Common Gene Pathways and Families Altered by DNA Methylation in Breast and Prostate Cancers

T K Day and T Bianco-Miotto DNA methylation in breast and 20:5 R215–R232 Review prostate cancers

Common gene pathways and families altered by DNA methylation in breast and prostate cancers

Tanya K Day and Tina Bianco-Miotto1,2 Correspondence Dame Roma Mitchell Cancer Research Laboratories, Discipline of Medicine, Hanson Institute, Adelaide Prostate should be addressed Cancer Research Centre, The University of Adelaide, Adelaide, South Australia, Australia to T Bianco-Miotto 1School of Paediatrics and Reproductive Health, The Robinson Institute and 2School of Agriculture, Food and Wine, Email The University of Adelaide, Waite Campus, PMB 1 Glen Osmond, Adelaide, South Australia 5064, Australia [email protected]

Abstract

Epigenetic modifications, such as DNA methylation, are widely studied in cancer as they are Key Words stable and easy to measure genome wide. DNA methylation changes have been used to " breast differentiate benign from malignant tissue and to predict tumor recurrence or patient " prostate outcome. Multiple genome wide DNA methylation studies in breast and prostate cancers " biomarker have identified genes that are differentially methylated in malignant tissue compared with " gene regulation non-malignant tissue or in association with hormone receptor status or tumor recurrence. " neoplasia Although this has identified potential biomarkers for diagnosis and prognosis, what is highlighted by reviewing these studies is the similarities between breast and prostate cancers. In particular, the gene families/pathways targeted by DNA methylation in breast and

Endocrine-Related Cancer prostate cancers have significant overlap and include homeobox genes, zinc finger transcription factors, S100 calcium binding proteins, and potassium voltage-gated family members. Many of the gene pathways targeted by aberrant methylation in breast and prostate cancers are not targeted in other cancers, suggesting that some of these targets may be specific to hormonal cancers. Genome wide DNA methylation profiles in breast and prostate cancers will not only define more specific and sensitive biomarkers for cancer diagnosis and prognosis but also identify novel therapeutic targets, which may be direct targets of agents that reverse DNA methylation or which may target novel gene families that

are themselves DNA methylation targets. Endocrine-Related Cancer (2013) 20, R215–R232

Introduction

Numerous diseases are associated with abnormal hor- important. Hormone receptor status of breast cancer is monal regulation including breast and prostate cancers. critical because it directs clinical decisions about adjuvant Breast cancer is a heterogeneous disease that can be hormone therapy. ERa-positive tumors are responsive to classified in many ways. The most primary classification anti-estrogenic treatments, including selective ER modu- is by hormone receptor status, namely estrogen receptor a lators like tamoxifen or aromatase inhibitors, which have (ERa) and progesterone receptor (PR), with more recent significantly increased the odds of survival from this recognition that the androgen receptor (AR) is also disease. However, w30% of women have ERa-negative

http://erc.endocrinology-journals.org q 2013 Society for Endocrinology Published by Bioscientiﬁca Ltd. DOI: 10.1530/ERC-13-0204 Printed in Great Britain Downloaded from Bioscientifica.com at 09/30/2021 01:27:26PM via free access Review T K Day and T Bianco-Miotto DNA methylation in breast and 20:5 R216 prostate cancers

tumors, and currently, there are no established hormone associated CpG islands is often linked to gene repression, therapies for the clinical management of women with either through a direct or indirect influence on the ERa-negative breast cancers. chromatin structure resulting in chromatin condensation Prostate cancer is also a heterogeneous disease and, if (Chan et al. 2000, Nakao 2001, Salozhin et al. 2005). diagnosed early, is potentially curable by surgery and/or radiotherapy. However, up to 20% of patients will relapse DNA methylation and cancer with metastatic disease within 5–10 years. The main therapy for patients with locally advanced or metastatic DNA methylation is important in maintaining the disease targets androgen production and its receptor. These integrity of the genome, and so it is hardly surprising therapies result in an initial period of tumor regression; that alterations in DNA methylation are commonly however, eventually, prostate cancers become unrespon- associated with disease. Numerous studies have supported sive and progress to the castrate-resistant state. Current the important role of DNA methylation in carcinogenesis therapies for advanced prostate cancer are not curative and (reviewed in Baylin & Jones (2011)), have shown that there are limited treatment options for patients that targeting aberrant DNA methylation is a promising cancer develop castrate-resistant prostate cancer (CRPC). therapeutic (Baylin & Jones 2011), and demonstrated Although the role of hormone receptors in breast and that DNA methylation can act as a disease biomarker prostate cancers is well established and utilized for (Jovanovic et al. 2010, Huang et al. 2011, Jeronimo et al. therapeutic treatments, new targets are needed for 2011, Chiam et al. 2012, Sandoval & Esteller 2012). hormone-resistant disease in breast and prostate cancers. Alterations in DNA methylation have been observed in Epigenetic modifications are critical events in tumorigen- multiple cancers, with global hypomethylation occurring esis and, in comparison to somatic mutations, occur at simultaneously with gene-specific promoter hypermethyl- a much higher frequency. Epigenetic modifications are ation (Esteller 2005, Karpinets & Foy 2005, Baylin & Ohm reversible changes to the genome and therefore are 2006). Global hypomethylation has been linked to excellent therapeutic targets. Epigenetic modifications activation of proto-oncogenes and chromosomal instabil- like DNA methylation are also stable and technically easy ity (Hake et al. 2004, Szyf et al. 2004, Szyf 2005). In to assess making them ideal biomarkers for diagnosis and contrast, gene-specific hypermethylation is associated prognosis. It is well established from candidate gene studies with inactivation of genes involved in DNA repair, cell that epigenetic modifications play an important role in cycle regulation, apoptosis, and tumor suppression Endocrine-Related Cancer hormone-dependent cancers (Chin et al. 2011, Huang et al. (Esteller 2005, Miyamoto & Ushijima 2005, Baylin & 2011, Jeronimo et al. 2011, Huynh et al. 2012); however, it Ohm 2006). is essential to expand our knowledge of epigenetic modifications that may occur commonly in hormonally Methods responsive disease states. In this review, we focus on genome wide DNA methylation studies in breast and This is a systematic review of genome wide DNA prostate cancers and in particular the overlap in aberrantly methylation studies in breast and prostate cancers, as methylated genes in these hormonally regulated cancers. reported on PubMed at May 2013. All studies with an unbiased genome wide DNA methylation analysis con- DNA methylation ducted in human breast and/or prostate cancer tissue, or cell lines derived from these tissues, were included. Cell Unlike mutations that alter the DNA sequence, epigenetic line studies were included only if they analyzed at least modifications regulate gene expression via chromatin two independent cell lines. We define ‘unbiased’ as remodeling. The most studied epigenetic modification is techniques with no selection of genomic regions analyzed DNA methylation, which is critical during embryogenesis, based on function, structure, or any other knowledge of imprinting, and X chromosome inactivation (Jaenisch & genes or regions. Techniques that analyze only gene Bird 2003, Salozhin et al. 2005, Nafee et al. 2008). DNA promoter CpG islands are included, although we acknowl- methylation involves the transfer of a methyl group from edge that these methods are selective by their nature. methyl donor S-adenosylmethionine to the 50 carbon of Genome wide techniques are those that assess methyl- cytosine. This occurs predominantly at CpG. CpG islands, ation on a global scale, usually at least 10 000 CpG sites, which are clusters of CpGs (O55%), are frequently found and where the methylation status can be linked to a within gene promoter regions. Methylation of promoter- genomic location. Gene expression and chromosome

copy number analysis of the same samples will be prostate using the Illumina Infinium HumanMethyla- discussed, where available. Studies investigating genome tion27 BeadChip. This was more pronounced for CpG wide changes in gene expression after treatment with islands but was common across all areas of the genome demethylating agents, but without corresponding gen- analyzed, although the HumanMethylation27 BeadChip ome wide DNA methylation data, were excluded on the predominantly assesses CpGs in gene proximal basis that these studies include both primary (direct) and promoters. Similarly, Kim et al. (2011c) observed overall secondary (indirect) effects of demethylation. The search hypermethylation in cancer using the same platform. In term ‘DNA methylation profiling cancer breast’ retrieved contrast, in the same study, ALU elements were 180 references and ‘DNA methylation profiling cancer hypomethylated in cancer compared with non-malig- prostate’ retrieved 56 references on 6th May 2013. Some of nant tissue (Kim et al. 2011c). These findings the papers reviewed here were not retrieved by these highlight the bias that can be introduced to DNA search terms in PubMed but were identified from methylation analysis by different methodologies reviewing the selected papers. In total, 22 breast cancer employed and the requirement for consideration of the and ten prostate cancer studies fitted our genome wide, technique used when interpreting genome wide DNA unbiased criteria. The techniques used for genome wide methylation results. DNA methylation analysis in breast or prostate cancer have been comprehensively reviewed elsewhere (Laird DNA methylation correlates with gene 2010) and therefore will not be discussed in this review. expression in breast and prostate cancers in genome wide studies Limitations and biases of genome wide DNA Historically, candidate gene studies have demonstrated methylation techniques a correlation between DNA methylation and gene Many techniques for genome wide methylation analysis expression. Genome wide DNA methylation studies have do not assess DNA methylation equally across the entire supported this correlation, particularly the association genome, instead focusing on CpGs within islands or gene between CpG island DNA hypermethylation and gene promoters. In breast cancer, Novak et al. (2008), using repression (Li et al. 2009, Van der Auwera et al. 2010, methyl DNA immunoprecipitation (IP) followed by a Dedeurwaerder et al. 2011, Fang et al. 2011, Kamalakaran promoter array, found that hypermethylation (38%) was et al. 2011, Sproul et al. 2011, Sun et al. 2011). Flanagan Endocrine-Related Cancer more frequent than hypomethylation (6%) in differen- et al. (2010), using familial breast cancers and tially methylated regions (DMRs) between tumor vs BRCA1/2-mutated cancers, combined DNA methylation normal. However, methyl DNA IP is biased for hyper- profiles that alone predicted BRCA status, with gene methylated DNA. Similarly, Ordway et al. (2007) found expression and copy number variation (CNV) and found that hypermethylation is more frequent than hypo- that genes with reduced expression were more likely to be methylation in breast cancer compared with non- in genomic regions with loss of heterozygosity and/or malignant tissue. Ordway et al. (2007) digested DNA high levels of DNA methylation. Using a panel of breast with McrBC, which has loose site specificity, allowing for cancer cell lines, it has also been shown that the the analysis of regional DNA methylation density. combination of gene dosage, allelic status, and DNA Samples were hybridized to OGHAv1.0 microarray methylation explains more gene expression changes (Ordway et al. 2006), which predominantly covers than either genomic element alone (Chari et al. 2010). promoter regions and transcription start sites. Both This highlights that in future studies, combining DNA these studies predominantly analyzed promoter regions methylation profiling with CNV and gene expression will rather than analyzing equally across the entire genome. be an effective tool to facilitate the identification of critical Hu et al. (2005) used methylation-specific digital genes involved in tumorigenesis. karyotyping (MSDK) to identify that in breast, tumor Similarly, in prostate cancer, Kim et al. (2011a) stromal and epithelial cells were hypomethylated relative demonstrated that gene promoter methylation was to normal stromal and epithelial cells. The MSDK associated with gene under-expression, irrespective of technique is biased for CpG islands, but regions analyzed the presence of a CpG island, while coding exon are distributed across the genome rather than clustered methylation was associated with over-expression. A around transcription start sites. Friedlander et al. (2012) study by Friedlander et al. (2012) also supported the reported hypermethylation in CRPC relative to benign correlation between DNA methylation and gene

