A reanalysis of published and unpublished data from epidemiological studies examines the association between height, body mass index, and the risk of developing ovarian cancer. Only about half the studies that have collected information on the relevance of women's height and body mass index to their risk of developing ovarian cancer have published their results, and findings are inconsistent. Here, we bring together the worldwide evidence, published and unpublished, and describe these relationships.Individual data on 25,157 women with ovarian cancer and 81,311 women without ovarian cancer from 47 epidemiological studies were collected, checked, and analysed centrally. Adjusted relative risks of ovarian cancer were calculated, by height and by body mass index.p<0.001); this relationship did not vary significantly by women's age, year of birth, education, age at menarche, parity, menopausal status, smoking, alcohol consumption, having had a hysterectomy, having first degree relatives with ovarian or breast cancer, use of oral contraceptives, or use of menopausal hormone therapy. For body mass index, there was significant heterogeneity (p<0.001) in the findings between ever-users and never-users of menopausal hormone therapy, but not by the 11 other factors listed above. The relative risk for ovarian cancer per 5 kg/m2 increase in body mass index was 1.10  in never-users and 0.95  in ever-users of hormone therapy.Ovarian cancer risk increased significantly with height and with body mass index, except in studies using hospital controls. For other study designs, the relative risk of ovarian cancer per 5 cm increase in height was 1.07  usually occur late in the disease process; fewer than one-third of ovarian cancers are detected before they have spread outside of the ovaries. There is no definitive evidence that screening reduces mortality from ovarian cancer, and given the poor prognosis of advanced ovarian cancer, there has been much research over recent years to increase understanding of this serious condition. There are recognized risk factors that increase the chance of developing ovarian cancer, such as increasing age, having fewer children, not having used oral contraceptives, and use of menopausal hormone therapy. Age and oral contraceptive use have by far the biggest impact on ovarian cancer risk.To date, there is no definitive information about the relevance of women's height, weight, and body mass index to their subsequent risk of developing ovarian cancer. There have been roughly 50 epidemiological studies of ovarian cancer, but only about half of these studies have published results on the association between body size and ovarian cancer risk, and so far, these findings have been inconsistent. Therefore, the researchers—an international collaboration of researchers studying ovarian cancer—re-analyzed the available epidemiological evidence to investigate the relationship between ovarian cancer risk and adult height, weight, and body mass index, and to examine the consistency of the findings across study designs.After an extensive literature search, the researchers identified 47 eligible studies that collected individual data on women's reproductive history, use of hormonal therapies, height, weight, and/or body mass index, and in which the principal investigators of each study accepted the invitation from the researchers to be involved in the re-analysis. The researchers combined data from the different studies. To ensure that women in one study were only directly compared with controls (similar women without ovarian cancer) in the same study, all analyses were routinely stratified by study, center within study, age, parity, use of oral contraceptives, use of hormonal therapy for menopause, and menopausal status or hysterectomy.2 increase in body mass index was 1.10 in women who had never taken menopausal hormone therapy but was only 0.95 in women who had previously taken menopausal hormone therapy.The 47 studies were conducted in 14 countries and comprised a total of 25,157 women with ovarian cancer (mostly from Europe and North America) and 81,311 women without ovarian cancer. The researchers found a significant increase in relative risk (1.07) of ovarian cancer per 5 cm increase in height. Furthermore, this risk did not vary depending on other studied factors—age, year of birth, education, age at menarche, parity, menopausal status, smoking, alcohol consumption, having had a hysterectomy, having first degree relatives with ovarian or breast cancer, use of oral contraceptives, or use of menopausal hormone therapy. However, the researchers found that for body mass index, the risks depended on whether women had ever taken menopausal hormone therapy: the relative risk for ovarian cancer per 5 kg/m2 per decade. The findings suggest an associated increase in ovarian cancer incidence of 3% per decade if all other factors relevant for ovarian cancer remain constant.These findings suggest that increasing height can be considered as a risk factor for ovarian cancer and that in women who have never taken menopausal hormone therapy, increased body mass index can be considered an additional risk factor. These findings have public health implications, especially in high-income countries, because the average height of women has increased by about 1 cm per decade and average body mass index has increased by about 1 kg/mhttp://dx.doi.org/10.1371/journal.pmed.1001200.Please access these web sites via the online version of this summary at MedicineNet, the US National Cancer Institute, Ovarian Cancer National Alliance, Macmillan Cancer SupportThe following organizations give more information on ovarian cancer which may be of use to patients:  About 50 epidemiological studies of ovarian cancer have collected information on the relevance of women's adult height and weight to their subsequent risk of ovarian cancer Our collaboration began in 1998, and since then potentially eligible epidemiological studies have been sought regularly, by searches of review articles and through computer-aided literature searches in MEDLINE, EMBASE, and PubMed using combinations of the search terms “ovarian cancer”, “ovary cancer”, “height”, “body mass index”, “body size”, “anthropometr*”. To be eligible for these analyses, studies needed to have collected individual data on women's reproductive history, use of hormonal therapies, height, weight, and/or body mass index, studied at least 200 women with ovarian cancer, and published their findings before 1 January 2009.  Studies that had collected relevant data but had not published on ovarian cancer and body size were sought by correspondence with colleagues, by discussions at collaborators meetings , and by electronic searches using the additional terms “cohort”, “prospective”, “women”, and “cancer risk”.We identified 51 eligible studies and invited principal investigators from each study to participate in the collaboration. Investigators from just one eligible study “Cases” are women with malignant epithelial or non-epithelial ovarian cancer and “controls” are women without ovarian cancer who had not undergone bilateral oophorectomy. Information sought from principal investigators about every control and about every case included their age, ethnic group, education, height, weight and/or body mass index, age at menarche, reproductive history, use of hormonal contraceptives, use of menopausal hormonal therapy, hysterectomy, family history of ovarian or breast cancer, and consumption of alcohol and tobacco. The information sought on these factors was for the time preceding the onset of disease for cases and for an equivalent time for controls. So that similar analytical methods could be used across studies, cohort studies were incorporated using a nested case–control design, in which up to four controls were selected at random and matched at follow-up by age of the case at entry into the cohort, age at cancer diagnosis, and, where appropriate, broad geographical region. In one cohort study “cases” were women with fatal ovarian cancer Principal investigators of 47 epidemiological studies included in the analyses International Classification of Diseases for OncologyInformation on the histological classification of the ovarian cancers was provided by principal investigators of all but seven O−E) numbers of women with ovarian cancer, together with their variances and covariances O−E values has the advantage of avoiding assumptions about the precise forms of any relations in the data. The stratified O−E values, together with their variances, were summed to yield both odds ratios (subsequently referred to as relative risks) and associated p-values. When only two groups were compared, relative risk estimates were obtained from the O−E value and its variance (var[O−E]) by the one-step method O−E]/var[O−E]). When more than two groups were compared, variances were estimated for every group, by treating the relative risks as floating absolute risks The analytical methods were similar to those used previously To ensure that women in one study were compared directly only with similar women in the same study, all analyses were routinely stratified by study, by centre within study, by age , parity , use of oral contraceptives , ever use of hormonal therapy for the menopause , and menopausal status or hysterectomy . Unknowns for each stratification variable were assigned to separate strata. Analyses in relation to height were also adjusted by body mass index, and analyses in relation to body mass index and to weight were also adjusted by height; results of sensitivity analyses to assess the effect of these mutual adjustments are presented. The effect on the main findings of other potential confounding factors  was examined by comparing results before and after stratification for each variable, in turn, and all simultaneously.2 increase in body mass index was estimated by fitting a log-linear trend across categories of height  and body mass index  using the median value within each category.The relative risk of ovarian cancer per 5 cm increase in height and per 5 kg/mp-values greater than 0.01 may well be due to chance. In the text and in figures summarizing the main results, 95% CIs are given.Results in the figures are presented by squares and lines, respectively, representing the relative risks and their corresponding 95% or 99% CIs or g-s CIs. The position of the square indicates the value of the relative risk, and its area is inversely proportional to the variance of the logarithm of the relative risk, thereby providing an indication of the amount of statistical information available for that particular estimate. When results from many studies or many subgroups