Predicting risk of breast cancer in postmenopausal women by hormone receptor status

Addition of polygenic risk score to a risk calculator for prediction of breast cancer in US Black women

BACKGROUND

Previous work in European ancestry populations has shown that adding a polygenic risk score (PRS) to breast cancer risk prediction models based on epidemiologic factors results in better discriminatory performance as measured by the AUC (area under the curve). Following publication of the first PRS to perform well in women of African ancestry (AA-PRS), we conducted an external validation of the AA-PRS and then evaluated the addition of the AA-PRS to a risk calculator for incident breast cancer in Black women based on epidemiologic factors (BWHS model).

METHODS

Data from the Black Women's Health Study, an ongoing prospective cohort study of 59,000 US Black women followed by biennial questionnaire since 1995, were used to calculate AUCs and 95% confidence intervals (CIs) for discriminatory accuracy of the BWHS model, the AA-PRS alone, and a new model that combined them. Analyses were based on data from 922 women with invasive breast cancer and 1844 age-matched controls.

RESULTS

AUCs were 0.577 (95% CI 0.556-0.598) for the BWHS model and 0.584 (95% CI 0.563-0.605) for the AA-PRS. For a model that combined estimates from the questionnaire-based BWHS model with the PRS, the AUC increased to 0.623 (95% CI 0.603-0.644).

CONCLUSIONS

This combined model represents a step forward for personalized breast cancer preventive care for US Black women, as its performance metrics are similar to those from models in other populations. Use of this new model may mitigate exacerbation of breast cancer disparities if and when it becomes feasible to include a PRS in routine health care decision-making.

Genetic Risk Scores and Missing Heritability in Ovarian Cancer

Ovarian cancers are curable by surgical resection when discovered early. Unfortunately, most ovarian cancers are diagnosed in the later stages. One strategy to identify early ovarian tumors is to screen women who have the highest risk. This opinion article summarizes the accuracy of different methods used to assess the risk of developing ovarian cancer, including family history, BRCA genetic tests, and polygenic risk scores. The accuracy of these is compared to the maximum theoretical accuracy, revealing a substantial gap. We suggest that this gap, or missing heritability, could be caused by epistatic interactions between genes. An alternative approach to computing genetic risk scores, using chromosomal-scale length variation should incorporate epistatic interactions. Future research in this area should focus on this and other alternative methods of characterizing genomes.

Is it time to consider population screening for fracture risk in postmenopausal women? A position paper from the International Osteoporosis Foundation Epidemiology/Quality of Life Working Group

The IOF Epidemiology and Quality of Life Working Group has reviewed the potential role of population screening for high hip fracture risk against well-established criteria. The report concludes that such an approach should strongly be considered in many health care systems to reduce the burden of hip fractures.

INTRODUCTION

The burden of long-term osteoporosis management falls on primary care in most healthcare systems. However, a wide and stable treatment gap exists in many such settings; most of which appears to be secondary to a lack of awareness of fracture risk. Screening is a public health measure for the purpose of identifying individuals who are likely to benefit from further investigations and/or treatment to reduce the risk of a disease or its complications. The purpose of this report was to review the evidence for a potential screening programme to identify postmenopausal women at increased risk of hip fracture.

METHODS

The approach took well-established criteria for the development of a screening program, adapted by the UK National Screening Committee, and sought the opinion of 20 members of the International Osteoporosis Foundation's Working Group on Epidemiology and Quality of Life as to whether each criterion was met (yes, partial or no). For each criterion, the evidence base was then reviewed and summarized.

RESULTS AND CONCLUSION

The report concludes that evidence supports the proposal that screening for high fracture risk in primary care should strongly be considered for incorporation into many health care systems to reduce the burden of fractures, particularly hip fractures. The key remaining hurdles to overcome are engagement with primary care healthcare professionals, and the implementation of systems that facilitate and maintain the screening program.

Fracture risk assessment by the FRAX model

The introduction of the FRAX algorithms has facilitated the assessment of fracture risk on the basis of fracture probability. FRAX integrates the influence of several well-validated risk factors for fracture with or without the use of bone mineral density. Since age-specific rates of fracture and death differ across the world, FRAX models are calibrated with regard to the epidemiology of hip fracture (preferably from national sources) and mortality (usually United Nations sources). Models are currently available for 73 nations or territories covering more than 80% of the world population. FRAX has been incorporated into more than 80 guidelines worldwide, although the nature of this application has been heterogeneous. The limitations of FRAX have been extensively reviewed. Arithmetic procedures have been proposed in order to address some of these limitations, which can be applied to conventional FRAX estimates to accommodate knowledge of dose exposure to glucocorticoids, concurrent data on lumbar spine bone mineral density, information on trabecular bone score, hip axis length, falls history, type 2 diabetes, immigration status and recency of prior fracture.

Validation of Breast Cancer Risk Models by Race/Ethnicity, Family History and Molecular Subtypes

Simple Summary

Several statistical models exist to predict a person’s risk of breast cancer. Risk assessment models can guide cancer screening approaches by identifying individuals who would benefit from additional screening. In this study, we compared the performance of four models in predicting the 5-year risk of breast cancer in a cohort of women aged 40–84 years who underwent screening mammography at three large health systems. Models showed comparable discrimination (ability to distinguish between cases and non-cases) and calibration (ability to accurately predict risk) overall, with no difference by race. Model discrimination was poorer for some cancer subtypes, and better for women with high BMI. The combined BRCAPRO+BCRAT model had improved calibration and discrimination among women with a family history of breast cancer. Our results can inform risk-based screening approaches by identifying women at a high risk of breast cancer.

Abstract

(1) Background: The purpose of this study is to compare the performance of four breast cancer risk prediction models by race, molecular subtype, family history of breast cancer, age, and BMI. (2) Methods: Using a cohort of women aged 40–84 without prior history of breast cancer who underwent screening mammography from 2006 to 2015, we generated breast cancer risk estimates using the Breast Cancer Risk Assessment tool (BCRAT), BRCAPRO, Breast Cancer Surveillance Consortium (BCSC) and combined BRCAPRO+BCRAT models. Model calibration and discrimination were compared using observed-to-expected ratios (O/E) and the area under the receiver operator curve (AUC) among patients with at least five years of follow-up. (3) Results: We observed comparable discrimination and calibration across models. There was no significant difference in model performance between Black and White women. Model discrimination was poorer for HER2+ and triple-negative subtypes compared with ER/PR+HER2−. The BRCAPRO+BCRAT model displayed improved calibration and discrimination compared to BRCAPRO among women with a family history of breast cancer. Across models, discriminatory accuracy was greater among obese than non-obese women. When defining high risk as a 5-year risk of 1.67% or greater, models demonstrated discordance in 2.9% to 19.7% of patients. (4) Conclusions: Our results can inform the implementation of risk assessment and risk-based screening among women undergoing screening mammography.

Performance of Breast Cancer Risk-Assessment Models in a Large Mammography Cohort

BACKGROUND

Several breast cancer risk-assessment models exist. Few studies have evaluated predictive accuracy of multiple models in large screening populations.

METHODS

We evaluated the performance of the BRCAPRO, Gail, Claus, Breast Cancer Surveillance Consortium (BCSC), and Tyrer-Cuzick models in predicting risk of breast cancer over 6 years among 35 921 women aged 40-84 years who underwent mammography screening at Newton-Wellesley Hospital from 2007 to 2009. We assessed model discrimination using the area under the receiver operating characteristic curve (AUC) and assessed calibration by comparing the ratio of observed-to-expected (O/E) cases. We calculated the square root of the Brier score and positive and negative predictive values of each model.

RESULTS

Our results confirmed the good calibration and comparable moderate discrimination of the BRCAPRO, Gail, Tyrer-Cuzick, and BCSC models. The Gail model had slightly better O/E ratio and AUC (O/E = 0.98, 95% confidence interval [CI] = 0.91 to 1.06, AUC = 0.64, 95% CI = 0.61 to 0.65) compared with BRCAPRO (O/E = 0.94, 95% CI = 0.88 to 1.02, AUC = 0.61, 95% CI = 0.59 to 0.63) and Tyrer-Cuzick (version 8, O/E = 0.84, 95% CI = 0.79 to 0.91, AUC = 0.62, 95% 0.60 to 0.64) in the full study population, and the BCSC model had the highest AUC among women with available breast density information (O/E = 0.97, 95% CI = 0.89 to 1.05, AUC = 0.64, 95% CI = 0.62 to 0.66). All models had poorer predictive accuracy for human epidermal growth factor receptor 2 positive and triple-negative breast cancers than hormone receptor positive human epidermal growth factor receptor 2 negative breast cancers.

CONCLUSIONS

In a large cohort of patients undergoing mammography screening, existing risk prediction models had similar, moderate predictive accuracy and good calibration overall. Models that incorporate additional genetic and nongenetic risk factors and estimate risk of tumor subtypes may further improve breast cancer risk prediction.

