Practical aspects of NGS-based pathways analysis for personalized cancer science and medicine

Oncotarget

Volumn 7, Issue 32, 2016, Pages 52493-52516

  Kotelnikova, Ekaterina A ^a,b,c   Pyatnitskiy, Mikhail ^a,f,g   Paleeva, Anna ^a   Kremenetskaya, Olga ^a,e   Vinogradov, Dmitriy ^a,b,d  

^a Personal Biomedicine   (Russian Federation)

^b INSTITUTE FOR INFORMATION TRANSMISSION PROBLEMS   (Russian Federation)

^c HOSPITAL CLÍNIC   (Spain)

^d MOSCOW STATE UNIVERSITY   (Russian Federation)

^e CENTER FOR THEORETICAL PROBLEMS OF PHYSICOCHEMICAL PHARMACOLOGY   (Russian Federation)

^f INSTITUTE OF BIOMEDICAL CHEMISTRY   (Russian Federation)

^g PIROGOV RUSSIAN NATIONAL RESEARCH MEDICAL UNIVERSITY   (Russian Federation)

Author keywords

Next generation sequencing (NGS); Pathways; Personalized medicine; Precision oncology; Systems biology

Indexed keywords

CANCER THERAPY; GOLD STANDARD; HUMAN; INFORMATION PROCESSING; NEXT GENERATION SEQUENCING; ONCOLOGY; PERSONALIZED MEDICINE; SYSTEMS BIOLOGY; GENETICS; HIGH THROUGHPUT SEQUENCING; NEOPLASM; PHARMACOGENETICS; PROCEDURES; STANDARDS; TRENDS;

TUMOR MARKER;

BIOMARKERS, TUMOR; HIGH-THROUGHPUT NUCLEOTIDE SEQUENCING; HUMANS; MEDICAL ONCOLOGY; NEOPLASMS; PHARMACOGENETICS; PRECISION MEDICINE; SYSTEMS BIOLOGY;

EID: 84982311569     PISSN: None     EISSN: 19492553     Source Type: Journal    
DOI: 10.18632/oncotarget.9370     Document Type: Review

References (235)

