A community effort to assess and improve drug sensitivity prediction algorithms

Nature Biotechnology

Volumn 32, Issue 12, 2014, Pages 1202-1212

  Costello, James C ^a,b,k   Heiser, Laura M ^c   Georgii, Elisabeth ^d   Gönen, Mehmet ^d   Menden, Michael P ^e   Wang, Nicholas J ^c   Bansal, Mukesh ^f   Ammad Ud Din, Muhammad ^d   Hintsanen, Petteri ^g   Khan, Suleiman A ^d   Mpindi, John Patrick ^g   Kallioniemi, Olli ^g   Honkela, Antti ^g   Aittokallio, Tero ^g   Wennerberg, Krister ^g   Collins, James J ^a,b,h   Gallahan, Dan ⁱ   Singer, Dinah ⁱ   Saez Rodriguez, Julio ^e   Kaski, Samuel ^d,g   Gray, Joe W ^c   Stolovitzky, Gustavo ^j   Abbuehl, Jean Paul ^l   Allen, Jeffrey ^m   Altman, Russ B ⁿ   Balcome, Shawn ^o   Battle, Alexis ^p   Bender, Andreas ^q   Berger, Bonnie ^r   Bernard, Jonathan ^l   Bhattacharjee, Madhuchhanda ^s,t   Bhuvaneshwar, Krithika ^u   Bieberich, Andrew A ^v   Boehm, Fred ^w,x   Califano, Andrea ^f   Chan, Christina ^y,z   Chen, Beibei ^m   Chen, Ting Huei ^aa   Choi, Jaejoon ^ab   Coelho, Luis Pedro ^ac   Cokelaer, Thomas ^e   Collins, James C ^a,b,h   Creighton, Chad J ^ad   Cui, Jike ^ae   Dampier, Will ^af   Davisson, V Jo ^v   De Baets, Bernard ^ag   Deshpande, Raamesh ^o   DiCamillo, Barbara ^ah   Dundar, Murat ^ai   Duren, Zhana ^aj   Ertel, Adam ^af   Fan, Haoyang ^w,x   Fang, Hongbin ^u   Gauba, Robinder ^u   Gottlieb, Assaf ⁿ   Grau, Michael ^ak   Gusev, Yuriy ^u   Ha, Min Jin ^aa   Han, Leng ^al   Harris, Michael ^u   Henderson, Nicholas ^w,x   Hejase, Hussein A ^y   Homicsko, Krisztian ^l   Hou, Jack P ^am   Hwang, Woochang ^ab   IJzerman, Adriaan P ^an   Karacali, Bilge ^ao   Keles, Sunduz ^w,x   Kendziorski, Christina ^w,x   Kim, Junho ^ab   Kim, Min ^m   Kim, Youngchul ^ap   Knowles, David A ^p   Koller, Daphne ^p   Lee, Junehawk ^ab,aq   Lee, Jae K ^ap   Lenselink, Eelke B ^an   Li, Biao ^ar   Li, Bin ^ae   Li, Jun ^al,as   Liang, Han ^ad,al   Ma, Jian ^am   Madhavan, Subha ^u,at   Mooney, Sean ^ar   Myers, Chad L ^o   Newton, Michael A ^w,x   Overington, John P ^au   Pal, Ranadip ^av   Peng, Jian ^r   Pestell, Richard ^af   Prill, Robert J ^aw   Qiu, Peng ^al   Rajwa, Bartek ^ax   Sadanandam, Anguraj ^l   Sambo, Francesco ^ah   Shin, Hyunjin ^ae   Song, Jiuzhou ^ay   Song, Lei ^u   Sridhar, Arvind ^l   Stock, Michiel ^ag   Sun, Wei ^aa   Ta, Tram ^w,x   Tadesse, Mahlet ^az   Tan, Ming ^u   Tang, Hao ^m   Theodorescu, Dan ^k   Toffolo, Gianna Maria ^ah   Tozeren, Aydin ^af   Trepicchio, William ^ae   Varoquaux, Nelle ^ba,bb,bc   Vert, Jean Philippe ^ba,bb,bc   Waegeman, Willem ^ag   Walter, Thomas ^ba,bb,bc   Wan, Qian ^av   Wang, Difei ^u,at   Wang, Wen ^o   Wang, Yong ^aj   Wang, Zhishi ^w,x   Wegner, Joerg K ^bd   Wu, Tongtong ^be   Xia, Tian ^o   Xiao, Guanghua ^m   Xie, Yang ^m   Xu, Yanxun ^al,bf   Yang, Jichen ^m   Yuan, Yuan ^ad,al   Zhang, Shihua ^aj   Zhang, Xiang Sun ^aj   Zhao, Junfei ^aj   Zuo, Chandler ^w,x   Van Vlijmen, Herman W T ^bd   Van Westen, Gerard J P ^au   more..