Comparative Validation of Breast Cancer Risk Prediction Models and Projections for Future Risk Stratification

BACKGROUND

External validation of risk models is critical for risk-stratified breast cancer prevention. We used the Individualized Coherent Absolute Risk Estimation (iCARE) as a flexible tool for risk model development and comparative model validation and to make projections for population risk stratification.

METHODS

Performance of two recently developed models, one based on the Breast and Prostate Cancer Cohort Consortium analysis (iCARE-BPC3) and another based on a literature review (iCARE-Lit), were compared with two established models (Breast Cancer Risk Assessment Tool and International Breast Cancer Intervention Study Model) based on classical risk factors in a UK-based cohort of 64 874 white non-Hispanic women (863 patients) age 35-74 years. Risk projections in a target population of US white non-Hispanic women age 50-70 years assessed potential improvements in risk stratification by adding mammographic breast density (MD) and polygenic risk score (PRS).

RESULTS

The best calibrated models were iCARE-Lit (expected to observed number of cases [E/O] = 0.98, 95% confidence interval [CI] = 0.87 to 1.11) for women younger than 50 years, and iCARE-BPC3 (E/O = 1.00, 95% CI = 0.93 to 1.09) for women 50 years or older. Risk projections using iCARE-BPC3 indicated classical risk factors can identify approximately 500 000 women at moderate to high risk (>3% 5-year risk) in the target population. Addition of MD and a 313-variant PRS is expected to increase this number to approximately 3.5 million women, and among them, approximately 153 000 are expected to develop invasive breast cancer within 5 years.

CONCLUSIONS

iCARE models based on classical risk factors perform similarly to or better than BCRAT or IBIS in white non-Hispanic women. Addition of MD and PRS can lead to substantial improvements in risk stratification. However, these integrated models require independent prospective validation before broad clinical applications.

Breast cancer risk assessment and management programs: A practical guide

Breast cancer risk assessment continues to evolve as emerging knowledge of breast cancer risk drivers and modifiers enables better identification of high-risk women who may benefit from increased screening or targeted risk-reduction protocols. The ongoing development of breast cancer Risk Assessment and Management Programs (RAMPs) presents an opportunity to decrease breast cancer disease incidence with evidence-based interventions. The goal of this review was to provide a practical guide for providers seeking to establish or update a breast cancer risk assessment and management program. We outline genetic/familial, personal, reproductive, and lifestyle-related factors while discussing the incorporation of risk modeling for precise risk estimate personalization. We further describe the process for determining a risk management plan: information gathering, generation of a risk profile, and articulation and implementation of risk reduction. We also include an overview of clinical workflows in breast cancer management programs and underlines the logistics of establishing a program as well as general principles for guiding the formulation of an individualized risk management plan. We discuss practical considerations, such as clinic structure and operation, allocation of resources, and patient education. Other critical aspects of program design, including identification of the target population, delineation of the core components of the clinical experience, definition of provider roles, description of referral mechanisms, and the launching of a marketing plan are also addressed. The process of risk assessment is both anxiety-provoking and empowering for women at increased risk. New knowledge has enabled strategies to both understand the risk and control it through evidence-based risk management. These benefits can now be realized by an increasing number of unaffected, high-risk patients collaborating with risk management practitioners. Continuation of these efforts will lead to further progress in both risk stratification and risk management of women at elevated breast cancer risk in the near future.

Somatic genetic aberrations in benign breast disease and the risk of subsequent breast cancer

It is largely unknown how the development of breast cancer (BC) is transduced by somatic genetic alterations in the benign breast. Since benign breast disease is an established risk factor for BC, we established a case-control study of women with a history of benign breast biopsy (BBB). Cases developed BC at least one year after BBB and controls did not develop BC over an average of 17 years following BBB. 135 cases were matched to 69 controls by age and type of benign change: non-proliferative or proliferation without atypia (PDWA). Whole-exome sequencing (WES) was performed for the BBB. Germline DNA (available from n = 26 participants) was utilized to develop a mutation-calling pipeline, to allow differentiation of somatic from germline variants. Among the 204 subjects, two known mutational signatures were identified, along with a currently uncatalogued signature that was significantly associated with triple negative BC (TNBC) (p = 0.007). The uncatalogued mutational signature was validated in 109 TNBCs from TCGA (p = 0.001). Compared to non-proliferative samples, PDWA harbors more abundant mutations at PIK3CA pH1047R (p < 0.001). Among the 26 BBB whose somatic copy number variation could be assessed, deletion of MLH3 is significantly associated with the mismatch repair mutational signature (p < 0.001). Matched BBB-cancer pairs were available for ten cases; several mutations were shared between BBB and cancers. This initial study of WES of BBB shows its potential for the identification of genetic alterations that portend breast oncogenesis. In future larger studies, robust personalized breast cancer risk indicators leading to novel interception paradigms can be assessed.

Cell-free miRNAs as non-invasive biomarkers in breast cancer: Significance in early diagnosis and metastasis prediction

Breast cancer is one of the genetic diseases causing a high mortality among women around the world. Despite the availability of advanced diagnostic tools and treatment strategies, the incidence of breast cancer is increasing every year. This is due to the lack of accurate and reliable biomarkers whose deficiency creates difficulty in early breast cancer recognition, subtypes determination, and metastasis prophecy. Although biomarkers such as ER, PR, Her2, Ki-67, and other genetic platforms e.g. MammaPrint®, Oncotype DX®, Prosigna® or EndoPredict® are available for determination of breast cancer diagnosis and prognosis. However, pertaining to heterogeneous nature, lack of sensitivity, and specificity of these markers, it is still incessant to overcome breast cancer burden. Therefore, a novel biomarker is urgently needed for therapeutic diagnosis and improving prognosis. Lately, it has become more evident that cell-free miRNAs might be useful as good non-invasive biomarkers that are associated with different events in carcinogenesis. For example, some known biomarkers such as miR-21, miR-23a, miR-34a are associated with molecular subtyping and different biomolecular aspects i.e. apoptosis, angiogenesis, metastasis, and miR-1, miR-10b, miR-16 are associated with drug response. Cell-free miRNAs present in human body fluids have proven to be potential biomarkers with significant prognostic and predictive values. Numerous studies have found a distinct expression profile of circulating miRNAs in breast tumour versus non-tumour and in early and advanced-stage, thus implicating its clinical relevance. This review article will highlight the importance of different cell-free miRNAs as a biomarker for early breast cancer detection, subtype classification, and metastasis forecast.

A decade of FRAX: how has it changed the management of osteoporosis?

The fracture risk assessment tool, FRAX^®, was released in 2008 and provides country-specific algorithms for estimating individualized 10-year probability of hip and major osteoporotic fracture (hip, clinical spine, distal forearm, and proximal humerus). Since its release, 71 models have been made available for 66 countries covering more than 80% of the world population. The website receives approximately 3 million visits annually. Following independent validation, FRAX has been incorporated into more than 80 guidelines worldwide. The application of FRAX in assessment guidelines has been heterogeneous with the adoption of several different approaches in setting intervention thresholds. Whereas most guidelines adopt a case-finding strategy, the case for FRAX-based community screening in the elderly is increasing. The relationship between FRAX and efficacy of intervention has been explored and is expected to influence treatment guidelines in the future.

Predicting breast cancer risk using personal health data and machine learning models

Among women, breast cancer is a leading cause of death. Breast cancer risk predictions can inform screening and preventative actions. Previous works found that adding inputs to the widely-used Gail model improved its ability to predict breast cancer risk. However, these models used simple statistical architectures and the additional inputs were derived from costly and / or invasive procedures. By contrast, we developed machine learning models that used highly accessible personal health data to predict five-year breast cancer risk. We created machine learning models using only the Gail model inputs and models using both Gail model inputs and additional personal health data relevant to breast cancer risk. For both sets of inputs, six machine learning models were trained and evaluated on the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial data set. The area under the receiver operating characteristic curve metric quantified each model’s performance. Since this data set has a small percentage of positive breast cancer cases, we also reported sensitivity, specificity, and precision. We used Delong tests (p < 0.05) to compare the testing data set performance of each machine learning model to that of the Breast Cancer Risk Prediction Tool (BCRAT), an implementation of the Gail model. None of the machine learning models with only BCRAT inputs were significantly stronger than the BCRAT. However, the logistic regression, linear discriminant analysis, and neural network models with the broader set of inputs were all significantly stronger than the BCRAT. These results suggest that relative to the BCRAT, additional easy-to-obtain personal health inputs can improve five-year breast cancer risk prediction. Our models could be used as non-invasive and cost-effective risk stratification tools to increase early breast cancer detection and prevention, motivating both immediate actions like screening and long-term preventative measures such as hormone replacement therapy and chemoprevention.

