Ruth Pfeiffer and colleagues describe models to calculate absolute risks for breast, endometrial, and ovarian cancers for white, non-Hispanic women over 50 years old using easily obtainable risk factors.Please see later in the article for the Editors' Summary Breast, endometrial, and ovarian cancers share some hormonal and epidemiologic risk factors. While several models predict absolute risk of breast cancer, there are few models for ovarian cancer in the general population, and none for endometrial cancer.Using data on white, non-Hispanic women aged 50+ y from two large population-based cohorts , we estimated relative and attributable risks and combined them with age-specific US-population incidence and competing mortality rates. All models included parity. The breast cancer model additionally included estrogen and progestin menopausal hormone therapy (MHT) use, other MHT use, age at first live birth, menopausal status, age at menopause, family history of breast or ovarian cancer, benign breast disease/biopsies, alcohol consumption, and body mass index (BMI); the endometrial model included menopausal status, age at menopause, BMI, smoking, oral contraceptive use, MHT use, and an interaction term between BMI and MHT use; the ovarian model included oral contraceptive use, MHT use, and family history or breast or ovarian cancer. In independent validation data  the breast and ovarian cancer models were well calibrated; expected to observed cancer ratios were 1.00  for breast cancer and 1.08 (95% CI: 0.97–1.19) for ovarian cancer. The number of endometrial cancers was significantly overestimated, expected/observed = 1.20 (95% CI: 1.11–1.29). The areas under the receiver operating characteristic curves  were 0.58 (95% CI: 0.57–0.59), 0.59 (95% CI: 0.56–0.63), and 0.68 (95% CI: 0.66–0.70) for the breast, ovarian, and endometrial models, respectively.These models predict absolute risks for breast, endometrial, and ovarian cancers from easily obtainable risk factors and may assist in clinical decision-making. Limitations are the modest discriminatory ability of the breast and ovarian models and that these models may not generalize to women of other races.Please see later in the article for the Editors' Summary In 2008, just three types of cancer accounted for 10% of global cancer-related deaths. That year, about 460,000 women died from breast cancer (the most frequently diagnosed cancer among women and the fifth most common cause of cancer-related death). Another 140,000 women died from ovarian cancer, and 74,000 died from endometrial (womb) cancer . Although these three cancers originate in different tissues, they nevertheless share many risk factors. For example, current age, age at menarche (first period), and parity (the number of children a woman has had) are all strongly associated with breast, ovarian, and endometrial cancer risk. Because these cancers share many hormonal and epidemiological risk factors, a woman with a high breast cancer risk is also likely to have an above-average risk of developing ovarian or endometrial cancer.Several statistical models  have been developed that estimate a woman's absolute risk (probability) of developing breast cancer over the next few years or over her lifetime. Absolute risk prediction models are useful in the design of cancer prevention trials and can also help women make informed decisions about cancer prevention and treatment options. For example, a woman at high risk of breast cancer might decide to take tamoxifen for breast cancer prevention, but ideally she needs to know her absolute endometrial cancer risk before doing so because tamoxifen increases the risk of this cancer. Similarly, knowledge of her ovarian cancer risk might influence a woman's decision regarding prophylactic removal of her ovaries to reduce her breast cancer risk. There are few absolute risk prediction models for ovarian cancer, and none for endometrial cancer, so here the researchers develop models to predict the risk of these cancers and of breast cancer.Absolute risk prediction models are constructed by combining estimates for risk factors from cohorts with population-based incidence rates from cancer registries. Models are validated in an independent cohort by testing their ability to identify people with the disease in an independent cohort and their ability to predict the observed numbers of incident cases. The researchers used data on white, non-Hispanic women aged 50 years or older that were collected during two large prospective US cohort studies of cancer screening and of diet and health, and US cancer incidence and mortality rates provided by the Surveillance, Epidemiology, and End Results Program to build their models. The models all included parity as a risk factor, as well as other factors. The model for endometrial cancer, for example, also included menopausal status, age at menopause, body mass index (an indicator of the amount of body fat), oral contraceptive use, menopausal hormone therapy use, and an interaction term between menopausal hormone therapy use and body mass index. Individual women's risk for endometrial cancer calculated using this model ranged from 1.22% to 17.8% over the next 20 years depending on their exposure to various risk factors. Validation of the models using data from the US Nurses' Health Study indicated that the endometrial cancer model overestimated the risk of endometrial cancer but that the breast and ovarian cancer models were well calibrated—the predicted and observed risks for these cancers in the validation cohort agreed closely. Finally, the discriminatory power of the models (a measure of how well a model separates people who have a disease from people who do not have the disease) was modest for the breast and ovarian cancer models but somewhat better for the endometrial cancer model.These findings show that breast, ovarian, and endometrial cancer can all be predicted using information on known risk factors for these cancers that is easily obtainable. Because these models were constructed and validated using data from white, non-Hispanic women aged 50 years or older, they may not accurately predict absolute risk for these cancers for women of other races or ethnicities. Moreover, the modest discriminatory power of the breast and ovarian cancer models means they cannot be used to decide which women should be routinely screened for these cancers. Importantly, however, these well-calibrated models should provide realistic information about an individual's risk of developing breast, ovarian, or endometrial cancer that can be used in clinical decision-making and that may assist in the identification of potential participants for research studies.http://dx.doi.org/10.1371/journal.pmed.1001492.Please access these websites via the online version of this summary at PLOS MedicinePerspective by Lars Holmberg and Andrew VickersThis study is further discussed in a US National Cancer Institute provides comprehensive information about cancer (in English and Spanish), including detailed information about breast cancer, ovarian cancer, and endometrial cancer;The Breast Cancer Risk Assessment Tool, the Surveillance, Epidemiology, and End Results Program, and on the prospective cohort study of screening and the diet and health study that provided the data used to build the models is also available on the NCI siteInformation on the Cancer Research UK, a not-for-profit organization, provides information about cancer, including detailed information on breast cancer, ovarian cancer, and endometrial cancerbreast cancer, ovarian cancer, and endometrial cancer; the not-for-profit organization Healthtalkonline also provides personal stories about dealing with breast cancer and ovarian cancerThe UK National Health Service Choices website has information and personal stories about  Several large chemoprevention studies of tamoxifen  and raloxifene  used BCRAT projected breast cancer risk to determine eligibility and to estimate needed sample sizes http://seer.cancer.gov/about/overview.html).We developed absolute risk prediction models for breast, endometrial, and ovarian cancer by combining data from two large prospective cohorts at the National Cancer Institute (NCI)—the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO) and the National Institutes of Health–AARP Diet and Health Study (NIH-AARP)—and from incidence and competing mortality rates in the NCI's Surveillance, Epidemiology, and End Results Program (SEER). The SEER cancer registries cover approximately 28% of the US population .We used data on white, non-Hispanic women from two NCI cohorts: the PLCO cohort and the NIH-AARP cohort.PLCO has been described in detail previously At entry, participants completed a self-administered lifestyle questionnaire. Cancers were ascertained via annual study updates and death certificates, and verified via review of medical records.NIH-AARP has been described previously Cancer cases were identified through linkage with state cancer registries with a 90% completeness rate In each study, we restricted the analysis to non-Hispanic, white women who completed the baseline questionnaire, had follow-up information, and had no personal history of the cancer of interest at baseline. For the ovarian and endometrial cancer models, we further excluded women with bilateral oophorectomy or hysterectomy, respectively , with age as the timescale. For each of the cancers, the primary outcome was incident, invasive epithelial cancer. For each outcome we first estimated RRs and 95% confidence intervals (CIs) separately for each cohort. However, we used RRs from a Cox model for the combined cohorts that included a study indicator as covariate (NIH-AARP versus PLCO). Analyses of the combined data that stratified the baseline hazard function in the Cox model on study yielded results in agreement to the third decimal point with this regression method. Proportionality of the hazard functions was assessed by visual inspection of hazard plots and Schoenfeld residuals.International Classification of Diseases for Oncology, Third EditionFor each RR model, women were considered at risk from the age at study entry (randomization for PLCO and completion of baseline questionnaire for NIH-AARP) until the age at the earliest of the following: (1) diagnosis of cancer of interest, (2) death, or (3) administrative censoring . In PLCO, women were also censored at date of unconfirmed self-reported cancers and non-epithelial cancers (the 2), age at menarche (see comment on coding below), number of live-born children , age at first birth , duration of oral contraceptive (OC) use , menopausal status and age at natural menopause , status and duration of menopausal hormone therapy (MHT) use , status and duration of estrogen and progestin MHT use , duration of unopposed estrogen MHT use , history of benign breast disease (yes/no) or breast biopsy , first-degree family history of breast cancer , first-degree family history of ovarian cancer , previous gynecologic surgery , history of endometriosis (yes/no), history of uterine fibroids (yes/no), use of tobacco  and alcohol consumption (drinks/day), and serum CA 125 level at baseline (PLCO only). For PLCO, we tested potential interactions with randomization arm and, for ovarian cancer, method of detection (screen-detected versus not screen-detected). In most instances, the PLCO and NIH-AARP questionnaires allowed for identically coded variables. To synchronize age at menarche , we randomly allocated women in the overlapping PLCO categories  to the NIH-AARP categories (≤12 y or 13–14 y) using the age at menarche distribution