Analyzing the cancer methylome through targeted bisulfite sequencing. Cancer Lett. 2013;340:171-178. [PMID: 23200671 DOI: 10.1016/j.canlet.2012.10.040]

Molecular epigenetics in the management of ovarian cancer: are we investigating a rational clinical promise?

Epigenetics is essentially a phenotypical change in gene expression without any alteration of the DNA sequence; the emergence of epigenetics in cancer research and mainstream oncology is fueling new hope. However, it is not yet known whether this knowledge will translate to improved clinical management of ovarian cancer. In this malignancy, women are still undergoing chemotherapy similar to what was approved in 1978, which to this day represents one of the biggest breakthroughs for treating ovarian cancer. Although liquid tumors are benefiting from epigenetically related therapies, solid tumors like ovarian cancer are not (yet?). Herein, we will review the science of molecular epigenetics, especially DNA methylation, histone modifications and microRNA, but also include transcription factors since they, too, are important in ovarian cancer. Pre-clinical and clinical research on the role of epigenetic modifications is also summarized. Unfortunately, ovarian cancer remains an idiopathic disease, for the most part, and there are many areas of patient management, which could benefit from improved technology. This review will also highlight the evidence suggesting that epigenetics may have pre-clinical utility in pharmacology and clinical applications for prognosis and diagnosis. Finally, drugs currently in clinical trials (i.e., histone deacetylase inhibitors) are discussed along with the promise for epigenetics in the exploitation of chemoresistance. Whether epigenetics will ultimately be the answer to better management in ovarian cancer is currently unknown; but we hope so in the future.

BayMeth: improved DNA methylation quantification for affinity capture sequencing data using a flexible Bayesian approach

Affinity capture of DNA methylation combined with high-throughput sequencing strikes a good balance between the high cost of whole genome bisulfite sequencing and the low coverage of methylation arrays. We present BayMeth, an empirical Bayes approach that uses a fully methylated control sample to transform observed read counts into regional methylation levels. In our model, inefficient capture can readily be distinguished from low methylation levels. BayMeth improves on existing methods, allows explicit modeling of copy number variation, and offers computationally efficient analytical mean and variance estimators. BayMeth is available in the Repitools Bioconductor package.

eMethylsorb: electrochemical quantification of DNA methylation at CpG resolution using DNA–gold affinity interactions

Base dependent affinity interaction of DNA with gold has been utilised to electrochemically quantify the methylation status of bisulphite treated DNA samples.