1932

Abstract

Despite significant progress in cancer research, current standard-of-care drugs fail to cure many types of cancers. Hence, there is an urgent need to identify better predictive biomarkers and treatment regimes. Conventionally, insights from hypothesis-driven studies are the primary force for cancer biology and therapeutic discoveries. Recently, the rapid growth of big data resources, catalyzed by breakthroughs in high-throughput technologies, has resulted in a paradigm shift in cancer therapeutic research. The combination of computational methods and genomics data has led to several successful clinical applications. In this review, we focus on recent advances in data-driven methods to model anticancer drug efficacy, and we present the challenges and opportunities for data science in cancer therapeutic research.

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2018-07-20
2024-05-26
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Literature Cited

  1. 1.  Huang ME, Ye YC, Chen SR, Chai JR, Lu JX et al. 1988. Use of all-trans retinoic acid in the treatment of acute promyelocytic leukemia. Blood 72:567–72
    [Google Scholar]
  2. 2.  Deininger M, Buchdunger E, Druker BJ 2005. The development of imatinib as a therapeutic agent for chronic myeloid leukemia. Blood 105:2640–53
    [Google Scholar]
  3. 3.  Paez JG, Janne PA, Lee JC, Tracy S, Greulich H et al. 2004. EGFR mutations in lung cancer: correlation with clinical response to gefitinib therapy. Science 304:1497–500
    [Google Scholar]
  4. 4.  Solomon BJ, Mok T, Kim DW, Wu YL, Nakagawa K et al. 2014. First-line crizotinib versus chemotherapy in ALK-positive lung cancer. New Engl. J. Med. 371:2167–77
    [Google Scholar]
  5. 5.  Holohan C, Van Schaeybroeck S, Longley DB, Johnston PG 2013. Cancer drug resistance: an evolving paradigm. Nat. Rev. Cancer 13:714–26
    [Google Scholar]
  6. 6.  Nitulescu GM, Margina D, Juzenas P, Peng Q, Olaru OT et al. 2016. Akt inhibitors in cancer treatment: the long journey from drug discovery to clinical use (review). Int. J. Oncol. 48:869–85
    [Google Scholar]
  7. 7.  Fassnacht M, Berruti A, Baudin E, Demeure MJ, Gilbert J et al. 2015. Linsitinib (OSI-906) versus placebo for patients with locally advanced or metastatic adrenocortical carcinoma: a double-blind, randomised, phase 3 study. Lancet Oncol 16:426–35
    [Google Scholar]
  8. 8.  Widakowich C, de Castro G Jr., de Azambuja E, Dinh P, Awada A 2007. Review: side effects of approved molecular targeted therapies in solid cancers. Oncologist 12:1443–55
    [Google Scholar]
  9. 9.  June CH, Warshauer JT, Bluestone JA 2017. Is autoimmunity the Achilles' heel of cancer immunotherapy?. Nat. Med. 23:540–47
    [Google Scholar]
  10. 10.  Sharma P, Hu-Lieskovan S, Wargo JA, Ribas A 2017. Primary, adaptive, and acquired resistance to cancer immunotherapy. Cell 168:707–23
    [Google Scholar]
  11. 11.  Jiang P, Liu XS 2015. Big data mining yields novel insights on cancer. Nat. Genet. 47:103–4
    [Google Scholar]
  12. 12.  Galluzzi L, Buque A, Kepp O, Zitvogel L, Kroemer G 2015. Immunological effects of conventional chemotherapy and targeted anticancer agents. Cancer Cell 28:690–714
    [Google Scholar]
  13. 13.  Lopez JS, Banerji U 2017. Combine and conquer: challenges for targeted therapy combinations in early phase trials. Nat. Rev. Clin. Oncol. 14:57–66
    [Google Scholar]
  14. 14.  Mahoney KM, Rennert PD, Freeman GJ 2015. Combination cancer immunotherapy and new immunomodulatory targets. Nat. Rev. Drug Discov. 14:561–84
    [Google Scholar]
  15. 15.  Sheng Z, Sun Y, Yin Z, Tang K, Cao Z 2017. Advances in computational approaches in identifying synergistic drug combinations. Briefings Bioinform 2017:bbx047
    [Google Scholar]
  16. 16.  Hu X, Zhang Z 2016. Understanding the genetic mechanisms of cancer drug resistance using genomic approaches. Trends Genet 32:127–37
    [Google Scholar]
  17. 17.  Roy S, Trinchieri G 2017. Microbiota: a key orchestrator of cancer therapy. Nat. Rev. Cancer 17:271–85
    [Google Scholar]
  18. 18.  Van Allen EM, Wagle N, Stojanov P, Perrin DL, Cibulskis K et al. 2014. Whole-exome sequencing and clinical interpretation of formalin-fixed, paraffin-embedded tumor samples to guide precision cancer medicine. Nat. Med. 20:682–88
    [Google Scholar]
  19. 19.  Robinson DR, Wu YM, Lonigro RJ, Vats P, Cobain E et al. 2017. Integrative clinical genomics of metastatic cancer. Nature 548:297–303
    [Google Scholar]
  20. 20.  Patch AM, Christie EL, Etemadmoghadam D, Garsed DW, George J et al. 2015. Whole-genome characterization of chemoresistant ovarian cancer. Nature 521:489–94
    [Google Scholar]