from the nationally representative NHANES study We considered the following risk factors (coding in parentheses): body mass index  computations. To accommodate missing data, we calculated AR for the breast cancer model in women with complete data. Using time on study as the timescale, adjustment for calendar period or additional censoring at age of diagnosis of any other cancer  or adjustment for gynecologic surgery did not change the results appreciably.Alcohol consumption was not ascertained in the PLCO control arm, and thus RRs for association with alcohol consumption were estimated from the PLCO intervention arm and NIH-AARP. RRs for duration of estrogen and progestin MHT use and duration of unopposed estrogen MHT use were estimated from women in the NIH-AARP cohort who had responded to the second questionnaire. Information for benign breast disease was missing on 20% of the women in the dataset, and we thus created an indicator for missing values. For all other variables, women with missing values were excluded as the number missing was very small  of cancer c  in the age interval  = λ10(a) rr1 as the product of the age-specific baseline hazard rate λ1(a) and the RR model, rr1 = exp(β'x), where x denotes covariates. We did not include covariates in the hazard λ2 for competing causes of death. The age-specific baseline hazard rates λ10(a) are computed by multiplying the age-specific SEER incidence rates, λ1*(a), by one minus the estimate of the AR, i.e., λ10(a) = λ1*(a)(1−AR1(a)), as outlined in c was obtained as one minus the number of women in the dataset divided by the sum of the multivariate RR estimates for cancer c over all women. We used a model with piecewise constant hazards to approximate We modeled λIn SEER, endometrial and ovarian cancer incidence rates are calculated based on all women in the population in a given age group. These rates thus are lower than rates that are based on women with an intact uterus or ovaries, respectively. We therefore adjusted the age-specific SEER rates by dividing them by the percentage of women who had not had a hysterectomy  survey E/O) ratios were calculated using the normal approximation to the Poisson distribution: E/O ratio above one indicates that the model overestimates cancer risk, and an E/O less than one indicates that the model underestimates cancer risk. We evaluated the discriminatory accuracy of the prediction models using the area under the receiver operating characteristic curve (AUC), with 95% bootstrap CIs.We compared the expected and the observed numbers of cases overall and in subgroups defined by risk factor combinations. The expected number of cases was calculated by summing the individual projected probabilities, given the baseline covariate values for each woman from entry (July 1990) into the NHS cohort to June 2004. The 95% CIs for the expected/observed (The characteristics of the women in the study and cancer incidence rates are shown in 2), estrogen and progestin MHT use , other MHT use , parity , age at first birth , premenopausal , age at menopause , benign breast diseases , family history of breast or ovarian cancer , and alcohol consumption . The largest RRs per category increase in the model , MHT use , parity , premenopausal , age at menopause , smoking , OC use , and an interaction term between MHT use and an indicator variable that was one for BMI<25 kg/m2 and zero otherwise. The largest RRs in the model were obtained for BMI, fitted with a trend RR = 1.72 per category increase (95% CI: 1.65–1.80); being a never smoker, with RR = 1.47 (95% CI: 1.22–1.78) compared to a current smoker; and the interaction term between MHT use and the BMI<25 kg/m2 indicator, RR = 1.61 (95% CI: 1.43–1.81)  .Among the 199,973 women included in the ovarian cancer analysis, 844 were diagnosed with ovarian carcinoma. The final RR model included family history of breast or ovarian cancer , duration of MHT use , parity , and OC use  . The larThe AR estimates used to compute baseline hazard rates for the models were 0.52 for breast cancer, 0.81 for endometrial cancer, and 0.43 for ovarian cancer.2 BMI category who had never smoked. For specific risk factor combinations, endometrial absolute risk can be higher than breast cancer absolute risk. For example, profile 8   in the NHS cohort was 0.58 (95% CI: 0.57–0.59). We also compared the performance of the new breast cancer model to that of NCI's BCRAT E/O = 1.00 (95% CI: 0.97–1.04). The number of cases was overpredicted significantly in cells defined by family history using either the coding for the new model or BCRAT (E/O = 1.30 (95% CI: 1.19–1.42), and for women with one and two relatives with breast cancer, E/O = 1.30 (95% CI: 1.18–1.43) and E/O = 1.78 (95% CI: 1.25–2.55), respectively. For the new breast cancer model, the predicted number of cases did not significantly differ from the observed, with E/O = 0.98 (95% CI: 0.89–1.08) for women with one first-degree relative with breast cancer and E/O = 0.72 (95% CI: 0.50–1.02) for women with two or more relatives with breast cancer. The overall AUC for BCRAT in the NHS validation data, 0.56 (95% CI: 0.55–0.58), was significantly lower than the AUC for the new breast cancer model .A total of 2,934 incident breast cancers were diagnosed among women included in the analyses, and the model predicted 2,930, resulting in an 96–1.04) . The modor BCRAT . For womE/O = 1.20 (95% CI: 1.11–1.30). The model significantly overestimated the observed NHS endometrial cancers in most subgroups . The number of ovarian cancers was overestimated for all covariate categories, albeit not statistically significantly, with the exception of the nulliparous group and women taking MHT for 10 or more years. The AUC was 0.59 (95% CI: 0.56–0.63).A total of 377 incident ovarian cancers were diagnosed among women included in the analyses, and the model predicted 406 , resultiCurrent American Society of Clinical Oncology guidelines indicate that premenopausal women and postmenopausal women with low risk of side effects and a 5-y projected BCRAT risk ≥1.66% may benefit from tamoxifen and/or raloxifene for breast cancer prevention We developed models that predict individualized probabilities of developing breast, endometrial, and ovarian cancers among US white women aged 50+ y. We chose these three cancers because they share several risk factors, presumably reflecting a common hormonal etiology, and because management decisions may depend jointly on these risks.2 or higher To our knowledge, there is no other absolute risk model for endometrial cancer, despite the fact that it is the fourth most common cancer in women Unlike most other models for breast cancer, our model includes factors that are potentially modifiable for the individual or in populations over time, such as alcohol consumption, BMI, and use of MHT. This model had slightly better discriminatory accuracy (AUC = 0.58) in the validation data than the widely used BCRAT (“Gail model”) (AUC = 0.56), which predicts breast cancer risk based on reproductive factors, number of breast biopsies, and atypical hyperplasia. However, despite this increase in AUC, the discriminatory accuracy of the breast cancer absolute risk model is still modest, and limits its clinical applicability, particularly for screening. Several breast cancer risk prediction models include non-modifiable risk factors such as family history e.g., , mammogrFor ovarian cancer, a model based on the NHS includes age at menopause, age at menarche, OC use, and tubal ligation To build the models, we combined data from two large prospective studies from well-characterized populations. We used another large cohort, the NHS cohort, to independently validate our models. The RR estimates agreed well with those in the NHS cohort for all three models. The discriminatory power, as assessed by the AUC, was 0.58 and 0.59 for the breast and ovarian cancer models, respectively. While these values indicate modest discriminatory ability, they are similar to those reported for other cancer risk models for these cancers The breast and ovarian model were well calibrated in the NHS cohort; however, because women in the NHS cohort were censored at the age of diagnosis of an in situ breast cancer, the breast model may have underestimated slightly. The endometrial model significantly over-predicted the number of endometrial cancers. This reflects the fact that the NHS cohort has considerably lower endometrial cancer rates  than thoWell-calibrated risk models, even those with modest discriminatory accuracy, have several public health applications. These include designing cancer prevention trials, assessing the absolute burden of disease in the population and in subgroups, and gauging the potential absolute reductions in risk from preventive strategies. Using risk models to select individuals for screening or other interventions usually requires high discriminatory accuracy There are several potential limitations to our models. The exact number of breast biopsies, an important predictor in BCRAT, was not available in our cohorts, and the models were restricted to women aged 50+ y. Our RR models were built using white, non-Hispanic women and may not generalize to other races or ethnicities. We adjusted the age-specific endometrial and ovarian cancer incidence rates for the prevalence of hysterectomy and oophorectomy among US women estimated from population-based surveys, but some residual error may exist. Another limitation is that MHT use was probably more prevalent during the period of study of PLCO and NIH-AARP, from 1993 to 2005, than it is currently. While such changes in the prevalence of risk factors would not affect model calibration, they could reduce the variation of risk in the population and hence reduce the discriminatory accuracy of the models. Among the strengths of our models are large study sample size, nearly complete end point ascertainment, and information on most of the important risk factors.BRCA1 or BRCA2 or with hereditary non-polyposis colorectal cancer (HNPCC). Each model is applicable to women without a prior diagnosis of that particular cancer, and thus in principle the breast cancer model can be applied to predict breast cancer risk for women with a prior diagnosis of any other cancer, including endometrial cancer. However, in applying the risk estimates, one needs to consider that a woman's risk may be altered by treatment for a previous cancer.Our models are not intended to predict the probability of the three cancers among women known to be at much higher than average risk, e.g., women with a mutation in In conclusion, we developed and assessed models that project the probabilities of developing breast, endometrial, or ovarian cancer among white, non-Hispanic women aged 50+ y. These models might improve the ability to identify potential participants for research studies and assist in clinical decision-making related to the risks of these cancers.Table S1Competing mortality rates per 100,000, 1992–2006, from causes other than breast, endometrial, or ovarian cancers obtained from US mortality data for white women. These data are collected by the National Center for Health Statistics.(DOCX)Click here for additional data file.Table S2Estimates from the Behavioral Risk Factor Surveillance System for percent of women with hysterectomy in SEER 13 areas, white women, 5-y age groups, 1992–2007 (2001 excluded).(DOCX)Click here for additional data file.Table S3Estimates from the NHANES 1999–2000 survey for percent of women with bilateral oophorectomy, white women, 5-y age groups.(DOCX)Click here for additional data file.


