Integrative cancer genomics: models, algorithms and analysis

doi:10.1007/s11704-016-5568-5

Front. Comput. Sci.

2017, Vol. 11

Issue (3) : 392-406 https://doi.org/10.1007/s11704-016-5568-5

REVIEW ARTICLE

Integrative cancer genomics: models, algorithms and analysis

Jinyu CHEN, Shihua ZHANG(

)

National Center for Mathematics and Interdisciplinary Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China

Download: PDF(441 KB)
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

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.

Keywords cancer genomics model algorithm data integration bioinformatics computational biology

Corresponding Author(s): Shihua ZHANG

Just Accepted Date: 21 July 2016 Online First Date: 31 October 2016 Issue Date: 25 May 2017

Cite this article:

Jinyu CHEN,Shihua ZHANG. Integrative cancer genomics: models, algorithms and analysis[J]. Front. Comput. Sci., 2017, 11(3): 392-406.

URL:

https://academic.hep.com.cn/fcs/EN/10.1007/s11704-016-5568-5
https://academic.hep.com.cn/fcs/EN/Y2017/V11/I3/392

1	HanahanD, Weinberg R A. The hallmarks of cancer. Cell, 2000, 100(1): 57–70 https://doi.org/10.1016/S0092-8674(00)81683-9
2	HanahanD, Weinberg R A. Hallmarks of cancer: the next generation. Cell, 2011, 144(5): 646–674 https://doi.org/10.1016/j.cell.2011.02.013
46	ZhangJ F, ZhangS H, WangY, 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 https://doi.org/10.1186/1752-0509-7-S2-S4
47	ZhangJ H, WuL Y, ZhangX S, Zhang S H. Discovery of co-occurring driver pathways in cancer. BMC Bioinformatics, 2014, 15: 271 https://doi.org/10.1186/1471-2105-15-271
48	LeisersonM D, BlokhD, SharanR, Raphael B J. Simultaneous identification of multiple driver pathways in cancer. Plos Computational Biology, 2013, 9(5): e1003054 https://doi.org/10.1371/journal.pcbi.1003054
49	AndersonK, LutzC, van DelftF W , BatemanC M, GuoY, ColmanS M, Kempski H, MoormanA V , TitleyI, Swansbury J, KearneyL , EnverT, Greaves M. Genetic variegation of clonal architecture and propagating cells in leukaemia. Nature, 2011, 469(7330): 356–361 https://doi.org/10.1038/nature09650
3	The Cancer Genome Atlas Research Network. Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature, 2008, 455(7216): 1061–1068 https://doi.org/10.1038/nature07385
4	The Cancer Genome Atlas Research Network. Integrated genomic analyses of ovarian carcinoma. Nature, 2011, 474(7353): 609–615 https://doi.org/10.1038/nature10166
5	The International Cancer Genome Consortium. International network of cancer genome projects. Nature, 2010, 464(7291): 993–998 https://doi.org/10.1038/nature08987
6	BarretinaJ, Caponigro G, StranskyN , VenkatesanK, Margolin A A, KimS , WilsonC J, Lehár J, KryukovG V , SonkinD, ReddyA, LiuM, Murray L, BergerM F , MonahanJ E, MoraisP, MeltzerJ, Korejwa A, Jané-ValbuenaJ, MapaF A, Thibault J, Bric-FurlongE , RamanP, Shipway A, EngelsI H , ChengJ, YuG K, YuJ, AspesiP Jr, de SilvaM, Jagtap K, JonesM D , WangL, HattonC, PalescandoloE , GuptaS, MahanS, SougnezC, Onofrio R C, LiefeldT , MacConaillL, Winckler W, ReichM , LiN, Mesirov J P, GabrielS B , GetzG, ArdlieK, ChanV, Myer V E, WeberB L , PorterJ, Warmuth M, FinanP , HarrisJ L, Meyerson M, GolubT R , MorrisseyM P, Sellers W R, SchlegelR , GarrawayL A. The cancer cell line encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature, 2012, 483(7391): 603–607 https://doi.org/10.1038/nature11003
