DNA methylation profiling reveals a predominant immune component in breast cancers

Abstract

Breast cancer is a molecularly, biologically and clinically heterogeneous group of disorders. Understanding this diversity is essential to improving diagnosis and optimizing treatment. Both genetic and acquired epigenetic abnormalities participate in cancer, but the involvement of the epigenome in breast cancer and its contribution to the complexity of the disease are still poorly understood. By means of DNA methylation profiling of 248 breast tissues, we have highlighted the existence of previously unrecognized breast cancer groups that go beyond the currently known ‘expression subtypes’. Interestingly, we showed that DNA methylation profiling can reflect the cell type composition of the tumour microenvironment, and in particular a T lymphocyte infiltration of the tumours. Further, we highlighted a set of immune genes having high prognostic value in specific tumour categories. The immune component uncovered here by DNA methylation profiles provides a new perspective for the importance of the microenvironment in breast cancer, holding implications for better management of breast cancer patients.

DNA methylation in the tumor microenvironment

“…This may be the epigenetic mechanism for tumor-induced immunosuppression. By performing DNA methylation profiling in normal breast tissues and infiltrating ductal carcinomas, Dedeurwaerder et al (2011) identified several hypomethylated genes correlated with high T lymphocyte infiltration, including lymphocyte transmembrane adaptor 1 (LAX1), signaling threshold regulating transmembrane adaptor 1 (SIT1), and ubiquitin associated and SH3 domain containing A (UBASH3A). Furthermore, the study revealed that hypermethylation of these genes accounted for an enhanced antitumor T-cell response, and had an independent prognostic value.…”

DNA Methylation

“…A recent whole-genome DNA methylation analysis by using the Illumina 27 K arrays suggests that DNA methylation profiling might expand current classifications of breast cancer subtypes [49,50]. The analysis of 248 breast cancer tumour samples, comprising a 'main set' of 123 samples (4 normal and 119 infiltrating ductal carcinomas (IDCs)), and a 'validation set' of 125 samples (8 normal and 117 IDCs), revealed an immune 'signature' in a mixed tumour stromal population, as also reported [51].…”

A Pathway-Based Classification of Breast Cancer Integrating Data on Differentially Expressed Genes, Copy Number Variations and MicroRNA Target Genes

“…The validation data set (n = 250), which was used for evaluating the classification performances of the classifiers, was also derived from four independent data sets (GSE3744, GSE5460, GSE5764, GSE20711) (Dedeurwaerder et al, 2011;Lu et al, 2008;Richardson et al, 2006;Turashvili et al, 2007). All samples were characterized by use of the Affymetrix Human Genome U133 Plus 2.0 array and normalized using the Robust Multi-array Average (RMA) method in R/BioConductor (http://www.bioconductor.org).…”

“…For a direct comparison between BluePrint and the 215-gene classifier, an independent validation set comprising 250 malignant breast tumors (Dedeurwaerder et al, 2011;Lu et al, 2008;Richardson et al, 2006;Turashvili et al, 2007) was adopted and subtype predictions were performed by using k-means clustering with the calculated centroids. To guarantee a fair comparison between BluePrint and the 215-gene classifier under same conditions, the subdivision of luminal types into luminal-A and luminal-B was not considered for this evaluation (Krijgsman et al, 2011).…”

“…In a training set of 327 tumors, a 215-gene signature, which is not encoded as individual genes, but rather as subnetworks within larger biological pathways, was identified. Finally, the classification performances of the gene signature were compared with those of BluePrint and Kao et al (2011) using an independent data set (Dedeurwaerder et al, 2011;Lu et al, 2008;Richardson et al, 2006;Turashvili et al, 2007).…”