Cost-effective methylome sequencing of cell-free DNA for accurately detecting and locating cancer
Mary L. Stackpole, Weihua Zeng, Shuo Li, Chun-Chi Liu, Yonggang Zhou, Shanshan He, Angela Yeh, Ziye Wang, Fengzhu Sun, Qingjiao Li, Zuyang Yuan, Asli Yildirim, Pin‐Jung Chen, Paul Winograd, Benjamin V. Tran, Yi‐Te Lee, Paul Shize Li, Zorawar S. Noor, Megumi Yokomizo, Preeti Ahuja, Yazhen Zhu, Hsian‐Rong Tseng, James S. Tomlinson, Edward B. Garon, Samuel W. French, Clara E. Magyar, Sarah Dry, Clara Lajonchere, Daniel H. Geschwind, Gina Choi, Sammy Saab, Frank Alber, Wing Hung Wong, Steven M. Dubinett, Denise R. Aberle, Vatche G. Agopian, Steven‐Huy B. Han, Xiaohui Ni, Wenyuan Li, Xianghong Jasmine Zhou
Abstract
Early cancer detection by cell-free DNA faces multiple challenges: low fraction of tumor cell-free DNA, molecular heterogeneity of cancer, and sample sizes that are not sufficient to reflect diverse patient populations. Here, we develop a cancer detection approach to address these challenges. It consists of an assay, cfMethyl-Seq, for cost-effective sequencing of the cell-free DNA methylome (with > 12-fold enrichment over whole genome bisulfite sequencing in CpG islands), and a computational method to extract methylation information and diagnose patients. Applying our approach to 408 colon, liver, lung, and stomach cancer patients and controls, at 97.9% specificity we achieve 80.7% and 74.5% sensitivity in detecting all-stage and early-stage cancer, and 89.1% and 85.0% accuracy for locating tissue-of-origin of all-stage and early-stage cancer, respectively. Our approach cost-effectively retains methylome profiles of cancer abnormalities, allowing us to learn new features and expand to other cancer types as training cohorts grow.