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Gut microbiome for predicting immune checkpoint blockade-associated adverse events
Muni Hu (Shanghai Jiao Tong University, Renji Hospital), Xiaolin Lin (Shanghai Jiao Tong University, Renji Hospital), Tiantian Sun (Shanghai Jiao Tong University, Renji Hospital), Xiaoyan Shao (Xuzhou Central Hospital), Xiaowen Huang (Shanghai Jiao Tong University, Renji Hospital), Weiwei Du (Xuzhou Central Hospital), Mengzhe Guo (Xuzhou Medical College), Xiaoqiang Zhu (Shanghai Jiao Tong University, Renji Hospital), Yilu Zhou (Shanghai Jiao Tong University, Renji Hospital), Tianying Tong (Shanghai Jiao Tong University, Renji Hospital), Fangfang Guo (First Affiliated Hospital Zhejiang University, Zhejiang University), Ting Han (Shanghai Jiao Tong University, Renji Hospital), Xiuqi Wu (Shanghai Jiao Tong University, Renji Hospital), Yi Shi (Shanghai Jiao Tong University), Xiuying Xiao (Shanghai Jiao Tong University, Renji Hospital), Youwei Zhang (Xuzhou Medical College, Xuzhou Central Hospital), Jie Hong (Shanghai Jiao Tong University, Renji Hospital), Haoyan Chen (Shanghai Jiao Tong University, Renji Hospital)
Résumé : 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 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. CONCLUSIONS: 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.