^a BOSTON UNIVERSITY   (United States)

^b Boston University   (United States)

^c Oregon Health and Science University Department of Biomedical Engineering   (United States)

^d AALTO UNIVERSITY   (Finland)

^e WELLCOME TRUST SANGER INSTITUTE   (United Kingdom)

^f Och Spine at New York Presbyterian Hospitals   (United States)

^g UNIVERSITY OF HELSINKI   (Finland)

^h HARVARD UNIVERSITY   (United States)

ⁱ NATIONAL CANCER INSTITUTE   (United States)

^j IBM T J WATSON RESEARCH CENTER   (United States)

^k UNIVERSITY OF COLORADO SCHOOL OF MEDICINE   (United States)

^l EPFL   (Switzerland)

^m UNIVERSITY OF TEXAS SOUTHWESTERN MEDICAL CENTER   (United States)

ⁿ STANFORD UNIVERSITY   (United States)

^o University of Minnesota   (United States)

^p Stanford University   (United States)

^q UNIVERSITY OF CAMBRIDGE   (United Kingdom)

^r MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^s UNIVERSITY OF PUNE   (India)

^t UNIVERSITY OF HYDERABAD   (India)

^u GEORGETOWN UNIVERSITY MEDICAL CENTER   (United States)

^v PURDUE UNIVERSITY   (United States)

^w University of Wisconsin Madison   (United States)

^x UNIVERSITY OF WISCONSIN SCHOOL OF MEDICINE AND PUBLIC HEALTH   (United States)

^y Michigan State University   (United States)

^z Michigan State University   (United States)

^aa IL   (United States)

^ab Korea Advanced Institute of Science and Technology (KAIST)   (South Korea)

^ac INSTITUTO DE MEDICINA MOLECULAR   (Portugal)

^ad BAYLOR COLLEGE OF MEDICINE   (United States)

^ae MILLENNIUM PHARMACEUTICALS INC   (United States)

^af DREXEL UNIVERSITY   (United States)

^ag GHENT UNIVERSITY   (Belgium)

^ah UNIVERSITY OF PADOVA   (Italy)

^ai IN 46202 USA   (United States)

^aj CHINESE ACADEMY OF SCIENCES   (China)

^ak PHILIPPS UNIVERSITY MARBURG   (Germany)

^al UNIVERSITY OF TEXAS MD ANDERSON CANCER CENTER   (United States)

^am University of Illinois at Urbana Champaign   (United States)

^an LEIDEN UNIVERSITY   (Netherlands)

^ao IZMIR INSTITUTE OF TECHNOLOGY   (Turkey)

^ap UNIVERSITY OF VIRGINIA   (United States)

^aq Korea Institute of Science and Technology Information (KISTI)   (South Korea)

^ar BUCK INSTITUTE FOR RESEARCH ON AGING   (United States)

^as MAX PLANCK INSTITUTE   (Germany)

^at GEORGETOWN UNIVERSITY   (United States)

^au EUROPEAN BIOINFORMATICS INSTITUTE   (United Kingdom)

^av Department of Electrical and Computer Engineering   (United States)

^aw IBM ALMADEN RESEARCH CENTER   (United States)

^ax PURDUE UNIVERSITY   (United States)

^ay UNIVERSITY OF MARYLAND   (United States)

^az GEORGETOWN UNIVERSITY   (United States)

^ba PSL RESEARCH UNIVERSITY   (France)

^bb INSTITUT CURIE   (France)

^bc INSERM U900   (France)

^bd JANSSEN RESEARCH FOUNDATION   (Belgium)

^be UNIVERSITY OF ROCHESTER MEDICAL CENTER   (United States)

^bf Rice University   (United States)

more..

