Abstract
Abstract Background: Cancers exploit changes in both genomic copy number and DNA methylation to promote growth and escape tumor-suppressor pathways. Under the 2-hit hypothesis, a single gene is likely to be altered by multiple mechanisms at the same time and integrated analysis can sharpen the focus on the most likely drivers. Requiring larger amounts of input material and imposing additional costs may limit multiplatform analysis, especially in studies using archival tissues with long clinical follow-up information where the nucleic acids are degraded and yields are generally lower than are obtained from fresh tissues. Taking advantage of similarities between methylation arrays and SNP arrays, we developed Epicopy, a robust computational method to identify DNA copy number variation (CNV) using high-density Illumina Human Methylation 450K methylation microarrays, thereby delivering two complementary genetic and epigenetic profiles from a single chip. Methods: Epicopy was developed using data from thyroid carcinoma samples arrayed by The Cancer Genome Atlas (TCGA) and subsequently validated on breast and lung small cell carcinoma TCGA datasets. Using Epicopy, we identified circumstances where CNV information can be reliably measured by methylation microarrays. Results: Using TCGA SNP microarrays as the gold standard to assess the performance of methylation derived CNV data from the thyroid, breast, and lung small cell carcinoma datasets, we showed that Epicopy is able to detect CNVs identified by SNP arrays at a sensitivity of 0.69 and specificity of 0.90. Frequently occurring CNVs identified using Genomic Identification of Significant Targets in Cancer (GISTIC), were identified with even higher accuracy. Conclusion: Epicopy provides a robust method to obtain both copy number and methylation information from a single methylation microarray experiment and will add value to methylation microarrays at no additional cost to the user. Tools to highlight regions of high sensitivity and specificity will also be provided to help users decide on the feasibility of using Epicopy to identify CNVs in regions of interest. Epicopy is implemented in the R statistical language and will be made available as a freestanding package as part of the Bioconductor bioinformatics software project. Citation Format: Soonweng Cho, Hyun-seok Kim, Leslie M. Cope, Christopher B. Umbricht. Epicopy: Measuring DNA copy number variation using high-density methylation microarrays. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 4873. doi:10.1158/1538-7445.AM2015-4873
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call