Gene expression phenotypic models that predict the activity of oncogenic pathways

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1 3 Nture Pulishing Group Gene expression phenotypic models tht predict the ctivity of oncogenic pthwys Erich Hung,, Seiichi Ishid,7, Jennifer Pittmn,3, Holly Dressmn,,4, Andre Bild,, Mrk Kloos, Mrk D Amico, Richrd G Pestell, Mike West,3 & Joseph R Nevins,,4, High-density DNA microrrys mesure expression of lrge numers of genes in one ssy. The ility to find underlying structure in complex gene expression dt sets nd rigorously test ssocition of tht structure with iologicl conditions is essentil to developing multi-fceted views of the gene ctivity tht defines cellulr phenotype. We sought to connect fetures of gene expression dt with iologicl hypotheses y integrting metgene ptterns from DNA microrry experiments in the chrcteriztion nd prediction of oncogenic phenotypes. We pplied these techniques to the nlysis of regultory pthwys controlled y the genes (Hrvey rt srcom virl oncogene homolog), (myelocytomtosis virl oncogene homolog) nd EF, EF nd EF3 (encoding EF trnscription fctors, nd 3, respectively). The phenotypic models ccurtely predict the EF ctivity of these pthwys in the context of norml cell prolifertion. Moreover, the metgene models trined with gene expression ptterns evoked y ectopic production of Myc or Rs proteins in primry tissue culture cells properly predict the ctivity of in vivo tumor models tht result from deregultion of the or pthwys. We conclude tht these gene expression phenotypes hve the potentil to chrcterize the complex genetic ltertions tht typify the neoplstic stte, whether in vitro or in vivo, in wy tht truly reflects the complexity of the regultory pthwys tht re ffected. We used recominnt denoviruses to express Rs, Myc or EF proteins in quiescent primry mouse emryo firolsts, using infections tht result in the ccumultion of these proteins to level equivlent to tht fter growth stimultion. We prepred RNA nd hyridized it to Metgene Metgene. Metgene Metgene Metgene Metgene.. Metgene Figure Metgene nlysis identifies differences in gene expression in cells expressing Myc, Rs nd EF proteins. () Myc nd Rs nlysis. Individul smples depicted in sctter plot on two dominnt fctors (metgenes) selected in pure discrimintion of the trining cses. Cells expressing either or re represented y closed symols, control cells y open symols. () EF nlysis. Metgene plot of smples from cells expressing EF, EF nd EF3 with metgenes derived from n ojective correltion screen of 8 genes selected in pirwise comprisons etween EF experiments versus controls. Control smples re indicted y open symols, cells expressing EF y red symols, cells expressing EF y green symols nd cells expressing EF3 y lue symols. Deprtment of Moleculr Genetics nd Microiology; Computtionl nd Applied Genomics Progrm, Duke Institute for Genome Sciences nd Policy; 3 Institute of Sttistics nd Decision Sciences; nd 4 Center for Genome Technology, Duke Institute for Genome Sciences nd Policy; Duke University, Durhm, North Crolin 77, USA. Lomrdi Comprehensive Cncer Center, Georgetown University, Wshington, DC 7, USA. Howrd Hughes Medicl Institute, Duke University Medicl Center, Durhm, North Crolin 77, USA. 7 Present ddress: Division of Phrmcology, Ntionl Institute of Helth Sciences -8-, Kmiyog, Setgy-Ku Tokyo 8-8, Jpn. Correspondence should e ddressed to J.R.N. (j.nevins@duke.edu). VOLUME 34 NUMBER JUNE 3 NATURE GENETICS