50	CampbellP J, Yachida S, MudieL J , StephensP J, Pleasance E D, StebbingsL A , MorsbergerL A, Latimer C, McLarenS , LinM L, McBride D J, VarelaI , Nik-ZainalS A, LeroyC, JiaM, Menzies A, ButlerA P , TeagueJ W, Griffin C A, BurtonJ , SwerdlowH, QuailM A, StrattonM R , Iacobuzio-DonahueC, Futreal P A. The patterns and dynamics of genomic instability in metastatic pancreatic cancer. Nature, 2010, 467(7319): 1109–1113 https://doi.org/10.1038/nature09460
51	WalterM J, ShenD, DingL, Shao J, KoboldtD C , ChenK, LarsonD E, McLellanM D , DoolingD, AbbottR, FultonR, Magrini V, SchmidtH , Kalicki-VeizerJ, O’Laughlin M, FanX , GrillotM, Witowski S, HeathS , FraterJ L, EadesW, TomassonM, Westervelt P, DiPersioJ F , LinkD C, MardisE R, LeyT J, Wilson R K, GraubertT A . Clonal architecture of secondary acute myeloid leukemia. The New England Journal of Medicine, 2012, 366(12): 1090–1098 https://doi.org/10.1056/NEJMoa1106968
7	GarnettM J, Edelman E J, HeidornS J , GreenmanC D, DasturA, LauK W, Greninger P, ThompsonI R , LuoX, SoaresJ, LiuQ, Iorio F, SurdezD , ChenL, MilanoR J, BignellG R, Tam A T, DaviesH , StevensonJ A, Barthorpe S, LutzS R , KogeraF, Lawrence K, McLaren-DouglasA , MitropoulosX, Mironenko T, ThiH , RichardsonL, ZhouW, JewittF, Zhang T, O’BrienP , BoisvertJ L, PriceS, HurW, Yang W, DengX , ButlerA, ChoiH G, ChangJ W, Baselga J, StamenkovicI , EngelmanJ A, SharmaS V, DelattreO, Saez-Rodriguez J, GrayN S , SettlemanJ, Futreal P A, HaberD A , StrattonM R, Ramaswamy S, McDermottU , BenesC H. Systematic identification of genomic markers of drug sensitivity in cancer cells. Nature, 2012, 483(7391): 570–575 https://doi.org/10.1038/nature11005
8	MullighanC, SuX, ZhangJ, Radtke I, PhillipsL A , MillerC B, MaJ, LiuW, Cheng C, SchulmanB A , HarveyR C, ChenI M, CliffordR J , CarrollW L, ReamanG, BowmanW P, Devidas M, GerhardD S , YangW, Relling M V, ShurtleffS A , CampanaD, Borowitz M J, PuiC H , SmithM, HungerS P, WillmanC 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 https://doi.org/10.1056/NEJMoa0808253
9	StrattonM R, Campbell P J, FutrealP A . The cancer genome. Nature, 2009, 458(7239): 719–724 https://doi.org/10.1038/nature07943
10	VazquezM, de la Torre V, ValenciaA . Chapter 14: Cancer genome analysis. Plos Computational Biology, 2012, 8(12): e1002824 https://doi.org/10.1371/journal.pcbi.1002824
11	VogelsteinB, Papadopoulos N, VelculescuV E , ZhouS B, DiazL A, KinzierK W. Cancer genome landscapes. Science, 2013, 339(6127): 1546–1558 https://doi.org/10.1126/science.1235122
12	WheelerD A, WangL H. From human genome to cancer genome: the first decade. Genome Research, 2013, 23(7): 1054–1062 https://doi.org/10.1101/gr.157602.113
13	ZhangJ H, ZhangS H. The discovery of mutated driver pathways in cancer: models and algorithms. 2016, arXiv:1604.01298
14	LiuZ Q, ZhangS H. Toward a systematic understanding of cancers: a survey of the pan-cancer study. Frontiers in Genetics, 2014, 5: 194 https://doi.org/10.3389/fgene.2014.00194
15	YatesL R, Campbell P J. Evolution of the cancer genome. Nature Reviews Genetics, 2012, 13(11): 795–806 https://doi.org/10.1038/nrg3317
16	SunY J, YaoJ, NowakN J, Goodison S. Cancer progression modeling using static sample data. Genome Biology, 2014, 15: 440 https://doi.org/10.1186/s13059-014-0440-0
17	WangJ G, Khiabanian H, RossiD , FabbriG, GatteiV, ForconiF, Laurenti L, MarascaR , PoetaG D, FoaR, PasqualucciL , GaidanoG, Rabadan R. Tumor evolutionary directed graphs and the history of chronic lymphocytic leukemia. Elife, 2014, 3: e02869 https://doi.org/10.7554/eLife.02869
