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 - Scorecard - 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
Clinical Scorecard: Functional Pathways of the Gut Microbiome Exceed Taxonomic Profiles in Predicting Responses to Immune Checkpoint Inhibitors in Non-Small Cell Lung Cancer: An Interpretable Machine Learning Analysis Using SHAP
At a Glance
Category
Detail
Condition
Non-Small Cell Lung Cancer (NSCLC)
Key Mechanisms
Gut microbiome functional profiles influence immune checkpoint inhibitor responses.
Target Population
Japanese individuals diagnosed with NSCLC undergoing immune checkpoint inhibitors.
Care Setting
Kanagawa Cancer Center, Yokohama, Japan.
Key Highlights
Functional profiles from MetaCyc pathways significantly correlate with treatment response.
A signature of four metabolic pathways predicts immune checkpoint inhibitor responses.
Taxonomic characteristics are less predictive than functional microbiome profiles.
Machine learning models enhance understanding of gut microbiome's role in treatment outcomes.
SHAP analysis provides interpretability for predictive models.
Guideline-Based Recommendations
Diagnosis
Evaluate gut microbiome characteristics for predicting treatment responses in NSCLC.
Management
Consider metabolic pathway signatures for precision intervention strategies.
Monitoring & Follow-up
Monitor changes in gut microbiome functional profiles during treatment.
Risks
Inconsistent clinical responses to immune checkpoint inhibitors may occur.
Patient & Prescribing Data
77 Japanese individuals with NSCLC receiving ICIs.
Functional gut microbiome profiles may guide treatment personalization.
Clinical Best Practices
Utilize functional microbiome assessments alongside traditional diagnostics.
Incorporate machine learning approaches to enhance predictive capabilities.
Focus on metabolic pathways for understanding treatment interactions.
