BINNING SOMATIC MUTATIONS BASED ON BIOLOGICAL KNOWLEDGE FOR PREDICTING SURVIVAL: AN APPLICATION IN RENAL CELL CARCINOMA

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1 BINNING SOMATIC MUTATIONS BASED ON BIOLOGICAL KNOWLEDGE FOR PREDICTING SURVIVAL: AN APPLICATION IN RENAL CELL CARCINOMA DOKYOON KIM, RUOWANG LI, SCOTT M. DUDEK, JOHN R. WALLACE, MARYLYN D. RITCHIE Center for Systems Genomcs, Department of Bochemstry and Molecular Bology, Pennsylvana State Unversty, Unversty Park, Pennsylvana, USA Emal: Enormous efforts of whole exome and genome sequencng from hundreds to thousands of patents have provded the landscape of somatc genomc alteratons n many cancer types to dstngush between drver mutatons and passenger mutatons. Drver mutatons show strong assocatons wth cancer clncal outcomes such as survval. However, due to the heterogenety of tumors, somatc mutaton profles are exceptonally sparse whereas other types of genomc data such as mrna or gene expresson contan much more complete data for all genomc features wth quanttatve values measured n each patent. To overcome the extreme sparseness of somatc mutaton profles and allow for the dscovery of combnatons of somatc mutatons that may predct cancer clncal outcomes, here we propose a new approach for bnnng somatc mutatons based on exstng bologcal knowledge. Through the analyss usng renal cell carcnoma dataset from The Cancer Genome Atlas (TCGA), we dentfed combnatons of somatc mutaton burden based on pathways, proten famles, evolutonary conversed regons, and regulatory regons assocated wth survval. Due to the nature of heterogenety n cancer, usng a bnnng strategy for somatc mutaton profles based on bologcal knowledge wll be valuable for mproved prognostc bomarkers and potentally for talorng therapeutc strateges by dentfyng combnatons of drver mutatons. Keywords: Somatc mutaton, pathway, somatc mutaton burden, survval analyss, renal cell carcnoma 1. Introducton Cancer s a complex and heterogeneous dsease, many of whch are caused by somatc mutatons or structural alteratons. Recent meta-dmensonal omcs data from The Cancer Genome Atlas (TCGA) or the Internatonal Cancer Genome Consortum (ICGC) have provded exceptonal opportuntes to nvestgate the complex genetc bass of dsease for mprovng the ablty to dagnose, treat, and prevent cancer [1,2]. Multple alteratons affectng cancer can be observed drectly as somatc mutatons or copy number changes, and ndrectly as changes n epgenomc, transcrptomc, and proteomc dmensons. In partcular, one of the man ssues n cancer research s to dstngush between drver mutatons and passenger mutatons based on somatc mutaton profles. Massve efforts of whole exome/genome sequencng from hundreds to thousands of patents have provded the landscape of somatc genomc alteratons n each specfc cancer or across several cancer types [3,4]. Drver mutatons show strong assocatons wth survval n several dfferent types of cancer [5]. Thus, evaluaton of survval models to predct the dsease trajectory of cancer patents based on somatc mutaton profles s one of the most mperatve foc n the development of predcton models for cancer prognoss. However, due to the heterogenety of tumors, somatc mutaton profles are exceptonally sparse whereas other types of genomc data such as mrna or gene

