Abstract 3766: Utilizing whole-transcriptome digital spatial profiling for glioblastoma clinical biomarker discovery
Authors: Adam Luo, Kelly Casella, Shivang Sharma, Ezra Baraban, Calixto-Hope Lucas, Eugene Shenderov
Journal: Cancer Research (2024)

Abstract Objective: IDH-wildtype glioblastoma (GBM) is the most aggressive brain tumor in adults. Notable for substantial inter- and intra-tumoral heterogeneity, GBM is highly resistant to standard-of-care treatment. Emerging spatial omics technologies, such as GeoMx Digital Spatial Profiling (DSP), offer novel approaches to interrogate the molecular features of regionally defined cellular ecosystems in cancer. Here, we leverage DSP to study whole-transcriptome gene expression signatures in a spatially defined manner across five IDH-wildtype GBMs. Methods: Formalin-fixed paraffin-embedded tumors were assessed histologically and three spatially distinct areas from each tumor were used to construct a custom “spatial” tissue microarray. From each set of three areas, we identified nine spatially distinct regions of interest (ROIs) for bulk transcriptomic analysis according to two histopathological features: distribution (cellular tumor core or tumor periphery) and vascular proximity (near or far). We evaluated normalized target expression data statistically using linear mixed modeling (LMM) with Benjamini-Hochberg correction. Results: 44 profiled ROIs and 8531 transcriptomic targets were analyzed following quality control trimming. 3D principal component analysis and unsupervised hierarchical clustering analysis showed clustering according to tumor specimen as well as distribution (core versus periphery), thereby substantiating the presence of inter- and intra-tumoral transcriptional heterogeneity. LMM-based differential gene expression analysis identified distinct molecular signatures when comparing core and periphery. Specifically, we observed elevated log2-transformed expression of migration and proliferation markers including LAMC1 (p=0.00297) and FABP7 (p=0.00434) in core ROIs and neurodevelopmental markers including NTRK2 (p=0.00734) and DAAM2 (p=0.00263) in periphery ROIs. Additionally, reactome analysis demonstrated considerable enrichment for the aberrant programmed cell death pathway (R-HSA-9645723, coverage=71.84%) in core ROIs and the MHC class II antigen presentation pathway (R-HSA-2132295, coverage=69.11%) in periphery ROIs. Interestingly, differential gene expression analysis did not reveal any biologically significant targets when subsetting for vascular proximity within cellular tumor core ROIs. However, reactome analysis of this regional subset demonstrated upregulation of various cellular proliferation pathways (G2/M transition, APC/C activity, DNA synthesis, etc.) within vessel-near ROIs when compared with vessel-far ROIs. Conclusions: We preliminarily characterize the molecular heterogeneity of GBM using a spatially guided approach. Ongoing analyses are focused on dataset validation, mixed cell deconvolution, further regional genotyping, and potential biomarkers. Citation Format: Adam Luo, Kelly Casella, Shivang Sharma, Ezra Baraban, Calixto-Hope Lucas, Eugene Shenderov. Utilizing whole-transcriptome digital spatial profiling for glioblastoma clinical biomarker discovery [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 3766.