2 3 Nture Pulishing Group Affymetrix MuK rrys. The dt set comprises totl of independent experiments. After n initil dt reduction to focus on those genes whose expression vried non-trivilly cross smples, we used singulr vlue decomposition methods to derive metgenes ; these re liner comintions of individul gene expression vlues tht together constitute the metgene nd tht hve the potentil to clssify nd predict cellulr phenotypes resulting from deregultion of oncogenic pthwys. We nlyzed smples of quiescent cells versus smples of cells expressing Myc or Rs proteins nd plotted the smples s function of two metgenes (Fig. ). Metgene does little to discriminte etween EF EF EF Figure Clssifictions sed on metgenes. () Fitted clssifictions for nd. Fitted clssifiction proilities for trining cses from the fctor regression nlysis. The vlues on the horizontl xis re estimtes of the overll metgene score in the regression. The corresponding vlues on the verticl xis re fitted/estimted clssifiction proilities with corresponding 9% proility intervls mrked s dshed lines to indicte uncertinty out these estimted vlues. Control smples re shown in lue nd or smples in red. () Fitted clssifictions for EF, EF nd EF3. Fitted clssifiction proilities for trining cses from the fctor regression nlysis. The vlues on the horizontl xis re estimtes of the overll metgene score in the regression. The corresponding vlues on the verticl xis re fitted/estimted clssifiction proilities with corresponding 9% proility intervls mrked s dshed lines to indicte uncertinty out these estimted vlues. Control smples re shown in lue nd EF smples in red. the quiescent nd Rs-expressing cells, ut metgene clenly distinguishes the smples. Likewise, metgene in the Myc nlysis ( different metgene from the Rs metgene ) discrimintes etween the quiescent cells nd those expressing Myc protein. These nlyses thus uncover gene expression ptterns tht identify the difference etween quiescent cell nd cell expressing Rs or Myc. We similrly nlyzed cells expressing EF (Fig. ). In this cse, comintion of three metgenes ws effective in distinguishing cells expressing EF from quiescent cells, nd further, in distinguishing the individul EFs from one nother. This ltter oservtion is importnt ecuse vrious studies hve suggested sutle ut overlpping differences in the ctivity of these EF proteins,3. These nlyses typiclly identify multiple metgenes tht provide some discrimintion of the phenotypes; y comining these individul metgenes using regulrized inry. regression methods, we cn crete compound metgene score tht defines nd tests the ility to discriminte etween phenotypic sttes. Ech metgene score is converted to proility scle using the inry regression Control Control EF EF EF Control EF Control EF Control EF3 Figure 3 Expression ptterns of discriminting genes. () Expression levels for nd metgenes. The expression vlues of genes selected for discriminting power re indicted y color, with lck representing the lowest level nd white the highest level of expression (color scle shown on the right). Ech column in the figure represents n individul smple, grouped s controls or s cells expressing Rs or Myc proteins. Ech row represents n individul gene, ordered from top to ottom ccording to regression coefficients. The vlues for ech gene depict stndrdized expression vlues (the men vlue for ech gene cross ll smples is sutrcted from ech vlue nd then divided y the stndrd devition). () Expression levels for EF metgenes. Detils re the sme s for. NATURE GENETICS VOLUME 34 NUMBER JUNE 3 7

3 3 Nture Pulishing Group Figure 4 Gene expression profiles predict the ctivity of oncogenic pthwys y cross-vlidtion nlysis. () nd prediction. One-t-time cross-vlidtion predictions of clssifiction proilities for trining cses. The vlues on the horizontl xis re estimtes of the overll metgene scores in the regression. The corresponding vlues on the verticl xis re estimted clssifiction proilities with corresponding 9% proility intervls mrked s dshed lines to indicte uncertinty out these estimted vlues. The nlysis nd predictions for ech smple re sed on the screened suset of most discrimintory genes. The control smples re shown in lue nd the or smples in red. () EF prediction. The nlyses re the sme s descried in. The estimted reltive proility of Rs- or Myc-expressing stte sed on this pproch (Fig. ) clerly illustrtes the cpcity of the metgene scores to properly clssify the smples s either Rs- or Myc-expressing cells. A similr nlysis for the individul EF metgenes (Fig. ) gin showed tht this method cn ccurtely clssify the smples sed on the expression of the oncogenic