A community effort to assess and improve drug sensitivity prediction algorithms

James C. Costello, Laura M. Heiser, Elisabeth Georgii, Mehmet Gönen, Michael P. Menden, Nicholas J. Wang, Mukesh Bansal, Muhammad Ammad-Ud-Din, Petteri Hintsanen, Suleiman A. Khan, John Patrick Mpindi, Olli Kallioniemi, Antti Honkela, Tero Aittokallio, Krister Wennerberg, James J. Collins, Dan Gallahan, Dinah Singer, Julio Saez-Rodriguez, Samuel KaskiJoe W. Gray, Gustavo Stolovitzky, Jean Paul Abbuehl, Jeffrey Allen, Russ B. Altman, Shawn Balcome, Alexis Battle, Andreas Bender, Bonnie Berger, Jonathan Bernard, Madhuchhanda Bhattacharjee, Krithika Bhuvaneshwar, Andrew A. Bieberich, Fred Boehm, Andrea Califano, Christina Chan, Beibei Chen, Ting Huei Chen, Jaejoon Choi, Luis Pedro Coelho, Thomas Cokelaer, James C. Collins, Chad J. Creighton, Jike Cui, Will Dampier, V. Jo Davisson, Bernard De Baets, Raamesh Deshpande, Barbara DiCamillo, Chad L. Myers, NCI DREAM Community

Research output: Contribution to journalArticlepeer-review

497 Scopus citations


Predicting the best treatment strategy from genomic information is a core goal of precision medicine. Here we focus on predicting drug response based on a cohort of genomic, epigenomic and proteomic profiling data sets measured in human breast cancer cell lines. Through a collaborative effort between the National Cancer Institute (NCI) and the Dialogue on Reverse Engineering Assessment and Methods (DREAM) project, we analyzed a total of 44 drug sensitivity prediction algorithms. The top-performing approaches modeled nonlinear relationships and incorporated biological pathway information. We found that gene expression microarrays consistently provided the best predictive power of the individual profiling data sets; however, performance was increased by including multiple, independent data sets. We discuss the innovations underlying the top-performing methodology, Bayesian multitask MKL, and we provide detailed descriptions of all methods. This study establishes benchmarks for drug sensitivity prediction and identifies approaches that can be leveraged for the development of new methods.

Original languageEnglish (US)
Pages (from-to)1202-1212
Number of pages11
JournalNature biotechnology
Issue number12
StatePublished - Dec 1 2014

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