A way forward for cancer prevention therapy: personalized risk assessment

Cancer is characterized by genetic and molecular aberrations whose number and complexity increase dramatically as cells progress along the spectrum of carcinogenesis. The pharmacologic application of agents in the context of a lower burden of dysregulated cellular processes constitutes an efficient strategy to enhance therapeutic efficacy, and underlies the rationale for using cancer prevention agents in high-risk populations. A longstanding barrier to implementing this strategy is that the risk in the general population is low for any given cancer, many people would have to be treated in order to benefit a few. Therefore, identifying and treating high-risk individuals will improve the risk: benefit ratio. Currently, risk is defined by considering a relatively low number of factors. A strategy that considers multiple factors has the ability to define a much-higher-risk cohort than the general population. This article will review the rationale for evaluating multiple risk factors so as to identify individuals at highest risk. It will use breast and lung cancer as examples, will describe currently available risk assessment tools, and will discuss ongoing efforts to expand the impact of this approach. The high potential of this strategy to provide a way forward for developing cancer prevention therapy will be highlighted.

Medication Use to Reduce Risk of Breast Cancer: US Preventive Services Task Force Recommendation Statement

IMPORTANCE

Breast cancer is the most common nonskin cancer among women in the United States and the second leading cause of cancer death. The median age at diagnosis is 62 years, and an estimated 1 in 8 women will develop breast cancer at some point in their lifetime. African American women are more likely to die of breast cancer compared with women of other races.

OBJECTIVE

To update the 2013 US Preventive Services Task Force (USPSTF) recommendation on medications for risk reduction of primary breast cancer.

EVIDENCE REVIEW

The USPSTF reviewed evidence on the accuracy of risk assessment methods to identify women who could benefit from risk-reducing medications for breast cancer, as well as evidence on the effectiveness, adverse effects, and subgroup variations of these medications. The USPSTF reviewed evidence from randomized trials, observational studies, and diagnostic accuracy studies of risk stratification models in women without preexisting breast cancer or ductal carcinoma in situ.

FINDINGS

The USPSTF found convincing evidence that risk assessment tools can predict the number of cases of breast cancer expected to develop in a population. However, these risk assessment tools perform modestly at best in discriminating between individual women who will or will not develop breast cancer. The USPSTF found convincing evidence that risk-reducing medications (tamoxifen, raloxifene, or aromatase inhibitors) provide at least a moderate benefit in reducing risk for invasive estrogen receptor-positive breast cancer in postmenopausal women at increased risk for breast cancer. The USPSTF found that the benefits of taking tamoxifen, raloxifene, and aromatase inhibitors to reduce risk for breast cancer are no greater than small in women not at increased risk for the disease. The USPSTF found convincing evidence that tamoxifen and raloxifene and adequate evidence that aromatase inhibitors are associated with small to moderate harms. Overall, the USPSTF determined that the net benefit of taking medications to reduce risk of breast cancer is larger in women who have a greater risk for developing breast cancer.

CONCLUSIONS AND RECOMMENDATION

The USPSTF recommends that clinicians offer to prescribe risk-reducing medications, such as tamoxifen, raloxifene, or aromatase inhibitors, to women who are at increased risk for breast cancer and at low risk for adverse medication effects. (B recommendation) The USPSTF recommends against the routine use of risk-reducing medications, such as tamoxifen, raloxifene, or aromatase inhibitors, in women who are not at increased risk for breast cancer. (D recommendation) This recommendation applies to asymptomatic women 35 years and older, including women with previous benign breast lesions on biopsy (such as atypical ductal or lobular hyperplasia and lobular carcinoma in situ). This recommendation does not apply to women who have a current or previous diagnosis of breast cancer or ductal carcinoma in situ.

Medication Use for the Risk Reduction of Primary Breast Cancer in Women: Updated Evidence Report and Systematic Review for the US Preventive Services Task Force

IMPORTANCE

Medications to reduce risk of breast cancer are effective for women at increased risk but also cause adverse effects.

OBJECTIVE

To update the 2013 US Preventive Services Task Force systematic review on medications to reduce risk of primary (first diagnosis) invasive breast cancer in women.

DATA SOURCES

Cochrane Central Register of Controlled Trials and Database of Systematic Reviews, EMBASE, and MEDLINE (January 1, 2013, to February 1, 2019); manual review of reference lists.

STUDY SELECTION

Discriminatory accuracy studies of breast cancer risk assessment methods; randomized clinical trials of tamoxifen, raloxifene, and aromatase inhibitors for primary breast cancer prevention; studies of medication adverse effects.

DATA EXTRACTION AND SYNTHESIS

Investigators abstracted data on methods, participant characteristics, eligibility criteria, outcome ascertainment, and follow-up. Results of individual trials were combined by using a profile likelihood random-effects model.

MAIN OUTCOMES AND MEASURES

Probability of breast cancer in individuals (area under the receiver operating characteristic curve [AUC]); incidence of breast cancer, fractures, thromboembolic events, coronary heart disease events, stroke, endometrial cancer, and cataracts; and mortality.

RESULTS

A total of 46 studies (82 articles [>5 million participants]) were included. Eighteen risk assessment methods in 25 studies reported low accuracy in predicting the probability of breast cancer in individuals (AUC, 0.55-0.65). In placebo-controlled trials, tamoxifen (risk ratio [RR], 0.69 [95% CI, 0.59-0.84]; 4 trials [n = 28 421]), raloxifene (RR, 0.44 [95% CI, 0.24-0.80]; 2 trials [n = 17 806]), and the aromatase inhibitors exemestane and anastrozole (RR, 0.45 [95% CI, 0.26-0.70]; 2 trials [n = 8424]) were associated with a lower incidence of invasive breast cancer. Risk for invasive breast cancer was higher for raloxifene than tamoxifen in 1 trial after long-term follow-up (RR, 1.24 [95% CI, 1.05-1.47]; n = 19 747). Raloxifene was associated with lower risk for vertebral fractures (RR, 0.61 [95% CI, 0.53-0.73]; 2 trials [n = 16 929]) and tamoxifen was associated with lower risk for nonvertebral fractures (RR, 0.66 [95% CI, 0.45-0.98]; 1 trial [n = 13 388]) compared with placebo. Tamoxifen and raloxifene were associated with increased thromboembolic events compared with placebo; tamoxifen was associated with more events than raloxifene. Tamoxifen was associated with higher risk of endometrial cancer and cataracts compared with placebo. Symptomatic effects (eg, vasomotor, musculoskeletal) varied by medication.

CONCLUSIONS AND RELEVANCE

Tamoxifen, raloxifene, and aromatase inhibitors were associated with lower risk of primary invasive breast cancer in women but also were associated with adverse effects that differed between medications. Risk stratification methods to identify patients with increased breast cancer risk demonstrated low accuracy.

World Cancer Research Fund International: Continuous Update Project-systematic literature review and meta-analysis of observational cohort studies on physical activity, sedentary behavior, adiposity, and weight change and breast cancer risk

PURPOSE

The purpose of the present study was to systematically review the complex associations between energy balance-related factors and breast cancer risk, for which previous evidence has suggested different associations in the life course of women and by hormone receptor (HR) status of the tumor.

METHODS

Relevant publications on adulthood physical activity, sedentary behavior, body mass index (BMI), waist and hip circumferences, waist-to-hip ratio, and weight change and pre- and postmenopausal breast cancer risk were identified in PubMed up to 30 April 2017. Random-effects meta-analyses were conducted to summarize the relative risks across studies.

RESULTS

One hundred and twenty-six observational cohort studies comprising over 22,900 premenopausal and 103,000 postmenopausal breast cancer cases were meta-analyzed. Higher physical activity was inversely associated with both pre- and postmenopausal breast cancers, whereas increased sitting time was positively associated with postmenopausal breast cancer. Although higher early adult BMI (ages 18-30 years) was inversely associated with pre- and postmenopausal breast cancers, adult weight gain and greater body adiposity increased breast cancer risk in postmenopausal women, and the increased risk was evident for HR+ but not HR- breast cancers, and among never but not current users of postmenopausal hormones. The evidence was less consistent in premenopausal women. There were no associations with adult weight gain, inverse associations with adult BMI (study baseline) and hip circumference, and non-significant associations with waist circumference and waist-to-hip ratio that were reverted to positive associations on average in studies accounting for BMI. No significant associations were observed for HR-defined premenopausal breast cancers.

CONCLUSION

Better understanding on the impact of these factors on pre- and postmenopausal breast cancers and their subtypes along the life course is needed.

Breast cancer risk prediction in women aged 35-50 years: impact of including sex hormone concentrations in the Gail model

Background

Models that accurately predict risk of breast cancer are needed to help younger women make decisions about when to begin screening. Premenopausal concentrations of circulating anti-Müllerian hormone (AMH), a biomarker of ovarian reserve, and testosterone have been positively associated with breast cancer risk in prospective studies. We assessed whether adding AMH and/or testosterone to the Gail model improves its prediction performance for women aged 35–50.