The clinical tumor-node-metastasis  classes of breast cancers provide important prognostic information. However, the possible association of TNM classes with reproductive factors has remained largely unexplored. Because every woman has a reproductive history, implications to outcome prediction are potentially significant.During the study period from 2002 through 2008, 5,614 pre- and 27,310 postmenopausal patients were identified in the Swedish Family-Cancer Database. Ordinal logistic regression analysis was used to estimate odds ratios (ORs) for TNM classes of breast cancers by histology. The reproductive variables were parity, age at first and last childbirth and time interval between first and last childbirth.Among postmenopausal patients, the ORs for high-T class (T2–T4) (tumor size ≥2 cm) and metastasis were decreased by parity. A late age at first and last childbirth associated with high-T class and the effects were higher for lobular (OR for late age at first childbirth  = 2.85) than ductal carcinoma. Overall, long time interval between first and last childbirth was related to high-T class and metastasis. However, a short time interval between first and last childbirth in patients with late age at first or last childbirth increased the risk of metastasis. Late age at last childbirth was associated with increased occurrence of lobular carcinoma in situ. Among premenopausal ductal carcinoma patients, nulliparity and early age at first childbirth were associated with high-T class.Increasing parity was protective against high-T class and metastasis; late ages at first and last childbirth were risk factors for high-T class in postmenopausal breast cancers. The current decline in parity and delayed age at first childbirth in many countries may negatively influence prognosis of breast cancer. Demographic changes over recent decades in developed countries and even among educated women in developing countries have modified reproductive patterns towards delayed childbearing and declining parity In developed countries, breast cancer is mainly a postmenopausal disease. The effects of reproductive factors on histology of breast cancer vary according to the menopausal status Only a few previous studies have examined the effect of reproductive factors on the stage or grade of histology-specific breast cancer We used the 2010 update of the Swedish Family-Cancer Database (FCD), which contains population-based data from the Swedish Cancer Registry, multigenerational registries, national censuses and death notifications Following the start of cancer registration in 1958, data on cancers in the FCD had a 4-digit diagnostic code according to the seventh revision of the International Classification of Disease (ICD). The codes for breast cancer were 170.1 and 170.2. Since 1993, ICD-O-2/ICD with histopathological data according to the Systematized Nomenclature of Medicine (SNOMED) has been used. This coding system gives a detailed tumor histology-topology. We analyzed ductal (SNOMED = 8500 and 8501), lobular (8520), tubular (8211) and mucinous (8480) carcinomas. Nonspecific adenocarcinoma (8140) was not included in our analysis . All breast cancer cases (100%) registered in the Cancer Registry were histologically verified Tumor size and local growth (T), regional lymph node involvement (N) and the presence of metastasis (M) according to the TNM classification system published by the American Joint Committee on Cancer have been available in the FCD since 2002 Parity , age at first and last childbirth , and time interval between first and last childbirth  were obtained from the FCD. The data for ages at menarche and menopause were not available in the dataset. It also contains longitudinal demographic and socio-economic data from the national censuses of 1960, 1970, 1980, and 1990 Ordinal logistic regression was used to calculate odds ratios (ORs) . The parity (number of children = 1), the age at first and last childbirth (20–29 years), and the time interval between first and last childbirth (1–4 years) were selected as references. The ORs were adjusted for age at diagnosis, region, occupation and, in some analyses, parity. To assess the effect of the median age at menopause, the analyses were stratified by age (≤50 and >50 years) Our study population comprised 23,045 ductal, 4,611 lobular, 1,047 tubular and 728 mucinous carcinoma patients. The analyses of the last two histologies are not presented in this report because of the small numbers of cases in the TNM classes for menopausal status and any reproductive factors. As there are correlations between the variables age at first and last childbirth and time interval between first and last childbirth, we stratified age at first and last childbirth by time interval between first and last childbirth. Alternatively, we examined the effect of parity and the time interval between first and last childbirth by adding the age at first or last childbirth to the adjustment; however, because of the small numbers of premenopausal breast cancer cases in any subclasses, this analysis was only applied to postmenopausal cases.The Lund regional Ethics Committee approved the study protocol. The corresponding author had full access to the FCD and had final responsibility for the decision to submit for publication.We identified 27,310 postmenopausal and 5,614 premenopausal breast cancer patients in the database. The histological type of breast cancer among postmenopausal patients was ductal in 68.5% of patients and lobular in 14.8% of patients, while premenopausal carcinomas were 77.1% ductal and 10.1% lobular.Our data included 10,794 T1, 13,767 N0 and 15,186 M0 cases of postmenopausal breast cancer . Parity The analyses shown in Data for postmenopausal ductal cancer are shown in The database included 4,328 cases of premenopausal ductal carcinoma, accounting for 77% of premenopausal cancers . The maiIn this large nation-wide follow-up study of 32,924 Swedish women with breast cancer, we found for postmenopausal cancer that multiparity was associated with a decreased risk of high-T class and distant metastases, while a late age at first or last childbirth increased the risk of high-T class. There was a general correlation between high-T class and distant metastases but not with lymph node metastases, which appeared not to be affected by reproductive factors. Risk of distant metastases was increased particularly in patients with a late age at first or last childbirth when the time interval between first and last childbirth was short, which is a typical reproductive pattern of educated women Our study used information on breast cancer patients diagnosed in the period 2002 to 2008, during which the histological classification system did not vary. Thanks to a complete cancer registration with verified histology, our study should be free of selection bias Reproductive factors induce physiological changes in the mammary gland such as rapid proliferation of breast tissue A study on 10,703 Danish women with breast cancer reported that early age at first childbirth was associated with a poor breast cancer prognosis The association of age at last childbirth and breast cancer risk or prognosis is unclear A population-based study in Finland showed that short time interval between first and last childbirth is a protective factor for lobular carcinoma, but not for ducal carcinoma In summary, increasing parity was protective against high-T class and metastasis. Late age at first and last childbirth were risk factors for high-T class in almost all postmenopausal breast cancers and the effects were stronger for lobular than ductal histology. Low parity and long time interval between first and last childbirth were risk factors for distant metastases. The observed variation in the associations of reproductive factors with TNM classes suggests that hormonal and other physiological changes during pregnancy and menopause play an important role in determining T class and metastatic spread, with implications to prognosis. The current decline in parity and delayed age at first childbirth in many global populations may counterbalance the favorable achievements of prevention, screening and treatment of breast cancer.


Hormonal therapy (HT) either estrogen alone  or estrogen plus progesterone (E+P) appears to increase the risk for breast cancer in Western countries. However, limited information is available on the association between HT and breast cancer in Asian women characterized mainly by dietary phytoestrogens intake and low prevalence of contraceptive pills prescription.A total of 65,723 women (20–79 years of age) without cancer or the use of Chinese herbal products were recruited from a nation-wide one-million representative sample of the National Health Insurance of Taiwan and followed from 1997 to 2008. Seven hundred and eighty incidents of invasive breast cancer were diagnosed. Using a reference group that comprised 40,052 women who had never received a hormone prescription, Cox proportional hazard models were constructed to determine the hazard ratios for receiving different types of HT and the occurrence of breast cancer.5,156 (20%) women ever used E+P, 2,798 (10.8%) ever used E-alone, and 17,717 (69%) ever used other preparation types. The Cox model revealed adjusted hazard ratios (HRs) of 2.05 (95% CI 1.37–3.07) for current users of E-alone and 8.65 (95% CI 5.45–13.70) for current users of E+P. Using women who had ceased to take hormonal medication for 6 years or more as the reference group, the adjusted HRs were significantly elevated and greater than current users and women who had discontinued hormonal medication for less than 6 years. Current users of either E-alone or E+P have an increased risk for invasive breast cancer in Taiwan, and precautions should be taken when such agents are prescribed. Many studies have shown that hormonal therapy (HT) with estrogen plus progesterone (E+P) increases the risk for breast cancer The database contained a total of 2,461 prevalent cases of invasive breast cancer that were diagnosed between 1997 and 2008. Within this population, we identified 780 patients who were newly diagnosed with invasive breast cancer during the ten-year study period (1999–2008) and were between the ages of 20 and 79 years. In comparison to women aged 30 to 39 years, the hazard ratios (HRs) for invasive breast cancer among never users of HT were 1.88 (95% CI 1.49–2.37) for women aged 40 to 49 years and 1.73 (95% CI 1.32–2.26) for women aged 50 to 59 years. The HR calculated from the Cox regression model for ever users of HT was significantly higher than that determined for never users. P<.001) linear dose–response relationship was observed between the risk for the development of invasive breast cancer and the prescribed dose of E-alone, mixed regimen and E+P HT. The comparison of current HT users to women who had ceased to use HT at least five years prior revealed that an increase of every 30 defined daily dose of E-alone, mixed regimen or E+P HT was significantly associated with an increased hazard ratio for invasive breast cancer among current HT users.When the reference group comprised women who ceased the intake of hormonal medication for 6 years or more, the adjusted HRs were still significantly elevated and