Multi-omics-driven kinetic modeling reveals metabolic adaptations and vulnerabilities in BRCA1-deficient ovarian cancer

Abstract

BRCA1-deficient ovarian cancer cells undergo extensive metabolic reprogramming, yet the network-level dynamics underlying their proliferation and treatment response remain poorly resolved. Here, we construct large-scale multi-omics-driven kinetic model populations of ovarian cancer metabolism to track how tumor cells adapt to changes in nutrient use, energy production, and metabolite dynamics over time. Across BRCA1 wild-type and mutant cells, these models expose distinct metabolic strategies shaped by transcriptional regulation and prioritize 28 enzyme-mediated vulnerabilities, including 24 linked to existing experimental or approved drugs and 4 previously uncharacterized targets in nucleotide and lipid synthesis. They further recapitulate a ceramide-linked metabolic stress signature shared across diverse chemotherapies. Mechanistic analysis traces the effects of BRCA1 loss to transcription-factor-mediated shifts in enzyme activity, outlining regulatory routes for network-level rewiring. Beyond ovarian cancer, this framework offers a generalizable blueprint for predicting metabolic vulnerabilities, drug responses, and adaptive mechanisms across diverse cancer and metabolic disease contexts. By coupling dynamic metabolism to therapeutic prediction, it delivers actionable hypotheses for biomarker discovery, patient stratification, target prioritization, and precision metabolic medicine.