Oncogene Identification using Filter based Approaches between Various Cancer Types in Lung

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1 World Academy of Sece, Egieerig ad Techology Ocogee Idetificatio usig Filter based Approaches betwee Various Cacer Types i Lug Michael Netzer*, Michael Seger*, Mahesh Visvaatha*, Berhard Pfeifer, Gerald H. Lushigto, Christia Baumgarter Abstract Lug cacer accouts for the most cacer related deaths for me as well as for wome. The idetificatio of cacer assoated gees ad the related pathways are essetial to provide a importat possibility i the prevetio of may types of cacer. I this work two filter approaches, amely the iformatio gai ad the biomarker idetifier (BMI) are used for the idetificatio of eret types of smallcell ad osmallcell lug cacer. A ew method to determie the BMI s is proposed to prioritize gees (i.e.,, secodary ad ) usig a kmeas clusterig approach. Sets of key gees were idetified that ca be foud i several pathways. It tured out that the modified BMI is well suited for microarray data ad therefore BMI is proposed as a powerful tool for the search for ew ad so far udiscovered gees related to cacer. Keywords lug cacer, micro arrays, data miig, feature selectio. I. INTRODUCTION UNG cacer accouts for the most cacer related deaths L (29%) for me as well as for wome ad follows with a very poor progosis a 5year survival rate of 15% (data for USA) [1]. The major types of lug cacer are smallcell ad osmallcell cacer. Nosmallcell cacer ca be further divided ito three major histological subtypes: squamouscell caroma, adeocaroma, ad largecell lug cacer [2]. The treatmet of lug cacer depeds o the cacer type ad the stage of cacer icludig surgery, radiatio therapy, chemotherapy ad targeted biological therapies. Biologists have kow for a log time that the partipatio of certai gees i spefic pathways are risk factors for multiple cacers. The idetificatio of these gees ad pathways is importat sice targetig them could provide a importat possibility i the prevetio of may types of cacer. Such gees iclude both ocogees ad ocopathways that are amplified i cacers ad activate the growth of tumors across eret orgas ad tumor suppressor gees *Authors cotributed equally to this work Michael Netzer, Michael Seger, Berhard Pfeifer ad Christia Baumgarter are with the Istitute of Electrical ad Bioegieerig, Uiversity for Health Seces, Medical Iformatics ad Techology (UMIT), A6060 Hall i Tirol, Austria; michael.etzer@umit.at. Mahesh Visvaatha ad Gerald H. Lushigto are with the Bioiformatics Core Fality Uiversity of Kasas Lawrece, KS 66047, USA; mvisvaatha@ku.edu havig the opposite effect (i.e., if active, they prevet multiple types of tumor growth ad developmet) [3]. DNA microarray techology eables the simultaeous moitorig of the expressio of thousads of gees resultig i a high dimesioality of the data subject to beig ivestigated. Chages i the expressio levels of sigle gees durig cacer developmet withi a give cell populatio may be assoated with cacer etiology ad developmet [4]. For extractio of those particular gees or features, however, sophisticated data miig approaches are required. Feature selectio, as a importat step i the data miig process, reduces dimesioality by searchig for represetative feature subsets with highly discrimiatory ability. I geeral, feature selectio methods ca be classified ito filters ad wrappers [5]. Filter methods rak features based o a quality measure (merit) depedig o the ability to distiguish betwee predefied classes (e.g., case vs. cotrol group). Wrappers use accuracy estimates provided by machie learig approaches to evaluate feature subsets. I geeral, feature subsets selected by wrappers are highly discrimiatory, with the drawback of a extesive computatioal cost. Filters are more effiet but less accurate. The calculated merit o the other had allows prioritizig features which is particularly importat for biological iterpretatio purposes. Espeally for small size datasets there are sigificat ereces i