  21. 21.  Nik-Zainal S, Davies H, Staaf J, Ramakrishna M, Glodzik D et al. 2016. Landscape of somatic mutations in 560 breast cancer whole-genome sequences. Nature 534:47–54
    [Google Scholar]
  22. 22.  Zehir A, Benayed R, Shah RH, Syed A, Middha S et al. 2017. Mutational landscape of metastatic cancer revealed from prospective clinical sequencing of 10,000 patients. Nat. Med. 23:703–13
    [Google Scholar]
  23. 23. Cancer Genome Atlas Res. Netw., Weinstein JN, Collisson EA, Mills GB, Shaw KR et al. 2013. The Cancer Genome Atlas Pan-Cancer analysis project. Nat. Genet. 45:1113–20
    [Google Scholar]
  24. 24. AACR Proj. GENIE Consort. 2017. AACR Project GENIE: powering precision medicine through an international consortium. Cancer Discov 7:818–31
    [Google Scholar]
  25. 25.  Hugo W, Shi H, Sun L, Piva M, Song C et al. 2015. Non-genomic and immune evolution of melanoma acquiring MAPKi resistance. Cell 162:1271–85
    [Google Scholar]
  26. 26.  Shoemaker RH 2006. The NCI60 human tumour cell line anticancer drug screen. Nat. Rev. Cancer 6:813–23
    [Google Scholar]
  27. 27.  Cragg GM 1998. Paclitaxel (Taxol®): a success story with valuable lessons for natural product drug discovery and development. Med. Res. Rev. 18:315–31
    [Google Scholar]
  28. 28.  Solit DB, Garraway LA, Pratilas CA, Sawai A, Getz G et al. 2006. BRAF mutation predicts sensitivity to MEK inhibition. Nature 439:358–62
    [Google Scholar]
  29. 29.  Barretina J, Caponigro G, Stransky N, Venkatesan K, Margolin AA et al. 2012. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483:603–7
    [Google Scholar]
  30. 30.  Melnick JS, Janes J, Kim S, Chang JY, Sipes DG et al. 2006. An efficient rapid system for profiling the cellular activities of molecular libraries. PNAS 103:3153–58
    [Google Scholar]
  31. 31.  Aguirre AJ, Meyers RM, Weir BA, Vazquez F, Zhang CZ et al. 2016. Genomic copy number dictates a gene-independent cell response to CRISPR/Cas9 targeting. Cancer Discov 6:914–29
    [Google Scholar]
  32. 32.  Tsherniak A, Vazquez F, Montgomery PG, Weir BA, Kryukov G et al. 2017. Defining a cancer dependency map. Cell 170:564–76.e16
    [Google Scholar]
  33. 33.  McDonald ER III, de Weck A, Schlabach MR, Billy E, Mavrakis KJ et al. 2017. Project DRIVE: a compendium of cancer dependencies and synthetic lethal relationships uncovered by large-scale, deep RNAi screening. Cell 170:577–92.e10
    [Google Scholar]
  34. 34.  Meyers RM, Bryan JG, McFarland JM, Weir BA, Sizemore AE et al. 2017. Computational correction of copy number effect improves specificity of CRISPR-Cas9 essentiality screens in cancer cells. Nat. Genet. 49:1779–84
    [Google Scholar]
  35. 35.  Seashore-Ludlow B, Rees MG, Cheah JH, Cokol M, Price EV et al. 2015. Harnessing connectivity in a large-scale small-molecule sensitivity dataset. Cancer Discov 5:1210–23
    [Google Scholar]
  36. 36.  Iorio F, Knijnenburg TA, Vis DJ, Bignell GR, Menden MP et al. 2016. A landscape of pharmacogenomic interactions in cancer. Cell 166:740–54
    [Google Scholar]
  37. 37.  Yu C, Mannan AM, Yvone GM, Ross KN, Zhang YL et al. 2016. High-throughput identification of genotype-specific cancer vulnerabilities in mixtures of barcoded tumor cell lines. Nat. Biotechnol. 34:419–23
    [Google Scholar]
  38. 38.  Peck D, Crawford ED, Ross KN, Stegmaier K, Golub TR, Lamb J 2006. A method for high-throughput gene expression signature analysis. Genome Biol 7:R61
    [Google Scholar]
  39. 39.  Du J, Bernasconi P, Clauser KR, Mani DR, Finn SP et al. 2009. Bead-based profiling of tyrosine kinase phosphorylation identifies SRC as a potential target for glioblastoma therapy. Nat. Biotechnol. 27:77–83
    [Google Scholar]
  40. 40.  Muellner MK, Uras IZ, Gapp BV, Kerzendorfer C, Smida M et al. 2011. A chemical-genetic screen reveals a mechanism of resistance to PI3K inhibitors in cancer. Nat. Chem. Biol. 7:787–93
    [Google Scholar]
  41. 41.  Sellers WR 2011. A blueprint for advancing genetics-based cancer therapy. Cell 147:26–31
    [Google Scholar]
  42. 42.  Arnould L, Gelly M, Penault-Llorca F, Benoit L, Bonnetain F et al. 2006. Trastuzumab-based treatment of HER2-positive breast cancer: an antibody-dependent cellular cytotoxicity mechanism?. Br. J. Cancer 94:259–67
    [Google Scholar]
  43. 43.  Gao H, Korn JM, Ferretti S, Monahan JE, Wang Y et al. 2015. High-throughput screening using patient-derived tumor xenografts to predict clinical trial drug response. Nat. Med. 21:1318–25
    [Google Scholar]
  44. 44.  Zitvogel L, Apetoh L, Ghiringhelli F, Kroemer G 2008. Immunological aspects of cancer chemotherapy. Nat. Rev. Immunol. 8:59–73
    [Google Scholar]
