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Integrative cancer genomics: models, algorithms and analysis

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Abstract

In the past decade, the remarkable development of high-throughput sequencing technology accelerates the generation of large amount of multiple dimensional data such as genomic, epigenomic, transcriptomic and proteomic data. The comprehensive data make it possible to understand the underlying mechanisms of biology and disease such as cancer systematically. It also provides great challenges for computational cancer genomics due to the complexity, scale and noise of data. In this article, we aim to review the recent developments and progresses of computational models, algorithms and analysis of complex data in cancer genomics. These topics of this paper include the identification of driver mutations, the genetic heterogeneity analysis, genomic markers discovery of drug response, pan-cancer scale analysis and so on.

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References

  1. Hanahan D, Weinberg R A. The hallmarks of cancer. Cell, 2000, 100(1): 57–70

    Article  Google Scholar 

  2. Hanahan D, Weinberg R A. Hallmarks of cancer: the next generation. Cell, 2011, 144(5): 646–674

    Article  Google Scholar 

  3. The Cancer Genome Atlas Research Network. Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature, 2008, 455(7216): 1061–1068

    Article  Google Scholar 

  4. The Cancer Genome Atlas Research Network. Integrated genomic analyses of ovarian carcinoma. Nature, 2011, 474(7353): 609–615

    Article  Google Scholar 

  5. The International Cancer Genome Consortium. International network of cancer genome projects. Nature, 2010, 464(7291): 993–998

    Article  Google Scholar 

  6. Barretina J, Caponigro G, Stransky N, Venkatesan K, Margolin A A, Kim S, Wilson C J, Lehár J, Kryukov G V, Sonkin D, Reddy A, Liu M, Murray L, Berger M F, Monahan J E, Morais P, Meltzer J, Korejwa A, Jané-Valbuena J, Mapa F A, Thibault J, Bric-Furlong E, Raman P, Shipway A, Engels I H, Cheng J, Yu G K, Yu J, Aspesi P Jr, de Silva M, Jagtap K, Jones M D, Wang L, Hatton C, Palescandolo E, Gupta S, Mahan S, Sougnez C, Onofrio R C, Liefeld T, MacConaill L, Winckler W, Reich M, Li N, Mesirov J P, Gabriel S B, Getz G, Ardlie K, Chan V, Myer V E, Weber B L, Porter J, Warmuth M, Finan P, Harris J L, Meyerson M, Golub T R, Morrissey M P, Sellers W R, Schlegel R, Garraway L A. The cancer cell line encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature, 2012, 483(7391): 603–607

    Article  Google Scholar 

  7. Garnett M J, Edelman E J, Heidorn S J, Greenman C D, Dastur A, Lau KW, Greninger P, Thompson I R, Luo X, Soares J, Liu Q, Iorio F, Surdez D, Chen L, Milano R J, Bignell G R, Tam A T, Davies H, Stevenson J A, Barthorpe S, Lutz S R, Kogera F, Lawrence K, McLaren-Douglas A, Mitropoulos X, Mironenko T, Thi H, Richardson L, Zhou W, Jewitt F, Zhang T, O’Brien P, Boisvert J L, Price S, Hur W, Yang W, Deng X, Butler A, Choi H G, Chang J W, Baselga J, Stamenkovic I, Engelman J A, Sharma S V, Delattre O, Saez-Rodriguez J, Gray N S, Settleman J, Futreal P A, Haber D A, Stratton M R, Ramaswamy S, McDermott U, Benes C H. Systematic identification of genomic markers of drug sensitivity in cancer cells. Nature, 2012, 483(7391): 570–575

    Article  Google Scholar 

  8. Mullighan C, Su X, Zhang J, Radtke I, Phillips L A, Miller C B, Ma J, Liu W, Cheng C, Schulman B A, Harvey R C, Chen I M, Clifford R J, Carroll W L, Reaman G, Bowman WP, Devidas M, Gerhard D S, Yang W, Relling M V, Shurtleff S A, Campana D, Borowitz M J, Pui C H, Smith M, Hunger S P, Willman C L, Downing J R, the Children’s Oncology Group. Deletion of IKZF1 and prognosis in acute lymphoblastic leukemia. The New England Journal of Medicine, 2009, 360(5): 470–480

