Sensitive detection of stage I lung adenocarcinoma using plasma cell-free DNA breakpoint motif profiling

• Published in: EBioMedicine 2022-07, Vol.81, p.104131-104131, Article 104131
• Main Authors: Guo, Wei, Chen, Xin, Liu, Rui, Liang, Naixin, Ma, Qianli, Bao, Hua, Xu, Xiuxiu, Wu, Xue, Yang, Shanshan, Shao, Yang, Tan, Fengwei, Xue, Qi, Gao, Shugeng, He, Jie
• Format: Article
• Language: English
• Subjects: Cell-free DNA; Early detection; Fragmentomics; Lung cancer; Whole-genome sequencing
• ISSN: 2352-3964; 2352-3964
• DOI: 10.1016/j.ebiom.2022.104131

BACKGROUNDEarly diagnosis benefits lung cancer patients with higher survival, but most patients are diagnosed after metastasis. Although cell-free DNA (cfDNA) analysis holds promise, its sensitivity for detecting early-stage lung cancer is unsatisfying. We leveraged cfDNA fragmentomics to develop a predictive model for invasive stage I lung adenocarcinoma (LUAD). METHODS292 stage I LUAD patients from three medical centers were included together with 230 healthy controls whose plasma cfDNA samples were profiled by whole-genome sequencing (WGS). Multiple cfDNA fragmentomic motif features and machine learning models were compared in the training cohort to select the best model. Model performance was assessed in the internal and external validation cohorts and an additional dataset. FINDINGSA logistic regression model using the 6bp-breakpoint-motif feature was selected. It yielded 98·0% sensitivity and 94·7% specificity in the internal validation cohort [Area Under the Curve (AUC): 0·985], while 92·5% sensitivity and 90·0% specificity were achieved in the external validation cohort (AUC: 0·954). It is sensitive for early-stage (100% sensitivity for minimally invasive adenocarcinoma, MIA) and