M, Sabel MS, Fullen DR, Johnson TM, Morri-son SJ. Efficient tumour formation by single human melanoma cells.Nature 2008;456:593–8.






2012;18:4122-4135. Published OnlineFirst June 6, 2012.Clin Cancer Res Felicia Soo-Lee Ng, Tan Boon Toh, Esther Hui-Ling Ting, et al. in Oligodendroglial TumorsProgenitor-like Traits Contribute to Patient Survival and Prognosis

Updated version

10.1158/1078-0432.CCR-11-3064doi:

Access the most recent version of this article at:

Material

Supplementary

http://clincancerres.aacrjournals.org/content/suppl/2012/06/06/1078-0432.CCR-11-3064.DC1

Access the most recent supplemental material at:

Cited articles

http://clincancerres.aacrjournals.org/content/18/15/4122.full#ref-list-1

This article cites 44 articles, 15 of which you can access for free at:

Citing articles

http://clincancerres.aacrjournals.org/content/18/15/4122.full#related-urls

This article has been cited by 3 HighWire-hosted articles. Access the articles at:

E-mail alerts related to this article or journal.Sign up to receive free email-alerts

Subscriptions

Reprints and

[email protected]

To order reprints of this article or to subscribe to the journal, contact the AACR Publications Department at

Permissions

Rightslink site. Click on "Request Permissions" which will take you to the Copyright Clearance Center's (CCC)

.http://clincancerres.aacrjournals.org/content/18/15/4122To request permission to re-use all or part of this article, use this link



http://clincancerres.aacrjournals.org/lookup/doi/10.1158/1078-0432.CCR-11-3064

http://clincancerres.aacrjournals.org/content/suppl/2012/06/06/1078-0432.CCR-11-3064.DC1

http://clincancerres.aacrjournals.org/content/18/15/4122.full#ref-list-1

http://clincancerres.aacrjournals.org/content/18/15/4122.full#related-urls

http://clincancerres.aacrjournals.org/cgi/alerts

mailto:[email protected]

http://clincancerres.aacrjournals.org/content/18/15/4122


progenitor-like traits contribute to patient survival and ... · imaging, diagnosis, prognosis...

Documents