expression, in this case also integrating chromosome copy and found more hypermethylated (2033) than hypo- number across metastatic CRPC compared with primary methylated (1473) DMRs in cancer compared with normal cancer and benign prostate tissue. Together, these studies breast tissue (Table 1). A similar study compared ductal in breast and prostate cancers demonstrate that the carcinomas in situ to non-malignant tissue with a CpG functional effects of gene silencing, initially identified island array after IP (Tommasi et al. 2009; Table 1). In this for a small subset of candidate genes, can be identified by study, O100 CpG islands were significantly altered in genome wide DNA methylation studies. Thus, future tumor compared with non-malignant tissue. These islands genome wide DNA methylation studies can provide an were positively methylated in O50% of cancer samples. additional avenue to identify novel genes silenced by DNA This demonstrates that not only can tumor tissue be methylation in tumorigenesis. differentiated from non-malignant using DNA methylation but also that hypermethylation can easily be distinguished from hypomethylation. Genome wide DNA methylation in Van der Auwera et al. (2010) used Illumina Infinium breast cancer HumanMethylation27 BeadChip to analyze normal breast tissues from ten healthy individuals and compared this to DNA methylation profiles discriminate tumor 62 breast tumor samples (19 were inflammatory breast from non-malignant breast tissue cancer; Table 1). The DNA methylation profiles divided Genome wide DNA methylation has been extensively the samples into three groups based on high, intermedi- applied to discriminate between tumor and normal or ate, and low DNA methylation levels, with the normal histologically non-malignant breast tissue. One of the first samples having low DNA methylation levels. When genome wide DNA methylation studies in breast cancer comparing DNA methylation between normal and developed MSDK to assess epithelial, myoepithelial, and tumor samples, 1352 CpG loci (1134 genes) were stromal fibroblasts from normal and cancer breast tissues differentially methylated (Van der Auwera et al. 2010). (Hu et al. 2005; Table 1). Although there were few For 77% of these CpG loci, there was significantly greater differences between tumor and normal myoepithelial methylation in tumors compared with normal. Another cells, DNA hypomethylation frequency was higher in study using the same technology found 6309 CpGs tumor epithelial and stromal cells when compared with differentially methylated between 119 tumors and normal cells of the same subtype (Hu et al. 2005). Based on four normal breast tissue samples (Dedeurwaerder et al. Endocrine-Related Cancer DNA methylation profiles, Ordway et al. (2007) identified 2011; Table 1). 220 loci that differentiated non-malignant from tumor Kamalakaran et al. (2011) identified several hundred tissue, with hypermethylation more common than hypo- differentially methylated loci between 11 adjacent non- methylation (Table 1). Validation was performed for 53 malignant breast tissues and 108 tumors (Table 1). Kim hypermethylated loci in 16 infiltrating ductal carcinomas et al. (2011b) pooled DNA from ten cancers and ten non- and 25 normal or benign breast tissues (Ordway et al. malignant matched adjacent tissues and identified 1181 2007). Sixteen DMRs were also identified with O95% differentially methylated CpGs (corresponding to 1043 specificity between breast cancer (103 tumors) and non- genes) with the vast majority (972) hypermethylated malignant breast tissue and blood (Ordway et al. 2007), (Table 1). Hill et al. (2011) found 291 probes (264 genes) suggesting that tissue type was not critical for methylation hypermethylated in breast cancer (nZ39) compared with detection. Differential methylation in tumors was not non-malignant breast tissue (nZ4) after removal of significantly associated with age, hormone receptor status, imprinted genes and X chromosome genes (Table 1). or family history (Ordway et al. 2007). Differential Additional studies have also compared tumor to non- methylation of one marker, GHSR, distinguished cancer malignant tissue and the number of genes identified that from normal with 90% sensitivity and 96% specificity discriminates the two depends on the filtering or analyses (Ordway et al. 2007). In future, combining multiple utilized. For example, Kim et al. (2012) used several markers with high specificity and sensitivity into a filtering processes to identify six genes, whereas Faryna biomarker panel may assist in developing a clinical test et al. (2012) identified 214 CpG islands but only one CpG to accurately differentiate breast tumor tissue from island (TAC1) was methylated in all ten cancer samples. non-malignant. The identification of thousands of loci with altered DNA Novak et al. (2008) utilized a promoter array after methylation between non-malignant and tumor tissue methyl DNA IP with 5-methyl-cytosine antibody (MeDIP) using genome wide methylation analyses raises several

Table 1 Summary of published genome wide DNA methylation studies in breast cancer

References Samples Methods Findings

Hu et al. (2005) Normal and tumor myoepithe- MSDK DNA hypomethylated in tumor epithelial lial, epithelial, stromal cells and stromal cells when compared to Immunomagnetic separation of normal cells cells from fresh tissue Ordway et al. (2007) Nine IDC; matched NM DNA methylation-dependent 220 loci distinguished tumor from NM. Validation 1: 16 IDC, 25 NM, 19 DNA fractionation; McrBC qPCR validation identified 16 DMR, normal blood digested DNA hybridized to independent of tumor stage and ER Validation 2: 103 IDC, 104 NM, OGHAv1.0 microarray status. GHSR DNA methylation 25 normal blood (Ordway et al. 2006) distinguished IDC from normal/benign (90% sensitivity and 96% specificity) Novak et al. (2008) 16 BCa, 5 NM, 4 pure cell MeDIP; Affymetrix GeneChip 3506 cancer-specific DMR: 2033 hyper- populations from tumor and Human Promoter 1.0R Array methylated, 1473 hypomethylated. normal; frozen tissue w90% of DMRs aberrantly methylated in O33% of tumors Li et al. (2009) MCF-7 MMSDK; Array CGH (CytoChip Significant methylation differences in MDA-MB-231 v2.0 BlueGnome); Gene introns and LINE1. Significant corre- Fresh cells expression Human U133 Plus lation between methylation of 2.0 (Affymetrix) promoter CpG islands and gene expression Tommasi et al. (2009) Six matched DCIS and NM MIRA-assisted microarray Greater than 100 significant CpG islands breast tissue. Frozen tissue analysis. MBD2b/MBD3L1 IP; altered in at least 3/6 DCIS examined. 1/3 Human CpG island array of the CpG islands were associated with (Agilent) homeobox superfamily members Chari et al. (2010) HCC38, HCC1008, HCC1143, Infinium HumanMethylation27 Combined DNA copy number, loss of HCC1395, HCC1599, BeadChip (Illumina); U133 heterozygosity and DNA methylation to HCC1937, HCC2218, BT474, Plus 2.0 gene expression identify genes disrupted in BCa MCF7, and MCF10A (normal) (Affymetix); CNV (Chari et al. 2006) Flanagan et al. Familial cancers: 11 BRCA1, 8 MeDIP and GeneChip Human Methylation profiling predicted BRCA (2010) BRCA2, 14 non-BRCA Promoter 1.0R Array mutation status. Significant number of

Endocrine-Related Cancer Macrodissected frozen tissue (Affymetrix); expression and genes with LOH had increased DNA Validation set: 14 BRCA1, 13 CNV (Waddell et al. 2010) methylation and decreased expression BRCA2, and 20 non-BRCA Macrodissected FFPE tissue Li et al. (2010) 12 ERCPRC Bca Infinium HumanMethylation27 93 hypermethylated; 55 hypomethylated 12 ERKPRK Bca BeadChip (Illumina) sites when comparing ERPRC to Frozen tissue ERPRK BCa. Hierarchical clustering Validation: 31 ERCPRC and 39 distinguished ERPRC from ERPRK. ERKPRK Bca Significant network: inflammatory Frozen tissue response and connective tissue disorders Ruike et al. (2010) HMEC, MCF7, MDA-MB-213, MeDIP; Sequencing on Illumina Compared to HMEC there was greater SKBR3, T47D, Hs578T, MDA- Genome Analyzer than fourfold increase in CpG island MB-453, HMC-1-8, MRK-nu-1 methylation in BCa cell lines Van der Auwera Ten normal breast tissues, 19 Infinium HumanMethylation27 Comparing breast tumors with normal: et al. (2010) IBC and 43 non-IBC. Frozen BeadChip (Illumina) 1353 CpG loci (1134 genes) were signi- tissue ficantly different; 77% were hyper- Gene expression by HGU133 methylated in tumor vs normal. plus 2.0 array (Affymetrix) Differentially methylated genes were related to focal adhesion, extracellular matrix receptor interaction, pathways in cancer, cytokine–cytokine receptor interaction and ether lipid metabolism. Unsupervised hierarchical cluster analysis separated BCa into low and high methylation. High-methylation group enriched for BCa from patients with distant metastasis and poor prognosis

