Identification of endometrial cancer methylation features using combined methylation analysis methods

Background DNA methylation is a stable epigenetic mark that is frequently altered in tumors. DNA meth-ylation features are attractive biomarkers for disease states given the stability of DNA methylation in living cells and in biologic specimens typically available for analysis. Widespread accumulation of methylation in regulatory elements in some cancers (specifically the CpG island methylator phenotype, CIMP) can play an important role in tumorigenesis. High reso-lution assessment of CIMP for the entire genome, however, remains cost prohibitive and requires quantities of DNA not available for many tissue samples of interest. Genome-wide scans of methylation have been undertaken for large numbers of tumors, and higher resolu-tion analyses for a limited number of cancer specimens. Methods for analyzing such large datasets and integrating findings from different studies continue to evolve. An approach for comparison of findings from a genome-wide assessment of the methylated component of tumor DNA and more widely applied methylation scans was developed. Methods Methylomes for 76 primary endometrial cancer and 12 normal endometrial samples were gen-erated using methylated fragment capture and second generation sequencing, MethylCapseq. Publically available Infinium HumanMethylation 450 data from The Cancer Genome Atlas (TCGA) were compared to MethylCap-seq data. Results Analysis of methylation in promoter CpG islands (CGIs) identified a subset of tumors with a methylator phenotype. We used a two-stage approach to develop a 13-region methylation signature associated with a hypermethylator state High level methylation for the 13-region methylation signatures was associated with mismatch repair deficiency, high mutation rate, and low somatic copy number alteration in the TCGA test set. In addition, the signature devised showed good agreement with previously described methylation clusters devised by TCGA. Conclusion We identified a methylation signature for a hypermethylator phenotype in endometrial can-cer and developed methods that may prove useful for identifying extreme methylation phe-notypes in other cancers.