BACKGROUND: Low-dose computed tomography (LDCT) suffers from a high false-positive rate in the evaluation of pulmonary nodules. Circulating tumor DNA (ctDNA) methylation is a promising complementary biomarker, but its detection is hindered by the highly fragmented nature of ctDNA. METHODS: We developed SAMT-Seq (Single-Strand Amplification Methylation-Targeted Sequencing), optimized for methylation detection in fragmented ctDNA. Lung cancer-specific methylation markers were identified from in-house cohort and TCGA database and validated in paired tissue and plasma samples from 30 early-stage lung cancer patients. A panel of 30 key markers was selected using LASSO regression in a training cohort (n=239). A Gaussian process classifier was developed and validated in two independent cohorts (n=59 and n=207). RESULTS: SAMT-Seq demonstrated superior analytical sensitivity and on-target efficiency compared to a standard commercial Swift method. The 30-marker classifier yielded area under the curve (AUC) of 0.95, 0.95, and 0.92 in the Training Cohort, Validation Cohort 1 and 2, respectively. Notably, it maintained robust performance across nodule types (solid/subsolid), sizes, smoking status, and insitu carcinoma. With a predefined threshold, the model achieved specificity of 100% and 92.16% in Validation Cohort 1 and 2, respectively, suggesting its potential utility in reducing false-positive classifications. CONCLUSIONS: We developed a high-specificity ctDNA methylation classifier that serves as a practical, complementary tool for risk stratification of pulmonary nodules, with the potential to significantly reduce unnecessary invasive procedures. Ongoing prospective diagnostic validation study are evaluating its clinical performance.
Guo et al. (Mon,) studied this question.