    Article  Google Scholar 

  9. Stratton M R, Campbell P J, Futreal P A. The cancer genome. Nature, 2009, 458(7239): 719–724

    Article  Google Scholar 

  10. Vazquez M, de la Torre V, Valencia A. Chapter 14: Cancer genome analysis. Plos Computational Biology, 2012, 8(12): e1002824

    Article  Google Scholar 

  11. Vogelstein B, Papadopoulos N, Velculescu V E, Zhou S B, Diaz L A, Kinzier K W. Cancer genome landscapes. Science, 2013, 339(6127): 1546–1558

    Article  Google Scholar 

  12. Wheeler D A, Wang L H. From human genome to cancer genome: the first decade. Genome Research, 2013, 23(7): 1054–1062

    Article  Google Scholar 

  13. Zhang J H, Zhang S H. The discovery of mutated driver pathways in cancer: models and algorithms. 2016, arXiv:1604.01298

    Google Scholar 

  14. Liu Z Q, Zhang S H. Toward a systematic understanding of cancers: a survey of the pan-cancer study. Frontiers in Genetics, 2014, 5: 194

    Google Scholar 

  15. Yates L R, Campbell P J. Evolution of the cancer genome. Nature Reviews Genetics, 2012, 13(11): 795–806

    Article  Google Scholar 

  16. Sun Y J, Yao J, Nowak N J, Goodison S. Cancer progression modeling using static sample data. Genome Biology, 2014, 15: 440

    Article  Google Scholar 

  17. Wang J G, Khiabanian H, Rossi D, Fabbri G, Gattei V, Forconi F, Laurenti L, Marasca R, Poeta G D, Foa R, Pasqualucci L, Gaidano G, Rabadan R. Tumor evolutionary directed graphs and the history of chronic lymphocytic leukemia. Elife, 2014, 3: e02869

    Google Scholar 

  18. Nik-Zainal S, Van Loo P, Wedge D C, Alexandrov L B, Greenman C D, Lau K W, Raine K, Jones D, Marshall J, Ramakrishna M, Shlien A, Cooke S L, Hinton J, Menzies A, Stebbings L A, Leroy C, Jia M, Rance R, Mudie L J, Gamble S J, Stephens P J, McLaren S, Tarpey P S, Papaemmanuil E, Davies H R, Varela I, McBride D J, Bignell G R, Leung K, Butler A P, Teague J W, Martin S, Jönsson G, Mariani O, Boyault S, Miron P, Fatima A, Langerød A, Aparicio S A, Tutt A, Sieuwerts A M, Borg A, Thomas G, Salomon A V, Richardson A L, Børresen-Dale A L, Futreal P A, Stratton M R, Campbell P J, Breast Cancer Working Group of the International Cancer Genome Consortium. The life history of 21 breast cancers. Cell, 2012, 149(5): 994–1007

    Article  Google Scholar 

  19. Liu Z Q, Zhang X S, Zhang S H. Breast tumor subgroups reveal diverse clinical predictive power. Scientific Reports, 2014, 4: 4002

    Article  Google Scholar 

  20. Hofree M, Shen J P, Carter H, Gross A, Ideker T. Network-based stratification of tumor mutations. Nature Methods, 2013, 10(11): 1108–1115

    Article  Google Scholar 

  21. Lu J, Getz G, Miska E A, Alvarez-Saavedra E, Lamb J, Peck D, Sweet-Cordero A, Ebert B L, Mark R H, Ferrando A A, Downing J R, Jacks T, Horvitz H R, Golub T R. Micro RNA expression profiles classify human cancers. Nature, 2005, 435(7043): 834–838

    Article  Google Scholar 

  22. Reis-Filho J S, Pusztai L. Gene expression profiling in breast cancer: classification, prognostication, and prediction. The Lancet, 2011, 378(9805): 1812–1823

    Article  Google Scholar 

  23. Kramer R, Cohen D. Functional genomics to new drug targets. Nature Reviews Drug Discovery, 2004, 3(11): 965–972

    Article  Google Scholar 

  24. Lamb J, Crawford E D, Peck D, Modell J W, Blat I C, Wrobel M J, Lerner J, Brunet J P, Subramanian A, Ross K N, Reich M, Hieronymus H, Wei G, Armstrong S A, Haggarty S J, Clemons P A, Wei R, Carr S A, Lander E S, Golub T R. The Connectivity Map: using geneexpression signatures to connect small molecules, genes, and disease. Science, 2006, 313(5795): 1929–1935

