iCAGES: integrated CAncer GEnome Score for comprehensively prioritizing driver genes in personal cancer genomes

Chengliang Dong, Yunfei Guo, Hui Yang, Zeyu He, Xiaoming Liu, Kai Wang, Chengliang Dong, Yunfei Guo, Hui Yang, Zeyu He, Xiaoming Liu, Kai Wang

Abstract

Cancer results from the acquisition of somatic driver mutations. Several computational tools can predict driver genes from population-scale genomic data, but tools for analyzing personal cancer genomes are underdeveloped. Here we developed iCAGES, a novel statistical framework that infers driver variants by integrating contributions from coding, non-coding, and structural variants, identifies driver genes by combining genomic information and prior biological knowledge, then generates prioritized drug treatment. Analysis on The Cancer Genome Atlas (TCGA) data showed that iCAGES predicts whether patients respond to drug treatment (P = 0.006 by Fisher's exact test) and long-term survival (P = 0.003 from Cox regression). iCAGES is available at http://icages.wglab.org .

Keywords: Cancer genomics; Machine learning; Precision medicine; Precision oncology; TCGA.

Figures

Fig. 1
Fig. 1
Analysis of each predictor selected for the radial SVM modeling for iCAGES variant score. a Correlation diagrams illustrating the pairwise Pearson correlation between all predictors and outcome variable in the training dataset. The color and size of the shaded region in the pie charts at the upper right indicate the level of correlation, with red and larger proportions of the shaded region indicating higher positive correlation. b Violin plots of scores from different predictors (different colors) in the training dataset in the TP (deleterious) and TN (neutral) groups. Each plot shows the median (indicated by the small white dot), the first through the third interquartile range (the thick, solid vertical band), and the density (different colors) of the predictor scores in each group
Fig. 2
Fig. 2
The iCAGES package as three layers. The input file contains all variants identified from the patient; it can be either in ANNOVAR input format or in VCF format. The first layer of iCAGES prioritizes mutations. It computes three different feature scores for annotating the gene, including the radial SVM score for each of its point coding mutations, CNV normalized peak score for each of its structural variations, and FunSeq2 score for each of its point non-coding mutations. The second layer of iCAGES prioritizes cancer driver genes. It takes three feature scores from the first layer, generates the corresponding Phenolyzer score for each mutated gene and computes a LR score for this gene (iCAGES gene score). The final level of iCAGES prioritizes targeted drugs. It first queries the DGIdb and FDA drug database for potential drugs that interact with mutated genes and their neighbors. Next, it calculates the joint probability for each drug being the most effective (iCAGES drug score) from three feature scores, which are iCAGES gene scores for its direct/indirect target, normalized BioSystems probability measuring the maximum relatedness of a drug’s direct target with each mutated gene (final target), and PubChem active probability measuring the bioactivity of the drug. The final output of iCAGES consists of three major elements, a prioritized list of mutations, a prioritized list of genes with their iCAGES gene scores, as well as a prioritized list of targeted drugs with their iCAGES drug scores
Fig. 3
Fig. 3
Performance of the first layer of iCAGES. a Performance of the radial SVM score evaluated on the COSMIC version 68 testing dataset (testing dataset I). A higher AUC score indicates better performance. The 95% CI was computed with 2000 stratified bootstrap replicates. b Performance of the radial SVM score evaluated on Cancer Gene Census genes from COSMIC version 68 testing dataset (testing dataset II)
Fig. 4
Fig. 4