1. Hanahan D, Weinberg RA. The hallmarks of cancer. Cell 2000; 100:57-70.
2. De Palma M, Hanahan D. The biology of personalized cancer medicine: facing individual complexities underlying hallmark capabilities. Mol Oncol 2012; 6:111-27.
3. Center For Drug Evaluation. Genomics-Table of Pharmacogenomic Biomarkers in Drug Labeling. [cited 2015 Oct 2]; Available from: http://www.fda.gov/ drugs/scienceresearch/researchareas/pharmacogenetics/ ucm083378.html
4. Yeh P, Chen H, Andrews J, Naser R, Pao W, Horn L. DNA-Mutation Inventory to Refine and Enhance Cancer Treatment (DIRECT): a catalog of clinically relevant cancer mutations to enable genome-directed anticancer therapy. Clin Cancer Res 2013; 19:1894-901.
5. McCann GA, Eisenhauer EL. Hereditary cancer syndromes with high risk of endometrial and ovarian cancer: surgical options for personalized care. J Surg Oncol 2015; 111:118-24.
6. Powell CB. Clinical management of patients at inherited risk for gynecologic cancer. Curr Opin Obstet Gynecol 2015; 27:14-22.
7. Weiss G, Glen W, Brandi H, Robert W, Ashish S, Susan G, Robert P, David M, Scott M, Eric T, David L, Vivek K. Evaluation and comparison of two commercially available targeted next-generation sequencing platforms to assist oncology decision making. Onco Targets Ther 2015;:959.
8. Shen T, Pajaro-Van de Stadt SH, Yeat NC, Lin JC-H. Clinical applications of next generation sequencing in cancer: from panels, to exomes, to genomes. Front Genet 2015; 6:215.
9. Beltran H, Yelensky R, Frampton GM, Park K, Downing SR, MacDonald TY, Jarosz M, Lipson D, Tagawa ST, Nanus DM, Stephens PJ, Mosquera JM, Cronin MT, et al. Targeted next-generation sequencing of advanced prostate cancer identifies potential therapeutic targets and disease heterogeneity. Eur Urol 2013; 63:920-6.
10. Zhao X, Wang A, Walter V, Patel NM, Eberhard DA, Hayward MC, Salazar AH, Jo H, Soloway MG, Wilkerson MD, Parker JS, Yin X, Zhang G, et al. Combined Targeted DNA Sequencing in Non-Small Cell Lung Cancer (NSCLC) Using UNCseq and NGScopy, and RNA Sequencing Using UNCqeR for the Detection of Genetic Aberrations in NSCLC. PLoS One 2015; 10:e0129280.
11. Green RC, Berg JS, Grody WW, Kalia SS, Korf BR, Martin CL, McGuire AL, Nussbaum RL, O'Daniel JM, Ormond KE, Rehm HL, Watson MS, Williams MS, et al. ACMG recommendations for reporting of incidental findings in clinical exome and genome sequencing. Genet Med 2013; 15:565-74.
12. Vogelstein B, Papadopoulos N, Velculescu VE, Zhou S, Diaz LA Jr, Kinzler KW. Cancer genome landscapes. Science 2013; 339:1546-58.
13. Mutz K-O, Heilkenbrinker A, Lönne M, Walter J-G, Stahl F. Transcriptome analysis using next-generation sequencing. Curr Opin Biotechnol 2013; 24:22-30.
14. Stirzaker C, Zotenko E, Song JZ, Qu W, Nair SS, Locke WJ, Stone A, Armstong NJ, Robinson MD, Dobrovic A, Avery-Kiejda KA, Peters KM, French JD, et al. Methylome sequencing in triple-negative breast cancer reveals distinct methylation clusters with prognostic value. Nat Commun 2015; 6:5899.
15. Bennett NC, Farah CS. Next-generation sequencing in clinical oncology: next steps towards clinical validation. Cancers 2014; 6:2296-312.
16. Gagan J, Van Allen EM. Next-generation sequencing to guide cancer therapy. Genome Med 2015; 7:80.
17. Sheridan C. Milestone approval lifts Illumina's NGS from research into clinic. Nat Biotechnol 2014; 32:111-2.
18. Shrager J, Tenenbaum JM. Rapid learning for precision oncology. Nat Rev Clin Oncol 2014; 11:109-18.
19. Rodríguez-Antona C, Taron M. Pharmacogenomic biomarkers for personalized cancer treatment. J Intern Med 2015; 277:201-17.
20. Gray SW, Cronin A, Bair E, Lindeman N, Viswanath V, Janeway KA. Marketing of personalized cancer care on the web: an analysis of Internet websites. J Natl Cancer Inst [Internet] 2015; 107. Available from: http://dx.doi. org/10.1093/jnci/djv030
21. Pham-Ledard A, Cowppli-Bony A, Doussau A, Prochazkova-Carlotti M, Laharanne E, Jouary T, Belaud-Rotureau M-A, Vergier B, Merlio J-P, Beylot-Barry M. Diagnostic and prognostic value of BCL2 rearrangement in 53 patients with follicular lymphoma presenting as primary skin lesions. Am J Clin Pathol 2015; 143:362-73.
22. Ito T, Hamasaki M, Matsumoto S, Hiroshima K, Tsujimura T, Kawai T, Shimao Y, Marutsuka K, Moriguchi S, Maruyama R, Miyamoto S, Nabeshima K. p16/CDKN2A FISH in Differentiation of Diffuse Malignant Peritoneal Mesothelioma From Mesothelial Hyperplasia and Epithelial Ovarian Cancer. Am J Clin Pathol 2015; 143:830-8.
23. Mertens F, Johansson B, Fioretos T, Mitelman F. The emerging complexity of gene fusions in cancer. Nat Rev Cancer 2015; 15:371-81.
24. Von Hoff DD, Stephenson JJ Jr, Rosen P, Loesch DM, Borad MJ, Anthony S, Jameson G, Brown S, Cantafio N, Richards DA, Fitch TR, Wasserman E, Fernandez C, et al. Pilot study using molecular profiling of patients' tumors to find potential targets and select treatments for their refractory cancers. J Clin Oncol 2010; 28:4877-83.
25. Van Allen EM, Wagle N, Levy MA. Clinical analysis and interpretation of cancer genome data. J Clin Oncol 2013; 31:1825-33.
26. Jones SJ, Laskin J, Li YY, Griffith OL, An J, Bilenky M, Butterfield YS, Cezard T, Chuah E, Corbett R, Fejes AP, Griffith M, Yee J, et al. Evolution of an adenocarcinoma in response to selection by targeted kinase inhibitors. Genome Biol 2010; 11:R82.
27. Ingenuity IPA-Integrate and understand complex 'omics data [Internet]. Ingenuity [cited 2015 Oct 2]; Available from: http://www.ingenuity.com/products/ipa
28. Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res 2000; 28:27-30.
29. Knox C, Law V, Jewison T, Liu P, Ly S, Frolkis A, Pon A, Banco K, Mak C, Neveu V, Djoumbou Y, Eisner R, Guo AC, et al. DrugBank 3.0: a comprehensive resource for "omics" research on drugs. Nucleic Acids Res 2011; 39:D1035-41.
30. Welch JS, Westervelt P, Ding L, Larson DE, Klco JM, Kulkarni S, Wallis J, Chen K, Payton JE, Fulton RS, Veizer J, Schmidt H, Vickery TL, et al. Use of whole-genome sequencing to diagnose a cryptic fusion oncogene. JAMA 2011; 305:1577-84.
31. Kolata G. In Gene Sequencing Treatment for Leukemia, Glimpses of the Future. NY Times [Internet] 2012 [cited 2015 Oct 2]; Available from: http://www.nytimes. com/2012/07/08/health/in-gene-sequencing-treatment-forleukemia-glimpses-of-the-future.html
32. Russell K, Shunyakov L, Dicke KA, Maney T, Voss A. A practical approach to aid physician interpretation of clinically actionable predictive biomarker results in a multiplatform tumor profiling service. Front Pharmacol 2014; 5:76.
