Structurally HMTs can be broadly categorized

Structurally, HMTs can be broadly categorized into three functional enzymatic families, the SET (Suppressor of variegation, Enhancer of zeste, Trithorax)-domain- containing methyltransferases, the non-SET DOT1-like (DOT1L) lysine methyltransferases, the PRDM family, containing a PR (PRDI-BF1-RIZ1 homologous) domain that is structurally and functionally similar to the SET domain, and the PRMT1 family which shares a common methyltransferase domain (Katoh, 2016, Mzoughi et al., 2016, Nguyen and Zhang, 2011, Schotta et al., 2004, Teyssier et al., 2010). In most cases, there is little redundancy between family members, owing to their methyl group- and cell type-specificity. This is highlighted by findings from loss-of-function mouse models and hereditary disorders associated with mental retardation and intellectual disability (Katoh, 2016). Breast cancer is the leading cause of cancer-related death in women world-wide (Kamangar et al., 2006). Comprehensive gene expression profiling has identified five major molecular subtypes of breast cancer: basal-like, luminal A, luminal B, HER2+/ER− and normal-like breast cancer (Perou et al., 2000, Sørlie et al., 2001), however, it is likely that many more subtypes exist (Curtis et al., 2012). There is mounting evidence that dysregulation of HMTs leads to imbalances in histone methylation patterns and contributes to the pathogenesis of a wide array of human cancers, including breast cancer. The hallmarks of sporadic breast cancer are somatic copy number alterations and “driver” mutations i.e., those mutations that confer a proliferative advantage on Cy3-dCTP to promote cancer development. Several large-scale sequencing efforts have led to the development of databases such as TCGA and COSMIC, which allow for the cataloging of somatic mutations in cancer. Analyses of these large datasets have revealed that HMTs are frequently mutated in cancer (Ciriello et al., 2015, Kudithipudi and Jeltsch, 2014, Nik-Zainal et al., 2016) and represent 5% of driver genes identified in whole-genome sequences of breast cancers (Nik-Zainal et al., 2016). Notably, basal-like breast cancers bear the highest frequencies of HMT gene amplifications, deletions, and mutations, whereas luminal A tumors have the lowest frequencies in every category of genetic alteration (Liu et al., 2015). These findings are consistent with triple-negative breast cancer (TNBC, a subset of basal-like cancers) exhibiting the highest degree of genomic instability. In addition to somatic mutations, single nucleotide polymorphisms (SNPs) found in genes encoding HMTs are associated with cancer risk susceptibility (Wang et al., 2012, Yoon et al., 2010) and clinical outcome (Crea et al., 2012). Moreover, acquired resistance to treatment is associated with elevated expression of HMTs (Borley and Brown, 2015, Magnani et al., 2012). HMTs therefore represent potential biomarkers or therapeutic targets in those patients in which HMTs are dysregulated.
Conclusions With the advent of large scale genomic and transcriptomic analyses of cancer, our ability to comprehensively interrogate the genome and epigenome of human cancers has greatly improved and is expected to inform future therapeutic strategies. Large scale sequencing efforts are uncovering common somatic mutations in breast cancers (Ciriello et al., 2015, Kudithipudi and Jeltsch, 2014, Nik-Zainal et al., 2016) as well as in other epithelial tumors (Kanchi et al., 2014), helping to stratify tumors into clinically relevant subtypes. Moreover, a recent study of 8000 cancer cases revealed that complex insertions/deletions are often overlooked or misannotated (Ye et al., 2016), indicating that their contribution to pathogenesis is currently underestimated. These studies, coupled with advances in technology, will help to define the “epigenomic landscape” and how it relates to gene expression profiles and phenotype. This review has highlighted what is known about the dysregulation of HMTs in breast cancer. Notably, some HMTs such as EZH2 and MLL function in large complexes with co-factors (Zhang et al., 2015) that may also be mutated in breast cancer. For example, PRC2 components EED and SUZ12 are emerging as tumor suppressors in their own right (Jene-Sanz et al., 2013, Koppens and Van Lohuizen, 2016), leading to further complexity in the interpretation of how HMT dysregulation impacts on phenotypes. Since functional data is still missing for many of the HMTs, the use of knockout or transgenic mice or CRISPR/Cas9 technology will be important for elucidating their function and guiding the development of targeted therapies for clinical use.