Methods

In a nested case-control study including ten prospective cohorts (1762 invasive cases/1890 matched controls) with pre-diagnostic serum/plasma samples, we estimated relative risks (RR) for the biomarkers and Gail risk factors using conditional logistic regression and random-effects meta-analysis. Absolute risk models were developed using these RR estimates, attributable risk fractions calculated using the distributions of the risk factors in the cases from the consortium, and population-based incidence and mortality rates. The area under the receiver operating characteristic curve (AUC) was used to compare the discriminatory accuracy of the models with and without biomarkers.

Results

The AUC for invasive breast cancer including only the Gail risk factor variables was 55.3 (95% CI 53.4, 57.1). The AUC increased moderately with the addition of AMH (AUC 57.6, 95% CI 55.7, 59.5), testosterone (AUC 56.2, 95% CI 54.4, 58.1), or both (AUC 58.1, 95% CI 56.2, 59.9). The largest AUC improvement (4.0) was among women without a family history of breast cancer.

Conclusions

AMH and testosterone moderately increase the discriminatory accuracy of the Gail model among women aged 35–50. We observed the largest AUC increase for women without a family history of breast cancer, the group that would benefit most from improved risk prediction because early screening is already recommended for women with a family history.

Electronic supplementary material

The online version of this article (10.1186/s13058-019-1126-z) contains supplementary material, which is available to authorized users.

Prospective validation of the NCI Breast Cancer Risk Assessment Tool (Gail Model) on 40,000 Australian women

Background

There is a growing interest in delivering more personalised, risk-based breast cancer screening protocols. This requires population-level validation of practical models that can stratify women into breast cancer risk groups. Few studies have evaluated the Gail model (NCI Breast Cancer Risk Assessment Tool) in a population screening setting; we validated this tool in a large, screened population.

Methods

We used data from 40,158 women aged 50–69 years (via the lifepool cohort) participating in Australia’s BreastScreen programme. We investigated the association between Gail scores and future invasive breast cancer, comparing observed and expected outcomes by Gail score ranked groups. We also used machine learning to rank Gail model input variables by importance and then assessed the incremental benefit in risk prediction obtained by adding variables in order of diminishing importance.

Results

Over a median of 4.3 years, the Gail model predicted 612 invasive breast cancers compared with 564 observed cancers (expected/observed (E/O) = 1.09, 95% confidence interval (CI) 1.00–1.18). There was good agreement across decile groups of Gail scores (χ² = 7.1, p = 0.6) although there was some overestimation of cancer risk in the top decile of our study group (E/O = 1.65, 95% CI 1.33–2.07). Women in the highest quintile (Q5) of Gail scores had a 2.28-fold increased risk of breast cancer (95% CI 1.73–3.02, p < 0.0001) compared with the lowest quintile (Q1). Compared with the median quintile, women in Q5 had a 34% increased risk (95% CI 1.06–1.70, p = 0.014) and those in Q1 had a 41% reduced risk (95% CI 0.44–0.79, p < 0.0001). Similar patterns were observed separately for women aged 50–59 and 60–69 years. The model’s overall discrimination was modest (area under the curve (AUC) 0.59, 95% CI 0.56–0.61). A reduced Gail model excluding information on ethnicity and hyperplasia was comparable to the full Gail model in terms of correctly stratifying women into risk groups.

Conclusions

This study confirms that the Gail model (or a reduced model excluding information on hyperplasia and ethnicity) can effectively stratify a screened population aged 50–69 years according to the risk of future invasive breast cancer. This information has the potential to enable more personalised, risk-based screening strategies that aim to improve the balance of the benefits and harms of screening.

Electronic supplementary material

The online version of this article (10.1186/s13058-018-1084-x) contains supplementary material, which is available to authorized users.

Risk prediction for estrogen receptor-specific breast cancers in two large prospective cohorts

BACKGROUND

Few published breast cancer (BC) risk prediction models consider the heterogeneity of predictor variables between estrogen-receptor positive (ER+) and negative (ER-) tumors. Using data from two large cohorts, we examined whether modeling this heterogeneity could improve prediction.

METHODS

We built two models, for ER+ (ModelER+) and ER- tumors (ModelER-), respectively, in 281,330 women (51% postmenopausal at recruitment) from the European Prospective Investigation into Cancer and Nutrition cohort. Discrimination (C-statistic) and calibration (the agreement between predicted and observed tumor risks) were assessed both internally and externally in 82,319 postmenopausal women from the Women's Health Initiative study. We performed decision curve analysis to compare ModelER+ and the Gail model (ModelGail) regarding their applicability in risk assessment for chemoprevention.

RESULTS

Parity, number of full-term pregnancies, age at first full-term pregnancy and body height were only associated with ER+ tumors. Menopausal status, age at menarche and at menopause, hormone replacement therapy, postmenopausal body mass index, and alcohol intake were homogeneously associated with ER+ and ER- tumors. Internal validation yielded a C-statistic of 0.64 for ModelER+ and 0.59 for ModelER-. External validation reduced the C-statistic of ModelER+ (0.59) and ModelGail (0.57). In external evaluation of calibration, ModelER+ outperformed the ModelGail: the former led to a 9% overestimation of the risk of ER+ tumors, while the latter yielded a 22% underestimation of the overall BC risk. Compared with the treat-all strategy, ModelER+ produced equal or higher net benefits irrespective of the benefit-to-harm ratio of chemoprevention, while ModelGail did not produce higher net benefits unless the benefit-to-harm ratio was below 50. The clinical applicability, i.e. the area defined by the net benefit curve and the treat-all and treat-none strategies, was 12.7 × 10- 6 for ModelER+ and 3.0 × 10- 6 for ModelGail.

CONCLUSIONS

Modeling heterogeneous epidemiological risk factors might yield little improvement in BC risk prediction. Nevertheless, a model specifically predictive of ER+ tumor risk could be more applicable than an omnibus model in risk assessment for chemoprevention.

Review of non-clinical risk models to aid prevention of breast cancer

A disease risk model is a statistical method which assesses the probability that an individual will develop one or more diseases within a stated period of time. Such models take into account the presence or absence of specific epidemiological risk factors associated with the disease and thereby potentially identify individuals at higher risk. Such models are currently used clinically to identify people at higher risk, including identifying women who are at increased risk of developing breast cancer. Many genetic and non-genetic breast cancer risk models have been developed previously. We have evaluated existing non-genetic/non-clinical models for breast cancer that incorporate modifiable risk factors. This review focuses on risk models that can be used by women themselves in the community in the absence of clinical risk factors characterization. The inclusion of modifiable factors in these models means that they can be used to improve primary prevention and health education pertinent for breast cancer. Literature searches were conducted using PubMed, ScienceDirect and the Cochrane Database of Systematic Reviews. Fourteen studies were eligible for review with sample sizes ranging from 654 to 248,407 participants. All models reviewed had acceptable calibration measures, with expected/observed (E/O) ratios ranging from 0.79 to 1.17. However, discrimination measures were variable across studies with concordance statistics (C-statistics) ranging from 0.56 to 0.89. We conclude that breast cancer risk models that include modifiable risk factors have been well calibrated but have less ability to discriminate. The latter may be a consequence of the omission of some significant risk factors in the models or from applying models to studies with limited sample sizes. More importantly, external validation is missing for most of the models. Generalization across models is also problematic as some variables may not be considered applicable to some populations and each model performance is conditioned by particular population characteristics. In conclusion, it is clear that there is still a need to develop a more reliable model for estimating breast cancer risk which has a good calibration, ability to accurately discriminate high risk and with better generalizability across populations.

Towards Prevention of Breast Cancer: What Are the Clinical Challenges?

The dramatic increase in breast cancer incidence compels a paradigm shift in our preventive efforts. There are several barriers to overcome before prevention becomes an established part of breast cancer management. The objective of this review is to identify the clinical challenges for improved breast cancer prevention and discuss current knowledge on breast cancer risk assessment methods, risk communication, ethics, and interventional efforts with the aim of covering the aspects relevant for a breast cancer prevention trial. Herein, the following five areas are discussed: (i) Adequate tools for identification of women at high risk of breast cancer suggestively entitled Prevent! Online. (ii) Consensus on the definition of high risk, which is regarded as mandatory for all risk communication and potential prophylactic interventions. (iii) Risk perception and communication regarding risk information. (iv) Potential ethical concerns relevant for future breast cancer prevention programs. (v) Risk-reducing programs involving multileveled prevention depending on identified risk. Taken together, devoted efforts from both policy makers and health care providers are warranted to improve risk assessment and risk counseling in women at risk for breast cancer to optimize the prevention of breast cancer. Cancer Prev Res; 11(5); 255-64. ©2018 AACR.

