Yang Yanping, Ye Maosong, Song Yijun, Xing Wenyu, Zhao Xing, Li Yufan, Shen Jiacheng, Zhou Jian, Arikawa Kinji, Wu Shengdi, Song Yuanlin, Xu Nuo
Shanghai Institute of Infectious Disease and Biosecurity, Fudan University, Shanghai, China.
Department of Pulmonary and Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai, China.
NPJ Biofilms Microbiomes. 2025 Jul 28;11(1):146. doi: 10.1038/s41522-025-00785-9.
The role of gut dysbiosis in shaping immunotherapy responses is well-recognized, yet its effect on the therapeutic efficacy of chemotherapy and immunotherapy combinations remains poorly understood. We analyzed gut microbiota in non-small cell lung cancer (NSCLC) patients treated with chemo-immunotherapy, comparing responders and non-responders using 16S rRNA sequencing. Responders showed higher microbial richness and abundance of specific genera like Faecalibacterium and Subdoligranulum, and the phylum Firmicutes. Support vector machine (SVM), a machine learning model based on microbial composition, predicted treatment efficacy with the area under the curve (AUC) values of 0.763 for genera and 0.855 for species. Metagenomic analysis revealed significant differences in metabolic pathways, with responders exhibiting higher short-chain fatty acids (SCFAs) production. Fecal microbiota transplantation (FMT) and SCFAs supplementation in mouse models enhanced treatment efficacy by promoting effector T cell activity in tumors. Our study suggests that gut microbiota, through SCFAs production, regulates chemo-immunotherapy efficacy, offering new strategies to improve NSCLC treatment outcomes.
肠道菌群失调在塑造免疫治疗反应中的作用已得到充分认识，但其对化疗和免疫治疗联合疗法疗效的影响仍知之甚少。我们分析了接受化疗免疫治疗的非小细胞肺癌（NSCLC）患者的肠道微生物群，使用16S rRNA测序比较了反应者和无反应者。反应者表现出更高的微生物丰富度以及特定菌属（如粪杆菌属和细小杆菌属）和厚壁菌门的丰度。支持向量机（SVM）是一种基于微生物组成的机器学习模型，预测治疗疗效时，菌属的曲线下面积（AUC）值为0.763，菌种的AUC值为0.855。宏基因组分析揭示了代谢途径的显著差异，反应者表现出更高的短链脂肪酸（SCFA）产量。在小鼠模型中进行粪便微生物群移植（FMT）和补充SCFAs可通过促进肿瘤中效应T细胞活性来提高治疗疗效。我们的研究表明，肠道微生物群通过产生SCFAs来调节化疗免疫治疗的疗效，为改善NSCLC治疗结果提供了新策略。
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