are presented in the figures, 99% CIs/g-s CIs are given, since because of the multiple testing, In high-income countries the average height has increased by about 1 cm per decade 2.Details of the women in the 47 participating studies are shown in p<0.001 for each). However, there was highly significant variation in the findings by study design (pheterogeneity<0.001 for each); this is illustrated in 2 increase in body mass index, grouped by study design. Studies contributing relatively small amounts of statistical information  are included in the “other” category for the relevant study design. The variation by study design is largely due to the qualitatively different results for case–control studies with hospital controls compared to results for case–control studies with population controls or for prospective studies. The possibility that some of the hospital controls were women with conditions affected by height and body mass index cannot be excluded, and so studies with hospital controls are excluded in subsequent analyses (see Ovarian cancer risk increased significantly both with increasing height and with increasing body mass index (yses see .p<0.001) for every 5 cm increase in height. Analyses were stratified by study, age, parity, menopausal status, hysterectomy, oral contraceptive use, use of hormone therapy for the menopause, and body mass index. It can be seen in The taller women were, the greater was their risk of ovarian cancer . The adjpheterogeneity = 0.02), and, given the large number of comparisons made, it may well be due to chance. There was no significant heterogeneity in the relationship between height and ovarian cancer risk between prospective studies and case–control studies with population controls (pheterogeneity = 0.07). Since the associations did not vary materially across the various subgroups studied and additional adjustment for other potential confounders had little effect, the overall relationship between height and the relative risk of ovarian cancer can be summarized as shown in The magnitude of the increase in the relative risk of ovarian cancer with increasing height did not vary substantially by age, year of birth, education, age at menarche, parity, use of oral contraceptives, menopausal status, use of menopausal hormone therapy, hysterectomy, smoking, alcohol consumption, or having first degree relatives with breast or ovarian cancer . The treThe heavier and the more obese women were, the greater was their risk of ovarian cancer . These rpheterogeneity<0.001). The relative risk of ovarian cancer increased with increasing body mass index among never-users of menopausal hormone therapy, but not among ever-users . Given that almost 60 subgroup-specific relative risks are shown in p-value of 0.02 for users of hormone therapy is of borderline significance, whereas the p-values of <0.001 for the trend in non-users and for the difference in the trends between hormone users and non-users is unlikely to be due to chance. Such differences were observed both in prospective studies  and in case–control studies with population controls  and did not differ when analyses were restricted to postmenopausal women.The magnitude of the increase in the relative risk of ovarian cancer with increasing body mass index did not vary significantly by women's age, year of birth, ethnic group, education, age at menarche, parity, use of oral contraceptives, menopausal status, hysterectomy, alcohol and tobacco use, or family history of breast or ovarian cancer . HoweverResults in prospective studies were not significantly different when the first 4 y of follow-up were excluded. The risk estimates associated with body mass index in ever-users and never-users of hormone therapy changed by less than 2% after further adjustment for seven other potential confounding factors: ethnic group, education, age at first birth, family history of ovarian or breast cancer, age at menarche, and alcohol and tobacco consumption, and simultaneous adjustment for all these factors also affected the relative risk estimate by less than 2%. Since factors other than hormone use did not have a major effect on the relationship between ovarian cancer and body mass index, the main results are summarized in pheterogeneity<0.001). Borderline malignant serous tumours make up about 5% of all the ovarian tumours included here. There were too few borderline malignant endometrioid tumours to examine separately. When analyses were restricted to never-users of menopausal hormone therapy, the associations with body mass index shown in 2 increase in body mass index of 1.33  and 1.04 , respectively, pheterogeneity<0.001); for the other tumour types the corresponding relative risks among never-users of hormone therapy were 1.05  for clear cell, 1.10  for endometrioid, and 1.14  for mucinous tumours.Data on tumour histology was available for 17,039 (68%) women with ovarian cancer included in the main analyses. The effects of increasing height and increasing body mass index did not differ significantly between epithelial and non-epithelial tumours or between clear cell, endometrioid, mucinous, or serous histology . However2 increase in body mass index with a relative risk of 1.019, in never-users of hormone therapy. In high-income countries, where average height has increased by about 1 cm per decade 2 per decade Based on the findings here, a 1 cm increase in height is associated with a relative risk of ovarian cancer of 1.014, and a 1 kg/mOur collaboration has brought together and re-analysed individual data from 47 studies on ovarian cancer risk associated with adult height, weight, and body mass index, that is, most of the available epidemiological information worldwide. Collectively, the findings show a highly significant increase in the risk of ovarian cancer with increasing values for each of the anthropometric variables examined. The increase in ovarian cancer risk with increasing height and with increasing body mass index did not vary materially by women's age, year of birth, ethnicity, education, age at menarche, parity, family history of ovarian or breast cancer, use of oral contraceptives, menopausal status, hysterectomy, or consumption of alcohol and tobacco. However use of hormone therapy for the menopause attenuated the relationship with body mass index, since an increase in ovarian cancer risk with increasing adiposity was found only in never-users of such therapy. The trends with increasing height and body mass index were broadly similar across the common histological subtypes of ovarian cancer, except for serous tumours of borderline malignancy, which comprise 5% of the total, where the increase in risk with increasing body mass index was considerably greater than for the other tumour subtypes.An advantage of seeking to review all epidemiological studies of ovarian cancer with relevant information on body size, published and unpublished, is that this helps avoid unduly selective emphasis on published results or just on some studies. Three eligible studies that did not contribute data to these analyses but that had published on ovarian cancer risk associated with height and/or body mass index, Results from case–control studies that used hospital controls differ qualitatively and significantly from those with other study designs. It seems unlikely that these differences are due to the retrospective reporting of height and/or weight, since the results differ substantially between the retrospective studies that used hospital controls and the retrospective studies that used population controls. While other factors might also be relevant, many of the retrospective studies with hospital controls were designed to examine the effects of hormonal factors on ovarian cancer risk, and controls were often recruited from orthopaedic wards, where the most common reason for admission among middle-aged women is for hip and knee replacement surgery, and the risk of these conditions increases markedly with increasing height and increasing body mass index In prospective studies, height and weight were generally recorded at the time that women were recruited into the cohort, and in case–control studies, women were generally asked about their height and weight some 1–5 y before the diagnosis of ovarian cancer (for cases) and at an equivalent time for controls. In the case–control studies with population controls, the retrospective reporting of height and weight may have been influenced by the cases' knowledge that they had ovarian cancer, but the similarity of the findings in such studies and in those with prospective recording of anthropometric factors suggests that this is probably not a serious problem here. Nonetheless, there is still likely to be some measurement error, not only in retrospective self-reported data but also with measurements made at entry into a prospective study, because women's weight  may change over time. Non-differential misclassification of these variables would dilute estimates of relative risk, although the effects are likely to be comparatively small The observed association between ovarian cancer and height did not differ significantly between prospective studies and retrospective studies with population controls, nor did it vary by the 12 personal characteristics of the women that were examined . VariatiThe association between ovarian cancer and body mass index is seen across all subgroups of women studied, except ever-users of menopausal hormone therapy. The relevance of the finding in users of menopausal hormones will be examined in a future report from this collaboration, where the direct relationship between ovarian cancer risk and use of menopausal hormone therapy will be examined, and the potential modification of any effect of hormone therapy by women's adiposity and other factors will be explored. Body mass index is in general a good measure of adiposity, although women with the same value for the index may vary in the relative contribution from fat and from muscle. The finding that use of hormone therapy largely eliminates the relationship between body mass index and ovarian cancer risk suggests that endogenous oestrogens may be relevant, at least among postmenopausal women: the well-characterized association between circulating oestrogen levels and increasing adiposity in postmenopausal women who do not use hormonal therapies is likely to be altered among users of these therapies 2 per decade in the generations of women now developing ovarian cancer Among women in high-income countries, average height has increased by about 1 cm per decade and average body mass index has increased by about 1 kg/mText S1PRISMA 2009 checklist.(DOC)Click here for additional data file.