18	Nik-ZainalS, Van Loo P, WedgeD C , AlexandrovL B, Greenman C D, LauK W , RaineK, JonesD, MarshallJ, Ramakrishna M, ShlienA , CookeS L, HintonJ, MenziesA, Stebbings L A, LeroyC , JiaM, RanceR, MudieL J, Gamble S J, StephensP J , McLarenS, TarpeyP S, PapaemmanuilE , DaviesH R, VarelaI, McBrideD J, Bignell G R, LeungK , ButlerA P, TeagueJ W, MartinS, Jönsson G, MarianiO , BoyaultS, MironP, FatimaA, Langerød A, AparicioS A , TuttA, Sieuwerts A M, BorgA , ThomasG, Salomon A V, RichardsonA L , Børresen-DaleA L , FutrealP A, Stratton M R, CampbellP J , Breast Cancer Working Group of the International Cancer Genome Consortium. The life history of 21 breast cancers. Cell, 2012, 149(5): 994–1007 https://doi.org/10.1016/j.cell.2012.04.023
19	LiuZ Q, ZhangX S, ZhangS H. Breast tumor subgroups reveal diverse clinical predictive power. Scientific Reports, 2014, 4: 4002
20	HofreeM, ShenJ P, CarterH, Gross A, IdekerT . Network-based stratification of tumor mutations. Nature Methods, 2013, 10(11): 1108–1115 https://doi.org/10.1038/nmeth.2651
21	LuJ, GetzG, MiskaE A, Alvarez-Saavedra E, LambJ , PeckD, Sweet- Cordero A, EbertB L , MarkR H, Ferrando A A, DowningJ R , JacksT, Horvitz H R, GolubT R . MicroRNA expression profiles classify human cancers. Nature, 2005, 435(7043): 834–838 https://doi.org/10.1038/nature03702
22	Reis-FilhoJ S, Pusztai L. Gene expression profiling in breast cancer: classification, prognostication, and prediction. The Lancet, 2011, 378(9805): 1812–1823 https://doi.org/10.1016/S0140-6736(11)61539-0
23	KramerR, CohenD. Functional genomics to new drug targets. Nature Reviews Drug Discovery, 2004, 3(11): 965–972 https://doi.org/10.1038/nrd1552
24	LambJ, Crawford E D, PeckD , ModellJ W, BlatI C, WrobelM J, Lerner J, BrunetJ P , SubramanianA, RossK N, ReichM, Hieronymus H, WeiG , ArmstrongS A, Haggarty S J, ClemonsP A , WeiR, CarrS A, LanderE S, Golub T R. The Connectivity Map: using geneexpression signatures to connect small molecules, genes, and disease. Science, 2006, 313(5795): 1929–1935 https://doi.org/10.1126/science.1132939
25	BansalM, YangJ, KaranC, Menden M P, CostelloJ C , TangH, XiaoG, LiY, AllenJ, ZhongR, Chen B, KimM , WangT, HeiserL M, RealubitR, Mattioli M, AlvarezM J , ShenY, NCI-DREAM Community, GallahanD , SingerD, Saez-Rodriguez J, XieY , StolovitzkyG, Califano A, NCI-DREAM Community. A community computational challenge to predict the activity of pairs of compounds. Nature Biotechnology, 2014, 32(12): 1213–1222 https://doi.org/10.1038/nbt.3052
26	CirielloG, MillerM L, AksoyB A, Senbabaoglu Y, SchultzN , SanderC. Emerging landscape of oncogenic signatures across human cancers. Nature Genetics, 2013, 45(10): 1127–1133 https://doi.org/10.1038/ng.2762
27	KandothC, McLellan M D, VandinF , YeK, NiuB F, LuC, XieM C, ZhangQ Y, McMichael J F, WyczalkowskiM A , LeisersonM D, MillerC A, WelchJ S, Walter M J, WendlM C , LeyT J, WilsonR K, RaphaelB J, Ding L. Mutational landscape and significance across 12 major cancer types. Nature, 2013, 502(7471): 333–339 https://doi.org/10.1038/nature12634
28	LawrenceM S, Stojanov P, MermelC H , RobinsonJ T, Garraway L A, GolubT R , MeyersonM, Gabriel S B, LanderE S , GetzG. Discovery and saturation analysis of cancer genes across 21 tumour types. Nature, 2014, 505(7484): 495–501 https://doi.org/10.1038/nature12912
29	ZackT I, Schumacher S E, CarterS L , CherniackA D, Saksena G, TabakB , LawrenceM S, ZhsngC Z, WalaJ, Mermel C H, SougnezC , GabrielS B, Hernandez B, ShenH , LairdP W, GetzG, MeyersonM, Beroukhim R. Pan-cancer patterns of somatic copy number alteration. Nature Genetics, 2013, 45(10): 1134–1140 https://doi.org/10.1038/ng.2760
