Evidence map›Paper›PMID 40629403›Full record

SynthesisJournal of translational medicine2025

Gut microbiome changes and cancer immunotherapy outcomes associated with dietary interventions: a systematic review of preclinical and clinical evidence.

Csenge Somodi, David Dora, Mátyás Horváth, Gabor Szegvari, Zoltan Lohinai

Abstract readSystematic Review
In one paragraph

Synthesis in Journal of translational medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed, 1 pooled it
–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

21 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Review
  6. Review
  7. Review
  8. Review
  9. Gut Microbiota and Extraintestinal Cancers: Mechanistic Insights and Microbiome-Targeted Interventions.JGH open : an open access journal of gastroenterology and hepatology · 2026
    Review
  10. Review
  11. Review
  12. Review
  13. Review
  14. Review
  15. Review
  16. Article
  17. Review
  18. Review
  19. Article
  20. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Csenge SomodiTranslational Medicine Institute, Semmelweis University, Tűzoltó Utca 37-47, 1094, Budapest, Hungary.
David DoraDepartment of Anatomy, Histology, and Embryology, Semmelweis University, Budapest, Hungary.
Mátyás HorváthTranslational Medicine Institute, Semmelweis University, Tűzoltó Utca 37-47, 1094, Budapest, Hungary.
Gabor SzegvariTranslational Medicine Institute, Semmelweis University, Tűzoltó Utca 37-47, 1094, Budapest, Hungary.
Zoltan LohinaiTranslational Medicine Institute, Semmelweis University, Tűzoltó Utca 37-47, 1094, Budapest, Hungary. lohinai.zoltan@semmelweis.hu.

Funding

Magyar Tudományos Akadémia Bolyai Research ScholarshipNemzeti Kutatási, Fejlesztési és Innovaciós Alap #142287Nemzeti Kutatási, Fejlesztési és Innovaciós Alap #146775
6 · The paper itself

Abstract

introductionCancer patient's survival has gradually improved due to immune checkpoint inhibitors (ICIs). Several studies showed a possible association between the intestinal microbiome and ICI efficacy. Strategies for modifying the composition of the gut microbiome encompass various dietary interventions, which may have distinct impacts on the outcomes of ICI-treated patients. In our systematic review, we explored how dietary habits correlate with therapeutic responses in cancer patients and cancer mouse models undergoing immunotherapy.

methodsA systematic review was conducted using search terms: "cancer", "immunotherapy", "diet", and "microbiome", from Medline, Web of Science, Scopus, and Cochrane Library databases. The outcomes in the clinical studies were overall response rate (ORR), overall survival (OS), or progression-free survival (PFS) in human studies. In mouse studies, change in tumor size was the endpoint. The comparator attributions were questionnaire-based dietary interventions.

resultsNineteen articles met the inclusion criteria and were included in the review (6 prospective cohort studies, 1 cross-sectional observational study, and 12 mouse studies). A consistent association was observed between high (vs. low) fiber consumption and improved therapeutic response with a pooled odds ratio of 5.79 when including all human prospective cohort studies. In mice, limited availability of methionine, cysteine, and low intake of leucine and glutamine was linked to reduced tumor progression. Combining ICIs with intermittent fasting or a fasting-mimicking diet significantly decreased tumor volume in mouse melanoma models. In humans, a higher relative abundance of short-chain fatty acid (SCFA) and lactic acid-producing bacteria-particularly Faecalibacterium prausnitzii and Akkermansia muciniphila-correlated with objective response rates (ORR). Similar microbiome alterations were observed in mouse models. Increased fiber intake enhanced ICI efficacy in mice by modulating the gut microbiome, primarily via elevated SCFA production-an effect also reflected in human studies.

conclusionIntermittent fasting, high fiber, and low sugar consumption are significantly associated with better ICI outcomes. The studies revealed alterations in microbiota composition linked to diet, and these findings were confirmed in animal models, regarding the production of SCFAs and lactic acid, as well as an increase in Bacteroidota/Bacillota ratio and microbial diversity.

Indexed as

DietGastrointestinal MicrobiomeImmunotherapyNeoplasmsAnimalsHumansMiceTreatment OutcomeCancerDietFiber intakeGut microbiomeImmunotherapyKetogenic diet

Identifiers

PMID40629403
PMCPMC12239337

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.