Human-mouse platform for data-driven patient stratification, target discovery, and validation in GBM

The primary factor for the dismal outcome of glioblastoma (GBM) is high inter- and intra-tumor heterogeneity. By using bulk RNA-seq, the Cancer Genome Atlas (TCGA) initiative provided robust gene expression-based identification of three GBM subtypes: Proneural (PN), Mesenchymal (MES), and Classical (CL). These molecular subtypes are not mutually exclusive and can co-exist within a single tumor but are important to comprehensively characterize because they represent ends of the spectrum of different molecular gradients that shape the GBM tumor microenvironment (TME). Hence, better understanding of the TME of different GBM subtypes is a critical first step towards improving patient stratification as well as identifying therapeutic vulnerabilities for different malignant cell subpopulation that make up mixed-subtype GBM tumors. Preliminary data in this application defines two distinct GBM MES subtypes, associated with different genetic drivers, TME cell compositions, and survival of patients. We propose to combine publicly available single-cell RNA-sequencing (scRNA-seq) data from 199 GBM patients with emerging high-resolution spatial transcriptomics technologies to dissect the TME cell composition, spatial tissue organization, cancer-intrinsic, and myeloid-driven immunosuppressive mechanisms underlying differences in survival between the two MES GBM subtypes (Aim 1). We will additionally develop a new machine learning approach for discovering immunomodulators of TME cell composition and combine it with existing computational tools to model cell-cell interactions across the integrated cohort, which will enable us to uncover new, context-specific therapeutic targets. In Aim 2, we will build and comprehensively immunophenotype genetically engineered mouse models (GEMMs) of the two new GBM MES subtypes to dissect the role of distinct genetic drivers on the GBM TME. The GEMMs will also provide a validation platform for the computationally discovered therapeutic targets with conserved function in human and mouse GBM. Hence, our study promises to uncover human-relevant cell-cell interactions and pathways that lead to an immunosuppressive TME in MES GBM and to identify new strategies for immunomodulatory treatment and patient stratification. Finally, this proposal will create an integrated human-murine, single-cell resolution resource that will provide an analysis and experimental platform for target discovery and pre-clinical validation for the two MES GBM subtypes. Project Number: 1R01CA308411-01 | Fiscal Year: 2026 | NIH Institute/Center: National Cancer Institute (NCI) | Principal Investigator: Alexander Tsankov | Institution: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI, NEW YORK, NY | Award Amount: $683,256 | Activity Code: R01 | Study Section: Molecular Cancer Diagnosis and Classification Study Section[MCDC] View on NIH RePORTER: https://reporter.nih.gov/project-details/11280025