33. Le Tourneau C, Paoletti X, Servant N, Bièche I, Gentien D, Rio Frio T, Vincent-Salomon A, Servois V, Romejon J, Mariani O, Bernard V, Huppe P, Pierron G, et al. Randomised proof-of-concept phase II trial comparing targeted therapy based on tumour molecular profiling vs conventional therapy in patients with refractory cancer: results of the feasibility part of the SHIVA trial. Br J Cancer 2014; 111:17-24.
34. Servant N, Roméjon J, Gestraud P, La Rosa P, Lucotte G, Lair S, Bernard V, Zeitouni B, Coffin F, Jules-Clément G, Yvon F, Lermine A, Poullet P, et al. Bioinformatics for precision medicine in oncology: principles and application to the SHIVA clinical trial. Front Genet 2014; 5:152.
35. DCTD - Major Initiatives [Internet]. [cited 2015 Oct 2]; Available from: http://dctd.cancer.gov/MajorInitiatives/ NCI-sponsored_trials_in_precision_medicine.htm
36. Beltran H, Eng K, Mosquera JM, Sigaras A, Romanel A, Rennert H, Kossai M, Pauli C, Faltas B, Fontugne J, Park K, Banfelder J, Prandi D, et al. Whole-Exome Sequencing of Metastatic Cancer and Biomarkers of Treatment Response. JAMA Oncol 2015; 1:466-74.
37. Zhao Y, Polley EC, Li M-C, Lih C-J, Palmisano A, Sims DJ, Rubinstein LV, Conley BA, Chen AP, Williams PM, Kummar S, Doroshow JH, Simon RM. GeneMed: An Informatics Hub for the Coordination of Next-Generation Sequencing Studies that Support Precision Oncology Clinical Trials. Cancer Inform 2015; 14:45-55.
38. Rodon J, Soria JC, Berger R, Batist G, Tsimberidou A, Bresson C, Lee JJ, Rubin E, Onn A, Schilsky RL, Miller WH, Eggermont AM, Mendelsohn J, et al. Challenges in initiating and conducting personalized cancer therapy trials: perspectives from WINTHER, a Worldwide Innovative Network (WIN) Consortium trial. Ann Oncol 2015; 26:1791-8.
39. DCTD - Major Initiatives [Internet]. [cited 2015 Oct 2]; Available from: http://dctd.cancer.gov/MajorInitiatives/ NCI-sponsored_trials_in_precision_medicine.htm
40. Brower V. NCI-MATCH pairs tumor mutations with matching drugs. Nat Biotechnol 2015; 33:790-1.
41. Do K, O'Sullivan Coyne G, Chen AP. An overview of the NCI precision medicine trials-NCI MATCH and MPACT. Chin Clin Oncol 2015; 4:31.
42. Dabrowski A, Chwiecko M. Oxygen radicals mediate depletion of pancreatic sulfhydryl compounds in rats with cerulein-induced acute pancreatitis. Digestion 1990; 47:15-9.
43. Wagle N, Grabiner BC, Van Allen EM, Hodis E, Jacobus S, Supko JG, Stewart M, Choueiri TK, Gandhi L, Cleary JM, Elfiky AA, Taplin ME, Stack EC, et al. Activating mTOR mutations in a patient with an extraordinary response on a phase I trial of everolimus and pazopanib. Cancer Discov 2014; 4:546-53.
44. Kitano H. Systems biology: a brief overview. Science 2002; 295:1662-4.
45. Ideker T, Galitski T, Hood L. A new approach to decoding life: systems biology. Annu Rev Genomics Hum Genet 2001; 2:343-72.
46. Werner HMJ, Mills GB, Ram PT. Cancer Systems Biology: a peek into the future of patient care? Nat Rev Clin Oncol 2014; 11:167-76.
47. Wolkenhauer O, Auffray C, Jaster R, Steinhoff G, Dammann O. The road from systems biology to systems medicine. Pediatr Res 2013; 73:502-7.
48. Lili LN, Matyunina LV, Walker LD, Daneker GW, McDonald JF. Evidence for the importance of personalized molecular profiling in pancreatic cancer. Pancreas 2014; 43:198-211.
49. Barillot E, Calzone L, Hupe P, Vert J-P, Zinovyev A. Computational systems biology of cancer. CRC Press; 2012.
50. Garcia-Murillas I, Schiavon G, Weigelt B, Ng C, Hrebien S, Cutts RJ, Cheang M, Osin P, Nerurkar A, Kozarewa I, Garrido JA, Dowsett M, Reis-Filho JS, et al. Mutation tracking in circulating tumor DNA predicts relapse in early breast cancer. Sci Transl Med 2015; 7:302ra133.
51. Ramsköld D, Luo S, Wang Y-C, Li R, Deng Q, Faridani OR, Daniels GA, Khrebtukova I, Loring JF, Laurent LC, Schroth GP, Sandberg R. Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells. Nat Biotechnol 2012; 30:777-82.
52. Graw S, Meier R, Minn K, Bloomer C, Godwin AK, Fridley B, Vlad A, Beyerlein P, Chien J. Robust gene expression and mutation analyses of RNA-sequencing of formalinfixed diagnostic tumor samples. Sci Rep 2015; 5:12335.
53. Tuononen K, Mäki-Nevala S, Sarhadi VK, Wirtanen A, Rönty M, Salmenkivi K, Andrews JM, Telaranta-Keerie AI, Hannula S, Lagström S, Ellonen P, Knuuttila A, Knuutila S. Comparison of targeted next-generation sequencing (NGS) and real-time PCR in the detection of EGFR, KRAS, and BRAF mutations on formalin-fixed, paraffin-embedded tumor material of non-small cell lung carcinoma-superiority of NGS. Genes Chromosomes Cancer 2013; 52:503-11.
54. Cancer Genome Atlas Research Network. Comprehensive molecular profiling of lung adenocarcinoma. Nature 2014; 511:543-50.
55. Krauthammer M, Kong Y, Ha BH, Evans P, Bacchiocchi A, McCusker JP, Cheng E, Davis MJ, Goh G, Choi M, Ariyan S, Narayan D, Dutton-Regester K, et al. Exome sequencing identifies recurrent somatic RAC1 mutations in melanoma. Nat Genet 2012; 44:1006-14.
56. Metzker ML. Sequencing technologies-the next generation. Nat Rev Genet 2010; 11:31-46.
57. Xuan J, Yu Y, Qing T, Guo L, Shi L. Next-generation sequencing in the clinic: promises and challenges. Cancer Lett 2013; 340:284-95.
58. Sboner A, Mu XJ, Greenbaum D, Auerbach RK, Gerstein MB. The real cost of sequencing: higher than you think! Genome Biol 2011; 12:125.
59. Ding L, Wendl MC, McMichael JF, Raphael BJ. Expanding the computational toolbox for mining cancer genomes. Nat Rev Genet 2014; 15:556-70.
60. Ewing AD, Houlahan KE, Hu Y, Ellrott K, Caloian C, Yamaguchi TN, Bare JC, P'ng C, Waggott D, Sabelnykova VY, ICGC-TCGA DREAM Somatic Mutation Calling Challenge participants, Kellen MR, Norman TC, et al. Combining tumor genome simulation with crowdsourcing to benchmark somatic single-nucleotide-variant detection. Nat Methods 2015; 12:623-30.
61. Roberts ND, Kortschak RD, Parker WT, Schreiber AW, Branford S, Scott HS, Glonek G, Adelson DL. A comparative analysis of algorithms for somatic SNV detection in cancer. Bioinformatics 2013; 29:2223-30.
62. Wilkerson MD, Cabanski CR, Sun W, Hoadley KA, Walter V, Mose LE, Troester MA, Hammerman PS, Parker JS, Perou CM, Hayes DN. Integrated RNA and DNA sequencing improves mutation detection in low purity tumors. Nucleic Acids Res 2014; 42:e107.
