Abstract
BackgroundWhile recent integrative analyses of copy number and gene expression data in breast cancer have revealed a complex molecular landscape with multiple subtypes and many oncogenic/tumour suppressor driver events, much less is known about the role of DNA methylation in shaping breast cancer taxonomy and defining driver events.ResultsHere, we applied a powerful integrative network algorithm to matched DNA methylation and RNA-Seq data for 724 estrogen receptor (ER)-positive (ER+) breast cancers and 111 normal adjacent tissue specimens from The Cancer Genome Atlas (TCGA) project, in order to identify putative epigenetic driver events and to explore the resulting molecular taxonomy. This revealed the existence of nine functionally deregulated epigenetic hotspots encompassing a total of 146 genes, which we were able to validate in independent data sets encompassing over 1000 ER+ breast cancers. Integrative clustering of the matched messenger RNA (mRNA) and DNA methylation data over these genes resulted in only two clusters, which correlated very strongly with the luminal-A and luminal B subtypes. Overall, luminal-A and luminal-B breast cancers shared the same epigenetically deregulated hotspots but with luminal-B cancers exhibiting increased aberrant DNA methylation patterns relative to normal tissue. We show that increased levels of DNA methylation and mRNA expression deviation from the normal state define a marker of poor prognosis. Our data further implicates epigenetic silencing of WNT signalling antagonists and bone morphogenetic proteins (BMP) as key events underlying both luminal subtypes but specially of luminal-B breast cancer. Finally, we show that DNA methylation changes within the identified epigenetic interactome hotspots do not exhibit mutually exclusive patterns within the same cancer sample, instead exhibiting coordinated changes within the sample.ConclusionsOur results indicate that the integrative DNA methylation and transcriptomic landscape of ER+ breast cancer is surprisingly homogeneous, defining two main subtypes which strongly correlate with luminal-A/B subtype status. In particular, we identify WNT and BMP signalling as key epigenetically deregulated tumour suppressor pathways in luminal ER+ breast cancer, with increased deregulation seen in luminal-B breast cancer.Electronic supplementary materialThe online version of this article (doi:10.1186/s13148-015-0159-0) contains supplementary material, which is available to authorized users.
Highlights
While recent integrative analyses of copy number and gene expression data in breast cancer have revealed a complex molecular landscape with multiple subtypes and many oncogenic/tumour suppressor driver events, much less is known about the role of DNA methylation in shaping breast cancer taxonomy and defining driver events
Identification and validation of Functional Epigenetic Modules (FEMs) modules in estrogen receptor (ER)+ breast cancer Given that the process of cellular differentiation is dictated by the specific activation/deactivation of signalling pathways and that this is largely controlled by epigenetics, we reasoned that putative epigenetic drivers of cancer could be identified by integrating DNA methylation with gene expression data in the context of a functional gene network, which incorporates pathwaylevel information, such as that provided by a comprehensive high-quality human protein interactome [15]
In order to identify interactome hotspots of simultaneous differential methylation and gene expression associated with ER+ breast cancer, we applied our FEM algorithm [11] (Fig. 1a) to the ER+ breast cancer subset of The Cancer Genome Atlas (TCGA), encompassing Illumina Infinium 450K DNA methylation and RNA-Seq data for 111 normal adjacent and 724 cancer samples
Summary
While recent integrative analyses of copy number and gene expression data in breast cancer have revealed a complex molecular landscape with multiple subtypes and many oncogenic/tumour suppressor driver events, much less is known about the role of DNA methylation in shaping breast cancer taxonomy and defining driver events. Large-scale integrative analyses of copy number and gene expression data in breast cancer have revealed a complex molecular landscape with many putative driver events [1,2,3,4]. Gao et al Clinical Epigenetics (2015) 7:126 Another mechanism that can lead to deregulation of gene expression in cancer is aberrant DNA methylation. Relatively little is known about how breast cancers would cluster if we were to perform an explicit integrative analysis of DNA methylation and gene expression
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