Metagenomic Profiling Identifies Gut Microbial Signatures Associated with Immunotherapy Response in Lung Cancer

A study published in Frontiers in Cellular and Infection Microbiology has made significant progress in understanding the relationship between the gut microbiome and immunotherapy response in lung cancer patients. Researchers at the Cancer Hospital of China Medical University conducted a comprehensive analysis of publicly available global metagenomic datasets to identify gut microbial signatures associated with immune response in lung cancer. The study found that certain bacterial taxa were significantly elevated in responders compared to non-responders, with antibiotic administration further amplifying this difference.

Key Takeaways:

• The study integrated publicly available global metagenomic datasets to identify gut microbial signatures associated with immune response in lung cancer.
• Microbial a-diversity was significantly elevated in responders compared to non-responders, with antibiotic administration further amplifying this difference.
• The researchers identified two pivotal microbial biomarkers, and , which were strongly associated with immunotherapy efficacy.
• A random forest-based classifier achieved robust predictive performance, with area under the curve (AUC) values of 0.82 and 0.79 at the species and genus levels, respectively.
• The findings underscore the prognostic relevance of specific taxa and establish a foundation for developing microbiome-informed, personalized immunotherapeutic strategies.
• The study was conducted by Yuhang Zhou and his team at the Cancer Hospital of China Medical University, with support from the National Natural Science Foundation of China and Liaoning Revitalization Talents Program.
• The researchers used machine learning approaches to construct a predictive framework for immunotherapy response.

Statistics:

• 209 fecal metagenomic samples were included in the study, comprising 154 baseline samples and 55 longitudinal samples collected during immunotherapy.
• The study identified 8 machine learning algorithms, with the optimal model selected to construct a predictive framework for immunotherapy response.
• The predictive performance of the random forest-based classifier achieved AUC values of 0.82 and 0.79 at the species and genus levels, respectively.
• Microbial a-diversity was significantly elevated in responders compared to non-responders, with a fold change of 2.34 at the species level.

Sources:

• Exploring fecal microbiota signatures associated with immune response and antibiotic impact in NSCLC: insights from metagenomic and machine learning approaches. Frontiers in Cellular and Infection Microbiology, 2025;15:1591076.
• Cancer Weekly. August 26, 2025; p 5670.