ESR1, ESR2) and androgen receptor (AR) genes have been suggested to play a role. We studied 258 African-American (AA) women with breast cancer and 259 hospital-based controls, as well as 349 Nigerian (NG) female breast cancer patients and 296 community controls. Three microsatellites, ESR1_TA, ESR2_CA and AR_CAG, in the ESR1, ESR2 and AR genes, respectively, were genotyped. Their repeat lengths were then analyzed as continuous and dichotomous variables. Analyses of continuous variables showed no association with breast cancer risk in either AA or NG at ESR1_TA; AA cases had shorter repeats in the long allele of ESR2_CA than AA controls , whereas NG patients had longer repeats in the short allele than NG controls ; and AA cases carried longer repeats in the short allele of AR_CAG than AA controls . When allele sizes were categorized as dichotomous variables, we discovered that women with two long alleles of ESR2_CA had increased risk of breast cancer . This is the first study to investigate these three microsatellites in hormonal receptor genes in relation to breast cancer risk in an indigenous African population. After adjusting for multiple-testing, our findings suggest that ESR2_CA is associated with breast cancer risk in Nigerian women, whereas ESR1_TA and AR_CAG seem to have no association with the disease among African American or Nigerian women.Genetic variants in hormone receptor genes may be crucial predisposing factors for breast cancer, and microsatellites in the estrogen receptor ( Breast cancer is a major global problem, as it is one of the most commonly diagnosed cancers among women. In the United States, breast cancer is the first leading and the second fatal cancer among female cancer patients Attention has long been paid to hormonal influences in breast cancer pathogenesis. Substantial evidence from both epidemiologic and experimental studies demonstrates a crucial role of hormones in the etiology of breast cancer. The indirect epidemiological support has come from the link between established reproductive breast cancer risk factors, such as early age at menarche and late age at menopause, and the exposures to estrogens and progesterone ESR1, ESR2) and androgen receptor (AR) genes have been hypothesized to be predisposing factors for breast cancer but the mechanisms are unknown. A dinucleotide TA repeat polymorphism (ESR1_TA) is located in the promoter region of ESR1 isoforms 1 and 2, as well as intron 1 and intron 2 of ESR1 isoforms 3 and 4, respectively (6q25.1), while a polymorphic dinucleotide CA tandem repeat (ESR2_CA) is located in intron 5 of ESR2 (14q23.2). To date, the functional property of these two microsatellites remains unknown, although gene expression could be modulated by such repeat nucleotide sequences Microsatellites/short tandem repeats (STRs) are informative genetic markers in the human genome and they could have biological functions according to their locations, such as affecting protein coding (in coding regions), or regulating gene expression (in regulatory regions) In the present study, we aimed to investigate whether ESR1_TA, ESR2_CA, and AR_CAG could be breast cancer susceptibility markers in African American (AA) and Nigerian (NG) women. We used a case-control study design with 258 AA cases and 259 AA controls, together with 349 NG cases and 296 NG controls.Informed consent was written and obtained from all participants. This study was approved by the Institutional Review Boards of the University of Chicago and the University of Ibadan.The Chicago Cancer Prone Study (CCPS): CCPS is an ongoing hospital-based case-control study aimed at understanding the genetic basis of young-onset breast cancer. Histologically confirmed breast cancer patients were recruited through the Cancer Risk Clinic at the University of Chicago. Cancer-free healthy controls were gender- and age-matched with cases and enrolled from individuals who visited the same hospital . Included in this study were 258 AA cases (age of onset  = 44.1±10.0 years [mean ± SD]) and 259 AA controls (age  = 46.8±10.9 years).The Nigerian Breast Cancer Study (NBCS): NBCS is an ongoing case-control study of breast cancer in Ibadan, Nigeria initiated in 1998. Breast cancer patients were recruited at the University College Hospital, Ibadan. Cancer-free healthy controls were randomly collected from a community adjoining the hospital, gender- and age-matched to the cases in the present study. The majority of the study subjects are Yoruban from Ibadan. NBCS contributed 349 NG cases (age of onset  = 47.5±11.7 years) and 296 NG controls (age  = 41.5±14.1 years) to this study.Unfortunately, estrogen receptor (ER) status was available for only a small subset of the breast cancer cases: 88 AA ER+ cases, 86 AA ER- cases, 23 NG ER+ cases, and 48 NG ER- cases .Genomic DNA was extracted from whole blood and evaluated for integrity by electrophoresis on agarose gel. Double-strand DNA was quantitated using Quant-iT™ PicoGreen dsDNA kit  and then quantified on Infinite® 200 PRO NanoQuant , according to the manufacturer’s instructions. To examine the potential sample contamination and gender discrepancy, samples were amplified by polymerase chain reaction (PCR) using AmpFℓSTR® Identifiler® PCR Amplification kit  and the PCR products were analyzed on Applied Biosystems 3130 DNA Analyzer . DNA fragment data were collected and then individually checked using GeneMapper® software , according to the manufacturer’s protocol.ESR1, ESR2 and AR, respectively: ESR1_TA_F: 5′-6FAM-AGACGCATGATATACTTCACC-3′, ESR1_TA_R: 5′-GTTCACTTGGGCTAGGATAT-3′ ; ESR2_CA_F: 5′-HEX-AACAAAATGTTGAATGAGTGGG-3′, ESR2_CA_R: 5′-GGTAAACCATGGTCTGTACC-3′ ; AR_CAG_F: 5′-HEX-TCCAGAATCTGTTCCAGAGCGTGC-3′, AR_CAG_R: 5′-GCTGTGAAGGTTGCTGTTCCTCAT-3′ . PCR was performed using PCRx Enhancer System  with conditions as follows: initial denaturation at 95°C for 5 min, followed by 35 amplification cycles at 95°C for 30 sec, annealing at 55°C for 50 sec and 72°C for 50 sec, followed by a final extension step at 72°C for 10 min. Fluorescently labeled fragments generated by PCR were run on Applied Biosystems 3130 DNA Analyzer and the repeat lengths were subsequently checked and assigned in GeneMapper® software. Genotypes were determined by two independent investigators who were blinded to subject disease status and all clinical information. Multiple homozygous subjects for each individual microsatellite were randomly chosen from the same studied populations and sequenced. Repeat lengths read by sequencing were successfully assigned to corresponding peak positions determined by fluorescence-based genotyping. Ninety-six samples were randomly selected and repeated to test the assay reproducibility.Primers were designed to amplify the fragments encompassing microsatellites ESR1_TA, ESR2_CA and AR_CAG present in http://genepop.curtin.edu.au/). In addition, we checked the microsatellite data by Micro-Checker v2.2.3, which was designed to identify genotyping errors due to an excess of homozygotes caused by non-amplified alleles , stutter peaks, or short allele dominance  (available at http://www.microchecker.hull.ac.uk/). Genotypes that did not deviate from Hardy-Weinberg Equilibrium (HWE) were eligible for statistical association analyses.Tests of conformance to Hardy-Weinberg genotypic expectations were carried out with Genepop v4.1  for ESR1_TA, 23  for ESR2_CA, and 20  for AR_CAG  and the long allele (L), according to the smaller and larger allele sizes determined, respectively. For homozygotes, two alleles are identical in peak positions; they were assigned as one S and one L, and later included in both the short and long allele analyses. For each microsatellite, the repeat lengths (mean ± standard deviation [SD]) were calculated under three categories: S, L, and the average of them. Wilcoxon rank-sum (Mann-Whitney) test was applied to compare the distributions of the repeat lengths, between case and control groups of AA and NG. Van Elteren’s test which is a stratified version of Wilcoxon rank-sum test was used to compare the repeat length in the pooled AA and NG sample set. Odds ratios (ORs) were also calculated by logistic regression analysis controlling for ascertainment, and with 95% confidence interval (CI) for three microsatellites, in AA, NG, and AA + NG. In addition, in order to avoid too-strong assumptions allele sizes were classified into dichotomous groups. Given that there is no No samples were detected to have contamination or gender issues, no discrepancy of allele calling was found between two investigators, and genotypes of 96 repeated samples were 100% consistent with previous determinations. Genotyping call rates were 99.8%, 100%, and 100% for ESR1_TA, ESR2_CA, and AR_CAG, respectively, in AA; genotyping call rates were 99.5%, 100%, and 99.8% for ESR1_TA, ESR2_CA, and AR_CAG, respectively, in NG. Calculations from Genepop and Micro-Checker showed that genotypes for all three microsatellites were in HWE and no potential genotyping errors were detected.The repeat length of ESR1_TA ranged from 9 to 26  in AA cases and 9 to 26  in AA controls, whereas the range was 9 to 27  in NG cases and 9 to 25  in NG controls. The repeat lengths (mean ± SD) of ESR1_TA were 17.31±2.86 and 17.36±2.78 in AA cases and controls, respectively; repeat lengths were 17.18±2.76 and 17.13±2.53 in NG cases and controls, respectively .P value of 0.039 in logistic regression in the pooled sample set of AA + NG, but it did not retain significance after correction for multiple-testing  in AA cases and 9 to 34  in AA controls, whereas it was 9 to 33  in NG cases and 9 to 33  in NG controls .P  = 0.036; logistic regression P  = 0.04, OR  = 0.91, 95% CI 0.83–1.00), whereas NG patients had longer S than NG controls   (P  = 0.0004)    . Thus, i01–1.17) . Accordi 0.0004) . NG indi36–2.54) . Similar10–1.74) .AR gene has repeat length ranging from 10 to 32  in AA cases and 7 to 31  in AA controls, whereas it is 9 to 31  in NG cases and 11 to 29  in NG controls . No statistically significant signal was obtained in either NG or AA + NG. In addition, we applied the dichotomous cut-offs of 20 and 22 for AR_CAG and then conducted genotypic logistic regression analysis. The results of this study provided no evidence that the AR_CAG genotypes can significantly influence the risk for breast cancer, in any populations   .ESR1 and ESR2 genes, respectively, have also been reported to be associated with other diseases such as bone mineral density AR (encoded by AR_CAG) has been reported to be associated with susceptibility to a number of human diseases, such as prostate cancer, male infertility, cryptorchidism, hirsutism, Spinal and Bulbar Muscular Atrophy, and Kennedy’s disease, among others Breast cancer is a hormone-dependent malignancy and cumulative exposure to sex hormones has been proposed to be linked to the development of breast cancer. Microsatellites ESR1_TA and ESR2_CA in the Analyzing each individual allele of a microsatellite is a way to examine its genetic/biological function. However, it is not easy to test whether an effect from a combination of alleles exists, and which alleles are actually involved. The combination could rely on repeat length , or allele frequency . It therefore creates too many assumptions and comparisons, especially when the function (if any) of exact allele(s) of a microsatellite remains unclear. Previous studies applied either the mean repeat length as a cut-off, or a cut-off previously reported, sometimes even ignoring the allele distribution differences among different ethnic populations. We chose to set the mean repeat length as a cut-off, because it divides the distribution of microsatellite repeat lengths in approximately in half, maintains adequate numbers in each allele category, and therefore provides the greatest power to detect statistically significant differences between cases and controls.Overall, the allele distributions of each individual microsatellite between AA and NG are similar . The allP value of 0.039 in logistic regression in AA + NG, but not in AA or NG; however, this P value did not reach the significant threshold of 0.0083. Additionally, no statistical significance was observed in continuous variable analysis. It seems that the risk impact of ESR1_TA in breast cancer is weak or is very likely to be absent in AA and NG. A large case-control study of ESR1 haplotype and postmenopausal breast cancer risk in Sweden has been carried out, but the ESR1_TA genotypes distribution deviated from HWE With the cut-off of ESR1_TA repeat length defined as 18, we found a CCDC170-ESR1 region), was identified in a Chinese population CCDC170 and 5′ upstream (63 kb away) of the nearest ESR1 isoform 4, a region covered by a single linkage disequilibrium (LD) block. This SNP has been replicated in Chinese and Japanese populations ESR1 in breast cancer etiology herein remains to be further clarified and explored. To date, neither ESR2 nor AR genes has been identified by breast cancer GWAS.In recent years, many genome-wide association studies (GWAS) have been conducted to search for breast cancer susceptibility loci. One of the GWAS hits, 6q25.1 . Thus, L turned out to be the protective allele in AA. However, the association results were negative when considering multiple-testing correction. So, the inherited predisposition of ESR2_CA to breast cancer in AA women was not fully supported. On the other hand, NG patients appeared to have longer S than NG controls , namely, S was the risk allele in NG. In the logistic regression analysis using the categorical cut-off of 23, ESR2_CA genotype overall distribution in cases and controls showed no significance in either AA or AA + NG, but in NG (P  = 0.0004). When comparing LL vs. SS + SL, statistical significances were also obtained in NG  and AA + NG . Although the association of ESR2_CA and breast cancer risk has not been reported in Finnish For ESR2_CA, our analysis of continuous variables showed that AA cases had shorter L than AA controls , but no statistical significance was obtained after adjusting for multiple-testing. Furthermore, we did not gain any significant indications from logistic regression analysis when a dichotomized cut-off of 20 was applied. Wang et al. have genotyped AR_CAG in 239 AA breast cancer women and 249 controls and found no significant association between AR_CAG and overall breast cancer risk using a cut-off of 22 AR_CAG repeats Among the three microsatellites examined, AR_CAG is the most commonly tested one in the literature . In the AR is located on the X chromosome. However, it has been reported that the short and long AR_CAG alleles were subjected to skewed inactivation with similar frequency It is noteworthy that there have been divergent findings about the AR_CAG and breast cancer risk . One posSince it has become clear in recent years that breast cancer genetic susceptibility is subtype specific, we conducted similar subset analysis stratified by ER status but found no significance . It is wTo our knowledge, there have been only three previous reports investigating the association between all the three microsatellites  and risk of breast cancer in women Table S1Continuous variable analysis of ESR1_TA, ESR2_CA, and AR_CAG alleles and breast cancer risk stratified by ER status in African American and Nigerian women.(DOC)Click here for additional data file.Table S2Categorical variable analysis of ESR1_TA, ESR2_CA, and AR_CAG alleles and breast cancer risk stratified by ER status in African American and Nigerian combined samples.(DOC)Click here for additional data file.


