Gut microbiome functional pathways outperform taxonomic profiles in predicting immune checkpoint inhibitor response in non-small cell lung cancer: an interpretable machine learning approach with SHAP

IntroductionLung cancer remains the leading cause of cancer mortality worldwide, with non-small cell lung cancer (NSCLC) accounting for the majority of cases. Although immune checkpoint inhibitors (ICIs) have transformed the therapeutic landscape of NSCLC, clinical responses remain highly variable.

Emerging evidence implicates the gut microbiome in modulating the outcomes of ICI treatment; however, most studies to date have focused on taxonomic composition rather than microbial functional capacity. This study aimed to systematically compare the predictive value of taxonomic versus functional gut microbiome features across multiple ICI-related outcomes.MethodsPretreatment fecal samples from 77 Japanese patients with NSCLC receiving ICIs were profiled using 16S rRNA sequencing.

Six feature sets, comprising three taxonomic (family, genus, and species) and three functional (KEGG Orthology, Enzyme Commission, and MetaCyc pathways), were assessed using permutational multivariate analysis of variance for their association with clinical outcomes, including treatment response, irAEs, progression-free survival, and overall survival.