Reproductive factors and subtypes of breast cancer defined by hormone receptor and histology

Reproductive factors are associated with reduced risk of breast cancer, but less is known about whether there is differential protection against subtypes of breast cancer. Assuming reproductive factors act through hormonal mechanisms they should protect predominantly against cancers expressing oestrogen (ER) and progesterone (PR) receptors. We examined the effect of reproductive factors on subgroups of tumours defined by hormone receptor status as well as histology using data from the NIHCD Women's Contraceptive and Reproductive Experiences (CARE) Study, a multicenter case–control study of breast cancer. We estimated odds ratios (ORs) and 95% confidence intervals (CIs) as measures of relative risk using multivariate unconditional logistic regression methods. Multiparity and early age at first birth were associated with reduced relative risk of ER + PR + tumours (P for trend=0.0001 and 0.01, respectively), but not of ER − PR − tumours (P for trend=0.27 and 0.85), whereas duration of breastfeeding was associated with lower relative risk of both receptor-positive (P for trend=0.0002) and receptor-negative tumours (P=0.0004). Our results were consistent across subgroups of women based on age and ethnicity. We found few significant differences by histologic subtype, although the strongest protective effect of multiparity was seen for mixed ductolobular tumours. Our results indicate that parity and age at first birth are associated with reduced risk of receptor-positive tumours only, while lactation is associated with reduced risk of both receptor-positive and -negative tumours. This suggests that parity and lactation act through different mechanisms. This study also suggests that reproductive factors have similar protective effects on breast tumours of lobular and ductal origin

The Potential for Enhancing the Power of Genetic Association Studies in African Americans through the Reuse of Existing Genotype Data

We consider the feasibility of reusing existing control data obtained in genetic association studies in order to reduce costs for new studies. We discuss controlling for the population differences between cases and controls that are implicit in studies utilizing external control data. We give theoretical calculations of the statistical power of a test due to Bourgain et al (Am J Human Genet 2003), applied to the problem of dealing with case-control differences in genetic ancestry related to population isolation or population admixture. Theoretical results show that there may exist bounds for the non-centrality parameter for a test of association that places limits on study power even if sample sizes can grow arbitrarily large. We apply this method to data from a multi-center, geographically-diverse, genome-wide association study of breast cancer in African-American women. Our analysis of these data shows that admixture proportions differ by center with the average fraction of European admixture ranging from approximately 20% for participants from study sites in the Eastern United States to 25% for participants from West Coast sites. However, these differences in average admixture fraction between sites are largely counterbalanced by considerable diversity in individual admixture proportion within each study site. Our results suggest that statistical correction for admixture differences is feasible for future studies of African-Americans, utilizing the existing controls from the African-American Breast Cancer study, even if case ascertainment for the future studies is not balanced over the same centers or regions that supplied the controls for the current study

Hormone-related risk factors for breast cancer in women under age 50 years by estrogen and progesterone receptor status: results from a case–control and a case–case comparison

INTRODUCTION: It has been suggested that hormonal risk factors act predominantly on estrogen receptor and progesterone receptor (ER/PR)-positive breast cancers. However, the data have been inconsistent, especially in younger women. METHODS: We evaluated the impact of age at menarche, pregnancy history, duration of breastfeeding, body mass index, combined oral contraceptive use, and alcohol consumption on breast cancer risk by ER/PR status in 1,725 population-based case patients and 440 control subjects aged 20 to 49 years identified within neighborhoods of case patients. We used multivariable unconditional logistic regression methods to conduct case–control comparisons overall as well as by ER/PR status of the cases, and to compare ER(+)PR(+ )with ER(-)PR(- )case patients. RESULTS: The number of full-term pregnancies was inversely associated with the risk of ER(+)PR(+ )breast cancer (p(trend )= 0.005), whereas recent average alcohol consumption was associated with an increased risk of ER(+)PR(+ )breast cancer (p(trend )= 0.03). Neither of these two factors was associated with the risk of ER(- )PR(- )breast cancer. Late age at menarche and a longer duration of breastfeeding were both associated with decreased breast cancer risk, irrespective of receptor status (all p(trend)≤ 0.03). CONCLUSION: Our results suggest that the number of full-term pregnancies and recent alcohol consumption affect breast cancer risk in younger women predominantly through estrogen and progesterone mediated by their respective receptors. Late age at menarche and breastfeeding may act through different hormonal mechanisms

