A clinically effective model based on cell-free DNA methylation and low-dose CT for risk stratification of pulmonary nodules
Wenhua Liang, Jinsheng Tao, Chao Cheng, Haitao Sun, Zhujia Ye, Shuangxiu Wu, Yubiao Guo, Jiaqing Zhang, Qunqing Chen, Dan Liu, Lunxu Liu, Hui Tian, Teng Lin, Nanshan Zhong, Jian‐Bing Fan, Jianxing He
Abstract
Accurate, non-invasive, and cost-effective tools are needed to assist pulmonary nodule diagnosis and management due to increasing detection by low-dose computed tomography (LDCT). We perform genome-wide methylation sequencing on malignant and non-malignant lung tissues and designed a panel of 263 differential DNA methylation regions, which is used for targeted methylation sequencing on blood cell-free DNA (cfDNA) in two prospectively collected and retrospectively analyzed multicenter cohorts. We develop and optimize an integrative model for risk stratification of pulmonary nodules based on 40 cfDNA methylation biomarkers, age, and five simple computed tomography (CT) imaging features using machine learning approaches and validate its good performance in two cohorts. Using the two-threshold strategy can effectively reduce unnecessary invasive surgeries, overtreatment costs, and injury for patients with benign nodules while advising immediate treatment for patients with lung cancer, which can potentially improve the overall diagnosis of lung cancer following LDCT/CT screening. • A concise methylation biomarker panel can non-invasively classify pulmonary nodules • The methylation model outperforms clinical tools in classifying pulmonary nodules • Five simple CT features and age significantly improve the diagnostic performance • Two-threshold strategy enables accurate risk stratification of pulmonary nodules Liang et al. present an integrated model consisting of 40 lung-cancer-specific cfDNA methylation markers, one clinical feature, and five CT imaging features for non-invasively accurate diagnosis and management of pulmonary nodules. The two-threshold strategy of the model can reduce unnecessary invasive surgeries and improve the overall diagnosis of lung cancer.