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Cellular fitness of MYC-driven cancer cells to genetic and pharmacologic perturbations in normoxia, hypoxia and 3D

GSE240980 Mus musculus Expression profiling by high throughput sequencing 8 samples Submitted 2024/06/26 Platform GPL24247
Summary
MYC and hypoxia gene clusters are the two most commonly recurring transcriptional programs in heterogenous tumors. Understanding the cellular fitness of MYC-driven cancer cells in normoxia and hypoxia may lead to identification of distinct cancer cell vulnerabilities. Using genome-wide CRISPR screenings of MYC-driven liver cancer cells cultured over 4 weeks in 21% and 1% oxygen in monolayer, and 21% oxygen in 3D spheroid, we not only identify over 600 essential genes under all three conditions but also context-specific fitness genes and pathways. Knockout of VHL-HIF1 pathway results in incompatible fitness defects under 21% oxygen versus 1% oxygen or 3D. Genetic deletion of each of the mitochondrial respiratory complex I-V has distinct cellular fitness outcomes. Notably, the organogenesis signaling pathways such as TGF-SMAD specifically limits the uncontrolled cell proliferation of 3D spheroids while inactivation of epigenetic modifiers (Bcor, Kmt2d, Mettl3 and Mettl14) has opposite outcomes in monolayer versus 3D. We also discover distinct metabolic requirement for fatty acid and cholesterol synthesis in three conditions. Chemical perturbations using 125 FDA approved oncology drugs reveal that MYC-driven liver cancer cells have different sensitivity to inhibition of tyrosine kinase receptors under hypoxia and 3D. Our resource study highlights unique epigenetic and metabolic dependency of MYC-driven cancer cells in different tumor environmental settings.
Published in
The context-dependent epigenetic and organogenesis programs determine 3D vs. 2D cellular fitness of MYC-driven murine liver cancer cells
Yang J, Fang J, Singh S et al. · Research square 2025 · PMID 38853928 · doi:10.21203/rs.3.rs-4390765/v4
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Also filed as BioProject PRJNA1005987 and SRA study SRP455401. Searching any of these in the dataset finder brings you back here.

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