2 expresson contan nearly complete data for all genomc features wth quanttatve values measured n each patent. Somatc mutatons, n contrast, occur wth low to ntermedate frequency among cancer patents (2-20%). Thus, t s common that patents do not share any somatc mutatons even though they have same clncal features such as prognoss [6]. Prevously, we proposed a framework for data ntegraton to predct clncal outcomes n ovaran cancer [7]. However, somatc mutaton profles were not approprate for predctng outcomes due to the sparseness. To overcome ths challenge, we developed a new strategy to use somatc mutaton profles by performng a bologcally based collapsng/bnnng of the mutatons to look for an accumulaton of somatc mutatons n specfc types of features based on bologcal knowledge such as pathways. Then, these somatc mutaton burden features n specfc pathways can be tested for assocaton wth cancer outcome such as survval. It may be desrable to focus on dentfyng drver pathways nstead of drver mutatons assocated wth survval because the patterns of altered pathways may be smlar even though patents wthn a cancer subtype mght have dverse mutatons [8]. The hypothess s that rather than lookng for the shared mutaton to be the drver, t s mportant to look for the shared pathway (or other bologcal feature) to be the drver. We appled grammatcal evoluton neural networks to dentfy not only specfc pathways assocated wth cancer survval, but also nteractons/combnatons of pathways. In addton, we tested not only pathways as bologcal features, but also proten famles, regulatory regons, and evolutonary conversed regons to test the assocaton between dfferent types of knowledge-based somatc mutaton burden and survval. To test the utlty of the proposed strategy, we appled our approach on somatc mutaton profles from renal cell carcnoma from TCGA, whch s the most common type of kdney cancer. 2. Methods 2.1. Data Somatc mutatons from renal cell carcnoma patents were retreved from the TCGA ( on 1 July Due to the extreme sparseness of somatc mutaton profle, we used all classes of somatc mutatons generated from the mutaton callng conducted by Baylor College of Medcne (BCM) as a mutaton annotaton format (MAF) and extracted patent mutaton profles where the analyte was a DNA sample from the tumor. Somatc mutatons from 417 patents wth renal cell carcnoma were retaned for subsequent analyss. Based on chromosomal postons, there were 27,194 unque somatc mutatons across all patents. As a clncal outcome, survval nformaton was downloaded for 417 patents as well as sex and age for adjustng potental confoundng factors when modelng BoBn BoBn s a flexble collapsng or bnnng method usng bologcal knowledge to automate the bnnng of low frequency varants for assocaton tests [9,10]. The man functon of BoBn

3 provdes access to comprehensve knowledge-guded mult-level bnnng. For example, bn boundares can be formed usng genomc locatons from: genes, regulatory regons, evolutonary conserved regons, and/or pathways. BoBn uses a bult-n database called the Lbrary of Knowledge Integraton (LOKI), whch s a repostory of data assembled from publc databases. LOKI contans multple data resources [11]. Smlar to germlne low frequency varants, somatc mutatons tend to occur wth low or ntermedate frequency among cancer patents. Thus, we used BoBn for bnnng somatc mutatons based on bologcal knowledge, such as pathway, n order to overcome the sparseness of somatc mutaton profles. Frst, we converted MAF to varant call format (VCF) as an nput for BoBn (Fg. 1). Then, we appled BoBn to generate KEGG pathway, Pfam, evolutonary conversed regon (ECR), and regulatory bn profles by accumulatng somatc mutatons n a specfc bn. BoBn s open source and avalable at Fg. 1. Illustraton of somatc mutaton analyss usng BoBn and ATHENA for predctng survval ATHENA The Analyss Tool for Hertable and Envronmental Network Assocatons (ATHENA) s a multfunctonal software package desgned to perform three essental functons to determne the metadmensonal models of complex dsease: (1) performng feature selectons from categorcal or contnuous ndependent features; (2) buldng addtve and nteracton models that explan or predct categorcal or contnuous clncal outcomes; (3) nterpretng the canddate models for use n further translatonal bonformatcs [7,12]. For ths analyss, we used Grammatcal Evoluton Neural Networks (GENN) as the modelng component. ATHENA s open source and avalable at

