Abstract Accurate prediction of patient drug response is required for successful personalized medicine. Cancer is a disease resulting from myriad genetic alterations. Accordingly, it should be possible to predict drug response of specific cancers using genetic and molecular signatures. To aid this effort, an ongoing collaboration established between Novartis and the Broad Institute called the Cancer Cell Line Encyclopedia (CCLE) has (i) comprehensively characterized genome-scale mRNA expression, copy number alteration and mutation profiles for nearly 1000 cancer cell line models spanning many tumor types and (ii) profiled ~500 of these cell lines against ~1000 anticancer compounds. Using these data, we are developing a scalable, extensible predictive modeling framework based on various supervised learning methods that include both categorical classification and linear regression approaches to predict compound sensitivity. By cross-validation of the generated models against test data sets, we quantitatively estimated model performance. Our initial results validate several preexisting genetic predictors of sensitivity. In addition, our models reported 70% or higher sensitivity and specificity for a number of compounds and yielded interesting potentially novel predictive features that, in some cases, outperform previously existing genetic predictors of sensitivity. Our integrative approach demonstrates that pharmacological profiling of large, genomically annotated cancer model systems, coupled with systematic predictive modeling, can uncover novel predictors of drug response and may ultimately aid patient stratification. This talk is also presented as Poster A4.