  45. 45.  Bossen C, Ingold K, Tardivel A, Bodmer JL, Gaide O et al. 2006. Interactions of tumor necrosis factor (TNF) and TNF receptor family members in the mouse and human. J. Biol. Chem. 281:13964–71
    [Google Scholar]
  46. 46.  Patel SJ, Sanjana NE, Kishton RJ, Eidizadeh A, Vodnala SK et al. 2017. Identification of essential genes for cancer immunotherapy. Nature 548:537–42
    [Google Scholar]
  47. 47.  Mbofung RM, McKenzie JA, Malu S, Zhang M, Peng W et al. 2017. HSP90 inhibition enhances cancer immunotherapy by upregulating interferon response genes. Nat. Commun. 8:451
    [Google Scholar]
  48. 48.  Pan D, Kobayashi A, Jiang P, Ferrari de Andrade L, Tay R et al. 2018. A major chromatin regulator determines resistance of tumor cells to T cell–mediated killing. Science 359:770–75
    [Google Scholar]
  49. 49.  Shaffer SM, Dunagin MC, Torborg SR, Torre EA, Emert B et al. 2017. Rare cell variability and drug-induced reprogramming as a mode of cancer drug resistance. Nature 546:431–35
    [Google Scholar]
  50. 50.  Meacham CE, Morrison SJ 2013. Tumour heterogeneity and cancer cell plasticity. Nature 501:328–37
    [Google Scholar]
  51. 51.  Lee AJ, Swanton C 2012. Tumour heterogeneity and drug resistance: personalising cancer medicine through functional genomics. Biochem. Pharmacol. 83:1013–20
    [Google Scholar]
  52. 52.  Galon J, Costes A, Sanchez-Cabo F, Kirilovsky A, Mlecnik B et al. 2006. Type, density, and location of immune cells within human colorectal tumors predict clinical outcome. Science 313:1960–64
    [Google Scholar]
  53. 53.  Dongre A, Rashidian M, Reinhardt F, Bagnato A, Keckesova Z et al. 2017. Epithelial-to-mesenchymal transition contributes to immunosuppression in breast carcinomas. Cancer Res 77:3982–89
    [Google Scholar]
  54. 54.  Gawad C, Koh W, Quake SR 2016. Single-cell genome sequencing: current state of the science. Nat. Rev. Genet. 17:175–88
    [Google Scholar]
  55. 55.  Wu AR, Wang J, Streets AM, Huang Y 2017. Single-cell transcriptional analysis. Annu. Rev. Anal. Chem. 10:439–62
    [Google Scholar]
  56. 56.  Spitzer MH, Nolan GP 2016. Mass cytometry: single cells, many features. Cell 165:780–91
    [Google Scholar]
  57. 57.  Zenobi R 2013. Single-cell metabolomics: analytical and biological perspectives. Science 342:1243259
    [Google Scholar]
  58. 58.  Izar B, Tirosh I, Stover E, Rotem A, Shah P et al. 2017. Dissecting treatment resistance in patients with ovarian cancer and PDX-models using single-cell RNA-sequencing. Proc. Am. Assoc. Cancer Res., Washington, DC, 1–5 Apr. Philadelphia: Am. Assoc. Cancer Res.
    [Google Scholar]
  59. 59.  Tirosh I, Izar B, Prakadan SM, Wadsworth MH II, Treacy D et al. 2016. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science 352:189–96
    [Google Scholar]
  60. 60.  Puram SV, Tirosh I, Parikh AS, Patel AP, Yizhak K et al. 2017. Single-cell transcriptomic analysis of primary and metastatic tumor ecosystems in head and neck cancer. Cell 171:1611–24.e24
    [Google Scholar]
  61. 61.  Yuan GC, Cai L, Elowitz M, Enver T, Fan G et al. 2017. Challenges and emerging directions in single-cell analysis. Genome Biol 18:84
    [Google Scholar]
  62. 62.  Zheng C, Zheng L, Yoo JK, Guo H, Zhang Y et al. 2017. Landscape of infiltrating T cells in liver cancer revealed by single-cell sequencing. Cell 169:1342–56.e16
    [Google Scholar]
  63. 63.  Angelo M, Bendall SC, Finck R, Hale MB, Hitzman C et al. 2014. Multiplexed ion beam imaging of human breast tumors. Nat. Med. 20:436–42
    [Google Scholar]
  64. 64.  Daillere R, Vetizou M, Waldschmitt N, Yamazaki T, Isnard C et al. 2016. Enterococcus hirae and Barnesiella intestinihominis facilitate cyclophosphamide-induced therapeutic immunomodulatory effects. Immunity 45:931–43
    [Google Scholar]
  65. 65.  Viaud S, Saccheri F, Mignot G, Yamazaki T, Daillere R et al. 2013. The intestinal microbiota modulates the anticancer immune effects of cyclophosphamide. Science 342:971–76
    [Google Scholar]
  66. 66.  Iida N, Dzutsev A, Stewart CA, Smith L, Bouladoux N et al. 2013. Commensal bacteria control cancer response to therapy by modulating the tumor microenvironment. Science 342:967–70
    [Google Scholar]
  67. 67.  Sivan A, Corrales L, Hubert N, Williams JB, Aquino-Michaels K et al. 2015. Commensal Bifidobacterium promotes antitumor immunity and facilitates anti-PD-L1 efficacy. Science 350:1084–89
    [Google Scholar]
  68. 68.  Vetizou M, Pitt JM, Daillere R, Lepage P, Waldschmitt N et al. 2015. Anticancer immunotherapy by CTLA-4 blockade relies on the gut microbiota. Science 350:1079–84
    [Google Scholar]