63. Fang H, Wu Y, Narzisi G, O'Rawe JA, Barrón LTJ, Rosenbaum J, Ronemus M, Iossifov I, Schatz MC, Lyon GJ. Reducing INDEL calling errors in whole genome and exome sequencing data. Genome Med 2014; 6:89.
64. Li H. Toward better understanding of artifacts in variant calling from high-coverage samples. Bioinformatics 2014; 30:2843-51.
65. Mose LE, Wilkerson MD, Hayes DN, Perou CM, Parker JS. ABRA: improved coding indel detection via assemblybased realignment. Bioinformatics 2014; 30:2813-5.
66. McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, Garimella K, Altshuler D, Gabriel S, Daly M, DePristo MA. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res 2010; 20:1297-303.
67. Alkodsi A, Louhimo R, Hautaniemi S. Comparative analysis of methods for identifying somatic copy number alterations from deep sequencing data. Brief Bioinform 2015; 16:242-54.
68. Liu B, Morrison CD, Johnson CS, Trump DL, Qin M, Conroy JC, Wang J, Liu S. Computational methods for detecting copy number variations in cancer genome using next generation sequencing: principles and challenges. Oncotarget 2013; 4:1868-81. doi: 10.18632/ oncotarget.1537.
69. Graham RP, Jin L, Knutson DL, Kloft-Nelson SM, Greipp PT, Waldburger N, Roessler S, Longerich T, Roberts LR, Oliveira AM, Halling KC, Schirmacher P, Torbenson MS. DNAJB1-PRKACA is specific for fibrolamellar carcinoma. Mod Pathol 2015; 28:822-9.
70. An X, Tiwari AK, Sun Y, Ding P-R, Ashby CR Jr, Chen Z-S. BCR-ABL tyrosine kinase inhibitors in the treatment of Philadelphia chromosome positive chronic myeloid leukemia: a review. Leuk Res 2010; 34:1255-68.
71. Kwak EL, Bang Y-J, Camidge DR, Shaw AT, Solomon B, Maki RG, Ou S-HI, Dezube BJ, Jänne PA, Costa DB, Varella-Garcia M, Kim W-H, Lynch TJ, et al. Anaplastic lymphoma kinase inhibition in non-small-cell lung cancer. N Engl J Med 2010; 363:1693-703.
72. Yoshihara K, Wang Q, Torres-Garcia W, Zheng S, Vegesna R, Kim H, Verhaak RGW. The landscape and therapeutic relevance of cancer-associated transcript fusions. Oncogene 2015; 34:4845-54.
73. Trapnell C, Roberts A, Goff L, Pertea G, Kim D, Kelley DR, Pimentel H, Salzberg SL, Rinn JL, Pachter L. Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat Protoc 2012; 7:562-78.
74. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 2014; 15:550.
75. Hardcastle TJ, Kelly KA. baySeq: empirical Bayesian methods for identifying differential expression in sequence count data. BMC Bioinformatics 2010; 11:422.
76. Laird PW, Jaenisch R. DNA methylation and cancer. Hum Mol Genet 1994; 3 Spec No:1487-95.
77. Stirzaker C, Taberlay PC, Statham AL, Clark SJ. Mining cancer methylomes: prospects and challenges. Trends Genet 2014; 30:75-84.
78. Rohlin A, Wernersson J, Engwall Y, Wiklund L, Björk J, Nordling M. Parallel sequencing used in detection of mosaic mutations: comparison with four diagnostic DNA screening techniques. Hum Mutat 2009; 30:1012-20.
79. Biesecker LG, Spinner NB. A genomic view of mosaicism and human disease. Nat Rev Genet 2013; 14:307-20.
80. Machiela MJ, Chanock SJ. Detectable clonal mosaicism in the human genome. Semin Hematol 2013; 50:348-59.
81. Friedman E, Efrat N, Soussan-Gutman L, Dvir A, Kaplan Y, Ekstein T, Nykamp K, Powers M, Rabideau M, Sorenson J, Topper S. Low-level constitutional mosaicism of a de novoBRCA1 gene mutation. Br J Cancer 2015; 112:765-8.
82. Yamaguchi K, Komura M, Yamaguchi R, Imoto S, Shimizu E, Kasuya S, Shibuya T, Hatakeyama S, Takahashi N, Ikenoue T, Hata K, Tsurita G, Shinozaki M, et al. Detection of APC mosaicism by next-generation sequencing in an FAP patient. J Hum Genet 2015; 60:227-31.
83. Pasmant E, Parfait B, Luscan A, Goussard P, Briand-Suleau A, Laurendeau I, Fouveaut C, Leroy C, Montadert A, Wolkenstein P, Vidaud M, Vidaud D. Neurofibromatosis type 1 molecular diagnosis: what can NGS do for you when you have a large gene with loss of function mutations? Eur J Hum Genet 2015; 23:596-601.
84. Krishnan VG, Ng PC. Predicting cancer drivers: are we there yet? Genome Med 2012; 4:88.
85. Dancey JE, Bedard PL, Onetto N, Hudson TJ. The genetic basis for cancer treatment decisions. Cell 2012; 148:409-20.
86. Bozic I, Antal T, Ohtsuki H, Carter H, Kim D, Chen S, Karchin R, Kinzler KW, Vogelstein B, Nowak MA. Accumulation of driver and passenger mutations during tumor progression. Proc Natl Acad Sci U S A 2010; 107:18545-50.
87. McFarland CD, Korolev KS, Kryukov GV, Sunyaev SR, Mirny LA. Impact of deleterious passenger mutations on cancer progression. Proc Natl Acad Sci U S A 2013; 110:2910-5.
88. Tamborero D, Gonzalez-Perez A, Perez-Llamas C, Deu-Pons J, Kandoth C, Reimand J, Lawrence MS, Getz G, Bader GD, Ding L, Lopez-Bigas N. Comprehensive identification of mutational cancer driver genes across 12 tumor types. Sci Rep 2013; 3:2650.
89. Lawrence MS, Stojanov P, Mermel CH, Robinson JT, Garraway LA, Golub TR, Meyerson M, Gabriel SB, Lander ES, Getz G. Discovery and saturation analysis of cancer genes across 21 tumour types. Nature 2014; 505:495-501.
90. Forbes SA, Beare D, Gunasekaran P, Leung K, Bindal N, Boutselakis H, Ding M, Bamford S, Cole C, Ward S, Kok CY, Jia M, De T, et al. COSMIC: exploring the world's knowledge of somatic mutations in human cancer. Nucleic Acids Res 2015; 43:D805-11.
91. Cancer Genome Atlas Research Network, Weinstein JN, Collisson EA, Mills GB, Shaw KRM, Ozenberger BA, Ellrott K, Shmulevich I, Sander C, Stuart JM. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet 2013; 45:1113-20.
92. Cerami E, Gao J, Dogrusoz U, Gross BE, Sumer SO, Aksoy BA, Jacobsen A, Byrne CJ, Heuer ML, Larsson E, Antipin Y, Reva B, Goldberg AP, et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov 2012; 2:401-4.
93. Cline MS, Craft B, Swatloski T, Goldman M, Ma S, Haussler D, Zhu J. Exploring TCGA Pan-Cancer data at the UCSC Cancer Genomics Browser. Sci Rep 2013; 3:2652.
94. Ng PC, Henikoff S. Predicting deleterious amino acid substitutions. Genome Res 2001; 11:863-74.
95. 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; 7:248-9.
96. Reva B, Antipin Y, Sander C. Predicting the functional impact of protein mutations: application to cancer genomics. Nucleic Acids Res 2011; 39:e118.
97. Shihab HA, Gough J, Cooper DN, Stenson PD, Barker GLA, Edwards KJ, Day INM, Gaunt TR. Predicting the functional, molecular, and phenotypic consequences of amino acid substitutions using hidden Markov models. Hum Mutat 2013; 34:57-65.