    Article  Google Scholar 

  25. Bansal M, Yang J, Karan C, Menden MP, Costello J C, Tang H, Xiao G, Li Y, Allen J, Zhong R, Chen B, Kim M, Wang T, Heiser L M, Realubit R, Mattioli M, Alvarez M J, Shen Y, NCI-DREAM Community, Gallahan D, Singer D, Saez-Rodriguez J, Xie Y, Stolovitzky G, Califano A, NCI-DREAM Community. A community computational challenge to predict the activity of pairs of compounds. Nature Biotechnology, 2014, 32(12): 1213–1222

    Article  Google Scholar 

  26. Ciriello G, Miller M L, Aksoy B A, Senbabaoglu Y, Schultz N, Sander C. Emerging landscape of oncogenic signatures across human cancers. Nature Genetics, 2013, 45(10): 1127–1133

    Article  Google Scholar 

  27. Kandoth C, McLellan M D, Vandin F, Ye K, Niu B F, Lu C, Xie M C, Zhang Q Y, McMichael J F, Wyczalkowski M A, Leiserson M D, Miller C A, Welch J S, Walter M J, Wendl M C, Ley T J, Wilson R K, Raphael B J, Ding L. Mutational landscape and significance across 12 major cancer types. Nature, 2013, 502(7471): 333–339

    Article  Google Scholar 

  28. Lawrence M S, Stojanov P, Mermel C H, Robinson J T, Garraway L A, Golub T R, Meyerson M, Gabriel S B, Lander E S, Getz G. Discovery and saturation analysis of cancer genes across 21 tumour types. Nature, 2014, 505(7484): 495–501

    Article  Google Scholar 

  29. Zack T I, Schumacher S E, Carter S L, Cherniack A D, Saksena G, Tabak B, Lawrence M S, Zhsng C Z, Wala J, Mermel C H, Sougnez C, Gabriel S B, Hernandez B, Shen H, Laird P W, Getz G, Meyerson M, Beroukhim R. Pan-cancer patterns of somatic copy number alteration. Nature Genetics, 2013, 45(10): 1134–1140

    Article  Google Scholar 

  30. Ding L, Getz G, Wheeler D A, Mardis E R, McLellan M D, Cibulskis K, Sougnez C, Greulich H, Muzny D M, Morgan M B, Fulton L, Fulton R S, Zhang Q Y, Wendl M C, Lawrence M S, Larson D E, Chen K, Dooling D J, Sabo A, Hawes A C, Shen H, Jhangiani S N, Lewis L R, Hall O, Zhu Y M, Mathew T, Ren Y, Yao J Q, Scherer S E, Clerc K, Metcalf G A, Ng B, Milosavljevic A, Gonzalez-Garay M L, Osborne J R, Meyer R, Shi X Q, Tang Y Z, Koboldt D C, Lin L, Abbott R, Miner T L, Pohl C, Fewell G, Haipek C, Schmidt H, Dunford-Shore B H, Kraja A, Crosby S D, Sawyer C S, Vickery T, Sander S, Robinson J, Winckler W, Baldwin J, Chirieac L R, Dutt A, Fennell T, Hanna M, Johnson B E, Onofrio R C, Thomas R K, Tonon G, Weir B A, Zhao X J, Ziaugra L, Zody M C, Giordano T, Orringer M B, Roth J A, Spitz M R, Wistuba I I, Ozenberger B, Good P J, Chang A C, Beer D G, Watson M A, Ladanyi M, Broderick S, Yoshizawa A, Travis W D, Pao W, Province M A, Weinstock G M, Varmus H E, Gabriel S B, Lander E S, Gibbs R A, Meyerson M, Wilson R K. Somatic mutations affect key pathways in lung adenocarcinoma. Nature, 2008, 455(7216): 1069–1075

    Article  Google Scholar 

  31. Sjöblom T, Jones S, Wood L D, Parsons D W, Lin J, Barber T D, Mandelker D, Leary RJ, Ptak J, Silliman N, Szabo S, Buckhaults P, Farrell C, Meeh P, Markowitz S D, Willis J, Dawson D, Willson J K, Gazdar A F, Hartigan J, Wu L, Liu C S, Parmigiani G, Park B H, Bachman K E, Papadopoulos N, Vogelstein B, Kinzler K W, Velculescu V E. The consensus coding sequences of human breast and colorectal cancers. Science, 2006, 314(5797): 268–274