the rakig betwee eret filter approaches due to the diversity of the uderlyig statistical models [6]. It is obvious that the uderlyig models leared from data iclude eret types of errors. The biasvariace decompositio as defied by Gema ad collegues [7] distiguishes betwee three types of errors: The bias error is a systematic compoet of the error. It results from ereces betwee the learig method ad the domai [8]. The variace error results from ereces betwee models of eret samples. The sum of bias ad variace is called total expected error of a learig method. The itrisic error is due to the ucertaity i the domai ad caot be leared [9]. I this work eret types of smallcell ad osmallcell lug cacer are compared usig iformatio gai (IG) [10] ad the biomarker idetifier (BMI) [11] as feature selectio methods. The IG computes the discrimiatory ability of every feature based o a etropy measure. The BMI, which was origially applied o metabolic data, combies various statistical measures to calculate a evaluatio score for feature rakig. The stregth of the BMI 1102

2 World Academy of Sece, Egieerig ad Techology is the ability to clearly er betwee, secodary ad marker cadidates with respect to their discrimiatory ability. For the categorizatio of gees ito these three groups usig BMI a ew method relyig o a kmeas clusterig approach is proposed. Δ Δ if Δ 1 = 1 else Δ with Δ = x x ref II. METHODS A. Research Data I this study, the gee expressio data sets from GlaxoSmithKlie (GSK) are examied which had released the geomic profilig data for over 300 cacer cell lies via the Natioal Cacer Istitute s cacer Bioiformatics Grid (cabig ) [12]. The ivestigated dataset applied i this work comprises data of 177 idividuals divided ito eret types of lug cacer: smallcell ( = 41), adeocaroma ( = 65), squamouscell ( = 34) ad largecell cacer ( = 37). Formally, the dataset ca be described as a set of tuples T, where T = {(c j,m) c j C, m M} with C = { smallcell cacer, adeocaroma, squamouscell, largecell cacer }, C is the set of class labels ad M is the set of features (gee expressios). The umber of measured gee expressios available i the database is 54,676. B. Feature Selectio usig the Iformatio Gai The IG describes how well a give feature separates betwee two or more classes based o a etropy measure. The IG with respect to class c j ca be defied as the erece betwee the etropy of class c j ad the coditioal etropy for class c j for a give feature f i. This meas that the expected reductio of etropy caused by partitioig the data accordig to feature f i ca be measured ad used for feature rakig [10, 13]. More formally the IG i feature F with relatio to C is the mutual iformatio betwee F ad C [14]: I(C, F) = H(C) H(C F), where = 1 H ( C) ) log2, the iitial etropy i C, c c j ) i H ( C F) fi ) H ( C f j ), the coditioal etropy i C = fi give F, ad, 1 H ( C f j ) = f j ) log 2, the etropy i C c f ) give a particular feature f j. C. Feature Selectio usig the Biomarker Idetifier The Biomarker idetifier (BMI) was developed for dichotomous test problems ad combies various statistical measures to discer the discrimiatory ability of features distiguishig betwee two classes of iterest. The BMI score for a feature f, a variat of the iitial method described i Baumgarter et al. [11], is defied as: ref BMI( f ) = λ TP 2 Δ with j j where λ is a scalig factor ad TP 2 is the product of the true positive (TP) values determied for both classes usig logistic regressio aalysis. The parameter Δ calculates relative chages i levels with respect to a referece group, ref ad deotes chages i the variace of data across the two cohorts. x is the mea value of levels i both classes. Usig BMI for microarray data, a list of gees raked by the BMI score is retured, represetig the ability of gees to distiguish betwee both cohorts. Note that a positive Δ ca be iterpreted as over expressio, a egative Δ value as uder expressio i the secod class compared to the chose referece class of a particular gee. D. Gee categorizatio A categorizatio