Author keywords

[No Author keywords available]

Indexed keywords

CELL CULTURE; DISEASES; DISTRIBUTED COMPUTER SYSTEMS; GENE EXPRESSION; REVERSE ENGINEERING;

BIOLOGICAL PATHWAYS; ENGINEERING ASSESSMENTS; GENE EXPRESSION MICROARRAY; GENOMIC INFORMATION; HUMAN BREAST CANCER CELLS; NATIONAL CANCER INSTITUTE; NON-LINEAR RELATIONSHIPS; PROTEOMIC PROFILING;

FORECASTING;

ALGORITHM; ARTICLE; BREAST CANCER CELL LINE; CONTROLLED STUDY; DRUG SENSITIVITY; EPIGENETICS; GENE EXPRESSION; GENE EXPRESSION PROFILING; GENOMICS; HUMAN; HUMAN CELL; MICROARRAY ANALYSIS; NONLINEAR SYSTEM; PHARMACOGENOMICS; PREDICTIVE VALUE; PRIORITY JOURNAL; PROTEOMICS; DRUG EFFECTS; DRUG RESISTANCE; GENE EXPRESSION REGULATION; GENETICS; NEOPLASMS; PROCEDURES;

ANTINEOPLASTIC AGENT;

ALGORITHMS; ANTINEOPLASTIC AGENTS; DRUG RESISTANCE, NEOPLASM; EPIGENOMICS; GENE EXPRESSION PROFILING; GENE EXPRESSION REGULATION, NEOPLASTIC; GENOMICS; HUMANS; NEOPLASMS; PROTEOMICS;

EID: 84906549588     PISSN: 10870156     EISSN: 15461696     Source Type: Journal    
DOI: 10.1038/nbt.2877     Document Type: Article

References (49)

