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A suite of DNA methylation markers that can detect most common human cancers

dataset
posted on 01.03.2018, 12:40 authored by Lukas Vrba, Bernard W. Futscher

Cancer-specific DNA methylation from the tumor derived fraction of cell free DNA found in blood samples could be used for minimally invasive detection and monitoring of cancer. The knowledge of marker regions with cancer-specific DNA methylation is necessary to the success of such a process. We analyzed the largest cancer DNA methylation dataset available—TCGA Illumina HumanMethylation450 data with over 8,500 tumors—in order to find cancer-specific DNA methylation markers for most common human cancers. First, we identified differentially methylated regions for individual cancer types and those were further filtered against data from normal tissues to obtain marker regions with cancer-specific methylation, resulting in a total of 1,250 hypermethylated and 584 hypomethylated marker CpGs. From hypermethylated markers, optimal sets of six markers for each TCGA cancer type were chosen that could identify most tumors with high specificity and sensitivity [area under the curve (AUC): 0.969-1.000] and a universal 12 marker set that can detect tumors of all 33 TCGA cancer types (AUC >0.84). In addition to hundreds of new DNA methylation markers, our approach also identified markers that are in current clinical use, SEPT9 and GSTP1, indicating the validity of our approach and a significant potential utility for the newly discovered markers. The hypermethylated markers are linked to polycomb associated loci and a significant fraction of the discovered markers is within noncoding RNA genes; one of the best markers is MIR129-2. Future clinical testing of herein discovered markers will confirm new markers that will improve minimally invasive diagnosis and monitoring for multiple cancers.

Funding

HHS | National Institutes of Health (NIH); This work was supported by the Maynard Chair in Breast Cancer Epigenomics at the University of Arizona Cancer Center and the Cancer Center Support Grant (P30 CA023074).

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