Performance of the second layer of iCAGES. a Performance of the iCAGES score compared to MutSigCV, evaluated on 14,169 TCGA patients. A higher AUC score indicates better performance. The 95% CI was computed with 2000 stratified bootstrap replicates (testing dataset I). b Performance of iCAGES compared to IntOgen, evaluated on data from 6748 patients used in the Rubio-Perez et al. study. Each bar represents the number of patients whose cancer driver gene can be identified by iCAGES or by IntOgen. Top One, Top Five, Top Ten and Top Twenty refer to using the top gene, top five genes, top ten genes, and top 20 genes from prediction, respectively. A significant advantage of iCAGES compared to other tools is indicated with ***P ≤ 0.0001 (Bonferroni correction; testing dataset II). c Performance of iCAGES compared to IntOgen, Phen-Gen, and MuSiC evaluated on data from 3178 patients used in the Kandoth et al. study (testing dataset III). d Performance of iCAGES compared to IntOgen, Phen-Gen, and MuSiC evaluated on data from 71 patients used in the Kandoth et al. study but not in the Rubio-Perez et al. study (testing dataset IV)
Fig. 5
Fig. 5
Performance of the third layer of iCAGES. a–c Kaplan–Meier survival curve for 124 TCGA patients with targeted therapy with unknown response whose data were also used in the Rubio-Perez et al. study (testing dataset I). aRed and blue curves represent patients whose treatments do and do not contain iCAGES-predicted first tier drugs, respectively. Red and blue areas represent the 95% confidence interval for the survival curve. bRed and blue curves represent patients whose treatments do and do not contain Rubio-Perez et al.-predicted drugs, respectively. cRed and blue curves represent patients whose treatments do and do not contain DGIdb-predicted drugs. d Number of TCGA patients with targeted therapy with complete response or progressive disease who received correct iCAGES-predicted drugs (blue), DGIdb drugs (gray), Rubio-Perez et al. tier one drugs (orange). e Number of patients used in Rubio-Perez et al. study who can potentially benefit from iCAGES (without pathway component from BioSystem) predicted drugs from three tiers (blue), iCAGES-predicted drugs (green), Rubio-Perez et al.-predicted drugs (orange). Significant advantage of iCAGES compared to other tools is indicated as ***P ≤ 0.0001 and Bonferroni correction (testing dataset III)
Fig. 6
Fig. 6
The web interface of iCAGES, as demonstrated using data from Imielinski et al. a The submission page for iCAGES. Users can enter data with the VCF format (default) or with ANNOVAR input format used in the ANNOVAR package. b Dynamic form for advanced users. Users can click “Advanced Options” and enter additional information, such as structural variations in BED format, cancer subtype, and drugs that this patient has been using. c Bubble plot output of the iCAGES package. The size of the bubbles indicates the weight of the iCAGES score and different colors indicate the category of the gene. Red, blue, and green indicate that this gene belongs to the Cancer Gene Census, the KEGG cancer pathway, or neither category, respectively. Pink bubbles that are connected to blue, green or red bubbles indicate targeted drugs. d The corresponding bar plot of the output. The length of the bar indicates the weight of the iCAGES score and different colors indicate the category of the gene

References

    1. Stratton MR, Campbell PJ, Futreal PA. The cancer genome. Nature. 2009;458(7239):719–24. doi: 10.1038/nature07943.
    1. Greenman C, Stephens P, Smith R, Dalgliesh GL, Hunter C, Bignell G, Davies H, Teague J, Butler A, Stevens C, et al. Patterns of somatic mutation in human cancer genomes. Nature. 2007;446(7132):153–8. doi: 10.1038/nature05610.
    1. Zender L, Spector MS, Xue W, Flemming P, Cordon-Cardo C, Silke J, Fan ST, Luk JM, Wigler M, Hannon GJ, et al. Identification and validation of oncogenes in liver cancer using an integrative oncogenomic approach. Cell. 2006;125(7):1253–67. doi: 10.1016/j.cell.2006.05.030.
    1. Zhang X, Jia H, Lu Y, Dong C, Hou J, Wang Z, Wang F, Zhong H, Wang L, Wang K. Exome sequencing on malignant meningiomas identified mutations in neurofibromatosis type 2 (NF2) and meningioma 1 (MN1) genes. Discov Med. 2014;18(101):301–11.
    1. Meyerson M, Gabriel S, Getz G. Advances in understanding cancer genomes through second-generation sequencing. Nat Rev Genet. 2010;11(10):685–96. doi: 10.1038/nrg2841.