scheme ito, secodary ad cadidate gees accordig to their discrimiatory ability is proposed. Primary gees reflect high (positive as well as egative) alteratios i their expressio levels. The prioritizatio ito secodary ad gees appears to be useful to distiguish betwee further promisig cadidates of which the latter group is more likely assoated with secodary gee regulatio pathways. For the IG empirical scores greater tha zero, greater tha the half maximum score ad greater tha twothirds of the maximum score (see Table I) are used. TABLE I. THRESHOLDS FOR PRIMARY, SECONDARY AND TERTIARY GENE SETS USING IG. Categorizatio of gees IG Primary 0.67 Secodary 0.67 > IG 0.5 Tertiary 0.5 > IG > 0 To determie adequate s for the BMI first a histogram of computed BMI scores was created (see Fig. 1). It is assumed that there are regios (or clusters) with strog (high absolute BMI score values, grey area i Fig. 1) ad weak discrimiatig gees (low BMI absolute score values, black area i Fig. 1). To discer such regios a partitioig clusterig algorithm o absolute BMI scores to get symmetric cutoffs was applied. I this work the kmeas algorithm [15, 16] with k=4 umber of clusters was used (three clusters represet gees categorized ito, secodary ad 1103

3 World Academy of Sece, Egieerig ad Techology gees, the cluster i the ceter of the histogram represets gees with weak or o discrimiatio). Kmeas groups the data objects by miimizig the sum of squared distaces betwee each data poit ad its cluster represetative based o a iterative procedure. TABLE III. NUMBER OF IDENTIFIED GENES USING BMI AND K MEANS CUTOFFS FOR DIFFERENT LUNG CANCER TYPES. Referece vs. compariso group Categorizatio of gees secodary Adeocaroma vs. smallcell Squamouscell vs. adeocaroma Squamouscell vs. largecell Squamouscell vs. smallcell Largecell vs. adeocaroma Largecell vs. smallcell Fig. 1 Histogram of calculated BMI scores (schematic illustratio). Grey areas idicate BMI scores of gees with good or excellet discrimiatio where the black area i the middle represets BMI values of gees with weak or o discrimiatio. TABLE II. Referece vs. compariso group Adeocaroma vs. smallcell Squamouscell vs. adeocaroma Squamouscell vs. largecell Squamouscell vs. smallcell Largecell vs. adeocaroma Largecell vs. smallcell CALCULATED BMI THRESHOLDS FOR PRIMARY, SECONDARY AND TERTIARY GENE SETS. BMI 590 Categorizatio of gees secodary 590 > BMI > BMI 47 BMI > BMI > BMI 30 BMI > BMI > BMI 20 BMI > BMI > BMI 40 BMI > BMI > BMI 30 BMI > BMI > BMI 30 TABLE IV. NUMBER OF IDENTIFIED GENES USING IG FOR DIFFERENT LUNG CANCER TYPES APPLYING THE THRESHOLDS DEFINED IN TABLE I. Referece vs. compariso group Categorizatio of gees secodary Adeocaroma vs. smallcell Squamouscell vs. adeocaroma Squamouscell vs. largecell Squamouscell vs. smallcell Largecell vs. adeocaroma Largecell vs. smallcell a) b) III. RESULTS The calculated BMI s for gee categorizatio usig the kmeas approach are depicted i Table II. The correspodig clusters ad s for BMI whe comparig adeocaroma vs. smallcell lug cacer are show i Fig. 2. The idetified umber of, secodary ad cadidate gees usig the BMI are depicted i Table III ad usig the IG i Table IV. The IG lacked the ability to clearly categorize gees ito the proposed scheme whe usig the clusterig approach (Fig. 3), resultig i a high umber of gees (2,531 gee cadidates for adeocaroma vs. smallcell lug cacer, residual data ot show). This might be explaied that IG scores do ot follow roughly a Gaussia distributio (compare Fig. 3b). Furthermore the IG does ot allow distiguishig betwee over ad uder expressio, because the IG solely delivers absolute values. Fig. 2 Idetified clusters o the BMI scores usig the kmeas algorithm for adeocaroma vs. smallcell lug cacer (a), ad the related histogram plot (b). Gree: gees; black: secodary gees; blue: gees. I the left figure (a) the absolute BMI scores are displayed accordig to their sorted rak (idex). 1104

