Gut microbiome for predicting immune checkpoint blockade-associated adverse events

• Published in: Genome medicine 2024-01, Vol.16 (1), p.16-18, Article 16
• Main Authors: Hu, Muni, Lin, Xiaolin, Sun, Tiantian, Shao, Xiaoyan, Huang, Xiaowen, Du, Weiwei, Guo, Mengzhe, Zhu, Xiaoqiang, Zhou, Yilu, Tong, Tianying, Guo, Fangfang, Han, Ting, Wu, Xiuqi, Shi, Yi, Xiao, Xiuying, Zhang, Youwei, Hong, Jie, Chen, Haoyan
• Format: Article
• Language: English
• Subjects: Adverse events; Algorithms; Biomarkers; Cancer therapies; Digestive system; Disease; Drugs; Enzymes; Genes; Gut microbiome; Hospitals; Immune checkpoint inhibitors; Immune-related adverse events; Inflammatory bowel disease; Intestinal microflora; Machine learning; Menaquinones; Metabolomics; Metadata; Microbiomes; Microbiota; Music in education; Patients; Physiological aspects; Programmed death 1 (PD-1)/programmed death ligand 1 (PD-L1); RNA; rRNA 16S; Shotguns; Software; Taxonomy; Transcriptomes
• ISSN: 1756-994X; 1756-994X
• DOI: 10.1186/s13073-024-01285-9

The impact of the gut microbiome on the initiation and intensity of immune-related adverse events (irAEs) prompted by immune checkpoint inhibitors (ICIs) is widely acknowledged. Nevertheless, there is inconsistency in the gut microbial associations with irAEs reported across various studies. We performed a comprehensive analysis leveraging a dataset that included published microbiome data (n = 317) and in-house generated data from 16S rRNA and shotgun metagenome samples of irAEs (n = 115). We utilized a machine learning-based approach, specifically the Random Forest (RF) algorithm, to construct a microbiome-based classifier capable of distinguishing between non-irAEs and irAEs. Additionally, we conducted a comprehensive analysis, integrating transcriptome and metagenome profiling, to explore potential underlying mechanisms. We identified specific microbial species capable of distinguishing between patients experiencing irAEs and non-irAEs. The RF classifier, developed using 14 microbial features, demonstrated robust discriminatory power between non-irAEs and irAEs (AUC = 0.88). Moreover, the predictive score from our classifier exhibited significant discriminative capability for identifying non-irAEs in two independent cohorts. Our functional analysis revealed that the altered microbiome in non-irAEs was characterized by an increased menaquinone biosynthesis, accompanied by elevated expression of rate-limiting enzymes menH and menC. Targeted metabolomics analysis further highlighted a notably higher abundance of menaquinone in the serum of patients who did not develop irAEs compared to the irAEs group. Our study underscores the potential of microbial biomarkers for predicting the onset of irAEs and highlights menaquinone, a metabolite derived from the microbiome community, as a possible selective therapeutic agent for modulating the occurrence of irAEs.