Clinical cancer research : an official journal of the American Association for Cancer Research · 2026
A Noninvasive Circulating Tumor DNA Methylation Classifier to Identify Benign Pulmonary Nodules.
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Guo Qiaomei, Deng Chaoqiang, Pan Xufeng, Liu Jiatao, Yang Guohua, Sun Yuteng, Shi Feng, Zhao Mingna, Wang Xueqing, Yang Shuxuan, Liang Xiaohui, Xu Wanxing, Ye Jingwen, Huang Xin, Wu Jiaojiao, Wang Wenyu, Guo Zhiwei, Hu Rongjun, Yu Yue, Zhang Yang, Lou Jiatao, Wang Lin
Аннотация
PURPOSE: 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.
EXPERIMENTAL DESIGN: 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).
RESULTS: 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.
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 studies are evaluating its clinical performance.
EXPERIMENTAL DESIGN: 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).
RESULTS: 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.
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 studies are evaluating its clinical performance.