Table 1 Continued

References Samples Methods Findings

Dedeurwaerder 119 tumors, 4 normal. Frozen Infinium HumanMethylation27 6309 CpGs differentially methylated et al. (2011) tissue BeadChip (Illumina) between normal and tumors. 2985 loci Validation set 117 tumors, 8 Affymetrix HG133 Plus 2.0 differentially methylated between normal. Frozen tissue GeneChip for expression normal and tumor identified two clusters that correlated with tumor grade and ER status. Using 55 genes with strong anti-correlation between methylation and expression status in univariate analysis, 32 were significant prognostic markers; 13/32 were immune related Fackler et al. (2011) 103 BCa, 6 NM distant to BCa. Infinium HumanMethylation27 Unsupervised hierarchical cluster analysis Frozen tissue. Fifteen epi- BeadChip (Illumina) identified two clusters; one enriched for thelium-enriched organoids ERC and one enriched for ERK BCa. from normal tissue Identified 100 CpGs associated with recurrence, these were enriched for homeobox genes Fang et al. (2011) 39 BCa; microdissected FFPE or Infinium HumanMethylation27 Methylation clustered into two groups: frozen tissue BeadChip (Illumina); Human HRC or HRC/K. Methylator phenotype Validation set nZ132 BCa; Genome U133A 2.0 (HRC group) associated with good microdissected FFPE or frozen expression (Affymetrix) outcome. Differentially methylated tissue genes enriched for PRC2 targets Hill et al. (2011) 39 primary BCa 4 matched Infinium HumanMethylation27 264 hypermethylated genes in CpG tumor/NM pairs BeadChip (Illumina); 5-aza islands, involved in cell adhesion, cell validated by PCR in T47D, proliferation, cell cycle, Wnt signaling MDA-MB-231, HCC1937 cells pathway. High-methylation group associated with relapse and ER/PRC. Low-methylation group associated with triple-negative tumors. DNA methylation clusters associated with ER/PR status (PZ0.001), tumor relapse (PZ0.035), and lymph node metastasis Z Endocrine-Related Cancer (P 0.042). Nine genes associated with relapse-free survival Kamalakaran et al. 108 tumors, 11 adjacent NM. MOMA Increased HOXA family methylation in (2011) Frozen tissue. 83 tumors used Expression data (Naume et al. luminal tumors compared to NM and as discovery set and 25 as 2007) basal or HER2 subtypes. 2259 loci validation set determined patient prognosis; 921 remained significant in multivariate Cox regression analysis Kim et al. (2011b) Ten cancers mixed, ten adjacent MeDIP, whole-genome 972 CpGs hypermethylated and 209 CpGs NM mixed amplification, followed by hypomethylated (1043 genes). IPA: Validation: MSP and qPCR hybridization on Human CpG ‘cellular development, embryonic expression in cell lines trea- island microarray (Agilent) development, tissue development’, ted with 5-aza ‘cellular movement, cancer, neuro- logical disease’, ‘cell death, embryonic development, cardiovascular disease’. STAT1 may be master regulator of multiple gene transcripts relevant to BCa Sproul et al. (2011) 19 BCa cell lines, normal HMEC, Infinium HumanMethylation27 In cell lines, hypermethylation of CpGs normal breast tissue, 47 BCa. BeadChip (Illumina) near transcription start associated with Frozen tissue Expression data (Neve et al. gene silencing. 120 genes divided cell 2006) lines into epithelial and mesechymal lineages and clustered BCa cases Sun et al. (2011) MDA-MB-231, BT20, T47D, Illumina bisulfite modified 149 genes with differential CpG island MCF7, ZR75.1, MDA-MB-468, deep sequencing; Illumina methylation within 5 kb of the 50 end of BT474, MCF10A (normal) mRNA-seq the gene and for which mRNA abun- Validation set: 129 BCa gene dance was inversely correlated with expression methylation status. ERC and ERK cell lines and tumors clustered according to methylation

Table 1 Continued

References Samples Methods Findings

Faryna et al. (2012) Two in situ BCa, eight invasive MBD2-Fc IP; Human CpG island Comparing tumor to NM, 214 CpG islands BCa, ten non-malignant tis- microarrays (Agilent) hypermethylated in O6 of 10 micro- sue (one from an affected arrays. TAC1 hypermethylated in all ten patient). Frozen tissue BCa. Significant overrepresentation of Validation set 1: 45 tumors and homeobox genes and genes involved in 11 controls transcription. Correlation with Validation set 2: 43 tumors and expression and aberrant methylation in 8 controls early BCa (in situ). Correlations between DNA methylation of BCAN and tumor grade, NXPH1 or KCTD8 with tumor proliferation. Association between SIM1 or KLF11 methylation and metastasis-free survival Kim et al. (2012) DNA pooled from three BCa MeDIA using MBD2bt; whole- Several hundred genes differentially and from three match-paired genome amplification, methylated between tumor and NM adjacent tissue. Frozen hybridization on Human CpG adjacent NM tissue. Several filtering tissue island microarray (Agilent) processes to identify six candidate genes: AXIN2, LHX2, NFIX, OTP, PTGER4, and WT1 Cancer Genome 802 BCa Infinium HumanMethylation27 574 probes used to cluster tumors into five Atlas N (2012) or HumanMethylation450 methylation groups. Group 3 was BeadChip (Illumina); gene hypermethylated and enriched for expression (Agilent custom luminal B subtype. Group 5 was low 244K); SNP (Affymetrix 6.0); DNA methylation, basal-like subtype, miRNA (Illumina GAIIx or and high-frequency TP53 mutation 122 adjacent NMHiSSeq 2000); Exome sequen- Supervised analysis compared group 3 to Frozen tissue cing Agilent SureSelect All groups 1, 2, and 4. 4283 genes Exome v2.0 kit or Nimblegen differentially methylated, 1899 genes SeqCap EZ Human Exome differentially expressed. 490 genes v2.0 hypermethylated and lower expression, including genes involved in ‘extracellular region part’ and ‘Wnt signaling

Endocrine-Related Cancer pathway’ Zheng & Zhao 2012) 30 BCa cell lines DMH, Human CpG island array No correlation between methylation and (Agilent) molecular subtype

MSDK, methylation-specific digital karyotyping; MMSDK, modified MSDK; MOMA, methylation oligonucleotide microarray analysis; PRC2, polycomb complex 2; 5-aza, 5-aza-20-deoxycytidine; BCa, breast cancer; CNV, copy number variation; CGH, comparative genomic hybridization; DCIS, ductal carcinoma in situ; DMH, differential methylation hybridization; DMR, differentially methylated regions; ER, estrogen receptor; FFPE, formalin fixed, paraffin embedded; GSEA, gene set enrichment analysis; HMEC, human mammary epithelial cells; HRC, hormone receptor-positive (ERCPRC); HRK, hormone receptor-negative (ERKPRK); IBC, inflammatory breast cancer; IDC, infiltrating ductal carcinoma; IP, immunoprecipitation; IPA, Ingenuity Pathway Analysis; miRNA, microRNA; LOH, loss of heterozygosity; MBD2bt, truncated methyl DNA binding domain; MeDIA, methylated DNA isolation assay; MeDIP, methyl DNA immunoprecipitation using a 5MeC MAB; MIRA, methylated CpG island recovery assay; MSP, methylation-specific PCR; NM, non-malignant; PR, progesterone receptor. If the information was available, details of the type of tissue used were also included.

possibilities for future study. The most pertinent of these subtype (luminal A or B, basal or HER2). DNA methylation are i) loci with aberrant DNA methylation can be profiles can differentiate hormone receptor-positive breast combined to generate a panel of biomarkers and ii) genes cancers from hormone receptor-negative cases (Li et al. previously unrecognized as methylation targets can be 2010, Dedeurwaerder et al. 2011, Fang et al. 2011, Hill et al. identified in an unbiased manner, providing potential 2011, Sun et al. 2011), although a study by Van der Auwera insight into the biology underlying the disease process. et al. (2010) did not find any association between hormone receptor status and DNA methylation profile (Table 1). It is important to note that there were several methodological DNA methylation profiles and breast cancer hormone differences between these studies, such as how the receptor status or subtype clustering analysis was performed and whether infor- Several studies have investigated whether genome wide mation from all CpG sites were used or only highly DNA methylation profiling can cluster breast cancers into methylated CpG sites. These methodological differences hormone receptor status (ER/PR positive or negative) or may explain the different results reported. Likewise, the

