Abstract 5290: Spatial multi-omics defines a shared glioblastoma infiltrative signature at the resection margin

Abstract Glioblastoma (GBM) remains an untreatable disease. Understanding of GBM’s infiltrative biology at the resection margin is limited, despite causing disease recurrence and progression. To address this, we generated a high-throughput single-nucleus (sn)RNA-seq and snATAC-seq multi-omic dataset from six tumors with distinct genomic drivers and combined it with spatial transcriptomics to characterize the unique molecular phenotype of GBM near the margin. By contrasting GBM-specific biology in matching “Core” vs. “Margin” dissections, we define unique, shared “GBM infiltration” and chromatin accessibility signatures near the margin. We prioritize EGFR as a top differentially expressed and accessible “Margin” marker across GBM subtypes, show its dynamic expression along a core-to-margin infiltration trajectory, and validate its role in migration through CRISPR/Cas9 deletion in two patient-derived models. ChIP-seq studies further corroborate preferential TEAD1 binding at EGFR’s accessible regulatory elements. This validated multi-omic dataset enables further studies into tumor and microenvironment biology in the context of residual GBM disease. Citation Format: Balagopal Pai, Susana Ramos, Wan Sze Cheng, Tanvi Joshi, Gabrielle Price, Jessica Tome-Garcia, Kristin Beaumont, Robert Sebra, Raymund Yong, Elena Zaslavsky, Nadejda M. Tsankova. Spatial multi-omics defines a shared glioblastoma infiltrative signature at the resection margin [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 5290.

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Journal
Cancer Research
Published
2025-04-21
DOI
https://doi.org/10.1158/1538-7445.am2025-5290
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
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article

Abstract 5290: Spatial multi-omics defines a shared glioblastoma infiltrative signature at the resection margin

Raymund L. Yong, Kristin G. Beaumont, Elena Zaslavsky, Susana Ramos et al.
1 links
Cancer Research
Radiomics and Machine Learning in Medical Imaging
article

Abstract 5290: Spatial multi-omics defines a shared glioblastoma infiltrative signature at the resection margin

Raymund L. Yong, Kristin G. Beaumont, Elena Zaslavsky, Susana Ramos, Balagopal Pai, Nadejda M. Tsankova, Jessica Tomé-García, Robert Sebra, Tanvi Joshi, Wan Sze Cheng, Gabrielle A. Price
article en

Abstract

Abstract Glioblastoma (GBM) remains an untreatable disease. Understanding of GBM’s infiltrative biology at the resection margin is limited, despite causing disease recurrence and progression. To address this, we generated a high-throughput single-nucleus (sn)RNA-seq and snATAC-seq multi-omic dataset from six tumors with distinct genomic drivers and combined it with spatial transcriptomics to characterize the unique molecular phenotype of GBM near the margin. By contrasting GBM-specific biology in matching “Core” vs. “Margin” dissections, we define unique, shared “GBM infiltration” and chromatin accessibility signatures near the margin. We prioritize EGFR as a top differentially expressed and accessible “Margin” marker across GBM subtypes, show its dynamic expression along a core-to-margin infiltration trajectory, and validate its role in migration through CRISPR/Cas9 deletion in two patient-derived models. ChIP-seq studies further corroborate preferential TEAD1 binding at EGFR’s accessible regulatory elements. This validated multi-omic dataset enables further studies into tumor and microenvironment biology in the context of residual GBM disease. Citation Format: Balagopal Pai, Susana Ramos, Wan Sze Cheng, Tanvi Joshi, Gabrielle Price, Jessica Tome-Garcia, Kristin Beaumont, Robert Sebra, Raymund Yong, Elena Zaslavsky, Nadejda M. Tsankova. Spatial multi-omics defines a shared glioblastoma infiltrative signature at the resection margin [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 5290.

Cancer ResearchVol. 85(8_Supplement_1)
Icahn School of Medicine at Mount Sinai (US)
Openalex Percentile: Top 88%
Radiomics and Machine Learning in Medical Imaging
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