demonstrated greater magnitudes than current users and women who ceased the intake of hormonal medication for less than 6 years, and the effect of age was maintained . In partTo our knowledge, this is the first study to employ a nation-wide representative cohort to examine the increased risk for invasive breast cancer among women in Taiwan who are undergoing treatment with HT. Because this issue has been heavily debated internationally, we must be cautious about potential confounding factors prior to generating any inferences. However, the following arguments provide a warning to individuals concerning the possible risks of HT. First, because the NHIRD collects all prescription information prospectively, we can rule out the possibility of a recall bias concerning intake dosages and different types of prescriptions: E-alone, E+P HT, progesterone only, or a mixed regimen. Second, in the present study, we included all of the patients who were newly diagnosed with invasive breast cancer between 1999 and 2008 from a simple random sample of one million subjects among the insured general population. Because the rate of insured individuals has been consistently above 96% since 1997, we can rule out the possibility of a selection bias. In fact, our current estimate of 63.7 new breast cancer cases per million person-years is very close to the 66.2 cases per million person-years that was calculated from the National Cancer Registry of Taiwan in 2005. Third, to minimise potential confounding factors according to the indication, we limited the analysis to postmenopausal women (55–79 years old of age), and an association between HT and invasive breast cancer risk was still observed, as summarised in The present findings shown in Breast cancer incidence was notably high in Western countries and the rates of all age groups exceeded those from Taiwan, Japan, and Hong Kong . AlthougIn this study and others The present study has some limitations. First, because the identities of the patients were not available in the NHI reimbursement database, we were unable to obtain any histopathology reports to verify the diagnoses. However, because approval for the registration of invasive breast cancer as a catastrophic illness is based on pathology and/or cytologic evidence and is followed by a full waiver of copayment, such a diagnosis is made only after very serious considerations and is generally accurate. The diagnostic accuracy of invasive breast cancer among the NHI data is corroborated by the considerable agreement between the incidence rate calculated herein and that determined by the National Cancer Registry of Taiwan, in which 95% of the breast cancers are accompanied by histopathologic validation. Second, we were unable to contact the patients directly regarding their use of HRT due to the encryption of their identification numbers; therefore, we cannot exclude the possibility that the subjects used additional phytoestrogenic herbs that were not prescribed. However, because the NHI system provides a comprehensive coverage and because the copayment for prescriptions is consistently 50 NT$ (New Taiwan Dollar) , which is generally less than the cost of herbs that are sold in the markets of Taiwan, the likelihood that the subjects purchased a large amount of other phytoestrogen-containing herbs outside of the NHI database is low. Furthermore, because we had previously limited the cohort to women who did not receive prescription Chinese herbal products during the 12 years of observation, the likelihood that the subjects purchased herbs outside of the services of the NHI becomes even lower. Furthermore, when we limited our analysis to postmenopausal women (55 to 79 years of age) who did not require post-coitus emergency contraceptives or birth control pills, the results showed the same trend as that observed among women aged between 20 to 79 years. However, we are unable to rule out the possibility that the occurrence of breast cancer in postmenopausal women was attributed by the use of a contraceptive pill early in life. Third, we were unable to validate the actual ingested dose of the prescribed HT recorded in the database. A large mean cumulative dose indicates that the patient continued to receive the same prescription for a long period and implies that the patient actually consumed the prescribed medication. Even if the patient did not consume all of the prescribed HT pills, the present findings would only underestimate the effect of the consumed HT. Fourth, because the reimbursement data did not include the selection patterns of phytoestrogen-rich foods, the relative weight and reproductive history of the women, we were unable to control for this factor in the model construction. Because the present study included females from a random sampling cohort, we assumed that such confounding factors would not bias the results.In summary, the present study found that current users of carefully prescribed E-alone or E+P in Taiwan have an increased risk for invasive breast cancer, especially elderly women. The overall risk seems to be less favourable for E+P in comparison to E-alone. Thus, we recommend that precautionary actions be taken toward the prescription of HT for any duration among postmenopausal women in Taiwan, and the current findings should be incorporated into the established guidelines and emerging risks and benefits of these agents.This study was initiated after approval by the review board of the Committee on Chinese Medicine and Pharmacy, Department of Health, Taiwan. All data were obtained from the NHI reimbursement database. The National Health Research Institutes of Taiwan anonymized and transformed the NHI reimbursement data as files suitable for research. Since the identification numbers and personal information of all individuals were not included in the above secondary files to protect the privacy of the individuals, the review board approved that written consents from patients were not required.International Classification of Diseases, Ninth Revision (ICD-9 ) In March of 1995, Taiwan established the National Health Insurance (NHI) programme, which routinely reimburses more than 96% of Taiwanese residents n = 495,836) or those with missing information concerning gender (n = 3). Age was calculated by subtracting the subject's birthday from the 1st day of July for each year. Second, subjects under 20  or over 79 years of age  were excluded to limit the study sample to the main consumers of hormones in Taiwan. All of the patients with invasive breast cancer were included in the study, and the diagnosis was verified by the NHI catastrophic illnesses registry during 1998-2008. Because all patients who are confirmed to have a catastrophic illness are exempt from all copayments, the presence of invasive breast cancer must be validated by tissue pathology to qualify for the registry, and the case is further classified into one of the following categories: the nipple and areola (ICD-9 codes 1740); central portion; upper-inner quadrant; lower-inner quadrant; upper-outer quadrant; lower-outer quadrant; axillary tail; unspecified ; other specified sites of the female breast (ICD-9 code 1748). We excluded two years, 1997 and 1998, to avoid the inclusion of 1926 prevalent cases. To control potential confounding factors, we further excluded 850 subjects who had ever used tamoxifen prior to any diagnosis of breast cancer. Finally, 305,633 subjects qualified for inclusion in the cohort. 10,309 Chinese herbal products (CHPs) were licensed by Taiwan government and covered by the NHI. Several popular Chinese herbal products (CHP) contain small amounts of estrogen-like substances The selection of study subjects from the random sample of one million individuals was performed as follows . First, A total of 25,671 women were prescribed at least one type of HT without an additional intake of any Chinese herbs during the study period from January 1, 1997 to December 31, 2008. All of the prescribed medications were covered under the NHI of Taiwan, and no drugs were dispensed at a pharmacy without a physician's prescription. The major indications for E-alone and E+P in women were contraception, dysmenorrhoea, hypogonadism (estrogen-deficient patients), menopausal hot flashes, and vaginal atrophy. The reimbursement database contained all the details regarding the prescribed conventional medicines, which included all of the types of HT and the commercial names of 14 types of estrogen-containing drugs and ten types of progestagen-containing drugs. The variables for HT usage that were included in the analyses were defined according to the specific proprietary preparation of HT used by the subjects during the 12-year study period. We categorised the types of preparations used as follows: E-alone; E+P; other preparations, including progesterone only and vaginal and other local treatments; combinations of the above preparation types.6 person-years at risk. Using 40,052 non-hormone users as the reference group, we constructed multivariate Cox proportional hazard models to estimate the hazard ratios and their 95% confidence intervals (CI) for new occurrences of invasive breast cancer after adjusting for age. To minimise the potential confounding by indications for HT, we conducted another Cox regression using women who had ceased prescription hormone intake for 6 years or more as the reference group. An estimate with a 95% CI that did not contain the number 1 was considered statistically significant. All the above analyses were conducted using the SAS ver. 9.2 software package .The incidence rate was summarised as the number of new invasive breast cancer patients/10Table S1a and 95% CI in Taiwanese and the United States.Prevalence of HRT use and age-adjusted breast cancer incidence rates(DOC)Click here for additional data file.Table S2a in Taiwan, Japan, Hong Kong and American white women.Age-specific breast cancer incidence rates(DOC)Click here for additional data file.