30	DingL, GetzG, WheelerD A, Mardis E R, McLellanM D , CibulskisK, Sougnez C, GreulichH , MuznyD M, MorganM B, FultonL, Fulton R S, ZhangQ Y , WendlM C, Lawrence M S, LarsonD E , ChenK, Dooling D J, SaboA , HawesA C, ShenH, JhangianiS N , LewisL R, HallO, ZhuY M, Mathew T, RenY , YaoJ Q, Scherer S E, ClercK , MetcalfG A, NgB, MilosavljevicA , Gonzalez-GarayM L, Osborne J R, MeyerR , ShiX Q, TangY Z, KoboldtD C, Lin L, AbbottR , MinerT L, PohlC, FewellG, Haipek C, SchmidtH , Dunford-ShoreB H, Kraja A, CrosbyS D , SawyerC S, Vickery T, SanderS , RobinsonJ, Winckler W, BaldwinJ , ChirieacL R, DuttA, FennellT, Hanna M, JohnsonB E , OnofrioR C, ThomasR K, TononG, Weir B A, ZhaoX J , ZiaugraL, ZodyM C, GiordanoT, Orringer M B, RothJ A , SpitzM R, Wistuba I I, OzenbergerB , GoodP J, ChangA C, BeerD G, Watson M A, LadanyiM , BroderickS, Yoshizawa A, TravisW D , PaoW, Province M A, WeinstockG M , VarmusH E, Gabriel S B, LanderE S , GibbsR A, Meyerson M, WilsonR K . Somatic mutations affect key pathways in lung adenocarcinoma. Nature, 2008, 455(7216): 1069–1075 https://doi.org/10.1038/nature07423
31	SjöblomT, JonesS, WoodL D, Parsons D W, LinJ , BarberT D, Mandelker D, LearyRJ , PtakJ, Silliman N, SzaboS , BuckhaultsP, Farrell C, MeehP , MarkowitzS D, WillisJ, DawsonD, Willson J K, GazdarA F , HartiganJ, WuL, LiuC S, Parmigiani G, ParkB H , BachmanK E, Papadopoulos N, VogelsteinB , KinzlerK W, Velculescu V E. The consensus coding sequences of human breast and colorectal cancers. Science, 2006, 314(5797): 268–274 https://doi.org/10.1126/science.1133427
32	StamatoyannopoulosJ A, Adzhubei I, ThurmanR E , KryukovG V, MirkinS M, SunyaevS R. Human mutation rate associated with DNA replication timing. Nature Genetics, 2009, 41(4): 393–395 https://doi.org/10.1038/ng.363
33	ChenC L, Rappailles A, DuquenneL , HuvetM, Guilbaud G, FarinelliL , AuditB, d’Aubenton-Carafa Y, ArneodoA , HyrienO, Thermes C. Impact of replication timing on non-CpG and CpG substitution rates in mammalian genomes. Genome Research, 2010, 20(4): 447–457 https://doi.org/10.1101/gr.098947.109
34	DeesN D, ZhangQ Y, KandothC, Wendl M C, SchierdingW , KoboldtD C, MooneyT B, CallawayM B , DoolingD, MardisE R, WilsonR K, Ding L. MuSiC: identifying mutational significance in cancer genomes. Genome Research, 2012, 22(8): 1589–1598 https://doi.org/10.1101/gr.134635.111
35	LawrenceM S, Stojanov P, PolakP , KryukovG V, Cibulskis K, SivachenkoA , CarterS L, Stewart C, MermelC H , RobertsS A, KiezunA, HammermanP S , McKennaA, DrierY, ZouL, Ramos A H, PughT J , StranskyN, HelmanE, KimJ, Sougnez C, AmbrogioL , NickersonE, Shefler E, CortésM L , AuclairD, Saksena G, VoetD , NobleM, DiCaraD, LinP, Lichtenstein L, HeimanD I , FennellT, Imielinski M, HernandezB , HodisE, BacaS, DulakA M, Lohr J, LandauD A , WuC J, Melendez-Zajgla J, Hidalgo-MirandaA , KorenA, McCarroll S A, MoraJ , LeeR S, Crompton B, OnofrioR , ParkinM, Winckler W, ArdlieK , GabrielS B, Roberts CW, BiegelJ A , StegmaierK, BassA J, GarrawayL A , MeyersonM, GolubT R, GordeninD A , SunyaevS, LanderE S, GetzG. Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature, 2013, 499(7457): 214–218 https://doi.org/10.1038/nature12213
36	YounA, SimonR. Identifying cancer driver genes in tumor genome sequencing studies. Bioinformatics, 2011, 27(2): 175–181 https://doi.org/10.1093/bioinformatics/btq630
37	TamboreroD, Gonzalez-Perez A, Lopez-BigasN . Oncodriveclust: exploiting the positional clustering of somatic mutations to identify cancer genes. Bioinformatics, 2013, 29(18): 2238–2244 https://doi.org/10.1093/bioinformatics/btt395
38	KorthauerK D, Kendziorski C. MADGiC: a model-based approach for identifying driver genes in cancer. Bioinformatics, 2015, 31(10): 1526–1535 https://doi.org/10.1093/bioinformatics/btu858
