Abstract
Approximately 200,000 women are diagnosed with breast cancer and about 40,000 patients will succumb to this disease per year. The vast majority of these deaths are due to metastatic disease, rather than the primary tumor, so better understanding of the terminal stages of breast cancer is of great importance for the design of improved clinical interventions. Mouse genetic studies from our laboratory previously demonstrated that like susceptibility to the development of breast cancer, a significant inherited component exists for disseminated disease. However, despite the tremendous success of GWAS in identifying inherited variants associated with cancer etiology, significantly less success has been achieved in identifying SNPs associated with disease prognosis. This may be due in part to the complexity of the metastatic process, which involves genetic, epigenetic as well as stochastic somatic events. The ability to directly detect inherited metastasis susceptibility loci in human populations may be further complicated by clinical interventions, including surgical resection, radiation and/or adjuvant therapy which may alter or interrupt the natural progression of the disease in patient cohorts, reducing the power of detection. Therefore to complement human studies of inherited susceptibility to progression, our laboratory has employed a dominant metastatic genetically engineered mouse model and a quantitative trait analysis strategy to identify genes associated with disseminated disease. The results of these studies will be presented in this presentation.
Citation Format: Kent W. Hunter. Using mouse models to identify breast cancer metastasis susceptibility genes. [abstract]. In: Proceedings of the AACR Special Conference on Post-GWAS Horizons in Molecular Epidemiology: Digging Deeper into the Environment; 2012 Nov 11-14; Hollywood, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2012;21(11 Suppl):Abstract nr IA21.