Purpose. To investigate symptom distress, quality of life, affective states, and inflammatory biomarkers before and after breast biopsy in women undergoing breast biopsy. Methods. A convenience sample of 47 women undergoing breast biopsy was assessed at the pre- and post-biopsy visits. The assessments included evaluation of fatigue, anxiety, depression, sleep disturbances, positive and negative affect, quality of life using validated self report measures, and a blood draw to determine markers of inflammation. Results. At the postbiopsy visit, a total of 15 participants were diagnosed with breast cancer, and 32 participants received negative biopsy result. The mean anxiety and sleep disturbances scores were in the clinically significant range for the total sample and for the biopsy positive (BC+) and biopsy negative (BC−) subgroups at both time points. For both subgroups, anxiety and sleep disturbances scores did not change significantly from pre- to post-biopsy. A subpopulation of participants in both groups reported moderate-to-severe anxiety, depression and fatigue levels at both time points. The inflammatory markers did not show consistent associations with psychosocial symptoms. Conclusions. A subset of participants in BC+ and BC− subgroups experience heightened symptom distress and negative impact on quality of life at both pre- and post-biopsy time points.  Widespread use of screening, along with treatment advances, has been credited with significantly reducing breast cancer mortality . HoweverSeveral studies have documented psychological and symptom distress associated with the screening process and with false positive or inconclusive results –9. For wEvidence suggests that among women undergoing breast cancer treatment, psychological distress is positively associated with adverse physical symptoms such as pain, fatigue, and nausea –17. StudInvestigation of psychological distress early on during the diagnostic process might help identify women at risk for chronic psychosocial issues later on during their cancer treatments. This in turn might help us identify specific risk/protective factors for such women and help devise focused interventions. A prior study has documented significant psychological distress, high rates of psychiatric syndromes, and impaired functioning in women at the time of prebiopsy surgical consultation , and othIn the present study, we investigated symptom distress, quality of life and affective states at two time points during the biopsy process, at the time of prebiopsy surgical consult and after biopsy, after participants have received either a positive or negative diagnosis. We hypothesized that individual distress symptoms  would be positively related to each other and would have a negative impact on the quality of life and affective state of the participants. We also hypothesized that distress would be more greatly reduced at the postbiopsy time point in women receiving a negative diagnosis compared to women receiving a positive diagnosis. In exploratory analyses, specific markers of inflammation were examined to evaluate their association with distress symptoms under investigation.The study protocol and procedures were approved by the University of Connecticut Health Center (UCHC) Institutional Review Board. Potential participants were recruited from the Breast Clinic at The Carole and Ray Neag Comprehensive Cancer Center at the UCHC. Participants included a convenience sample accrued from a group of consecutive patients referred to the surgeons at the cancer center due an abnormal breast imaging study or palpable breast abnormality. Participants were approached and invited for study participation by the study staff during their initial surgical consult if biopsy was planned. Written consents were obtained prior to any study procedures. Study eligibility criteria were as follows: women 18 years of age or older, without a prior history of breast cancer, with an abnormal breast imaging study or a palpable breast mass in whom biopsy was recommended. Patients with history of nonbreast malignancies were eligible if they have been disease-free for 5 or more years prior to study enrollment and were deemed by their physician at low risk for recurrence.Participants completed study assessments at two time points . The firThe measures included standardized demographic assessments, symptom questionnaires, and measures of quality of life and affective state. Fatigue was assessed using the Brief Fatigue Inventory (BFI). The BFI is a 9-item questionnaire for evaluation of fatigue in patients with cancer . It is dTwo inflammatory markers, C-reactive protein (CRP), and interleukin-6 (IL-6), were assayed at both time points during the study. For this purpose, blood samples were collected from participants via inner elbow venipuncture and collected into vacutainer tubes. Samples were prepared via centrifuge, and serum was collected and subsequently frozen at −80°C. Serum was then analyzed for levels of CRP and IL-6 using Luminex Multiplex Bead assays according to manufacturer's instructions .t-tests were used to compare the severity of symptoms at the prebiopsy and postbiopsy visits, as well as to compare the cancer positive and cancer negative groups at the postbiopsy visit. Patient subgroups were divided and compared at both visits for the total sample (regardless of diagnosis) and for breast cancer positive (BC+) and breast cancer negative (BC−) subgroups separately, and severity of symptoms was evaluated based on established cutoffs for depression, anxiety, and fatigue questionnaires. Pearson correlations were computed to evaluate relationships between symptoms , affective state, and quality of life at both time points. Partial correlations were computed in order to allow for determination of associations between all study measures with appropriate covariates included. Potential covariates included participant's age, time interval between pre- and post-biopsy time points as well as several other pertinent demographic/medical variables.All data analyses were performed using SPSS version 17.0 . Means and standard deviations were computed to describe the sample and To ensure normal distribution of scores, values above or below three standard deviations of the mean for CRP and IL-6 were removed for further analyses. Square root and natural log transformations were then conducted for CRP and IL-6, respectively. Bivariate correlations were computed in order to determine associations between the behavioral measures and biological markers (CRP and IL-6) and between these variables and potential covariates. Finally, residualized change scores were computed to further examine relationships between study measures over time controlling for baseline values.A total of 47 patients completed both study assessments at pre- and post-biopsy visits and had a confirmed biopsy result. At the postbiopsy visit, 15 participants (31.9%) were diagnosed with breast cancer, and 32 participants (68.1%) received a negative biopsy. The average age of participants was 52.93 years (SD = 10.67); 56.6 years (SD = 8.93) for participants with positive biopsy; 51.22 years (SD = 11.11) for participants with negative biopsy results. Notably, higher number of participants (43%) in the positive biopsy group were 60 years of age or older, and this group had no participants under the age of 45. Comparatively, only 18% of participants in the negative biopsy group were above 60 years of age and 31% of participants in this group were under 45 years of age. Participants were mostly Caucasian (51.1%) with smaller numbers of African-American (4.3%) and Hispanic (6.4%) women, with similar numbers in both biopsy positive and biopsy negative subgroups. Information on ethnicity was not available for 18 of 47 participants. The mean interval of time between completion of the two sets of study questionnaires was 25.5 days (SD = 26).t (14) = −.202, P = .843) or the BC− (t (31) = 1.067, P = .294) subgroup. Similarly, sleep disturbances also remained unchanged from pre to post-biopsy for both the BC+ (t (13) = .915, P = .377) and BC− (t (31) = .293, P = .771) subgroups. The mean scores for depressive and fatigue symptoms were in the normal ranges as defined for these measures [F   = 4.241,P  ≤.05).The descriptive statistics for all study outcome measures for both breast cancer positive (BC+) and breast cancer negative (BC−) are displayed in measures , 29 at bT1: 36.4%) to post-biopsy (T2: 27.3%) time points. The subgroup of patients reporting moderate to severe level of fatigue at both time points was higher in the BC− subgroup (T1: 28.1% and T2: 28.1%) than the BC+ subgroup (T1: 20% and T2: 13.3%). None of the differences or changes in percentages were statistically significant. Analyses of severity of depressive symptoms indicated that most patients remained in the normal-to-mild range at both time points.Participants were divided in three subgroups based on established cutoffs for the respective scales , 29 to iCorrelational analyses using mean scores  were conP's >.10). Similarly the effect of time and cancer status as well as their interactions was not significant for affective states of the participants (P's >.10).We also conducted regression analyses to examine within subjects, between subjects, and interaction effects for time point and diagnostic status across all variables of interest. For all of the assessed symptoms, that is, anxiety, depression, sleep, and fatigue, the effects of time  or of cancer status or their interactions was not significant (P < 0.01). On the other hand, BC+ patients were found to have a negative correlation with IL-6 and quality of life (P < 0.01) and a positive correlation between CRP and level of fatigue (P < 0.05). For the total sample, regardless of diagnosis, IL-6 correlated to level of depression (P < 0.01).The markers of inflammation (CRP and IL-6) did not show consistent correlations with specific symptoms or affective states. However, we did find sporadic correlations. For example, BC− patients were found to have a positive correlation with IL-6 and level of depression  and more importantly, cancer status, did not have a significant impact on any of the distress symptoms under investigation. Additionally, several of the symptoms under investigation correlated with each other in both subgroups. Also, to our surprise, certain symptoms such as percentage of patients experiencing moderate-severe fatigue was higher in the BC− subgroup compared to the BC+ subgroup at both time points.The study results are partially consistent with other prior work that has found women with specific personality factors, prebiopsy high levels of anxiety, and negative coping mechanisms (such as cognitive avoidance coping) had high level of distress and anxiety after biopsy , 12. OurFatigue and depression in patients undergoing breast cancer treatments or in breast cancer survivors has been associated with chronic inflammation –25. In tThere are several limitations of the present study, including a relatively small sample size and convenience sample. Collection of demographic and clinical assessments was also limited in an effort to reduce patient burden. Comprehensive assessment of these factors  could haThe present study identified a subset of women with heightened distress despite negative biopsy results. Future work should attempt to identify these vulnerable women prior to mammograms and consider development of specific distress management strategies for these women at this emotionally stressful time. Future projects should also attempt to monitor this subset of women, both from a psychological standpoint and to ensure their compliance with monitoring recommendations. Limited sample size of the present study restricted further analyses of the impact of age difference between BC+ and BC− subgroups. Future studies should investigate the impact of this critical variable  on psychTo our knowledge, the present study is the first study of its kind with longitudinal and comprehensive evaluation of symptom distress, affective state, and quality of life in women undergoing the biopsy phase of breast cancer diagnosis. The study results suggest that majority of patients continue to farewell even after receiving a positive diagnosis. However, a subset of patients, irrespective of diagnostic status, experience heightened symptom distress with negative impact on their quality of life. Thus, psychosocial interventions may be warranted early on, especially in this subset of patients, to address psychological and symptom distress and improve quality of life.


