Cancer ResearchCancer BiologyPeer-reviewed journal article
Spatial multi-omics defines a shared infiltrative signature in glioblastoma at the resection margin
Balagopal PaiiD, Susana Isabel RamosiD, Wan Sze ChengiD, Tanvi JoshiiD, Emine ÖzeniD, Lakshmi Shree Kulumani MahadevaniD, Thenzing J. Silva-HurtadoiD, Gabrielle A. PriceiD, Jessica Tomé-GarcíaiD, German NudelmaniD, Sanjana ShroffiD, Kristin G. BeaumontiD, Raymund L.M. YongiD, Robert SebraiD, Elena ZaslavskyiD, Nadejda M. TsankovaiD
Abstract
Glioblastoma (GBM) remains an untreatable disease. Understanding GBMs 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 furthermore corroborate preferential TEAD1 binding at EGFRs accessible regulatory elements. This validated multi-omic dataset enables further studies into tumor and microenvironment biology in the context of residual GBM disease.
- organism: human
- assay: scRNA-seq
- assay: scATAC-seq
- assay: spatial transcriptomics
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- research focus: drug response / drug discovery
- approach: deep learning
- approach: statistical model
- Citations
- 17
- FWCI
- 9.99
- Field percentile
- top 1%
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