98. Schwarz JM, Rödelsperger C, Schuelke M, Seelow D. MutationTaster evaluates disease-causing potential of sequence alterations. Nat Methods 2010; 7:575-6.
99. Pon JR, Marra MA. Driver and passenger mutations in cancer. Annu Rev Pathol 2015; 10:25-50.
100. Marx V. Cancer genomes: discerning drivers from passengers. Nat Methods 2014; 11:375-9.
101. Shihab HA, Gough J, Cooper DN, Day INM, Gaunt TR. Predicting the functional consequences of cancer-associated amino acid substitutions. Bioinformatics 2013; 29:1504-10.
102. Douville C, Carter H, Kim R, Niknafs N, Diekhans M, Stenson PD, Cooper DN, Ryan M, Karchin R. CRAVAT: cancer-related analysis of variants toolkit. Bioinformatics 2013; 29:647-8.
103. Kaminker JS, Zhang Y, Watanabe C, Zhang Z. CanPredict: a computational tool for predicting cancer-associated missense mutations. Nucleic Acids Res 2007; 35:W595-8.
104. Khurana E, Fu Y, Colonna V, Mu XJ, Kang HM, Lappalainen T, Sboner A, Lochovsky L, Chen J, Harmanci A, Das J, Abyzov A, Balasubramanian S, et al. Integrative annotation of variants from 1092 humans: application to cancer genomics. Science 2013; 342:1235587.
105. 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.
106. Hong B, van den Heuvel APJ, Prabhu VV, Zhang S, El-Deiry WS. Targeting tumor suppressor p53 for cancer therapy: strategies, challenges and opportunities. Curr Drug Targets 2014; 15:80-9.
107. Nag S, Zhang X, Srivenugopal KS, Wang M-H, Wang W, Zhang R. Targeting MDM2-p53 interaction for cancer therapy: are we there yet? Curr Med Chem 2014; 21:553-74.
108. Jackson RA, Chen ES. Synthetic lethal approaches for assessing combinatorial efficacy of chemotherapeutic drugs. Pharmacol Ther [Internet] 2016; Available from: http:// dx.doi.org/10.1016/j.pharmthera.2016.01.014
109. Muller FL, Aquilanti EA, DePinho RA. Collateral Lethality: A new therapeutic strategy in oncology. Trends Cancer Res 2015; 1:161-73.
110. Morris LGT, Chan TA. Therapeutic targeting of tumor suppressor genes. Cancer 2015; 121:1357-68.
111. Jones S, Anagnostou V, Lytle K, Parpart-Li S, Nesselbush M, Riley DR, Shukla M, Chesnick B, Kadan M, Papp E, Galens KG, Murphy D, Zhang T, et al. Personalized genomic analyses for cancer mutation discovery and interpretation. Sci Transl Med 2015; 7:283ra53.
112. Rubio-Perez C, Tamborero D, Schroeder MP, Antolín 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; 27:382-96.
113. American Society of Clinical Oncology. The state of cancer care in america, 2015: a report by the american society of clinical oncology. J Oncol Pract 2015; 11:79-113.
114. Wang L, Liu H, Chute CG, Zhu Q. Cancer based pharmacogenomics network supported with scientific evidences: from the view of drug repurposing. BioData Min 2015; 8:9.
115. Martinez-Ledesma E, de Groot JF, Verhaak RGW. Seek and destroy: relating cancer drivers to therapies. Cancer Cell 2015; 27:319-21.
116. Van Allen EM, Wagle N, Stojanov P, Perrin DL, Cibulskis K, Marlow S, Jane-Valbuena J, Friedrich DC, Kryukov G, Carter SL, McKenna A, Sivachenko A, Rosenberg M, et al. Whole-exome sequencing and clinical interpretation of formalin-fixed, paraffin-embedded tumor samples to guide precision cancer medicine. Nat Med 2014; 20:682-8.
117. Meric-Bernstam F, Johnson A, Holla V, Bailey AM, Brusco L, Chen K, Routbort M, Patel KP, Zeng J, Kopetz S, Davies MA, Piha-Paul SA, Hong DS, et al. A decision support framework for genomically informed investigational cancer therapy. J Natl Cancer Inst [Internet] 2015; 107. Available from: http://dx.doi.org/10.1093/jnci/djv098
118. Romond EH, Perez EA, Bryant J, Suman VJ, Geyer CE Jr, Davidson NE, Tan-Chiu E, Martino S, Paik S, Kaufman PA, Swain SM, Pisansky TM, Fehrenbacher L, et al. Trastuzumab plus adjuvant chemotherapy for operable HER2-positive breast cancer. N Engl J Med 2005; 353:1673-84.
119. Sutton L-A, Rosenquist R. Clonal evolution in chronic lymphocytic leukemia: impact of subclonality on disease progression. Expert Rev Hematol 2015; 8:71-8.
120. Aparicio S, Caldas C. The implications of clonal genome evolution for cancer medicine. N Engl J Med 2013; 368:842-51.
121. Whirl-Carrillo M, McDonagh EM, Hebert JM, Gong L, Sangkuhl K, Thorn CF, Altman RB, Klein TE. Pharmacogenomics knowledge for personalized medicine. Clin Pharmacol Ther 2012; 92:414-7.
122. Griffith M, Griffith OL, Coffman AC, Weible JV, McMichael JF, Spies NC, Koval J, Das I, Callaway MB, Eldred JM, Miller CA, Subramanian J, Govindan R, et al. DGIdb: mining the druggable genome. Nat Methods 2013; 10:1209-10.
123. Kaplun A, Hogan JD, Schacherer F, Peter AP, Krishna S, Braun BR, Nambudiry R, Nitu MG, Mallelwar R, Albayrak A. PGMD: a comprehensive manually curated pharmacogenomic database. Pharmacogenomics J [Internet] 2015; Available from: http://dx.doi.org/10.1038/tpj.2015.32
124. Sosman JA, Kim KB, Schuchter L, Gonzalez R, Pavlick AC, Weber JS, McArthur GA, Hutson TE, Moschos SJ, Flaherty KT, Hersey P, Kefford R, Lawrence D, et al. Survival in BRAF V600-mutant advanced melanoma treated with vemurafenib. N Engl J Med 2012; 366:707-14.
125. Prahallad A, Sun C, Huang S, Di Nicolantonio F, Salazar R, Zecchin D, Beijersbergen RL, Bardelli A, Bernards R. Unresponsiveness of colon cancer to BRAF(V600E) inhibition through feedback activation of EGFR. Nature 2012; 483:100-3.
126. Curtis C, Shah SP, Chin S-F, Turashvili G, Rueda OM, Dunning MJ, Speed D, Lynch AG, Samarajiwa S, Yuan Y, Gräf S, Ha G, Haffari G, et al. The genomic and transcriptomic architecture of 2, 000 breast tumours reveals novel subgroups. Nature 2012; 486:346-52.
127. Fernandez-Retana J, Lasa-Gonsebatt F, Lopez-Urrutia E, Coronel-Martínez J, Cantu De Leon D, Jacobo-Herrera N, Peralta-Zaragoza O, Perez-Montiel D, Reynoso-Noveron N, Vazquez-Romo R, Perez-Plasencia C. Transcript profiling distinguishes complete treatment responders with locally advanced cervical cancer. Transl Oncol 2015; 8:77-84.
128. Gaba RC, Groth JV, Parvinian A, Guzman G, Casadaban LC. Gene expression in hepatocellular carcinoma: pilot study of potential transarterial chemoembolization response biomarkers. J Vasc Interv Radiol 2015; 26:723-32.
129. Estevez-Garcia P, Rivera F, Molina-Pinelo S, Benavent M, Gómez J, Limón ML, Pastor MD, Martinez-Perez J, Paz-Ares L, Carnero A, Garcia-Carbonero R. Gene expression profile predictive of response to chemotherapy in metastatic colorectal cancer. Oncotarget 2015; 6:6151-9. doi: 10.18632/oncotarget.3152.
