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<p>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&#xa0;—&#xa0;including dietary modification, bacterial consortia, and fecal microbiota transplantation&#xa0;—&#xa0;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.</p>

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Gut dysbiosis in oncology: a risk factor for immunoresistance

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月14日

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  • Andrew Allan Almonte,
  • Simon Thomas,
  • Valerio Iebba,
  • Guido Kroemer,
  • Lisa Derosa,
  • Laurence Zitvogel

摘要

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.