Ultrasensitive Detection of Stage I Lung Adenocarcinoma Using Cell-Free DNA Breakpoint Motif Profiling: A Prospective Cohort Study

Background: Early diagnosis benefits lung cancer patients with higher survival, but most patients are diagnosed after metastasis. Although cell-free DNA (cfDNA) analysis holds promise for early detection, the current sensitivity in early-stage lung cancer is unsatisfying. We aimed to establish a predictive model using cfDNA fragmentomics for high-sensitivity detection of invasive Stage I lung adenocarcinoma (ADC). Methods: Stage I lung ADC patients from three medical centers were enrolled whose plasma cfDNA samples were profiled by whole-genome sequencing (WGS). Multiple cfDNA fragmentomic features and machine learning models were compared in the training cohort (Center I) to build a predictive model. Its performance was assessed in an internal (Center I) and external (Center II/III) validation cohort. Findings: 292 patients were enrolled in the study. We constructed a logistic regression model using cfDNA 6bp breakpoint-motif feature in the training cohort (cancer: 150, healthy: 115). The model yielded high sensitivity (98·0%) and specificity (94·7%) in the internal validation cohort [cancer: 102, healthy: 75, Area Under the Curve (AUC): 0·985], while a 92·5% sensitivity and a 90·0% specificity were achieved in the external validation cohort (cancer: 40, healthy: 40, AUC: 0·954). The model exhibited an exceptional ability for identifying early-stage (100% sensitivity for minimally invasive adenocarcinoma, MIA) and <1 cm (92·9%-97·7% sensitivity) tumors. Furthermore, its predictive power remained high when reducing sequencing depth to 0·5× (AUC: 0·977 and 0·931 for the two validation cohorts). Interpretation: We established an ultrasensitive assay for detecting Stage I lung adenocarcinoma with early-stage, small-size tumors using the cfDNA breakpoint-motif feature. Funding: National Natural Science Foundation of China (82002451), Institutional Fundamental Research Funds (2018PT32033), Ministry of Education, Innovation Team Development Project (IRT- 17R10), Beijing Hope Run Special Fund of Cancer Foundation of China (LC2019B15). Declaration of Interest: XC, RL, HB, XW, and YS are employees of Nanjing Geneseeq Technology Inc., China. All other authors have declared no conflicts of interest. Ethical Approval: All study protocols were approved by the ethics committee of each hospital and following the Good Clinical Practice Guidelines of the International Conference on Harmonization.