130. Costello JC, Heiser LM, Georgii E, Gönen M, Menden MP, Wang NJ, Bansal M, Ammad-ud-din M, Hintsanen P, Khan SA, Mpindi J-P, Kallioniemi O, Honkela A, et al. A community effort to assess and improve drug sensitivity prediction algorithms. Nat Biotechnol 2014; 32:1202-12.
131. Kao J, Salari K, Bocanegra M, Choi Y-L, Girard L, Gandhi J, Kwei KA, Hernandez-Boussard T, Wang P, Gazdar AF, Minna JD, Pollack JR. Molecular profiling of breast cancer cell lines defines relevant tumor models and provides a resource for cancer gene discovery. PLoS One 2009; 4:e6146.
132. Geeleher P, Cox NJ, Huang RS. Clinical drug response can be predicted using baseline gene expression levels and in vitro drug sensitivity in cell lines. Genome Biol 2014; 15:R47.
133. Li B, Shin H, Gulbekyan G, Pustovalova O, Nikolsky Y, Hope A, Bessarabova M, Schu M, Kolpakova-Hart E, Merberg D, Dorner A, Trepicchio WL. Development of a Drug-Response Modeling Framework to Identify Cell Line Derived Translational Biomarkers That Can Predict Treatment Outcome to Erlotinib or Sorafenib. PLoS One 2015; 10:e0130700.
134. Yang W, Soares J, Greninger P, Edelman EJ, Lightfoot H, Forbes S, Bindal N, Beare D, Smith JA, Thompson IR, Ramaswamy S, Futreal PA, Haber DA, et al. Genomics of Drug Sensitivity in Cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells. Nucleic Acids Res 2013; 41:D955-61.
135. Barretina J, Caponigro G, Stransky N, Venkatesan K, Margolin AA, Kim S, Wilson CJ, Lehár J, Kryukov GV, Sonkin D, Reddy A, Liu M, Murray L, et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 2012; 483:603-7.
136. Reinhold WC, Sunshine M, Liu H, Varma S, Kohn KW, Morris J, Doroshow J, Pommier Y. CellMiner: a webbased suite of genomic and pharmacologic tools to explore transcript and drug patterns in the NCI-60 cell line set. Cancer Res 2012; 72:3499-511.
137. Wilding JL, Bodmer WF. Cancer cell lines for drug discovery and development. Cancer Res 2014; 74:2377-84.
138. Sonkin D. Expression signature based on TP53 target genes doesn't predict response to TP53-MDM2 inhibitor in wild type TP53 tumors. Elife [Internet] 2015; 4. Available from: http://dx.doi.org/10.7554/eLife.10279
139. Haibe-Kains B, El-Hachem N, Birkbak NJ, Jin AC, Beck AH, Aerts HJWL, Quackenbush J. Inconsistency in large pharmacogenomic studies. Nature 2013; 504:389-93.
140. Cancer Cell Line Encyclopedia Consortium, Genomics of Drug Sensitivity in Cancer Consortium. Pharmacogenomic agreement between two cancer cell line data sets. Nature 2015; 528:84-7.
141. Sonkin D, Hassan M, Murphy DJ, Tatarinova TV. Tumor suppressors status in cancer cell line Encyclopedia. Mol Oncol 2013; 7:791-8.
142. Ahmed D, Eide PW, Eilertsen IA, Danielsen SA, Eknæs M, Hektoen M, Lind GE, Lothe RA. Epigenetic and genetic features of 24 colon cancer cell lines. Oncogenesis 2013; 2:e71.
143. Singh Nanda J, Kumar R, Raghava GPS. dbEM: A database of epigenetic modifiers curated from cancerous and normal genomes. Sci Rep 2016; 6:19340.
144. Shen H, Laird PW. Interplay between the cancer genome and epigenome. Cell 2013; 153:38-55.
145. Easwaran H, Tsai H-C, Baylin SB. Cancer epigenetics: tumor heterogeneity, plasticity of stem-like states, and drug resistance. Mol Cell 2014; 54:716-27.
146. Khatri P, Sirota M, Butte AJ. Ten years of pathway analysis: current approaches and outstanding challenges. PLoS Comput Biol 2012; 8:e1002375.
147. Mutation Consequences and Pathway Analysis working group of the International Cancer Genome Consortium. Pathway and network analysis of cancer genomes. Nat Methods 2015; 12:615-21.
148. Kuperstein I, Grieco L, Cohen DPA, Thieffry D, Zinovyev A, Barillot E. The shortest path is not the one you know: application of biological network resources in precision oncology research. Mutagenesis 2015; 30:191-204.
149. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, Harris MA, Hill DP, Issel-Tarver L, et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet 2000; 25:25-9.
150. Liberzon A, Subramanian A, Pinchback R, Thorvaldsdóttir H, Tamayo P, Mesirov JP. Molecular signatures database (MSigDB) 3.0. Bioinformatics 2011; 27:1739-40.
151. Ogata H, Goto S, Sato K, Fujibuchi W, Bono H, Kanehisa M. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res 1999; 27:29-34.
152. Nishimura D. BioCarta. Biotech Software & Internet Report 2001; 2:117-20.
153. Croft D, Mundo AF, Haw R, Milacic M, Weiser J, Wu G, Caudy M, Garapati P, Gillespie M, Kamdar MR, Jassal B, Jupe S, Matthews L, et al. The Reactome pathway knowledgebase. Nucleic Acids Res 2014; 42:D472-7.
154. Kamburov A, Pentchev K, Galicka H, Wierling C, Lehrach H, Herwig R. ConsensusPathDB: toward a more complete picture of cell biology. Nucleic Acids Res 2011; 39:D712-7.
155. Cerami EG, Gross BE, Demir E, Rodchenkov I, Babur O, Anwar N, Schultz N, Bader GD, Sander C. Pathway Commons, a web resource for biological pathway data. Nucleic Acids Res 2011; 39:D685-90.
156. Franceschini A, Szklarczyk D, Frankild S, Kuhn M, Simonovic M, Roth A, Lin J, Minguez P, Bork P, von Mering C, Jensen LJ. STRING v9.1: protein-protein interaction networks, with increased coverage and integration. Nucleic Acids Res 2013; 41:D808-15.
157. Chatr-Aryamontri A, Breitkreutz B-J, Oughtred R, Boucher L, Heinicke S, Chen D, Stark C, Breitkreutz A, Kolas N, O'Donnell L, Reguly T, Nixon J, Ramage L, et al. The BioGRID interaction database: 2015 update. Nucleic Acids Res 2015; 43:D470-8.
158. Jewison T, Su Y, Disfany FM, Liang Y, Knox C, Maciejewski A, Poelzer J, Huynh J, Zhou Y, Arndt D, Djoumbou Y, Liu Y, Deng L, et al. SMPDB 2.0: big improvements to the Small Molecule Pathway Database. Nucleic Acids Res 2014; 42:D478-84.
159. Schaefer CF, Anthony K, Krupa S, Buchoff J, Day M, Hannay T, Buetow KH. PID: the Pathway Interaction Database. Nucleic Acids Res 2009; 37:D674-9.
160. An O, Pendino V, D'Antonio M, Ratti E, Gentilini M, Ciccarelli FD. NCG 4.0: the network of cancer genes in the era of massive mutational screenings of cancer genomes. Database 2014; 2014:bau015.
161. Kuperstein I, Bonnet E, Nguyen H-A, Cohen D, Viara E, Grieco L, Fourquet S, Calzone L, Russo C, Kondratova M, Dutreix M, Barillot E, Zinovyev A. Atlas of Cancer Signalling Network: a systems biology resource for integrative analysis of cancer data with Google Maps. Oncogenesis 2015; 4:e160.
162. Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell 2011; 144:646-74.
