AI and Cancer Research ── Cancer biology ── 2026-10-23
Cancer molecular biology and AI
- Abstract
Metabolic reprogramming is a hallmark of many diseases, particularly cancer, yet current methods for analyzing metabolic states from gene expression data often lack the resolution and interpretability needed for clinical applications. Gene-based signature approaches reduce the structure of metabolic networks, are susceptible to statistical biases, and eventually show poor reproducibility across studies. A network-structured, mechanistically grounded alternative is therefore needed. We developed CircumSpectrum, a computational framework that converts gene expression profiles into flux-based metabolic fingerprints by integrating genome-scale metabolic models with machine learning classifiers trained across 142 functional metabolic subsystems. The framework is validated using multiple independent transcriptomic cohorts of patients treated with metformin — a widely used anti-diabetes drug with growing relevance in oncology — spanning healthy individuals, type II diabetic patients, and cancer cell lines. Performance is benchmarked against conventional gene signature and pathway enrichment methods using bootstrapped classification accuracy. Here we show that CircumSpectrum surpasses conventional gene signature methods in distinguishing treatment effects and maintains reproducibility across datasets, platforms, and species. Applied to metformin-treated cohorts, the framework uncovers drug-induced metabolic adaptations at the phenotypic, subsystem, and individual reaction levels that are not detected by standard transcriptomic analyses, including disease-specific divergences between healthy and diabetic metabolic responses and conserved pathway rewiring across cancer types. CircumSpectrum enables multi-scale metabolic profiling from patient cohorts, experimental models, and single-cell data, providing structured and interpretable insights into complex phenotypes from gene expression data. This approach has broad potential for identifying metabolic vulnerabilities, characterizing drug mechanisms of action, and supporting precision therapeutic strategies across disease contexts. Cells constantly adapt their metabolism in response to disease or treatment, but current methods for studying these changes from gene activity data are often imprecise and hard to reproduce. We developed CircumSpectrum, a computational tool that converts gene expression measurements into detailed metabolic “fingerprints” by combining large-scale biological network models with machine learning. We tested it using metformin, a common anti-diabetes drug with emerging uses in cancer, across multiple patient studies and cancer types. CircumSpectrum identified metabolic changes that standard methods missed, and did so consistently across different datasets and even different species. These findings suggest that CircumSpectrum could help researchers better understand how drugs work at the metabolic level, potentially guiding more precise treatment strategies for diabetes, cancer, and other metabolic diseases. Polley et al. present CircumSpectrum, a framework transforming RNA-seq into flux-based metabolic fingerprints via genome-scale models and machine learning. They benchmark the framework on metformin across clinical and cancer cohorts, and find that it performs favorably against conventional gene-signature methods and suggests drug mechanisms at the phenotypic, subsystem, and reaction levels.
Journal IF-equivalent: 6.0 (OpenAlex 2-year mean citedness, value as of 2026-10-08, retrieved 2026-10-09; not the official Clarivate IF)Reference: Polley N, Kaoma T, Nazarov PV, Sarry JE. Inferring flux-based metabolic fingerprints from gene expression to predict drug response and facilitate patient stratification. Commun Med. 2026 Oct 21 [Epub ahead of print]. doi:10.1038/s43856-026-01970-3.Checked: Abstract only