In sub-Project 2 we will conduct a series of in vitro, and in silico investigations to assign a gene function to each validated risk variant and examine whether loss or gain of function of these genes in breast epithelial or stromal cells alter phenotypes in vitro in a 3-D model of breast morphogenesis and oncogenesis. As an initial assessment of potential gene function, we will use the DASL assay to determine the level of 24,000 RNA transcripts in breast tumor and normal tissue, from women for whom we also have an lllumina 540 GWAS data available. This will enable us to conduct expression quantitative trait locus (eQTL) analyses of cis and trans associations between >2.5 million SNPs (genotyped and imputed) and the ievels of each transcript and transcription patterns (Aim 1). We will develop an online tool and make these data publicly available that breast cancer researchers will then be able to use to conduct their own analyses (Aim 1). Using computational techniques we will conduct Bayesian Network analyses, and Gene-set enrichment analyses to identify networks of genes in which alterations of expression can be linked to specific germline risk variants (Aim 2). For risk variants that are in intergenic regions and potential enhancers, we will use Chromosomal Conformation Capture (3C) assays to examine whether these risk loci physically interact with distant DNA loci across the genome (Aim 3). Finally, for the genes that are identified in Aims 1-3, we will explore whether overexpression or knockdown of these genes alters the phenotypes of breast epithelial and stromal cells in a 3-D model of breast cancer development (Aim 4). These approaches are all directed at elucidating the mechanisms by which germline risk variants alter risk of breast cancer, information that may then lead to development of pharmacologic approaches to breast cancer prevention and treatment.
In sub-Project 2 we will conduct a series of laboratory and computational investigations to assign a gene function to each validated genetic risk variant for breast cancer. We will also examine how loss or gain of function of these genes alter phenotypes in a 3-D model of breast morphogenesis and oncogenesis. Data generated by this proposal will be critical in translating GWAS data into clinical targets that can be utilized for early detection, risk stratification, and drug development for the prevention and treatment of breast cancer.
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|Prentice, Ross L (2014) Postmenopausal hormone therapy and the risks of coronary heart disease, breast cancer, and stroke. Semin Reprod Med 32:419-25|
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|Cai, Qiuyin; Zhang, Ben; Sung, Hyuna et al. (2014) Genome-wide association analysis in East Asians identifies breast cancer susceptibility loci at 1q32.1, 5q14.3 and 15q26.1. Nat Genet 46:886-90|
|Milne, Roger L; Herranz, Jesus; Michailidou, Kyriaki et al. (2014) A large-scale assessment of two-way SNP interactions in breast cancer susceptibility using 46,450 cases and 42,461 controls from the breast cancer association consortium. Hum Mol Genet 23:1934-46|
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