DNA methylation profiling from circulating tumor DNA for early-detection of colorectal cancer.

person Wei Zhang Colorectal Surgery Department, Changhai Hospital, Shanghai, China info_outline Wei Zhang, Jinke Sui, Xianrui Wu, Fuao Cao, Guanyu Yu, Chenyang Wang, Zhihong Zhang, Bingsi Li, Shuai Fang, Han Han-Zhang, Zheng Lou, Haifeng Gong, Lianjie Liu, Nanxin Zheng, Liqiang Hao, Ping Lan

Authors person Wei Zhang Colorectal Surgery Department, Changhai Hospital, Shanghai, China info_outline Wei Zhang, Jinke Sui, Xianrui Wu, Fuao Cao, Guanyu Yu, Chenyang Wang, Zhihong Zhang, Bingsi Li, Shuai Fang, Han Han-Zhang, Zheng Lou, Haifeng Gong, Lianjie Liu, Nanxin Zheng, Liqiang Hao, Ping Lan Organizations Colorectal Surgery Department, Changhai Hospital, Shanghai, China, Changhai Hospital, Shanghai, China, Burning Rock Biotech, Guangzhou, China, Burning Rock Biotech, Shanghai, China, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China Abstract Disclosures Research Funding Other Foundation Background: Colorectal cancer (CRC) develops as a result of neoplastic progression, which often takes decades, providing a window for early detection. Unfortunately, there has been little success in developing blood-based screening method due to the low amount of ctDNA present in the circulation, especially in patients with early stage disease. The role of aberrant DNA methylation, occurring very early in tumorigenesis, has been well elucidated. In this prospective study, we evaluated the potentiality of DNA methylation status obtained from ctDNA as an early detection method. Methods: Panel Design: Methylation data of tumor samples (12 types, n = 4,772), adjacent normal (8 types, n = 411), and normal white blood cells (n = 656) from TCGA and GSE were compared. Differentially methylated sites were extracted using modified wald-test with an adjusted p-value < 0.05 and fold-change > 2. Our panel covers 80,672 CpG sites, spanning 1.05Mb of human genome. We performed targeted bisulfite sequencing on plasma samples of 67 (stage I: 13, II:29, III: 23, IV: 2) Chinese CRC patients and 144 healthy individuals to construct a model for deriving markers that are differentially methylated and their associated weight. The model was validated in 2 independent cohorts. Results: We constructed a model using a support vector machine (SVM)-based machine learning classifier based on top 4,000 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 91%, specificity of 98% and area under curve (AUC) of 98.6%. The model was subsequently validated in 2 independent cohorts: one consisted of 57 stage I-III CRC patients and 74 healthy individuals and another one with 47 stage IV patients and the same 74 healthy individuals. The model yielded a sensitivity of 83% and 95% for the early and late stage cohorts, respectively. A specificity of 95% was obtained for both cohorts. Conclusions: Our findings demonstrated the potential of profiling DNA methylation, which can effectively distinguish cancerous from healthy, for the purpose of screening. This method has potential to serve as a supplementary or alternative approach in early detection.