39	WuG M, FengX, SteinL. A human functional protein interaction network and its application to cancer data analysis. Genome Biology, 2010, 11(5): R53 https://doi.org/10.1186/gb-2010-11-5-r53
40	VandinF, UpfalE, RaphaelB J. Algorithms for detecting significantly mutated pathways in cancer. Journal of Computational Biology, 2011, 18(3): 507–522 https://doi.org/10.1089/cmb.2010.0265
41	LeisersonM D M, VandinF, WuH T, Dobson J R, EldridgeJ V , ThomasJ L, Papoutsaki A, KimY , NiuB F, McLellan M, LawrenceM S , Gonzalez-PerezA, Tamborero D, ChengY W , RyslikG A, Lopez-Bigas N, GetzG , DingL, Raphael B J. Pan-cancer network analysisidentifies combinations of rare somatic mutations across pathways and protein complexes. Nature Genetics, 2015, 47(2): 106–114 https://doi.org/10.1038/ng.3168
42	CeramiE, DemirE, SchultzN, Taylor B S, SanderC . Automated network analysis identifies core pathways in glioblastoma. Plos One, 2010, 5(2): e8918 https://doi.org/10.1371/journal.pone.0008918
43	YeangC H, McCormick F, LevineA . Combinatorial patterns of somatic gene mutations in cancer. The FASEB Journal, 2008, 22(8): 2605–2622 https://doi.org/10.1096/fj.08-108985
44	VandinF, UpfalE, RaphaelB J. De novo discovery of mutated driver pathways in cancer. Genome Research, 2012, 22(2): 375–385 https://doi.org/10.1101/gr.120477.111
45	ZhaoJ F, ZhangS H, WuL Y, Zhang X S. Efficient methods for identifying mutated driver pathways in cancer. Bioinformatics, 2012, 28(22): 2940–2947 https://doi.org/10.1093/bioinformatics/bts564
52	WuX C, Northcott P A, DubucA , DupuyA J, ShihD J, WittH, Croul S, BouffetE , FultsD W, Eberhart C G, GarziaL , Van MeterT, ZagzagD, JabadoN, Schwartzentruber J, MajewskiJ , ScheetzT E, Pfister S M, KorshunovA , LiX N, Scherer SW, ChoY J , AkagiK, MacDonald T J, KosterJ , McCabeM G, SarverA L, CollinsV P, Weiss W A, LargaespadaD A , CollierL S, TaylorM D. Clonal selection drives genetic divergence of metastatic medulloblastoma. Nature, 2012, 482(7386): 529–533 https://doi.org/10.1038/nature10825
53	QiaoY, Quinlan A R, JazaeriA A , VerhaakR G, Wheeler D A, MarthG T . SubcloneSeeker: a computational framework for reconstructing tumor clone structure for cancer variant interpretation and prioritization. Genome Biology, 2014, 15(8): 443 https://doi.org/10.1186/s13059-014-0443-x
54	RothA, Khattra J, YapD , WanA, LaksE, BieleJ, Ha G, AparicioS , Bouchard-CôtéA , ShahS P. PyClone: statistical inference of clonal population structure in cancer. Nature Methods, 2014, 11(4): 396–398 https://doi.org/10.1038/nmeth.2883
55	XiaH, LiuY N, WangM 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 https://doi.org/10.1109/TCBB.2014.2366114
56	FischerA, Vázquez-García I, IllingworthC J , MustonenV. Highdefinition reconstruction of clonal composition in cancer. Cell Reports, 2014, 7(5): 1740–1752 https://doi.org/10.1016/j.celrep.2014.04.055
57	LeeJ, Mueller P, SenguptaS , GulukotaK, JiY. Bayesian inference for tumor subclones accounting for sequencing and structural variant. 2014, arXiv:1409.7158
58	NavinN, Kendall J, TrogeJ , AndrewsP, Rodgers L, McIndooJ , CookK, Stepansky A, LevyD , EspositoD, Muthuswamy L, KrasnitzA , McCombieW R, HicksJ, WiglerM. Tumour evolution inferred by single-cell sequencing. Nature, 2011, 472(7341): 90–94 https://doi.org/10.1038/nature09807
59	HouY, SongL T, ZhuP, Zhang B, TaoY , XuX, LiF Q, WuK, LiangJ, ShaoD, Wu H J, YeX F , YeC, WuR H, JianM, Chen Y, XieW , ZhangR R, ChenL, LiuX, Yao X T, ZhengH C , YuC, LiQ B, GongZ L, Mao M, YangX , YangL, LiJ X, WangW, Lu Z H, GuN , LaurieG, BolundL, KristiansenK , WangJ, YangH M, LiY R, Zhang X Q, WangJ . Single-cell exome sequencing and monoclonal evolution of a JAK2-negative myeloproliferative neoplasm. Cell, 2012, 148(5): 873–885 https://doi.org/10.1016/j.cell.2012.02.028
