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<p>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 <i>Faecalibacterium</i> and <i>Subdoligranulum</i>, 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.</p>

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Gut microbiota and SCFAs improve the treatment efficacy of chemotherapy and immunotherapy in NSCLC

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月28日

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  • Yanping Yang,
  • Maosong Ye,
  • Yijun Song,
  • Wenyu Xing,
  • Xing Zhao,
  • Yufan Li,
  • Jiacheng Shen,
  • Jian Zhou,
  • Kinji Arikawa,
  • Shengdi Wu,
  • Yuanlin Song,
  • Nuo Xu

摘要

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.