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. The success of precision medicine hinges on our ability to effectively translate genomic data into actionable, customized prognosis and treatment regimens for individual patients. This requires identifying a genomic disease signature from a patient, then matching it with the most effective therapeutic intervention. As a scientific community, we are moving toward this goal, but many questions still remain, including, what data are needed to develop these genomic signatures and what methods are needed to extract the appropriate information from high-dimensional genomic data sets? The first step in addressing these challenges is to generate comprehensive drug sensitivity profiling measurements across many drugs, many disease (sub)types, and many genomic profiling technologies. Several of these data sets have been generated with a focus on cancer biology 1-9 , and in particular breast cancer 4, 5, [10] [11] [12] [13] [14] . From these data, the bottleneck then becomes identifying robust computational approaches that connect genomic profiles to drug and disease response. In the past 20 years, there has been measurable improvement in breast cancer outcomes with a steady decrease in mortality 15 . The identification of HER2 amplification and subsequent discovery of HER2-targeted therapies (e.g., trastuzumab (Herceptin), lapatinib (Tykerb)) demonstrates that the identification of genomic biomarkers can be used to effectively guide treatment decisions and improve outcomes. However, identification of such biomarkers is complicated by substantial genomic and epigenomic heterogeneity in breast cancer 2, 10, 16, 17 , indicating that multiple 'drivers' may serve as targets for breast cancer treatment. Effective personalized treatments will require matching therapeutic interventions to the complex genomic context of each patient. The ideal data set(s) to build predictive models linking genomic context to treatment would be systematically characterized drug sensitivities across a large cohort of patients, but these data are time-intensive to generate, prohibitively expensive, and limited in the scope of drugs that can be tested. Performing such assays in cell culture and focusing on breast cancer affords the opportunity to learn the factors that contribute to building effective predictive models in a tractable biological system; these factors can then be used for the design of marker-based clinical trials. Comparisons have shown that cell lines mirror many aspects of the 'omic' diversity found in primary tumors 4, 18, 19 , suggesting that they can be used as a proxy for characterizing the response to therapeutic interventions. Previous work has characterized relationships between genomic profiles and drug response 1, 3, 6, 7, 20 and several drug sensitivity prediction algorithms have been proposed 1, 3, [20] [21] [22] [23] ; however, a thorough and unbiased comparison of such methods has not been reported.
In particular, REF proposed Bayesian multi-task multiple kernel learning (BMTMKL) to predict drug responses on new human breast cancer cell lines.
A community effort to assess and improve drug sensitivity prediction algorithms
Nature Biotechnology
Nature Biotechnology
Medicine