Gail and others developed a model (GAIL) using age-at-menarche, age-at-birth of first live child, number of previous benign breast biopsy examinations, and number of first-degree-relatives with breast cancer as well as baseline age-specific breast cancer risks for predicting the 5-year risk of invasive breast cancer for Caucasian women. However, the validity of the model for projecting risk in South-East Asian women is uncertain. We evaluated GAIL and attempted to improve its performance for Singapore women of Chinese, Malay and Indian origins.E) with the observed (O) by the ratio (E/O) and 95% confidence interval (CI) and the respective concordance statistics estimated.Data from the Singapore Breast Screening Programme (SBSP) are used. Motivated by lower breast cancer incidence in many Asian countries, we utilised race-specific invasive breast cancer and other cause mortality rates for Singapore women to produce GAIL-SBSP. By using risk factor information from a nested case-control study within SBSP, alternative models incorporating fewer then additional risk factors were determined. Their accuracy was assessed by comparing the expected cases . Except for women who had two or more first-degree-relatives with breast cancer, satisfactory prediction was present in almost all risk categories. This agreement was reflected in Chinese and Malay, but not in Indian women. We also found that a simplified model (S-GAIL-SBSP) including only age-at-menarche, age-at-birth of first live child and number of first-degree-relatives performed similarly with associated concordance statistics of 0.5997. Taking account of body mass index and parity did not improve the calibration of S-GAIL-SBSP.From 28,883 women, GAIL-SBSP predicted 241.83 cases during the 5-year follow-up while 241 were reported  provides a simpler approach for projecting absolute risk of invasive breast cancer in South-East Asia women. Nevertheless its role in counseling the individual women regarding their risk of breast cancer remains problematical and needs to be validated in independent data. The best-known statistical model available for predicting an individual woman’s chance of developing breast cancer is that derived using information from regularly screened Caucasian women from the USA participating in the Breast Cancer Detection Demonstration Project (BCDDP). This moet al is the baseline age-specific composite breast cancer rate for age-group j, Bj is the age-specific breast cancer incidence rate and AR is the attributable risk in the broader age category within which j falls. When developing GAIL, AR for the USA Caucasian population was found to be approximately constant in those less than 50 years at AR−49=0.4771, and for older women at AR50+=0.4736.In equation (1), i of age a=τj-1, the probability of remaining breast cancer free up to the age, τj, is estimated by S(τj)=S(τj − 1)exp(−bjrijΔ). In addition, the age-specific hazard cj of dying of other causes is assumed to be the same for all subjects in the age-group j. The probability of surviving competing risks up to the end of the age-group j, τj, is estimated by C(τj)=C(τj − 1)exp(−cjΔ), where C(0)=1.For an individual in risk group rij, by combining these with data providing information on a, Bj and cj.The Fortran program BCPTCARE of the National Cancer Institute calculates equation (1), for given values SBSP recruited 29,193 female permanent residents and citizens of Singapore, including 24,609 ethnically Chinese, 1,630 Malay and 1,434 Indian, from 01 October 1994 to 28 February 1997. Women were eligible with no previously diagnosed cancers (except non-melanoma of the skin), no mammography within the past year or biopsy within the last 6 months. Prior to mammography, all attendees completed a questionnaire including demographics; reproductive and family histories; smoking; and menopausal hormone therapy use,11.in situ) at the time of breast cancer screening. In order to focus on incident breast cancer, women were included only if they were followed-up to be alive without disease (5 in situ) for the next 3 years. As the prevalent breast cancers are not included, the study women have a lower absolute risk than the general female population[in situ disease who then developed an invasive cancer were considered as invasive in the year of this latter diagnosis. Women with unknown age-at-menarche or date-of-diagnosis were excluded.Included in the risk evaluation are those who were disease-free  and National Cancer Centre Institutional Review Board (NC08-041). As this was a large population based study, with full anonymity of all data, direct consent from the participants is waivered.in situ disease) or developed invasive breast cancer before 2006 were defined as case patients. Control subjects were selected from those who did not have a breast cancer diagnosis at the time of study. These were matched to cases by 5-year at age-at-entry groups and calendar year of entry into the SBSP program and ethnicity. Data from these women was used to build a model to project absolute invasive breast cancer risk.To study risk factors for breast cancer, a nested case-control study was conducted within SBSP in 2006. Women wBj, and cj for the period 2003-2007 (Table Breast cancer incidence and death status for SBSP participants were notified as either detected through SBSP or subsequently through record linkage with the Singapore Cancer Registry (SCR). All whose death status was not captured were assumed alive at 1 March 2009. SCR includes all cases of cancer occurring in citizens and permanent residents (population near 5 million) between 1968 and 2008. Annual invasive breast cancer cases and annual non-breast cancer deaths are obtained from SCR and annual population numbers from Singapore Resident Population report (2003-2007). These wβ,  is:k age categories are concerned then, under the null hypothesis, ∑(O – E)2/E follows a χ2 distribution with k degrees of freedom[To estimate the probability of invasive breast cancer for a different population one can assume that all the components necessary are already contained in GAIL. That is, the regression coefficients, , Table , BCDDP, p±1.96×1O,3. If k  freedom.O=241, E=401.54, E/O=1.67). Consequently our study aim was modified, to one investigating features of the local population which might influence the ultimate predictions while preserving the Gail-based modelling approach.In a preliminary investigation, we noted that applying GAIL unchanged to Singaporean women substantially overestimated the number of invasive breast cancer cases: , parity (parity), smoking (smoking), body mass index (bmi), use of hormonal replacement therapy (hrt), use of oral contraception (oc) and waist-to-hip ratio (whr). The body mass index was categorized as <23.0, 23.0–27.4 and ≥27.5 kg/m2  following the World Health Organization (WHO) guideline for Asian populations[P<0.05 was used for statistical significance to select variables for multivariate modeling. Finally E-GAIL-SBSP was created by taking only those variables with prognostic significance into the model.As it is known that the etiological factors for breast cancer vary according to, for example, ethnicity and/or geographical location of the women, the risk factors concerned and/or their weightings in the established Gail model may require some modification. To explore this, we used subjects from the nested case-control study. Relative odds were obtained by use of multiple logistic regression with the same independent variables and coding as GAIL-SBSP Table . A simplulations. To avoiscriminatory accuracy was measured by the age-specific concordance statistic, using a logistic regression model of breast cancer status on the estimated risks. Thus each model was assessed by use of the area under the receiver operating characteristics curve (AUC) created by computing sensitivity and specificity[Model dicificity. The CI cificity. The varE/O=1.00, CI=0.88 to 1.14) – suggesting good model calibration . This satisfactory prediction was also seen within all age groups . This agreement was reflected in Chinese and Malay, but not in the relatively few Indian women as 17 cases were observed and only 10.16 predicted .Of the 29,193 women in SBSP, 28,883 were available for the 5-year risk assessment. A total of 241 invasive cases were observed and these are categorized by ethnicity and age-group together with the numbers predicted by GAIL-SBSP  while for those with no history there was excellent calibration .In general, predictions were good amongst the various risk categories Table . HoweverRR) was derived (Table RRs for each of the risk categories are given in Table P=0.359).To estimate the relative risk function, we analyzed 439 invasive breast cancer cases (121 diagnosed at screening and 318 subsequently) and 1,198 controls from the nested case-control study Table . As far RRs between the nested case-control study and BCDDP are largest only in the groups where the number of first-degree-relatives with breast cancer is two or more.The differences in the RRs and predicted 241.80 cases   Table . The satE/O=0.67, CI=0.44 to 1.01) while for those without a biopsy there was a very good calibration .Again similar to GAIL-SBSP, S-GAIL-SBSP predictions were relatively close amongst the various risk categories Table . Howeverbmi, use of hormonal replacement therapy, use of oral contraception and waist-to-hip ratio to estimate the regression coefficients  and hence formulated E-GAIL-SBSP together with Singapore race-specific estimates of Bj and cj  and so was not able to satisfactorily capture the number of cases among the various risk categories . Moreover, E-GAIL-SBSP statistically significantly underestimated the number of cases among women with two or more first-degree-relatives with breast cancer .Overall, E-GAIL-SBSP predicted 289.47 cases  for 60 - 69 years, and 0.8938 (CI=0.70 to 1.00) for 70 - 74 years.The unweighted average concordance statistics were very similar, and not statistically significantly different, with AUC = 0.6098 (CI=0.57 to 0.65), 0.5997 (CI=0.56 to 0.64), and 0.6162 (CI=0.58 to 0.65) for GAIL-SBSP, S-GAIL-SBSP and E-GAIL-SBSP, respectively . However, the model under predicted for the very few women who had two or more first-degree-relatives with breast cancer. Prediction was satisfactory for Chinese women but over-predicted for the Malay and Indian women. Since these latter groups each comprise of only 5% of the population studied, the accuracy of these specific predictions requires further investigation in residents of the Malaysian Peninsula and the Indian Sub-Continent. Nevertheless, the results for the Chinese women suggests the potential that GAIL can be improved for South-East Asian populations by using local (and/or updated) estimates of incidence and competing mortality rates.Retaining the GAIL model structure, but applying Singapore national and race-specific invasive breast cancer and other cause mortality rates, to develop GAIL-SBSP resulted in absolute risk projections that worked reasonably well as assessed by the comparison of observed and expected cases across all age groups and amongst the majority of risk categories Tables  &4. In tAlthough the performance of GAIL-SBSP is in general satisfactory, at least amongst Singapore-Chinese women, one might anticipate that taking into account implications of the different health systems and etiological factors may produce improved prediction. For example, only 10% of the subjects in the SBSP cohort had had a mammogram in the previous year and this may have an impact on the apparent natural history of disease. More generally, women from much of the Asia-Pacific region do not receive regular mammogram screening. Also, sS-GAIL-SBSP, which included age-at-menarche, age-at-birth of first live child and number of first-degree-relatives with breast cancer was well calibrated in the total SBSP cohort and across most subgroups Tables  &4. It wbmi and parity were independent predictors of risk. Thus their inclusion in E-GAIL-SBSP, with modified AR and RRs derived from the SBSP cohort, marginally improved the discriminatory power (AUC = 0.62) but overestimated the predicted breast cancer cases substantially in, for example, the highest decile  to those of 50 and more years, they are applicable to younger women but further validation is needed. Further, we started the “clock” three years after negative screening which implies the SBSP based models are pertinent to women thought to be free of breast cancer. A woman who has just had a negative breast examination and mammogram, as the United Kingdom breast screening programme has shown, has about one third the absolute risk of breast cancer in the following three years. NeverthN=216) or had been residents in a western country for 10 years or less (N=421)[The strengths of this study include the use of a predominately postmenopausal group of women from three ethnic groups, Chinese, Malay and Indian, drawn from a very large screening program in which more than 29,000 women were randomly chosen to participate. Further, since neither Asian- or Chinese-Occidental (born in or migrated to the West) women were systematically included in the development of the Gail-based models,2. We be (N=421).Limitations of using SBSP data for individual absolute risk predictions include the inability of the nested case-control study to estimate elaborate models with sufficient precision. Also our validation data included relatively small numbers of breast cancer cases, especially amongst Singapore-Malay and -Indian women and those with two or more affected first-degree relatives. Furthermore, as with retrospective studies in general, the level of ascertainment of incident cases is of concern. However, Singapore is a small island where all citizens and permanent residents are registered in the population registry with a unique registration number. Also cancer notification is mandatory and this enables near complete ascertainment of breast cancer incidence by linkage of SBSP participants with the Singapore Cancer Register database.In conclusion, we found that among South-East Asian postmenopausal women, the GAIL type model could be refined using race-specific estimates of invasive breast cancer incidence and other cause mortality rates. A model which includes age-at-menarche, age-at-birth of first live child and number of first-degree-relatives with breast cancer appears to provide a simpler approach for projecting absolute risk of invasive breast cancer in South-East Asia women. Nevertheless its role in counseling the individual women regarding their risk of breast cancer remains problematical and needs to be validated in independent data.The authors declare that they have no competing interests.FG, DM, SWD, DBM, KSC conceived the study and participated in its design. FG, DM, DBM, CHG, YFS participated in data analysis and interpretation. FG, DM, KYC, YFS, CHG drafted the manuscript. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2407/12/529/prepub


P≤0.05) with the risk of overall breast cancer in the same direction as previously reported: rs10069690 (5p15/TERT), rs999737 (14q24/RAD51L1), rs13387042 (2q35/TNP1), rs1219648 (10q26/FGFR2), rs8170 (19p13/BABAM1), rs17817449 (16q12/FTO), and rs13329835 (16q23/DYL2). A marginally significant association (P<0.10) was found for three additional SNPs: rs1045485 (2q33/CASP8), rs4849887 (2q14/INHBB), and rs4808801 (19p13/ELL). Three additional SNPs, including rs1011970 (9p21/CDKN2A/2B), rs941764 (14q32/CCDC88C), and rs17529111 (6q14/FAM46A), showed a significant association in analyses conducted by breast cancer subtype. The risk of breast cancer was elevated with an increasing number of risk variants, as measured by quintile of the genetic risk score, from 1.00 (reference), to 1.75 (1.30–2.37), 1.56 (1.15–2.11), 2.02 (1.50–2.74) and 2.63 (1.96–3.52), respectively, (P = 7.8×10–10). Results from this study highlight the need for large genetic studies in AAs to identify risk variants impacting this population.Genome-wide association studies (GWAS), conducted mostly in European or Asian descendants, have identified approximately 67 genetic susceptibility loci for breast cancer. Given the large differences in genetic architecture between the African-ancestry genome and genomes of Asians and Europeans, it is important to investigate these loci in African-ancestry populations. We evaluated index SNPs in all 67 breast cancer susceptibility loci identified to date in our study including up to 3,300 African-American women , recruited in the Southern Community Cohort Study (SCCS) and the Nashville Breast Health Study (NBHS). Seven SNPs were statistically significant ( Breast cancer is one of the most common malignancies diagnosed among women worldwide, including women of African descendent. African American (AA) women experience a disproportionate burden of breast cancer. Age-adjusted mortality rate of this cancer is more than 40% higher in AAs than in European-ancestry populations. AA women tend to be diagnosed with breast cancer at a younger age and with more aggressive types of the disease, such as ER- (estrogen receptor negative) and ER−/PR−/HER2-  breast cancer. Within AAs, women having a higher African ancestry level, estimated by ancestry informative markers (AIMs), have been shown to have an increased likelihood of ER−/PR- versus ER+/PR+ breast cancers BRCA1, BRCA2, TP53, and PTEN) and four moderate-penetrance genes  have been discovered for breast cancer To date, four high-penetrance genes  were selected randomly from those who were cancer-free and frequency-matched to cases in a 1∶1 ratio on age at enrollment (±1 year), recruitment method, and sample type .This study uses resources from the Southern Community Cohort Study (SCCS) and the Nashville Breast Health Study (NBHS). The NBHS is a population-based case-control study Written, informed consent was obtained from all participants prior to interview, and the study protocols have been approved by Institutional Review Boards at Vanderbilt University (for NBHS and SCCS) and Meharry Medical College (for SCCS).P<10–6), 4) genotyping not concordant with previous data, 5) duplicates or “cryptic” duplicates, 6) first-degree relative, 7) ethnic outliers based on a subset of 37,000 uncorrelated markers which passed QC. SNPs were excluded for any of the following reasons: 1) call rate <95%, 2) deviated from HWE in controls at P<10–7, 3) genotyping discrepancies in more than 2% of duplicate samples. Data cleaning was conducted within the whole COGS Project. After QC, a total of 199,961 SNPs for 1,116 cases and 932 controls were included in the dataset. We then performed principal component analyses (PCA) using a set of 4,613 uncorrelated SNPs . Five additional participants (three cases and two controls) were excluded due to >6 σ away from the means of PCA1 and PCA2. In total, 1,113 cases and 930 controls from the SCCS and NBHS were successfully genotyped by the COGS SNP array.Genotyping for samples described above was conducted using the protocol for the COGS Project as described elsewhere In our previous project, nine GWAS-identified SNPs were genotyped using Taqman/Sequenom in 810 cases and 1,784 controls from the NBHS and SCCS http://med.stanford.edu/tanglab/software/frappe.html), which implements an Estimation-Maximization algorithm for simultaneously inferring each individual’s ancestry proportion and allele frequencies in the ancestral populations Individual African ancestry level was estimated from 612 AIMs included in the COGS SNP array using the program frappe  