proteins. The reltive expression vlues of genes selected for Rs, Myc nd EF discrimintions re shown in Figure 3. Ech row represents n individul gene, ordered from top to ottom s function of estimted regression coefficients. The expression vlues were plotted fter stndrdiztion cross the smples y tking the men expression vlue of ll smples, sutrcting it from the vlue of ech smple nd then dividing y the stndrd devition. Distinctions in the pttern of expression of this group of genes etween the quiescent cells nd the cells expressing Myc, Rs or EF re clerly evident. An exmintion of the list of genes (Supplementry Tles online) identifies some similrities to previously descried nlyses for Myc 4, in tht numer of the genes encode generl growth regultory proteins or proteins involved in metolic processes ssocited with cell growth. The nture of the genes in the Rs discriminting group is less cler, lthough it seems to include numer of genes ssocited with cell growth signling events, such s those encoding Rc3, TNF, gunine nucleotide inding protein nd cyclin G, s well s generl cellulr metolism (phospholipse, poly(a) + inding protein, dul specificity phosphtse). Finlly, the EF discriminting metgenes EF EF EF identify genes known to e EF trgets ut, more profoundly, include lrge numer of genes not previously descried s EF trgets. An importnt spect of this nlysis is the ility to test nd vlidte the clssifictions. This llows us to determine whether metgene pttern cn ctully predict the sttus of smple treted s n unknown. To do this, we used cross-vlidtion testing in which smple is omitted from the dt set, the metgene model is regenerted from the remining smples nd the omitted smple is tested ginst the model. Ech smple is treted s n unknown nd used s vlidtion smple. The composition of ech metgene is lso treted s n unknown nd reselected in ech prediction. Becuse the prediction does not include (h) (h) Figure Prediction of gene ctivtion pthwys fter stimultion of cell growth. () nd nlysis. Ten trces for nd representing simultions of 34, Monte Crlo Mrkov Chin itertions ech. Ech predictive model represents prediction of distriution of possiilities. Ech simultion represents rndom wlk of 34, steps through the distriution of possiilities for prticulr model. () EF nlysis. The nlyses re the sme s descried in. EF EF EF (h) (h) (h) 8 VOLUME 34 NUMBER JUNE 3 NATURE GENETICS

4 3 Nture Pulishing Group the vlidtion smple, it is true prediction. The results of one-t-time cross-vlidtion ssys for predicting Rs, Myc nd EFs re shown in Figure 4. Uncertinty intervls reflect the reltively smll numer of smples nd the inherent vriility nd resulting miguity therein. Indeed, one of the quiescent cell smples (#) is clerly predicted incorrectly. On further investigtion, we found tht this smple ws not completely synchronized in quiescent stte fter serum deprivtion. This highlights the power of the nlysis to identify those instnces tht devite from the expected. Nevertheless, it is quite cler from these nlyses tht the metgenes selected for their ility to iden Figure Prediction of tumors tht result from deregultion of nd. () Prediction of ctivtion in mouse tumor smples. RNA ws prepred from series of mouse mmmry tumors tht were derived from mice trnsgenic for the MMTV- ( smples), MMTV- (3 smples) or MMTV-NEU (7 smples) oncogenes. In ddition, 7 smples of norml mmmry tissue were nlyzed. Hyridiztion ws crried out using the MuK GeneChips, nd the results were used to predict clssifiction proilities sed on the trined pttern derived from the nlysis of Mycexpressing firolsts. The Myc tumors re indicted in red nd the other tumors, s well s the norml tissue, re in lue. Smple 3 is Neuinduced tumor. () Prediction of ctivtion in mouse tumor smples. The results of the hyridiztions descried in pnel were used to predict clssifiction proilities sed on the trined pttern derived from the nlysis of Rs-expressing firolsts. The Rs tumors re indicted in red nd the other tumors nd norml tissue re in lue. Smples 3, 3, 3, 33 nd 34 re Myc-induced tumors (indicted y rrows). tify cells expressing Rs, Myc nd EF cn lso ccurtely predict the stte of cell on the sis of the expression of group of relevnt genes. As n independent vlidtion of the use of the Rs, Myc nd EF metgenes to predict the ctivity of these pthwys, we tested their ility to predict the ctivity of these proteins in norml, physiologicl setting or, specificlly, to predict the ctivity of the relevnt pthwys fter stimultion of cell prolifertion. Activity of oth Rs nd Myc ccumultes erly in the growth response, within 3 h of the growth stimultion, wheres ctivity of EFs rises lter in the growth response. The inry regression models tht we used for these predictions ssign coefficients to ech gene in prticulr metgene model nd then evlute the resulting proilities for Myc nd Rs sttes. We prepred RNA from mouse emryo firolsts (MEFs) t vrious time intervls fter serum stimultion. We prepred proes nd hyridized them to the Affymetrix mouse rrys to ssy gene expression t ech time point. We then predicted the ctivities of ech smple using the metgene models identified for Rs, Myc nd EFs (Fig. ). Both the Rs nd Myc metgenes predict ctivity t the erly time points (Fig. ), consistent with the known time profiles of ccumultion of Myc nd Rs ctivity. In contrst to the Rs nd Myc profiles, the EF metgenes predicted ctivity much lter in the growth response ( h; Fig. ), coincident with the time tht EF ctivity is known to ccumulte. As finl test of predictive ility, we used the metgenes to predict the Myc nd Rs stte in series of mmmry tumors tht developed in trnsgenic mice expressing either Myc or Rs from the MMTV enhncer,. As controls, we lso nlyzed mmmry tumors induced y MMTV-directed expression of the oncogene ERBB (lso clled NEU or HER-) s well s smples of norml mmmry tissue. We prepred RNA from the vrious smples, used it to generte proes nd then hyridized them to Affymetrix MuK GeneChips. We then used ech smple s vlidtion set for prediction y either the Myc or the Rs metgenes defined in the firolst experiments. The Myc metgenes predicted Myc tumors with high degree of confidence (Fig. ) nd clerly seprted them from most of the other smples tht represent tumors expressing either Rs or ErB (Fig. ). Although ech of the Rs tumors ws ccurtely predicted nd clerly seprted from the ErB tumors or norml tissue, there ws little distinction etween the Rs tumors nd the Myc tumors (Fig. ). Recent studies hve shown tht lrge frction of tumors induced y MMTV- lso develop point muttions in 7. We confirmed this oservtion, t lest in principle, in the smple set of tumors nlyzed here. Specificlly, 3 of Myc tumors hd muttions in. This is in contrst with erlier work tht identified muttions in K-RAS in MMTV- tumors ut estlishes the connection etween Rs dysfunction nd Myc. The fct tht the Myc tumors re predicted y oth the Myc nd the Rs metgenes is consistent with the ctivtion of Rs in oth sets of tumors nd further underscores the power of the metgene nlysis in identifying underlying iologicl phenotypes. Interpreting gene expression dt to understnd the underlying iology is multistep process tht involves identifying structure in dt, testing the ssocition of tht structure with iologicl phenomen nd evluting the roustness y testing the cpcity of the identified structure to ccurtely predict iologicl sttes. The models we descrie here represent complex moleculr phenotypes composed of not only hundreds to thousnds of gene expression fetures, ut lso the interreltion of these fetures. Thus, we focus more on distinctive comintions of multiple genes with diverse gene expression profiles thn on genes tht shre high degree of similrity in their regultion or genes whose expression chnges mrkedly in mgnitude in experiments. The power NATURE GENETICS VOLUME 34 NUMBER JUNE 3 9

