Predictable regulation of gut microbiome in immunotherapeutic efficacy of gastric cancer.

Abstract

Immunotherapy has showcased remarkable progress in the management of gastric cancer (GC), prompting the need to proactively identify and classify patients suitable for immunotherapy. Here, 30 patients were enrolled and stratified into three groups (PR, partial response; SD, stable disease; PD, progressive disease) based on efficacy assessment. 16S rRNA sequencing were performed to analyze the gut microbiome signature of patients at three timepoints. We found that immunotherapy interventions perturbed the gut microbiota of patients. Additionally, although differences at the enterotype level did not distinguish patients' immunotherapy response, we identified 6, 7, and 19 species that were significantly enriched in PR, SD, and PD, respectively. Functional analysis showed that betalain biosynthesis and indole alkaloid biosynthesis were significantly different between the responders and non-responders. Furthermore, machine learning model utilizing only bacterial biomarkers accurately predicted immunotherapy efficacy with an Area Under the Curve (AUC) of 0.941. Notably, Akkermansia muciniphila and Dorea formicigenerans played a significant role in the classification of immunotherapy efficacy. In conclusion, our study reveals that gut microbiome signatures can be utilized as effective biomarkers for predicting the immunotherapy efficacy for GC.