Zhang Bin, Liang Han, Liu Weiran, Zhou Xinlan, Qiao Sitan, Li Fuqiang, Tian Pengfei, Li Chenguang, Ma Yuchen, Zhang Hua, Zhang Zhenfa, Nanjo Shigeki, Russo Alessandro, Puig-Butillé Joan Anton, Wu Kui, Wang Changli, Zhao Xin, Yue Dongsheng
Department of Lung Cancer, Tianjin Lung Cancer Center, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.
The Institute of Precision Health, BGI-Shenzhen, Shenzhen, China.
Transl Lung Cancer Res. 2022 Oct;11(10):2094-2110. doi: 10.21037/tlcr-22-647.
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.
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.
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.
This study provides a novel non-invasive diagnostic approach to discriminate between benign and malignant pulmonary nodules to avoid unnecessary invasive procedures.
区分良性和恶性肺结节是一项诊断挑战，检测不准确可能导致不必要的侵入性检查。游离DNA（cfDNA）已成功用于检测各种实体瘤。在本研究中，我们开发了一种全基因组方法，以探索通过低深度全基因组测序（LD-WGS）获得的cfDNA测序读数的特征，用于诊断肺结节。
对从420份血浆样本中提取的cfDNA进行LD-WGS，这些样本来自经计算机断层扫描（CT）确定直径不超过30 mm的肺结节患者。分析cfDNA的测序读数分布模式，并用于建立区分良性和恶性肺结节的模型。
我们基于cfDNA的拷贝数改变（CNA）提出了加权读数分布差异（WRDD）的概念，以构建良性和恶性诊断（BEMAD）算法模型。在360份血浆样本的训练队列中，该模型在10倍交叉验证中实现了平均受试者操作特征（ROC）曲线下面积（AUC）值为0.84。该模型在60份血浆样本的独立队列中得到验证，AUC值为0.87。BEMAD模型能够以74%的灵敏度和86%的特异性区分良性和恶性结节。此外，使用BEMAD模型对cfDNA的关键特征进行分析，确定了与微卫星不稳定性相关的重复区域，微卫星不稳定性是肿瘤发生 的一个重要指标。
本研究提供了一种新颖的非侵入性诊断方法，用于区分良性和恶性肺结节，以避免不必要的侵入性检查。