60	XuX, HouY, YinX Y, Bao L, TangA F , SongL T, LiF Q, TsangS, Wu K, WuH J , HeW M, ZengL, XingM J, Wu R H, JiangH , LiuX, CaoD D, GuoG W, Hu X D, GuiY T , LiZ, XieW Y, SunX J, Shi M, CaiZ M , WangB, ZhongM M, LiJ X, Lu Z H, GuN , ZhangX Q, Goodman L, BolundL , WangJ, YangH M, KristiansenK , DeanM, LiY R, WangJ. Single-cell exome sequencing reveals single-nucleotide mutation characteristics of a kidney tumor. Cell, 2012, 148(5): 886–895 https://doi.org/10.1016/j.cell.2012.02.025
61	MooreM J. From birth to death: the complex lives of eukaryotic mRNAs. Science, 2005, 309(5740): 1514–1518 https://doi.org/10.1126/science.1111443
62	ChuangH, HofreeM, IdekerT. A decade of systems biology. Annual Reviews Cell and Developmental Biology, 2010, 26: 721–744 https://doi.org/10.1146/annurev-cellbio-100109-104122
63	OrphanidesG, Reinberg D. A unified theory of gene expression. Cell, 2002, 108(4): 439–451 https://doi.org/10.1016/S0092-8674(02)00655-4
64	JaenischR, BirdA. Epigenetic regulation of gene expression: how the genome integrates intrinsic and environmental signals. Nature Genetics, 2003, 33: 245–254 https://doi.org/10.1038/ng1089
65	ZhangW, ZhuJ, SchadtE E, Liu J S. A bayesian partition method for detecting pleiotropic and epistatic eQTL modules. Plos Computational Biology, 2010, 6(1): e1000642 https://doi.org/10.1371/journal.pcbi.1000642
66	MankooP K, ShenR, SchultzN, Levine D A, SanderC . Time to recurrence and survival in serous ovarian tumors predicted from integrated genomic profiles. Plos One, 2011, 6(11): e24709 https://doi.org/10.1371/journal.pone.0024709
67	KutalikZ, Beckmann J S, BergmannS . A modular approach for integrative analysis of large-scale gene-expression and drug-response data. Nature Biotechnology, 2008, 26(5): 531–539 https://doi.org/10.1038/nbt1397
68	ChenJ Y, ZhangS H. Integrative analysis for identifying joint modular patterns of gene-expression and drug-response data. Bioinformatics, 2016, 32(11): 1724–1732 https://doi.org/10.1093/bioinformatics/btw059
69	WittenD 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 https://doi.org/10.2202/1544-6115.1470
70	ChenK, ChanK S, StensethN 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 https://doi.org/10.1111/j.1467-9868.2011.01002.x
71	MaX, XiaoL, WongW 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 https://doi.org/10.1073/pnas.1417808111
72	ZhangS H, LiuC C, LiW Y, Shen H, LairdP W , ZhouX J. Discovery of multi-dimensional modules by integrative analysis of cancer genomic data. Nucleic Acids Research, 2012, 40(19): 9379–9391 https://doi.org/10.1093/nar/gks725
73	ZhangS H, LiQ J, LiuJ, 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 https://doi.org/10.1093/bioinformatics/btr206
74	ZitnikM, ZupanB. Data fusion by matrix factorization. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2015, 37(1): 41–53 https://doi.org/10.1109/TPAMI.2014.2343973
75	LiW Y, ZhangS H, LiuC C, Zhou X J. Identifying multi-layer gene regulatory modules from multi-dimensional genomic data. Bioinformatics, 2012, 28(19): 2458–2466 https://doi.org/10.1093/bioinformatics/bts476
76	KonstantinopoulosP A, Spentzos D, CannistraS A . Gene-expression profiling in epithelial ovarian cancer. Nature Clinical Practice Oncology, 2008, 5(10): 577–587 https://doi.org/10.1038/ncponc1178
77	CareyL A, PerouC M, LivasyC A, Dressler L G, CowanD , ConwayK, KaracaG, TroesterM A , TseC K, Edmiston S, DemingS L , GeradtsJ, CheangM C, NielsenT O, Moorman P G, EarpH S , MillikanR 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 https://doi.org/10.1001/jama.295.21.2492
