Abstract
e20038 Background: The frequent false-positive results of lung cancer screening of low-dose computed tomography (LDCT) remains a challenge. In this study, we aimed to establish a novel panel of cancer-specific methylated regions detected from circulating cell-free DNA (cfDNA) for differentiating malignant pulmonary nodules (PNs) from benign lesions in high-risk patients. Methods: We performed DNA methylation profiling by high throughput DNA bisulfite sequencing in tissue samples (nodule size < 3 cm in diameter) to learn methylation patterns that differentiate cancerous tumors from benign lesions. Then we filtered out differential methylation patterns in circulating cfDNA that are consistent with tissue expression patterns and built an assay for plasma sample classification. Area under the curve was computed for each receiving operation curve, and 95% confidence intervals were also estimated by bootstrapping with 1,000 iterations. Results: We first performed the whole genome methylation sequencing of 100 tissue samples from PNs to learn cancer-specific methylation patterns, and obtained 2,406 differential hypermethylated regions and 16,028 differential hypomethylated regions. These tissue-derived DNA methylation markers were further filtered using a training set of 42 plasma samples and 11 markers were ultimately identified to build a diagnostic prediction model. This methylation panel provided promising results in an independent validation set of additional 17 plasma samples, with a sensitivity of 66.67% (95% CI: 0.25–1.00) and a specificity of 79.17% for differentiating patients with malignant tumor (N = 12) from patients with benign lesions (n = 5). A further validation study of this methylation panel in the cancer genome atlas data set demonstrated significant discriminatory performance in distinguishing patients with lung cancer from subjects without malignancy (area under the curve = 0.88). Conclusions: Our model based on the measurement of circulating cfDNA methylation provide a minimally invasive procedure for differentiation of early-stage lung cancer and nonmalignant pulmonary nodules, and may guide management of positive LDCT screening results. Additional clinical studies with larger sample size are needed to establish the robustness of this model.
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