. Sub-Project 3 (Epidemiology: genes, modifiable risk factors, and risk prediction for breast cancer) will extend existing genome-wide association studies (GWAS) as well as the additional discovery efforts in Sub-Project 1 of this application by characterizing the relationships among newly-discovered common breast cancer risk alleles, known breast cancer risk factors, and breast cancer incidence. This Sub-Project will accelerate the translation of GWAS findings to clinical and public health practice by suggesting particular biologic mechanisms for breast cancer etiology (Aims 1-3) and by evaluating the clinical validity and utility of genetic risk prediction models in a variety of settings (Aims 4-6). The Sub-Project encompasses six specific aims: 1) Conduct a GWAS for new loci that interact with other, known breast cancer alleles;2) assess gene-gene interactions among known breast cancer risk alleles;3) assess gene-environment interactions between known breast cancer risk alleles and established risk factors, most notably modifiable exposures such as postmenopausal hormone therapy;4) construct and compare the clinical validity of risk prediction algorithms that combine information on multiple breast cancer alleles and known risk factors;5) evaluate the performance of these algorithms in high-risk families;and 6) assess genetic modification of prophylactic tamoxifen treatment.
This Sub-Project will accelerate the translation of GWAS findings to clinical and public health practice. It will generate hypotheses regarding the basic biology of breast cancer, which can be investigated in Sub-Project 2 of this proposal. A deeper understanding of breast cancer biology may eventually lead to better prevention and treatment strategies for breast cancer, which kills over 40,000 U.S. women every year. This Sub-Project will also generate risk prediction algorithms and new information about women who may be more likely to benefit from tamoxifen chemoprevention or hormone therapy, which could have immediate clinical and public health applications.
|Dite, Gillian S; MacInnis, Robert J; Bickerstaffe, Adrian et al. (2016) Breast Cancer Risk Prediction Using Clinical Models and 77 Independent Risk-Associated SNPs for Women Aged Under 50 Years: Australian Breast Cancer Family Registry. Cancer Epidemiol Biomarkers Prev 25:359-65|
|Easton, Douglas F; Lesueur, Fabienne; Decker, Brennan et al. (2016) No evidence that protein truncating variants in BRIP1 are associated with breast cancer risk: implications for gene panel testing. J Med Genet 53:298-309|
|Painter, Jodie N; O'Mara, Tracy A; Marquart, Louise et al. (2016) Genetic Risk Score Mendelian Randomization Shows that Obesity Measured as Body Mass Index, but not Waist:Hip Ratio, Is Causal for Endometrial Cancer. Cancer Epidemiol Biomarkers Prev 25:1503-1510|
|Wen, Wanqing; Shu, Xiao-Ou; Guo, Xingyi et al. (2016) Prediction of breast cancer risk based on common genetic variants in women of East Asian ancestry. Breast Cancer Res 18:124|
|Darabi, Hatef; Beesley, Jonathan; Droit, Arnaud et al. (2016) Fine scale mapping of the 17q22 breast cancer locus using dense SNPs, genotyped within the Collaborative Oncological Gene-Environment Study (COGs). Sci Rep 6:32512|
|Bonilla, Carolina; Lewis, Sarah J; Martin, Richard M et al. (2016) Pubertal development and prostate cancer risk: Mendelian randomization study in a population-based cohort. BMC Med 14:66|
|(2016) PALB2, CHEK2 and ATM rare variants and cancer risk: data from COGS. J Med Genet 53:800-811|
|Han, Mi-Ryung; Long, Jirong; Choi, Ji-Yeob et al. (2016) Genome-wide association study in East Asians identifies two novel breast cancer susceptibility loci. Hum Mol Genet 25:3361-3371|
|Karami, Sara; Han, Younghun; Pande, Mala et al. (2016) Telomere structure and maintenance gene variants and risk of five cancer types. Int J Cancer 139:2655-2670|
|(2016) Functional mechanisms underlying pleiotropic risk alleles at the 19p13.1 breast-ovarian cancer susceptibility locus. Nat Commun 7:12675|
Showing the most recent 10 out of 123 publications