Identification of ex vivo therapeutic vulnerabilities in diffuse gliomas using a functional genomics approach

Abstract Diffuse gliomas are aggressive brain tumors that lack effective therapies. Despite advances in the molecular characterization of gliomas, targeted treatments have failed to improve patient outcomes to date. Here, we employed a functional genomics approach to rapidly identify therapeutic vulnerabilities and provide a strategy to individualize patient treatment. Our process combined comprehensive genomic and transcriptomic tumor profiling, custom algorithms and visualization software for data integration, and preclinical 3-dimensional ex vivo models (mcancers) for drug screening to assess response to therapeutic agents targeting specific molecular alterations. A total of 32 cases, including 5 oligodendrogliomas (1 primary and 4 recurrent), 5 astrocytomas (1 primary and 4 recurrent), and 22 glioblastomas (5 primary and 17 recurrent), were included in the study. Immunohistochemistry and integrated genomics confirmed that mcancers maintain key molecular alterations, including mutations, chromosomal rearrangements and focal amplifications, cellularity, and heterogeneity, faithfully recapitulating the original tumor. A total of 119 single drugs and 183 combinations were tested, including a panel of 26-brain penetrant targeted agents (18 single drugs and 8 combinations) that was curated to target common glioma oncogenic pathways and was tested across all cases. A strong correlation was observed between therapeutic vulnerabilities of glioma mcancers to targeted agents (EGFR, MET, PDGFR, HDAC inhibitors, etc.) and clinical experience. Single agent activity of panel compounds in the ex vivo setting was generally poor, with only 35% mCancers exhibiting > 70% growth inhibition at Cmax. In contrast, combination strategies, especially ones including epigenetic drugs, were significantly more effective (affecting 22 out of 32 mCancers), underscoring the molecular complexity of gliomas and the need for clinical trials testing combination treatments. Our data identified a number of novel combination treatments with significant efficacy across glioma subtypes and molecular profiles, suggesting that our functional genomics pipeline can guide treatment selection and inform clinical trial enrollment for gliomas.