Volume 36, No 2, Feb 2026

ISSN: 1001-0602 
EISSN: 1748-7838 2018 
impact factor 17.848* 
(Clarivate Analytics, 2019)

Volume 36 Issue 2, February 2026: 103-120   |  Open Access

REVIEW ARTICLE

Gut dysbiosis in oncology: a risk factor for immunoresistance

Andrew Allan Almonte^1,† , Simon Thomas^1,† , Valerio Iebba^1,2 , Guido Kroemer^3,4,5,6 , Lisa Derosa^1,7,* , Laurence Zitvogel^1,*

¹Tumor Immunology and Cancer Immunotherapy, Université Paris-Saclay, Gustave Roussy, Inserm UMR1015, Villejuif, France
²Department of Medical, Surgical and Health Sciences, University of Trieste, Trieste, Italy
³Université Paris Cité, Sorbonne Université, Inserm, Centre de Recherche des Cordeliers, Paris, France
⁴Centre de Recherche des Cordeliers, Equipe labellisée par la Ligue contre le Cancer, Institut Universitaire de France, Paris, France
⁵Metabolomics and Cell Biology Platforms, Institut Gustave Roussy, Université Paris-Saclay, INSERM US23/CNRS UAR 3655, Villejuif, France
⁶Institut du Cancer Paris CARPEM, Department of Biology, Hôpital Européen Georges Pompidou, AP-HP, Paris, France
⁷Gustave Roussy, Department of Medical Oncology, Villejuif, France
^†These authors contributed equally: Andrew Allan Almonte, Simon Thomas
Correspondence: Lisa Derosa(deros.lisa@gmail.com)Laurence Zitvogel(Laurence.Zitvogel@gustaveroussy.fr)

The gut microbiome is recognized as a determinant of response to immune checkpoint inhibitor (ICI) therapies in cancer. However, the clinical translation of microbiome science has been hampered by inconsistent definitions of dysbiosis, inadequate biomarker frameworks, and limited mechanistic understanding. In this review, we synthesize the current state of knowledge on how gut microbial composition and function influence ICI efficacy, highlighting both correlative and causal evidence. We discuss computational approaches based on α-diversity or taxonomic abundance and argue for more functionally and clinically informative models, such as the topological score (TOPOSCORE) and other dysbiosis indices derived from machine learning. Using retrospective analyses of metagenomic datasets from thousands of patients and healthy controls, we examine microbial patterns that distinguish responders from non-responders. We also explore how dysbiosis perturbs immunoregulatory pathways, including bile acid metabolism, gut permeability, and mucosal immunomodulation. Finally, we assess emerging therapeutic strategies aimed at correcting microbiome dysfunction — including dietary modification, bacterial consortia, and fecal microbiota transplantation — and describe how they are being deployed in multiple clinical trials. We conclude with a brief discussion of the ONCOBIOME initiative, which works with international partners to incorporate microbiome science into oncology workflows. By refining our understanding of gut–immune interactions and translating it into action, microbiome-informed oncology may unlock new therapeutic potential for patients previously resistant to immunotherapy.

https://doi.org/10.1038/s41422-025-01212-6

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