patient cohorts were different between these studies and methylation analysis will need to be combined with other so this may contribute to the differing outcomes. The genome wide analyses to correctly cluster breast cancer majority of genome wide DNA methylation studies have subtypes. It is also possible that DNA methylation found that ERCPRC tumors have higher levels of subtypes are different to the subtypes identified by gene DNA methylation compared with ERKPRK tumors (Li expression and may provide additional information that et al. 2010, Fackler et al. 2011, Fang et al. 2011, Hill et al. assists in the clinical setting. Further research is required to 2011). Li et al. (2010) found 148 altered CpG sites delineate these options and determine how subtypes (93 hypermethylated and 55 hypomethylated) in identified by DNA methylation profiling differ to subtypes ERCPRC breast cancers relative to ERKPRK tumors identified by gene expression. (Table 1). Fackler et al. (2011) identified 40 CpG probes that had an overall specificity of 89% and sensitivity of DNA methylation profiles and breast cancer prognosis 90% for classifying ERC from ERK tumors. Hill et al. (2011) used cluster analysis to show that ERCPRC tumors Identifying biomarkers for breast cancer prognosis by DNA had high methylation, whereas triple-negative breast methylation profiling have also been the subject of cancers had low methylation (Table 1). Genome wide intensive investigations. One of the first biomarker studies DNA methylation studies in breast cancer cell lines have found that when breast cancer samples were divided into also shown clustering according to hormone receptor high and low DNA methylation levels, the high group was status based on DNA methylation levels (Sun et al. 2011). enriched for patients with a poor prognosis (Van der Most critically, these genome wide DNA methylation Auwera et al. 2010). Using a clustering approach, Fang et al. studies demonstrate that an adequately powered study of (2011) found that the group of breast cancers with high appropriate clinical samples should identify methylation DNA methylation levels was ER/PRC and that this group differences based on hormone receptor status. With has a lower risk of relapse and better overall survival. By additional future study, this may serve as a basis for the selecting genes with a strong anti-correlation between development of an improved clinical test to identify the DNA methylation and expression status, univariate Cox hormone status of breast cancers. regression analysis identified 32 genes that were signi- Several studies have also assessed DNA methylation ficant prognostic markers in breast cancer (Dedeurwaerder and its association with breast cancer subtypes. A study et al. 2011). Further filtering of these genes identified 11 profiling 30 breast cancer cell lines did not find any genes associated with immunity, and high expression of Endocrine-Related Cancer correlation between methylation and breast cancer sub- these genes was associated with a good clinical outcome; types, although the basal cell type was associated with two 10/11 were independent prognostic markers by multi- DNA methylation clusters (Zheng & Zhao 2012). Dedeur- variate analysis (Dedeurwaerder et al. 2011). Using a waerder et al. (2011) identified six DNA methylation genome wide DNA methylation approach on 82 breast clusters and HER2-positive tumors, basal-like and luminal cancers, 100 CpGs were associated with recurrence; A cancer each belonged to a cluster, but three clusters were homeobox genes were enriched in this set of hypermethy- not associated with a predominant molecular subtype. lated loci (Fackler et al. 2011). Focusing just on homeobox Kamalakaran et al. (2011) also performed DNA methyl- genes, 60 homeobox loci were associated with recurrence, ation cluster analysis and found that one cluster was and these were an independent predictor of recurrence predominantly luminal A (22/30 samples), the second (Fackler et al. 2011). It is important to note that this study cluster was highly correlated with basal-like (7/8 samples), design was biased toward hypermethylated loci. and the third cluster contained a mixture of subtypes. Using cluster analysis of genome wide DNA Recently, The Cancer Genome Atlas assessed DNA methylation data to divide breast cancers into three methylation profiles in 802 breast cancers and identified groups, high, intermediate, and low DNA methylation that the group with high DNA methylation was associated levels, the high DNA methylation group was associated with the luminal B subtype while the group with low with relapse but there was no association with disease-free methylation was associated with the basal-like subtype survival with any of the groups (Hill et al. 2011). However, (Table 1; Cancer Genome Atlas N 2012). Future studies when the probes/genes associated with a single clinical need to include a more detailed investigation of the feature (hormone receptor status, tumor relapse, and methylation differences between breast cancer subtypes to lymph node status) were assessed individually using determine whether there is a methylation signature that Fisher’s exact tests with false discovery rate correction, can identify breast cancer subtypes. It is possible that nine genes were identified that were significantly

associated with relapse-free survival (Hill et al. 2011). This hypermethylated in prostate cancer, HOXD3 and BMP7 demonstrates the importance of dividing cancers into were validated by 5-aza re-expression in vitro in DU145 appropriate groups for analysis. Kamalakaran et al. (2011) cells (Kron et al. 2009). These studies identified several identified 2259 loci that divided patients into good and candidate genes with differential DNA methylation and poor prognosis groups; 921 of these remained significant gene expression between cancerous and non-malignant after multivariate and Cox regression analysis. Faryna et al. tissue. Future functional studies of these genes will clarify (2012), although with a small samples size, identified their role in prostate tumorigenesis. methylation of two genes associated with metastasis-free Kobayashi et al. (2011) identified 87 CpG biomarkers survival. Further study should be directed at determining in a large clinical prostate cancer cohort, almost all of the minimal number of loci required to effectively stratify which were hypermethylated in cancer (83/87; Table 2). patients by prognosis. Extensive future work is also Kim et al. (2011c) identified 1034 differentially methylated required to follow on from all these prognostic studies to genes in prostate cancer (Table 2). The highest functional validate these observations in additional clinical cohorts. network identified from these genes was ‘Cancer, Cellular In addition, biological characterization of relevant loci is Movement, Hematological System Development and required to understand how methylation at particular loci Function’, with TNFa as the central mediator. Despite contributes to or influences breast cancer prognosis. methylation discovery being performed with a single pooled sample each for tumor and non-malignant, of ten genes with promoter hypermethylation selected for Genome wide DNA methylation in expression analysis in an independent patient cohort prostate cancer (nZ17), most genes showed reduced expression, support- ing the methylation results reported by Kim et al. (2011c). DNA methylation profiles that discriminate tumor Coolen et al. (2010) mined public gene expression from non-malignant prostate tissue databases to identify candidate regions of long-range EightgenomewideDNAmethylationstudieshave epigenetic silencing (LRES; Table 2). Regions were classi- compared non-malignant and tumor tissue. Chung et al. fied as candidates for LRES if they i) contained four or more (2008) used methylated CpG island amplification coupled consecutive repressed or silent genes in two prostate with representational difference analysis of prostate cancer clinical data sets; ii) essentially lacked upregulated cancer cell lines (pooled DU145, PC3, and LNCaP) and probes, and iii) had probes that were upregulated in at Endocrine-Related Cancer identified eight genes with promoter hypermethylation least 2/4 prostate cancer cell lines after 5-aza (Coolen et al. (Table 2). These genes were validated in prostate cancer 2010). Genome wide methylation in LNCaP and PrEC was compared with non-malignant tissue (nZ20) and analyzed by MeDIP-chip, and results were presented for were re-expressed by the DNA methylation inhibitor seven candidate regions. Hypermethylation was identified 5-aza-20-deoxycytidine (5-aza). FAM84A (NSE1) and for a number of individual genes and the HOX-A and SPOCK2 hypermethylation was the optimal biomarker SERPINB gene families, with limited validation in clinical combination in differentiating cancer from non- samples (Coolen et al. 2010). Kim et al. (2011a) identified malignant tissue (sensitivity, 80%; specificity, 95%; and 2481 cancer-specific methylation events in clinical cancer accuracy, 96%; Chung et al. 2008). Kim et al. (2011d) (Table 2) and coupled this with gene expression. Methyl- combined genome wide DNA methylation and gene ation, 5-aza re-expression, and chromatin mapping data expression in pooled LNCaP-FGC, DU-145, and PC-3 from LNCaP and PrEC cells, together with clinical cells compared with RWPE-1 non-malignant cultured methylation and expression data, also demonstrated that prostate cells (Table 2). Three genes exhibited concordant promoter methylation regulates alternative transcript methylation and expression changes, had a CpG island, expression for the known cancer-related genes RASSF1, and enhanced expression after 5-aza treatment. In clinical NDRG2, and APC (Kim et al. 2011a). A recent study not samples, EFEMP1 methylation was the optimal marker to only identified differential DNA methylation changes differentiate prostate cancer from benign prostatic hyper- between non-malignant and tumor tissue but also plasia (sensitivity, 95.3% and specificity, 86.6%). EFEMP1 identified DNA methylation changes in histologically gene expression was also reduced in an independent non-malignant tissue adjacent to tumor (Yang et al. prostate cancer cohort (Kim et al. 2011d). Kron et al. (2009) 2013). This highlights the critical nature of sample choice, performed differential methylation hybridization (DMH) particularly with respect to non-malignant tissue for of prostate cancer samples (Table 2). Of the genes comparison. It also suggests that some DNA methylation

Table 2 Summary of published genome wide DNA methylation studies in prostate cancer.

References Samples Methods Findings

Chung et al. (2008) DU145, PC3, LNCaP, 20 MCA-RDA; sequenced 198 34 promoter-associated CpG islands. Filtered through matched microdis- random clones cell line panel; eight genes hypermethylated in PCa sected PCa–NM pairs vs NM, re-expressed by 5-aza in vitro Kron et al. (2009) 3C3or4C4 PCa DMH; microarray with 27/100 top methylated genes PCa vs NM (lymphocytes) (nZ10 each; frozen 7776 CpG islands (Yan homeobox or T-box genes. 3C3vs4C4 – 23/100 top tissue) et al. 2001) methylated genes are homeobox genes Ref: lymphocytes Coolen et al. (2010) LNCaP, DU145, PC3, MeDIP-chip; GeneChip 47 regions of LRES in PCa, based on gene expression in MDA-2A, PrEC Human Promoter 1.0R clinical cohorts and cell lines after 5-aza treatment. arrays (Affymetrix) Genome wide methylation reported for seven LRES Kim et al. (2011a) LNCaP, PrEC 6 adjacent MethylPlex NGS (Illumina); CpG island methylation increased with progression NM, 2 NM, 5 PCa, 4 Human GE 44K microar- (benign 13%, localized 19%, and metastatic 22%). metastatic CRPC ray (Agilent) 2481 regions with cancer-specific differential methylation Kim et al. (2011d) LNCaP-FGC, DU-145, Infinium HumanMethyla- 106 genes hypermethylated in PCa cell lines compared PC-3, RWPE-1 tion27 BeadChip and to RWPE-1. Filtered by greater than twofold Validation: 97 BPH, 106 Human HT-12 Gene difference in gene expression in PCa cell lines, PCa; frozen tissue Expression BeadChip presence of a CpG island, re-expression with 5-aza. (Illumina) Four candidates validated in clinical cohort Kobayashi et al. 95 PCa 86 adjacent NM Infinium HumanMethyla- 87 CpG biomarkers for NM vs PCa, 83 hypermethylated (2011) (includes 70 matched tion27 BeadChip in PCa. 69 CpGs associate with recurrence. Gleason pairs); microdis- (Illumina) grade not distinguishable by methylation sected frozen tissue Kim et al. (2011c) Pooled matched PCa Infinium HumanMethyla- General hypermethylation in PCa; ALU-specific hypo- and NM (nZ12 tion27 BeadChip methylation in cancer. 972 CpGs hypermethylated; patients); microdis- (Illumina) 209 CpGs hypomethylated (1043 genes) sected FFPE tissue Friedlander et al. Metastatic CRPC nZ15 Infinium HumanMethyla- 513 probes with differential methylation between (2012) (14 individuals) tion27 BeadChip CRPC and BPH. Aberrant methylation more frequent

