PROJECT SUMMARY The epidermal growth factor (EGFR) oncogene is amplified and drives tumor growth in 55% of adult glioblastomas (GBMs). However, EGFR inhibitors have failed to demonstrate clinical benefit in GBM, presenting one of the most fundamental challenges facing the field of neuro-oncology. As highlighted by the National Cancer Institute’s recent think tank on progress in GBM, despite clear signals about the genomic underpinnings of GBM, including the high frequency of EGFR amplification, new drug development programs have stalled because of the high risk of clinical failures. Intra-tumoral genetic heterogeneity, and the poor brain-plasma ratios of many drug candidates, are thought to play a major role in clinical failure. Building on the team’s recent discoveries demonstrating that EGFR is amplified almost exclusively on extrachromosomal DNA particles (ecDNA), driving intra-tumoral genetic heterogeneity, accelerated tumor evolution, and EGFR inhibitor resistance, and their discovery of actionable metabolic dependencies that arise when EGFR becomes amplified, this proposal will identify proteins on which EGFR-amplified GBMs selectively depend for survival, even in highly heterogeneous tumors. This proposal integrates a hypothesis-driven approach with unbiased discovery using activity-based protein profiling (ABPP). In clinically relevant patient-derived models of GBM, this proposal takes a chemistry- first approach to discover both actionable dependencies that arise when EGFR is amplified and ligands that engage these proteins, which can be made to be highly brain-penetrant. By deploying fully functionalized (FF) small-molecule libraries to enable direct progression from phenotypic screening to target identification in living GBM cells, including in patient-derived GBMs with amplified EGFR, this proposal is poised to inform actionable therapeutic targets for patients in vivo. The proposed integrated approach provides a rapid route towards initiating new drug development that directly addresses the fundamental challenges of GBM.