Several therapeutic agents have been approved for treating multiple myeloma (MM), a cancer of bone marrow resident plasma cells. Predictive biomarkers for drug response could help guide clinical strategies to optimize outcomes. Here, we present an integrated functional genomic analysis of tumor samples from MM patients that were assessed for their ex vivo drug sensitivity to 37 drugs, clinical variables, cytogenetics, mutational profiles, and transcriptomes. This analysis revealed a MM transcriptomic topology that generates "footprints" in association with ex vivo drug sensitivity that have both predictive and mechanistic applications. Validation of the transcriptomic footprints for the anti-CD38 monoclonal antibody daratumumab and the nuclear export inhibitor selinexor demonstrated that these footprints can accurately classify clinical responses. The analysis further revealed that daratumumab and selinexor have anti-correlated mechanisms of resistance, and treatment with a selinexor-based regimen immediately after a daratumumab-containing regimen was associated with improved survival in three independent clinical trials, supporting an evolutionary-based strategy involving sequential therapy. These findings suggest that this unique repository and computational framework can be leveraged to inform underlying biology and to identify therapeutic strategies to improve treatment of MM.