78	KonstantinopoulosP A, Spentzos D, KarlanB Y , TaniguchiT, Fountzilas E, FrancoeurN , LevineD 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 https://doi.org/10.1200/JCO.2009.27.5719
79	VerhaakR G, Hoadley K A, PurdomE , WangV, QiY, WilkersonM D , MillerC R, DingL, GolubT, Mesirov J P, AlexeG , LawrenceM, O’Kelly M, TamayoP , WeirB A, Gabriel S, WincklerW , GuptaS, Jakkula L, FeilerH S , HodgsonJ G, JamesC D, SarkariaJ N , BrennanC, KahnA, SpellmanP T , WilsonR K, SpeedT P, GrayJ W, Meyerson M, GetzG , PerouC M, HayesD 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 https://doi.org/10.1016/j.ccr.2009.12.020
80	LiuZ Q, ZhangS H. Tumor characterization and stratification by integrated molecular profiles reveals essential pan-cancer features. BMC Genomics, 2015, 16: 503 https://doi.org/10.1186/s12864-015-1687-x
81	CurtisC, ShahS P, ChinS F, Turashvili G, RuedaO M , DunningM J, SpeedD, LynchA G, Samarajiwa S, YuanY , GräfS, HaG, HaffariG, Bashashati A, RussellR , McKinneyS; METABRIC Group, LangerødA , GreenA, Provenzano E, WishartG , PinderS, WatsonP, MarkowetzF, Murphy L, EllisI , PurushothamA, Børresen-Dale A L, BrentonJ D , TavaréS, CaldasC, AparicioS. The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature, 2012, 486(7403): 346–352
82	ParkerJ S, Mullins M, CheangM C , LeungS, VoducD, VickeryT, Davies S, FauronC , HeX, HuZ, QuackenbushJ F , StijlemanI J, Palazzo J, MarronJ S , NobelA B, MardisE, NielsenT O, Ellis M J, PerouC M , BernardP S. Supervised risk predictor of breast cancer based on intrinsic subtypes. Journal of Clinical Oncology, 2009, 27(8): 1160–1167 https://doi.org/10.1200/JCO.2008.18.1370
83	ShoemakerR H. The NCI60 human tumor cell line screen. Nature Reviews Cancer, 2006, 6: 813–823 https://doi.org/10.1038/nrc1951
84	EduatiF, Mangravite L M, WangT , TangH, BareJ C, HuangR, Norman T, KellenM , MendenM P, YangJ C, ZhanX W, Zhong R, XiaoG H , XiaM H, AbdoN, KosykO, NIEHS-NCATS-UNC DREAM Toxicogenetics Collaboration, FriendS, DearryA, SimeonovA, Tice R R, RusynI , WrightF A, Stolovitzky G, XieY , Saez-RodriguezJ. Prediction of human population responses to toxic compounds by a collaborative competition. Nature Biotechnology, 2015, 33(9): 933–940 https://doi.org/10.1038/nbt.3299
85	ZhaoJ, ZhangX S, ZhangS H. Predicting cooperative drug effects through the quantitative cellular profiling of response to individual drugs. CPT: Pharmacometrics & Systems Pharmacology, 2014, 3(2): 1–7 https://doi.org/10.1038/psp.2013.79
86	The Cancer Genome Atlas Research Network, WeinsteinJ N , CollissonE A, MillsG B, ShawK R, Ozenberger B A, EllrottK , ShmulevichI, SanderC, StuartJ M. The cancer genome atlas pan-cancer analysis project. Nature Genetics, 2013, 45(10): 1113–1120
87	ReimandJ, WagihO, BaderG D.The mutational landscape of phosphorylation signaling in cancer. Scientific Reports, 2013, 3: 2651 https://doi.org/10.1038/srep02651
88	WitteT, PlassC, GerhauserC. Pan-cancer patterns of DNA methylation. Genome Medicine, 2014, 6(8): 66 https://doi.org/10.1186/s13073-014-0066-6
89	GevaertO, Tibshirani R, PlevritisS K . Pancancer analysis of DNA methylation-driven genes using MethylMix. Genome Biology, 2015, 16: 17 https://doi.org/10.1186/s13059-014-0579-8
90	YangX F, ShaoX J, GaoL, 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 https://doi.org/10.1093/hmg/ddv172
91	YangX F, ShaoX J, GaoL, Zhang S H. Comparative pan-cancer DNA methylation analysis reveals cancer common and specific patterns. Briefings in Bioinformatics, 2016, doi:10.1093/bib/bbw063 https://doi.org/10.1093/bib/bbw063

