Multi-omic integration identifies broad drug resistance mechanisms and strategies to therapeutically reprogram cancer cells

TL;DR

This study uses multi-omic data to uncover how cancer cells become resistant to drugs and identifies ways to reverse this resistance.

Contribution

The paper introduces a novel integrative framework combining drug-response and omic data to identify and reprogram drug resistance mechanisms.

Findings

Resistant cancer cells show coordinated activation of stress-adaptation and survival pathways.

NFE2L2 is identified as a central regulator linking mutations to oxidative-stress programs.

Rosiglitazone re-sensitized resistant cells to chemotherapy by reducing NFE2L2 activity.

Abstract

Broad drug resistance arises from diverse transcriptional, metabolic, and genetic adaptations, yet the unifying features that sustain cross-resistant phenotypes remain unclear. We developed an integrative framework combining PRISM drug-response data with transcriptomic, metabolomic, and mutational profiles to define the molecular programs associated with broad resistance and to nominate compounds capable of reversing them. 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 analyses revealed metabolic reprogramming as a conserved feature of resistance, and patient cohort analyses showed that resistance-associated alterations correlated with shorter progression-free survival. Computational perturbagen screening identified compounds predicted to counteract these transcriptional signatures, converging on regulators of NFE2L2 activity. Experimental testing confirmed that rosiglitazone reduced NFE2L2-associated gene expression and re-sensitized resistant cells to chemotherapy, demonstrating a scalable strategy for rational phenotypic reprogramming.

•Multi-omic integration with drug screening data defines mechanisms of drug resistance•Network modeling identifies NFE2L2 as a key regulator of resistance programs•Altered resistance programs in patients have been shown to be negatively correlated with survival•Perturbagen screening identifies compounds that reprogram resistance programs

Multi-omic integration with drug screening data defines mechanisms of drug resistance

Network modeling identifies NFE2L2 as a key regulator of resistance programs

Altered resistance programs in patients have been shown to be negatively correlated with survival

Perturbagen screening identifies compounds that reprogram resistance programs

Health sciences

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