Abstract Glioblastoma (GBM) is the most common primary malignant brain tumor with an abysmal 15-month median survival. Therefore, novel therapeutic interventions are urgently needed. EGFR is a receptor tyrosine kinase that is mutated in over 50% of GBM and is a logical target for precision oncology approaches. While aberrant EGFR signaling has been successfully targeted in other cancers, early attempts to target EGFR in GBM clinical trials have not been successful. Since these early trials, several studies have revealed that GBM EGFR biology is unique and cannot be generalized from other EGFR-driven neoplasms. To better understand EGFR biology in a GBM-specific context, we have characterized the GBM transcriptome with RNAseq, the epigenome with CUT&RUN, and the kinase proteome with Multiplexed inhibitor beads with Mass Spectrometry (MIB-MS), collectively referred to as ‘multiomics’, after modeling EGFR resistance. An isogenic mouse astrocyte (mAc) model of GBM was genetically engineered to overexpress EGFRvIII (CEv3), the most common EGFR mutation in GBM. Expression of EGFRvIII induces a unique multiomic profile that mediates many hallmark cancer phenotypes including proliferation and stemness. Chronic EGFR resistance was modeled both in vitro and in vivo through continuous exposure to erlotinib or gefitinib, EGFR tyrosine kinase inhibitors (TKI). Additionally, acute EGFR resistance was modeled using a single exposure to EGFR TKI afatinib or neratinib in vitro over a 48-hour time course. Preliminary multiomic characterization of these data has revealed several targets that can be further investigated for therapeutic exploitation. Further investigation into these targets using orthotopic allografts with CEv3 cells shows that combinatorial therapy with neratinib and abemacilib, a CDK inhibitor, significantly (p < 0.001, n= 20 mice per group) extends survival (56 days) compared to neratinib alone (31.5 days). Future integrated multiomic analysis aims to elucidate the synergistic relationship between neratinib and abemacilib.
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