  69. 69.  Routy B, Le Chatelier E, Derosa L, Duong CPM, Alou MT et al. 2018. Gut microbiome influences efficacy of PD-1-based immunotherapy against epithelial tumors. Science 359:91–97
    [Google Scholar]
  70. 70.  Gopalakrishnan V, Spencer CN, Nezi L, Reuben A, Andrews MC et al. 2018. Gut microbiome modulates response to anti–PD-1 immunotherapy in melanoma patients. Science 359:97–103
    [Google Scholar]
  71. 71.  Matson V, Fessler J, Bao R, Chongsuwat T, Zha Y et al. 2018. The commensal microbiome is associated with anti-PD-1 efficacy in metastatic melanoma patients. Science 359:104–8
    [Google Scholar]
  72. 72.  Stringer AM, Gibson RJ, Logan RM, Bowen JM, Yeoh AS, Keefe DM 2008. Faecal microflora and beta-glucuronidase expression are altered in an irinotecan-induced diarrhea model in rats. Cancer Biol. Ther. 7:1919–25
    [Google Scholar]
  73. 73.  Franzosa EA, Hsu T, Sirota-Madi A, Shafquat A, Abu-Ali G et al. 2015. Sequencing and beyond: integrating molecular ‘omics’ for microbial community profiling. Nat. Rev. Microbiol. 13:360–72
    [Google Scholar]
  74. 74.  Kostic AD, Ojesina AI, Pedamallu CS, Jung J, Verhaak RG et al. 2011. PathSeq: software to identify or discover microbes by deep sequencing of human tissue. Nat. Biotechnol. 29:393–96
    [Google Scholar]
  75. 75.  Kostic AD, Gevers D, Pedamallu CS, Michaud M, Duke F et al. 2012. Genomic analysis identifies association of Fusobacterium with colorectal carcinoma. Genome Res 22:292–98
    [Google Scholar]
  76. 76.  Castellarin M, Warren RL, Freeman JD, Dreolini L, Krzywinski M et al. 2012. Fusobacterium nucleatum infection is prevalent in human colorectal carcinoma. Genome Res 22:299–306
    [Google Scholar]
  77. 77.  Bullman S, Pedamallu CS, Sicinska E, Clancy TE, Zhang X et al. 2017. Analysis of Fusobacterium persistence and antibiotic response in colorectal cancer. Science 358:1443–48
    [Google Scholar]
  78. 78.  Mabert K, Cojoc M, Peitzsch C, Kurth I, Souchelnytskyi S, Dubrovska A 2014. Cancer biomarker discovery: current status and future perspectives. Int. J. Radiat. Biol. 90:659–77
    [Google Scholar]
  79. 79.  Freedman D 2009. Statistical Models: Theory and Practice Cambridge, UK: Cambridge Univ. Press
  80. 80.  Kleinbaum DG 1998. Survival analysis, a self‐learning text. Biometr. J. 40:107–8
    [Google Scholar]
  81. 81.  James G, Witten D, Hastie T, Tibshirani R 2013. An Introduction to Statistical Learning: With Applications in R New York: Springer-Verlag
  82. 82.  Keir ME, Butte MJ, Freeman GJ, Sharpe AH 2008. PD-1 and its ligands in tolerance and immunity. Annu. Rev. Immunol. 26:677–704
    [Google Scholar]
  83. 83. Cancer Genome Atlas Netw. 2015. Genomic classification of cutaneous melanoma. Cell 161:1681–96
    [Google Scholar]
  84. 84.  Jiang P, Lee W, Li X, Johnson C, Liu JS et al. 2018. Genome-scale signatures of gene interaction from compound screens predict clinical efficacy of targeted cancer therapies. Cell Syst 6:343–54
    [Google Scholar]
  85. 85.  Zou H, Hastie T 2005. Regularization and variable selection via the elastic net. J. R. Stat. Soc. B 67:301–20
    [Google Scholar]
  86. 86.  Hastie T, Tibshirani R, Friedman JH 2009. The Elements of Statistical Learning: Data Mining, Inference, and Prediction New York: Springer-Verlag
  87. 87.  Tibshirani R 1996. Regression shrinkage and selection via the Lasso. J. R. Stat. Soc. B 58:267–88
    [Google Scholar]
  88. 88.  Garnett MJ, Edelman EJ, Heidorn SJ, Greenman CD, Dastur A et al. 2012. Systematic identification of genomic markers of drug sensitivity in cancer cells. Nature 483:570–75
    [Google Scholar]
  89. 89.  Jiang P, Freedman ML, Liu JS, Liu XS 2015. Inference of transcriptional regulation in cancers. PNAS 112:7731–36
    [Google Scholar]
  90. 90.  Filipits M, Rudas M, Jakesz R, Dubsky P, Fitzal F et al. 2011. A new molecular predictor of distant recurrence in ER-positive, HER2-negative breast cancer adds independent information to conventional clinical risk factors. Clin. Cancer Res. 17:6012–20
    [Google Scholar]
  91. 91.  Smola AJ, Scholkopf B 2004. A tutorial on support vector regression. Stat. Comput. 14:199–222
    [Google Scholar]
  92. 92.  Costello JC, Heiser LM, Georgii E, Gonen M, Menden MP et al. 2014. A community effort to assess and improve drug sensitivity prediction algorithms. Nat. Biotechnol. 32:1202–12
    [Google Scholar]
  93. 93.  Fan J, Lv J 2010. A selective overview of variable selection in high dimensional feature space. Stat. Sin. 20:101–48
    [Google Scholar]
  94. 94.  Zhao P, Yu B 2006. On model selection consistency of Lasso. J. Mach. Learn. Res. 7:2541–63
    [Google Scholar]