1. Barretina, J. et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483, 603-607 (2012).
2. Cancer Genome Atlas Network. Comprehensive molecular portraits of human breast tumours. Nature 490, 61-70 (2012).
3. Garnett, M.J. et al. Systematic identification of genomic markers of drug sensitivity in cancer cells. Nature 483, 570-575 (2012).
4. Heiser, L.M. et al. Subtype and pathway specific responses to anticancer compounds in breast cancer. Proc. Natl. Acad. Sci. USA 109, 2724-2729 (2012).
5. International Cancer Genome Consortium. et al. International network of cancer genome projects. Nature 464, 993-998 (2010).
6. Lamb, J. et al. The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease. Science 313, 1929-1935 (2006).
7. Yang, W. et al. Genomics of Drug Sensitivity in Cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells. Nucleic Acids Res. 41, D955-D961 (2013).
8. Shoemaker, R.H. The NCI60 human tumour cell line anticancer drug screen. Nat. Rev. Cancer 6, 813-823 (2006).
9. Wilson, T.R. et al. Widespread potential for growth-factor-driven resistance to anticancer kinase inhibitors. Nature 487, 505-509 (2012).
10. Curtis, C. et al. The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature 486, 346-352 (2012).
11. Reis-Filho, J.S. & Pusztai, L. Gene expression profiling in breast cancer: classification, prognostication, and prediction. Lancet 378, 1812-1823 (2011).
12. Sorlie, T. et al. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc. Natl. Acad. Sci. USA 98, 10869-10874 (2001).
13. van 't Veer, L.J. et al. Gene expression profiling predicts clinical outcome of breast cancer. Nature 415, 530-536 (2002).
14. Wu, J. et al. Identification and functional analysis of 9p24 amplified genes in human breast cancer. Oncogene 31, 333-341 (2012).
15. Howlader, N. et al. SEER Cancer Statistics Review, 1975-2010 (National Cancer Insitute, Bethesda, MD, 2013).
16. Stephens, P.J. et al. The landscape of cancer genes and mutational processes in breast cancer. Nature 486, 400-404 (2012).
17. Wood, L.D. et al. The genomic landscapes of human breast and colorectal cancers. Science 318, 1108-1113 (2007).
18. Kao, J. et al. Molecular profiling of breast cancer cell lines defines relevant tumor models and provides a resource for cancer gene discovery. PLoS ONE 4, e6146 (2009).
19. Neve, R.M. et al. A collection of breast cancer cell lines for the study of functionally distinct cancer subtypes. Cancer Cell 10, 515-527 (2006).
20. Daemen, A. et al. Modeling precision treatment in breast cancer. Genome Biol. 14, R110 (2013).
21. Bussey, K.J. et al. Integrating data on DNA copy number with gene expression levels and drug sensitivities in the NCI-60 cell line panel. Mol. Cancer Ther. 5, 853-867 (2006).
22. Masica, D.L. & Karchin, R. Collections of simultaneously altered genes as biomarkers of cancer cell drug response. Cancer Res. 73, 1699-1708 (2013).
23. Menden, M.P. et al. Machine learning prediction of cancer cell sensitivity to drugs based on genomic and chemical properties. PLoS ONE 8, e61318 (2013).
24. Harrell, F.E. Regression Modeling Strategies (Springer, New York, 2001).
25. Marbach, D. et al. Wisdom of crowds for robust gene network inference. Nat. Methods 9, 796-804 (2012).
26. Kanehisa, M. & Goto, S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28, 27-30 (2000).
27. Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. USA 102, 15545-15550 (2005).
28. Schölkopf, B. & Smola, A.J. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond (MIT Press, 2001).
29. Shawe-Taylor, J. & Cristianni, N. Kernel Methods for Pattern Analysis (Cambridge University Press, New York, NY, 2004).
30. Liberzon, A. et al. Molecular signatures database (MSigDB) 3.0. Bioinformatics 27, 1739-1740 (2011).
31. Vaske, C.J. et al. Inference of patient-specific pathway activities from multi-dimensional cancer genomics data using PARADIGM. Bioinformatics 26, i237-i245 (2010).
32. Gönen, M. & Alpaydin, E. Multiple kernel learning algorithms. J. Mach. Learn. Res. 12, 2211-2268 (2011).
33. Caruana, R. Multitask learning. Mach. Learn. 28, 41-75 (1997).
34. Breiman, L. Random forests. Mach. Learn. 45, 5-32 (2001).
35. Friedman, J., Hastie, T. & Tibshirani, R. Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw. 33, 1-22 (2010).
36. Leiserson, M.D., Blokh, D., Sharan, R. & Raphael, B.J. Simultaneous identification of multiple driver pathways in cancer. PLoS Comput. Biol. 9, e1003054 (2013).
37. Fallahi-Sichani, M., Honarnejad, S., Heiser, L.M., Gray, J.W. & Sorger, P.K. Comparing drug activity across cell line banks reveals systematic variation in properties other than potency. Nat. Chem. Biol. 9, 708-714 (2013).
38. Kwong, L.N. et al. Oncogenic NRAS signaling differentially regulates survival and proliferation in melanoma. Nat. Med. 18, 1503-1510 (2012).
39. Hanahan, D. & Weinberg, R.A. Hallmarks of cancer: the next generation. Cell 144, 646-674 (2011).
40. Rantala, L.M., Kwon, S., Korkola, J.E. & Gray, J.W. Expanding the diversity of image-based RNAi screen applications using cell spot microarrays. Microarrays 2, 97-114 (2013).
41. Margolin, A.A. et al. Systematic analysis of challenge-driven improvements in molecular prognostic models for breast cancer. Sci. Transl. Med. 5, 181re1 (2013).
42. Costello, J.C. & Stolovitzky, G. Seeking the wisdom of crowds through challenge-based competitions in biomedical research. Clin. Pharmacol. Ther. 93, 396-398 (2013).
43. Venkatraman, E.S. & Olshen, A.B. A faster circular binary segmentation algorithm for the analysis of array CGH data. Bioinformatics 23, 657-663 (2007).
44. Bengtsson, H., Wirapati, P. & Speed, T.P. A single-array preprocessing method for estimating full-resolution raw copy numbers from all Affymetrix genotyping arrays including GenomeWideSNP 5 & 6. Bioinformatics 25, 2149-2156 (2009).
45. Griffith, M. et al. Alternative expression analysis by RNA sequencing. Nat. Methods 7, 843-847 (2010).
46. Fackler, M.J. et al. Genome-wide methylation analysis identifies genes specific to breast cancer hormone receptor status and risk of recurrence. Cancer Res. 71, 6195-6207 (2011).
47. Tibes, R. et al. Reverse phase protein array: validation of a novel proteomic technology and utility for analysis of primary leukemia specimens and hematopoietic stem cells. Mol. Cancer Ther. 5, 2512-2521 (2006).
48. Kuo, W.L. et al. A systems analysis of the chemosensitivity of breast cancer cells to the polyamine analogue PG-11047. BMC Med. 7, 77 (2009).
49. Monks, A. et al. Feasibility of a high-flux anticancer drug screen using a diverse panel of cultured human tumor cell lines. J. Natl. Cancer Inst. 83, 757-766 (1991).