Cancer as a dysregulated epigenome allowing cellular growth advantage at the expense of the host.

Although at the genetic level cancer is caused by diverse mutations, epigenetic modifications are characteristic of all cancers, from apparently normal precursor tissue to advanced metastatic disease, and these epigenetic modifications drive tumour cell heterogeneity. We propose a unifying model of cancer in which epigenetic dysregulation allows rapid selection for tumour cell survival at the expense of the host. Mechanisms involve both genetic mutations and epigenetic modifications that disrupt the function of genes that regulate the epigenome itself. Several exciting recent discoveries also point to a genome-scale disruption of the epigenome that involves large blocks of DNA hypomethylation, mutations of epigenetic modifier genes and alterations of heterochromatin in cancer (including large organized chromatin lysine modifications (LOCKs) and lamin-associated domains (LADs)), all of which increase epigenetic and gene expression plasticity. Our model suggests a new approach to cancer diagnosis and therapy that focuses on epigenetic dysregulation and has great potential for risk detection and chemoprevention.

Redefining CpG islands using hidden Markov models.

The DNA of most vertebrates is depleted in CpG dinucleotide: a C followed by a G in the 5' to 3' direction. CpGs are the target for DNA methylation, a chemical modification of cytosine (C) heritable during cell division and the most well-characterized epigenetic mechanism. The remaining CpGs tend to cluster in regions referred to as CpG islands (CGI). Knowing CGI locations is important because they mark functionally relevant epigenetic loci in development and disease. For various mammals, including human, a readily available and widely used list of CGI is available from the UCSC Genome Browser. This list was derived using algorithms that search for regions satisfying a definition of CGI proposed by Gardiner-Garden and Frommer more than 20 years ago. Recent findings, enabled by advances in technology that permit direct measurement of epigenetic endpoints at a whole-genome scale, motivate the need to adapt the current CGI definition. In this paper, we propose a procedure, guided by hidden Markov models, that permits an extensible approach to detecting CGI. The main advantage of our approach over others is that it summarizes the evidence for CGI status as probability scores. This provides flexibility in the definition of a CGI and facilitates the creation of CGI lists for other species. The utility of this approach is demonstrated by generating the first CGI lists for invertebrates, and the fact that we can create CGI lists that substantially increases overlap with recently discovered epigenetic marks. A CGI list and the probability scores, as a function of genome location, for each species are available at http://www.rafalab.org.