  95. 95.  Siemers NO, Holloway JL, Chang H, Chasalow SD, Ross-MacDonald PB et al. 2017. Genome-wide association analysis identifies genetic correlates of immune infiltrates in solid tumors. PLOS ONE 12:e0179726
    [Google Scholar]
  96. 96.  Li B, Liu JS, Liu XS 2017. Revisit linear regression-based deconvolution methods for tumor gene expression data. Genome Biol 18:127
    [Google Scholar]
  97. 97.  Altrock PM, Liu LL, Michor F 2015. The mathematics of cancer: integrating quantitative models. Nat. Rev. Cancer 15:730–45
    [Google Scholar]
  98. 98.  Norton L, Simon R 1977. Tumor size, sensitivity to therapy, and design of treatment schedules. Cancer Treat. Rep. 61:1307–17
    [Google Scholar]
  99. 99.  Citron ML, Berry DA, Cirrincione C, Hudis C, Winer EP et al. 2003. Randomized trial of dose-dense versus conventionally scheduled and sequential versus concurrent combination chemotherapy as postoperative adjuvant treatment of node-positive primary breast cancer: first report of Intergroup Trial C9741/Cancer and Leukemia Group B Trial 9741. J. Clin. Oncol. 21:1431–39
    [Google Scholar]
  100. 100.  Michor F, Beal K 2015. Improving cancer treatment via mathematical modeling: surmounting the challenges is worth the effort. Cell 163:1059–63
    [Google Scholar]
  101. 101.  Chmielecki J, Foo J, Oxnard GR, Hutchinson K, Ohashi K et al. 2011. Optimization of dosing for EGFR-mutant non-small cell lung cancer with evolutionary cancer modeling. Sci. Transl. Med. 3:90ra59
    [Google Scholar]
  102. 102.  Leder K, Pitter K, LaPlant Q, Hambardzumyan D, Ross BD et al. 2014. Mathematical modeling of PDGF-driven glioblastoma reveals optimized radiation dosing schedules. Cell 156:603–16
    [Google Scholar]
  103. 103.  Chen JC, Alvarez MJ, Talos F, Dhruv H, Rieckhof GE et al. 2014. Identification of causal genetic drivers of human disease through systems-level analysis of regulatory networks. Cell 159:402–14
    [Google Scholar]
  104. 104.  Chuang HY, Lee E, Liu YT, Lee D, Ideker T 2007. Network-based classification of breast cancer metastasis. Mol. Syst. Biol. 3:140
    [Google Scholar]
  105. 105.  Hofree M, Shen JP, Carter H, Gross A, Ideker T 2013. Network-based stratification of tumor mutations. Nat. Methods 10:1108–15
    [Google Scholar]
  106. 106.  Jiang P, Wang H, Li W, Zang C, Li B et al. 2015. Network analysis of gene essentiality in functional genomics experiments. Genome Biol 16:239
    [Google Scholar]
  107. 107.  Nelander S, Wang W, Nilsson B, She QB, Pratilas C et al. 2008. Models from experiments: combinatorial drug perturbations of cancer cells. Mol. Syst. Biol. 4:216
    [Google Scholar]
  108. 108.  Korkut A, Wang W, Demir E, Aksoy BA, Jing X et al. 2015. Perturbation biology nominates upstream-downstream drug combinations in RAF inhibitor resistant melanoma cells. eLife 4:e04640
    [Google Scholar]
  109. 109. Early Breast Cancer Trialists' Collab. Group. 2005. Effects of chemotherapy and hormonal therapy for early breast cancer on recurrence and 15-year survival: an overview of the randomised trials. Lancet 365:1687–717
    [Google Scholar]
  110. 110.  Paik S, Shak S, Tang G, Kim C, Baker J et al. 2004. A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. New Engl. J. Med. 351:2817–26
    [Google Scholar]
  111. 111.  Paik S, Tang G, Shak S, Kim C, Baker J et al. 2006. Gene expression and benefit of chemotherapy in women with node-negative, estrogen receptor-positive breast cancer. J. Clin. Oncol. 24:3726–34
    [Google Scholar]
  112. 112.  Albain KS, Barlow WE, Shak S, Hortobagyi GN, Livingston RB et al. 2010. Prognostic and predictive value of the 21-gene recurrence score assay in postmenopausal women with node-positive, oestrogen-receptor-positive breast cancer on chemotherapy: a retrospective analysis of a randomised trial. Lancet Oncol 11:55–65
    [Google Scholar]
  113. 113.  van ’t Veer LJ, Dai H, van de Vijver MJ, He YD, Hart AA et al. 2002. Gene expression profiling predicts clinical outcome of breast cancer. Nature 415:530–36
    [Google Scholar]
  114. 114.  Cardoso F, van ’t Veer LJ, Bogaerts J, Slaets L, Viale G et al. 2016. 70-gene signature as an aid to treatment decisions in early-stage breast cancer. New Engl. J. Med. 375:717–29
    [Google Scholar]
  115. 115.  Parker JS, Mullins M, Cheang MC, Leung S, Voduc D et al. 2009. Supervised risk predictor of breast cancer based on intrinsic subtypes. J. Clin. Oncol. 27:1160–67
    [Google Scholar]
  116. 116.  Chia SK, Bramwell VH, Tu D, Shepherd LE, Jiang S et al. 2012. A 50-gene intrinsic subtype classifier for prognosis and prediction of benefit from adjuvant tamoxifen. Clin. Cancer Res. 18:4465–72
    [Google Scholar]