    1. Gonzalez-Perez A, Perez-Llamas C, Deu-Pons J, Tamborero D, Schroeder MP, Jene-Sanz A, Santos A, Lopez-Bigas N. IntOGen-mutations identifies cancer drivers across tumor types. Nat Methods. 2013;10(11):1081–2. doi: 10.1038/nmeth.2642.
    1. Carter H, Chen S, Isik L, Tyekucheva S, Velculescu VE, Kinzler KW, Vogelstein B, Karchin R. Cancer-specific high-throughput annotation of somatic mutations: computational prediction of driver missense mutations. Cancer Res. 2009;69(16):6660–7. doi: 10.1158/0008-5472.CAN-09-1133.
    1. Kaminker JS, Zhang Y, Watanabe C, Zhang Z. CanPredict: a computational tool for predicting cancer-associated missense mutations. Nucleic Acids Res. 2007;35(Web Server issue):W595–8. doi: 10.1093/nar/gkm405.
    1. Carter H, Samayoa J, Hruban RH, Karchin R. Prioritization of driver mutations in pancreatic cancer using cancer-specific high-throughput annotation of somatic mutations (CHASM) Cancer Biol Ther. 2010;10(6):582–7. doi: 10.4161/cbt.10.6.12537.
    1. Lawrence MS, Stojanov P, Polak P, Kryukov GV, Cibulskis K, Sivachenko A, Carter SL, Stewart C, Mermel CH, Roberts SA, et al. Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature. 2013;499(7457):214–8. doi: 10.1038/nature12213.
    1. Dees ND, Zhang Q, Kandoth C, Wendl MC, Schierding W, Koboldt DC, Mooney TB, Callaway MB, Dooling D, Mardis ER, et al. MuSiC: identifying mutational significance in cancer genomes. Genome Res. 2012;22(8):1589–98. doi: 10.1101/gr.134635.111.
    1. Youn A, Simon R. Identifying cancer driver genes in tumor genome sequencing studies. Bioinformatics. 2011;27(2):175–81. doi: 10.1093/bioinformatics/btq630.
    1. Reva B, Antipin Y, Sander C. Predicting the functional impact of protein mutations: application to cancer genomics. Nucleic Acids Res. 2011;39(17):e118. doi: 10.1093/nar/gkr407.
    1. Shihab HA, Gough J, Cooper DN, Day IN, Gaunt TR. Predicting the functional consequences of cancer-associated amino acid substitutions. Bioinformatics. 2013;29(12):1504–10. doi: 10.1093/bioinformatics/btt182.
    1. Fu Y, Liu Z, Lou S, Bedford J, Mu X, Yip KY, Khurana E, Gerstein M. FunSeq2: A framework for prioritizing noncoding regulatory variants in cancer. Genome Biol. 2014;15(10):480. doi: 10.1186/s13059-014-0480-5.
    1. Gnad F, Baucom A, Mukhyala K, Manning G, Zhang Z. Assessment of computational methods for predicting the effects of missense mutations in human cancers. BMC Genomics. 2013; 14 Suppl 3:S7.
    1. Javed A, Agrawal S, Ng PC. Phen-Gen: combining phenotype and genotype to analyze rare disorders. Nat Methods. 2014;11(9):935–7. doi: 10.1038/nmeth.3046.
    1. Chang X, Wang K. wANNOVAR: annotating genetic variants for personal genomes via the web. J Med Genet. 2012;49(7):433–6. doi: 10.1136/jmedgenet-2012-100918.
    1. Yang H, Robinson PN, Wang K. Phenolyzer: phenotype-based prioritization of candidate genes for human diseases. Nat Methods. 2015;12(9):841–3. doi: 10.1038/nmeth.3484.
    1. Ng S, Collisson EA, Sokolov A, Goldstein T, Gonzalez-Perez A, Lopez-Bigas N, Benz C, Haussler D, Stuart JM. PARADIGM-SHIFT predicts the function of mutations in multiple cancers using pathway impact analysis. Bioinformatics. 2012;28(18):i640–6. doi: 10.1093/bioinformatics/bts402.
