{
  "abstract": "Differences in microbiome composition profoundly influence drug response, yet methods to model the metabolic impact of microbes on host cells and therapeutics remain limited. We present a microbiome-aware computational framework combining machine learning and genome-scale metabolic models to predict combination therapies for colorectal cancer (CRC) in the presence of Fusobacterium nucleatum (Fn) and other pathogenic, probiotic, and commensal microbes. The model learned predictive metabolic flux signatures from 6,514 drug combination profiles in CRC cell lines and predicted synergistic drug combinations across both microbe-free and microbe-associated contexts. Model performance was supported through prospective comparison with newly reported drug combinations, in vitro drug synergy assays, microbiome co-culture experiments, and targeted metabolic perturbations of predicted pathway dependencies. Pharmacological perturbations in asymmetric co-cultures revealed phosphoinositol metabolism and cysteine transport as key determinants of Fn-dependent drug synergy. Together, this work introduces a scalable strategy for discovering microbiome-dependent combination therapies, including chemotherapies, immunotherapy, and probiotics.",
  "abstract_shown": true,
  "affiliations": [
    "Gilbert S. Omenn Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA",
    "Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA",
    "Center for Chemical Genomics, University of Michigan, Ann Arbor, MI 48109, USA",
    "Department of Molecular and Cellular Pathology, University of Michigan, Ann Arbor, MI 48103, USA",
    "Department of Computer Science and Engineering, University of Michigan, Ann Arbor, MI 48109, USA",
    "Rogel Cancer Center, University of Michigan, Ann Arbor, MI 48109, USA",
    "Institute for Data and AI in Society, University of Michigan, Ann Arbor, MI 48109, USA"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 9,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Annie J. Badenoch"
    },
    {
      "affs": [
        1
      ],
      "name": "Zeyang Pang"
    },
    {
      "affs": [
        1
      ],
      "name": "Carolina H. Chung"
    },
    {
      "affs": [
        2
      ],
      "name": "Aaron Robida"
    },
    {
      "affs": [
        3
      ],
      "name": "Bretton Badenoch"
    },
    {
      "affs": [
        4
      ],
      "name": "Ritish Natesan"
    },
    {
      "affs": [
        1
      ],
      "name": "Layth Kakish"
    },
    {
      "affs": [
        1,
        4,
        5
      ],
      "name": "Jiahe Li"
    },
    {
      "affs": [
        0,
        1,
        5,
        6
      ],
      "name": "Sriram Chandrasekaran"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/ec6bdphbcs8gg.bib",
  "cited_by": 1,
  "doi": "10.1016/j.crmeth.2026.101549",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13721753/",
  "id": "ec6bdphbcs8gg",
  "issns": [
    "2667-2375"
  ],
  "landing_url": "https://doi.org/10.1016/j.crmeth.2026.101549",
  "language": "en",
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Article"
    ],
    [
      "openalex",
      "1312|Microbial Metabolic Engineering and Bioproduction"
    ]
  ],
  "openalex": "W7131796543",
  "pdf_url": null,
  "pmcid": "PMC13721753",
  "pmid": "42594868",
  "published": "2026-08-13",
  "publisher": "Elsevier",
  "ris": "https://research.draconitic.com/paper/ec6bdphbcs8gg.ris",
  "sources": [
    {
      "first_seen": "2026-11-03T12:22:23Z",
      "native_id": "PMC13721753",
      "source": "pmc",
      "url": "https://doi.org/10.1016/j.crmeth.2026.101549"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "openalex:1312|Microbial Metabolic Engineering and Bioproduction",
      "subfield": "molecular-biology"
    }
  ],
  "title": "Modeling microbiome modulation of tumor metabolic networks to predict synergistic therapies",
  "title_text": "Modeling microbiome modulation of tumor metabolic networks to predict synergistic therapies",
  "type": "peer-reviewed",
  "updated_at": "2026-11-03T12:22:23Z",
  "url": "https://research.draconitic.com/paper/ec6bdphbcs8gg",
  "venue": "Cell Reports Methods",
  "versions": []
}