  117. 117.  Sgroi DC, Carney E, Zarrella E, Steffel L, Binns SN et al. 2013. Prediction of late disease recurrence and extended adjuvant letrozole benefit by the HOXB13/IL17BR biomarker. J. Natl. Cancer Inst. 105:1036–42
    [Google Scholar]
  118. 118.  Sanft T, Aktas B, Schroeder B, Bossuyt V, DiGiovanna M et al. 2015. Prospective assessment of the decision-making impact of the Breast Cancer Index in recommending extended adjuvant endocrine therapy for patients with early-stage ER-positive breast cancer. Breast Cancer Res. Treat. 154:533–41
    [Google Scholar]
  119. 119.  Bartlett JM, Bloom KJ, Piper T, Lawton TJ, van de Velde CJ et al. 2012. Mammostrat as an immunohistochemical multigene assay for prediction of early relapse risk in the tamoxifen versus exemestane adjuvant multicenter trial pathology study. J. Clin. Oncol. 30:4477–84
    [Google Scholar]
  120. 120.  Fan C, Oh DS, Wessels L, Weigelt B, Nuyten DS et al. 2006. Concordance among gene-expression-based predictors for breast cancer. New Engl. J. Med. 355:560–69
    [Google Scholar]
  121. 121.  Salazar R, Roepman P, Capella G, Moreno V, Simon I et al. 2011. Gene expression signature to improve prognosis prediction of stage II and III colorectal cancer. J. Clin. Oncol. 29:17–24
    [Google Scholar]
  122. 122.  Yamanaka T, Oki E, Yamazaki K, Yamaguchi K, Muro K et al. 2016. 12-Gene recurrence score assay stratifies the recurrence risk in stage II/III colon cancer with surgery alone: the SUNRISE study. J. Clin. Oncol. 34:2906–13
    [Google Scholar]
  123. 123.  Erho N, Crisan A, Vergara IA, Mitra AP, Ghadessi M et al. 2013. Discovery and validation of a prostate cancer genomic classifier that predicts early metastasis following radical prostatectomy. PLOS ONE 8:e66855
    [Google Scholar]
  124. 124.  Knezevic D, Goddard AD, Natraj N, Cherbavaz DB, Clark-Langone KM et al. 2013. Analytical validation of the oncotype DX prostate cancer assay—a clinical RT-PCR assay optimized for prostate needle biopsies. BMC Genom 14:690
    [Google Scholar]
  125. 125.  Kratz JR, He J, Van Den Eeden SK, Zhu ZH, Gao W et al. 2012. A practical molecular assay to predict survival in resected non-squamous, non-small-cell lung cancer: development and international validation studies. Lancet 379:823–32
    [Google Scholar]
  126. 126.  Simon R 2015. Sensitivity, specificity, PPV, and NPV for predictive biomarkers. J. Natl. Cancer Inst. 107:djv153
    [Google Scholar]
  127. 127.  Sharma P, Allison JP 2015. Immune checkpoint targeting in cancer therapy: toward combination strategies with curative potential. Cell 161:205–14
    [Google Scholar]
  128. 128.  Masucci GV, Cesano A, Hawtin R, Janetzki S, Zhang J et al. 2016. Validation of biomarkers to predict response to immunotherapy in cancer: volume I—pre-analytical and analytical validation. J. Immunother. Cancer 4:76
    [Google Scholar]
  129. 129.  Davoli T, Uno H, Wooten EC, Elledge SJ 2017. Tumor aneuploidy correlates with markers of immune evasion and with reduced response to immunotherapy. Science 355:eaaf8399
    [Google Scholar]
  130. 130.  Cogdill AP, Andrews MC, Wargo JA 2017. Hallmarks of response to immune checkpoint blockade. Br. J. Cancer 117:1–7
    [Google Scholar]
  131. 131.  Snyder A, Makarov V, Merghoub T, Yuan J, Zaretsky JM et al. 2014. Genetic basis for clinical response to CTLA-4 blockade in melanoma. New Engl. J. Med. 371:2189–99
    [Google Scholar]
  132. 132.  Le DT, Uram JN, Wang H, Bartlett BR, Kemberling H et al. 2015. PD-1 blockade in tumors with mismatch-repair deficiency. New Engl. J. Med. 372:2509–20
    [Google Scholar]
  133. 133.  Nishino M, Ramaiya NH, Hatabu H, Hodi FS 2017. Monitoring immune-checkpoint blockade: response evaluation and biomarker development. Nat. Rev. Clin. Oncol. 14:655–68
    [Google Scholar]
  134. 134.  Yang JJ, Landier W, Yang W, Liu C, Hageman L et al. 2015. Inherited NUDT15 variant is a genetic determinant of mercaptopurine intolerance in children with acute lymphoblastic leukemia. J. Clin. Oncol. 33:1235–42
    [Google Scholar]
  135. 135.  Dean L 2012. Mercaptopurine therapy and TPMT genotype. Medical Genetics Summaries V Pratt, H McLeod, L Dean, A Malheiro, W Rubinstein Bethesda, MD: Natl. Cent. Biotechnol. Inform https://www.ncbi.nlm.nih.gov/books/NBK100660/
    [Google Scholar]
  136. 136.  Adam de Beaumais T, Fakhoury M, Medard Y, Azougagh S, Zhang D et al. 2011. Determinants of mercaptopurine toxicity in paediatric acute lymphoblastic leukemia maintenance therapy. Br. J. Clin. Pharmacol. 71:575–84
    [Google Scholar]
  137. 137.  Eduati F, Mangravite LM, Wang T, Tang H, Bare JC et al. 2015. Prediction of human population responses to toxic compounds by a collaborative competition. Nat. Biotechnol. 33:933–40