Endocrine-Related Cancer seven liver and eight (Illumina); aCGH Human (10.5%) than CNV (2.1%). Sixteen genes methylated soft tissue Genome microarray and and copy loss in the same tumor in R66% of Whole-Human Gene samples. Pathways of genes structurally and epi- Expression 44K microar- genetically altered: androgen biosynthesis, p53 ray (Agilent) pathway, IGF1–protein kinase B signaling cascade Mahapatra et al. 40 matched NM; 198 Inﬁnium HumanMethyla- Differentially methylated genes for: tumor vs normal, (2012) PCa, macrodissected tion27 BeadChip 147; recurrence vs no recurrence, 75; clinical recur- frozen tissue; (Illumina) rence vs biochemical recurrence, 16; systemic recur- divided into: non- rence vs local recurrence, 68 recurrence (75), recurrence (123), clinical recurrence (80), BCR (43), systemic recurrence (36), and local recurrence (44) Yang et al. (2013) Five normal (no cancer), MeDIP-chip; ENCODE HG18 615 (537 hypomethylatedC78 hypermethylated) four histologically DNA methylation arrays probes differentially methylated in NM tissue NM (with PCa) (Roche NimbleGen) (from PCa tissues) compared to normal prostate Microdissected tissue tissue. DNA methylation changes seen in NM tissue adjacent to tumor Validation: 26 histologically NM tissue from men with PCa

5-aza, 5-aza-20-deoxycytidine; aCGH, array comparative genomic hybridization; BCR, biochemical recurrence; BPH, benign prostatic hyperplasia; CRPC, castrate-resistant prostate cancer; DMH, differential methylation hybridization; FFPE, formalin fixed, paraffin embedded; GE, gene expression; LRES, long- range epigenetic silencing; MCA-RDA, methylated CpG island amplification coupled with representational difference analysis; MeDIP, methyl DNA immunoprecipitation using a 5MeC MAB; NGS, next-generation sequencing; NM, non-malignant; PCa, prostate cancer; PrEC, cultured non-malignant prostate epithelial cell line. If the information was available, details of the type of tissue used were also included.

changes are seen very early in the tumorigenesis process, biomarkers based on their putative and known biological or alternatively ‘spread’ from the tumor into the surround- functions. Further research could investigate the ing tissue, potentially promoting a suitable environment functional role of differentially methylated genes ident- for tumor growth. Future studies should investigate these ified in this study and also identify whether combinations possibilities. The studies discussed here in general of these could act as biomarkers of disease progression. included limited validation of DNA methylation; both Two subsequent studies have identified panels of in terms of clinical cohorts and relationship with other differentially methylated CpGs associated with pro- genome wide data such as chromosome copy number, gression or recurrence in prostate cancer clinical cohorts. gene expression, and re-expression by demethylating Kobayashi et al. (2011) identified 69 CpGs associated with agents. Future work should include more systematic time to biochemical recurrence. These CpGs were located validation of the genome wide DNA methylation analyses in the promoters of both novel and known cancer-related currently reported. genes; further study is required to investigate the function of genes without a previously identified role in cancer. In the same study, Gleason grade could not be distinguished DNA methylation profiles in CRPC by methylation profile. Similarly, Mahapatra et al. (2012) Kim et al. (2011a) analyzed DNA methylation of four found 75 genes differentially methylated between recur- metastatic prostate cancer samples; however, results for rence and no recurrence, 68 genes differentially methyl- these samples were grouped with other cancers making ated between systemic recurrence and local recurrence, analysis difficult. Friedlander et al. (2012) integrated and 16 genes differentially methylated between clinical genome wide chromosome copy number, gene expression, recurrence and biochemical recurrence. Overall, there and DNA methylation across metastatic CRPC compared were fewer genes methylated in patients with clinical with primary cancer and benign prostate. Sixteen genes recurrence compared with patients with only biochemical had methylation and copy loss in the same tumor in 66% or recurrence (Mahapatra et al. 2012). Validation of 14 genes more of samples, and methylation changes were more in an independent cohort (nZ20 per group) supported the common than copy number alteration in CRPC. Moreover, genome wide data. However, the cohorts in each of these androgen biosynthesis, the p53 pathway, and the insulin- studies were small and validation is required in larger like growth factor 1–protein kinase B signaling cascade independent cohorts. were identified as pathways of genes frequently altered by Endocrine-Related Cancer aberrant copy number or DNA methylation changes Similarities between DNA methylation et al (Friedlander . 2012). Androgen biosynthesis is a key profiles in breast and prostate cancers mechanism for maintaining androgen signaling following androgen deprivation therapy (Cai & Balk 2011), and loss We have performed a meta-analysis of the currently of p53 plays an important role in regulating AR activity reported genome wide DNA methylation studies in breast across the genome (Guseva et al. 2012). Together, this and prostate cancers. We identified genes or gene families re-iterates the potential of DNA methylation to identify (HUGO Gene Nomenclature, http://www.genenames.org/) novel therapeutic targets in advanced prostate cancer. commonly targeted for aberrant DNA methylation as those that were identified in at least four different studies (Table 3). In performing this meta-analysis, we utilized the DNA methylation profiles in prostate cancer gene lists provided with the publication. We note that this progression and recurrence analysis was biased by differences between studies, the Kim et al. (2011a) reported that total promoter-associated biggest difference being whether data was just genome CpG island methylation increases in parallel with prostate wide DNA methylation or whether genes were identified cancer progression but did not correlate this increased by other filtering processes such as combining DNA methylation with any specific individual genomic loci. methylation with gene expression or CNV. From 22 breast The first genome wide DNA methylation study to identify cancer studies, we identified 425 genes that were individual CpGs associated with prostate cancer pro- commonly subject to aberrant methylation. Ninety-five gression was reported by Kron et al. (2009), who performed genes were commonly aberrantly methylated in prostate DMH of pure primary gleason pattern 3 (PP3) and PP4. cancer. The lower number of genes identified in prostate From the top 100 differentially methylated genes, BMP7 cancer is most likely due to the lower number of genome and HOXD3 were selected for analysis as potential wide DNA methylation studies currently published for

Table 3 Genes identiﬁed as differentially methylated in breast and prostate cancers.

Breast cancer Prostate cancer

Genes NM vs T Prognosis HR status Cell line NM vs T Recurrence

Homeobox gene/gene family HOXA J, K, M B HOXB E, L, M B B 3 HOXC E, L, M 2, 4 8 3, 7 HOXD D, E, J, L, M 4 3, 7 IRX1 D, L, M 4 7 ISL E, L, M I LBX L47 LHX D, G, H, L, M 4 7, 8 MNX1 47 NKX D, L, M B, C 4 1 7 PAX/OTX D, E, L, M POU D, E, G, M B, C 8 SIX6 D, E, M 4 7, 8 TBX E, M I VAX1 47 Gene family ADAMTSL E, G, K, M B, E B3G E, J, M B, E 6 CD E, K, M A B, I 5 6 8 CDH E, M 66 CDKN E, G, L, M I 4, 5 6, 7 COL E, K, M B E, I CXC G, K, M C 6 CYB E I 6, 8 ELF C6,8 EPH E, K, M 6 FGF M I 6, 8 FOX D, E, L, M 8 GAS MB6,88 GPR E, G, M A, C 5 6 Endocrine-Related Cancer HIST1 E, J, M 6 IL E, M C I ITG MCB,I6 KCN D, E, M B, C I 6 KLF D, E, G, L, M NEURO E, M 4 8 PCDH D, E, G, L, M PPP L, M A I 2 6 PRDM D, F, K, L, M RAB D, E, G, K, M C 6, 8 RASSF E, M 4, 5 RUNX E, M C B 4, 5 7 8 S100 MC 66 SEMA E, L, M I SFRP E, M C, E SLC D, E, F, G, J, K, M, A, C 6, 8 6, 8 ST8 E, L, M C SYN E, J, M I 6 8 TMEM D, E, M I 6 6, 8 TRIM D, E, G, M C I ZBTB J, K, M I ZNF D, E, G, J, K, L, M C, E E, I 6, 8 6, 8 Gene ACADL E, M E B APC E, M 4, 5 GSTP1 E, M C 4, 5 6, 8 HIF3A E 6, 8 GPR180 E, M E E

Table 3 Continued

Breast cancer Prostate cancer

Genes NM vs T Prognosis HR status Cell line NM vs T Recurrence

MAT1A E, M E 6 NFIX D, G, H, K RECK E, M C, E B TNFRSF10D E, M E 4 8 WT1 D, H, L, M 4, 5 7

Cell line comparison, NM vs T; HR, hormone receptor status; NM, non-malignant; T, tumor. A, Dedeurwaerder et al. (2011);B,Fackler et al. (2011);C,Fang et al. (2011);D,Faryna et al. (2012);E,Hill et al. (2011);F,Hu et al. (2005);G,Kamalakaran et al. (2011);H,Kim et al. (2012);I,Li et al. (2010);J,Novak et al. (2008);K,Ordway et al. (2007);L,Tommasi et al. (2009);M,Van der Auwera et al. (2010).1,Chung et al. (2008);2,Coolen et al. (2010);3,Friedlander et al. (2012);4,Kim et al. (2011a);5,Kim et al. (2011c);6,Kobayashi et al. (2011);7,Kron et al. (2009);8,Mahapatra et al. (2012).