Over the last decade several breast cancer risk alleles have been identified which has led to an increased interest in individualised risk prediction for clinical purposes.We investigate the performance of an up-to-date 18 breast cancer risk single-nucleotide polymorphisms (SNPs), together with mammographic percentage density (PD), body mass index (BMI) and clinical risk factors in predicting absolute risk of breast cancer, empirically, in a well characterised Swedish case-control study of postmenopausal women. We examined the efficiency of various prediction models at a population level for individualised screening by extending a recently proposed analytical approach for estimating number of cases captured.P = 4.69 × 10-4). Adding mammographic PD, BMI and all 18 SNPs to a Swedish Gail model improved the discriminatory accuracy (the AUC statistic) from 55% to 62%. The net reclassification improvement was used to assess improvement in classification of women into low, intermediate, and high categories of 5-year risk (P = 8.93 × 10-9). For scenarios we considered, we estimated that an individualised screening strategy based on risk models incorporating clinical risk factors, mammographic density and SNPs, captures 10% more cases than a screening strategy using the same resources, based on age alone. Estimates of numbers of cases captured by screening stratified by age provide insight into how individualised screening programs might appear in practice.The performance of a risk prediction model based on an initial set of seven breast cancer risk SNPs is improved by additionally including eleven more recently established breast cancer risk SNPs (Taken together, genetic risk factors and mammographic density offer moderate improvements to clinical risk factor models for predicting breast cancer. Breast cancer screening aims to detect the disease early in women and thereby reduce mortality from breast cancer. It may not be cost-effective to screen all women equally often, but rather to allocate resources disproportionately across women at different risks of developing breast cancer. To identify high- and low-risk groups, a model for estimating a woman's individual risk is needed. One of the earliest and most widely used risk models for sporadic breast cancer is the Gail model . The modOver the past decade, several common, low penetrance risk alleles for breast cancer have been identified by genome-wide association studies (GWAS), which has led to a recent increased interest in individualised risk prediction for clinical purposes ,7. The pet al. , and can be written as:Gail et al.  presentet. In this equation the term 2S(t)/2S(a) corresponds to the conditional probability of surviving other causes from age a to t, and the exponential term corresponds to surviving without breast cancer from age a to age t. At age t, there is an instantaneous probability h1(t)r(t)dt of developing breast cancer. The baseline hazard, h1(t), is estimated by multiplying age-specific breast cancer incidence rates, 2h(t) and is assumed to be the same for all individuals. The population attributable risk is a function of the relative risk model r(t), and can be determined according to an approach described by Bruzzi et al. [et al. [h1, 2 hand r are constant within five-year intervals. We estimated the age-specific breast cancer incidence rates and hazard of dying from other causes from the Swedish Cancer and Cause of Death registries, respectively [see Table A1 in Additional file where i et al. . In the The Gail relative risk model  incorporMammographic density has been consistently shown to be strongly associated with breast cancer and has previously been considered in breast cancer risk-prediction models -5. Due tWe used the Gail approach to estimate the 5-year and 10-year absolute risk of breast cancer based on age and various combinations of genetic and non-genetic risk factors. We evaluated various models for breast cancer risk based on subsets of women with data on (i) Gail, PD and BMI variables, (ii) Gail and SNP variables and (iii) Gail, PD, BMI and SNP variables.et al. [et al. [We used the Hosmer-Lemeshow test to assess calibration of the prediction models based on comparing observed and expected outcomes within deciles of estimated risk. As in Mealiffe et al. , we firset al. . To asse [et al.  to test et al. [To assess the ability of a new test to reclassify subjects accurately into higher or lower risk categories, we evaluated the two statistics suggested by Pencina et al.  for asseet al. .et al. [For the English population Pashayan et al.  have evaet al. , using . Based on a subset of women with complete data on Gail variables and SNPs, a statistically significant improvement in AUCs was seen when adding The7 to the Swe-Gail model. Improvement was further enhanced when the recently discovered 11 SNPs were added . Furthermore, a gain in AUCs was observed from including these 11 SNPs when the baseline model also included PD and BMI. We finally selected a subset of women with complete data on Gail variables, SNPs, PD and BMI and compared the performances of the Swe-Gail model and a model additionally including PD, BMI and The18. The latter model, referred to as the full model herein, obtained an AUC of 0.619, improving the AUC by 0.067 (P = 3.24 × 10-9). In this subset, with PD and BMI only we observed on AUC of 0.541 (95% CI = 0.515 to 0.568), with The18 only we observed an AUC of 0.589 (95% CI = 0.563 to 0.614) and with PD, BMI and The18 we observed an AUC of 0.600 (95% CI = 0.575 to 0.626).We first examined the classification abilities of models with and without PD and BMI, but including Swe-Gail risk factors age at menarche, age at first live birth, family history, benign breast disease, in 1,739 cases and 1,672 controls , intermediate , and elevated risk categories , based on five-year absolute risk estimates (Table 1 (= 2.41%) to correspond to the first quartile of the estimated risk based on the Swe-Gail model and ε2 (= 4.11%) to correspond to the third quartile. The NRI value for the comparison of the Swe-Gail model with the full model was 0.170 . In total, 46% of women were reclassified. Reclassification based on the full model was overall in the right direction, with an upward shift in risk categories for cases and a downward shift for controls. The global IDI measure was 0.004 . Using the cut-off values suggested by Mealiffe et al. [1 = 1.5%,ε2 = 2%, the NRI value for comparing the same two models was estimated to be 0.193 .Assuming three risk categories, we used reclassification tables to compare pairs of models in terms of their assignment of women to low  approach of screening all women aged 40 to 75 years at three cut-offs of eligibility [see Table A4 of Additional file P = 4.69 × 10-4). We further show that including mammographic PD, BMI and the 18 SNPs, in the baseline Swe-Gail model, is strongly associated with positive reclassification .In the present study we have investigated the potential gain in combining SNP information with clinical information and mammographic percentage density for the prediction of absolute risk of developing breast cancer, in the Swedish population, utilising the Gail approach . We haveet al. [The value of the AUC statistic, for assessing discrimination based on absolute risks calculated from the Gail model, which we observed is low compared with what has been observed in some studies carried out in the US. Rockhill et al.  observedet al.  an AUC oet al.  .et al. [et al. [et al. [In our study we observed improvements of 2 to 3% in AUC values as a result of adding mammographic density to risk-prediction models, which is slightly more than the 1% improvement observed by Tice  [et al. . Chen et [et al.  estimateet al. [We examined the usefulness of the 18 markers on a population level, with respect to screening. Using published effect estimates for the 18 markers and the clinical variables we evaluated several approaches to individualised screening, against age only-based screening, in women aged 40 to 75 years (at different 10-year risk cut-offs for defining eligibility for screening). We showed for the Swedish female population that a personalised screening approach based on a risk prediction model incorporating age, Gail model variables, PD, BMI and 18 SNPs captures significantly more breast cancer cases than screening approaches using equal resources based on age and Gail model variables and on age alone. The individualised screening strategies investigated here correspond very loosely to a strategy where all women are screened at baseline (e.g. at age 40 years) and at a small number of occasions , in order to ascertain personalised risk and between these occasions women are recommended to attend screening at intervals tailored to their personalised risk. In practice, rather than reducing the total number of mammograms, as in Pashayan et al. , and in It is now recognised that stratification according to genetic risk scores may improve the efficiency of screening programs . With onThe strengths of the present study are the population-based setting with a high participation rate and the detailed information on key breast cancer risk factors, including mammographic density. To our knowledge this is the first study to assess the prediction performance of the currently established 18 breast cancer risk SNPs empirically.There are limitations to the present study. Two of the variables in our prediction models varied slightly in definition from those used in the standard Gail model. For these variables we were forced to use internal effect estimates. Any bias in estimating discriminatory accuracy is, however, expected to be negligible. Further limitations are that the study is focused on postmenopausal women and that the family history variable used in our study is very crude. More sophisticated approaches have been described that more specifically describe the nature of the family history ,41, usinThe approach used for assessing efficiency of individualised screening programs is simplistic. It assumes that women being screened are under constant surveillance and that cancer is instantaneously detectable without error. Moreover, our approach was based on further simplifying assumptions, for example that effect sizes are age independent. Related to this particular condition is our assumption that women aged less than 50 years have the same risk distribution of those women aged 50 years. However, in reality the relative risk associated with family history is higher at younger ages . One wayMore refined approaches for evaluating screening strategies need to be developed and applied. It is important to incorporate breast cancer mortality as well as incidence and to at least partially reflect that breast cancer is a complex disease with a number of subtypes (which receive different treatments) and that patient survival outlooks vary. Accurately predicting an individuals risk of developing and dying from breast cancer remains a challenge. Microsimulation may prove a useful tool for accounting for the complicated processes of disease progression and detection when evaluating the efficiency of screening strategies . Using mTaken together, genetic risk factors and mammographic density offer moderate improvements to clinical risk factor models for predicting breast cancer.AUC: area under the receiver operating characteristic curve; BMI: body mass index; CI: confidence interval; GWAS: genome-wide association studies; IDI: integrated discrimination improvement; NRI: net reclassification improvement; PD: percentage density; SNPs: single nucleotide polymorphisms.The authors declare that they have no competing interests.HD, KH, KC, JL and PH were involved in planning the study. WZ and JL administrated the genotyping analysis. HD and KH performed the statistical analysis. HD, KH, KC and PH drafted the manuscript, and all the authors approved the manuscript.Supplementary tables. (a1) Age-specific composite (2h(t)), for breast cancer using 2005 data from the Swedish cancer registry and cause of death registry . (a2) Measures of model calibration and discrimination for the Swe-Gail model and the full model. (a3) Expected and observed counts of case patients for subgroups of predicted risk for Swe-Gail model and the full model. (a4) Percentage of individuals eligible for screening and the percentage of cases potentially detectable by screening in the population undergoing screening, across different  screening strategies based on different cut-off of 10-year absolute risk for developing breast cancer. (a5) Percentage of individuals eligible for screening and the percentage of cases potentially detectable by screening in the population undergoing screening, across different screening strategies based on different cut-off of 10-year absolute risk for developing breast cancer, stratified by age.Click here for fileSupplementary methods. Full methods accompanying this manuscript.Click here for file


Women known to carry a BRCA1/2 mutation have to make complex decisions regarding the management of their increased cancer risk, such as whether or not to undergo risk-reducing surgery and how to communicate this risk information to family members. These women may face a unique set of supportive care needs, although unlike the situation for breast and other cancer patients, few supportive care services for mutation carriers exist outside the Familial Cancer Centres. The aim of this presentation is to describe the unmet support needs of women with a known BRCA 1/2 mutation volunteering for a randomised control trial (RCT) testing the effectiveness of a telephone based peer support program.Participants were recruited through Familial Cancer Centres in Victoria, New South Wales and South Australia. Women between the ages of 18-70, who tested positive for a BRCA1/2 mutation within the last 4 years were eligible for participation in the study. Participants were recruited as part of an ongoing RCT. Women completed a baseline survey after enrolling in the study and prior to randomisation. The baseline survey included a 16-item scale assessing unmet support needs, such as dealing with fears about developing cancer, wanting reassurance that feelings are normal, and talking to others in a similar situation. Women were asked to indicate their level of need in the past month on each item using a 5-point likert scale.216 participants have completed the baseline survey to date (response rate of 46%). The average age of participants was 47.2 years (SD:13.6) and 44.0% (n=95) had a previous diagnosis of breast or ovarian cancer. The mean time since notification of carrier status was 2.5 years (SD=2.1).The average number of moderate to very high needs among all participants was 4.7 (S.D 4.6). Twenty six percent (n=52) of participants had no moderate to very high needs, while 23.1% (n=46) had a moderate to very high need on eight or more items. The most commonly endorsed moderate to very high needs were: “dealing with uncertainty about the future” (42%), “dealing with the impact a faulty gene has had on your family” (40%), and “dealing with fears about developing cancer” (39%). The least endorsed items were: “dealing with insurance issues” (61% indicated no need), “dealing with feelings of isolation” (53% no need) and “obtaining more information about your risk for breast cancer” (53%). The average number of moderate to very high unmet needs was not related to previous history of cancer (P=0.94), having children (p=0.87) or time since receiving BRCA1/2 mutation status results (P=0.36).Many women with a known BRCA 1/2 mutation have unmet supportive care needs that are not currently being addressed. Level of unmet need is not related to time since receiving test results, history of cancer or having children. The RCT trial will assess whether a telephone-based peer support program meets these unmet needs.