    [Google Scholar]
  138. 138.  Abdo N, Xia M, Brown CC, Kosyk O, Huang R et al. 2015. Population-based in vitro hazard and concentration-response assessment of chemicals: the 1000 genomes high-throughput screening study. Environ. Health Perspect. 123:458–66
    [Google Scholar]
  139. 139.  Patlewicz G, Fitzpatrick JM 2016. Current and future perspectives on the development, evaluation, and application of in silico approaches for predicting toxicity. Chem. Res. Toxicol. 29:438–51
    [Google Scholar]
  140. 140.  Raies AB, Bajic VB 2016. In silico toxicology: computational methods for the prediction of chemical toxicity. Wiley Interdiscip. Rev. Comput. Mol. Sci. 6:147–72
    [Google Scholar]
  141. 141.  Gayvert KM, Madhukar NS, Elemento O 2016. A data-driven approach to predicting successes and failures of clinical trials. Cell Chem. Biol. 23:1294–301
    [Google Scholar]
  142. 142.  Sanz F, Pognan F, Steger-Hartmann T, Diaz C, eTox et al. 2017. Legacy data sharing to improve drug safety assessment: the eTOX project. Nat. Rev. Drug Discov. 16:811–12
    [Google Scholar]
  143. 143.  Emery CM, Vijayendran KG, Zipser MC, Sawyer AM, Niu L et al. 2009. MEK1 mutations confer resistance to MEK and B-RAF inhibition. PNAS 106:20411–16
    [Google Scholar]
  144. 144.  Paraiso KH, Fedorenko IV, Cantini LP, Munko AC, Hall M et al. 2010. Recovery of phospho-ERK activity allows melanoma cells to escape from BRAF inhibitor therapy. Br. J. Cancer 102:1724–30
    [Google Scholar]
  145. 145.  Held MA, Langdon CG, Platt JT, Graham-Steed T, Liu Z et al. 2013. Genotype-selective combination therapies for melanoma identified by high-throughput drug screening. Cancer Discov 3:52–67
    [Google Scholar]
  146. 146.  Szakacs G, Annereau JP, Lababidi S, Shankavaram U, Arciello A et al. 2004. Predicting drug sensitivity and resistance: profiling ABC transporter genes in cancer cells. Cancer Cell 6:129–37
    [Google Scholar]
  147. 147.  Ludwig JA, Szakacs G, Martin SE, Chu BF, Cardarelli C et al. 2006. Selective toxicity of NSC73306 in MDR1-positive cells as a new strategy to circumvent multidrug resistance in cancer. Cancer Res 66:4808–15
    [Google Scholar]
  148. 148.  Kirkwood JM, Bastholt L, Robert C, Sosman J, Larkin J et al. 2012. Phase II, open-label, randomized trial of the MEK1/2 inhibitor selumetinib as monotherapy versus temozolomide in patients with advanced melanoma. Clin. Cancer Res. 18:555–67
    [Google Scholar]
  149. 149.  Kwong LN, Costello JC, Liu H, Jiang S, Helms TL et al. 2012. Oncogenic NRAS signaling differentially regulates survival and proliferation in melanoma. Nat. Med. 18:1503–10
    [Google Scholar]
  150. 150.  Li J, Xu M, Yang Z, Li A, Dong J 2010. Simultaneous inhibition of MEK and CDK4 leads to potent apoptosis in human melanoma cells. Cancer Investig 28:350–56
    [Google Scholar]
  151. 151.  Hugo W, Zaretsky JM, Sun L, Song C, Moreno BH et al. 2016. Genomic and transcriptomic features of response to anti-PD-1 therapy in metastatic melanoma. Cell 165:35–44
    [Google Scholar]
  152. 152.  Riaz N, Havel JJ, Makarov V, Desrichard A, Urba WJ et al. 2017. Tumor and microenvironment evolution during immunotherapy with nivolumab. Cell 171:934–49.e15
    [Google Scholar]
  153. 153.  Leek JT, Scharpf RB, Bravo HC, Simcha D, Langmead B et al. 2010. Tackling the widespread and critical impact of batch effects in high-throughput data. Nat. Rev. Genet. 11:733–39
    [Google Scholar]
  154. 154.  Singal G, Miller PG, Agarwala V, He J, Gossai A et al. 2017. Development and validation of a real-world clinico-genomic database. Am. Soc. Clin. Oncol. 35:2514
    [Google Scholar]
  155. 155.  Van Allen EM, Miao D, Schilling B, Shukla SA, Blank C et al. 2015. Genomic correlates of response to CTLA-4 blockade in metastatic melanoma. Science 350:207–11 Erratum. 2016 Science 352:aaf8264
    [Google Scholar]
  156. 156.  Snyder A, Nathanson T, Funt SA, Ahuja A, Buros Novik J et al. 2017. Contribution of systemic and somatic factors to clinical response and resistance to PD-L1 blockade in urothelial cancer: an exploratory multi-omic analysis. PLOS Med 14:e1002309
    [Google Scholar]
  157. 157.  Deng J, Dong W, Socher R, Li L-J, Li K, Fei-Fei L 2009. Imagenet: a large-scale hierarchical image database. Proc. Comput. Vis. Pattern Recognit., Miami, Fla., 20–25 June248–55 New York: IEEE
    [Google Scholar]
  158. 158.  Krizhevsky A, Sutskever I, Hinton GE 2012. Imagenet classification with deep convolutional neural networks. Proc. Int. Conf. Neural Inf. Process. Syst., Lake Tahoe, Nev., 3–6 Dec F Pereira, CJC Burgess, L Bottou, KQ Weinberger 1097–105 Red Hook, NY: Curran Assoc.
