Array-Based DNA Methylation Profiling for Breast Cancer Subtype Discrimination

Abstract

BackgroundAbnormal DNA methylation is well established for breast cancer and contributes to its progression by silencing tumor suppressor genes. DNA methylation profiling platforms might provide an alternative approach to expression microarrays for accurate breast tumor subtyping. We sought to determine whether the distinction of the inflammatory breast cancer (IBC) phenotype from the non-IBC phenotype by transcriptomics could be sustained by methylomics.Methodology/Principal FindingsWe performed methylation profiling on a cohort of IBC (N = 19) and non-IBC (N = 43) samples using the Illumina Infinium Methylation Assay. These results were correlated with gene expression profiles. Methylation values allowed separation of breast tumor samples into high and low methylation groups. This separation was significantly related to DNMT3B mRNA levels. The high methylation group was enriched for breast tumor samples from patients with distant metastasis and poor prognosis, as predicted by the 70-gene prognostic signature. Furthermore, this tumor group tended to be enriched for IBC samples (54% vs. 24%) and samples with a high genomic grade index (67% vs. 38%). A set of 16 CpG loci (14 genes) correctly classified 97% of samples into the low or high methylation group. Differentially methylated genes appeared to be mainly related to focal adhesion, cytokine-cytokine receptor interactions, Wnt signaling pathway, chemokine signaling pathways and metabolic processes. Comparison of IBC with non-IBC led to the identification of only four differentially methylated genes (TJP3, MOGAT2, NTSR2 and AGT). A significant correlation between methylation values and gene expression was shown for 4,981 of 6,605 (75%) genes.Conclusions/SignificanceA subset of clinical samples of breast cancer was characterized by high methylation levels, which coincided with increased DNMT3B expression. Furthermore, an association was observed with molecular signatures indicative of poor patient prognosis. The results of the current study also suggest that aberrant DNA methylation is not the main force driving the molecular biology of IBC.

A functional methylome map of ulcerative colitis

“…Together with other factors (Hampe et al 1999;Stoll et al 2004;Franke et al 2008Franke et al , 2010, epigenetic modifications may explain parts of the remaining disease risk. While disease-relevant epigenetic modifications as well as their interaction with environmental factors has been shown for other diseases (van Overveld et al 2003;Mann et al 2004;Heijmans et al 2008;Rakyan et al 2008Rakyan et al , 2011aPetronis 2010), such effects have not been shown in ulcerative colitis. Therefore, this study aimed to create a functional epigenetic map by combining genome-wide data of differentially methylated regions, methylation variable positions, and transcriptome data, reflecting potential effects of the epigenetic variations.…”

“…Epigenetic patterns are highly tissue-specific (Rakyan et al 2008) and can be influenced by environmental factors (Mann et al 2004;Heijmans et al 2008). Recent technological advances have assessed the degree of methylation of specific CpG sites as regulators of disease-associated transcripts (van Overveld et al 2003;Petronis 2010). Consequently, epigenetic modifications represent promising candidates for elucidating processes of disease manifestation beyond the identified risk loci.…”

Downregulation of ARFGEF1 and CAMK2B by promoter hypermethylation in breast cancer cells

“…The combinational approach for identifying candidate genes of which expression is confined to cancer tissue has proven useful by successfully mining and characterizing ARFGEF1 and CAMK2B. A previous genome-wide study in breast cancer that used an Illumina methylation array indicates that 7.8% of the total genes underwent methylation change in the tumor tissue (8). Another study on ovary cancers also showed ∼7.8% of methylation change (14).…”

“…HOXB13 and HNF1B are tumor suppressors and were identified from breast cancer tissue by the differential methylation hybridization (DMH) method (7). MOGAT2 and NTSR2 were also identified from breast cancer tissue by Illumina Infinium methylation array (8). The hypermethylated and hypomethylated genes obtained by microarray assay are available at the public databases EBI (European bioinformatics institute) (http://www.ebi.ac.uk/gxa/) and GEO (http://www.ncbi.nlm.…”

DNA Methylation

“…One of the first genome wide DNA methylation studies in breast cancer developed methylation-specific digital karyotyping (MSDK) to assess epithelial, myoepithelial, and stromal fibroblasts from normal abreast and cancer tissues [71]. Furthermore, genome wide DNA methylation studies in breast cancer identified gene families that were commonly identified as differentially methylated between non-malignant and tumour included transcription factors (FOX, KLF, PRDM, ZBTB, and ZNF) and gene families involved in cell transport of proteins or vesicles(RAB and SLC) or involvement in cell adhesion (CDH and PCDH) [71][72][73][74]. The pathways and gene families do not appear to have a strong link to hormone metabolism or signalling, it is likely that these genes are not drivers of cancer but rather are secondary events that occur as part of the tumourigenic process [75,76].…”