A Noninvasive Circulating Tumor DNA Methylation Classifier to Identify Benign Pulmonary Nodules

Low-dose computed tomography 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.

We developed single-strand amplification methylation-targeted sequencing (SAMT-Seq), optimized for methylation detection in fragmented ctDNA. Lung cancer-specific methylation markers were identified from in-house cohort and The Cancer Genome Atlas database and validated in paired tissue and plasma samples from 30 patients with early-stage lung cancer. A panel of 30 key markers was selected using least absolute shrinkage and selection operator (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).

SAMT-Seq demonstrated superior analytic sensitivity and on-target efficiency compared with a standard commercial Swift method. The 30-marker classifier yielded area under the curve values of 0.95, 0.95, and 0.92 in the training cohort and validation cohorts 1 and 2, respectively. Notably, it maintained robust performance across nodule types (solid/subsolid), sizes, smoking status, and in situ 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.

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 studies are evaluating its clinical performance.