Evidence map›Paper›PMID 42594868›Full record

ArticleCell reports methods2026

Modeling microbiome modulation of tumor metabolic networks to predict synergistic therapies.

Annie J Badenoch, Zeyang Pang, Carolina H Chung, Aaron Robida, Bretton Badenoch, Ritish Natesan, Layth Kakish, Jiahe Li, Sriram Chandrasekaran

Abstract read
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In one paragraph

Article in Cell reports methods, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors.

Annie J BadenochGilbert S. Omenn Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
Zeyang PangDepartment of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA.
Carolina H ChungDepartment of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA.
Aaron RobidaCenter for Chemical Genomics, University of Michigan, Ann Arbor, MI 48109, USA.
Bretton BadenochDepartment of Molecular and Cellular Pathology, University of Michigan, Ann Arbor, MI 48103, USA.
Ritish NatesanDepartment of Computer Science and Engineering, University of Michigan, Ann Arbor, MI 48109, USA.
Layth KakishDepartment of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA.
Jiahe LiDepartment of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, 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.
Sriram ChandrasekaranGilbert 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; 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. Electronic address: csriram@umich.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

cancer therapycolon cancercombination therapiesCP: microbiologyCP: systems biologydrug discoveryimmunotherapymachine learningmetabolic modelingmetabolismmicrobiomesystems biology

Identifiers

PMID42594868

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.