Abstract 4304: Network-based inference identifies cell state-specific drugs targeting master regulator vulnerabilities in diffuse midline glioma

Diffuse Midline Glioma (DMG) are fatal pediatric brain tumors with no therapies. We leveraged network-based methodologies to dissect the heterogeneity of DMG tumors and to discover Master Regulator (MR) proteins representing pharmacologically accessible, mechanistic determinants of molecularly distinct cell states. We produced the first DMG regulatory network from 122 publicly available RNAseq profiles with ARACNe (Basso et al. Nat Genet 2005), and inferred sample-specific MR protein activity with VIPER (Alvarez et al. Nat Genet 2016) based on the differential expression of their targets. 7 of the top 25 most active MRs found comprise a well-characterized MR block (MRB2) (Paull et al.Cell 2021), frequently activated across aggressive tumors, and enriched in DMG patient MR signatures (Fisher’s Exact Test p=4.4 × 10−18). A CRISPR/Cas9 KO screen across 3 DMG patient cell lines identified a set of 73/77 essential genes that were enriched in the MR signature of 80% of patient samples (GSEA p=0.000034). FOXM1 emerged as an essential MR, significantly activated across virtually all patients. We then generated RNAseq profiles following perturbation with ~300 oncology drugs in 2 DMG cell lines most representative of patient MR signatures, and used this to identify drugs that invert patient MR activity profiles using the NYS/CA Dept. of Health approved OncoTreat algorithm (Alvarez et al. Nat Genet 2018). OncoTreat predicted sensitivity to HDAC, MEK, CDK, PI3K, and proteosome inhibitors in subsets of patients, overlapping with published DMG drug screens. Importantly, 80% of OncoTreat-predicted drugs (p