Decision model for durable clinical benefit from front- or late-line immunotherapy alone or with chemotherapy in non-small cell lung cancer
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View abstract on PubMed
Summary
This summary is machine-generated.This study identifies key biomarkers for predicting durable clinical benefits from immune checkpoint inhibitors in non-small cell lung cancer. A transparent decision tree model accurately predicts patient response, aiding treatment selection.
Area Of Science
- Oncology
- Immunotherapy
- Biomarker Discovery
Background
- Predictive biomarkers for immune checkpoint inhibitors (ICIs) in non-small cell lung cancer (NSCLC) lack conclusive evidence.
- Machine learning models for ICI treatment prediction are often opaque and impractical for clinical use.
Purpose Of The Study
- To provide robust evidence for predictive biomarkers in NSCLC.
- To develop a transparent decision tree model for predicting durable clinical benefits (DCBs) from ICIs.
Main Methods
- Consolidated data from 3,288 ICI-treated NSCLC patients across real-world and clinical trial cohorts.
- Examined over 50 features to identify significant biomarkers for DCB prediction.
- Developed and validated a decision tree model (DT10) incorporating clinicopathological and genomic markers.
Main Results
- Identified tumor histology, PD-L1 expression, tumor mutational burden, and specific gene mutations (EGFR, KRAS, KEAP1, STK11, TP53) as significant predictors.
- The DT10 model achieved an AUC of 0.82 in predicting DCB, outperforming other models.
- DT10-predicted responders showed longer survival and an inflamed tumor immune phenotype, while non-responders exhibited a desert immune phenotype.
Conclusions
- The developed decision tree model effectively predicts durable clinical benefit from ICI treatment in NSCLC.
- The model provides clinicians with valuable, cost-effective insights for treatment efficacy prediction across different patient groups and treatment lines.
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