prostate cancer compared with breast cancer. The number of these genes identiﬁed as aberrantly methylated combined genes (466 unique genes) were analyzed by in both breast and prostate cancers have previously been Ingenuity Pathway Analysis (IPA) Ingenuity Systems shown to be methylated in breast, prostate, and other (www.ingenuity.com) and identiﬁed Embryonic Develop- cancers: APC, CDKN2A, GSTP1, RASSF1, RUNX3, S100A2, ment, Organismal Development and Cancer as the top SFRP1, and WT1 (Table 4). However, a large number of network (Fig. 1). these genes have not been previously shown to be methy- Surprisingly, there were 54 genes in common between lated in breast and prostate cancers or cancer in general, the breast and prostate cancer gene lists (Fig. 2). A small including genes from the SLC family. Interestingly, the

HOXA3 HOXB13

HOXD4 HOXD3 HOXD12 KCNA6

Endocrine-Related Cancer ST8SIA1 HOXB9 ST8SIA2

HOXD10 KCNA5 KCNAB2 α-N-acetylneuraminate ZBTB16 α-2,8-Sialyltransferase

ST8SIA3 HOXD8 HOXD13 KCNA2 KCNA4 HOXD9 HOXD11 KCNA3 ST8SIA5 RAB31 KCNA1

HOXA9 CD34

SLC27A6 ITGA8 HOXA7 ZNF22 HOXA10 RNA polymerase II

HOXA11 S100A12

Figure 1 Ingenuity Pathway Analysis of genes identiﬁed with aberrant methylation studies in breast and prostate cancers was Embryonic Development, in breast cancer (red), prostate cancer (green), and both (yellow). The top Organismal Development, Cancer. network identiﬁed from the genes aberrantly methylated in four or more

an important key role in early embryonic development, but more recently, they have been associated with disease including cancer.

Genes commonly subject to aberrant DNA methylation in breast cancer

Numerous breast cancer genome wide DNA methylation studies identified homeobox genes as differentially methylated between non-malignant and tumor or associated with hormone receptor status or breast cancer prognosis (Ordway et al. 2007, Novak et al. 2008, Tommasi et al. 2009, Van der Auwera et al. 2010, Fackler et al. 2011, Figure 2 Venn diagram showing the genes commonly altered by DNA methylation Fang et al. 2011, Hill et al. 2011, Kamalakaran et al. 2011; in breast (red) and prostate (green). Fifty-four genes were common Table 3). Other gene families that were commonly between breast and prostate cancers. identified as differentially methylated between non- malignant and tumor included transcription factors (FOX, KLF, PRDM, ZBTB, and ZNF) and gene families only study to investigate both breast and prostate cancers involved in cell transport of proteins or vesicles (RAB and identified four genes to be hypermethylated in both SLC) or involvement in cell adhesion (CDH and PCDH) cancers; one of these four genes was a member of the (Table 3; Hu et al. 2005, Ordway et al. 2007, Novak et al. SLC family (Chung et al. 2008). Further study is required to 2008, Tommasi et al. 2009, Van der Auwera et al. 2010, Hill determine the functional effects of these proteins on et al. 2011, Kamalakaran et al. 2011). The ST8 family of prostate and breast cancer and to validate these results in ovarian cancer tumor suppressor genes was commonly other clinical cohorts. Using the Gene Functional Classi- identified in non-malignant vs tumor and prognosis as fication Tool from the DAVID Bioinformatics Resources, was the RECK tumor suppressor gene (Table 3). IPA of the there was an enrichment in this gene list for homeobox genes methylated in breast cancer identified some genes (19/54; Table 4; Huang da et al. 2009a,b). There was biological functions, such as posttranslational modifi- Endocrine-Related Cancer also enrichment of genes on chromosome 7 and this is due cation and lipid metabolism, that were only identified in to the homeobox A cluster on 7p15.2 (HOXA1, HOXA10, breast cancer and not in prostate cancer. This may be due HOXA11, HOXA13, HOXA4, HOXA5, HOXA7,and to the differences in the gene list size between breast and HOXA9). Interestingly, despite clearly identified CpG prostate cancers. In terms of the canonical pathways, the islands (Fig. 3), there is limited evidence for aberrant top ones for breast cancer-specific genes were transcrip- methylation of these enriched genes in cancers other than tional regulatory network in embryonic stem cells and breast or prostate, identifying them as potential hormonal caveolar-mediated endocytosis signaling. Given that the cancer-specific genes. This requires future study to clarify. pathways and gene families do not appear to have a Homeobox genes are transcription factors known to have strong link to hormone metabolism or signaling, it is

Table 4 Genes aberrantly methylated in breast and prostate cancers.

APC FOXE3 HOXA1 HOXD4 MAT1A SIX6 CD48 GAS6 HOXA10 HOXD9 NEUROG1 SLC34A2 CD8A GAS7 HOXA11 IRX1 NKX2–5 SLC9A3 CDH13 GPR150 HOXA13 ITGA11 POU3F3 SYN2 CDH5 GPR62 HOXA4 KCNK9 RAB34 TMEM74 CDKN1C GPR83 HOXA5 KCNQ1 RASSF1 TNFRSF10D CDKN2A GSTP1 HOXA7 KLK10 RUNX3 WT1 CXCL1 HIF3A HOXA9 LBX1 S100A2 ZNF154 FGF6 HIST1H3A HOXD3 LHX9 SFRP1 ZNF80

Homeobox genes are in bold.

Scale 50 kb HG19 chr7: 27 150 000 27 200 000 Chromosome bands localized by FISH mapping clones 7p15.2 7p15.2 Gene annotations from ENCODE/GENCODE version 7 Basic RefSeq genes HOXA1 HOXA3 ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( HOXA-AS3 ) ) ) ) ) ) ) ) ) ) ) HOXA-AS4 ) ) HOXA11 ( ( HOXA13 ( HOXA1 HOXA3 ( ( ( ( ( ( ( ( ( ( ( HOXA4 HOXA5 ( HOXA7 ( MIR196B HOXA11–AS) ) HOTTIP ) ) HOTAIRM1 HOXA6 ( ( HOXA9 ( HOTAIRM1 HOXA-AS3 ) ) ) ) HOXA10 ( HOXA2 HOXA10–HOXA9 ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( HOXA10 ( ( ( ( ( ( ( ( CpG islands (islands <300 bases are light green) CpG: 77 CpG: 24 CpG: 44 CpG: 87 CpG: 207 CpG: 91 CpG: 53 CpG: 33 CpG: 113 CpG: 17 CpG: 49 CpG: 24 CpG: 37 CpG: 72 CpG: 201 CpG: 36 CpG: 114 CpG: 69 CpG: 116 CpG: 67 CpG: 32 CpG: 63 CpG: 28 CpG: 183 CpG: 41 CpG: 45 CpG: 28 CpG: 97 CpG: 46 CpG: 94

Figure 3 Genomic features of the 7p15.2 chromosomal region. The genes commonly corresponding CpG islands are shown using the human genome browser at altered by methylation in breast and prostate cancers were enriched for UCSC (http://genome.ucsc.edu/; Kent et al. 2002). this region, which contains the HOXA gene cluster. Genes in the region and

likely that these genes are not drivers of cancer but rather and recurrence than for cell line or cancer vs non- are secondary events that occur as part of the malignant. IPA identified biological functions associated tumorigenic process. with genes methylated in prostate cancer (Inflammatory Response and Immune Cell Trafficking) that were not evident with genes from breast cancer. The top canonical Genes identified as commonly targeted pathways for prostate cancer were bladder cancer signaling by DNA methylation in prostate cancer and Wnt/b-catenin signaling. There was substantial overlap between different studies of Endocrine-Related Cancer genome wide DNA methylation in prostate cancer Future directions (Table 3). GSTP1 frequently exhibits prostate cancer- specific gene promoter methylation (Goering et al. 2012), While this review demonstrates that genome wide DNA and this was reflected in the studies reviewed here (Table 3). methylation analysis in breast and prostate cancers can The homeobox or T-box gene families were commonly provide novel data and identify gene pathways not usually identified in cancer compared to non-malignant and associated with these cancers, further study is required. during progression (Kron et al. 2009). Despite the limi- First, it is clear that studies that integrate multiple data tations of this study introduced using pooled lymphocytes sources will produce the highest quality targets. as a reference, many homeobox genes were also identified Additional studies that integrate genome wide DNA in other studies (Kim et al. 2011a, Friedlander et al. 2012). methylation, gene expression, CNV, and analysis of Where studies had more similarities, there was greater other histone modifications are essential in prostate overlap. For example, in prostate cancer vs non-malignant, cancer, and to a lesser extent, breast cancer. These studies 16/25 genes identified by Mahapatra et al. (2012) over- must be performed in appropriately powered clinical lapped with those identified by Kobayashi et al. (2011). cohorts with the relevant patient samples. Studies of this Similarly, there were many common genes in studies where nature should produce an unbiased, high-quality cell lines were used. This suggests that the degree of ‘roadmap’ of all sites in the genome that are under the variation in methylation between samples is sufficiently influence of DNA methylation. Secondly, a meta-analysis smallthatgenomewideDNAmethylationanalysis of currently reported studies, using the raw data, should be techniques can reliably and robustly detect methylation conducted. While the meta-analysis presented here changes. The degree of similarity observed also reflects the demonstrates that there are novel and overlapping variability within each disease state, with less overlap genes/gene families subject to aberrant DNA methylation identified between studies of prostate cancer progression in breast and prostate cancers, careful comparison of this

data with genome wide DNA methylation data from other Funding cancers is essential. This will minimize variation between This work was supported by grants from the Prostate Cancer Foundation of studies resulting from inconsistent statistical analyses and Australia Young Investigator Grant (T K Day, YIG03); The United States generate an unbiased list of candidate genes, some of Department of Defense Prostate Cancer Research Program Training Fellowship (T K Day, PC080400); The University of Adelaide Barbara Kidman which are likely to be breast and/or prostate speciﬁc, Fellowship (T K Day); W Bruce Hall Cancer Council of South Australia making them good candidates for being drivers of disease. Research Fellowship (T Bianco-Miotto); Cancer Council SA and SAHMRI Drivers of disease are also potential therapeutic candidates Beat Cancer Project (T Bianco-Miotto, APP1030945). Views and opinions of the authors do not necessarily reﬂect those of the USA Army, the for late-stage breast or prostate cancer, for which there are Department of Defense or the Cancer Council of SA. currently limited therapeutic options.