It is unclear whether circulating insulin or glucose levels are associated with increased risk of colorectal cancer. Few prospective studies have examined this question, and only one study had repeated measurements.We conducted a prospective study of colorectal cancer risk using the subsample of women in the Women's Health Initiative study whose fasting blood samples, collected at baseline and during follow-up, were analysed for insulin and glucose. Cox proportional hazards models were used to assess associations with colorectal cancer risk in both baseline and time-dependent covariates analyses.−1) with the lowest tertile (<89.5 mg dl−1): 1.74, 95% confidence interval (CI) 0.97–3.15 and 2.25, 95% CI: 1.12–4.51, respectively. Serum insulin and homeostasis model assessment were not associated with risk. Analyses of repeated measurements supported the baseline results.Among 4902 non-diabetic women with baseline fasting serum insulin and glucose values, 81 incident cases of colorectal cancer were identified over 12 years of follow-up. Baseline glucose levels were positively associated with colorectal cancer and colon cancer risk: multivariable-adjusted hazard ratio (HR) comparing the highest (⩾99.5 mg dlThese data suggest that elevated serum glucose levels may be a risk factor for colorectal cancer in postmenopausal women. Obesity, diabetes, and physical inactivity show consistent positive associations with risk of colorectal cancer . InsulinEpidemiologic studies that have examined the association of colorectal cancer with circulating insulin and/or glucose levels have yielded conflicting results  clinical trials (CTs) and observational study (OS).N=93 676) and CTs (N=68 132) of hormone therapy, dietary modification (low-fat dietary intervention), and calcium plus vitamin D supplementation  and a 1% sample of women in the OS (N=1062). All WHI participants had provided a fasting blood sample at baseline. Women in the 6% subsample additionally provided fasting blood samples at years 1, 3, and 6 of follow-up, and women in the 1% sample of women in the OS additionally provided fasting blood samples in year 3 (−1 (8 cases and 556 non-cases), leaving 81 cases and 4821 non-cases for analysis.The present analysis is based on a 6% random sample of women in the CTs , 3049 (57.4%-year 3), and 2976 (56.0%-year 6). Of the 5476 women with a baseline glucose measurement, the numbers with measurements during follow-up were 3587 (65.5%-year 1), 3227 (58.9%-year 3), and 2980 (54.4%-year 6). The proportions were similar for different blood draws among cases and non-cases.Cancer outcomes were initially ascertained through either self-administered questionnaires at semi-annual visits (CTs) or through questionnaires that were mailed annually (OS). Potential cases were then confirmed by centralised review of pathology reports, discharge summaries, operative and radiology reports, and tumour registry abstracts by trained medical adjudicators. After the end of CT period (2005), outcomes in all women were identified on an annual basis by mailed questionnaires. The rate of confirmation of locally adjudicated outcomes by central adjudication was 94% for colorectal cancer  , where storage at −70 °C was maintained. In the CT, serum samples used for the insulin and glucose measurements went through one freeze/thaw cycle, while those from the OS went through at least two cycles. Since analyses were stratified on participation in the OS IN, USA) . Monthly300 BMD; . MonthlyCox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (95% CIs) for the associations of serum insulin and glucose with risk of colorectal cancer, with duration of follow-up (days) as the time scale. For these analyses, study participants were considered to be at risk from their date of enrollment until the date of diagnosis of their colorectal cancer, termination of follow-up (30 September 2010), loss to follow-up, withdrawal from the study, or death, whichever occurred first. Event times of participants who had not developed colorectal cancer by the end of follow-up, who had died, or who had withdrawn from the study before the end of follow-up, were censored.μIU ml−1) × fasting glucose (mg dl−1))/22.5) , body mass index (kg m−2-continuous), alcohol intake (drinks per week-continuous), physical activity (MET-hours per week-continuous), family history of colorectal cancer , and ethnicity . In addition, dummy variables for participation in the OS or specific treatment arm of one of the CTs were included in all models. We carried out stratified analyses by stratifying BMI and physical activity at their median values  and treating glucose and insulin as continuous variables. Tests for trend were performed by assigning the median value to each category and modelling this variable as a continuous variable.We estimated the risk of colorectal cancer in association with baseline insulin and glucose levels, and with a measure of insulin resistance, the homeostasis model assessment-insulin resistance (HOMA-IR) index , a total of 81 colorectal cancer cases were ascertained in the cohort of 4902 non-diabetic women with fasting serum insulin and glucose values at baseline. Sixty-five cases had colon cancer; six had cancer of the rectosigmoid junction; and ten had rectal cancer. In all, 38 cases and 2372 non-cases were not in any of the CT intervention groups.Compared with non-cases, on average cases were older, less physically active , and more likely to be non-Hispanic white . In otheP<0.0001) and baseline glucose showed a moderately strong association with HOMA-IR , whereas insulin was strongly correlated with HOMA-IR . Although levels of glucose and insulin at follow-up visits were correlated with baseline levels, the strength of these correlations decreased with increasing interval between measurements (data not shown).Baseline serum glucose and insulin levels were modestly correlated  vs lowest tertile (<89.5 mg dl−1) was 1.74, 95% CI 0.97–3.15, P for trend 0.06. When the HRs for insulin and glucose were mutually adjusted, the HR for highest vs lowest tertile of insulin was 0.88, 95% CI: 0.47–1.65, P for trend 0.70, and that for glucose was 1.72, 95% CI: 0.94–3.15, P for trend 0.07. The HR per 1 mg dl−1 of glucose was 1.031, 95% CI: 1.009–1.054, P for trend=0.0066. When the analysis was restricted to cases of colon cancer (N=65), HRs for the middle and highest tertiles vs the lowest tertile of glucose were 1.72 (95% CI: 0.85–3.48) and 2.25 (95% CI: 1.12–4.51), respectively, P for trend 0.02, whereas insulin and HOMA-IR were not associated with risk. Further adjustment for total serum cholesterol did not affect the results (data not shown). The HR per 1 mg dl−1 of glucose was 1.034, 95% CI: 1.009–1.060, P for trend=0.0066.In both age-adjusted and multivariable-adjusted analyses, baseline insulin levels and HOMA-IR were not associated with colorectal cancer risk . However−2): HR per mg dl−1 1.029  and 1.031 , respectively. Similar results were seen for colon cancer. HRs for glucose with colorectal cancer within strata of physical activity (dichotomised at the median of 5.75 MET-hour per week) were 1.039  and 1.027 , respectively. Similar results were seen for colon cancer. Neither insulin nor HOMA-IR was associated with risk.The positive association of glucose with colorectal cancer was very similar in high and low BMI groups (⩾27.76 and <27.76 kg mN=17), the associations were unchanged: HR of colorectal cancer for the highest vs lowest tertile of glucose was 1.81, 95% CI 0.93–3.51, P for trend 0.07. In the analysis restricted to women in the CT (10 OS cases excluded), insulin and HOMA-IR showed no association, but the association of glucose with colorectal cancer was strengthened: HRs for intermediate and highest tertiles relative to the lowest: 1.60, 95% CI: 0.82–3.14, and 2.10, 95% CI: 1.08–4.06, P for trend 0.03.In analyses excluding cases diagnosed within the first 2 years of follow-up  also showed positive trends but were not statistically significant due to the smaller number of subjects with measurements obtained during these specific periods. As in the baseline analysis, no associations were seen with insulin or HOMA-IR.In the time-dependent covariates analysis, the average (mean) of the glucose measurements obtained during follow-up until the date of diagnosis of the case (excluding measurements within 1 year of diagnosis for cases) was associated with significantly increased risk of colorectal (76 cases) and colon cancer (61 cases): HR per 1 mg dlThe results of the present study suggest that baseline fasting serum glucose levels are positively associated with risk of colorectal cancer in postmenopausal women, whereas insulin and HOMA-IR are not associated with risk. The association was not affected by adjustment for insulin level or by the exclusion of cases diagnosed within the first 2 years following enrollment. Baseline glucose appeared to be more strongly associated with colon cancer than with all colorectal cancers. Analyses of the repeated measures obtained during follow-up using time-dependent covariates showed a statistically significant positive association of fasting glucose with colorectal and colon cancer, but power for the time-lagged models was diminished due to smaller available sample sizes.in-situ carcinoma)  , whereasvs lowest quartile of glucose . The fact that the association of glucose with colorectal cancer was somewhat strengthened when the analysis was restricted to women in the CT is consistent with the possibility that differences between the CT and OS populations may in part account for the differences in results between the two studies.In a previous study conducted within the Women's Health Initiative Observational Study , previous studies of the association of insulin and glucose with colorectal cancer risk have included only baseline measurements. Our time-dependent covariate analysis supported the existence of a statistically significant positive association with average glucose level during follow-up. Similar trends were observed when analysing measurements obtained during various time windows before the date of diagnosis of the cases, although the results were not statistically significant. Moreover, few studies have analysed HOMA-IR, a more direct measure of insulin resistance, which can only be computed using fasting insulin and glucose values, and which has been shown to reflect insulin resistance assessed by the euglycaemic clamp more accurately than fasting insulin alone (The present study benefited from the availability of repeated measurements of serum insulin and glucose over 12 years of follow-up. With one exception (Stocks In conclusion, in this cohort study of postmenopausal women, elevated fasting serum glucose, but not insulin or HOMA-IR, was associated with roughly a two-fold increased risk of colorectal cancer. This association was robust and was not affected by adjustment for insulin or by exclusion of cases diagnosed in the first 2 years following enrollment. In time-dependent covariate analyses, trends using the average of all measurements showed a statistically significant positive association of glucose with colorectal and colon cancer risk. Our findings, together with those of several previous studies, suggest that insulin resistance may be related to increased risk of colorectal cancer, though the specific biologic mechanism remains to be elucidated.


