A Novel Approach for Non-Invasive Diagnosis of Pulmonary Nodules Using Low-Depth Whole Genome Sequencing of Cell-Free DNA

Background: The differentiation between benign and malignant pulmonary nodules is a diagnostic challenge. Here we developed a genome-wide approach to explore the characteristics of cell-free DNA (cfDNA) reads distributions using low-depth sequencing (LD-seq) to diagnose pulmonary nodules. Methods: Low-depth whole genome sequencing (WGS) was performed on cfDNA extracted from 420 plasma samples from individuals with pulmonary nodules which were no more than 30mm in diameter determined by CT scan. The read distribution patterns of cfDNA were analyzed and used to establish the model for distinguishing benign from malignant pulmonary nodules. Findings: We proposed the concept of weighted reads distribution difference (WRDD) based on copy number alteration (CNA) of cfDNA to construct the benign and malignant diagnostic (BEMAD) model. In the training cohort including 360 plasma samples, the model achieved an average area under the curve (AUC) value of 0.84 in 10-fold cross-validation. The model was validated in an independent cohort of 60 plasma samples, obtaining an AUC of 0.87. The analysis of crucial features in the BEMAD model identified repeat regions which were associated with microsatellite instability (MSI). Interpretation: This study provides a novel non-invasive approach for diagnosis of benign and malignant pulmonary nodules, which has the potential of being applicable to the management of undiagnosed pulmonary nodules, avoiding unnecessary invasive procedures. Funding: This work was supported by National Key Research and Development Program of China Grant (grant number 2016YFC0905501 to CLW); National Natural Science Foundation of China (grant number 81772484 to CLW); and Guangdong Enterprise Key Laboratory of Human Disease Genomics (grant number 2020B1212070028 to KW). Declaration of Interest: None to declare. Ethical Approval: This study was approved by the Ethical Committee of Tianjin Medical University Cancer Hospital (Approval No. bc2016014, bc2018009, bc2019091), and all participants provided written informed consent.