    Article  Google Scholar 

  32. Stamatoyannopoulos J A, Adzhubei I, Thurman R E, Kryukov G V, Mirkin S M, Sunyaev S R. Human mutation rate associated with DNA replication timing. Nature Genetics, 2009, 41(4): 393–395

    Article  Google Scholar 

  33. Chen C L, Rappailles A, Duquenne L, Huvet M, Guilbaud G, Farinelli L, Audit B, d’Aubenton-Carafa Y, Arneodo A, Hyrien O, Thermes C. Impact of replication timing on non-CpG and CpG substitution rates in mammalian genomes. Genome Research, 2010, 20(4): 447–457

    Article  Google Scholar 

  34. Dees N D, Zhang Q Y, Kandoth C, Wendl M C, Schierding W, Koboldt D C, Mooney T B, Callaway M B, Dooling D, Mardis E R, Wilson R K, Ding L. MuSiC: identifying mutational significance in cancer genomes. Genome Research, 2012, 22(8): 1589–1598

    Article  Google Scholar 

  35. Lawrence M S, Stojanov P, Polak P, Kryukov G V, Cibulskis K, Sivachenko A, Carter S L, Stewart C, Mermel C H, Roberts S A, Kiezun A, Hammerman P S, McKenna A, Drier Y, Zou L, Ramos A H, Pugh T J, Stransky N, Helman E, Kim J, Sougnez C, Ambrogio L, Nickerson E, Shefler E, Cortés M L, Auclair D, Saksena G, Voet D, Noble M, DiCara D, Lin P, Lichtenstein L, Heiman D I, Fennell T, Imielinski M, Hernandez B, Hodis E, Baca S, Dulak A M, Lohr J, Landau D A, Wu C J, Melendez-Zajgla J, Hidalgo-Miranda A, Koren A, McCarroll S A, Mora J, Lee R S, Crompton B, Onofrio R, Parkin M, Winckler W, Ardlie K, Gabriel S B, Roberts CW, Biegel J A, Stegmaier K, Bass A J, Garraway L A, Meyerson M, Golub T R, Gordenin D A, Sunyaev S, Lander E S, Getz G. Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature, 2013, 499(7457): 214–218

    Article  Google Scholar 

  36. Youn A, Simon R. Identifying cancer driver genes in tumor genome sequencing studies. Bioinformatics, 2011, 27(2): 175–181

    Article  Google Scholar 

  37. Tamborero D, Gonzalez-Perez A, Lopez-Bigas N. Oncodriveclust: exploiting the positional clustering of somatic mutations to identify cancer genes. Bioinformatics, 2013, 29(18): 2238–2244

    Article  Google Scholar 

  38. Korthauer K D, Kendziorski C. MADGiC: a model-based approach for identifying driver genes in cancer. Bioinformatics, 2015, 31(10): 1526–1535

    Article  Google Scholar 

  39. Wu G M, Feng X, Stein L. A human functional protein interaction network and its application to cancer data analysis. Genome Biology, 2010, 11(5): R53

    Article  Google Scholar 

  40. Vandin F, Upfal E, Raphael B J. Algorithms for detecting significantly mutated pathways in cancer. Journal of Computational Biology, 2011, 18(3): 507–522

    Article  MathSciNet  Google Scholar 

  41. Leiserson M D M, Vandin F, Wu H T, Dobson J R, Eldridge J V, Thomas J L, Papoutsaki A, Kim Y, Niu B F, McLellan M, Lawrence M S, Gonzalez-Perez A, Tamborero D, Cheng Y W, Ryslik G A, Lopez-Bigas N, Getz G, Ding L, Raphael B J. Pan-cancer network analysis identifies combinations of rare somatic mutations across pathways and protein complexes. Nature Genetics, 2015, 47(2): 106–114

    Article  Google Scholar 

  42. Cerami E, Demir E, Schultz N, Taylor B S, Sander C. Automated network analysis identifies core pathways in glioblastoma. Plos One, 2010, 5(2): e8918

