Gut microbial markers of immunotherapy response in melanoma: a cross-cohort analysis including the first Russian dataset

Melanoma is an aggressive malignancy with a significant risk of mortality. In recent years, treatment strategies have undergone a paradigm shift with the advent of immunotherapy, particularly immune checkpoint inhibitors (ICIs). Despite notable clinical success, a substantial proportion of patients fail to respond or eventually develop resistance to ICIs. Emerging evidence highlights the gut microbiota as a critical modulator of host immune responses and is one of the potential determinants of immunotherapy efficacy. We performed a cross-cohort analysis of gut microbiome profiles from melanoma patients treated with ICIs. The study integrated the first Russian cohort (62 patients) with six previously published international datasets, comprising a total of 490 patients across seven cohorts. In all cases, metagenomic sequencing was performed using various Illumina platforms, and raw sequencing data were processed using a unified bioinformatic pipeline. Analysis revealed 527 metagenome-assembled genomes (MAGs) significantly associated with treatment outcome: 239 with response and 288 with non-response. Notably, the species Faecalibacterium sp900539945, Phocaeicola vulgatus, Bifidobacterium adolescentis, Faecalibacterium taiwanense, and Gemmiger qucibialis were consistently associated with response, while Enterobacter ludwigii was linked to non-response. Analysis of the Russian cohort revealed both conserved and population-specific microbial signatures, highlighting the coexistence of globally shared and region-dependent microbiome features. Our results also show that species-level annotations may obscure opposing response associations within the same taxa, highlighting the need for MAGs or strain profiling. Together, this study demonstrates that cross-cohort analysis enables the identification of robust and reproducible bacterial markers of immunotherapy response, providing a foundation for microbiome-based prediction and modulation strategies in melanoma.