    [Google Scholar]
  159. 159.  Cerami E, Gao J, Dogrusoz U, Gross BE, Sumer SO et al. 2012. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov 2:401–4
    [Google Scholar]
  160. 160.  Cerami EG, Gross BE, Demir E, Rodchenkov I, Babur O et al. 2011. Pathway Commons, a web resource for biological pathway data. Nucleic Acids Res 39:D685–90
    [Google Scholar]
  161. 161.  Rhodes DR, Yu J, Shanker K, Deshpande N, Varambally R et al. 2004. ONCOMINE: a cancer microarray database and integrated data-mining platform. Neoplasia 6:1–6
    [Google Scholar]
  162. 162.  Gillet JP, Calcagno AM, Varma S, Marino M, Green LJ et al. 2011. Redefining the relevance of established cancer cell lines to the study of mechanisms of clinical anti-cancer drug resistance. PNAS 108:18708–13
    [Google Scholar]
  163. 163.  Gillet JP, Varma S, Gottesman MM 2013. The clinical relevance of cancer cell lines. J. Natl. Cancer Inst. 105:452–58
    [Google Scholar]
  164. 164.  Daemen A, Griffith OL, Heiser LM, Wang NJ, Enache OM et al. 2013. Modeling precision treatment of breast cancer. Genome Biol 14:R110
    [Google Scholar]
  165. 165.  Geeleher P, Cox NJ, Huang RS 2014. Clinical drug response can be predicted using baseline gene expression levels and in vitro drug sensitivity in cell lines. Genome Biol 15:R47
    [Google Scholar]
  166. 166.  Gentles AJ, Newman AM, Liu CL, Bratman SV, Feng W et al. 2015. The prognostic landscape of genes and infiltrating immune cells across human cancers. Nat. Med. 21:938–45
    [Google Scholar]
  167. 167.  Feng C, Araki M, Kunimoto R, Tamon A, Makiguchi H et al. 2009. GEM-TREND: a web tool for gene expression data mining toward relevant network discovery. BMC Genom 10:411
    [Google Scholar]
  168. 168.  Wang Z, Monteiro CD, Jagodnik KM, Fernandez NF, Gundersen GW et al. 2016. Extraction and analysis of signatures from the Gene Expression Omnibus by the crowd. Nat. Commun. 7:12846
    [Google Scholar]
  169. 169.  Rosenberg SA, Restifo NP 2015. Adoptive cell transfer as personalized immunotherapy for human cancer. Science 348:62–68
    [Google Scholar]
  170. 170.  Lim WA, June CH 2017. The principles of engineering immune cells to treat cancer. Cell 168:724–40
    [Google Scholar]
  171. 171.  Ott PA, Hu Z, Keskin DB, Shukla SA, Sun J et al. 2017. An immunogenic personal neoantigen vaccine for patients with melanoma. Nature 547:217–21
    [Google Scholar]
  172. 172.  Sahin U, Derhovanessian E, Miller M, Kloke BP, Simon P et al. 2017. Personalized RNA mutanome vaccines mobilize poly-specific therapeutic immunity against cancer. Nature 547:222–26
    [Google Scholar]
  173. 173.  Davis ME, Chen ZG, Shin DM 2008. Nanoparticle therapeutics: an emerging treatment modality for cancer. Nat. Rev. Drug Discov. 7:771–82
    [Google Scholar]
  174. 174.  Shalem O, Sanjana NE, Zhang F 2015. High-throughput functional genomics using CRISPR-Cas9. Nat. Rev. Genet. 16:299–311
    [Google Scholar]
  175. 175.  Manguso RT, Pope HW, Zimmer MD, Brown FD, Yates KB et al. 2017. In vivo CRISPR screening identifies Ptpn2 as a cancer immunotherapy target. Nature 547:413–18
    [Google Scholar]
  176. 176.  Bruna A, Rueda OM, Greenwood W, Batra AS, Callari M et al. 2016. A biobank of breast cancer explants with preserved intra-tumor heterogeneity to screen anticancer compounds. Cell 167:260–74.e22
    [Google Scholar]
  177. 177.  Trifiletti DM, Sturz VN, Showalter TN, Lobo JM 2017. Towards decision-making using individualized risk estimates for personalized medicine: a systematic review of genomic classifiers of solid tumors. PLOS ONE 12:e0176388
    [Google Scholar]
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