    1. Hou JP, Ma J. DawnRank: discovering personalized driver genes in cancer. Genome Med. 2014;6(7):56. doi: 10.1186/s13073-014-0056-8.
    1. Bertrand D, Chng KR, Sherbaf FG, Kiesel A, Chia BK, Sia YY, Huang SK, Hoon DS, Liu ET, Hillmer A, et al. Patient-specific driver gene prediction and risk assessment through integrated network analysis of cancer omics profiles. Nucleic Acids Res. 2015;43(7):e44. doi: 10.1093/nar/gku1393.
    1. Reimand J, Bader GD. Systematic analysis of somatic mutations in phosphorylation signaling predicts novel cancer drivers. Mol Syst Biol. 2013;9:637. doi: 10.1038/msb.2012.68.
    1. Martelotto LG, Ng C, De Filippo MR, Zhang Y, Piscuoglio S, Lim R, Shen R, Norton L, Reis-Filho JS, Weigelt B. Benchmarking mutation effect prediction algorithms using functionally validated cancer-related missense mutations. Genome Biol. 2014;15(10):484. doi: 10.1186/s13059-014-0484-1.
    1. Wang K, Li M, Hakonarson H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 2010;38(16):e164. doi: 10.1093/nar/gkq603.
    1. Kumar P, Henikoff S, Ng PC. Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm. Nat Protoc. 2009;4(7):1073–81. doi: 10.1038/nprot.2009.86.
    1. Adzhubei I, Jordan DM, Sunyaev SR. Predicting functional effect of human missense mutations using PolyPhen-2. Curr Protoc Hum Genet. 2013;7:7–20.
    1. Chun S, Fay JC. Identification of deleterious mutations within three human genomes. Genome Res. 2009;19(9):1553–61. doi: 10.1101/gr.092619.109.
    1. Schwarz JM, Rodelsperger C, Schuelke M, Seelow D. MutationTaster evaluates disease-causing potential of sequence alterations. Nat Methods. 2010;7(8):575–6. doi: 10.1038/nmeth0810-575.
    1. Shihab HA, Gough J, Cooper DN, Stenson PD, Barker GL, Edwards KJ, Day IN, Gaunt TR. Predicting the functional, molecular, and phenotypic consequences of amino acid substitutions using hidden Markov models. Hum Mutat. 2013;34(1):57–65. doi: 10.1002/humu.22225.
    1. Davydov EV, Goode DL, Sirota M, Cooper GM, Sidow A, Batzoglou S. Identifying a high fraction of the human genome to be under selective constraint using GERP++ PLoS Comput Biol. 2010;6(12):e1001025. doi: 10.1371/journal.pcbi.1001025.
    1. Cooper GM, Stone EA, Asimenos G, Program NCS, Green ED, Batzoglou S, Sidow A. Distribution and intensity of constraint in mammalian genomic sequence. Genome Res. 2005;15(7):901–13. doi: 10.1101/gr.3577405.
    1. Kircher M, Witten DM, Jain P, O’Roak BJ, Cooper GM, Shendure J. A general framework for estimating the relative pathogenicity of human genetic variants. Nat Genet. 2014;46(3):310–5. doi: 10.1038/ng.2892.
    1. Carter H, Douville C, Stenson PD, Cooper DN, Karchin R. Identifying Mendelian disease genes with the variant effect scoring tool. BMC Genomics. 2013;14(Suppl 3):S3. doi: 10.1186/1471-2164-14-S3-S3.
    1. Garber M, Guttman M, Clamp M, Zody MC, Friedman N, Xie X. Identifying novel constrained elements by exploiting biased substitution patterns. Bioinformatics. 2009;25(12):i54–62. doi: 10.1093/bioinformatics/btp190.
    1. Alexandrov LB, Nik-Zainal S, Wedge DC, Aparicio SA, Behjati S, Biankin AV, Bignell GR, Bolli N, Borg A, Borresen-Dale AL, et al. Signatures of mutational processes in human cancer. Nature. 2013;500(7463):415–21. doi: 10.1038/nature12477.
