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Abstract Immune checkpoint inhibitors (ICIs) have revolutionized melanoma treatment, yet patient responses remain highly variable, underscoring the need for predictive biomarkers. Emerging evidence suggests that gut microbiome composition influences ICI efficacy, though findings remain inconsistent across studies. Here, we present a meta-analysis of seven melanoma-associated microbiome cohorts (N=678) using a standardized computational pipeline to integrate microbial species, biosynthetic gene clusters (BGCs), and functional pathways. We identifyFaecalibacteriumSGB15346 as a key species enriched in responders, alongside RiPP biosynthetic class and pathways involved in short-chain fatty acid fermentation. Conversely, dTDP-sugar biosynthesis correlates with non-response. Our results highlight microbial signatures and metabolic pathways associated with ICI outcomes, offering potential targets for microbiome-based interventions in personalized immunotherapy.
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Melody: meta-analysis of microbiome association studies for discovering generalizable microbial signatures
Abstract Standard protocols for meta-analysis of association studies are inadequate for microbiome data due to their complex compositional structure, leading to inaccurate and unstable microbial signature selection. To address this issue, we introduce Melody, a framework that generates, harmonizes, and combines study-specific summary association statistics to powerfully and robustly identify microbial signatures in meta-analysis. Comprehensive and realistic simulations demonstrate that Melody substantially outperforms existing approaches in prioritizing true signatures. In the meta-analyses of five studies on colorectal cancer and eight studies on the gut metabolome, we showcase the superior stability, reliability, and predictive performance of Melody-identified signatures.
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- Award ID(s):
- 2054346
- PAR ID:
- 10628853
- Publisher / Repository:
- Springer Science + Business Media
- Date Published:
- Journal Name:
- Genome Biology
- Volume:
- 26
- Issue:
- 1
- ISSN:
- 1474-760X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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