Abstract

Abstract Diffuse Midline Gliomas (DMGs) are pediatric brain tumors that arise in the pons, thalamus or the brainstem. DMGs present a pressing clinical problem since they cannot be surgically removed and respond poorly to radio- and chemotherapies. Like adult gliomas, DMGs present a high degree of intra-tumoral heterogeneity, with cells adopting different levels of differentiation similar to oligodendrocytes, astrocytes, precursor and mesenchymal cells. These different phenotypes possibly play distinct roles in tumor progression. Yet, we lack a good understanding of the underlying mechanisms of DMG heterogeneity. Here, we present a novel strategy to uncover regulator genes of DMG heterogeneity in a comprehensive and unbiased fashion, using an integration of machine learning with Cas9-based Perturb-seq. We first use a new data-driven modeling technique, termed single cell regulatory-driven clustering (scRegClust; Larsson et al., BioRxiv 2023) to uncover transcriptional modules, and their upstream transcriptional and kinase regulators. The goal of this analysis is to identify regulator genes that control cellular processes relevant to the disease, thereby enhancing our understanding of DMGs and uncover potential therapeutic targets. Applied to recently available single-cell data from 90 DMG patients, scRegClust revealed 160 regulators of meta-modules including oligodendrocyte-like, oligodendrocyte-progenitor-like, astrocyte-like and mesenchymal like cells. To validate the importance of these regulators, we are establishing a Perturb-seq experimental pipeline by combining pooled CRISPR knock-out and single cell RNA sequencing in patient-derived DMG cell lines. By perturbing the candidate regulators both in vitro and in vivo we aim to evaluate their impact on DMG tumorigenicity. This comprehensive strategy will ultimately increase our understanding of the molecular processes in DMGs and present the functional significance of these regulators and their involvement in disease progression.

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