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Published on : 2025-12-21 15:31:40
Article Code: AMJ-21-12-2025-66666
Title : Gut Microbiome Signatures Predicting Immune Checkpoint InhibitorResponse in Metastatic Melanoma. A Multi-Omics Machine Learning Analysis
Author(s) : Test user
Abstract :
Background: Response rates to immune checkpoint inhibitors (ICIs) in metastatic melanoma remain heterogeneous(40-60%), with gut microbiome composition emerging as a key modulator of anti-tumor immunity. However,predictive microbiome signatures lack validation across diverse cohorts and omic layers. Objectives: To develop and validate a multi-omics model integrating gut microbiome, metabolomic, and clinical data to predict ICI response in metastatic melanoma patients. Methods: We analyzed 456 patients with metastatic melanoma from six academic centers (2019-2023). Shotgun metagenomic sequencing of fecal samples was performed pre-treatment, alongside plasma metabolomics (LC-MS) and immune profiling (CyTOF). Machine learning models (XGBoost, random forest) were trained to predict objective response (RECIST 1.1) at 6 months. External validation was performed on an independent cohort (n=112). Results: A 12-feature microbiome-metabolome signature achieved AUC 0.91 (95% CI: 0.88-0.94) in predicting ICI response. Key predictors included Akkermansia muciniphila abundance (OR=2.34, p=0.001),short-chain fatty acid production capacity, and baseline CD8+ T-cell exhaustion markers. The model stratified patients into high-response (ORR 72.3%) and low-response (ORR 18.7%) groups (p=0.001). External validation AUC was 0.87. Conclusions: Multi-omics integration of gut microbiome and metabolomic data robustly predicts ICI response in melanoma, offering a non-invasive tool for treatment stratification. This framework may extend to other cancer types where microbiome-immune crosstalk is operative.