for each study participant by multiplying the number of risk alleles (0/1/2) of each SNP by the weight  for that SNP, and then summing them together. Since data for a complete set of SNPs were only available for the 1,110 cases and 929 controls genotyped using COGS SNP array, GRS analysis was conducted among these subjects.P<0.1) with direction of association consistent with previous reports. SNP rs1219648 was not included in the COGS SNP array and only 66% women of the COGS subjects had data available for this SNP, genotyped in our previous project through Sequenom (23). SNP rs13387042 was genotyped in both by the COGS SNP array and Sequenom (23). This SNP, however, did not reach P<0.1 in the samples analyzed using COGS SNP array and thus was not included in the GRS analyses. Thus, these two SNPs, rs1219648 and rs13387042, were excluded in GRS analyses. The remaining eight SNPs, rs10069690, rs999737, rs8170, rs17817449, rs13329835, rs1045485, rs4849887, and rs4808801 were included in the GRS analyses. All statistical analyses were conducted in SAS, version 9.3, with the use of two-tailed tests.We constructed a GRS using SNPs that showed a statistically significant association with breast cancer risk in this study. In the present study, ten SNPs were associated with breast cancer , and both of them were included in the present study. Similarly, in the locus 16q12/TOX3, SNPs rs4784227 and rs3803662 were discovered in Asians 2 = 1.0), was used as a substitute. SNPs rs17271951 and rs3803662 are not in LD in HapMap Africans (r2 = 0.03), and both of them were included in the final analyses. In the 10q26/FGFR2 locus, both rs1219648 and rs2981582 were discovered in Europeans but were in moderate LD in HapMap Africans (r2 = 0.25). For the 6q25/TAB2 locus, rs9485370 was used to replace the originally reported SNP rs9485372 2 = 1 in HapMap Africans). For the other 21 loci, one index SNP (reported in previous GWAS) per locus was selected. Therefore, in total, 28 SNPs in previously reported 25 loci were investigated in the present study. Five of them were deviated from Hardy-Weinberg test with P<0.05, including rs1045485, rs10995190, rs13281615, rs889312, and rs999737.Two SNPs have been discovered in the 10q21/P = 0.04, binomial sign test). Five SNPs were nominally statistically significant (P≤0.05) in the same direction as previously reported  1.19 (1.05–1.36) and P-value 0.007. Notably, the MAF is very low (0.047 in controls) for SNP rs999737 at 14q24/RAD51L1, however, a strong association was observed for this SNP, with OR (95% CI) 1.59 (1.15–2.19) and P-value 0.005. Allelic OR (95% CI) for the other 3 SNPs are 1.17 (1.04–1.33) for rs13387042 (P = 0.011), 1.17 (1.04–1.33) for rs1219648 (P = 0.011), and 1.25 (1.07–1.47) for rs8170 (P = 0.006). The CASP8 SNP (rs1045485) identified previously through a candidate gene approach has a low frequency (MAF = 0.065) in AAs. A marginally significant association was found for this SNP with OR (95% CI) of 1.25 (0.96–1.62) and P-value 0.096. Significant association was observed for SNP rs4973768 at 3p24/SLC4A7 in this study, however, in the opposite direction as previously reported. No association was observed with the other 21 SNPs. Among them, the minor allele in rs10771399 at 12p11/PTHLH is very rare in AAs with MAF = 0.034 in controls.Of the 28 SNPs, 19 SNPs had an OR of breast cancer risk in the same direction as initial reports. This is higher than expected under the null hypothesis (reported . SNP rs1P≤0.05). The OR (95% CI) was 1.21 (1.06–1.37) for rs17817449 at 16q12/FTO (P = 0.004) and 1.16 (1.01–1.32) for rs13329835 at 16q23/DYL2 (P = 0.037). A marginally significant association (P<0.10) in the same direction as in Europeans was found for two additional SNPs, with ORs (95% CI) of 1.13 (0.99–1.30) for rs4849887 at 2q14/INHBBP (P = 0.075), and 1.13 (0.98–1.29) for rs4808801 at 19p13/ELL (P = 0.091). The 22q13/MKL1 SNP rs6001930 showed an association (P = 0.035), however the direction was inconsistent with that observed in Europeans. No associations were identified for the other 37 SNPs, among which four SNPs have a MAF <0.05, including rs11552449 at 1p13/AP4B1, rs1353747 at 5q11/PDE4D, rs11780156 at 8q24/MYC, and rs11571833 at 13q13/BRCA2.Of the 42 SNPs, one for each of the 42 loci recently discovered in European-ancestry populations P≤0.05) or marginally significant (P≤0.1) association with any of these three subtypes of breast cancer are presented. Three SNPs that were not associated with overall breast cancer showed nominally statistical significance (P≤0.05) in analysis by subtypes in the same direction as previously reported. SNPs rs1011970 at 9p21/CDKN2A/2B and rs941764 at 14q32/CCDC88C were associated with ER+ breast cancer with ORs (95% CI) 1.27 (1.05–1.54) (P = 0.014) and 1.26 (1.02–1.56) (P = 0.032), respectively. SNP rs17529111 at 6q14/FAM46A was associated with ER−/PR−/HER2− tumor with OR (95% CI) of 1.97 (1.02–3.82) (P = 0.043). Differences in the strength of the association were also observed for three other SNPs across breast cancer subtypes. SNP rs17817449 at 16q12/FTO showed an association with ER+ tumor with OR (95% CI) of 1.32 (1.09–1.60) but not with ER− or ER−/PR−/HER2− tumors. Both rs10069690 at 5p15/TERT and rs999737 at 14q24/RAD51L1 showed the strongest association for ER−/PR−/HER2− breast cancer though associations were also observed for ER+ and ER− cancers.Association results between SNPs with risk of breast cancer by subtypes, including ER+, ER-, and ER−/PR−/HER2−, are presented in P = 7.8×10–10). Such associations also were observed for all three subtypes of breast cancer.Significant associations were observed between GRS and risk of breast cancer . ORs (95P<0.05) and three SNPs were marginally significantly associated (P<0.10) with overall breast cancer risk in the same association direction as previously reported. Three additional SNPs showed a significant association (P<0.05) when stratified by breast cancer subtype. GRS analyses showed significant associations with the risk of overall or subtype of breast cancer.In the present study, we investigated associations of 70 index SNPs in 67 breast cancer susceptibility loci identified to date in up to 1,231 cases and 2,069 controls of AA women. We found that seven SNPs were significantly associated  was originally discovered in a GWAS of AAs FGFR2, association was observed in our previous study BABAM1 was only investigated in a pooled study from Africans and AAs and no association was observed CASP8 SNP (rs1045485) and RAD51L SNP (rs999737) have a MAF <5% in African-ancestry populations. Therefore, it is not surprising that these two SNPs were not replicated in all previous studies of African-ancestry populations Among the ten SNPs that showed significant or marginally significant association with overall breast cancer risk in the present study, six have been investigated in previous studies of African-ancestry populations TOX3 was replicated in the WHI FGFR2) was significantly associated with breast cancer risk in two studies MRPS30KLF4We did not replicate associations for the other 60 reported SNPs with the risk of overall breast cancer. Inconsistent results were reported for some of them in previous studies of African-ancestry populations. For example, SNP rs3803662 at 16q12/In general, our results that approximately 90% of index SNPs were not replicated in AAs are consistent with results from previous studies in African-ancestry populations TNP1, 5q11/MAP3K1, 10q26/FGFR2, and 19p13/BABAM1 loci Because of the large difference in genetic architecture between African-ancestry and European/Asian ancestry populations, failure to replicate most of the reported SNPS in AAs is not surprising. Most, if not all, index SNPs identified in GWAS are associated with breast cancer risk through their strong LD with causal variants. African-ancestry populations have shorter LD and more genetic variations than European/Asian ancestry populations and may have different SNPs in LD with the causal variant. This may be the major reason why index SNPs are not replicated in African descendants. For example, in the BWHS, originally reported index SNPs rs10941679 and rs3803662 were not replicated, but other SNPs in these regions, rs16901937 and rs3104746, were associated with breast cancer To our knowledge, this is the first study in AAs that has evaluated index SNPs in all breast cancer susceptibility loci identified to date. However, the sample size in our study is relatively small, especially when stratified by breast cancer subtype. Some of the null associations observed in this study could be due to inadequate statistical power. Meta-analysis by pooling together all existing data in the AA populations will increase the statistical power to evaluate the effects of these variants in AAs. The other limitation of this study is that we only investigated index SNP in each locus. Large-scale fine mapping studies are needed to identify genetic risk variants at these loci in African-ancestry populations. Such work will be very helpful to identify causal variants for breast cancer.In summary, in this African-ancestry population study, we replicated approximately 10% of index SNPs in 67 breast-cancer susceptibility loci. Heterogeneity was observed across the breast cancer subtype. These results show the complexity in applying GWAS findings to African-ancestry populations. Large-scale studies in AAs are needed to discover genetic risk variants which impact this population.Table S1African genetic ancestry proportion of study participants estimated by AIMs.(DOCX)Click here for additional data file.Table S2Association of breast cancer risk with 60 SNPs located in reported breast-cancer susceptibility loci in African Americans with P>0.1.(DOCX)Click here for additional data file.