163. Villaveces JM, Jimenez RC, Habermann BH. KEGGViewer, a BioJS component to visualize KEGG Pathways. F1000Res 2014; 3:43.
164. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 2003; 13:2498-504.
165. Saito R, Smoot ME, Ono K, Ruscheinski J, Wang P-L, Lotia S, Pico AR, Bader GD, Ideker T. A travel guide to Cytoscape plugins. Nat Methods 2012; 9:1069-76.
166. Kutmon M, van Iersel MP, Bohler A, Kelder T, Nunes N, Pico AR, Evelo CT. PathVisio 3: an extendable pathway analysis toolbox. PLoS Comput Biol 2015; 11:e1004085.
167. Dixon P, Higginson I. AIDS and cancer pain treated with slow release morphine. Postgrad Med J 1991; 67 Suppl 2:S92-4.
168. MetaCore | Thomson Reuters [Internet]. [cited 2015 Oct 2]; Available from: http://www.thomsonreuters.com/metacore
169. Elsevier. Biological Research-Pathway Studio | Elsevier [Internet]. [cited 2015 Oct 2]; Available from: http://www. elsevier.com/solutions/pathway-studio
170. Dennis G Jr, Sherman BT, Hosack DA, Yang J, Gao W, Lane HC, Lempicki RA. DAVID: Database for Annotation, Visualization, and Integrated Discovery. Genome Biol 2003; 4:P3.
171. Wang J, Duncan D, Shi Z, Zhang B. WEB-based GEne SeT AnaLysis Toolkit (WebGestalt): update 2013. Nucleic Acids Res 2013; 41:W77-83.
172. Finotello F, Di Camillo B. Measuring differential gene expression with RNA-seq: challenges and strategies for data analysis. Brief Funct Genomics 2015; 14:130-42.
173. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, Mesirov JP. Gene set enrichment analysis: a knowledge-based approach for interpreting genomewide expression profiles. Proc Natl Acad Sci U S A 2005; 102:15545-50.
174. Hänzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics 2013; 14:7.
175. Barbie DA, Tamayo P, Boehm JS, Kim SY, Moody SE, Dunn IF, Schinzel AC, Sandy P, Meylan E, Scholl C, Fröhling S, Chan EM, Sos ML, et al. Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1. Nature 2009; 462:108-12.
176. Tarca AL, Draghici S, Khatri P, Hassan SS, Mittal P, Kim J-S, Kim CJ, Kusanovic JP, Romero R. A novel signaling pathway impact analysis. Bioinformatics 2009; 25:75-82.
177. Haynes WA, Higdon R, Stanberry L, Collins D, Kolker E. Differential expression analysis for pathways. PLoS Comput Biol 2013; 9:e1002967.
178. Jia P, Zhao Z. Personalized pathway enrichment map of putative cancer genes from next generation sequencing data. PLoS One 2012; 7:e37595.
179. Oshlack A, Wakefield MJ. Transcript length bias in RNAseq data confounds systems biology. Biol Direct 2009; 4:14.
180. Young MD, Wakefield MJ, Smyth GK, Oshlack A. Gene ontology analysis for RNA-seq: accounting for selection bias. Genome Biol 2010; 11:R14.
181. Wang X, Cairns MJ. SeqGSEA: a Bioconductor package for gene set enrichment analysis of RNA-Seq data integrating differential expression and splicing. Bioinformatics 2014; 30:1777-9.
182. Mi G, Di Y, Emerson S, Cumbie JS, Chang JH. Length bias correction in gene ontology enrichment analysis using logistic regression. PLoS One 2012; 7:e46128.
183. Xiong Q, Ancona N, Hauser ER, Mukherjee S, Furey TS. Integrating genetic and gene expression evidence into genome-wide association analysis of gene sets. Genome Res 2012; 22:386-97.
184. Mo Q, Wang S, Seshan VE, Olshen AB, Schultz N, Sander C, Powers RS, Ladanyi M, Shen R. Pattern discovery and cancer gene identification in integrated cancer genomic data. Proc Natl Acad Sci U S A 2013; 110:4245-50.
185. MacNeil SM, Johnson WE, Li DY, Piccolo SR, Bild AH. Inferring pathway dysregulation in cancers from multiple types of omic data. Genome Med 2015; 7:61.
186. Vaske CJ, Benz SC, Sanborn JZ, Earl D, Szeto C, Zhu J, Haussler D, Stuart JM. Inference of patient-specific pathway activities from multi-dimensional cancer genomics data using PARADIGM. Bioinformatics 2010; 26:i237-45.
187. 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:i640-6.
188. Schroeder MP, Gonzalez-Perez A, Lopez-Bigas N. Visualizing multidimensional cancer genomics data. Genome Med 2013; 5:9.
189. Perez-Llamas C, Lopez-Bigas N. Gitools: analysis and visualisation of genomic data using interactive heat-maps. PLoS One 2011; 6:e19541.
190. 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; 10:1081-2.
191. Krzywinski M, Schein J, Birol I, Connors J, Gascoyne R, Horsman D, Jones SJ, Marra MA. Circos: an information aesthetic for comparative genomics. Genome Res 2009; 19:1639-45.
192. Medvedev P, Stanciu M, Brudno M. Computational methods for discovering structural variation with nextgeneration sequencing. Nat Methods 2009; 6:S13-20.
193. Koboldt DC, Zhang Q, Larson DE, Shen D, McLellan MD, Lin L, Miller CA, Mardis ER, Ding L, Wilson RK. VarScan 2: somatic mutation and copy number alteration discovery in cancer by exome sequencing. Genome Res 2012; 22:568-76.
194. Neuman JA, Isakov O, Shomron N. Analysis of insertiondeletion from deep-sequencing data: software evaluation for optimal detection. Brief Bioinform 2013; 14:46-55.
195. Ghoneim DH, Myers JR, Tuttle E, Paciorkowski AR. Comparison of insertion/deletion calling algorithms on human next-generation sequencing data. BMC Res Notes 2014; 7:864.
196. Wang Q, Jia P, Li F, Chen H, Ji H, Hucks D, Dahlman KB, Pao W, Zhao Z. Detecting somatic point mutations in cancer genome sequencing data: a comparison of mutation callers. Genome Med 2013; 5:91.
197. Wang C, Davila JI, Baheti S, Bhagwate AV, Wang X, Kocher J-PA, Slager SL, Feldman AL, Novak AJ, Cerhan JR, Thompson EA, Asmann YW. RVboost: RNA-seq variants prioritization using a boosting method. Bioinformatics 2014; 30:3414-6.
198. Piskol R, Ramaswami G, Li JB. Reliable identification of genomic variants from RNA-seq data. Am J Hum Genet 2013; 93:641-51.
199. Radenbaugh AJ, Ma S, Ewing A, Stuart JM, Collisson EA, Zhu J, Haussler D. RADIA: RNA and DNA integrated analysis for somatic mutation detection. PLoS One 2014; 9:e111516.
200. Boeva V, Popova T, Bleakley K, Chiche P, Cappo J, Schleiermacher G, Janoueix-Lerosey I, Delattre O, Barillot E. Control-FREEC: a tool for assessing copy number and allelic content using next-generation sequencing data. Bioinformatics 2012; 28:423-5.
201. Magi A, Tattini L, Cifola I, D'Aurizio R, Benelli M, Mangano E, Battaglia C, Bonora E, Kurg A, Seri M, Magini P, Giusti B, Romeo G, et al. EXCAVATOR: detecting copy number variants from whole-exome sequencing data. Genome Biol 2013; 14:R120.
