A community effort to assess and improve drug sensitivity prediction algorithms

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.
MaGNeT grant 5U54CA121852-08; National Institutes of Health, National Cancer Institute (U54 CA 112970); Stand Up To Cancer-American Association for Cancer Research Dream Team Translational Cancer Research (SU2C-AACR-DT0409); Prospect Creek Foundation; Howard Hughes Medical Institute (HHMI); Academy of Finland (Finnish Center of Excellence in Computational Inference Research COIN) (251170--140057)