Dietary energy restriction may protect against cancer. In parts of the Netherlands, mostly in larger cities, periods of chronically impaired nutrition and even severe famine (Hunger Winter 1944–1945) existed during the 1930s and World War II (1940–1945). We studied the association between energy restriction during childhood and early adulthood on the risk of ovarian cancer later in life. In 1986, the Netherlands Cohort Study was initiated. A self-administered questionnaire on dietary habits and other cancer risk factors was completed by 62,573 women aged 55–69 years at baseline. Follow-up for cancer was established by record linkage to the Netherlands Cancer Registry. After 16.3 years of follow-up, 364 invasive epithelial ovarian cancer cases and 2220 subcohort members  with complete information confounders were available for case-cohort analyses. In multivariable analysis, ovarian cancer risk was lower for participants with an unemployed father during the 1930s (Hazard Ratio (HR), 0.70; 95% Confidence Interval (CI), 0.47–1.06) compared to participants with an employed father as well as for participants living in a city during World War II  compared to participants living in the country-side. Residence in a Western City during the famine (Hunger Winter) was not associated with a decreased risk. Our results show a relation between proxy variables for modest energy restriction over a longer period of time during childhood or early adulthood and a reduced ovarian cancer risk. Ovarian cancer is the seventh most common malignancy among women living in the world, with approximately 225,000 newly diagnosed ovarian cancer cases and almost 140,000 deaths due to ovarian cancer in 2008. Oral contraceptive use, parity and tubal ligation have been associated with a reduced ovarian cancer risk. In animal studies an inverse association between energy restriction and cancer has been found for mice In the Netherlands, periods of chronically impaired nutrition existed in parts of the population during the economic depression in the 1930s and the earlier years of World War II (1940–1944). Also, a substantial part of the population experienced a severe famine during World War II, the so-called Hunger winter (1944–1945), especially in the Western Cities of the country.This unique setting provided the opportunity to study the association between energy restriction during childhood and early adulthood on the risk of ovarian cancer later in life. We evaluated this within the Netherlands cohort study on diet and cancer.The NLCS started in September 1986, when 62,573 women, 55–69 years of age at baseline, were enrolled in the cohort. A detailed description of this prospective cohort study has been reported elsewhere. For efficiency reasons, data processing and analysis were based on the case-cohort approach, in which the cases were enumerated for the entire cohort (providing the numerator information for estimating incidence rates), while the accumulated person-years for the entire cohort were estimated from a subcohort randomly sampled from the entire cohort at baseline (providing the denominator information for estimating incidence rates).On recruitment, participants were informed in writing of the details of the study and its objectives. In accordance with the regulations at that time, written informed consent was obtained when participants returned the completed baseline questionnaire. The Medical Ethics Committee of Maastricht University/ University Hospital Maastricht, the Netherlands, has approved the study.After 16.3 years of follow-up (until 31 December 2002), information regarding vital status was available for all 2,589 female subcohort members. After the exclusion of women who had reported a prevalent malignancy  or to have undergone an oophorectomy (n = 32), 2,406 female subcohort members were available for analysis.Incident cancer cases occurring in the entire cohort have been identified by record linkage to the Netherlands cancer registry and the national pathology register (PALGA). The method of record linkage has been described earlier.During 16.3 years of follow-up, 421 incident, microscopically confirmed, primary ovarian cancer cases  were identified. After excluding non-epithelial tumors (N = 13) and borderline invasive tumors (N = 14), 394 invasive epithelial ovarian carcinoma cases remained eligible for analysis.Individual food intake data of our participants during childhood and early adulthood was not available, therefore we used the employment status of the father during the Economic Depression (1932–1940), residence during the Second World War (1940–1944), and residence in the winter of 1944–1945 (Hunger winter) as proxy variables to classify participants into groups of energy restriction. These variables were used since the caloric intake was reported to be lower in the families of the unemployed during the years of the economic depression Participants reporting to have lived abroad during the War years or during the Hunger winter were excluded from the analyses (leaving 381 cases and 2293 subcohort members), since their reported whereabouts make it difficult to speculate on the degree of energy restriction experienced.In all models, age was included to adjust for the increasing cancer risk with age. Confounders were selected in two steps: some were chosen a priori to be included in the models, and other potential confounders were only included if they changed age-adjusted hazard ratios (HRs) of any of the exposure variables by more than ten percent. The a priori selected covariables were: use of oral contraceptives (ever versus never) and parity (continuous). As potential confounders we further investigated age at menarche (continuous), age at menopause (continuous), hysterectomy (possible/probable or no), height (continuous) and cigarette smoking . None of the potential confounders did change the age-adjusted HRs by more than 10% and the final model included therefore age, parity and use of oral contraceptives. Cohort members with missing values on any of the confounders were excluded, leaving 364 cases and 2220 subcohort members. In an additional multivariable model, we mutually adjusted for all exposure variables for energy restriction. Height and age at menarche could be intermediate factors and were therefore investigated by comparing the multivariable model to the model including either height or age at menarche. Furthermore, we explored associations in 5-year age groups based on year of birth, because the timing of exposure in relation to a possible susceptible period in life, such as menarche, may be important.HRs and corresponding 95% confidence intervals (95% CI) for ovarian carcinoma risk were estimated in age-adjusted and multivariable adjusted analyses using the Cox proportional hazards model The most frequent subtypes of ovarian cancer observed in this population were serous carcinoma (49%), mucinous carcinoma (9%), endometrioid carcinoma (9%) and adenocarcinoma – not otherwise specified (24%).Descriptive data did not show large differences in the distribution over the categories of the exposure variables between the cases and the subcohort, although cases were on average taller (165.8 versus 165.3 cm) than subcohort members . The repAge-adjusted and multivariable adjusted hazard ratios are shown in The risk of ovarian cancer was lower  for participants living in a city during World War II in comparison to participants living in the country-side. After mutual adjustment for the other exposure variables for energy restriction, the HR was 0.72, .The risk of ovarian cancer for residents of a Western City during the Hunger Winter was 0.93  compared to participants who lived in the non-West part of the country. After mutual adjustment for the other indicators for energy restriction, the HR was 0.97 .When the analyses were restricted to serous ovarian cancer cases, results were very similar (data not shown).p for interaction, 0.65). For individuals living in a city during World War II relative to those living in rural areas, estimates in each birth cohort were not different . The risk estimates for Living in a Western City or Western rural area during the Hunger Winter of 1944–1945 were not consistent . When the analyses were stratified according to age at diagnosis (before or after age 70), results were very similar (data not shown).In The multivariable models were additionally adjusted for height and age at menarche which had little effect on the rate ratios (data not shown).In this prospective cohort study, we used father's unemployment status during the Economic depression as an indicator of moderate energy restriction in childhood and city residence during World War II as indicators of moderate energy restriction during early adulthood. Both indicators were associated with a decrease in the risk of ovarian cancer. We found no association however between residence in a city in the Western Netherlands as an indicator of severe energy restriction and cancer risk.Misclassification of energy restriction is possible since the exposure was measured indirectly by proxy variables. We believe these indicators of energy restriction can be reasonably adequate however, as previously discussed by Dirx et al. There are several publications that show the existence of a period of chronically impaired nutrition during the economic depression in the 1930s A substantial part of the population experienced a severe famine during World War II, the so-called Hunger winter (1944–1945), especially in the Western part of the country. Body Mass Index (BMI) reflects the balance of energy intake and expenditure. Energy restriction can be expected to cause a lower BMI. However, studies between body composition at different ages and ovarian cancer risk are not in agreement with our findings. In the Nurseś Health Study, birth weight was not, while body fatness at age 5 and 10 were inversely associated with ovarian cancer risk. Another explanation may be that not energy restriction, but nutrient composition is of importance. During the war years, especially the proportion of fat decreased.A lower risk of ovarian cancer would be in accordance with the theory postulated by Fathalla, who suggested that every ovulation causes trauma to the ovarian epithelium and stimulation of mitosis.In this large prospective cohort study, we were able to consider several potential confounders. Height and age of menarche do not appear to be intermediate factors however in the presumed relation of energy restriction during childhood and early adulthood and ovarian cancer. We believe that this study is the first to look at measures of modest energy restriction during childhood and early adulthood and the risk of ovarian cancer in later life and to find an inverse association. No relation was seen however between presumed severe energy restriction during the Hunger Winter and ovarian cancer risk, perhaps because the exposure period was much shorter (November 1944-May 1945). Other relevant studies include the study by Elias et al Overall, using fathers occupation and wartime residence data as proxy measures for exposure, our study suggests that moderate energy restriction during adolescence was associated with a decrease in ovarian cancer risk. We found that our measure of more severe energy restriction during a shorter period of time was not. Other studies are needed to confirm these findings.