4 2.4. Grammatcal Evoluton Neural Networks (GENN) Varous computatonal methods have been developed to dentfy non-lnear nteractons between genomc varables that have small or large man effects such as a mult-factor dmensonalty reducton (MDR) [13]. However, MDR performs an exhaustve search of all possble combnaton of nteractng loc to generate mult-locus predctor models. The search space ncreases exponentally wth the number of varables and became nfeasble when ntegratng metadmensonal genomcs data. Thus, stochastc methods usng evolutonary algorthm have been developed and shown to utlze the full dmensonalty of the data wthout exhaustvely evaluatng all possble combnatons of varables [14,15]. Artfcal Neural Network (ANN) s a flexble and robust machne learnng technque desgned to mtate neurons n the bran to solve complex problems. ANN s a good canddate for dentfyng complex and non-lnear nteractons that nfluence varance n an outcome of nterest. Generally, the method for applyng ANN to a classfcaton problem s to use gradent descent algorthm such as backpropagaton to ft the weghts of the network gven nput varables and network archtecture. However, the varables and network archtecture are not known a pror. In order to smultaneously optmze the nput varables, weghts, and network structures, evolutonary algorthm approaches have been developed and appled [15,16]. Genetc programmng, a specalzaton of genetc algorthms, s an evolutonary algorthm-based method that uses survval of the fttest to evolve optmal solutons from a populaton of random solutons [17]. Grammatcal evoluton s a more flexble verson of genetc programmng snce t can also evolve functonal solutons, or computer program, va grammar rules [15]. The detals of the grammar rules were descrbed n a prevous study [15]. The GENN algorthm s brefly descrbed as follows: (1) The data s dvded nto fve parts for fve cross valdaton wth 4/5 for tranng and 1/5 for testng. (2) A random populaton of bnary strngs s generated to be ANNs usng a Backus-Naur grammar. The total populaton s dvded nto demes as sub-populatons across a user-defned number of CPUs for parallelzaton. (3) All ANNs are evaluated wth tranng data, and the solutons wth the lowest predcton errors are selected for crossover and reproducton. The new populaton s composed of mutated orgnal solutons and new random solutons. (4) Step 3 s repeated for a set number of generatons. Mgraton of best solutons also occurs between demes durng evoluton for a pre-defned number of tmes. (5) The best soluton at the fnal generaton s tested usng the remanng 1/5 test dataset and ftness s recorded. (6) Steps 2-5 are repeated four more tmes, each tme usng a dfferent 4/5 of the tranng data and 1/5 of testng data.

5 2.5. Survval ftness functon The goal of ths study s to predct censored survval outcome based on somatc mutaton burden generated from BoBn. In general, t s dffcult to drectly predct raw survval data va measurng of goodness-of-ft, such as R 2, due to the censored observatons. Thus, an approprate measure of goodness-of-ft should be requred for predctng censored survval data. Martngale resduals are defned as the dfference between the cumulatve hazards assgned to an ndvdual wth falure tme t and ts observed status, δ = 0 censored, δ = 1event [18]. Thus, martngale resduals could be ntutvely nterpreted as the surplus deaths. Martngale resduals are calculated from the ftted Cox model as M = δ Λ ( t) (1) where Λ s a cumulatve hazard functon [18]. Accordng to the model, the result of cumulatve hazard functon reflects the number of expected death events per ndvduals falng at t. The range of martngale resduals s between negatve nfnty and 1 because the cumulatve hazard functon does not have upper lmt. However, the sum of all martngale resduals s zero. Each patent wth a negatve martngale resdual s nterpreted as a good prognoss, whereas one wth a postve martngale resdual s nterpreted as a poor prognoss. The martngale resdual of each patent s obtaned from the reduced model wth no genomc effects from somatc mutatons. Thus, martngale resduals can be used as a new contnuous outcome snce they reflect the unexplaned porton beyond what s explaned by the adjusted clncal covarates excludng the genomc features [18]. In addton, another advantage of martngale resdual s that the model can be adjusted by potental confounders such as sex and age. As a proof of concept, we adjusted for age and sex when calculatng martngale resduals usng survval R package. Snce the dstrbuton of martngale resduals s more exponentally shaped, the assumpton of R 2, whch has normally dstrbuted resduals, s not satsfed. Thus, a new ftness functon was proposed for measurng the mean absolute dfferences (MAD) between observed martngale resduals ( M ) and predcted martngale resduals ( M x ) from GENN wth genomc covarate vector x [19]. The new ftness functon s formulated as follows: M (2) M x MAD M Ftness functon = 1 MAD (3) The output of the MAD ftness functon wll be from 0 to 1. The model wth 1 ftness score represents the best predctve model whereas the one wth 0 ftness score means the worst predctve model. We used MAD for the subsequent experments Experment setup = Fgure 1 shows the overvew of the analyss ppelne, whch conssts of a bnnng step usng