    Article  Google Scholar 

  43. Yeang C H, McCormick F, Levine A. Combinatorial patterns of somatic gene mutations in cancer. The FASEB Journal, 2008, 22(8): 2605–2622

    Article  Google Scholar 

  44. Vandin F, Upfal E, Raphael B J. De novo discovery of mutated driver pathways in cancer. Genome Research, 2012, 22(2): 375–385

    Article  Google Scholar 

  45. Zhao J F, Zhang S H, Wu L Y, Zhang X S. Efficient methods for identifying mutated driver pathways in cancer. Bioinformatics, 2012, 28(22): 2940–2947

    Article  Google Scholar 

  46. Zhang J F, Zhang S H, Wang Y, Zhang X S. Identification of mutated core cancer modules by integrating somatic mutation, copy number variation, and gene expression data. BMC Systems Biology, 2013, 7(Suppl 2): S4

    Article  Google Scholar 

  47. Zhang J H, Wu L Y, Zhang X S, Zhang S H. Discovery of co-occurring driver pathways in cancer. BMC Bioinformatics, 2014, 15: 271

    Article  Google Scholar 

  48. Leiserson M D, Blokh D, Sharan R, Raphael B J. Simultaneous identification of multiple driver pathways in cancer. Plos Computational Biology, 2013, 9(5): e1003054

    Article  Google Scholar 

  49. Anderson K, Lutz C, van Delft F W, Bateman C M, Guo Y, Colman S M, Kempski H, Moorman A V, Titley I, Swansbury J, Kearney L, Enver T, Greaves M. Genetic variegation of clonal architecture and propagating cells in leukaemia. Nature, 2011, 469(7330): 356–361

    Article  Google Scholar 

  50. Campbell P J, Yachida S, Mudie L J, Stephens P J, Pleasance E D, Stebbings L A, Morsberger L A, Latimer C, McLaren S, Lin M L, McBride D J, Varela I, Nik-Zainal S A, Leroy C, Jia M, Menzies A, Butler A P, Teague J W, Griffin C A, Burton J, Swerdlow H, Quail M A, Stratton M R, Iacobuzio-Donahue C, Futreal P A. The patterns and dynamics of genomic instability in metastatic pancreatic cancer. Nature, 2010, 467(7319): 1109–1113

    Article  Google Scholar 

  51. Walter M J, Shen D, Ding L, Shao J, Koboldt D C, Chen K, Larson D E, McLellan MD, Dooling D, Abbott R, Fulton R, Magrini V, Schmidt H, Kalicki-Veizer J, O’Laughlin M, Fan X, Grillot M, Witowski S, Heath S, Frater J L, Eades W, Tomasson M, Westervelt P, DiPersio J F, Link D C, Mardis E R, Ley T J, Wilson R K, Graubert T A. Clonal architecture of secondary acute myeloid leukemia. The New England Journal of Medicine, 2012, 366(12): 1090–1098

    Article  Google Scholar 

  52. Wu X C, Northcott P A, Dubuc A, Dupuy A J, Shih D J, Witt H, Croul S, Bouffet E, Fults D W, Eberhart C G, Garzia L, Van Meter T, Zagzag D, Jabado N, Schwartzentruber J, Majewski J, Scheetz T E, Pfister SM, Korshunov A, Li X N, Scherer SW, Cho Y J, Akagi K, MacDonald T J, Koster J, McCabe M G, Sarver A L, Collins V P, Weiss W A, Largaespada D A, Collier L S, Taylor M D. Clonal selection drives genetic divergence of metastatic medulloblastoma. Nature, 2012, 482(7386): 529–533

    Article  Google Scholar 

  53. Qiao Y, Quinlan A R, Jazaeri A A, Verhaak R G, Wheeler D A, Marth G T. Subclone Seeker: a computational framework for reconstructing tumor clone structure for cancer variant interpretation and prioritization. Genome Biology, 2014, 15(8): 443

    Article  Google Scholar 

  54. Roth A, Khattra J, Yap D, Wan A, Laks E, Biele J, Ha G, Aparicio S, Bouchard-Côté A, Shah S P. PyClone: statistical inference of clonal population structure in cancer. Nature Methods, 2014, 11(4): 396–398

    Article  Google Scholar 

  55. Xia H, Liu Y N, Wang M H, Li A. Identification of genomic aberrations in cancer subclones from heterogeneous tumor samples. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2015, 12(3): 679–685

