TMIC-52. DECODING METABOLIC INTERACTIONS IN GLIOBLASTOMA TUMOR MICROENVIRONMENT THROUGH COMPREHENSIVE SPATIAL TRANSCRIPTOMIC AND PROTEOMIC ANALYSIS

Abstract INTRODUCTION Within the GBM microenvironment, distinct micro-niches arise from independent cancer cell lineages with unique metabolic needs, characterized as Fast Cycling Cells (FCCs) and Slow Cycling Cells (SCCs). FCCs preferentially utilize aerobic glycolysis for energy, whereas treatment-resistant SCCs depend on lipid metabolism. Our study decodes the molecular and spatial heterogeneity within the GBM microenvironment, focusing on the dichotomy of immune infiltrates and the metabolic interactions between SCCs and the immune compartment in human GBM patients. METHODS We performed spatial transcriptomics and proteomics combined with geospatially resolved single-cell neighborhood analysis of IHC-stained GBM tissues using CellProfiler and the SNAQ algorithm that we developed to investigate the specific immune microenvironment of SCCs and FCCs. Holotomography and fluorescence-based time-lapse imaging coupled with flow cytometry were used to study lipid transfer between immune cells and GBM cells. Finally, the effects of pharmacological and genetic targeting of lipid transfer were investigated both in vitro and in vivo. RESULTS Our study establishes a connection between spatial metabolic heterogeneity and immune diversity, highlighting how specific tumor cell lineages correlate with characteristic immune micro-niches. In particular, SCCs exploit the metabolic properties of recruited immunosuppressive myeloid-derived cells, which metabolically support tumor cells by facilitating lipid transport and supporting SCC metabolism. Our results show that inhibiting lipid transfer affect SCCs, remodels the immune microenvironment, delays tumor growth and improves disease outcome. Our study also demonstrates a correlation between predicted sensitivity to lipid-modifying drugs, such as statins, and the SCC phenotype. This correlation could enable stratification and selection of patients most likely to benefit from such treatments. CONCLUSIONS This study establishes critical connections between the TME, cellular metabolism, anti-tumor immunity, and treatment vulnerabilities. Our research provides novel insights into how metabolic exchanges contribute to tumor progression and suggests that targeting metabolic interactions could be an effective therapeutic strategy for GBM.