[1]

FCS-0392-15568-SHZ_suppl_1

Download

[1]	Tao HAN, Hailong SUN, Yangqiu SONG, Yili FANG, Xudong LIU. Find truth in the hands of the few: acquiring specific knowledge with crowdsourcing[J]. Front. Comput. Sci., 2021, 15(4): 154315-.
[2]	Yu HU, Tiezheng NIE, Derong SHEN, Yue KOU, Ge YU. An integrated pipeline model for biomedical entity alignment[J]. Front. Comput. Sci., 2021, 15(3): 153321-.
[3]	Jingke XU, Yidan ZHAO, Ge YU. An evaluation and query algorithm for the influence of spatial location based on RkNN[J]. Front. Comput. Sci., 2021, 15(2): 152604-.
[4]	Ruidong YAN, Yi LI, Deying LI, Weili WU, Yongcai WANG. SSDBA: the stretch shrink distance based algorithm for link prediction in social networks[J]. Front. Comput. Sci., 2021, 15(1): 151301-.
[5]	Wangli HAO, Ian Max ANDOLINA, Wei WANG, Zhaoxiang ZHANG. Biologically inspired visual computing: the state of the art[J]. Front. Comput. Sci., 2021, 15(1): 151304-.
[6]	Zhumin CHEN, Xueqi CHENG, Shoubin DONG, Zhicheng DOU, Jiafeng GUO, Xuanjing HUANG, Yanyan LAN, Chenliang LI, Ru LI, Tie-Yan LIU, Yiqun LIU, Jun MA, Bing QIN, Mingwen WANG, Jirong WEN, Jun XU, Min ZHANG, Peng ZHANG, Qi ZHANG. Information retrieval: a view from the Chinese IR community[J]. Front. Comput. Sci., 2021, 15(1): 151601-.
[7]	Qianchen YU, Zhiwen YU, Zhu WANG, Xiaofeng WANG, Yongzhi WANG. Estimating posterior inference quality of the relational infinite latent feature model for overlapping community detection[J]. Front. Comput. Sci., 2020, 14(6): 146323-.
[8]	Cheng WANG, Kyung Tae KIM, Hee Yong YOUN. PopFlow: a novel flow management scheme for SDN switch of multiple flow tables based on flow popularity[J]. Front. Comput. Sci., 2020, 14(6): 146505-.
[9]	Xiaochen LIU, Chunhe XIA, Tianbo WANG, Li ZHONG, Xiaojian LI. A behavior-aware SLA-based framework for guaranteeing the security conformance of cloud service[J]. Front. Comput. Sci., 2020, 14(6): 146808-.
[10]	Chengbo YANG, Long ZHENG, Chuangyi GUI, Hai JIN. Efficient FPGA-based graph processing with hybrid pull-push computational model[J]. Front. Comput. Sci., 2020, 14(4): 144102-.
[11]	Di MA, Songcan CHEN. Bayesian compressive principal component analysis[J]. Front. Comput. Sci., 2020, 14(4): 144303-.
[12]	Chune LI, Yongyi MAO, Richong ZHANG, Jinpeng HUAI. A revisit to MacKay algorithm and its application to deep network compression[J]. Front. Comput. Sci., 2020, 14(4): 144304-.
[13]	Yanwei ZHOU, Bo YANG. Practical continuous leakage-resilient CCA secure identity-based encryption[J]. Front. Comput. Sci., 2020, 14(4): 144804-.
[14]	Muhammad Aminur RAHAMAN, Mahmood JASIM, Md. Haider ALI, Md. HASANUZZAMAN. Bangla language modeling algorithm for automatic recognition of hand-sign-spelled Bangla sign language[J]. Front. Comput. Sci., 2020, 14(3): 143302-.
[15]	Ebuka IBEKE, Chenghua LIN, Adam WYNER, Mohamad Hardyman BARAWI. A unified latent variable model for contrastive opinion mining[J]. Front. Comput. Sci., 2020, 14(2): 404-416.

Viewed

Full text

Abstract

Cited

Shared

Discussed