Abstract 816: DNA methylation profiling from circulating tumor DNA for early diagnosis of non-small cell lung cancer

Introduction: Lung cancer found at an early stage carries much-improved prognosis. Unfortunately, there has been little success in developing blood-based diagnostic method. Profiling somatic mutations from ctDNA has shown great promise for cancer diagnosis, prognosis, and surveillance. Despite the substantial advances made in ctDNA detection techniques, the detection rate remains low for early stage disease. The role of aberrant DNA methylation in the process of tumorigenesis both at individual genes and a genome-wide scale has been well elucidated. It occurs very early in cancer development, thus capable of serving as a diagnostic biomarker. In this prospective study, we evaluated the potentiality of DNA methylation status obtained from ctDNA as an early diagnostic marker for NSCLC. Methods: Panel Design: Methylation data of tumor samples (12 types, n=4772), adjacent normal (8 types, n=411), and normal white blood cells (WBC, n=656) from TCGA and GSE were compared. Differentially methylated sites were extracted using modified wald-test with an adjusted p-value 2. Our panel covers 80,672 CpG sites, spanning 1.05Mb of human genome. We performed targeted bisulfite sequencing on plasma samples of 359 early stage Chinese NSCLC patients and 144 healthy individuals to interrogate their methylation statuses with an average sequencing depth of 1,000x. The Results: The training cohort consisted of 359 early stage Chinese lung cancer patients (266 stage IA, 25 stage IB, 43 stage II and 25 stage III) with a median age of 51 and 144 Chinese healthy individuals with a median age of 59. We constructed a diagnostic classification model using a support vector machine (SVM)-based machine learning classifier based on top 3,000 differentially methylated regions (DMRs) selected by random forest between tumor and normal plasma samples. Subsequently, 5-fold cross-validation with 100-time repeats were performed to gain a robust estimation of model performance, achieving a sensitivity of 79.4%, specificity of 95.4% and area under curve (AUC) of 95.2%. The model was subsequently validated in an independent cohort, consisting of 79 early stage Chinese NSCLC patients and 74 healthy individuals with comparable clinical characteristics as the patients in the training cohort. The model yielded a sensitivity of 77% and specificity of 90% in the validation cohort, suggesting its robustness. Conclusions: Overall, our findings demonstrated in a large clinical cohort t