Abstract 5460: Discovery and validation of effective combination therapies targeting cell state-specific master regulator vulnerabilities by network-based protein activity inference in diffuse midline glioma

Abstract Diffuse Midline Glioma (DMG) are fatal pediatric brain tumors with no effective systemic therapies. We leveraged network-based methodologies to dissect tumor heterogeneity, discover Master Regulators (MR) representing pharmacologically accessible vulnerabilities of distinct DMG cell states, and validated candidate MR-reversing drugs predicted by the NYS CLIA-certified OncoTarget and OncoTreat algorithms (Cancer Discov 2023) in vivo. We first interrogated single cell DMG regulatory networks generated by ARACNe (Nat Genet 2005) with publicly available gene expression signatures from 3, 039 tumor cells across 6 patients using VIPER (Nat Genet 2016) to infer single cell regulatory protein activity. Clustering of cells by protein activity defined 7 patient-independent cell states with distinct MR profiles reflecting known glial lineage markers (OPC-like-S1, OPC-like-S2, OC-like-S1, OC-like-S2, Cycling, AC-like, and AC/OPC-like). We then generated drug-induced differential protein activity from RNAseq profiles following perturbation with 372 oncology drugs in two DMG cell lines that together recapitulate the MR signatures of the cell states and used this to identify drugs that invert tumor MR activity profiles using OncoTreat. Candidate drugs predicted by OncoTarget (inhibitors of individual MRs) and OncoTreat were distinct across the cell states, and we selected five drugs targeting the OPC/cycling-like cells (Trametinib, Dinaciclib, Avapritinib, Mocetinostat, and Etoposide), and four drugs targeting the AC-like cells (Ruxolitinib, Venetoclax, Napabucasin, Larotrectonib) for further validation as these states comprised most tumor cells across patients. We generated single-cell RNAseq for 95, 687 cells after 5 days of treatment with either vehicle control (n = 4) or candidate drug (n = 2-3/drug) in subcutaneous SU-DIPG-17 mouse models. We show this model recapitulates cell states seen in patients and confirm reduction in tumor growth and significant depletion of either OPC/cycling-like cells or AC-like cells in line with our drug predictions for 8/9 candidate drugs. We then treated a syngeneic (DIPG4423) orthotopic DMG model with each drug and demonstrate significant differences in survival with Avapritinib, Dinaciclib, and Trametinib. Notably, the combination of drugs targeting OPC/cycling-like and AC-like cells (i. e. Trametinib+Ruxolitinib and Avapritinib+Venetoclax) showed significantly lower tumor volumes after 2 weeks of treatment as compared to vehicles or each drug alone, and significant survival differences for some combinations. This work provides a precision medicine platform to nominate much-needed novel drug combinations addressing DMG tumor heterogeneity for further study to improve outcomes in this devastating disease. Citation Format: Ester Calvo Fernandez, Lorenzo Tomassoni, Xu Zhang, Aleksandar Obradovic, Pasquale Laise, Aaron T. Griffin, Lukas Vlahos, Junqiang Wang, Hanna E. Minns, Diana V. Morales, Christian Simmons, Matthew Gallito, Hong-Jian Wei, Zhiguo Zhang, Robyn Gartrell, Luca Szalontay, Stergios Zacharoulis, Cheng-Chia Wu, Andrea Califano, Jovana Pavisic. Discovery and validation of effective combination therapies targeting cell state-specific master regulator vulnerabilities by network-based protein activity inference in diffuse midline glioma abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts) ; 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84 (6Suppl): Abstract nr 5460.