6 BoBn and a modelng step usng ATHENA. After convertng MAF to VCF, BoBn was used to generate pathway, Pfam, ECR, regulatory bn profles. Then, we used ATHENA to buld addtve/nteracton models assocated wth survval. Martngale resduals and each bn profle can be used as an nput for ATHENA. For buldng GENN models, we randomly splt the nput dataset nto two groups, 4/5 dataset (n=333) for learnng models and 1/5 dataset (n=84) for the valdaton. Ths s ndependent of the cross-valdaton (CV) procedure. The CV procedure was performed on the learnng dataset whch s 4/5 of the total dataset. Based on GENN results from 5-fold CV, the features from each model across 5 CVs were selected, and then, we reran GENN usng selected features to generate the fnal model from entre tranng dataset. Lastly, the fnal GENN model can be used to predct survval from the valdaton dataset. To avod over-fttng, the valdaton dataset was not used for the entre learnng step. Table 1 shows the GENN parameters for the analyss. Based on the output of the fnal GENN model as predcted martngale resduals, the valdaton dataset was dvded nto two sub groups, low-rsk group and hgh-rsk group, by the medan threshold of predcted martngale resduals. Then, survval analyss was performed usng survval R package. 3. Results and Dscusson Table 1. GENN parameter settngs Parameter Value Number of demes (CPUs) 20 Populaton sze/ Deme 5,000 Number of generatons 1,000 Number of mgratons 20 Probablty of crossover 0.9 Probablty of mutaton 0.01 Ftness functon 1 MAD 3.1. Bnnng somatc mutatons usng BoBn To predct survval based on somatc mutaton burden, BoBn was used to generate KEGG pathway, Pfam, ECR, and regulatory bn profles. Somatc mutaton burden analyss can be based when usng bns consstng of extremely small number of mutatons, thus bns from KEGG pathway, Pfam, ECR, and regulatory regons wth more than 10 mutatons were selected for the further study. The total number of KEGG pathway, Pfam, ECR, and regulatory bns were 272, 922, 250, and 41, respectvely. Snce somatc mutaton profles were conducted by whole-exome sequencng, regulatory bn profles had a relatvely small number of bns compared to other bns. Fgure 2 shows the dfference of sparseness between raw somatc mutaton profles and pathway bn profles.

7 Fg. 2. Dfference of sparseness between raw somatc mutaton profles and KEGG pathway bn profles. For somatc mutaton profles, mutatons from chromosome 22 were extracted for generatng the heatmap fgure. Each black dot represents the presence of ether somatc mutaton n the left heatmap or mutaton burden n a pathway n the rght heatmap GENN modelng for somatc mutaton burden A smulaton study was conducted to demonstrate the valdty of the proposed survval ftness functon and martngale resduals as a new outcome for predctng survval (data not shown) [Km et al., submtted]. Accordng to the results from the smulaton data, martngale resduals performed well as a new outcome n terms of fndng true survval genes and lmted false postves usng GENN. Next, somatc mutaton profles n renal cell carcnoma were analyzed to dentfy addtve/nteracton models based on knowledge-based somatc mutaton burden. After generatng pathway, Pfam, ECR, and regulatory bns usng BoBn, GENN models were traned to predct survval from the valdaton dataset. The fnal model of GENN s the evolved neural network wth optmzed nput varables, weghts, and network structure to dentfy addtve or nteracton models that predct survval outcome. Fgure 3 shows the best GENN models from each bn profle: KEGG pathway, Pfam, ECR, and regulatory bns, respectvely. Fnally, the fnal GENN model was used to predct survval from the valdaton dataset, whch conssted of 84 patents. The ftness scores from the valdaton dataset for each of the best models wth pathway, Pfam, ECR, and regulatory bn profles were 0.641, 0.67, 0.665, and 0.654, respectvely (Fg 3 and Table 2). Among four dfferent bn profles, Pfam bn profles showed the best performance for predctng survval. Table 2. Performance comparson between dfferent types of bn profles. Performance was measured from the valdaton dataset. GENN model 1 - MAD Permutaton p-value KEGG pathway bns Pfam bns ECR bns Regulatory bns Integraton

