Precision medicine is based on the concept that optimal treatments for individual patients can be selected based on the molecular features of the individual tumors. This requires strategies to link molecular features and response. The Cancer Genome Atlas (TCGA) project and other international efforts have measured genomic, transcriptional and epigenomic heterogeneity in breast tumors in an effort to define the breast cancer genomic landscape. We and others have shown that collections of breast cancer cell lines mirror many of the recurrent omic features and pathways found in primary tumors thereby supporting their use as models to identify response signatures. We now report an assessment of responses of a collection of 70 breast cancer cell lines to 138 experimental or approved therapeutic agents. We use this information to identify molecular signatures that are significantly associated with responses to these agents in the cell lines. We apply these signatures to molecular data from the TCGA to assess the predicted frequencies of response to various therapies and to identify additional characteristics of these tumors. Finally, we describe a computational predictor of response that can be used to rank order therapeutic compounds for treatment of individual patients. Based on these developments, we propose clinical trial strategies to assess the clinical utility of omics-guided treatments derived from in vitro measurements. These strategies should ensure that sufficient patients are enrolled in specific experimental therapeutic trials to provide the necessary statistical power to observe the predicted response.