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Multi-omic integration identifies mechanisms of Broad Drug Resistance and opportunities for therapeutic reprogramming cancer cells

GSE309935 Homo sapiens Expression profiling by high throughput sequencing 8 samples Submitted 2025/11/15 Platform GPL30173
Summary
Broad drug resistance in cancer arises through diverse transcriptional, metabolic, and genetic adaptations, yet the shared molecular programs that sustain cross-resistant phenotypes remain incompletely defined. This study integrates PRISM drug-response data with transcriptomic, metabolomic, and mutational profiles to characterize the molecular features associated with broad drug resistance and to identify compounds capable of reversing resistance-associated gene signatures. Resistant cell lines exhibited coordinated activation of extracellular matrix remodeling, stress-adaptation pathways, and survival signaling, with NFE2L2 emerging as a central regulatory hub linking upstream mutations to oxidative-stress transcriptional programs. Multi-omic integration further revealed metabolic reprogramming as a conserved hallmark of resistance, and analyses of clinical cohorts demonstrated that resistance-associated alterations were associated with reduced progression-free survival. Computational perturbagen screening nominated compounds predicted to counteract resistance-associated transcriptional signatures. Experimental validation confirmed that rosiglitazone suppressed NFE2L2-associated gene expression programs and restored chemotherapy sensitivity in resistant models, supporting a scalable framework for rational phenotypic reprogramming. This GEO submission provides raw RNA-seq data generated from compound-treated OE19 cells used to experimentally validate candidate re-sensitization strategies.
Published in
Multi-omic integration identifies broad drug resistance mechanisms and strategies to therapeutically reprogram cancer cells
Mersich I, Blagg BSJ, Ali A · iScience 2026 · PMID 41492468 · doi:10.1016/j.isci.2025.114293
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Also filed as BioProject PRJNA1364013 and SRA study SRP644548. Searching any of these in the dataset finder brings you back here.

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