Development and validation of a circulating tumor DNA-based machine learning model for predicting immunotherapy response in non-small cell lung cancer.

Background: Current biomarkers, such as programmed death-ligand 1 (PD-L1) and tumor mutational burden (TMB), have limited predictive value for immune checkpoint inhibitor (ICI) response in non-small cell lung cancer (NSCLC). Machine learning analysis of circulating tumor DNA (ctDNA) can enhance patient stratification via liquid biopsy genomic signatures. We aimed to build a support vector machine (SVM) model using baseline ctDNA to predict ICI benefit in advanced NSCLC. Methods: We trained an SVM model on pretreatment ctDNA whole-exome sequencing (WES) data from the OAK trial cohort (n=303) to predict durable clinical benefit (DCB): response or stable disease (SD) ≥6 months. We used least absolute shrinkage and selection operator (LASSO) regression to select 41 predictive somatic mutations. Model performance was validated in the independent POPLAR trial cohort (n=97) and a regional multicenter cohort (n=41). Receiver operating characteristic (ROC) curve analysis and Kaplan-Meier methods were used to evaluate predictive accuracy and survival outcomes. Results: The ctDNA-based SVM model achieved high accuracy across cohorts: area under the ROC curve (AUC) =0.87 in OAK, 0.92 in POPLAR, and 0.83 in the local cohort. Patients classified as SVM-low (score >0.55) had significantly longer median progression-free survival (mPFS) [12.80 Conclusions: Our ctDNA-based SVM model accurately predicts DCB and survival outcomes in NSCLC patients receiving ICIs. By using a single baseline liquid biopsy, this model could streamline immunotherapy decision-making without requiring longitudinal monitoring.