Data from The Functional Transcriptomic Landscape Informs Therapeutic Strategies in Multiple Myeloma

Several therapeutic agents have been approved for treating multiple myeloma, a cancer of bone marrow–resident plasma cells. Predictive biomarkers for drug response could help guide clinical strategies to optimize outcomes. In this study, we present an integrated functional genomic analysis of tumor samples from patients multiple myeloma that were assessed for their ex vivo drug sensitivity to 37 drugs, clinical variables, cytogenetics, mutational profiles, and transcriptomes. This analysis revealed a multiple myeloma 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 mAb daratumumab (DARA) and the nuclear export inhibitor selinexor (SELI) demonstrated that these footprints can accurately classify clinical responses. The analysis further revealed that DARA and SELI have anticorrelated mechanisms of resistance, and treatment with a SELI-based regimen immediately after a DARA-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 multiple myeloma.

Significance: Functional genomic analysis of primary multiple myeloma samples elucidated predictive biomarkers for drugs and molecular pathways mediating therapeutic response, which revealed a rationale for sequential therapy to maximize patient outcomes.