Estrogen receptor α polymorphisms and postmenopausal breast cancer risk

Item does not contain fulltextBACKGROUND: The estrogen receptor alpha (ESR1) is a mediator of estrogen response in the breast. The most studied variants in this gene are the PvuII and XbaI polymorphisms, which have been associated to lower sensitivity to estrogen. We evaluated whether these polymorphisms were associated with breast cancer risk by means of an association study in a population of Caucasian postmenopausal women from the Rotterdam study and a meta-analysis of published data. METHODS: The PvuII and XbaI polymorphisms were genotyped in 3,893 women participants of the Rotterdam Study. Baseline information was obtained through a questionnaire. We conducted logistic regression analyses to assess the risk of breast cancer by each of the ESR1 genotypes. Meta-analyses of all publications on these relations were done by retrieving literature from Pubmed and by further checking the reference lists of the articles obtained. RESULTS: There were 38 women with previously diagnosed breast cancer. During follow-up, 152 were additionally diagnosed. The logistic regression analyses showed no difference in risk for postmenopausal breast cancer in carriers of the PvuII or XbaI genotypes neither in overall, incident or prevalent cases. No further evidence of a role of these variants was found in the meta-analysis. CONCLUSIONS: Our results suggest that the ESR1 polymorphisms do not play a role in breast cancer risk in Caucasian postmenopausal women

Estrogen receptor alpha gene polymorphism and endometrial cancer risk – a case-control study

Background: Estrogen is an established endometrial carcinogen. One of the most important mediators of estrogenic action is the estrogen receptor alpha. We have investigated whether polymorphic variation in the estrogen receptor alpha gene (ESR1) is associated with endometrial cancer risk. Methods: In 702 cases with invasive endometrial cancer and 1563 controls, we genotyped five markers in ESR1 and used logistic regression models to estimate odds ratios (OR) and 95 percent confidence intervals (CI). Results: We found an association between rs2234670, rs2234693, as well as rs9340799, markers in strong linkage disequilibrium (LD), and endometrial cancer risk. The association with rs9340799 was the strongest, OR 0.75 (CI 0.60–0.93) for heterozygous and OR 0.53 (CI 0.37–0.77) for homozygous rare compared to those homozygous for the most common allele. Haplotype models did not fit better to the data than single marker models. Conclusion: We found that intronic variation in ESR1 was associated with endometrial cancer risk

Multi-Variant Pathway Association Analysis Reveals the Importance of Genetic Determinants of Estrogen Metabolism in Breast and Endometrial Cancer Susceptibility

Peer reviewe

ESR1 and EGF genetic variation in relation to breast cancer risk and survival

The main purposes of this thesis were to analyse common genetic variation in candidate genes and candidate pathways in relation to breast cancer risk, prognosticators and survival, to develop statistical methods for genetic association analysis for evaluating the joint importance of genes, and to investigate the potential impact of adding genetic information to clinical risk factors for projecting individualised risk of developing breast cancer over specific time periods. In Paper I we studied genetic variation in the estrogen receptor α and epidermal growth factor genes in relation to breast cancer risk and survival. We located a region in the estrogen receptor α gene which showed a moderate signal for association with breast cancer risk but were unable to link common variation in the epidermal growth factor gene with breast cancer aetiology or prognosis. In Paper II we investigated whether suspected breast cancer risk SNPs within genes involved in androgen-to-estrogen conversion are associated with breast cancer prognosticators grade, lymph node status and tumour size. The strongest association was observed for a marker within the CYP19A1 gene with histological grade. We also found evidence that a second marker from the same gene is associated with histological grade and tumour size. In Paper III we developed a novel test of association which incorporates multivariate measures of categorical and continuous heterogeneity. In this work we described both a single-SNP and a global multi-SNP test and used simulated data to demonstrate the power of the tests when genetic effects differ across disease subtypes. In Paper IV we assessed the extent to which recently associated genetic risk variants improve breast cancer risk-assessment models. We investigated empirically the performance of eighteen breast cancer risk SNPs together with mammographic density and clinical risk factors in predicting absolute risk of breast cancer. We also examined the usefulness of various prediction models considered at a population level for a variety of individualised breast cancer screening approaches. The goal of a genetic association study is to establish statistical associations between genetic variants and disease states. Each variant linked to a disease can lead the way to a better understanding of the underlying biological mechanisms that govern the development of a disease. Increased knowledge of molecular variation provides the opportunity to stratify populations according to genetic makeup, which in turn has the potential to lead to improved disease prevention programs and improved patient care

Genetic variation in the estrogen metabolic pathway and mammographic density as an intermediate phenotype of breast cancer

