Abstract 767: Discovering the geospatial framework in glioblastoma: Uncovering hotspots for glioma stem cell hide-out and their therapeutic implications

Introduction: Glioblastoma or GBM is an aggressive brain cancer with a 5 year survival rate below 10 percent. Glioma stem cells or GSCs drive GBM formation, growth, and resistance. Previously, analysis of 17,601 phase contrast images from 15 GBM patients revealed that neurodevelopmental GSCs form smaller, uniform clusters, while mesenchymal and injury-response GSCs exhibit complex, irregular growth patterns. This new study examines the spatial distribution and local cellular niche of GSCs along a neurodevelopmental gradient in primary GBM. We develop a geospatial map of GSCs in relation to other cell-types, identifying community patterns of organization. Methods: We profiled 14 primary, treatment-naive GBM tissue sections (5 micrometer thick) on a spatial transcriptomics platform (Xenium), utilizing a custom 414 gene panel to score the GSC, GBM and other cell signatures. We segmented cell boundaries using both Baysor and Proseg algorithms and performed hematoxylin-eosin (HE) staining of tissue sections post-Xenium run. We performed spatial clustering using SpaGCN and Banksy, followed by spatial distribution and spatial point pattern analysis (Ripley’s, Moran’s I). Results: Across the 14 tissues, we segmented 5, 705, 825 cells and annotated each cell with a cell-type label using pre-defined gene sets. This resulted in 15.2 percent GSC-like cells, 9.0% oligodendrocytic-OPC-like, 8.7% astrocytic-mesenchymal-like, 6.7% neuronal-like, 15.0% immune, 7.1% endothelial, and the rest of the cells showing mixed signatures. In total we identified 110-140 spatial clusters, which we classified into 8 domains by grouping highly correlated spatial clusters using Pearson correlation. All cell-types were differentially distributed both proportionally and spatially. Domains with highly correlated grouped clusters exhibited distinct spatial arrangements of cells as well. For instance, GSC-like cells were confined to unique spatial domains with specific geometric properties across cellular hierarchies. Likewise, immune and endothelial cells displayed enrichment in preferred domains, with some co-localized with GSC-enriched regions while others were devoid of these cell types. Therefore, we find that these geospatial topology maps could identify spatial enrichment patterns and distributional trends. Conclusions: This study deciphers GBM’s geospatial topology, where annotated cell-types are mapped to specific niches. Though the ultimate aim of the study will be to eliminate GSC-like cells in GBM, we posit that by revealing cell community patterns, we aim to shift therapeutic strategies from targeting single cells or molecules to biomarkers that address entire cell communities within which these GSCs are spatially and functionally connected to. This framework lays the groundwork for future biomarker discovery and therapeutic interventions in GBM. Citation Format: Shamini Ayyadhury, Fatemah Al Solaiman, Yuna Lee, Alyona Ivanova, Ana Nikolic, Farzaneh Aboulizadeh, Melanie Peralta, Patty Sachamitr, Michelle M. Kushida, Nicole I. Park, Fiona J. Coutinho, Owen Whitley, Panagiotis Prinos, Cheryl H. Arrowsmith, Sam Weiss, Sheila Mansouri, Gelareh Zadeh, Peter B. Dirks, Troy Ketela, H. Artee Luchman, Gary D. Bader, Trevor J. Pugh. Discovering the geospatial framework in glioblastoma: Uncovering hotspots for glioma stem cell hide-out and their therapeutic implications [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 767.