Acknowledgements The authors gratefully acknowledge Dylan McCullough for assistance with Conclusion this mansucript. Genome wide methylation studies in breast and prostate cancers have identified in an unbiased manner genes and gene families with aberrant methylation in non-malignant References vs tumor, or with cancer progression or associated with Baylin SB & Ohm JE 2006 Epigenetic gene silencing in cancer – a mechanism for early oncogenic pathway addiction? Nature Reviews. hormone receptor status. This demonstrates the potential Cancer 6 107–116. (doi:10.1038/nrc1799) of these techniques to enhance our understanding of what Baylin SB & Jones PA 2011 A decade of exploring the cancer epigenome – controls gene expression in hormonally regulated cancers. biological and translational implications. Nature Reviews. Cancer 11 726–734. (doi:10.1038/nrc3130) Our analysis of the genes identified with aberrant methyl- Cai C & Balk SP 2011 Intratumoral androgen biosynthesis in prostate ation in breast and prostate cancers clearly showed that cancer pathogenesis and response to therapy. Endocrine-Related Cancer there was substantial overlap between genes identified 18 R175–R182. (doi:10.1530/ERC-10-0339) Cancer Genome Atlas N 2012 Comprehensive molecular portraits of across different studies, both within and between cancer human breast tumours. Nature 490 61–70. (doi:10.1038/nature11412) types. The observed overlap between breast and prostate Chan MF, Liang G & Jones PA 2000 Relationship between transcription and DNA methylation. Current Topics in Microbiology and Immunology 249 cancers may be a result of common underlying hormonal 75–86. (doi:10.1007/978-3-642-59696-4_5) growth pathways, although this requires further analyses. Chari R, Lockwood WW, Coe BP, Chu A, Macey D, Thomson A, Davies JJ, MacAulay C & Lam WL 2006 SIGMA: a system for integrative genomic

Endocrine-Related Cancer In conclusion, the studies reviewed here demonstrate microarray analysis of cancer genomes. BMC Genomics 7 324. (doi:10. the potential of identifying epigenetically regulated genes 1186/1471-2164-7-324) that are involved in cancer initiation or progression, and Chari R, Coe BP, Vucic EA, Lockwood WW & Lam WL 2010 An integrative therefore biomarkers of prognosis and diagnosis, and that multi-dimensional genetic and epigenetic strategy to identify aberrant genes and pathways in cancer. BMC Systems Biology 4 67. (doi:10.1186/ may be targets for epigenetic-modifying agents. However, 1752-0509-4-67) further validation in additional clinical cohorts is required Chiam K, Ricciardelli C & Bianco-Miotto T 2012 Epigenetic biomarkers in to confirm the targets of aberrant methylation in breast prostate cancer: current and future uses. Cancer Letters [in press]. (doi:10.1016/j.canlet.2012.02.011) and prostate cancers. In addition, methylation results Chin SP, Dickinson JL & Holloway AF 2011 Epigenetic regulation of need to be linked to alterations in gene expression, prostate cancer. Clinical Epigenetics 2 151–169. (doi:10.1007/s13148- chromosome copy number alterations, and other epige- 011-0041-7) Chung W, Kwabi-Addo B, Ittmann M, Jelinek J, Shen L, Yu Y & Issa JP 2008 netic marks, all of which work together to contribute to Identification of novel tumor markers in prostate, colon and breast the transcriptional picture of a cell. Only with a fully cancer by unbiased methylation profiling. PLoS ONE 3 e2079. integrated, genome wide analysis, can the downstream (doi:10.1371/journal.pone.0002079) Coolen MW,StirzakerC,SongJZ,StathamAL,KassirZ,MorenoCS,YoungAN, effects of these genetic and epigenetic changes on breast Varma V, Speed TP, Cowley M et al. 2010 Consolidation of the cancer and prostate cancer cells be determined. In doing so, we genome into domains of repressive chromatin by long-range epigenetic will identify the key drivers in hormonal tumorigenesis silencing (LRES) reduces transcriptional plasticity. Nature Cell Biology 12 235–246. (doi:10.1038/ncb2023) and elucidate key targets for chemoprevention and Dedeurwaerder S, Desmedt C, Calonne E, Singhal SK, Haibe-Kains B, therapeutics. Defrance M, Michiels S, Volkmar M, Deplus R, Luciani J et al. 2011 DNA methylation profiling reveals a predominant immune component in breast cancers. EMBO Molecular Medicine 3 726–741. (doi:10.1002/ emmm.201100801) Declaration of interest Esteller M 2005 Aberrant DNA methylation as a cancer-inducing The authors declare that there is no conflict of interest that could be mechanism. Annual Review of Pharmacology and Toxicology 45 629–656. perceived as prejudicing the impartiality of the review reported. (doi:10.1146/annurev.pharmtox.45.120403.095832)

Fackler MJ, Umbricht CB, Williams D, Argani P, Cruz LA, Merino VF, non-luminal subtypes and can identify relapse risk independent of Teo WW, Zhang Z, Huang P, Visvananthan K et al. 2011 Genome-wide other clinical variables. Molecular Oncology 5 77–92. (doi:10.1016/ methylation analysis identifies genes specific to breast cancer hormone j.molonc.2010.11.002) receptor status and risk of recurrence. Cancer Research 71 6195–6207. Karpinets TV & Foy BD 2005 Tumorigenesis: the adaptation of mammalian (doi:10.1158/0008-5472.CAN-11-1630) cells to sustained stress environment by epigenetic alterations and Fang F, Turcan S, Rimner A, Kaufman A, Giri D, Morris LG, Shen R, Seshan V, succeeding matched mutations. Carcinogenesis 26 1323–1334. Mo Q, Heguy A et al. 2011 Breast cancer methylomes establish an (doi:10.1093/carcin/bgi079) epigenomic foundation for metastasis. Science Translational Medicine 3 Kent WJ, Sugnet CW, Furey TS, Roskin KM, Pringle TH, Zahler AM & 75ra25. (doi:10.1126/scitranslmed.3001875) Haussler D 2002 The human genome browser at UCSC. Genome Research Faryna M, Konermann C, Aulmann S, Bermejo JL, Brugger M, Diederichs S, 12 996–1006. (doi:10.1101/gr.229102) Rom J, Weichenhan D, Claus R, Rehli M et al. 2012 Genome-wide Kim JH, Dhanasekaran SM, Prensner JR, Cao X, Robinson D, methylation screen in low-grade breast cancer identifies novel Kalyana-Sundaram S, Huang C, Shankar S, Jing X, Iyer M et al. 2011a epigenetically altered genes as potential biomarkers for tumor Deep sequencing reveals distinct patterns of DNA methylation in diagnosis. FASEB Journal 26 4937–4950. (doi:10.1096/fj.12-209502) prostate cancer. Genome Research 21 1028–1041. (doi:10.1101/gr. Flanagan JM, Cocciardi S, Waddell N, Johnstone CN, Marsh A, 119347.110) Henderson S, Simpson P, da Silva L, Khanna K, Lakhani S et al. 2010 Kim JH, Kang HS, Kim TW & Kim SJ 2011b Differential methylation DNA methylome of familial breast cancer identifies distinct profiles hybridization profiling identifies involvement of STAT1-mediated defined by mutation status. American Journal of Human Genetics 86 pathways in breast cancer. International Journal of Oncology 39 955–963. 420–433. (doi:10.1016/j.ajhg.2010.02.008) (doi:10.3892/ijo.2011.1075) Friedlander TW, Roy R, Tomlins SA, Ngo VT, Kobayashi Y, Azameera A, Kim SJ, Kelly WK, Fu A, Haines K, Hoffman A, Zheng T & Zhu Y 2011c Rubin MA, Pienta KJ, Chinnaiyan A, Ittmann MM et al. 2012 Common Genome-wide methylation analysis identifies involvement of TNF-a structural and epigenetic changes in the genome of castration-resistant mediated cancer pathways in prostate cancer. Cancer Letters 302 47–53. prostate cancer. Cancer Research 72 616–625. (doi:10.1158/0008-5472. (doi:10.1016/j.canlet.2010.12.010) CAN-11-2079) Kim YJ, Yoon HY, Kim SK, Kim YW, Kim EJ, Kim IY & Kim WJ 2011d Goering W, Kloth M & Schulz WA 2012 DNA methylation changes in EFEMP1 as a novel DNA methylation marker for prostate cancer: array- prostate cancer. Methods in Molecular Biology 863 47–66. (doi:10.1007/ based DNA methylation and expression profiling. Clinical Cancer 978-1-61779-612-8_4) Research 17 4523–4530. (doi:10.1158/1078-0432.CCR-10-2817) Guseva NV, Rokhlin OW, Bair TB, Glover RB & Cohen MB 2012 Inhibition Kim MS, Lee J, Oh T, Moon Y, Chang E, Seo KS, Hoehn BD, An S & Lee JH of p53 expression modifies the specificity of chromatin binding by the 2012 Genome-wide identification of OTP gene as a novel methylation androgen receptor. Oncotarget 3 183–194. marker of breast cancer. Oncology Reports 27 1681–1688. (doi:10.3892/ Hake SB, Xiao A & Allis CD 2004 Linking the epigenetic ‘language’ of or.2012.1691) covalent histone modifications to cancer. British Journal of Cancer 90 Kobayashi Y, Absher DM, Gulzar ZG, Young SR, McKenney JK, Peehl DM, 761–769. (doi:10.1038/sj.bjc.6601575) Brooks JD, Myers RM & Sherlock G 2011 DNA methylation profiling Hill VK, Ricketts C, Bieche I, Vacher S, Gentle D, Lewis C, Maher ER & reveals novel biomarkers and important roles for DNA methyl- Latif F 2011 Genome-wide DNA methylation profiling of CpG islands transferases in prostate cancer. Genome Research 21 1017–1027. in breast cancer identifies novel genes associated with tumorigenicity. (doi:10.1101/gr.119487.110) Cancer Research 71 2988–2999. (doi:10.1158/0008-5472.CAN-10-4026) Endocrine-Related Cancer Kron K, Pethe V, Briollais L, Sadikovic B, Ozcelik H, Sunderji A, Hu M, Yao J, Cai L, Bachman KE, van den Brule F, Velculescu V & Polyak K Venkateswaran V, Pinthus J, Fleshner N, van der Kwast T et al. 2009 2005 Distinct epigenetic changes in the stromal cells of breast cancers. Discovery of novel hypermethylated genes in prostate cancer using Nature Genetics 37 899–905. (doi:10.1038/ng1596) genomic CpG island microarrays. PLoS ONE 4 e4830. (doi:10.1371/ Huang Y, Nayak S, Jankowitz R, Davidson NE & Oesterreich S 2011 journal.pone.0004830) Epigenetics in breast cancer: what’s new? Breast Cancer Research 13 225. Laird PW 2010 Principles and challenges of genomewide DNA methylation (doi:10.1186/bcr2925) analysis. Nature Reviews. Genetics 11 191–203. (doi:10.1038/nrg2732) Huang da W, Sherman BT & Lempicki RA 2009a Bioinformatics et al. enrichment tools: paths toward the comprehensive functional analysis Li J, Gao F, Li N, Li S, Yin G, Tian G, Jia S, Wang K, Zhang X, Yang H of large gene lists. Nucleic Acids Research 37 1–13. (doi:10.1093/nar/ 2009 An improved method for genome wide DNA methylation gkn923) profiling correlated to transcription and genomic instability in two Huang da W, Sherman BT & Lempicki RA 2009b Systematic and integrative breast cancer cell lines. BMC Genomics 10 223. (doi:10.1186/1471-2164- analysis of large gene lists using DAVID bioinformatics resources. 10-223) Nature Protocols 4 44–57. (doi:10.1038/nprot.2008.211) Li L, Lee KM, Han W, Choi JY, Lee JY, Kang GH, Park SK, Noh DY, Yoo KY & Huynh KT, Chong KK, Greenberg ES & Hoon DS 2012 Epigenetics of Kang D 2010 Estrogen and progesterone receptor status affect genome- estrogen receptor-negative primary breast cancer. Expert Review of wide DNA methylation profile in breast cancer. Human Molecular Molecular Diagnostics 12 371–382. (doi:10.1586/erm.12.26) Genetics 19 4273–4277. (doi:10.1093/hmg/ddq351) Jaenisch R & Bird A 2003 Epigenetic regulation of gene expression: how the Mahapatra S, Klee EW, Young CY, Sun Z, Jimenez RE, Klee GG, Tindall DJ & genome integrates intrinsic and environmental signals. Nature Genetics Donkena KV 2012 Global methylation profiling for risk prediction of 33 (Suppl) 245–254. (doi:10.1038/ng1089) prostate cancer. Clinical Cancer Research 18 2882–2895. (doi:10.1158/ Jeronimo C, Bastian PJ, Bjartell A, Carbone GM, Catto JW, Clark SJ, 1078-0432.CCR-11-2090) Henrique R, Nelson WG & Shariat SF 2011 Epigenetics in prostate Miyamoto K & Ushijima T 2005 Diagnostic and therapeutic applications of cancer: biologic and clinical relevance. European Urology 60 753–766. epigenetics. Japanese Journal of Clinical Oncology 35 293–301. (doi:10.1016/j.eururo.2011.06.035) (doi:10.1093/jjco/hyi088) Jovanovic J, Ronneberg JA, Tost J & Kristensen V 2010 The epigenetics of Nafee TM, Farrell WE, Carroll WD, Fryer AA & Ismail KM 2008 Epigenetic breast cancer. Molecular Oncology 4 242–254. (doi:10.1016/j.molonc. control of fetal gene expression. BJOG: an International Journal of 2010.04.002) Obstetrics and Gynaecology 115 158–168. (doi:10.1111/j.1471-0528. Kamalakaran S, Varadan V, Giercksky Russnes HE, Levy D, Kendall J, 2007.01528.x) Janevski A, Riggs M, Banerjee N, Synnestvedt M, Schlichting E et al. Nakao M 2001 Epigenetics: interaction of DNA methylation and 2011 DNA methylation patterns in luminal breast cancers differ from chromatin. Gene 278 25–31. (doi:10.1016/S0378-1119(01)00721-1)

