Skip to content
DraconiticResearch
Search Sources API
181,686 studies31 sourcesUpdated 56 min ago
Peer-reviewedCC BY-NC-ND 4.0Cell Reports Methods

Modeling microbiome modulation of tumor metabolic networks to predict synergistic therapies

  • Annie J. Badenoch1
  • Zeyang Pang2
  • Carolina H. Chung2
  • Aaron Robida3
  • Bretton Badenoch4
  • Ritish Natesan5
  • Layth Kakish2
  • Jiahe Li2,5,6
  • Sriram Chandrasekaran1,2,6,7
7 affiliations
  1. 1Gilbert S. Omenn Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
  2. 2Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
  3. 3Center for Chemical Genomics, University of Michigan, Ann Arbor, MI 48109, USA
  4. 4Department of Molecular and Cellular Pathology, University of Michigan, Ann Arbor, MI 48103, USA
  5. 5Department of Computer Science and Engineering, University of Michigan, Ann Arbor, MI 48109, USA
  6. 6Rogel Cancer Center, University of Michigan, Ann Arbor, MI 48109, USA
  7. 7Institute for Data and AI in Society, University of Michigan, Ann Arbor, MI 48109, USA
Read full textDOICite

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.

Related

  1. Metabolic reprogramming and metabolic homeostasis in podocyte injury: novel mechanistic insights and natural product therapies
    Zhongjie Qu, Wenqing Ba, Meijing Tang +3
    Frontiers in cell and developmental biologyPeer-reviewedCC BYFull textGeneral biochemistryEurope PMC
  2. Editorial: Dysbiosis and microbiome modulation in cancer prevention and therapy
    Rohini Ravindran Nair, Ehud Wainstein, Suresh Singh Yadav
    Frontiers in OncologyPeer-reviewedCC BY 4.0Full textMolecular biology & gene regulationCrossref
  3. Targeted Modulation of the Gut Microbiota in Obesity: From Dietary Interventions to Bacteriophage-Based Precision Therapies
    Sthefhany Nohemí Rodríguez-Arellano, Jean Pierre González-Gómez, José Roberto Aguirre-Sánchez +4
    NutrientsPeer-reviewedCC BY 4.0Molecular biology & gene regulationCrossref
  4. Faecal microbiota transplant revisited - potential metabolic outcomes of regional microbiome mismatches.
    Elisabeth A A O'Flaherty, Douwe van Sinderen
    Microbiome research reportsPeer-reviewedCC BYFull textMolecular biology & gene regulationEurope PMC
  5. Gut microbiome composition and oxalate-related metabolic functions in staghorn calcium oxalate stone formers: a shotgun metagenomic analysis
    Yadong Lu, Tzi Shin Toh, Lydia Lim +3
    Scientific ReportsPeer-reviewedCC BY-NC-ND 4.0Full textMolecular biology & gene regulationCrossref

Cite

BibTeXRIS
BibTeX
@article{badenoch2026modeling,
  title = {{Modeling microbiome modulation of tumor metabolic networks to predict synergistic therapies}},
  author = {Annie J. Badenoch and Zeyang Pang and Carolina H. Chung and Aaron Robida and Bretton Badenoch and Ritish Natesan and Layth Kakish and Jiahe Li and Sriram Chandrasekaran},
  journal = {Cell Reports Methods},
  year = {2026},
  month = aug,
  doi = {10.1016/j.crmeth.2026.101549},
  url = {https://doi.org/10.1016/j.crmeth.2026.101549},
  note = {CC BY-NC-ND 4.0},
}
RIS
TY  - JOUR
TI  - Modeling microbiome modulation of tumor metabolic networks to predict synergistic therapies
AU  - Annie J. Badenoch
AU  - Zeyang Pang
AU  - Carolina H. Chung
AU  - Aaron Robida
AU  - Bretton Badenoch
AU  - Ritish Natesan
AU  - Layth Kakish
AU  - Jiahe Li
AU  - Sriram Chandrasekaran
PY  - 2026
DA  - 2026/08/13
JO  - Cell Reports Methods
DO  - 10.1016/j.crmeth.2026.101549
UR  - https://doi.org/10.1016/j.crmeth.2026.101549
AB  - 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.
SN  - 2667-2375
ER  -