Evaluation of unclassified variants in the breast cancer susceptibility genes BRCA1 and BRCA2 using five methods: results from a population-based study of young breast cancer patients

Introduction Efforts are ongoing to determine the significance of unclassified variants (UVs) in the breast cancer susceptibility genes BRCA1/BRCA2, but no study has systematically assessed whether women carrying the suspected deleterious UVs have characteristics commonly seen among women carrying known deleterious or disease-causing mutations in BRCA1/BRCA2. Methods We sequenced BRCA1/BRCA2 in 1,469 population-based female breast cancer patients diagnosed between the ages of 20 and 49 years. We used existing literature to classify variants into known deleterious mutations, polymorphic variants, and UVs. The UVs were further classified as high risk or low risk based on five methods: allele frequency, Polyphen algorithm, sequence conservation, Grantham matrix scores, and a combination of the Grantham matrix score and sequence conservation. Furthermore, we examined whether patients who carry the variants classified as high risk using these methods have risk characteristics similar to patients with known deleterious BRCA1/BRCA2 mutations (early age at diagnosis, family history of breast cancer or ovarian cancer, and negative estrogen receptor/progesterone receptor). Results We identified 262 distinct BRCA1/BRCA2 variants, including 147 UVs, in our study population. The BRCA1 UV carriers, but not the BRCA2 UV carriers, who were classified as high risk using each classification method were more similar to the deleterious mutation carriers with respect to family history than those carriers classified as low risk. For example, the odds ratio of having a first-degree family history for the high-risk women classified using Polyphen was 3.39 (95% confidence interval = 1.16 to 9.94) compared with normal/polymorphic BRCA1 carriers. The corresponding odds ratio of low-risk women was 1.53 (95% confidence interval = 1.07 to 2.18). The odds ratio for high-risk women defined by allele frequency was 2.00 (95% confidence interval = 1.14 to 3.51), and that of low-risk women was 1.30 (95% confidence interval = 0.87 to 1.93). Conclusion The results suggest that the five classification methods yielded similar results. Polyphen was particularly better at isolating BRCA1 UV carriers likely to have a family history of breast cancer or ovarian cancer, and may therefore help to classify BRCA1 UVs. Our study suggests that these methods may not be as successful in classifying BRCA2 UVs

Androgens and the breast

Androgens have important physiological effects in women while at the same time they may be implicated in breast cancer pathologies. However, data on the effects of androgens on mammary epithelial proliferation and/or breast cancer incidence are not in full agreement. We performed a literature review evaluating current clinical, genetic and epidemiological data regarding the role of androgens in mammary growth and neoplasia. Epidemiological studies appear to have significant methodological limitations and thus provide inconclusive results. The study of molecular defects involving androgenic pathways in breast cancer is still in its infancy. Clinical and nonhuman primate studies suggest that androgens inhibit mammary epithelial proliferation and breast growth while conventional estrogen treatment suppresses endogenous androgens. Abundant clinical evidence suggests that androgens normally inhibit mammary epithelial proliferation and breast growth. Suppression of androgens using conventional estrogen treatment may thus enhance estrogenic breast stimulation and possibly breast cancer risk. Addition of testosterone to the usual hormone therapy regimen may diminish the estrogen/progestin increase in breast cancer risk but the impact of this combined use on mammary gland homeostasis still needs evaluation

Menopausal hormone use and ovarian cancer risk: individual participant meta-analysis of 52 epidemiological studies

Background Half the epidemiological studies with information about menopausal hormone therapy and ovarian cancer risk remain unpublished, and some retrospective studies could have been biased by selective participation or recall. We aimed to assess with minimal bias the effects of hormone therapy on ovarian cancer risk. Methods Individual participant datasets from 52 epidemiological studies were analysed centrally. The principal analyses involved the prospective studies (with last hormone therapy use extrapolated forwards for up to 4 years). Sensitivity analyses included the retrospective studies. Adjusted Poisson regressions yielded relative risks (RRs) versus never-use. Findings During prospective follow-up, 12 110 postmenopausal women, 55% (6601) of whom had used hormone therapy, developed ovarian cancer. Among women last recorded as current users, risk was increased even with <5 years of use (RR 1·43, 95% CI 1·31–1·56; p<0·0001). Combining current-or-recent use (any duration, but stopped <5 years before diagnosis) resulted in an RR of 1·37 (95% CI 1·29–1·46; p<0·0001); this risk was similar in European and American prospective studies and for oestrogen-only and oestrogen-progestagen preparations, but differed across the four main tumour types (heterogeneity p<0·0001), being definitely increased only for the two most common types, serous (RR 1·53, 95% CI 1·40–1·66; p<0·0001) and endometrioid (1·42, 1·20–1·67; p<0·0001). Risk declined the longer ago use had ceased, although about 10 years after stopping long-duration hormone therapy use there was still an excess of serous or endometrioid tumours (RR 1·25, 95% CI 1·07–1·46, p=0·005). Interpretation The increased risk may well be largely or wholly causal; if it is, women who use hormone therapy for 5 years from around age 50 years have about one extra ovarian cancer per 1000 users and, if its prognosis is typical, about one extra ovarian cancer death per 1700 users

