Gut microbiome for predicting immune checkpoint blockade-associated adverse events

作者：Muni Hu, Xiaolin Lin, Tiantian Sun, Xiaoyan Shao, Xiaowen Huang, Weiwei Du, Mengzhe Guo, Xiaoqiang Zhu, Yilu Zhou, Tianying Tong, Fangfang Guo, Ting Han, Xiuqi Wu, Yi Shi, Xiuying Xiao, Youwei Zhang, Jie Hong, Haoyan Chen · 发表于：Genome Medicine · 年份：2024 · DOI：10.1186/s13073-024-01285-9 · 被引用次数：55 · 研究领域：Gut microbiota and health、Cancer Immunotherapy and Biomarkers、Ferroptosis and cancer prognosis

BACKGROUND: 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. METHODS: 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. RESULTS: 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 meta...