    Article  Google Scholar 

  56. Fischer A, Vázquez-García I, Illingworth C J, Mustonen V. Highdefinition reconstruction of clonal composition in cancer. Cell Reports, 2014, 7(5): 1740–1752

    Article  Google Scholar 

  57. Lee J, Mueller P, Sengupta S, Gulukota K, Ji Y. Bayesian inference for tumor subclones accounting for sequencing and structural variant. 2014, arXiv:1409.7158

    Google Scholar 

  58. Navin N, Kendall J, Troge J, Andrews P, Rodgers L, McIndoo J, Cook K, Stepansky A, Levy D, Esposito D, Muthuswamy L, Krasnitz A, McCombie W R, Hicks J, Wigler M. Tumour evolution inferred by single-cell sequencing. Nature, 2011, 472(7341): 90–94

    Article  Google Scholar 

  59. Hou Y, Song L T, Zhu P, Zhang B, Tao Y, Xu X, Li F Q, Wu K, Liang J, Shao D, Wu H J, Ye X F, Ye C, Wu R H, Jian M, Chen Y, Xie W, Zhang R R, Chen L, Liu X, Yao X T, Zheng H C, Yu C, Li Q B, Gong Z L, Mao M, Yang X, Yang L, Li J X, Wang W, Lu Z H, Gu N, Laurie G, Bolund L, Kristiansen K, Wang J, Yang H M, Li Y R, Zhang X Q, Wang J. Single-cell exome sequencing and monoclonal evolution of a JAK2-negative myeloproliferative neoplasm. Cell, 2012, 148(5): 873–885

    Article  Google Scholar 

  60. Xu X, Hou Y, Yin X Y, Bao L, Tang A F, Song L T, Li F Q, Tsang S, Wu K, Wu H J, He W M, Zeng L, Xing M J, Wu R H, Jiang H, Liu X, Cao D D, Guo G W, Hu X D, Gui Y T, Li Z, Xie W Y, Sun X J, Shi M, Cai Z M, Wang B, Zhong M M, Li J X, Lu Z H, Gu N, Zhang X Q, Goodman L, Bolund L, Wang J, Yang H M, Kristiansen K, Dean M, Li Y R, Wang J. Single-cell exome sequencing reveals single-nucleotide mutation characteristics of a kidney tumor. Cell, 2012, 148(5): 886–895

    Article  Google Scholar 

  61. Moore M J. From birth to death: the complex lives of eukaryotic mRNAs. Science, 2005, 309(5740): 1514–1518

    Article  Google Scholar 

  62. Chuang H, Hofree M, Ideker T. A decade of systems biology. Annual Reviews Cell and Developmental Biology, 2010, 26: 721–744

    Article  Google Scholar 

  63. Orphanides G, Reinberg D. A unified theory of gene expression. Cell, 2002, 108(4): 439–451

    Article  Google Scholar 

  64. Jaenisch R, Bird A. Epigenetic regulation of gene expression: how the genome integrates intrinsic and environmental signals. Nature Genetics, 2003, 33: 245–254

    Article  Google Scholar 

  65. Zhang W, Zhu J, Schadt E E, Liu J S. A bayesian partition method for detecting pleiotropic and epistatic eQTL modules. Plos Computational Biology, 2010, 6(1): e1000642

    Article  MathSciNet  Google Scholar 

  66. Mankoo P K, Shen R, Schultz N, Levine D A, Sander C. Time to recurrence and survival in serous ovarian tumors predicted from integrated genomic profiles. Plos One, 2011, 6(11): e24709

    Article  Google Scholar 

  67. Kutalik Z, Beckmann J S, Bergmann S. A modular approach for integrative analysis of large-scale gene-expression and drug-response data. Nature Biotechnology, 2008, 26(5): 531–539

    Article  Google Scholar 

  68. Chen J Y, Zhang S H. Integrative analysis for identifying joint modular patterns of gene-expression and drug-response data. Bioinformatics, 2016, 32(11): 1724–1732

    Article  Google Scholar 

  69. Witten D M, Tibshirani R J. Extensions of sparse canonical correlation analysis with applications to genomic data. Statistical Applications in Genetics and Molecular Biology, 2009, 8(1): 1–27

