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We present a generalizable framework combining machine-learning and genome-scale metabolic modeling to prioritize combination therapies for colorectal cancer (CRC) in the presence of\n                  <jats:italic>Fusobacterium nucleatum<\/jats:italic>\n                  (\n                  <jats:italic>Fn<\/jats:italic>\n                  ) and other pathogenic, probiotic, and commensal microbes. Trained on 6,514 drug combinations in microbe-free CRC cell lines, the model predicted synergistic combinations in both microbe-free and microbe-associated contexts and generalized to immunotherapy-associated conditions. Predictions were validated using an asymmetric co-culture system that mimics the colon\u2019s normoxic\u2013anaerobic gradient, confirming synergistic combinations in HCT116 cells with\n                  <jats:italic>Fn<\/jats:italic>\n                  , including drugs not typically used in CRC therapy. Mechanistic analysis and targeted pharmacological perturbations revealed phospho-inositol metabolism and cysteine transport as key determinants of\n                  <jats:italic>Fn<\/jats:italic>\n                  -dependent drug synergy. Together, this work introduces a scalable, microbiome-aware framework to enable discovery of context-specific combination therapies.\n                <\/jats:p>","DOI":"10.64898\/2026.02.25.707963","type":"posted-content","created":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T06:00:25Z","timestamp":1772172025000},"source":"Crossref","is-referenced-by-count":0,"title":["Modeling Microbiome Modulation of Tumor Metabolic Networks to Predict Synergistic Therapies"],"prefix":"10.64898","author":[{"given":"Annie J","family":"Badenoch","sequence":"first","affiliation":[{"name":"Gilbert S. 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