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Gut microbiome functional pathways outperform taxonomic profiles in predicting immune checkpoint inhibitor response
Feifei Wei1,2, Yoshiro Nakahara3,4, Junya Isobe5
1Division of Cancer Immunotherapy, Kanagawa Cancer Center Research Institute, Yokohama, Japan.
Introduction:
Lung 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.
Methods:
Pretreatment 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. Machine-learning models were subsequently developed based on MetaCyc pathway features to predict treatment response, with nested internal and external validation to ensure robustness and SHapley Additive exPlanations (SHAP) analysis for model interpretability.
Results:
Of all the feature sets tested, the functional profiles derived from the MetaCyc pathways exhibited the strongest association with the RECIST-defined response. A four-pathway signature, comprising PWY-4984 (urea cycle), SALVADEHYPOX-PWY (adenosine nucleotide degradation), OANTIGEN-PWY (O-antigen biosynthesis in E. coli), and PWY-5088 (L-glutamate degradation VIII to propanoate), achieved robust predictive performance, substantially outperforming any single feature. SHAP analysis confirmed that the primary drivers of responder classification were pathways involved in nitrogen metabolism and short-chain fatty acid biosynthesis.
Conclusions:
In this study, gut microbial functional profiles consistently outperformed taxonomic features in predicting ICI response in patients with NSCLC. These findings suggest that metabolic pathway-based signatures may capture functional microbiome-host interactions more effectively and hold greater promise as translatable, safer targets for precision intervention, particularly through metabolite-oriented strategies.
Insights
Gut microbiome function, not just its composition, predicts non-small cell lung cancer (NSCLC) response to immune checkpoint inhibitors (ICIs). Functional pathways, particularly those in nitrogen metabolism, offer promising targets for improving ICI therapy effectiveness.
Area of Science:
- Oncology
- Microbiome Research
- Immunotherapy
Background:
- Lung cancer, particularly non-small cell lung cancer (NSCLC), is a leading cause of cancer mortality globally.
- Immune checkpoint inhibitors (ICIs) have improved NSCLC treatment, but patient responses vary significantly.
- The gut microbiome's role in modulating ICI efficacy is increasingly recognized, yet studies often focus on taxonomic composition over functional capacity.
Purpose of the Study:
- To systematically compare the predictive power of gut microbiome taxonomic versus functional features for various immune checkpoint inhibitor (ICI)-related outcomes in NSCLC patients.
- To identify specific microbial functional pathways associated with treatment response, immune-related adverse events (irAEs), progression-free survival (PFS), and overall survival (OS).
Main Methods:
- 16S rRNA sequencing was used to profile pretreatment fecal samples from 77 Japanese NSCLC patients receiving ICIs.
- Six feature sets (three taxonomic: family, genus, species; three functional: KEGG Orthology, Enzyme Commission, MetaCyc pathways) were analyzed for associations with clinical outcomes.
- Machine-learning models based on MetaCyc pathways were developed and validated to predict treatment response, with SHapley Additive exPlanations (SHAP) for interpretability.
Main Results:
- Functional profiles derived from MetaCyc pathways showed the strongest association with RECIST-defined treatment response compared to all other feature sets.
- A four-pathway signature (PWY-4984, SALVADEHYPOX-PWY, OANTIGEN-PWY, PWY-5088) demonstrated robust predictive performance for treatment response.
- SHAP analysis identified pathways involved in nitrogen metabolism and short-chain fatty acid biosynthesis as key predictors of responder classification.
Conclusions:
- Gut microbial functional profiles significantly outperform taxonomic features in predicting ICI response in NSCLC patients.
- Metabolic pathway-based signatures offer a more effective approach to capturing functional microbiome-host interactions relevant to ICI therapy.
- Functional microbiome signatures hold promise as translatable and safer targets for precision medicine interventions in NSCLC, potentially through metabolite-oriented strategies.