Introduction: Several studies have examined the effect of genetic variants in genes involved in the estrogen metabolic pathway on mammographic density, but the number of loci studied and the sample sizes evaluated have been small and pathways have not been evaluated comprehensively. In this study, we evaluate the association between mammographic density and genetic variants of the estrogen metabolic pathway. Methods: A total of 239 SNPs in 34 estrogen metabolic genes were studied in 1,731 Swedish women who participated in a breast cancer case-control study, of which 891 were cases and 840 were controls. Film mammograms of the medio-lateral oblique view were digitalized and the software Cumulus was used for computer-assisted semi-automated thresholding of mammographic density. Generalized linear models controlling for possible confounders were used to evaluate the effects of SNPs on mammographic density. Results found to be nominally significant were examined in two independent populations. The admixture maximum likelihood-based global test was performed to evaluate the cumulative effect from multiple SNPs within the whole metabolic pathway and three subpathways for androgen synthesis, androgen-to-estrogen conversion and estrogen removal. Results: Genetic variants of genes involved in estrogen metabolism exhibited no appreciable effect on mammographic density. None of the nominally significant findings were validated. In addition, global analyses on the overall estrogen metabolic pathway and its subpathways did not yield statistically significant results. Conclusions: Overall, there is no conclusive evidence that genetic variants in genes involved in the estrogen metabolic pathway are associated with mammographic density in postmenopausal women

Combined effect of CCND1 and COMT polymorphisms and increased breast cancer risk

<p>Abstract</p> <p>Background</p> <p>Estrogens are crucial tumorigenic hormones, which impact the cell growth and proliferation during breast cancer development. Estrogens are metabolized by a series of enzymes including COMT, which converts catechol estrogens into biologically non-hazardous methoxyestrogens. Several studies have also shown the relationship between estrogen and cell cycle progression through activation of CCND1 transcription.</p> <p>Methods</p> <p>In this study, we have investigated the independent and the combined effects of commonly occurring CCND1 (Pro241Pro, A870G) and COMT (Met108/158Val) polymorphisms to breast cancer risk in two independent Caucasian populations from Ontario (1228 breast cancer cases and 719 population controls) and Finland (728 breast cancer cases and 687 population controls). Both COMT and CCND1 polymorphisms have been previously shown to impact on the enzymatic activity of the coded proteins.</p> <p>Results</p> <p>Here, we have shown that the high enzymatic activity genotype of CCND1^High(AA) was associated with increased breast cancer risk in both the Ontario [OR: 1.3, 95%CI (1.0–1.69)] and the Finland sample [OR: 1.4, 95%CI (1.01–1.84)]. The heterozygous COMT^Medium(MetVal) and the high enzymatic activity of COMT^High(ValVal) genotype was also associated with breast cancer risk in Ontario cases, [OR: 1.3, 95%CI (1.07–1.68)] and [OR: 1.4, 95%CI (1.07–1.81)], respectively. However, there was neither a statistically significant association nor increased trend of breast cancer risk with COMT^High(ValVal) genotypes in the Finland cases [OR: 1.0, 95%CI (0.73–1.39)]. In the combined analysis, the higher activity alleles of the COMT and CCND1 is associated with increased breast cancer risk in both Ontario [OR: <b>2.22</b>, 95%CI (1.49–3.28)] and Finland [OR: <b>1.73</b>, 95%CI (1.08–2.78)] populations studied. The trend test was statistically significant in both the Ontario and Finland populations across the genotypes associated with increasing enzymatic activity.</p> <p>Conclusion</p> <p>Using two independent Caucasian populations, we have shown a stronger combined effect of the two commonly occurring CCND1 and COMT genotypes in the context of breast cancer predisposition.</p

Assessing interactions between the associations of common genetic susceptibility variants, reproductive history and body mass index with breast cancer risk in the breast cancer association consortium: a combined case-control study.

INTRODUCTION: Several common breast cancer genetic susceptibility variants have recently been identified. We aimed to determine how these variants combine with a subset of other known risk factors to influence breast cancer risk in white women of European ancestry using case-control studies participating in the Breast Cancer Association Consortium. METHODS: We evaluated two-way interactions between each of age at menarche, ever having had a live birth, number of live births, age at first birth and body mass index (BMI) and each of 12 single nucleotide polymorphisms (SNPs) (10q26-rs2981582 (FGFR2), 8q24-rs13281615, 11p15-rs3817198 (LSP1), 5q11-rs889312 (MAP3K1), 16q12-rs3803662 (TOX3), 2q35-rs13387042, 5p12-rs10941679 (MRPS30), 17q23-rs6504950 (COX11), 3p24-rs4973768 (SLC4A7), CASP8-rs17468277, TGFB1-rs1982073 and ESR1-rs3020314). Interactions were tested for by fitting logistic regression models including per-allele and linear trend main effects for SNPs and risk factors, respectively, and single-parameter interaction terms for linear departure from independent multiplicative effects. RESULTS: These analyses were applied to data for up to 26,349 invasive breast cancer cases and up to 32,208 controls from 21 case-control studies. No statistical evidence of interaction was observed beyond that expected by chance. Analyses were repeated using data from 11 population-based studies, and results were very similar. CONCLUSIONS: The relative risks for breast cancer associated with the common susceptibility variants identified to date do not appear to vary across women with different reproductive histories or body mass index (BMI). The assumption of multiplicative combined effects for these established genetic and other risk factors in risk prediction models appears justified.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are