Somatic, and in a minor scale also germ line, epigenetic aberrations are fundamental to carcinogenesis, cancer progression, and tumor phenotype. DNA methylation is the most extensively studied and arguably the best understood epigenetic mechanisms that become altered in cancer. Both somatic loss of methylation (hypomethylation) and gain of methylation (hypermethylation) are found in the genome of malignant cells. In general, the cancer cell epigenome is globally hypomethylated, while some regions—typically gene-associated CpG islands—become hypermethylated. Given the profound impact that DNA methylation exerts on the transcriptional profile and genomic stability of cancer cells, its characterization is essential to fully understand the complexity of cancer biology, improve tumor classification, and ultimately advance cancer patient management and treatment.
A plethora of methods have been devised to analyze and quantify DNA methylation alterations. Several of the early-developed methods relied on the use of methylation-sensitive restriction enzymes, whose activity depends on the methylation status of their recognition sequences. Among these techniques, methylation-sensitive amplification length polymorphism (MS-AFLP) was developed in the early 2000s, and successfully adapted from its original gel electrophoresis fingerprinting format to a microarray format that notably increased its throughput and allowed the quantification of the methylation changes. This array-based platform interrogates over 9500 independent loci putatively amplified by the MS-AFLP technique, corresponding to the NotI sites mapped throughout the human genome.
DNA methylation Methylation-specific amplified fragment length polymorphism Methylation microarray Epigenomics
Methylation specific restriction enzyme
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We are grateful to our colleagues at the Jichi Medical School, Saitama, for the essential collaboration during the development, testing, validation, and application of the MS-AFLP arrays. We also are thankful to Dr. Lauro Sumoy for productive discussion during the writing of this chapter. This work was supported in part by the Spanish Ministry of Health Plan Nacional de I + D + I, ISCIII, FEDER, FIS PI09/02444, PI12/00511, and PI15/01763 grants, and 2014-SGR-1269 from the Agència de Gestió d’Ajuts Universitaris i de Recerca (AGAUR).
Supplementary Fig. 2Distance to nearest neighbor distribution of the recognition sequences in the human genome of commercially available methylation-sensitive restriction enzymes (PDF 2662 kb)
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