5 3 Nture Pulishing Group of this nlysis is shown y its ility to ccurtely distinguish individul EF smples s well s to predict tumors rising from deregultion of specific oncogenes. Notly, the nlysis mkes no distinction etween primry, secondry or tertiry trgets of the regultory proteins. Our focus ws to identify unique trnscriptionl fetures, regrdless of whether they represent direct trgets or those frther downstrem. Our intention ws to mke the ssocition first nd therey inform further moleculr or iochemicl experimenttion to understnd the mechnistic sis of tht ssocition. Although they re powerful first step in orgnizing inherently complex dt, clustering lgorithms re ill-suited for model uilding. Similrity metrics generted y such pproches relte gene expression vlues or individul experiments to one nother ut not to phenotypic stte. In contrst, the metgene modeling methods we used here relte gene expression dt to one nother in the context of phenotypic stte nd permit explicit nd systemtic discovery of gene expression ptterns pertinent to tht phenotype. They further fcilitte cross-vlidtion testing of the model tht quntifies uncertinty in tht model. Therefore, we cn identify nd discrd models tht indequtely reflect (tht is, predict) phenotypes in vlidtion testing. The comintion of Byesin methods of nlysis nd rigorous cross-vlidtion testing lso prevents over-fitting in models, common circumstnce when explntory vriles fr outnumer experiments. The ultimte test of model is whether it hs relile predictive cpcity when pplied out of the milieu in which it ws trined. These methods hve een pplied previously to predict clinicl phenotypes of cncers,8, ut it is cler from the work we descrie here tht this pproch is more generlly pplicle to exmining ny physiologic stte. Finlly, we note tht lrge ody of work hs documented the role of somtic genetic ltertions in the genesis of tumors, including the ltertion of not just one criticl pthwy ut mny, nd tht vriety of comintions of such events proly contriutes to the development of tumor. The ility to ccurtely predict the ctivtion of n oncogenic pthwy, such s tht initited y Rs or Myc or loss of R, sed on the nlysis of gene expression profiles reflecting these pthwys my llow more roust nlysis of cncers y detecting the consequences of the genetic ltertion. The fct tht in vitro overexpression of Myc nd Rs in MEFs evokes identifile ptterns of gene expression tht ppropritely predict the deregultion of Myc nd Rs in trnsgenic mouse mmmry tumor tissues in quntittive nd proilistic mnner suggests tht our metgene models cn distinguish gene expression ptterns fundmentl to these ctivities. METHODS Cells nd viruses. We prepred primry wild-type MEFs from emryos t 3. d post coitum s descried previously. The cells were pssged on -mm tissue culture dishes nd mintined with Dulecco s modified Egle medium (DMEM) supplemented with % het-inctivted fetl ovine serum (HIFBS). The firolsts were expnded to pssge 4 nd plted t finl density of 4 cells mm s determined y trypsiniztion nd counting with hemcytometer. We then rendered the MEFs quiescent y strvtion for 48 h in DMEM with.% HIFBS. We prepred nd functionlly vlidted the recominnt denoviruses Ad-EF, Ad-EF, Ad-EF3, Ad-Myc, Ad-Rs L nd Ad- CMV-GFP s previously descried,3. We counted quiescent MEFs once more for ccurte multiplicities nd infected t the multiplicities descried in 3 ml DMEM in mm HEPES uffer. After infection, we dded ml DMEM with.% HIFBS to ech plte nd llowed incutions to proceed for 8 h. RNA preprtion. After 8 h of incution with recominnt denoviruses, we collected infected MEFs in 4 ml TRIZol Regent. We isolted totl RNA ccording to mnufcturer protocols nd ssessed qulity with n Agilent Gene-On-A-Chip mchine. Anlysis of tumors. MMTV-, MMTV- nd MMTV-NEU trnsgenic mice hve een descried previously,. We isolted RNA from freshly dissected mmmry tumors from mice of ech genotype using Tri-ZOL regent (Amion) following the mnufcturer s instructions 4. We used RNA smples from the tumors for sequence nlysis to detect muttions in nd used RT PCR to generte DNA for nlysis. Primers used for mplifiction nd cycle sequencing re shown in Supplementry Figure online. DNA microrry nlysis. We used Affymetrix MuK A/B GeneChips for ll experiments. We prepred the trgets for Affymetrix DNA microrry nlysis s