The use of liquid biopsies for cancer detection and management is rapidly gaining prominence1. Current methods for the detection of circulating tumour DNA involve sequencing somatic mutations using cell-free DNA, but the sensitivity of these methods may be low among patients with early-stage cancer given the limited number of recurrent mutations2,3,4,5. By contrast, large-scale epigenetic alterations—which are tissue- and cancer-type specific—are not similarly constrained6 and therefore potentially have greater ability to detect and classify cancers in patients with early-stage disease. Here we develop a sensitive, immunoprecipitation-based protocol to analyse the methylome of small quantities of circulating cell-free DNA, and demonstrate the ability to detect large-scale DNA methylation changes that are enriched for tumour-specific patterns. We also demonstrate robust performance in cancer detection and classification across an extensive collection of plasma samples from several tumour types. This work sets the stage to establish biomarkers for the minimally invasive detection, interception and classification of early-stage cancers based on plasma cell-free DNA methylation patterns.
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R markdowns (either knit or raw) and scripts used to generate the findings in this study have been deposited on Zenodo (DOIs in Supplementary Table 13). All the cell line datasets generated and/or analysed during the current study are available in the Gene Expression Omnibus repository under accession code GSE79838. The cfMeDIP–seq next-generation sequencing data for patient samples that support the findings of this study are available upon request from the corresponding author to comply with institutional ethics regulation. Source data for Fig. 1b and Extended Data Fig. 3e are provided in Supplementary Table 9, and for Fig. 1c in Supplementary Table 10. Additional source data can be found on Zenodo (Supplementary Table 13).
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This study was conducted with support from the University of Toronto McLaughlin Centre (MC-2015-02), the Canadian Institutes of Health Research (CIHR FDN 148430 and CIHR New Investigator Salary award 201512MSH-360794-228629), Ontario Institute for Cancer Research (OICR) with funds from the province of Ontario, Canada Research Chair (950-231346), and the Princess Margaret Cancer Foundation to D.D.D.C. as well as Canadian Cancer Society (CCSRI 701717) to R.J.H., CCSRI 704716 to R.J.H. and D.D.D.C. and CCSRI 703827 to M.M.H. Recruitment of healthy individuals was supported by Cancer Care Ontario Chair of Population Health and CCSRI 020214 awarded to R.J.H. Collection of lung cancer samples was supported by the Alan B. Brown chair in molecular genomics and the Lusi Wong Lung Cancer Early Detection Program to G.L. We acknowledge the Princess Margaret Genomics Centre for carrying out the next-generation sequencing and the Bioinformatics and HPC Core, Princess Margaret Cancer Centre for their expertise in generating the next-generation sequencing data.
Nature thanks E. Collisson, A. Teschendorff and the other anonymous reviewer(s) for their contribution to the peer review of this work.