Assessment of performance of the Gail model for predicting breast cancer risk: a systematic review and meta-analysis with trial sequential analysis

BACKGROUND

The Gail model has been widely used and validated with conflicting results. The current study aims to evaluate the performance of different versions of the Gail model by means of systematic review and meta-analysis with trial sequential analysis (TSA).

METHODS

Three systematic review and meta-analyses were conducted. Pooled expected-to-observed (E/O) ratio and pooled area under the curve (AUC) were calculated using the DerSimonian and Laird random-effects model. Pooled sensitivity, specificity and diagnostic odds ratio were evaluated by bivariate mixed-effects model. TSA was also conducted to determine whether the evidence was sufficient and conclusive.

RESULTS

Gail model 1 accurately predicted breast cancer risk in American women (pooled E/O = 1.03; 95% CI 0.76-1.40). The pooled E/O ratios of Caucasian-American Gail model 2 in American, European and Asian women were 0.98 (95% CI 0.91-1.06), 1.07 (95% CI 0.66-1.74) and 2.29 (95% CI 1.95-2.68), respectively. Additionally, Asian-American Gail model 2 overestimated the risk for Asian women about two times (pooled E/O = 1.82; 95% CI 1.31-2.51). TSA showed that evidence in Asian women was sufficient; nonetheless, the results in American and European women need further verification. The pooled AUCs for Gail model 1 in American and European women and Asian females were 0.55 (95% CI 0.53-0.56) and 0.75 (95% CI 0.63-0.88), respectively, and the pooled AUCs of Caucasian-American Gail model 2 for American, Asian and European females were 0.61 (95% CI 0.59-0.63), 0.55 (95% CI 0.52-0.58) and 0.58 (95% CI 0.55-0.62), respectively. The pooled sensitivity, specificity and diagnostic odds ratio of Gail model 1 were 0.63 (95% CI 0.27-0.89), 0.91 (95% CI 0.87-0.94) and 17.38 (95% CI 2.66-113.70), respectively, and the corresponding indexes of Gail model 2 were 0.35 (95% CI 0.17-0.59), 0.86 (95% CI 0.76-0.92) and 3.38 (95% CI 1.40-8.17), respectively.

CONCLUSIONS

The Gail model was more accurate in predicting the incidence of breast cancer in American and European females, while far less useful for individual-level risk prediction. Moreover, the Gail model may overestimate the risk in Asian women and the results were further validated by TSA, which is an addition to the three previous systematic review and meta-analyses.

TRIAL REGISTRATION

PROSPERO CRD42016047215 .

Using Resistin, glucose, age and BMI to predict the presence of breast cancer

Background

The goal of this exploratory study was to develop and assess a prediction model which can potentially be used as a biomarker of breast cancer, based on anthropometric data and parameters which can be gathered in routine blood analysis.

Methods

For each of the 166 participants several clinical features were observed or measured, including age, BMI, Glucose, Insulin, HOMA, Leptin, Adiponectin, Resistin and MCP-1. Machine learning algorithms (logistic regression, random forests, support vector machines) were implemented taking in as predictors different numbers of variables. The resulting models were assessed with a Monte Carlo Cross-Validation approach to determine 95% confidence intervals for the sensitivity, specificity and AUC of the models.

Results

Support vector machines models using Glucose, Resistin, Age and BMI as predictors allowed predicting the presence of breast cancer in women with sensitivity ranging between 82 and 88% and specificity ranging between 85 and 90%. The 95% confidence interval for the AUC was [0.87, 0.91].

Conclusions

These findings provide promising evidence that models combining age, BMI and metabolic parameters may be a powerful tool for a cheap and effective biomarker of breast cancer.

Electronic supplementary material

The online version of this article (10.1186/s12885-017-3877-1) contains supplementary material, which is available to authorized users.

Genetic and environmental factors and serum hormones, and risk of estrogen receptor-positive breast cancer in pre- and postmenopausal Japanese women

Breast cancer incidence in Japanese women has more than tripled over the past two decades. We have previously shown that this marked increase is mostly due to an increase in the estrogen receptor (ER)-positive, HER2-negative subtype. We conducted a case-control study; ER-positive, HER2-negative breast cancer patients who were diagnosed since 2011 and women without disease were recruited. Environmental factors, serum levels of testosterone and 25-hydroxyvitamin D, and common genetic variants reported as predictors of ER-positive breast cancer or found in Asian women were evaluated between patients and controls in pre- and postmenopausal women. To identify important risk predictors, risk prediction models were created by logistic regression models. In premenopausal women, two environmental factors (history of breastfeeding, and history of benign breast disease) and four genetic variants (TOX3-rs3803662, ESR1-rs2046210, 8q24-rs13281615, and SLC4A7-rs4973768) were considered to be risk predictors, whereas three environmental factors (body mass index, history of breastfeeding, and hyperlipidemia), serum levels of testosterone and 25-hydroxyvitamin D, and two genetic variants (TOX3-rs3803662 and ESR1-rs2046210) were identified as risk predictors. Inclusion of common genetic variants and serum hormone measurements as well as environmental factors improved risk assessment models. The decline in the birthrate according to recent changes of lifestyle might be the main cause of the recent notable increase in the incidence of ER-positive breast cancer in Japanese women.

Estrogen receptors in breast and bone: from virtue of remodeling to vileness of metastasis

Bone metastasis is a prominent cause of morbidity and mortality in cancer. High rates of bone colonization in breast cancer, especially in the subtype expressing estrogen receptors (ERs), suggest tissue-specific proclivities for metastatic tumor formation. The mechanisms behind this subtype-specific organ-tropism remains largely elusive. Interestingly, as the major driver of ER+ breast cancer, ERs also have important roles in bone development and homeostasis. Thus, any agents targeting ER will also inevitably affect the microenvironment, which involves the osteoblasts and osteoclasts. Yet, how such microenvironmental effects are integrated with direct therapeutic responses of cancer cells remain poorly understood. Recent findings on ER mutations, especially their enrichment in bone metastasis, raised even more provocative questions on the role of ER in cancer-bone interaction. In this review, we evaluate the importance of ERs in bone metastasis and discuss new avenues of investigation for bone metastasis treatment based on current knowledge.

FRAX Update

The fracture risk assessment tool, FRAX, was released in 2008 and provides country-specific algorithms for estimating individualized 10-year probability of hip and major osteoporotic fracture (hip, clinical spine, distal forearm, and proximal humerus). Since its release, models are now available for 63 countries, covering 79% of the world population. The website receives approximately 3 million visits annually. Following independent validation, FRAX has been incorporated into more than 80 guidelines worldwide. However, the application of FRAX in guidelines has been heterogeneous with the adoption of several different approaches to setting intervention thresholds. The relationship between FRAX and efficacy of intervention has been explored and is expected to influence treatment guidelines in the future. A more unified approach to setting intervention thresholds with FRAX is a research priority.

Family history and risk of breast cancer: an analysis accounting for family structure

Purpose

Family history is an important risk factor for breast cancer incidence, but the parameters conventionally used to categorize it are based solely on numbers and/or ages of breast cancer cases in the family and take no account of the size and age-structure of the woman’s family.

Methods

Using data from the Generations Study, a cohort of over 113,000 women from the general UK population, we analyzed breast cancer risk in relation to first-degree family history using a family history score (FHS) that takes account of the expected number of family cases based on the family’s age-structure and national cancer incidence rates.

Results

Breast cancer risk increased significantly (P_trend < 0.0001) with greater FHS. There was a 3.5-fold (95% CI 2.56–4.79) range of risk between the lowest and highest FHS groups, whereas women who had two or more relatives with breast cancer, the strongest conventional familial risk factor, had a 2.5-fold (95% CI 1.83–3.47) increase in risk. Using likelihood ratio tests, the best model for determining breast cancer risk due to family history was that combining FHS and age of relative at diagnosis.

Conclusions

A family history score based on expected as well as observed breast cancers in a family can give greater risk discrimination on breast cancer incidence than conventional parameters based solely on cases in affected relatives. Our modeling suggests that a yet stronger predictor of risk might be a combination of this score and age at diagnosis in relatives.

Electronic supplementary material

The online version of this article (doi:10.1007/s10549-017-4325-2) contains supplementary material, which is available to authorized users.