    1. Kandoth C, McLellan MD, Vandin F, Ye K, Niu B, Lu C, Xie M, Zhang Q, McMichael JF, Wyczalkowski MA, et al. Mutational landscape and significance across 12 major cancer types. Nature. 2013;502(7471):333–9. doi: 10.1038/nature12634.
    1. Forbes SA, Beare D, Gunasekaran P, Leung K, Bindal N, Boutselakis H, Ding M, Bamford S, Cole C, Ward S, et al. COSMIC: exploring the world’s knowledge of somatic mutations in human cancer. Nucleic Acids Res. 2014;43(Database issue):D805-11.
    1. Kanehisa M, Araki M, Goto S, Hattori M, Hirakawa M, Itoh M, Katayama T, Kawashima S, Okuda S, Tokimatsu T, et al. KEGG for linking genomes to life and the environment. Nucleic Acids Res. 2008;36(Database issue):D480–484.
    1. UniProt C. Activities at the Universal Protein Resource (UniProt) Nucleic Acids Res. 2014;42(Database issue):D191–198.
    1. Zhao M, Sun J, Zhao Z. TSGene: a web resource for tumor suppressor genes. Nucleic Acids Res. 2013;41(Database issue):D970–6. doi: 10.1093/nar/gks937.
    1. Forbes SA, Bindal N, Bamford S, Cole C, Kok CY, Beare D, Jia M, Shepherd R, Leung K, Menzies A, et al. COSMIC: mining complete cancer genomes in the Catalogue of Somatic Mutations in Cancer. Nucleic Acids Res. 2011;39(Database issue):D945–50. doi: 10.1093/nar/gkq929.
    1. Wu CH, Apweiler R, Bairoch A, Natale DA, Barker WC, Boeckmann B, Ferro S, Gasteiger E, Huang H, Lopez R, et al. The Universal Protein Resource (UniProt): an expanding universe of protein information. Nucleic Acids Res. 2006;34(Database issue):D187–91. doi: 10.1093/nar/gkj161.
    1. Auer PL, Johnsen JM, Johnson AD, Logsdon BA, Lange LA, Nalls MA, Zhang G, Franceschini N, Fox K, Lange EM, et al. Imputation of exome sequence variants into population- based samples and blood-cell-trait-associated loci in African Americans: NHLBI GO Exome Sequencing Project. Am J Hum Genet. 2012;91(5):794–808. doi: 10.1016/j.ajhg.2012.08.031.
    1. Li H, Glusman G, Hu H, Shankaracharya, Caballero J, Hubley R, Witherspoon D, Guthery SL, Mauldin DE, Jorde LB, et al. Relationship estimation from whole-genome sequence data. PLoS Genet. 2014;10(1):e1004144. doi: 10.1371/journal.pgen.1004144.
    1. Genomes Project Consortium. Abecasis GR, Auton A, Brooks LD, DePristo MA, Durbin RM, Handsaker RE, Kang HM, Marth GT, McVean GA. An integrated map of genetic variation from 1,092 human genomes. Nature. 2012;491(7422):56–65. doi: 10.1038/nature11632.
    1. Rubio-Perez C, Tamborero D, Schroeder MP, Antolin AA, Deu-Pons J, Perez-Llamas C, Mestres J, Gonzalez-Perez A, Lopez-Bigas N. In silico prescription of anticancer drugs to cohorts of 28 tumor types reveals targeting opportunities. Cancer Cell. 2015;27(3):382–96. doi: 10.1016/j.ccell.2015.02.007.
    1. Imielinski M, Greulich H, Kaplan B, Araujo L, Amann J, Horn L, Schiller J, Villalona-Calero MA, Meyerson M, Carbone DP. Oncogenic and sorafenib-sensitive ARAF mutations in lung adenocarcinoma. J Clin Invest. 2014;124(4):1582–6. doi: 10.1172/JCI72763.
