Source: europepmc · Origin: IT · Candeliere F, Busi E, Cerri S, Sola L, Lombardi M, Greco S, Pedroni S, Amaretti A, Raimondi S, Chiavelli C, Vitale MG, B · Cancer immunology, immunotherapy : CII · 2026-05-26
URL: https://pubmed.ncbi.nlm.nih.gov/42189287/
AI rationale (4/5, tier: emerging): Large-scale metagenomic cohort linking enterotype-specific dysbiosis patterns to immune response; mechanistic bridge between microbiota composition and host phenotype.
The gut microbiota appears to play a critical role in modulating antitumor immune responses and influencing the efficacy of cancer immunotherapy drugs such as immune checkpoint inhibitors. However, the identification of consistent microbial biomarkers of response remains a significant challenge. This lack of consensus is largely driven by multi-source heterogeneity, including geographic variations in lifestyle, and high inter-individual variability. We hypothesize that these inconsistencies arise because microbiome composition is not uniform but organized into distinct enterotypes. To address this, we performed an integrated metagenomic analysis of 569 fecal samples from oncological patients affected by different tumor types treated with immunotherapy. The samples were clustered into two main enterotypes, E1 and E2, each of them containing two subclusters. A total of 166 species (e.g., Collinsella spp., Blautia spp., Bacteroides spp.) were identified as enterotype-specific biomarkers. A preliminary independent concordance assessment of these biomarkers was conducted in 19 oncologic patients with exceptional response to immunotherapy, providing an initial confirmation of selected enterotype-associated signals. Furthermore, we evaluated the predictive potential of gut microbiota profiles for immunotherapy outcomes through machine learning techniques. The models showed encouraging, albeit moderate, performance in the heterogeneous full dataset, supporting the potential of microbiome-based stratification as an exploratory framework for patient classification, while indicating that further validation is needed before clinical application.