Breast Cancer Screening in the Precision Medicine Era: Risk-Based Screening in a Population-Based Trial

Ongoing controversy over the optimal approach to breast cancer screening has led to discordant professional society recommendations, particularly in women age 40 to 49 years. One potential solution is risk-based screening, where decisions around the starting age, stopping age, frequency, and modality of screening are based on individual risk to maximize the early detection of aggressive cancers and minimize the harms of screening through optimal resource utilization. We present a novel approach to risk-based screening that integrates clinical risk factors, breast density, a polygenic risk score representing the cumulative effects of genetic variants, and sequencing for moderate- and high-penetrance germline mutations. We demonstrate how thresholds of absolute risk estimates generated by our prediction tools can be used to stratify women into different screening strategies (biennial mammography, annual mammography, annual mammography with adjunctive magnetic resonance imaging, defer screening at this time) while informing the starting age of screening for women age 40 to 49 years. Our risk thresholds and corresponding screening strategies are based on current evidence but need to be tested in clinical trials. The Women Informed to Screen Depending On Measures of risk (WISDOM) Study, a pragmatic, preference-tolerant randomized controlled trial of annual vs personalized screening, will study our proposed approach. WISDOM will evaluate the efficacy, safety, and acceptability of risk-based screening beginning in the fall of 2016. The adaptive design of this trial allows continued refinement of our risk thresholds as the trial progresses, and we discuss areas where we anticipate emerging evidence will impact our approach.

Risk Factors That Increase Risk of Estrogen Receptor-Positive and -Negative Breast Cancer

Background

Risk factors may differentially influence development of estrogen receptor (ER)-positive vs -negative breast cancer. We examined associations with strong, prevalent risk factors by ER subtype.

Methods

Of 1 279 443 women age 35 to 74 years participating in the Breast Cancer Surveillance Consortium, 14 969 developed ER-positive and 3617 developed ER-negative invasive breast cancer. We calculated hazard ratios (HRs) using Cox regression and compared ER subtype hazard ratios at representative ages or by menopausal status using Wald tests. All statistical tests were two-sided.

Results

For women age 40 years, compared with no prior biopsy, ER-positive vs ER-negative HRs were 1.53 (95% CI = 1.30 to 1.81) vs 1.26 (95% CI = 0.90 to 1.76) for nonproliferative disease, 1.63 (95% CI = 1.23 to 2.17) vs 1.41 (95% CI = 0.78 to 2.57) for proliferative disease without atypia, and 4.47 (95% CI = 2.88 to 6.96) vs 0.20 (95% CI = 0.02 to 2.51) for proliferative disease with atypia. Benign disease proliferation risk was stronger for ER-positive than ER-negative cancer for women age 35 years (Wald P = .04), age 40 years (Wald P = .04), and age 50 years (Wald P = .06). Among pre/perimenopausal women, body mass index (BMI) had a stronger association with ER-negative than ER-positive cancer (obese II/III vs. normal weight: HR = 1.52, 95% CI = 1.19 to 1.94; vs 1.21, 95% CI = 1.08 to 1.36). Increasing BMI similarly increased ER-positive and ER-negative cancer risk among postmenopausal hormone users (Wald P = .15) and nonusers (Wald P = .08). Associations with ER subtype varied by race/ethnicity across all ages (P < .001) and by family history of breast cancer and breast density for specific ages.

Conclusions

Strength of risk factor associations differed by ER subtype. Separate risk models for ER subtypes may improve identification of women for targeted prevention strategies.

Accounting for individualized competing mortality risks in estimating postmenopausal breast cancer risk

PURPOSE

Accurate risk assessment is necessary for decision-making around breast cancer prevention. We aimed to develop a breast cancer prediction model for postmenopausal women that would take into account their individualized competing risk of non-breast cancer death.

METHODS

We included 73,066 women who completed the 2004 Nurses' Health Study (NHS) questionnaire (all ≥57 years) and followed participants until May 2014. We considered 17 breast cancer risk factors (health behaviors, demographics, family history, reproductive factors) and 7 risk factors for non-breast cancer death (comorbidities, functional dependency) and mammography use. We used competing risk regression to identify factors independently associated with breast cancer. We validated the final model by examining calibration (expected-to-observed ratio of breast cancer incidence, E/O) and discrimination (c-statistic) using 74,887 subjects from the Women's Health Initiative Extension Study (WHI-ES; all were ≥55 years and followed for 5 years).

RESULTS

Within 5 years, 1.8 % of NHS participants were diagnosed with breast cancer (vs. 2.0 % in WHI-ES, p = 0.02), and 6.6 % experienced non-breast cancer death (vs. 5.2 % in WHI-ES, p < 0.001). Using a model selection procedure which incorporated the Akaike Information Criterion, c-statistic, statistical significance, and clinical judgement, our final model included 9 breast cancer risk factors, 5 comorbidities, functional dependency, and mammography use. The model's c-statistic was 0.61 (95 % CI [0.60-0.63]) in NHS and 0.57 (0.55-0.58) in WHI-ES. On average, our model under predicted breast cancer in WHI-ES (E/O 0.92 [0.88-0.97]).

CONCLUSIONS

We developed a novel prediction model that factors in postmenopausal women's individualized competing risks of non-breast cancer death when estimating breast cancer risk.

Relationship of Predicted Risk of Developing Invasive Breast Cancer, as Assessed with Three Models, and Breast Cancer Mortality among Breast Cancer Patients

PURPOSE

Breast cancer risk prediction models are used to plan clinical trials and counsel women; however, relationships of predicted risks of breast cancer incidence and prognosis after breast cancer diagnosis are unknown.

METHODS

Using largely pre-diagnostic information from the Breast Cancer Surveillance Consortium (BCSC) for 37,939 invasive breast cancers (1996-2007), we estimated 5-year breast cancer risk (<1%; 1-1.66%; ≥1.67%) with three models: BCSC 1-year risk model (BCSC-1; adapted to 5-year predictions); Breast Cancer Risk Assessment Tool (BCRAT); and BCSC 5-year risk model (BCSC-5). Breast cancer-specific mortality post-diagnosis (range: 1-13 years; median: 5.4-5.6 years) was related to predicted risk of developing breast cancer using unadjusted Cox proportional hazards models, and in age-stratified (35-44; 45-54; 55-69; 70-89 years) models adjusted for continuous age, BCSC registry, calendar period, income, mode of presentation, stage and treatment. Mean age at diagnosis was 60 years.

RESULTS

Of 6,021 deaths, 2,993 (49.7%) were ascribed to breast cancer. In unadjusted case-only analyses, predicted breast cancer risk ≥1.67% versus <1.0% was associated with lower risk of breast cancer death; BCSC-1: hazard ratio (HR) = 0.82 (95% CI = 0.75-0.90); BCRAT: HR = 0.72 (95% CI = 0.65-0.81) and BCSC-5: HR = 0.84 (95% CI = 0.75-0.94). Age-stratified, adjusted models showed similar, although mostly non-significant HRs. Among women ages 55-69 years, HRs approximated 1.0. Generally, higher predicted risk was inversely related to percentages of cancers with unfavorable prognostic characteristics, especially among women 35-44 years.

CONCLUSIONS

Among cases assessed with three models, higher predicted risk of developing breast cancer was not associated with greater risk of breast cancer death; thus, these models would have limited utility in planning studies to evaluate breast cancer mortality reduction strategies. Further, when offering women counseling, it may be useful to note that high predicted risk of developing breast cancer does not imply that if cancer develops it will behave aggressively.

Performance of the Breast Cancer Risk Assessment Tool Among Women Age 75 Years and Older

BACKGROUND

The Breast Cancer Risk Assessment Tool (BCRAT, "Gail model") is commonly used for breast cancer prediction; however, it has not been validated for women age 75 years and older.

METHODS

We used Nurses' Health Study (NHS) data beginning in 2004 and Women's Health Initiative (WHI) data beginning in 2005 to compare BCRAT's performance among women age 75 years and older with that in women age 55 to 74 years in predicting five-year breast cancer incidence. BCRAT risk factors include: age, race/ethnicity, age at menarche, age at first birth, family history, history of benign breast biopsy, and atypia. We examined BCRAT's calibration by age by comparing expected/observed (E/O) ratios of breast cancer incidence. We examined discrimination by computing c-statistics for the model by age. All statistical tests were two-sided.

RESULTS

Seventy-three thousand seventy-two NHS and 97 081 WHI women participated. NHS participants were more likely to be non-Hispanic white (96.2% vs 84.7% in WHI, P < .001) and were less likely to develop breast cancer (1.8% vs 2.0%, P = .02). E/O ratios by age in NHS were 1.16 (95% confidence interval [CI] = 1.09 to 1.23, age 57-74 years) and 1.31 (95% CI = 1.18 to 1.45, age ≥ 75 years, P = .02), and in WHI 1.03 (95% CI = 0.97 to 1.09, age 55-74 years) and 1.10 (95% CI = 1.00 to 1.21, age ≥ 75 years, P = .21). E/O ratio 95% confidence intervals crossed one among women age 75 years and older when samples were limited to women who underwent mammography and were without significant illness. C-statistics ranged between 0.56 and 0.58 in both cohorts regardless of age.