    1. Wagle N, Grabiner BC, Van Allen EM, Hodis E, Jacobus S, Supko JG, Stewart M, Choueiri TK, Gandhi L, Cleary JM, et al. Activating mTOR mutations in a patient with an extraordinary response on a phase I trial of everolimus and pazopanib. Cancer Discov. 2014;4(5):546–53. doi: 10.1158/-13-0353.
    1. Zhu Y, Qiu P, Ji Y. TCGA-assembler: open-source software for retrieving and processing TCGA data. Nat Methods. 2014;11(6):599–600. doi: 10.1038/nmeth.2956.
    1. Kim TM, Xi R, Luquette LJ, Park RW, Johnson MD, Park PJ. Functional genomic analysis of chromosomal aberrations in a compendium of 8000 cancer genomes. Genome Res. 2013;23(2):217–27. doi: 10.1101/gr.140301.112.
    1. Dong C, Wei P, Jian X, Gibbs R, Boerwinkle E, Wang K, Liu X. Comparison and integration of deleteriousness prediction methods for nonsynonymous SNVs in whole exome sequencing studies. Hum Mol Genet. 2015;24(8):2125–37. doi: 10.1093/hmg/ddu733.
    1. Liu X, Jian X, Boerwinkle E. dbNSFP v2.0: a database of human non-synonymous SNVs and their functional predictions and annotations. Hum Mutat. 2013;34(9):E2393–402. doi: 10.1002/humu.22376.
    1. Liu X, Jian X, Boerwinkle E. dbNSFP: a lightweight database of human nonsynonymous SNPs and their functional predictions. Hum Mutat. 2011;32(8):894–9. doi: 10.1002/humu.21517.
    1. David Meyer ED, Hornik K, Weingessel A, Leisch F. Package ‘e1071’. 2014. .
    1. Xavier Robin NT, Hainard A, Tiberti N, Lisacek F, Sanchez J-C, Müller M. Package ‘pROC’. 2014. .
    1. Tobias Sing OS, Beerenwinkel N, Lengauer T. Package ‘ROCR’. 2013. .
    1. Wang Y, Xiao J, Suzek TO, Zhang J, Wang J, Bryant SH. PubChem: a public information system for analyzing bioactivities of small molecules. Nucleic Acids Res. 2009;37(Web Server issue):W623–33. doi: 10.1093/nar/gkp456.
    1. Vogelstein B, Papadopoulos N, Velculescu VE, Zhou S, Diaz LA, Jr, Kinzler KW. Cancer genome landscapes. Science. 2013;339(6127):1546–58. doi: 10.1126/science.1235122.
    1. Ellis LM, Hicklin DJ. VEGF-targeted therapy: mechanisms of anti-tumour activity. Nat Rev Cancer. 2008;8(8):579–91. doi: 10.1038/nrc2403.
    1. Lovly CM, McDonald NT, Chen H, Ortiz-Cuaran S, Heukamp LC, Yan Y, Florin A, Ozretic L, Lim D, Wang L, et al. Rationale for co-targeting IGF-1R and ALK in ALK fusion-positive lung cancer. Nat Med. 2014;20(9):1027–34. doi: 10.1038/nm.3667.
    1. Zook JM, Chapman B, Wang J, Mittelman D, Hofmann O, Hide W, Salit M. Integrating human sequence data sets provides a resource of benchmark SNP and indel genotype calls. Nat Biotechnol. 2014;32(3):246–51. doi: 10.1038/nbt.2835.
    1. Li H. Tabix: fast retrieval of sequence features from generic TAB-delimited files. Bioinformatics. 2011;27(5):718–9. doi: 10.1093/bioinformatics/btq671.
    1. Santarius T, Shipley J, Brewer D, Stratton MR, Cooper CS. A census of amplified and overexpressed human cancer genes. Nat Rev Cancer. 2010;10(1):59–64. doi: 10.1038/nrc2771.
    1. Futreal PA, Coin L, Marshall M, Down T, Hubbard T, Wooster R, Rahman N, Stratton MR. A census of human cancer genes. Nat Rev Cancer. 2004;4(3):177–83. doi: 10.1038/nrc1299.