8 (a) Pathway bns (Ftness score: 0.641) (b) Pfam bns (Ftness score: 0.67) (c) ECR bns (Ftness score: 0.665) (d) Regulatory bns (Ftness score: 0.654) Fg. 3. Best GENN models from each knowledge-based somatc mutaton profles. PSUB, PMUL, and PADD are a subtracton, multplcaton, and addton actvaton node, respectvely. Knowledge features such as pathway, Pfam, ECR, regulatory regons are shown n the gray boxes. (a) pathway-based mutaton profles (b) Pfam-based mutaton profles (c) ECR-based mutaton profles (d) regulatory-based mutaton profles To buld an nteracton model between dfferent knowledge-guded bns assocated wth survval n renal cancer, we ntegrated pathway, Pfam, ECR, and regulatory bn profles. The fnal ntegraton model was generated usng GENN wth varables from the best models of each bn profle. The fnal ntegraton model was also used to predct survval for the valdaton dataset. In terms of predctve power, the ntegraton model showed the best performance wth a ftness score of (Table 2). The selected features n the fnal ntegraton model are methyl-cpg bndng doman and nsulnase (Peptdase famly M16) from Pfam bn profle, ecr_vertebrate_chr3_band1567 and

9 ecr_vertebrate_chr15_band356 from ECR bn profles, and regulatory_eomes from regulatory bn profles (Fg. 4). To test the statstcal sgnfcance of each GENN model, permutaton testng was performed. The survval outcome for the valdaton dataset was randomly permuted 1000 tmes and permutaton p-values of each GENN model were obtaned from the 1000 random valdaton sets (Table 2). The ntegraton model showed a sgnfcant result (P = 0.026) whle other GENN models were not sgnfcant based on permutaton testng. In addton, survval analyss was performed for two sub groups, low-rsk and hgh-rsk groups, whch were dvded by a medan threshold of predcted martngale resduals for the valdaton dataset. Kaplan-Meer analyss showed that the two groups generated from the ntegraton model were sgnfcantly dfferent based on survval (Fg. 5). Fg. 4. Integraton model contanng varables from dfferent knowledge-based mutaton profles. Red, green, and blue boxes represent Pfam, ECR, and regulatory regon features, respectvely. PADD represents an addton actvaton node. A ftness score of the ntegraton model was Bologcal nterpretaton Four pathways, argnne and prolne metabolsm, adherens juncton, toxoplasmoss, and rheumatod arthrts, were found n the GENN models. Adherens junctons are one of the most relevant junctonal complexes n the kdney epthelum and adherens juncton dsrupton s assocated wth cell prolferaton, nvason, and angogeness n renal cell carcnoma [20]. In addton, argnne and prolne metabolsm has been shown to be mportant n renal cell carcnoma usng proteomc and metabolc profles [21]. Interestngly, rheumatod arthrts was dentfed as one of the features n the fnal model. Assocatons between rheumatod arthrts and kdney cancer have been reported n many studes. For example, nflxmab, ant-tumor necross factor α (TNF-α) antbody, s lcensed for use n rheumatod arthrts and TNF-α mght be a therapeutc target n renal cell carcnoma [22]. Notably, the top pathway model showed complex and non-lnear nteractons between pathways assocated wth survval. Ths suggests t s mportant to consder