202. Favero F, Joshi T, Marquard AM, Birkbak NJ, Krzystanek M, Li Q, Szallasi Z, Eklund AC. Sequenza: allele-specific copy number and mutation profiles from tumor sequencing data. Ann Oncol 2015; 26:64-70.
203. Cancer Genome Atlas Network. Comprehensive molecular characterization of human colon and rectal cancer. Nature 2012; 487:330-7.
204. Hoffmann S, Otto C, Doose G, Tanzer A, Langenberger D, Christ S, Kunz M, Holdt LM, Teupser D, Hackermüller J, Stadler PF. A multi-split mapping algorithm for circular RNA, splicing, trans-splicing and fusion detection. Genome Biol 2014; 15:R34.
205. Tang J, Fang F, Miller DF, Pilrose JM, Matei D, Huang TH-M, Nephew KP. Global DNA methylation profiling technologies and the ovarian cancer methylome. Methods Mol Biol 2015; 1238:653-75.
206. Anders S, McCarthy DJ, Chen Y, Okoniewski M, Smyth GK, Huber W, Robinson MD. Count-based differential expression analysis of RNA sequencing data using R and Bioconductor. Nat Protoc 2013; 8:1765-86.
207. Feng H, Qin Z, Zhang X. Opportunities and methods for studying alternative splicing in cancer with RNA-Seq. Cancer Lett 2013; 340:179-91.
208. Cibulskis K, Lawrence MS, Carter SL, Sivachenko A, Jaffe D, Sougnez C, Gabriel S, Meyerson M, Lander ES, Getz G. Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nat Biotechnol 2013; 31:213-9.
209. Saunders CT, Wong WSW, Swamy S, Becq J, Murray LJ, Cheetham RK. Strelka: accurate somatic small-variant calling from sequenced tumor-normal sample pairs. Bioinformatics 2012; 28:1811-7.
210. Tang X, Baheti S, Shameer K, Thompson KJ, Wills Q, Niu N, Holcomb IN, Boutet SC, Ramakrishnan R, Kachergus JM, Kocher J-PA, Weinshilboum RM, Wang L, et al. The eSNV-detect: a computational system to identify expressed single nucleotide variants from transcriptome sequencing data. Nucleic Acids Res 2014; 42:e172.
211. Ye K, Schulz MH, Long Q, Apweiler R, Ning Z. Pindel: a pattern growth approach to detect break points of large deletions and medium sized insertions from paired-end short reads. Bioinformatics 2009; 25:2865-71.
212. Albers CA, Lunter G, MacArthur DG, McVean G, Ouwehand WH, Durbin R. Dindel: accurate indel calls from short-read data. Genome Res 2011; 21:961-73.
213. Narzisi G, O'Rawe JA, Iossifov I, Fang H, Lee Y-H, Wang Z, Wu Y, Lyon GJ, Wigler M, Schatz MC. Accurate de novo and transmitted indel detection in exome-capture data using microassembly. Nat Methods 2014; 11:1033-6.
214. Yoon S, Xuan Z, Makarov V, Ye K, Sebat J. Sensitive and accurate detection of copy number variants using read depth of coverage. Genome Res 2009; 19:1586-92.
215. Xie C, Tammi MT. CNV-seq, a new method to detect copy number variation using high-throughput sequencing. BMC Bioinformatics 2009; 10:80.
216. Boeva V, Zinovyev A, Bleakley K, Vert J-P, Janoueix-Lerosey I, Delattre O, Barillot E. Control-free calling of copy number alterations in deep-sequencing data using GCcontent normalization. Bioinformatics 2011; 27:268-9.
217. Xi R, Hadjipanayis AG, Luquette LJ, Kim T-M, Lee E, Zhang J, Johnson MD, Muzny DM, Wheeler DA, Gibbs RA, Kucherlapati R, Park PJ. Copy number variation detection in whole-genome sequencing data using the Bayesian information criterion. Proc Natl Acad Sci U S A 2011; 108:E1128-36.
218. Carter SL, Cibulskis K, Helman E, McKenna A, Shen H, Zack T, Laird PW, Onofrio RC, Winckler W, Weir BA, Beroukhim R, Pellman D, Levine DA, et al. Absolute quantification of somatic DNA alterations in human cancer. Nat Biotechnol 2012; 30:413-21.
219. Louhimo R, Lepikhova T, Monni O, Hautaniemi S. Comparative analysis of algorithms for integration of copy number and expression data. Nat Methods 2012; 9:351-5.
220. Xing M. Molecular pathogenesis and mechanisms of thyroid cancer. Nat Rev Cancer 2013; 13:184-99.
221. Kim D, Salzberg SL. TopHat-Fusion: an algorithm for discovery of novel fusion transcripts. Genome Biol 2011; 12:R72.
222. Jia W, Qiu K, He M, Song P, Zhou Q, Zhou F, Yu Y, Zhu D, Nickerson ML, Wan S, Liao X, Zhu X, Peng S, et al. SOAPfuse: an algorithm for identifying fusion transcripts from paired-end RNA-Seq data. Genome Biol 2013; 14:R12.
223. Torres-García W, Zheng S, Sivachenko A, Vegesna R, Wang Q, Yao R, Berger MF, Weinstein JN, Getz G, Verhaak RGW. PRADA: pipeline for RNA sequencing data analysis. Bioinformatics 2014; 30:2224-6.
224. Peters TJ, Buckley MJ, Statham AL, Pidsley R, Samaras K, V Lord R, Clark SJ, Molloy PL. De novo identification of differentially methylated regions in the human genome. Epigenetics Chromatin 2015; 8:6.
225. Pedersen BS, Schwartz DA, Yang IV, Kechris KJ. Comb-p: software for combining, analyzing, grouping and correcting spatially correlated P-values. Bioinformatics 2012; 28:2986-8.
226. Li S, Garrett-Bakelman FE, Akalin A, Zumbo P, Levine R, To BL, Lewis ID, Brown AL, D'Andrea RJ, Melnick A, Mason CE. An optimized algorithm for detecting and annotating regional differential methylation. BMC Bioinformatics 2013; 14 Suppl 5:S10.
227. Witte T, Plass C, Gerhauser C. Pan-cancer patterns of DNA methylation. Genome Med 2014; 6:66.
228. Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res 2015; 43:e47.
229. González-Vallinas M, Vargas T, Moreno-Rubio J, Molina S, Herranz J, Cejas P, Burgos E, Aguayo C, Custodio A, Reglero G, Feliu J, Ramírez de Molina A. Clinical relevance of the differential expression of the glycosyltransferase gene GCNT3 in colon cancer. Eur J Cancer 2015; 51:1-8.
230. Shukla HD, Mahmood J, Vujaskovic Z. Integrated proteogenomic approach for early diagnosis and prognosis of cancer. Cancer Lett [Internet] 2015; Available from: http:// dx.doi.org/10.1016/j.canlet.2015.08.003
231. Anders S, Reyes A, Huber W. Detecting differential usage of exons from RNA-seq data. Genome Res 2012; 22:2008-17.
232. Wang W, Qin Z, Feng Z, Wang X, Zhang X. Identifying differentially spliced genes from two groups of RNA-seq samples. Gene 2013; 518:164-70.
233. Shen S, Park JW, Huang J, Dittmar KA, Lu Z-X, Zhou Q, Carstens RP, Xing Y. MATS: a Bayesian framework for flexible detection of differential alternative splicing from RNA-Seq data. Nucleic Acids Res 2012; 40:e61.
234. Lend AK, Kazantseva A, Kivil A, Valvere V, Palm K. Diagnostic significance of alternative splice variants of REST and DOPEY1 in the peripheral blood of patients with breast cancer. Tumour Biol 2015; 36:2473-80.
235. Jackson C, Browell D, Gautrey H, Tyson-Capper A. Clinical Significance of HER-2 Splice Variants in Breast Cancer Progression and Drug Resistance. Int J Cell Biol 2013; 2013:973584.