    Article  MathSciNet  MATH  Google Scholar 

  70. Chen K, Chan K S, Stenseth N C. Reduced rank stochastic regression with a sparse singular value decomposition. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 2012, 74(2): 203–221

    Article  MathSciNet  Google Scholar 

  71. Ma X, Xiao L, Wong W H. Learning regulatory programs by threshold SVD regression. Proceedings of the National Academy of Sciences of the United States of America, 2014, 111(44): 15675–15680

    Article  Google Scholar 

  72. Zhang S H, Liu C C, Li W Y, Shen H, Laird P W, Zhou X J. Discovery of multi-dimensional modules by integrative analysis of cancer genomic data. Nucleic Acids Research, 2012, 40(19): 9379–9391

    Article  Google Scholar 

  73. Zhang S H, Li Q J, Liu J, Zhou X J. A novel computational framework for simultaneous integration of multiple types of genomic data to identify microRNA-gene regulatory modules. Bioinformatics, 2011, 27(13): 401–409

    Article  Google Scholar 

  74. Zitnik M, Zupan B. Data fusion by matrix factorization. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2015, 37(1): 41–53

    Article  Google Scholar 

  75. Li W Y, Zhang S H, Liu C C, Zhou X J. Identifying multi-layer gene regulatory modules from multi-dimensional genomic data. Bioinformatics, 2012, 28(19): 2458–2466

    Article  Google Scholar 

  76. Konstantinopoulos P A, Spentzos D, Cannistra S A. Gene-expression profiling in epithelial ovarian cancer. Nature Clinical Practice Oncology, 2008, 5(10): 577–587

    Article  Google Scholar 

  77. Carey L A, Perou C M, Livasy C A, Dressler L G, Cowan D, Conway K, Karaca G, Troester M A, Tse C K, Edmiston S, Deming S L, Geradts J, Cheang M C, Nielsen T O, Moorman P G, Earp H S, Millikan R C. Race, breast cancer subtypes, and survival in the carolina breast cancer study. The Journal of the American Medical Association, 2006, 295(21): 2492–2502

    Article  Google Scholar 

  78. Konstantinopoulos P A, Spentzos D, Karlan B Y, Taniguchi T, Fountzilas E, Francoeur N, Levine D A, Cannistra S A. Gene expression profile of BRCAness that correlates with responsiveness to chemotherapy and with outcome in patients with epithelial ovarian cancer. Journal of Clinical Oncology, 2010, 28(22): 3555–3561

    Article  Google Scholar 

  79. Verhaak R G, Hoadley K A, Purdom E, Wang V, Qi Y, Wilkerson M D, Miller C R, Ding L, Golub T, Mesirov J P, Alexe G, Lawrence M, O’Kelly M, Tamayo P, Weir B A, Gabriel S, Winckler W, Gupta S, Jakkula L, Feiler H S, Hodgson J G, James C D, Sarkaria J N, Brennan C, Kahn A, Spellman P T, Wilson R K, Speed T P, Gray J W, Meyerson M, Getz G, Perou C M, Hayes D N, Cancer Genome Atlas Research Network. Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1. Cancer Cell, 2010, 17(1): 98–110

    Article  Google Scholar 

  80. Liu Z Q, Zhang S H. Tumor characterization and stratification by integrated molecular profiles reveals essential pan-cancer features. BMC Genomics, 2015, 16: 503

    Article  Google Scholar 

  81. Curtis C, Shah S P, Chin S F, Turashvili G, Rueda O M, Dunning M J, Speed D, Lynch A G, Samarajiwa S, Yuan Y, Gräf S, Ha G, Haffari G, Bashashati A, Russell R, McKinney S; METABRIC Group, Langerød A, Green A, Provenzano E, Wishart G, Pinder S, Watson P, Markowetz F, Murphy L, Ellis I, Purushotham A, Børresen-Dale A L, Brenton J D, Tavaré S, Caldas C, Aparicio S. The genomic and transcriptomic architecture of 2, 000 breast tumours reveals novel subgroups. Nature, 2012, 486(7403): 346–352

    Google Scholar 

  82. Parker J S, Mullins M, Cheang M C, Leung S, Voduc D, Vickery T, Davies S, Fauron C, He X, Hu Z, Quackenbush J F, Stijleman I J, Palazzo J, Marron J S, Nobel A B, Mardis E, Nielsen T O, Ellis M J, Perou C M, Bernard P S. Supervised risk predictor of breast cancer based on intrinsic subtypes. Journal of Clinical Oncology, 2009, 27(8): 1160–1167