4 World Academy of Sece, Egieerig ad Techology a) b) Fig. 3 Idetified clusters o IG scores usig the kmeas clusterig algorithm for adeocaroma vs. smallcell caroma (a) (red: gees; gree: secodary gees; blue: gees), ad the related histogram plot (b). IV. DISCUSSION AND CONCLUSION I this work gee expressios of eret types of smallcell ad osmallcell lug cacer are compared. The feature selectio methods IG ad BMI were applied to search for the best discrimiatig gees whe comparig pairs of eret cacer types ad categorize them ito, secodary ad cadidate gees. It tured out that fixed s are iappropriate for categorizig gees because the umber of gees rages from 0 to 615 for the eret data sets whe usig empirical IG cutoffs. Based o this aspect a ew method for adjustig s usig a kmeas clusterig approach was developed. Due to the characteristics of roughly Gaussia distributed scores whe usig the BMI method it excelletly turs out the gee cluster, represetig a rage beyod the 99 th percetile of calculated BMI scores. Furthermore the BMI method is very useful to distiguish betwee over ad uder expressed gees. Iterpretig the distributio of BMI scores it also poits out a geeral tedecy to higher or lower over or respectively uder expressed gees i a micro array experimet (see Fig. 4). Fig. 4 Histogram plot of BMI scores for comparig squamouscell vs. adeocaroma idicatig a higher ratio of uder expressed gees (BMIscores < 0). At this poit it is also importat to map the top rakig markers with their gees ad biological pathways. Therefore pathway aalysis was performed usig HGD (Hyper Geometric Distributio) techique to validate the top raked gees ad the assoated pathways [17]. These gees were foud to be part of Cell Commuicatio, Focal adhesio, T cell receptor sigalig pathway, ECMreceptor iteractio pathway, Cell Cycle ad P53 sigalig pathways. The list of top raked gee ames ad their assoated pathways by comparig squamouscell lug caroma vs. largecell lug caroma is show i Table V (usig BMI) ad Table VI (usig IG). Focal adhesio, cell cycle, P53 sigalig ad ECMreceptors pathways play a sigificat role i small cell lug cacer ad o small cell lug cacer. These gees are ivolved i redug the cellcycle progressio ad degradatio of resistace to apoptosis sigals as observed i the small cell lug cacer pathway models. Gees like collage, cycli d1 that have bee idetified as oe of the key gees are also resposible for costitutively upregulatio i lug cacer cell lies. They have bee foud to be ecteiasdi 743 (ET743; Yodelis, Trabectedi) a marie aticacer aget that iduced loglastig objective remissios ad tumor cotrol i a subset of patiets with lug caroma. Hece these gees idetified through this approach ca play a sigificat role i distiguishig various cacer types i lug. TABLE V. TOP TEN RANKED PRIMARY MARKERS AND PATHWAYS SQUAMOUSCELL VS. LARGECELL USING BMI. Affymetrix ID Gee Name Pathways Ivolved 37892_at collage, type XI, alpha 1 Cell Commuicatio, Focal adhesio, ECMreceptor iteractio _at orthodeticle homolog _at collage, type XI, alpha 1 Cell Commuicatio, Focal adhesio, ECMreceptor iteractio, Cell cycle _at otthump _at glucago _a_at similar to hypothetical protei FLJ _at /a Complemet _s_at Coagulatio fibrioge gamma chai cascades, Small cell lug cacer _x_at /a _s_at /a 1105

5 World Academy of Sece, Egieerig ad Techology TABLE VI. TOP TEN RANKED PRIMARY MARKERS AND PATHWAYS SQUAMOUSCELL VS. LARGECELL USING IG. Affymetrix ID Gee Name Pathways Ivolved _at isoleucyltrna sythetase 2, mitochodrial Valie, leue ad isoleue biosythesis, AmioacyltRNA biosythesis _s_at /a _at _at _at _s_at _at _at _at swi/sf related, matrix assoated, acti depedet regulator of chromati, subfamily e, member 1 chromosome 14 ope readig frame 132 chromosome 3 ope readig frame 60 cycli d1 at rich iteractive domai 5b (mrf1like) discoidi, cub ad lccl domai cotaiig 1 protei phosphatase 2c, magesiumdepedet, catalytic subuit Chromati Remodelig by hswi/snf ATPdepedet Complexes, Cotrol of Gee Expressio by Vitami D Receptor Cell cycle, p53 sigalig pathway, Wt sigalig pathway, Focal adhesio, Small cell lug cacer, Nosmall cell lug cacer _at ucleolar protei 9 I order to cross validate the fidigs based o the selected cell lies used i the cabig TM gee expressio studies should be probed for expressio profile of the idetified gees ad the correspodig protei levels. Similar profilig of the tumor tissues from mouse ad huma tumors would further validate fidigs from BMI ad IG. A additioal level of validatio could ivolve pharmacologically treatig the cells with kow atitumor agets ad profilig the same gees to determie potetial efficacy. The biological studies might cofirm the accuracy of the iformatics tools developed ad also poit toward selective biomarkers that may be of sigificace i diagostic ad progostic applicatios. Usig IG ad BMI sets of key gees which ca be foud i several pathways could be idetified. Espeally the BMI combied with dyamic s is well suited for aalyzig microarray experimets ad therefore BMI as a powerful tool for the exploratio of ew ad so far udiscovered gees assoated with cacer is proposed. For future work it is iteded to further study the predictive value of discovered gee sets to aid i risk predictio i lug cacer. ACKNOWLEDGMENT This publicatio was made possible by grat umber P20 RR from the Natioal Ceter for Research Resources (NCRR), a compoet of the Natioal Istitutes of Health (NIH) ad the Austria GENAU project Bioiformatics Itegratio Network. REFERENCES [1] A. Jemal, R. Siegel, E. Ward, Y. Hao, J. Xu, T. Murray ad M.J. Thu, Cacer Statistics, CA Cacer J Cli, vol 58, pp. 7196, [2] R.S. Herbst, J.V. Heymach, S.M. Lippma, Lug cacer., N Egl J Med., vol. 360, pp. 878, [3] I.G. Campbell, S.E. Russell, D.Y. Choog, K.G. Motgomery, M.L. Ciavarella, C.S. Hooi, B.E. Cristiao, P.B. Pearso, W.A. Phillips, Mutatio of the pik3ca gee i ovaria ad breast cacer, Cacer Res., vol. 64, pp , [4] R. Hewett ad P. Kijsaayothi, Tumor classificatio rakig from microarray data, BMC Geomics, vol. 9, [5] C. Baumgarter ad A. Graber, Data miig ad kowledge discovery i metabolomics, I Masseglia F, Pocelet P, Teisseire M (eds.) Successes ad ew directios i data miig. Idea Group Ic, 2007, pp [6] M. Netzer, G. Milloig, M. Osl, B. Pfeifer, S. Prau, J. Villiger, W. Vogel ad C. Baumgarter, A ew esemblebased algorithm for idetifyig breath gas marker cadidates i liver disease usig io molecule reactio mass spectrometry (IMRMS), Bioiformatics, vol. 25, pp , [7] S. Gema, E. Bieestock ad R. Doursat, Neural etworks ad the bias/variace dilemma., Neural Computatio, vol. 4, pp. 158, [8] P. Putte ad M. Somere, A biasvariace aalysis of a real world learig problem: the coil challege Machie Learig, vol. 57, pp , [9] I.H. Witte ad E. Frak, Data Miig: Practical Machie Learig Tools ad Techiques, Secod Editio. Morga Kaufma Publishers Ic., Sa Frasco, CA, USA, [10] R.J. Quila, C4.5: Programs for Machie Learig. Sa Frasco: Morga Kaufma, [11] C. Baumgarter ad D. Baumgarter, Biomarker discovery, disease classificatio, ad similarity query processig o highthroughput ms/ms data of ibor errors of metabolism. J Biomol Scree, vol. 11, pp , [12] NCI, t.publicidetifier=woost00041#; last visited o April 9 th, [13] M. Osl, S. Dreiseitl, B. Pfeifer, K. Weiberger, H. Klocker, G. Bartsch, G. Schäfer, B. Tilg, A. Graber, ad C. Baumgarter, A ew rulebased data miig algorithm for idetifyig metabolic markers i prostate cacer usig tadem mass spectrometry. Bioiformatics, vol. 24, pp , [14] J.D. Nelso, Fidig useful questios: o Bayesia diagostity, probability, impact, ad iformatio gai. Psychol Rev., pp , [15] J.B. MacQuee (1967): "Some Methods for classificatio ad Aalysis of Multivariate Observatios, Proceedigs of 5th Berkeley Symposium o Mathematical Statistics ad Probability", Berkeley, Uiversity of Califoria Press, 1: [16] J.A. Hartiga ad M.A. Wog, A kmeas clusterig algorithm. JR Stat. Soc. Ser. CAppl. Stat, 28:100108, [17] R. Barriot, J. Poix., A. Groppi, A. Barre., N. Goffard., D. Sherma., I. Dutour ad A. de Daruvar, New strategy for the represetatio ad the itegratio of biomolecular kowledge at a cellular scale. Nucleic Ads Res., vol. 32, pp ,