To determine: (i) the mammographic parenchymal patterns in Malaysian women and whether the breasts are dense on mammogram; (ii) the effect of age on breast density; (iii) the effect of parity on breast density; (iv) the difference in breast parenchymal patterns among the major races of women in Malaysia.This was a descriptive cross-sectional study of 1,784 patients  who had undergone mammography during the 1-year study period. Majority of women (41.7%) were aged between 51 and 60 years and majority (43%) had 3–4 children. The Tabar classification (Pattern I - V) was used to evaluate breast parenchymal patterns on mammogram. Tabar Pattern I was further divided into 3 sub-groups . The different patterns were then grouped into dense  and not dense  breasts. The SPSS package was used for statistical analysis.Majority (59%) of Malaysian women had dense breasts  and 41% did not have dense breasts . Age and parity were inversely related to breast density (p < 0.0001). Chinese women (65.7%) had the highest percentage of dense breasts , followed by the Indians (57.2%) and the Malays (50.5%).Majority of women had dense breasts but Pattern IV, which has been associated with increased risk of breast cancer, was seen in only 5% of the women. The breast density reduced steadily with increasing age and parity. There was no statistically significant difference in breast density in the three main races. The terms mammographic parenchymal patterns and mammographic density are widely used to describe the proportion of dense to lucent areas in the breast as seen on mammogram. The appearance of breast tissue on mammogram varies according to its composition . The glaMammographic density has been reported as a risk factor for breast carcinoma . The aneThis study was approved by the hospital technical and ethical committee. Patient informed consent was not obtained as this was a retrospective review.This was a descriptive cross-sectional study of 1,784 women who had mammography at our hospital over a one-year period from September 2004 to August 2005. Those who had breast cancer and those on hormone replacement therapy were also included. Women whose mammograms were not available for review were excluded.Two-view  film-screen mammography examinations were performed using the Siemens Mammomat 3000 (Germany).The method of classification based on anatomic-mammographic correlation developed by Tabar  was usedTabar Pattern I was found to have a wide spectrum of density and therefore it was further divided into 3 sub-groups . In PattDense breast on mammogram was defined as at least 50% of the breast containing dense tissue. Therefore, Pattern II, III, and IA were classified as not dense. Pattern IB, IC, IV, and V were classified as dense.The cranial-caudal and medial-lateral oblique views were viewed by a radiologist who was blinded to the women’s clinical data.Prevalence odds ratios (OR) were used to express the degree of association between mammographic patterns and the three selected factors, i.e. age, parity and race. A binary logistic regression model was used to allow for the effects of several potential confounders. Results were considered as statistically significant if the p-value was 0.05 or less and 95% confidence intervals (CI) were reported throughout the paper. The multiple logistic regression analyses were performed using the SPSS package.There were 95 (5%) women below the age of 40, 526 (30%) who were 41–50 years old, 840 (47%) who were 51–60 years old and 323 (18%) women older than 60 years. There were 238 (13%) nulliparous women, 481 (27%) had 1 or 2 children, 775 (44%) had 3 or 4 children, and 290 (16%) had more than 4 children. There were 981 (55%) Malay women, 571 (32%) Chinese, 214 (12%) Indians and 18 (1%) were women of other races.Majority (77%) had Pattern I . PatternPattern I sub-type that predominated in each age cohort reduced in density (from Pattern IC to IA) as the age increased . In the Pattern II that represented complete fatty replacement was seen predominantly in the two cohorts of 51–60 years and above 60 years (39% and 35% respectively). The highest occurrence of Pattern IV was in the cohort of 51–60 years (49%) while the highest occurrence of Pattern V was seen in the cohort of 41–50 years (42%). Breast density reduced with increasing age . The cohThe majority of women who were nulliparous and those with 1 or 2 children had Pattern IB (28%) and IC (26%) . The majThere was an inverse association between parity and breast density. Nulliparous women had the highest odds of having dense breasts, OR 2.18 (p < 0.0001).The majority with Pattern II breasts that had undergone complete fatty replacement were Malay women (61%). The occurrence of Pattern II was similar amongst the Chinese and Indians .In Malays and Indians, non-dense Pattern IA was predominant (30% each) . The majEthnic Chinese had the highest percentage of dense breasts (66%), followed by Malays (57%), and then Indians (51%) . The othMammographic density has been reported as a risk factor for breast carcinoma , 4–8. A It has been found that the prevalence of breast cancer is more than twice as high in women with Pattern IV compared with women with Pattern I-III . The assOur study showed that the majority of women had dense breasts but this was due to Pattern IB and IC, not due to Pattern IV that has been associated with increased risk of breast cancer –11. The The incidence of dense breasts in our population is important to note, even though it is due to Pattern IB and IC. This is because dense breasts raise the possibility of missing early lesions that are small. Therefore, radiologists involved with breast imaging should remember to use ultrasound as an adjunct to mammography of dense breasts.In post-menopausal breast involution, the lobules reduce in size, cells lining the acinar diminish, ductal epithelium atrophies and there is regression of adjacent connective tissue. These fibroglandular tissues are gradually replaced by fat . InvolutScreening mammograms taken at regular intervals have documented the transition from predominantly fibroglandular tissue (Pattern I) to predominantly fat tissue (Pattern II and III) .Our study showed a similar decline in radiographic density with increasing age as reported in previous studies. This decline in density was best seen in Pattern I where the transition of pattern predominance from IC to IA was seen with progressively increasing age.The majority of women with Pattern IV in our study were in the cohort of 51–60 years. It is interesting to note that it is this age group that leads the breast cancer incidence in our country .It has been noted that Pattern IV appears resistant to the process of involution , 9. The During pregnancy the lobules increase in size and there is proliferation of lobular acini . When laOur study showed a similar decline in radiographic density with increasing parity as previously reported. This decline in density was best seen in Pattern I where there was a predominance of Pattern IB and IC in the nulliparous and low parity groups to predominance of Pattern IA and IB in those with 3 and more children.The finding that the ethnic Chinese had the highest odds of having dense breasts was not statistically significant. However, it is interesting to note that breast cancer incidence is highest amongst the ethnic Chinese in Malaysia . The ASREthnic Indians, who had the lowest percentage of dense breasts, came second after the Chinese in the incidence of breast cancer. Pattern IV was highest amongst Malay women who had the lowest incidence of breast cancer. These findings indicate that in our study population, breast parenchymal pattern and density were not good indicators of breast cancer risk.This study used a combination of qualitative and quantitative assessment. The mammograms were initially qualitatively classified according to Tabar patterns. Pattern IB, IC, IV, and V were then classified as dense because at least 50% of the breast contained dense tissue. This method of subjective assessment has several limitations, the most important being the low reproducibility. Two readers would have reduced subjectivity. However, at the time the study was conducted, there was only one trained reader available. There have been reports that quantitative assessment is superior to qualitative. However, a report comparing two quantitative (Cumulus and Madena software) and two qualitative (Wolfe and Tabar) methods showed the same overall associations between breast density and the risk factors for breast cancer .The hospital at which the study was conducted is not a breast screening centre and women on hormone replacement therapy (HRT) had been included in this study. The use of HRT may influence breast density. However, an earlier study at the same centre showed that HRT did not cause any significant increase in breast density . The incOther potential factors that could affect mammographic patterns were not documented in this study. For example, age at menarche, age at first childbirth, body mass index (BMI) and height could all play a role in determining parenchymal patterns and breast density.Finally, the women involved in this study were not truly representative of Malaysian women as the study population was based in an urban setting. Dietary habits, different lifestyles and levels of education could all contribute to differences in mammographic densities.The majority of women in this study population (59%) had dense breasts . Pattern IV, which has been associated with increased risk of breast cancer, was seen in only 5% of the women. Breast density reduced steadily with increasing age and parity. There was no statistically significant difference in breast density among the 3 major races


Hereditary breast and ovarian cancer syndromes are rare genetic disorders conferring a significant lifetime risk of developing breast and/or ovarian cancer. Women with a strong family history of breast and ovarian cancer face decisions regarding genetic testing, cancer surveillance and risk reducing surgery.This paper draws on interview data from a group of thirty-two Pakeha New Zealand women living with an increased risk of breast and ovarian cancer.“Getting on with it” emerged as a dominant theme, as the way in which most of these women approached their risk. “Getting on with it” appears to be a deeply entrenched social, cultural and gendered expectation in New Zealand, perhaps influenced by our history as a settler society and the more recent influences of neo-liberal governance. This approach to risk is influential in guiding the management decisions that these women are making.Mothering is central to the identity of many of the participants in this study. They mother their children within the context of the increased cancer risk. These women identify a strongly felt responsibility to be there to care for their children. Alongside a strongly voiced desire to “get on with it”, they use their role as mothers to motivate their decisions regarding risk reducing surgery. I argue that choosing to undergo the removal of healthy body parts in order to reduce risk and remain alive to fulfil role expectations provides a symbolic and gendered representation of women as carers and nurturers.