Dietary Patterns and Gut Microbiome Modulation in Cancer Immunotherapy: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Accumulating evidence suggests that gut microbiome composition influences response to immune checkpoint inhibitor (ICI) therapy in cancer patients. Dietary interventions represent a modifiable approach to optimize microbiome profiles for improved immunotherapy outcomes. This systematic review and meta-analysis quantifies the association between dietary interventions targeting gut microbiome modulation and clinical outcomes in cancer immunotherapy. Methods: We conducted a systematic search of PubMed, Embase, Web of Science, Cochrane CENTRAL, and Scopus databases from January 2010 to August 2025, following a prospectively registered protocol (PROSPERO: CRD42025395817). We included clinical studies evaluating dietary interventions (≥7 days duration) with quantitative microbiome assessment and immunotherapy outcomes. Two reviewers independently performed study selection, data extraction, and quality assessment using Risk of Bias 2 (RoB-2) and Newcastle-Ottawa Scale tools. Meta-analysis was performed using random-effects models with the metafor package in R. Evidence certainty was assessed using GRADE methodology. Results: Eight studies encompassing 1,247 cancer patients met inclusion criteria. Dietary interventions included high-fiber diets (n=4 studies), Mediterranean diet patterns (n=2), prebiotic supplementation (n=1), and combined probiotic-prebiotic approaches (n=1). Meta-analysis revealed that dietary interventions were associated with significantly improved objective response rates to ICI therapy compared to standard care (pooled odds ratio [OR] 2.27, 95% confidence interval [CI] 1.48-3.46, p=0.0002). Statistical heterogeneity was moderate (I²=65%). Leave-one-out sensitivity analysis confirmed robust findings (OR range: 2.01-2.67). A post-hoc subgroup analysis suggested consistent effects across cancer types. Publication bias assessment revealed minimal evidence of small-study effects (Egger/Begg tests are underpowered with <10 studies; non-significant p values do not prove absence of bias.). Conclusions: Dietary interventions targeting gut microbiome modulation are associated with improved objective response rates to immune checkpoint inhibitor therapy, with moderate certainty evidence. These findings are promising and may inform clinical trials and pilot implementation; however, routine clinical adoption is premature without confirmatory randomized trials.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.043 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".