We propose an integrated multiple-PI Project to systematically discover and replicate additional common genetic variants associated with breast cancer, assess their biological significance, and develop evidence based assessments of the clinical validity of prediction algorithms using these variants, and their suitability for translation into clinical practice. In sub-Project 1 we will combine the resources of (a) major GWAS for breast cancer amounting to >15,000 cases and (b) three pre-existing Consortia with over 48,000 additional cases to provide the large sample size needs necessary in the replication phase of GWAS. We will fine map the associated loci in collaboration with the major Consortia conducting GWAS for breast cancer in Asian and African-American women. In sub-Project 2 we will conduct a series of investigations to (a) assign a gene function to each replicated risk variant by measuring expression of 24,000 RNA transcripts in breast tumor tissue and normal tissue, from women for whom we also have an lllumina 540 GWAS available;by identifying networks of genes in which alterations of expression can be linked to specific germline risk variants;and by using Chromosomal Conformation Capture assays to examine whether associated intergenic regions fold physically in a way that brings them into contact with distant genie regions. We will also (b) examine whether loss or gain of function of the genes implicated in (a) in breast epithelial cells or stromal cells alter phenotypes in vitro in a 3-D model of breast morphogenesis and oncogenesis. In sub- Project 3, we will develop breast cancer prediction models that can be used to stratify women according to breast cancer risk. We will attempt to discover gene-gene interactions by reanalyzing the GWAS data, and we will systematically examine the genome-wide significant gene variants for effect modification by established breast cancer risk factors, using the largest set of prospective studies available. We will develop and refine risk models that incorporate both the germline risk factors and the established non-genetic risk factors, and also assess these in a cohort of women with higher familial risk of breast cancer (to specifically address the clinical needs of women at high risk due to a strong family history of breast cancer). Finally, we will analyze data from the major trials of primary prevention of breast cancer to address the question of whether the protective effect of tamoxifen is altered by risk status for our prediction models, data with a direct bearing on clinical decision-making with respect to chemoprevention for women at known high risk.

Public Health Relevance

The proposed Project is highly relevant to the translation of discoveries from existing Genome-Wide Association Studies into useful clinical activities. We will discover additional breast cancer associated loci, attempt to narrow the number of potentially causal variants at each locus, assess the biological mechanism behind the implicated genes, and assemble the associated variants into risk prediction algorithms that we will rigorously test for clinical validity.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Program--Cooperative Agreements (U19)
Project #
3U19CA148065-04S1
Application #
8871938
Study Section
Special Emphasis Panel (ZCA1-SRLB-4 (J1))
Program Officer
Rogers, Scott
Project Start
2010-07-15
Project End
2015-06-30
Budget Start
2013-07-30
Budget End
2015-06-30
Support Year
4
Fiscal Year
2014
Total Cost
$574,120
Indirect Cost
$161,550
Name
Harvard University
Department
Public Health & Prev Medicine
Type
Schools of Public Health
DUNS #
149617367
City
Boston
State
MA
Country
United States
Zip Code
02115
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
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
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
Silvestri, Valentina; Barrowdale, Daniel; Mulligan, Anna Marie et al. (2016) Male breast cancer in BRCA1 and BRCA2 mutation carriers: pathology data from the Consortium of Investigators of Modifiers of BRCA1/2. Breast Cancer Res 18:15

Showing the most recent 10 out of 123 publications