EPCO-42. A SYSTEMS SCALE MODEL FOR PERSONALIZED PRECISION ONCOLOGY APPLICATIONS TO GBM

Abstract

Abstract Glioblastoma (GBM) is the most aggressive and lethal type of brain tumor, and its treatment remains challenging due to the complexity of the underlying molecular mechanisms. Here, we present gbmMINER, a causal and mechanistic transcriptional regulatory network model for GBM that integrates multi-omics and clinical data with a new network quantization approach using the MINER algorithm. gbmMINER accurately grouped 9,728 genes into 3,797 regulons and identified 179 transcriptional programs that stratify GBM into 23 disease states, associated with distinct clinical outcomes. We demonstrate that the activity profiles of regulons and programs in the quantized network model accurately predict overall survival, identifying patients with significantly longer OS of up to 42 months. Through extensive characterization using independent cohort datasets and genome-wide CRISPR screening data, we demonstrate the accuracy of gbmMINER in delineating causal and mechanistic insights into well-known and novel prognostic somatic mutations in GBM. Finally, through high-throughput drug screening, we demonstrate that the activity of regulons and programs accurately predicts the sensitivity of patient-derived glioma stem-like cells (PD-GSCs) to anticancer drugs. In theory, gbmMINER could be implemented to better stratify patients based on prognosis and potentially identify precision therapeutic strategies based on network activity.