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Deconvolving cell type and pathway heterogeneity in disorders of the central nervous system: intertumoral and spatial heterogeneity in glioma


Glioma is the most common primary brain malignancy and remains a challenging and intractable disease. Two major barriers inhibiting progress in therapeutic development are the unclear clinical implications of molecular differences across patients and inevitable tumor recurrence likely due to the difficulty in removing the entire malignant compartment during the initial resection surgery. Here, I present a strategy to begin combatting these challenges by leveraging flexible and scalable sequencing analysis pipelines to deconvolve complex glioblastoma cellularity and discover functional drivers of disease progression and recurrence. I first describe an approach that aims to better characterize therapeutically relevant patterns of intertumoral glioblastoma heterogeneity by classifying tumors based on functional pathway enrichment rather than gene expression alone, which identified E2F1 as a key proliferation driver specifically in E2F1-activated tumors. We also found a biological parallel of one of our candidate gene lists, as it correlated significantly with markers of endothelial, mural, and mesenchymal-like glioma cells. These findings suggest that pathway enrichment is a more functionally relevant grouping metric and that we can use this approach to identify therapeutic targets in specific patient subsets. I then describe an approach aimed at addressing the challenge of recurrence by characterizing the cellular and molecular differences between the core and edge regions of recurrent glioma at single cell resolution. Using MRI-guided biopsy target selection and a multifaceted snRNA-seq analysis pipeline, we thoroughly profiled both malignant and non-malignant cell types in the edge region and identified a functional “state switching” event in mesenchymal-like malignant cells, with vascular remodeling consistently enriched in the tumor core and proliferation consistently upregulated in the tumor edge. Robust sequencing analysis pipelines thus represent an effective approach to identifying key functional drivers in multiple contexts and demonstrate the potential to identify targetable candidate cells and pathways in glioma.

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