DNA methylation signatures for breast cancer classification and prognosis

Moshe Szyf
<span title="">2012</span> <i title="Springer Nature"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/odidfbcgarekjpyyfxpdptajxa" style="color: black;">Genome Medicine</a> </i> &nbsp;
Breast cancer classi cation, staging and prognosis: the importance of DNA methylation signatures Breast cancer is a heterogeneous disease with very different therapeutic responses and outcomes. It has traditionally been staged by histopathological criteria that are based on size, level of invasiveness and lymph node infiltration, and by immunochemical characterization of cell surface receptors, including estrogen receptor (ER), the progesterone receptor (PR) and the human epidermal growth
more &raquo; ... receptor 2 (HER2). However, in many instances staging breast cancer fails to predict prognosis or therapeutic response because of the heterogeneity of the disease. More recently, molecular approaches focusing on gene expression profiles have been used. Classifications based on gene expression profiles have expanded the detailed classification of breast cancer by revealing cellular identity profiles, with particular emphasis on presence of stem cells and the nature of the immune response to the tumor. ese new molecular-based classifications are termed 'intrinsic subtypes of breast cancer' because they reveal the molecular identity of the breast cancer cell in the tumor rather than its stage. Several distinct tumor types and normal breast-like intrinsic classes of breast cancer were previously described [1] (see list in Table 1 ). ese different subtypes are found in all stages of breast cancer, even in the early stages, and therefore serve as early prognostic and therapeutic predictors. ey have contri buted prognostic value in breast cancer management as they are now guiding prediction of patient relapse, survival and response to chemotherapy. However, there are still major challenges in accurate early prediction of breast cancer, prognosis, and prediction of therapeutic outcome. ere is significant room for improving our predictive and prognostic tools, particularly in guiding therapeutic choices. DNA methylation is a molecular modification of DNA that is tightly associated with gene function and celltype-specific gene function and therefore provides an exquisite identity descriptor of a cell. In the recent past, DNA methylation analysis was targeted at a few candidate genes using either methylation-sensitive restriction enzymes or gene-specific DNA methylation mapping by Abstract Changes in gene expression that reset a cell program from a normal to a diseased state involve multiple genetic circuitries, creating a characteristic signature of gene expression that defines the cell's unique identity. Such signatures have been demonstrated to classify subtypes of breast cancers. Because DNA methylation is critical in programming gene expression, a change in methylation from a normal to diseased state should be similarly reflected in a signature of DNA methylation that involves multiple gene pathways. Whole-genome approaches have recently been used with different levels of success to delineate breast-cancer-specific DNA methylation signatures, and to test whether they can classify breast cancer and whether they could be associated with specific clinical outcomes. Recent work suggests that DNA methylation signatures will extend our ability to classify breast cancer and predict outcome beyond what is currently possible. DNA methylation is a robust biomarker, vastly more stable than RNA or proteins, and is therefore a promising target for the development of new approaches for diagnosis and prognosis of breast cancer and other diseases. Here, I review the scientific basis for using DNA methylation signatures in breast cancer classification and prognosis. I discuss the role of DNA methylation in normal gene regulation, the aberrations in DNA methylation in cancer, and candidate-gene and whole-genome approaches to classify breast cancer subtypes using DNA methylation markers.
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