Abstract 144: Spatial transcriptomic landscape of the primary and recurrent glioblastoma tumor microenvironment

Abstract

Abstract Differences in clonality, genomic and epigenetic alterations are known to occur between primary Glioblastoma (pGBM) and recurrent (rGBM), but this has been insufficient to yield a promising therapeutic approach. Recent studies show that the GBM tumor microenvironment (TME), composed of reactive glial cells, microglial subtypes, neuronal activity, myeloid cells, lymphocytes, etc., plays a crucial role in the invasiveness and evolution of the glioma cells. Nevertheless, how the spatial architecture and interplay among these cells facilitates GBM resurgence is largely unknown. To this point, we developed a pipeline for characterizing the TME changes using single nucleus and spatial transcriptomics (ST). We retrospectively collected matched, fresh-frozen, IDH-wildtype pGBM and rGBM samples (n=14, 7 patients) from the Mayo Clinic Neuroscience Biobank. Single nucleus RNA sequencing was performed using the 10x Chromium platform. Matched FFPE blocks from 8 samples were collected from the Mayo Clinic pathology department for 4 of the 7 patients. A region of interest was identified from the corresponding histopathology slide by a pathologist, avoiding areas of necrosis and hemorrhage. The region was collected from the block using a 5mm biopsy punch. The tissue was re-paraffinized, and a 5µm thick section was used to perform ST with the 10x Visium for FFPE platform. We performed transcriptomic and image texture-based clustering of capture dots along with differential gene expression analysis. Based on gene expression, we identified and characterized conserved regions across samples. We used single nucleus data to infer cell composition. Spatial correlation and autocorrelation statistics were calculated on features, such as gene expression, gene set scores, cell type prediction, and antibody capture. The features’ relative spatial changes were compared between pGBM and rGBM. Our analyses showed high heterogeneity within and across samples, but we found higher molecular and textural heterogeneity in rGBM samples compared to pGBM. We found higher androgen response activity (p < 0.001) presents diffusely (I: 0.09) throughout rGBM tissue but localized to mesenchymal-like regions in pGBM (I: 0.15). KRAS signaling signature was decreased in pGBM (p < 0.01) and increased in rGBM (p < 0.01). This increase was significantly overexpressed in cluster 0 for rGBM (p=0.02), which is an area characterized by neural progenitor signature. No significant difference in KRAS or EGFR magnitude or distribution of expression was present for either GBM type, indicating an independent mechanism for pGBM. We established a pipeline for assessing the spatio-temporal changes of the GBM TME. We found evidence indicating differences in the type of growth signaling, the cells bing targeted, and their distribution across the TME between pGBM and rGBM. Our work suggests how ST can help us better understand the intricate dynamics of GBM. Citation Format: Jean R. Clemenceau, Paola Suarez Meade, Mark E. Jentoft, Alfredo Quiñones-Hinojosa, Tae Hyun Hwang. Spatial transcriptomic landscape of the primary and recurrent glioblastoma tumor microenvironment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 144.