Mutations in CHEK2 predispose to a range of cancer types including breast cancer. A meta-analysis of all association studies estimated the risk of breast cancer among carriers of 1100delC to be increased by 2.7-fold (9) and increased by 4.7-fold among carriers with a positive family history of breast cancer . We estimated the risk of breast cancer in a woman who has a CHEK2 mutation depending on her family history of breast cancer. Out data suggest that carriers of a truncating mutation of CHEK2  have 2.9 – fold increased risk of breast cancer in the Polish population. The risk was higher for women with at least one first-degree relative with breast cancer (OR = 4.5), and for women with at least one second-degree relative with breast cancer (OR = 3.5). If both a first- and second-degree relative was affected with breast cancer, the odds ratio was 6.4. We estimate the lifetime risks for carriers of CHEK2 truncating mutations to be from 21 to 37% depending family history of breast cancer in first- and second degree relatives. CHEK2 mutation screening detects a clinically meaningful risk of breast cancer.


Black women are more likely than white women to have an aggressive subtype of breast cancer that is associated with higher mortality and this may contribute to the observed black-white difference in mortality. However, few studies have investigated the black-white disparity in mortality risk stratified by breast cancer subtype, defined by estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER2) status. Furthermore, it is not known whether additional consideration of p53 protein status influences black-white differences in mortality risk observed when considering subtypes defined by ER, PR and HER2 status.Four biomarkers were assessed by immunohistochemistry in paraffin-embedded breast tumor tissue from 1,204  women with invasive breast cancer, aged 35–64 years at diagnosis, who accrued a median of 10 years’ follow-up. Multivariable Cox proportional hazards regression models were fit to assess subtype-specific black-white differences in mortality risk.No black-white differences in mortality risk were observed for women with triple negative  subtype. However, older (50–64 years) black women had greater overall mortality risk than older white women if they had been diagnosed with luminal A (ER-positive [ER+] or PR+ plus HER2-) breast cancer . This black-white difference among older women was further confined to those with luminal A/p53- tumors . Tests for homogeneity of race-specific HRs comparing luminal A to triple negative subtype and luminal A/p53- to luminal A/p53+ subtype did not achieve statistical significance, although statistical power was limited.Our findings suggest that the subtype-specific black-white difference in mortality risk occurs mainly among older women diagnosed with luminal A/p53- breast cancer, which is most likely treatable. These results further suggest that factors other than subtype may be relatively more important in explaining the increased mortality risk seen in older black women. Black women have higher risk of death after breast cancer diagnosis than white women  or PR+ plus HER2-), luminal B (ER+ or PR+ plus HER2+), and HER2-enriched (ER-/PR-/HER2+) subtype -16. GeneTN breast tumors, which account for 10-25% of all invasive breast cancers ,20, havep53 is a tumor suppressor gene, which encodes the p53 protein p53 is a te women  than in r tissue ,37. One We have previously shown that white women with invasive breast cancer participating in the Women’s Contraceptive and Reproductive Experiences (CARE) Study who had higher body mass index (BMI) had higher mortality risk than those with a normal (not overweight) BMI; but this association did not hold for black women . Here, wThe participants for this analysis are women from two study sites, Detroit and Los Angeles (LA), participating in the Women’s CARE Study, a population-based case–control study designed to examine risk factors for invasive breast cancer among US-born black women and white women including those of Hispanic ethnicity . The WomParaffin-embedded tumor blocks were obtained from pathology laboratories where diagnoses were made for 1,333 participating breast cancer cases , approximately 80% of those requested. Tumor blocks were carefully reviewed and evaluated in the centralized pathology laboratory of Dr. Michael F. Press at the University of Southern California.in situ (n = 56) or no tumor tissue (n = 46); had insufficient tissue for assay (n = 3); had other problems (n = 14); or only hematoxlin-and-eosin stained tissue sections were received (n = 8). The expression of ER, PR, HER2, and p53 was determined for the remaining 1,206 samples .We excluded 127 case samples because the tumor blocks contained only carcinoma The expression of ER and PR was determined using previously published immunohistochemistry (IHC) methods ,41. ImmuHER2 expression was determined by IHC using the 10H8 monoclonal antibody ,44 to asThe expression of p53 protein was determined by IHC using the monoclonal mouse antibodies DO7  and BP 53-12-1 (Biogenex) to measure p53 nuclear protein immunostaining. Based on findings from previous studies, comparing p53 mutations in exons 2–11 with p53 protein expression levels ,45, ≥10%The Women’s CARE Study collected tumor stage, tumor histologic grade, and other tumor characteristics. We excluded two more women because they were missing information on tumor stage, resulting in the final sample size of 1,204  women for the analyses.Women were followed up annually for vital status, date of death and cause of death using standard SEER follow-up procedures. Women from Detroit were followed through December 31, 2004; follow-up extended until December 31, 2007 in LA.We used Pearson Chi-squared tests to compare frequency distributions of categorical variables between black women and white women.Adjusted estimates of the hazard ratio (HR) of death, a measure of relative risk, and its 95% confidence interval (CI), comparing black women to white women, were calculated for each breast cancer subtype of interest using Cox proportional hazards regression models . Two CoxKaplan-Meier breast cancer-specific curves  were conP value less than 0.05 as statistically significant when testing for homogeneity of HRs across subtypes of breast cancer. All statistical analyses were performed using SAS version 9.2 software .We considered a two-sided P < 0.001, Table P = 0.16) or in younger women 35 to 49 years of age (P = 0.98), whereas older black women 50 to 64 years of age were more likely to be diagnosed with HER2+ tumors than white women in the same age group (P = 0.04).During a median follow-up of 10 years , 272 (141 black and 131 white) women died specifically from breast cancer and 63 (39 black and 24 white) women died from other causes. Compared with white women, black women were more likely to be diagnosed with ER-, PR-, TN, p53+, non-localized, or higher grade tumors , but not among those diagnosed with TN breast cancer  to 2.22 .Although black-white differences in mortality after breast cancer diagnosis were observed only among older women diagnosed with luminal A and luminal A/p53- subtype, no tests for homogeneity of race-specific HRs across subtypes achieved statistical significance (results not shown).In the current analysis of 1,204 women 35 to 64 years of age, with a median follow-up of 10 years, we did not observe any statistically significant black-white differences in cancer-specific or all-cause mortality among women diagnosed with TN subtype. We did, however, find that black women had statistically significant greater all-cause mortality risk than white women among those ages 50–64 years who were diagnosed with luminal A tumors, and more specifically among those diagnosed with luminal A/p53- breast cancer. However, no tests for homogeneity of race-specific HRs comparing luminal A to TN subtype and luminal A/p53- to luminal A/p53+ subtype achieved statistical significance.The results from four previous epidemiologic studies that compared mortality risk or survival in black and white women diagnosed with luminal A or TN or basal-like subtype are inconsistent -7. One sAn analysis comparing the outcomes of 405 black women with 4,412 nonblack women who had stage I-III breast cancer and who participated in a National Cancer Institute-sponsored randomized phase III trial also provides supporting evidence for our results . Breast p53 are associated with resistance to chemotherapy, radiotherapy, and poor prognosis [Based on our knowledge, this is the first study to examine if the overexpression status of p53 protein impacts the black-white disparities in mortality of TN or luminal A breast cancer. Our data showed that p53 protein expression status could impact black-white mortality differences, and this was most evident for older women diagnosed with luminal A breast cancer. A possible explanation for no black-white difference in mortality risk for older women with luminal A/p53+ tumor is that luminal A/p53+ tumor is currently less likely to be treatable for either black women or white women since mutations in rognosis ,32. The rognosis ,52 and lp53 mutations. Although previous research shows that p53 protein expression and p53 mutation status determined by FISH analysis are strongly correlated, our assessment of p53 protein expression by IHC may have misclassified some tumors. Fifth, although the agreement in the classification for ER and PR status between the SEER registry and centralized laboratory was substantial [This study had several limitations. First, we were unable to request tissue for all eligible women diagnosed with invasive breast cancer in the two study sites because of funding constraints. However, we obtained paraffin-embedded tissue for 80% of the samples requested. Second, we did not have breast cancer treatment information available and therefore did not adjust for treatments in our analyses. Although we have presumed that controlling for age, stage of disease, and the status of the four tumor markers has provided some control for treatment, previous studies have reported that black women may receive less optimal treatment than white women -58. Blacstantial , we repeOur findings suggest that the black-white difference in mortality risk is mainly among women 50 years or older diagnosed with luminal A/p53- breast cancer, a subtype for which treatments exist. These results further suggest that factors other than subtype may be relatively more important in explaining the increased mortality risk seen in older black women.AA: African American; BMI: Body mass index; ER: Estrogen receptor; PR: Progesterone receptor; HER: Human epidermal growth factor receptor; TN: Triple negative; CARE: Contraceptive and reproductive experiences; LA: Los Angeles; IHC: Immunohistochemistry; FISH: Fluorescent in situ hybridization; HR: Hazard ratio; CI: Confidence interval.The authors declare that they have no competing interests.RS, DMD, BLS, and LB participated in the study design and supervised the collection and assembly of data. KEM, PAM, RTB, JAM, SGF, and JS supervised or participated in the collection and assembly of data. HM conducted data analyses and drafted the manuscript with LB’s input. All authors participated in the revision of the manuscript and have read and approved the final version.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2407/13/225/prepub