    1. Kanehisa M, Goto S, Sato Y, Furumichi M, Tanabe M. KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res. 2012;40(Database issue):D109–14. doi: 10.1093/nar/gkr988.
    1. Adzhubei IA, Schmidt S, Peshkin L, Ramensky VE, Gerasimova A, Bork P, Kondrashov AS, Sunyaev SR. A method and server for predicting damaging missense mutations. Nat Methods. 2010;7(4):248–9. doi: 10.1038/nmeth0410-248.
    1. Merlo LM, Pepper JW, Reid BJ, Maley CC. Cancer as an evolutionary and ecological process. Nat Rev Cancer. 2006;6(12):924–35. doi: 10.1038/nrc2013.
    1. Stephens PJ, Tarpey PS, Davies H, Van Loo P, Greenman C, Wedge DC, Nik-Zainal S, Martin S, Varela I, Bignell GR, et al. The landscape of cancer genes and mutational processes in breast cancer. Nature. 2012;486(7403):400–4.
    1. Ding L, Getz G, Wheeler DA, Mardis ER, McLellan MD, Cibulskis K, Sougnez C, Greulich H, Muzny DM, Morgan MB, et al. Somatic mutations affect key pathways in lung adenocarcinoma. Nature. 2008;455(7216):1069–75. doi: 10.1038/nature07423.
    1. Weir BA, Woo MS, Getz G, Perner S, Ding L, Beroukhim R, Lin WM, Province MA, Kraja A, Johnson LA, et al. Characterizing the cancer genome in lung adenocarcinoma. Nature. 2007;450(7171):893–8. doi: 10.1038/nature06358.
    1. Riely GJ, Kris MG, Rosenbaum D, Marks J, Li A, Chitale DA, Nafa K, Riedel ER, Hsu M, Pao W, et al. Frequency and distinctive spectrum of KRAS mutations in never smokers with lung adenocarcinoma. Clin Cancer Res. 2008;14(18):5731–4. doi: 10.1158/1078-0432.CCR-08-0646.
    1. Marks JL, Broderick S, Zhou Q, Chitale D, Li AR, Zakowski MF, Kris MG, Rusch VW, Azzoli CG, Seshan VE, et al. Prognostic and therapeutic implications of EGFR and KRAS mutations in resected lung adenocarcinoma. J Thorac Oncol. 2008;3(2):111–6. doi: 10.1097/JTO.0b013e318160c607.
    1. Geer LY, Marchler-Bauer A, Geer RC, Han L, He J, He S, Liu C, Shi W, Bryant SH. The NCBI BioSystems database. Nucleic Acids Res. 2010;38(Database issue):D492–6. doi: 10.1093/nar/gkp858.
    1. Griffith M, Griffith OL, Coffman AC, Weible JV, McMichael JF, Spies NC, Koval J, Das I, Callaway MB, Eldred JM, et al. DGIdb: mining the druggable genome. Nat Methods. 2013;10(12):1209–10. doi: 10.1038/nmeth.2689.
    1. Wang Y, Xiao J, Suzek TO, Zhang J, Wang J, Zhou Z, Han L, Karapetyan K, Dracheva S, Shoemaker BA, et al. PubChem’s BioAssay Database. Nucleic Acids Res. 2012;40(Database issue):D400–12. doi: 10.1093/nar/gkr1132.
    1. Consortium EP. The ENCODE (ENCyclopedia Of DNA Elements) Project. Science. 2004;306(5696):636–40. doi: 10.1126/science.1105136.
    1. Yuan Y, Van Allen EM, Omberg L, Wagle N, Amin-Mansour A, Sokolov A, Byers LA, Xu Y, Hess KR, Diao L, et al. Assessing the clinical utility of cancer genomic and proteomic data across tumor types. Nat Biotechnol. 2014;32(7):644–52. doi: 10.1038/nbt.2940.
    1. Consortium EP. An integrated encyclopedia of DNA elements in the human genome. Nature. 2012;489(7414):57–74. doi: 10.1038/nature11247.

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