Based on data from 2002-2006, SEER estimated that in 2009, 192,370 American women were diagnosed with breast cancer and 40,170 died of the disease (Horner 2009). Although familial and/or early onset breast cancer does not represent the majority of the disease, these cases are often associated with poor prognosis. Further, because of their early age at diagnosis, these cases have a disproportionately large impact in terms of years of life lost to the disease. The high-risk breast cancer susceptibility genes BRCA1, BRCA2, PTEN, and TP53 were all discovered more than a decade ago. Currently, mutation screening of these genes plays an important role in the clinical management of women with a strong family history of the disease or syndromic evidence for the presence of a gene mutation. At the other end of the risk spectrum, genome-wide association studies have identified a number of common alleles with very modest effects on breast cancer;their clinical utility has yet to be established. However, taken together, the known spectrum of genetic effects only explain about a third of the overall familial excess of breast cancer. It should be emphasized that, at present, the vast majority of women seen at familial cancer clinics are counseled on the basis of their family history alone because they do not have mutations in the known susceptibility genes. Accordingly, the long-term objective of this project is to identify the majority of genes responsible for the unexplained component of inherited breast cancer risk. Over the last few years, new DNA sequencing technologies - often referred to as "next generation" or "massively parallel" sequencing - have been maturing rapidly. They are now ripe for application to research questions in genetic susceptibility, for which linkage analysis is confounded by extensive genetic heterogeneity and candidate gene studies technologically limited to small numbers of genes. Taking advantage of breast cancer genetics resources that have been gathered by international consortia over the last 15-plus years, two massively parallel sequencing strategies will be used to pursue the long term objective of this project: 1) resequencing all of the gene exons in the human genome from a series of breast cancer cases who have strong family history that is not explained by one of the currently known high-risk susceptibility genes;and 2) resequencing the gene exons of all of the genes in biochemical pathways that have been implicated in breast cancer susceptibility from a series of 2,400 early onset breast cancer cases and frequency-matched controls. The collaborative team assembled for this project has collected the largest breast cancer family resource extant, has unique expertise in breast cancer genetics, has the statistical and bioinformatic skills required to analyze massive resequencing data, and has the experience required to build wider consortia as necessary. Thus this team and project are poised to meet their long-term breast cancer susceptibility gene identification objective and thereby solve the "problem of missing heritability" in breast cancer genetics.
Currently, clinical cancer genetics applied to families with a history of breast and/ or ovarian cancer is only useful to the minority of families in which there is a BRCA1, BRCA2, TP53, or PTEN mutation;unfortunately, mutations in these genes only explain a minority of such families. This project will apply new DNA sequencing technologies to an outstanding resource of breast cancer cases and families to identify the majority of genes that contribute early onset and familial breast cancer. In the long term, discovery of these genes will lead to more effective prevention programs and, potentially, improved treatments.
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|COMPLEXO; Southey, Melissa C; Park, Daniel J et al. (2013) COMPLEXO: identifying the missing heritability of breast cancer via next generation collaboration. Breast Cancer Res 15:402|
|Pope, Bernard J; Nguyen-Dumont, Tu; Odefrey, Fabrice et al. (2013) FAVR (Filtering and Annotation of Variants that are Rare): methods to facilitate the analysis of rare germline genetic variants from massively parallel sequencing datasets. BMC Bioinformatics 14:65|
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