Abstract 3875: Glioblastoma mutational profiles drive cancer cell signaling and immune evasion

Abstract Glioblastoma multiforme (GBM) is the most common malignant brain tumor in adults with a median survival below two years. Characterization of GBM through gene expression profiling and DNA sequencing has identified disease subgroups and commonly altered genes and pathways, including alterations in the genes encoding neurofibromin-1 (NF1) and the epidermal growth factor receptor (EGFR). NF1 and EGFR are involved in receptor tyrosine kinase (RTK), RAS/MEK/ERK, and PI3K signaling, and together, are genetically altered in over 60% of adult GBMs. Interestingly, NF1 and EGFR alterations in GBM are almost always mutually exclusive. Here, we designed a multimodal approach integrating spatial proteomics, transcriptomics, and genomics to measure cell composition, immune cell function, cancer cell signaling, cellular spatial organization, gene expression, transcriptional subtypes, and genome alterations in 80 NF1 and EGFR altered human GBMs with extensively curated treatment and survival data. We quantified the spatial expression of 42 proteins in over 31 million single cells measuring RTK/RAS/PI3K and Rb signaling and features of the TME, including 23 cell types covering tumor cells, immune cells, and brain resident cells. In addition to tumor genotype labels, we classified each GBM tumor by its transcriptional subtype (i.e., mesenchymal, classical, or proneural). We identified genotype-phenotype associations in GBM including hyperactive RAS signaling cellular niches, a macrophage-rich TME, and an epithelial-mesenchymal signature in NF1 altered tumors and hyperactive RTK signaling and an immune desert TME in EGFR amplified tumors. Furthermore, we observed spatial heterogeneity in cancer cell signaling patterns suggestive of the presence of different tumor cell clones within different regions of each GBM tumor. Together, we identify several genotype-associated features of GBMs, reinforcing the value of integrating spatial multi-omic data for generation of genotype-driven therapeutic hypotheses, with implications for GBM genotype-based therapeutic stratification. Citation Format: Maryam Pourmaleki, Brian D. Greenstein, Caitlin J. Jones, Subhiksha Nandakumar, Daniel A. Navarrete, Smrutiben Mehta, Carl Campos, Travis J. Hollmann, Nicholas D. Socci, Tejus Bale, Sohrab P. Shah, Ingo K. Mellinghoff. Glioblastoma mutational profiles drive cancer cell signaling and immune evasion [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(6_Suppl):Abstract nr 3875.