SNP-SNP interactions in breast cancer susceptibility

BACKGROUND: Breast cancer predisposition genes identified to date (e.g., BRCA1 and BRCA2) are responsible for less than 5% of all breast cancer cases. Many studies have shown that the cancer risks associated with individual commonly occurring single nucleotide polymorphisms (SNPs) are incremental. However, polygenic models suggest that multiple commonly occurring low to modestly penetrant SNPs of cancer related genes might have a greater effect on a disease when considered in combination. METHODS: In an attempt to identify the breast cancer risk conferred by SNP interactions, we have studied 19 SNPs from genes involved in major cancer related pathways. All SNPs were genotyped by TaqMan 5'nuclease assay. The association between the case-control status and each individual SNP, measured by the odds ratio and its corresponding 95% confidence interval, was estimated using unconditional logistic regression models. At the second stage, two-way interactions were investigated using multivariate logistic models. The robustness of the interactions, which were observed among SNPs with stronger functional evidence, was assessed using a bootstrap approach, and correction for multiple testing based on the false discovery rate (FDR) principle. RESULTS: None of these SNPs contributed to breast cancer risk individually. However, we have demonstrated evidence for gene-gene (SNP-SNP) interaction among these SNPs, which were associated with increased breast cancer risk. Our study suggests cross talk between the SNPs of the DNA repair and immune system (XPD-[Lys751Gln] and IL10-[G(-1082)A]), cell cycle and estrogen metabolism (CCND1-[Pro241Pro] and COMT-[Met108/158Val]), cell cycle and DNA repair (BARD1-[Pro24Ser] and XPD-[Lys751Gln]), and within carcinogen metabolism (GSTP1-[Ile105Val] and COMT-[Met108/158Val]) pathways. CONCLUSION: The importance of these pathways and their communication in breast cancer predisposition has been emphasized previously, but their biological interactions through SNPs have not been described. The strategy used here has the potential to identify complex biological links among breast cancer genes and processes. This will provide novel biological information, which will ultimately improve breast cancer risk management

Enhanced Statistical Tests for GWAS in Admixed Populations: Assessment using African Americans from CARe and a Breast Cancer Consortium

While genome-wide association studies (GWAS) have primarily examined populations of European ancestry, more recent studies often involve additional populations, including admixed populations such as African Americans and Latinos. In admixed populations, linkage disequilibrium (LD) exists both at a fine scale in ancestral populations and at a coarse scale (admixture-LD) due to chromosomal segments of distinct ancestry. Disease association statistics in admixed populations have previously considered SNP association (LD mapping) or admixture association (mapping by admixture-LD), but not both. Here, we introduce a new statistical framework for combining SNP and admixture association in case-control studies, as well as methods for local ancestry-aware imputation. We illustrate the gain in statistical power achieved by these methods by analyzing data of 6,209 unrelated African Americans from the CARe project genotyped on the Affymetrix 6.0 chip, in conjunction with both simulated and real phenotypes, as well as by analyzing the FGFR2 locus using breast cancer GWAS data from 5,761 African-American women. We show that, at typed SNPs, our method yields an 8% increase in statistical power for finding disease risk loci compared to the power achieved by standard methods in case-control studies. At imputed SNPs, we observe an 11% increase in statistical power for mapping disease loci when our local ancestry-aware imputation framework and the new scoring statistic are jointly employed. Finally, we show that our method increases statistical power in regions harboring the causal SNP in the case when the causal SNP is untyped and cannot be imputed. Our methods and our publicly available software are broadly applicable to GWAS in admixed populations