descried y the mnufcturer. Briefly, we synthesized doule-strnded cdna from totl RNA ( µg strting mteril) isolted from tissue culture hrvests. We generted iotin-leled crna y in vitro trnscription from the DNA. The crna ws frgmented efore hyridiztion, nd we prepred hyridiztion cocktil tht included the frgmented crna, proe rry controls, ovine serum lumin nd herring sperm DNA. We hyridized the crna to the oligonucleotide proes on the proe rry for -h incution t 4 C. Immeditely fter the hyridiztion, the hyridized proe rry underwent n utomted wshing nd stining protocol on n Affymetrix fluidics sttion. We scnned the DNA chips with the Affymetrix GeneChip scnner nd processed the signls y the GeneChip expression nlysis lgorithm (v.; Affymetrix). Sttisticl nlysis methods. For the metgene regression nlysis of expression dt, we used methods essentilly s descried previously for the nlysis of rest cncer smples. These methods re sed on inry regression models comined with singulr vlue decompositions nd with stochstic regulriztion using Byesin nlysis. A proility model estimtes clssifiction proility for ech of the two possile sttes (control versus experiment) for ech smple. This proility is structured s proit regression model in which the expression levels of genes re scored y regression prmeters tht re estimted nd used to compute resulting clssifiction proilities for oth trining nd vlidtion smples. The estimted regression vector itself is importnt not only in defining the predictive clssifiction, ut lso in scoring genes s to their contriution to the clssifiction. Note: Supplementry informtion is ville on the Nture Genetics wesite. COMPETING INTERESTS STATEMENT The uthors declre tht they hve no competing finncil interests. Received 9 Ferury; ccepted 3 April 3 Pulished online 8 My 3; doi:.38/ng7. West, M. et l. Predicting the clinicl sttus of humn rest cncer y using gene expression profiles. Proc. Ntl. Acd. Sci. USA 98, 4 47 ().. Nevins, J.R. Towrd n understnding of the functionl complexity of the EF nd Retinolstom fmilies. Cell Growth Differ. 9, 8 93 (998). 3. Dyson, N. The regultion of EF y prb-fmily proteins. Genes Dev., 4 (998). 4. Coller, H.A. et l. Expression nlysis with oligonucleotide microrrys revels tht regultes genes involved in growth, cell cycle, signling, nd dhesion. Proc. Ntl. Acd. Sci. USA 97, 3 3 ().. Berss, D.J., Lee, R.L., Troyer, D.A., Pestell, R.G. & Windle, J.J. p WAF/CIP deficiency hs opposite effects in tumors rising from MMTV-Rs nd MMTV-Myc trnsgenic mice. Cncer Res., ().. Lee, R.J. et l. Cyclin D is required for trnsformtion y ctivted Neu nd is induced through n EF-dependent signling pthwy. Mol. Cell. Biol., 7 83 (). 7. D Crus, C.M. et l. c- induces mmmry tumorigenesis y mens of preferred pthwy involving spontneous Krs muttions. Nt. Med. 7, 3 39 (). 8. Pomeroy, S.L. et l. Prediction of centrl nervous system emryonl tumour outcome sed on gene expression. Nture 4, (). 9. Shipp, M.A. et l. Diffuse lrge B-cell lymphom outcome prediction y geneexpression profiling nd supervised mchine lerning. Nt. Med. 8, 8 74 ().. Golu, T.R. et l. Moleculr clssifiction of cncer: clss discovery nd clss prediction y gene expression monitoring. Science 8, 3 37 (999).. vn T Veer, L.J. et l. Gene expression profiling predicts clinicl outcome of rest cncer. Nture 4, 3 3 ().. Nevins, J.R., DeGregori, J., Jkoi, L. & Leone, G. Functionl nlysis of EF. Methods Enzymol. 83, 9 (997). 3. DeGregori, J., Leone, G., Miron, A., Jkoi, L. & Nevins, J.R. Distinct roles for EF proteins in cell growth control nd poptosis. Proc. Ntl. Acd. Sci. USA 94, 74 7 (997). 4. Bromerg, J.F. et l. Stt3 s n oncogene. Cell 98, 9 33 (999). 3 VOLUME 34 NUMBER JUNE 3 NATURE GENETICS

6 ERRATA Gene expression phenotypic models tht predict the ctivity of oncogenic pthwys E Hung, S Ishid, J Pittmnn, H Dressmn, A Bild, M Kloos, M D Amico, R G Pestell, M West & J R Nevins Nt. Genet. 34, 3 (3). Figure 3 ws reproduced poorly in print. A corrected version ppers elow. 3 Nture Pulishing Group NATURE GENETICS VOLUME 34 NUMBER 4 AUGUST 3 4