Naume B, Zhao X, Synnestvedt M, Borgen E, Russnes HG, Lingjaerde OC, of different lineages in breast cancer. PNAS 108 4364–4369. Stromberg M, Wiedswang G, Kvalheim G, Karesen R et al. 2007 Presence (doi:10.1073/pnas.1013224108) of bone marrow micrometastasis is associated with different recurrence Sun Z, Asmann YW, Kalari KR, Bot B, Eckel-Passow JE, Baker TR, Carr JM, risk within molecular subtypes of breast cancer. Molecular Oncology 1 Khrebtukova I, Luo S, Zhang L et al. 2011 Integrated analysis of gene 160–171. (doi:10.1016/j.molonc.2007.03.004) expression, CpG island methylation, and gene copy number in breast Neve RM, Chin K, Fridlyand J, Yeh J, Baehner FL, Fevr T, Clark L, Bayani N, cancer cells by deep sequencing. PLoS ONE 6 e17490. (doi:10.1371/ Coppe JP, Tong F et al. 2006 A collection of breast cancer cell lines for journal.pone.0017490) the study of functionally distinct cancer subtypes. Cancer Cell 10 Szyf M 2005 DNA methylation and demethylation as targets for anticancer 515–527. (doi:10.1016/j.ccr.2006.10.008) therapy. Biochemistry 70 533–549. (doi:10.1007/s10541-005-0147-7) Novak P, Jensen T, Oshiro MM, Watts GS, Kim CJ & Futscher BW 2008 Szyf M, Pakneshan P & Rabbani SA 2004 DNA demethylation and cancer: Agglomerative epigenetic aberrations are a common event in human therapeutic implications. Cancer Letters 211 133–143. (doi:10.1016/ breast cancer. Cancer Research 68 8616–8625. (doi:10.1158/0008-5472. j.canlet.2004.04.009) CAN-08-1419) Tommasi S, Karm DL, Wu X, Yen Y & Pfeifer GP 2009 Methylation Ordway JM, Bedell JA, Citek RW, Nunberg A, Garrido A, Kendall R, of homeobox genes is a frequent and early epigenetic Stevens JR, Cao D, Doerge RW, Korshunova Y et al. 2006 Comprehen- event in breast cancer. Breast Cancer Research 11 R14. (doi:10.1186/ sive DNA methylation profiling in a human cancer genome identifies bcr2233) novel epigenetic targets. Carcinogenesis 27 2409–2423. (doi:10.1093/ Van der Auwera I, Yu W, Suo L, Van Neste L, van Dam P, Van Marck EA, carcin/bgl161) Pauwels P, Vermeulen PB, Dirix LY & Van Laere SJ 2010 Array-based Ordway JM, Budiman MA, Korshunova Y, Maloney RK, Bedell JA, DNA methylation profiling for breast cancer subtype discrimination. Citek RW, Bacher B, Peterson S, Rohlfing T, Hall J et al. 2007 PLoS ONE 5 e12616. (doi:10.1371/journal.pone.0012616) Identification of novel high-frequency DNA methylation changes in Waddell N, Arnold J, Cocciardi S, da Silva L, Marsh A, Riley J, breast cancer. PLoS ONE 2 e1314. (doi:10.1371/journal.pone.0001314) Johnstone CN, Orloff M, Assie G, Eng C et al. 2010 Subtypes of familial Ruike Y, Imanaka Y, Sato F, Shimizu K & Tsujimoto G 2010 Genome-wide breast tumours revealed by expression and copy number profiling. analysis of aberrant methylation in human breast cancer cells using Breast Cancer Research and Treatment 123 661–677. (doi:10.1007/ methyl-DNA immunoprecipitation combined with high-throughput s10549-009-0653-1) sequencing. BMC Genomics 11 137. (doi:10.1186/1471-2164-11-137) Yan PS, Chen CM, Shi H, Rahmatpanah F, Wei SH, Caldwell CW & Salozhin SV, Prokhorchuk EB & Georgiev GP 2005 Methylation of Huang TH 2001 Dissecting complex epigenetic alterations in breast DNA – one of the major epigenetic markers. Biochemistry 70 525–532. cancer using CpG island microarrays. Cancer Research 61 8375–8380. (doi:10.1007/s10541-005-0146-8) Yang B, Bhusari S, Kueck J, Weeratunga P, Wagner J, Leverson G, Huang W Sandoval J & Esteller M 2012 Cancer epigenomics: beyond genomics. & Jarrard DF 2013 Methylation profiling defines an extensive field Current Opinion in Genetics & Development 22 50–55. (doi:10.1016/j.gde. defect in histologically normal prostate tissues associated with prostate 2012.02.008) cancer. Neoplasia 15 399–408. (doi:10.1593/neo.13280) Sproul D, Nestor C, Culley J, Dickson JH, Dixon JM, Harrison DJ, Zheng S & Zhao Z 2012 DNA methylation profiling distinguishes three Meehan RR, Sims AH & Ramsahoye BH 2011 Transcriptionally clusters of breast cancer cell lines. Chemistry & Biodiversity 9 848–856. repressed genes become aberrantly methylated and distinguish tumors (doi:10.1002/cbdv.201100354)

Received in ﬁnal form 6 June 2013 Endocrine-Related Cancer Accepted 28 June 2013 Made available online as an Accepted Preprint 1 July 2013