    Article  Google Scholar 

  83. Shoemaker R H. The NCI60 human tumor cell line screen. Nature Reviews Cancer, 2006, 6: 813–823

    Article  Google Scholar 

  84. Eduati F, Mangravite L M, Wang T, Tang H, Bare J C, Huang R, Norman T, Kellen M, Menden M P, Yang J C, Zhan XW, Zhong R, Xiao G H, Xia M H, Abdo N, Kosyk O, NIEHS-NCATS-UNC DREAM Toxicogenetics Collaboration, Friend S, Dearry A, Simeonov A, Tice R R, Rusyn I, Wright F A, Stolovitzky G, Xie Y, Saez-Rodriguez J. Prediction of human population responses to toxic compounds by a collaborative competition. Nature Biotechnology, 2015, 33(9): 933–940

    Article  Google Scholar 

  85. Zhao J, Zhang X S, Zhang S H. Predicting cooperative drug effects through the quantitative cellular profiling of response to individual drugs. CPT: Pharmacometrics & Systems Pharmacology, 2014, 3(2): 1–7

    Google Scholar 

  86. The Cancer Genome Atlas Research Network, Weinstein J N, Collisson E A, Mills G B, Shaw K R, Ozenberger B A, Ellrott K, Shmulevich I, Sander C, Stuart J M. The cancer genome atlas pan-cancer analysis project. Nature Genetics, 2013, 45(10): 1113–1120

    Article  Google Scholar 

  87. Reimand J, Wagih O, Bader G D. The mutational landscape of phosphorylation signaling in cancer. Scientific Reports, 2013, 3: 2651

    Article  Google Scholar 

  88. Witte T, Plass C, Gerhauser C. Pan-cancer patterns of DNA methylation. Genome Medicine, 2014, 6(8): 66

    Article  Google Scholar 

  89. Gevaert O, Tibshirani R, Plevritis S K. Pancancer analysis of DNA methylation-driven genes using Methyl Mix. Genome Biology, 2015, 16: 17

    Article  Google Scholar 

  90. Yang X F, Shao X J, Gao L, Zhang S H. Systematic DNA methylation analysis of multiple cell lines reveals common and specific patterns within and across tissues of origin. Human Molecular Genetics, 2015, 24(15): 4374–4384

    Article  Google Scholar 

  91. Yang X F, Shao X J, Gao L, Zhang S H. Comparative pan-cancer DNA methylation analysis reveals cancer common and specific patterns. Briefings in Bioinformatics, 2016, doi:10.1093/bib/bbw063

    Google Scholar 

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant Nos. 61379092, 61422309, 61621003 and 11131009), the Strategic Priority Research Program of the Chinese Academy of Sciences (CAS) (XDB13040600), the Outstanding Young Scientist Program of CAS, Key Research Program of Frontier Sciences, CAS (QYZDB-SSW-SYS008), and the Key Laboratory of Random Complex Structures and Data Science, CAS (2008DP173182).

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Correspondence to Shihua Zhang.

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Jinyu Chen is a PhD student at Academy of Mathematics and Systems Science, Chinese Academy of Sciences, China. Her research interests are mainly in bioinformatics, cancer genomics, pattern recognition and data mining.

Shihua Zhang received the PhD degree in applied mathematics and bioinformatics from the Academy of Mathematics and Systems Science, Chinese Academy of Sciences, China in 2008 with the highest honor. He has been in the same institute and worked as an assistant professor since 2008. His research interests are mainly in pattern recognition and bioinformatics. He has won various honors including Outstanding Young Scientist Program of CAS (2014) and Youth Science and Technology Award of China (2013). He is the awardee of the NSFC Excellent Young Scholars Program in 2014. Now he serves as an Editorial Board Member of Scientific Reports, Current Bioinformatics and an Associate Editor of BMC Genomics, Frontiers in Bioinformatics and Computational Biology, respectively. He is also a member of the IEEE, ISCB and SIAM.

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Chen, J., Zhang, S. Integrative cancer genomics: models, algorithms and analysis. Front. Comput. Sci. 11, 392–406 (2017). https://doi.org/10.1007/s11704-016-5568-5

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