A central concern regarding the widespread use of menopausal hormones is the increased breast cancer risk as recently described in several large studies. Since many women nevertheless have considerable benefit from this therapy, mainly alleviation of menopausal symptoms, it would be of outmost value if we were able to identify those women in whom the use of menopausal hormones should be discouraged due to an unacceptably increased breast cancer risk. Since this treatment could be seen as a representation of any hormonal exposures, it can serve as a model for how other hormonal factors, such as age at menopause and BMI, interact with breast cancer susceptibility genes. We will study genetic variation in pathways that regulate estrogen exposure (estrogen metabolism pathway) and regulate response to estrogen exposure (receptors and associated proteins, transcription cofactors and responsive genes) influence the risk of breast cancer. Further, we want to investigate how the influence of this genetic variation is affected by intake of exogenous hormones. The influence of estrogen in the breast tissue is mediated via one of its receptors, estrogen receptor a (ER) and transcription induced by the ER is dependent on co-regulators. We have experimentally, using T47D cells and expression arrays, identified estrogen responsive genes and genes that are associated with ER status in primary breast cancers. We have also constructed a database of gene expression data that integrates all six publicly available and some unpublished breast cancer microarray datasets. The purpose of the database is to facilitate the mining of genes with robust associations, with clinicopathological variables in human breast cancer. We propose a systematic approach for gene association study that first employs bioinformatics and genomic approaches to expand the number of likely candidate genes from several to hundreds. In addition, by using haplotype-tagging SNPs (ht-SNPs), we reduce the genotyping load by approximately 75%, while retaining thorough characterization of each gene under evaluation.
| Kumar, Pallavi; Wright, Alexi A; Hatfield, Laura A et al. (2017) Family Perspectives on Hospice Care Experiences of Patients with Cancer. J Clin Oncol 35:432-439 |
| Wright, Alexi A; Keating, Nancy L; Ayanian, John Z et al. (2016) Family Perspectives on Aggressive Cancer Care Near the End of Life. JAMA 315:284-92 |
| International Multiple Sclerosis Genetics Consortium (IMSGC); Beecham, Ashley H; Patsopoulos, Nikolaos A et al. (2013) Analysis of immune-related loci identifies 48 new susceptibility variants for multiple sclerosis. Nat Genet 45:1353-60 |
| Salmela, Elina; Lappalainen, Tuuli; Liu, Jianjun et al. (2011) Swedish population substructure revealed by genome-wide single nucleotide polymorphism data. PLoS One 6:e16747 |
| International Multiple Sclerosis Genetics Consortium (see original citation for additional authors) (2011) Genetic risk and a primary role for cell-mediated immune mechanisms in multiple sclerosis. Nature 476:214-9 |
| Low, Yen Ling; Li, Yuqing; Humphreys, Keith et al. (2010) Multi-variant pathway association analysis reveals the importance of genetic determinants of estrogen metabolism in breast and endometrial cancer susceptibility. PLoS Genet 6:e1001012 |
| Lango Allen, Hana (see original citation for additional authors) (2010) Hundreds of variants clustered in genomic loci and biological pathways affect human height. Nature 467:832-8 |
| Elks, Cathy E; Perry, John R B; Sulem, Patrick et al. (2010) Thirty new loci for age at menarche identified by a meta-analysis of genome-wide association studies. Nat Genet 42:1077-85 |
| Gaudet, Mia M; Milne, Roger L; Cox, Angela et al. (2009) Five polymorphisms and breast cancer risk: results from the Breast Cancer Association Consortium. Cancer Epidemiol Biomarkers Prev 18:1610-6 |
| Ahmed, Shahana; Thomas, Gilles; Ghoussaini, Maya et al. (2009) Newly discovered breast cancer susceptibility loci on 3p24 and 17q23.2. Nat Genet 41:585-90 |
Showing the most recent 10 out of 13 publications