10 nteractons assocated wth survval, whch mght have crucal roles n molecular pathogeness, progresson, and prognoss of renal cell carcnoma, that are not easly detected by tradtonal pathway analyss approaches. For Pfam model, a combnaton of methyl-cpg bndng doman, cd1 famly poly A polymerase, ubqutn carboxyl-termnal hydrolase famly 1, nsulnase (peptdase famly M16), and nuclear envelope localzaton doman was found to be assocated wth survval. In partcular, the epgenetc slencng of cancer-related genes such as ABCG2 has been shown to be assocated wth renal cell carcnoma va beng medated through recrutment of a group of protens, called methyl-cpg bndng doman (MBD) [23]. Epgenetc control of the ubqutn carboxyl termnal hydrolase famly 1, whch plays an mportant role n cell growth and dfferentaton, can be dsturbed n renal cell carcnoma [24]. Several evolutonary conversed regons were also selected from ECR model. Many genes located n selected evolutonary conversed regons such as BAP1, EIF4G1, EBAG9, or FBN1 were found as mport genes nvolved n several cancers. In partcular, BAP1 loss defnes a new class of renal cell carcnoma [25]. In addton, somatc mutaton burden n the regulatory regons of CACNA1A, LOXHD1, DNAH5, DCC, or EOMES mght play a functonally sgnfcant role n renal cell carcnoma survval. In the ntegraton model, where we used multple knowledge sources, methyl-cpg bndng doman and nsulnase (Peptdase famly M16) from Pfam model, chr3_band1567 and chr15_band356 from ECR model, and EOMES from regulatory model were selected. Combnaton of somatc mutaton burden based on multple bologcal knowledge sources mght reflect the complex molecular pathogeness and progresson of renal cell carcnoma. Fg. 5. Kaplan-Meer survval plots for the valdaton dataset. Valdaton dataset was dvded nto hgh-rsk and low-rsk groups based on a medan value of predcted outputs from the ntegraton model. 4. Conclusons In ths study, we proposed a new approach of bnnng somatc mutaton based on bologcal knowledge for predctng survval n order to overcome the extreme sparseness of somatc mutaton profles. Through the analyss usng renal cell carcnoma dataset, we dentfed nteracton/combnatons of somatc mutaton burden based on pathway, proten famles,

11 evolutonary conversed regons, and regulatory regons assocated wth survval. Knowledge guded bnnng/collapsng somatc mutatons dramatcally reduce the sparseness of profles as well as search space, from 27,194 mutatons to 272 pathways. In terms of predctve power, some of the GENN models were not sgnfcant based on the permutaton test. However, t mght be due to the ftness functon based on mean absolute dfference. Even though survval outcome of 84 patents were shuffled, a mean dfference between observed martngale resduals and predcted martngale resduals could not be too large. In addton, a small number of patents n the valdaton dataset lmt our power as sample sze s often a lmtng factor. Improvng the methodology by ncorporatng drectonalty of the pathway when bnnng somatc mutatons could also potentally ncrease the predctve power of pathway-based approach. Notably, the predctve power of the ntegraton model outperformed other models from sngle types of bologcal knowledge sources. These results suggest that each bologcal knowledge source can be complementary to the predcton power of survval because each knowledge source has ts specfc bologcal context. Furthermore, the survval analyss for the valdaton dataset demonstrated that somatc mutaton burden based on bologcal knowledge showed sgnfcant assocatons wth cancer prognoss n renal cell carcnoma. The present study underpns our on-gong work. Frst, not only somatc mutatons but also germlne mutatons can be regarded as mportant genomc features that are assocated wth cancer outcomes [5]. Thus, as one of promsng future works, t would be valuable to combne both types of mutatons to nvestgate the assocatons wth cancer outcomes. It would be also nterestng to nvestgate whether known somatc mutatons nfluence the models. In addton, the proposed approach could be appled to explore assocatons wth other cancer clncal outcomes such as stage, grade, recurrence, or metastass. Furthermore, the current approach by bologcal-based bns such as pathways may unnecessarly nject nose nto the model. It would be ntrgung to apply an adquate flterng step to the mutatons n future studes. Due to the nature of heterogenety n cancer, usng a bnnng strategy for somatc mutaton profles based on bologcal knowledge wll be valuable for mproved prognostc bomarkers and talorng therapeutc strateges by dentfyng nteractons/combnatons of drver mutatons. Acknowledgments Ths work was funded by NIH grant R01 LM010040, NHLBI grant U01 HL065962, and CTSI: UL1 RR Ths work s also supported by a grant wth the Pennsylvana Department of Health usng Tobacco CURE Funds. References 1. Cancer Genome Atlas Research N (2013) Comprehensve molecular characterzaton of clear cell renal cell carcnoma. Nature 499: Internatonal Cancer Genome Consortum (2010) Internatonal network of cancer genome projects. Nature 464: Cancer Genome Atlas Research N, Wensten JN, Collsson EA, Mlls GB, Shaw KR, et al. (2013) The Cancer Genome Atlas Pan-Cancer analyss project. Nat Genet 45:

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