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  • The Gut Microbiome as a Predictor of Immune Checkpoint Inhibitor Efficacy and Immune-Related Toxicity: A Translational Evidence Gap Analysis

  • Fatemeh Aghajani,1,*
    1. Shiraz University of Medical Science


  • Introduction: The gut microbiome has been linked to the efficacy and toxicity of immune checkpoint inhibitors (ICIs), and many reviews have summarised these associations. Most are taxon-centred and descriptive, and reported signatures are inconsistent across studies. How far this evidence has moved from association towards clinical use is less clear. We mapped the evidence for microbiome-based prediction and modulation of ICI efficacy and immune-related adverse events (irAEs) by translational stage and identified gaps.
  • Methods: We conducted an evidence gap analysis using a scoping review approach, following the Arksey and O'Malley framework and PRISMA-ScR. PubMed was searched on 12 October 2026 with two strategies, one for efficacy (systematic reviews, trials and validation studies) and one for toxicity prediction. We included (1) systematic reviews and meta-analyses of the gut microbiome and ICI outcomes, (2) clinical trials of microbiome modulation in patients receiving ICIs, and (3) primary studies of microbiome-based prediction with a validation or prospective design. We excluded reviews of antibiotic or proton pump inhibitor exposure alone, bibliometric analyses, tumour microbiome and preclinical studies. Evidence was mapped to four stages: discovery, validation, intervention and clinical utility. One reviewer screened the records and charted the data.
  • Results: The searches returned 563 records (505 unique) and 60 were included: 26 systematic reviews or meta-analyses, 20 trials of microbiome modulation (16 reported, 4 protocols) and 14 prediction studies. At the discovery stage, a meta-analysis of 26 studies (1,542 patients) in NSCLC and melanoma linked higher alpha diversity to better survival (OS HR 0.52; PFS HR 0.58) and Akkermansia to response (OR 2.15), yet a pan-cancer analysis found that strain-level signatures generalised across cohorts only when ICI regimens matched. At the validation stage, seven prediction studies tested models in independent data, with varying performance; a commercial stool test had sensitivity 81% and specificity 50% in a validation cohort of 63 patients. At the intervention stage, of 16 reported trials four were randomised and the single-arm studies that reported size enrolled 10 to 69 patients; a meta-analysis of 25 trials and cohorts gave a pooled response rate of 40% (95% CI 31–49%), but these estimates were non-comparative. For toxicity, one study built an irAE classifier (AUC 0.88) and tested it in two independent cohorts, and high Bacteroidetes abundance was associated with a lower risk of severe colitis on anti-CTLA-4 therapy (OR 0.34). We found no study testing a microbiome-guided treatment decision.
  • Conclusion: In aggregate, higher microbial diversity is consistently associated with better ICI outcomes, but specific taxa and signatures depend on the regimen, validation is limited, and modulation studies are small and mostly single-arm. Prediction of irAEs is far less developed than prediction of response, and evidence for clinical utility is lacking. Priorities are regimen-specific validation cohorts with standardised methods, prospective irAE prediction studies, randomised trials of modulation with predefined microbiome endpoints, and trials testing microbiome-guided decisions.
  • Keywords: gut microbiome; immune checkpoint inhibitors; biomarkers; immune-related adverse events; faecal micr

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