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 - Summary - MDSpire
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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
To evaluate the predictive significance of taxonomic versus functional gut microbiome characteristics on immune checkpoint inhibitor (ICI) therapy outcomes in non-small cell lung cancer (NSCLC).
Key Findings:
Functional profiles from MetaCyc pathways showed the strongest correlation with treatment response, indicating their predictive power.
A signature of four specific pathways was identified as highly predictive of treatment outcomes.
Key factors influencing responder classification were linked to nitrogen metabolism and short-chain fatty acid biosynthesis, highlighting their importance.
Interpretation:
Functional microbiome profiles provide a more accurate reflection of interactions between the microbiome and host, suggesting potential for precision interventions focused on metabolic pathways, which may enhance treatment efficacy.
Limitations:
Study limited to a specific ethnic group (Japanese individuals), which may affect generalizability to other populations.
Sample size of 77 may limit the robustness of findings and their applicability.
Conclusion:
Functional characteristics of the gut microbiome are superior to taxonomic features in predicting responses to ICIs in NSCLC, highlighting the importance of metabolic pathways in treatment outcomes.
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