CONCLUSIONS

BCRAT accurately predicted breast cancer for women age 75 years and older who underwent mammography and were without significant illness but had modest discrimination. Models that consider individual competing risks of non-breast cancer death may improve breast cancer risk prediction for older women.

Breastfeeding and breast cancer risk by receptor status--a systematic review and meta-analysis

BACKGROUND

Breastfeeding is inversely associated with overall risk of breast cancer. This association may differ in breast cancer subtypes defined by receptor status, as they may reflect different mechanisms of carcinogenesis. We conducted a systematic review and meta-analysis of case-control and prospective cohort studies to investigate the association between breastfeeding and breast cancer by estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) status.

DESIGN

We searched the PubMed and Scopus databases and bibliographies of pertinent articles to identify relevant articles and used random-effects models to calculate summary odds ratios (ORs) and 95% confidence intervals (CIs).

RESULTS

This meta-analysis represents 27 distinct studies (8 cohort and 19 case-control), with a total of 36 881 breast cancer cases. Among parous women, the risk estimates for the association between ever (versus never) breastfeeding and the breast cancers negative for both ER and PR were similar in three cohort and three case-control studies when results were adjusted for several factors, including the number of full-term pregnancies (combined OR 0.90; 95% CI 0.82-0.99), with little heterogeneity and no indication of publication bias. In a subset of three adjusted studies that included ER, PR, and HER2 status, ever breastfeeding showed a stronger inverse association with triple-negative breast cancer (OR 0.78; 95% CI 0.66-0.91) among parous women. Overall, cohort studies showed no significant association between breastfeeding and ER+/PR+ or ER+ and/or PR+ breast cancers, although one and two studies (out of four and seven studies, respectively) showed an inverse association.

CONCLUSIONS

This meta-analysis showed a protective effect of ever breastfeeding against hormone receptor-negative breast cancers, which are more common in younger women and generally have a poorer prognosis than other subtypes of breast cancer. The association between breastfeeding and receptor-positive breast cancers needs more investigation.

Alcohol intake and breast cancer in the European prospective investigation into cancer and nutrition

Alcohol intake has been associated to breast cancer in pre and postmenopausal women; however results are inconclusive regarding tumor hormonal receptor status, and potential modifying factors like age at start drinking. Therefore, we investigated the relation between alcohol intake and the risk of breast cancer using prospective observational data from the European Prospective Investigation into Cancer and Nutrition (EPIC). Up to 334,850 women, aged 35-70 years at baseline, were recruited in ten European countries and followed up an average of 11 years. Alcohol intake at baseline and average lifetime alcohol intake were calculated from country-specific dietary and lifestyle questionnaires. The study outcomes were the Hazard ratios (HR) of developing breast cancer according to hormonal receptor status. During 3,670,439 person-years, 11,576 incident breast cancer cases were diagnosed. Alcohol intake was significantly related to breast cancer risk, for each 10 g/day increase in alcohol intake the HR increased by 4.2% (95% CI: 2.7-5.8%). Taking 0 to 5 g/day as reference, alcohol intake of >5 to 15 g/day was related to a 5.9% increase in breast cancer risk (95% CI: 1-11%). Significant increasing trends were observed between alcohol intake and ER+/PR+, ER-/PR-, HER2- and ER-/PR-HER2- tumors. Breast cancer risk was stronger among women who started drinking prior to first full-time pregnancy. Overall, our results confirm the association between alcohol intake and both hormone receptor positive and hormone receptor negative breast tumors, suggesting that timing of exposure to alcohol drinking may affect the risk. Therefore, women should be advised to control their alcohol consumption.

At risk or not at risk? A meta-analysis of the prognostic accuracy of psychometric interviews for psychosis prediction

An accurate detection of individuals at clinical high risk (CHR) for psychosis is a prerequisite for effective preventive interventions. Several psychometric interviews are available, but their prognostic accuracy is unknown. We conducted a prognostic accuracy meta-analysis of psychometric interviews used to examine referrals to high risk services. The index test was an established CHR psychometric instrument used to identify subjects with and without CHR (CHR+ and CHR-). The reference index was psychosis onset over time in both CHR+ and CHR- subjects. Data were analyzed with MIDAS (STATA13). Area under the curve (AUC), summary receiver operating characteristic curves, quality assessment, likelihood ratios, Fagan's nomogram and probability modified plots were computed. Eleven independent studies were included, with a total of 2,519 help-seeking, predominately adult subjects (CHR+: N=1,359; CHR-: N=1,160) referred to high risk services. The mean follow-up duration was 38 months. The AUC was excellent (0.90; 95% CI: 0.87-0.93), and comparable to other tests in preventive medicine, suggesting clinical utility in subjects referred to high risk services. Meta-regression analyses revealed an effect for exposure to antipsychotics and no effects for type of instrument, age, gender, follow-up time, sample size, quality assessment, proportion of CHR+ subjects in the total sample. Fagan's nomogram indicated a low positive predictive value (5.74%) in the general non-help-seeking population. Albeit the clear need to further improve prediction of psychosis, these findings support the use of psychometric prognostic interviews for CHR as clinical tools for an indicated prevention in subjects seeking help at high risk services worldwide.

A comparison between different prediction models for invasive breast cancer occurrence in the French E3N cohort

Breast cancer remains a global health concern with a lack of high discriminating prediction models. The k-nearest-neighbor algorithm (kNN) estimates individual risks using an intuitive tool. This study compares the performances of this approach with the Cox and the Gail models for the 5-year breast cancer risk prediction. The study included 64,995 women from the French E3N prospective cohort. The sample was divided into a learning (N = 51,821) series to learn the models using fivefold cross-validation and a validation (N = 13,174) series to evaluate them. The area under the receiver operating characteristic curve (AUC) and the expected over observed number of cases (E/O) ratio were estimated. In the two series, 393 and 78 premenopausal and 537 and 98 postmenopausal breast cancers were diagnosed. The discrimination values of the best combinations of predictors obtained from cross-validation ranged from 0.59 to 0.60. In the validation series, the AUC values in premenopausal and postmenopausal women were 0.583 [0.520; 0.646] and 0.621 [0.563; 0.679] using the kNN and 0.565 [0.500; 0.631] and 0.617 [0.561; 0.673] using the Cox model. The E/O ratios were 1.26 and 1.28 in premenopausal women and 1.44 and 1.40 in postmenopausal women. The applied Gail model provided AUC values of 0.614 [0.554; 0.675] and 0.549 [0.495; 0.604] and E/O ratios of 0.78 and 1.12. This study shows that the prediction performances differed according to menopausal status when using parametric statistical tools. The k-nearest-neighbor approach performed well, and discrimination was improved in postmenopausal women compared with the Gail model.

Development of a risk assessment tool for projecting individualized probabilities of developing breast cancer for Chinese women

The optimal approach regarding breast cancer screening for Chinese women is unclear due to the relative low incidence rate. A risk assessment tool may be useful for selection of high-risk subsets of population for mammography screening in low-incidence and resource-limited developing country. The odd ratios for six main risk factors of breast cancer were pooled by review manager after a systematic research of literature. Health risk appraisal (HRA) model was developed to predict an individual's risk of developing breast cancer in the next 5 years from current age. The performance of this HRA model was assessed based on a first-round screening database. Estimated risk of breast cancer increased with age. Increases in the 5-year risk of developing breast cancer were found with the existence of any of included risk factors. When individuals who had risk above median risk (3.3‰) were selected from the validation database, the sensitivity is 60.0% and the specificity is 47.8%. The unweighted area under the curve (AUC) was 0.64 (95% CI = 0.50-0.78). The risk-prediction model reported in this article is based on a combination of risk factors and shows good overall predictive power, but it is still weak at predicting which particular women will develop the disease. It would be very helpful for the improvement of a current model if more population-based prospective follow-up studies were used for the validation.

Association between oestrogens receptor expressions in breast cancer and comorbidities: a cross-sectional, population-based study

Background

Breast cancer with oestrogen receptor expression is common in older women. Several factors, such as age and reproductive hormone exposure, have been associated with oestrogen receptor expression in breast cancer. However, the association between comorbidities and the oestrogen receptor expression has been poorly studied. We hypothesized that there was an association between burden comorbidity and breast cancer with oestrogen receptor expression in older women.

Objective

To determine whether oestrogen receptor expression in breast cancer was associated with burden comorbidity in community-dwelling women.

Methods

A total of 1,707 women with breast cancer registered on the list of a breast cancer registry were included. The recorded data included: age, Charlson Comorbidity Index score≥1, breast cancer characteristics (coded according to the International Classification of Diseases for Oncology), and breast cancer pathological stage (the pathological-tumour-node-metastasis, Scarff Bloom Richardson, and hormonal status of oestrogen receptor, progesterone receptor, and human epidermal growth factor receptor).

