A novel approach for the non-invasive diagnosis of pulmonary nodules using low-depth whole-genome sequencing of cell-free DNA
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Background: Differentiating between benign and malignant pulmonary nodules is a diagnostic challenge, and inaccurate detection can result in unnecessary invasive procedures. Cell-free DNA (cfDNA) has been successfully utilized to detect various solid tumors. In this study, we developed a genome-wide approach to explore the characteristics of cfDNA sequencing reads obtained by low-depth whole-genome sequencing (LD-WGS) to diagnose pulmonary nodules. Methods: LD-WGS was performed on cfDNA extracted from 420 plasma samples from individuals with pulmonary nodules that were no more than 30 mm in diameter, as determined by computed tomography (CT). The sequencing read distribution patterns of cfDNA were analyzed and used to establish a model for distinguishing benign from malignant pulmonary nodules. Results: We proposed the concept of weighted reads distribution difference (WRDD) based on the copy number alterations (CNAs) of cfDNA to construct a benign and malignant diagnostic (BEMAD) algorithm model. In a training cohort of 360 plasma samples, the model achieved an average area under the receiver operating characteristic (ROC) 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 value of 0.87. The BEMAD model could distinguish benign from malignant nodules at a sensitivity of 74% and a specificity of 86%. Furthermore, analysis of the critical features of the cfDNA using the BEMAD model identified repeat regions that were associated with microsatellite instability, which is an important indicator of tumorigenesis. Conclusions: This study provides a novel non-invasive diagnostic approach to discriminate between benign and malignant pulmonary nodules to avoid unnecessary invasive procedures.
Originalsprog | Engelsk |
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Tidsskrift | Translational Lung Cancer Research |
Vol/bind | 11 |
Udgave nummer | 10 |
Sider (fra-til) | 2094-2110 |
Antal sider | 17 |
ISSN | 2218-6751 |
DOI | |
Status | Udgivet - 2022 |
Bibliografisk note
Funding Information:
The authors appreciate the academic support from the AME Lung Cancer Collaborative Group. We thank Giuseppe Giaccone and Olivier Elemento from Weill-Cornell Medicine for helpful comments. We also thank Li Zhang from Sun Yat-sen University Cancer Center for helpful discussions. Computational and sequencing resources were provided by China National GeneBank (CNGB). This study made use of data generated by The Chinese University of Hong Kong (CUHK) Circulating Nucleic Acids Research Group, as reported by Jiang et al. in Cancer Discov and by the Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA, as reported by Mathios et al. in Nat Commun. We thank Hu Xi from The CUHK (Data for EGAS00001003409) and Jillian Phallen from Johns Hopkins University School of Medicine (Data for EGAS00001005340) for providing help with data access (https://ega-archive.org/studies/EGAS00001003409, Accession No. EGAS00001003409; and https://ega-archive.org/studies/EGAS00001005340, Accession No. EGAS00001005340). Finally, we thank H. Nikki March, PhD, from Liwen Bianji (Edanz) (https://www. liwenbianji.cn/) for editing the English text of a draft of this manuscript. Funding: This work was supported by the National Key Research and Development Program of China Grant (No. 2016YFC0905501 to CW), the National Natural Science Foundation of China (No. 82173038 to DY), and the Guangdong Enterprise Key Laboratory of Human Disease Genomics (No. 2020B1212070028 to KW).
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