Evidence map›Paper›PMID 42168704›Full record

ArticleCommunications medicine2026

Meta-analytic microbiome target discovery for immune checkpoint inhibitor response in advanced melanoma.

Xinyang Zhang, Himel Mallick, Ali Rahnavard

Abstract read
In one paragraph

Article in Communications medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Longitudinal omics data analysis: approaches and applications.Computational and structural biotechnology journal · 2026
    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

3 authors.

Xinyang ZhangComputational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC, USA.
Himel MallickDivision of Biostatistics, Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York, NY, USA. him4004@med.cornell.edu.ORCID http://orcid.org/0000-0003-4956-2429
Ali RahnavardComputational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC, USA. rahnavard@gwu.edu.ORCID http://orcid.org/0000-0002-9710-0248

Funding

National Science Foundation (NSF) 2109688
6 · The paper itself

Abstract

backgroundImmune checkpoint inhibitors have transformed melanoma therapy, yet only a subset of patients achieve durable responses. Gut microbes have been linked to response, but reported biomarkers vary across studies. We aim to identify reproducible microbial features and test their generalizability across cohorts and treatment settings.

methodsWe reprocessed stool metagenomic sequencing data from 15 melanoma cohorts (763 samples from 484 individuals), including 12 cohorts treated with immune checkpoint inhibitors alone and 3 trials combining immune checkpoint inhibitors with fecal microbiota transplantation. Using a unified analysis pipeline, we profiled microbial species, metabolic pathways, and biosynthetic gene clusters, and analyzed their associations with treatment response using Tweedie regression, random-effects meta-analysis, and multimodal integration with leave-one-dataset-out validation.

resultsHere, we show that responders in immune checkpoint inhibitor-only cohorts are enriched for several short-chain fatty acid-producing commensals, whereas non-responders show higher abundance of taxa associated with disrupted gut communities. In fecal microbiota transplantation plus immune checkpoint inhibitor trials, response associates with distinct communities and shifts in amino-acid, nucleotide and cofactor metabolism. Across cohorts, multiview prediction models repeatedly select gene clusters linked to antimicrobial peptides and surface polysaccharides, but cross-study discrimination remains modest.

conclusionsMicrobiome signatures of response are treatment-context dependent and are not captured by a single universal species. These harmonized findings prioritize microbial taxa and functions for mechanistic studies and future microbiome-informed interventions.

Identifiers

PMID42168704
PMCPMC13194946

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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.