e20017 Background: Lung squamous cell carcinoma (LUSC) is the second most common subtype of lung cancer. Due to its low driver gene mutation rate, immunotherapy has become the first-line treatment for most patients. However, comprehensive tools to predict immunotherapy efficacy remain inadequately established. The role of gut microbiota and their metabolites in predicting response to immune checkpoint inhibitor (ICI) therapy for LUSC is largely unclear. This study aimed to explore the predictive value of gut microbiota and their metabolites for ICI efficacy in LUSC patients, as well as their regulatory effects on the immune system. Methods: Twenty-seven LUSC patients were enrolled and stratified into two groups by progression-free survival (PFS): responders (PFS ≥ 6 months, n = 16) and non-responders (PFS < 6 months, n = 11). A total of 54 fecal samples were collected at three time points: baseline (before immunotherapy), response (partial response PR), and progression (progressive disease PD). Integrated metagenomic and untargeted metabolomic analyses were performed within and between the two groups. Machine learning models were used to identify potential biomarkers linked to treatment outcomes. Results: Baseline gut microbiome composition was comparable between the two cohorts. During immunotherapy, non-responders showed progressive gut microbiota dysregulation, along with increased microbial diversity. Genera including Cryptobacterium, Faecalibacterium, Alistipes, and Lactimicrobium were identified as potential key determinants of treatment response before immunotherapy initiation. Moreover, relative abundances of probiotic genera (Romboutsia, Lactobacillus, Akkermansia, Lactiplantibacillus, Ligilactobacillus) and "pathogenic" genera (Finegoldia, Mycoplasma, Desulfobulbus, Peptoniphilus, Mycobacterium, Streptococcus) emerged as promising indicators for real-time monitoring of ICI efficacy. Metabolomic profiling revealed that lipid metabolic pathways play a pivotal role in immunotherapy, with multiple lipid metabolites showing significant level alterations. Furthermore, machine learning models identified static biomarkers (PG(i-19:0/PGE2), SM(d18:2(4E,14Z)/6-keto-PGF1α)) and dynamic biomarkers (DL-2-hydroxystearic acid, isoleucyl-valine, 22-hydroxydocosanoic acid, (9Z)-octadecenoic acid), which are highly promising for early prediction and dynamic monitoring of ICI efficacy. Conclusions: Our findings demonstrate that gut microbiota and their metabolites may modulate the efficacy of immunotherapy in LUSC patients. The identified static and dynamic biomarkers possess substantial potential for the early prediction and real-time monitoring of treatment responses.
Ding et al. (Thu,) studied this question.