bioRxivJClub
Patient-specific metabolic fluxes reflect molecular subtype and heterogeneity in breast cancer
bioRxiv · · Preprint · Open access
Ruiz Robles, Rincion-Ballesteros + 3 more
Abstract ↗AI summary
The abstract is read at the publisher; the summary is JClub's.
Patient-specific genome-scale metabolic modeling of 1,223 TCGA breast tumor and normal samples yields flux profiles that distinguish tumor from normal with ROC-AUC up to 0.989.
- Why it matters: Gene expression only indirectly reflects metabolic activity, leaving a gap in patient-level, context-specific characterization of breast cancer metabolism and heterogeneity.
- What they did: The study inferred metabolic fluxes using patient-specific genome-scale metabolic models plus supervised and unsupervised machine learning under a uniform physiological medium, evaluated with nested cross-validation.
- The result: Flux profiles also predict receptor status and molecular subtype (AUC = 0.966) and show continuous within-tumor metabolic variation, enabling a reproducible framework for mapping heterogeneity.
The findingWhy it mattersWhat they didThe result
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