Results

Breast cancer with oestrogen receptor expression was identified in 1,378 patients (80·7%). The fully-adjusted logistic regression showed that oestrogen receptor expression was associated with Charlson Comorbidity Index score≥1 (odds ratio [OR] = 1·91,95%confidence interval [CI] = [1.01–3.61], P = 0·048), progesterone receptor expression (OR = 16·64, 95%CI = [11.62–23.81], P<0·001), human epidermal growth factor receptor (OR = 0·54, 95%CI = [0.34–0.84], P = 0·007), age (OR = 1.02, 95%CI = [1.00–1.03], P = 0.008), Scarff Bloom Richardson grade II and grade III (OR = 0·21with 95%CI = [0.10–0.44] and OR = 0·06 with 95%CI = [0.03–0.12], P<0·001).

Conclusion

Our findings provide new data showing an independent positive association between burden comorbidity and breast cancer with oestrogen receptor expression. This result confirms that evaluation of oestrogen receptor expression in breast cancer should not be limited to hormonal factors stratified by age.

Breast cancer risk in older women: results from the NIH-AARP Diet and Health Study

BACKGROUND

Divergent risk factors exist for premenopausal and postmenopausal breast cancers, but it is unclear whether differences by age exist among postmenopausal women.

METHODS

We examined relationships among 190,872 postmenopausal women, ages 50-71 years recruited during 1995-1996 for the NIH-AARP Diet and Health Study, in whom 7,384 incident invasive breast carcinomas were identified through 2006. Multivariable Cox regression hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated for breast cancer risk factors by age (50-59, 60-69, ≥70 years).

RESULTS

The only factor showing significant statistical heterogeneity by age (p(het) = 0.001) was menopausal hormone therapy duration, but trends were apparent across all ages and the strongest association prevailed among women 60-69 years. Although other risk factors did not show statistically significant heterogeneity by age, we did observe attenuated relations for parity and late age at first birth among older women [e.g., HR for age at first birth ≥30 vs. 20-24 = 1.62 (95% CI 1.23-2.14) for women 50-59 years vs. 1.12 (0.96-1.31) for ≥70 years]. In contrast, risk estimates associated with alcohol consumption and BMI tended to be slightly stronger among the oldest subjects [e.g., HR for BMI ≥35 vs. 18.5-24.9 = 1.24 (95% CI 0.97-1.58) for 50-59 years vs. 1.46 (1.26-1.70) for ≥70 years]. These differences were somewhat more pronounced for estrogen receptor positive and ductal cancers, tumors predominating among older women. Breast cancer family history, physical activity, and previous breast biopsies did not show divergent associations by age.

CONCLUSION

Although breast cancer risk factor differences among older women were not large, they may merit further consideration with respect to individualized risk prediction.

Genome-wide association study identifies 25 known breast cancer susceptibility loci as risk factors for triple-negative breast cancer

Triple-negative (TN) breast cancer is an aggressive subtype of breast cancer associated with a unique set of epidemiologic and genetic risk factors. We conducted a two-stage genome-wide association study of TN breast cancer (stage 1: 1529 TN cases, 3399 controls; stage 2: 2148 cases, 1309 controls) to identify loci that influence TN breast cancer risk. Variants in the 19p13.1 and PTHLH loci showed genome-wide significant associations (P < 5 × 10(-) (8)) in stage 1 and 2 combined. Results also suggested a substantial enrichment of significantly associated variants among the single nucleotide polymorphisms (SNPs) analyzed in stage 2. Variants from 25 of 74 known breast cancer susceptibility loci were also associated with risk of TN breast cancer (P < 0.05). Associations with TN breast cancer were confirmed for 10 loci (LGR6, MDM4, CASP8, 2q35, 2p24.1, TERT-rs10069690, ESR1, TOX3, 19p13.1, RALY), and we identified associations with TN breast cancer for 15 additional breast cancer loci (P < 0.05: PEX14, 2q24.1, 2q31.1, ADAM29, EBF1, TCF7L2, 11q13.1, 11q24.3, 12p13.1, PTHLH, NTN4, 12q24, BRCA2, RAD51L1-rs2588809, MKL1). Further, two SNPs independent of previously reported signals in ESR1 [rs12525163 odds ratio (OR) = 1.15, P = 4.9 × 10(-) (4)] and 19p13.1 (rs1864112 OR = 0.84, P = 1.8 × 10(-) (9)) were associated with TN breast cancer. A polygenic risk score (PRS) for TN breast cancer based on known breast cancer risk variants showed a 4-fold difference in risk between the highest and lowest PRS quintiles (OR = 4.03, 95% confidence interval 3.46-4.70, P = 4.8 × 10(-) (69)). This translates to an absolute risk for TN breast cancer ranging from 0.8% to 3.4%, suggesting that genetic variation may be used for TN breast cancer risk prediction.

Mammographic breast density as a risk factor for breast cancer: awareness in a recently screened clinical sample

BACKGROUND

Breast density is an established, independent risk factor for breast cancer. Despite this, density has not been included in standard risk models or routinely disclosed to patients. However, this is changing in the face of legal mandates and advocacy efforts. Little information exists regarding women's awareness of density as a risk factor, their personal risk, and risk management options.

METHODS

We assessed awareness of density as a risk factor and whether sociodemographic variables, breast cancer risk factors. and perceived breast cancer risk were associated with awareness in 344 women with a recent screening mammogram at a tertiary care center.

FINDINGS

Overall, 62% of women had heard about density as a risk factor and 33% had spoken to a provider about breast density. Of the sample, 18% reported that their provider indicated that they had high breast density. Awareness of density as a risk factor was greater among White women and those with other breast cancer risk factors.

CONCLUSION

Our results suggest that although a growing number of women are aware of breast density as a risk factor, this awareness varies. Growing mandates for disclosure suggest the need for patient education interventions for women at increased risk for the disease and to ensure all women are equally aware of their risks.

Female breast cancer and alcohol consumption: a review of the literature

CONTEXT

Consumption of alcoholic beverages is one of the single most important known and modifiable risk factor for human cancer. Among women, breast cancer is the most common cancer worldwide and the leading cause of cancer-related mortality. Consumption of alcoholic beverages is causally associated with female breast cancer and the association shows a linear dose-response relationship. The role of heavy drinking has been long recognized and even a moderate intake is associated with an increased risk for breast cancer. The present review is an update of the current evidence on the association between alcohol consumption and breast cancer risk. The aim is to gain further insight into this association and to improve our current understanding of the effects of the major modifying factors.

EVIDENCE ACQUISITION

Epidemiologic and experimental studies published since the most recent International Agency for Research on Cancer (IARC) Monograph on alcoholic beverages were identified in PubMed using a combination of keywords such as alcohol, breast cancer, polymorphisms, menopausal status.

EVIDENCE SYNTHESIS

Cumulative lifetime consumption, drinking frequency, drinking patterns and timing of exposure each modulate the association between alcohol consumption and breast cancer. Hormonal status, genetic polymorphisms, and nutritional factors may interact with ethanol metabolism and further influence breast cancer risk.

CONCLUSIONS

Better standardization among experimental and epidemiologic designs in assessing alcohol intake and timing of exposure may improve our understanding of the heterogeneity observed across studies, possibly allowing the quantification of the effects of occasional heavy drinking and the identification of a window of higher susceptibility to breast cancer development.

Body mass index and breast cancer risk according to postmenopausal estrogen-progestin use and hormone receptor status

To assess the joint relationships among body mass index, menopausal status, and breast cancer according to breast cancer subtype and estrogen-progestin medication use, we conducted a meta-analysis of 89 epidemiologic reports published in English during 1980-2012 identified through a systematic search of bibliographic databases. Pooled analysis yielded a summary risk ratio of 0.78 (95% confidence interval (CI): 0.67, 0.92) for hormone receptor-positive premenopausal breast cancer associated with obesity (body mass index (weight (kg)/height (m)(2)) ≥30 compared with <25). Obesity was associated with a summary risk ratio of 1.39 (95% CI: 1.14, 1.70) for receptor-positive postmenopausal breast cancer. For receptor-negative breast cancer, the summary risk ratios of 1.06 (95% CI: 0.70, 1.60) and 0.98 (95% CI: 0.78, 1.22) associated with obesity were null for both premenopausal and postmenopausal women, respectively. Elevated postmenopausal breast cancer risk ratios associated with obesity were limited to women who never took estrogen-progestin therapy, with risk ratios of 1.42 (95% CI: 1.30, 1.55) among never users and 1.18 (95% CI: 0.98, 1.42) among users; too few studies were available to examine this relationship according to receptor subtype. Future research is needed to confirm whether obesity is unrelated to receptor-negative breast cancer in populations of postmenopausal women with low prevalence of hormone medication use.

Assessing breast cancer risk models in Marin County, a population with high rates of delayed childbirth

INTRODUCTION

This study was designed to compare the Breast Cancer Risk Assessment Tool (BCRAT; Gail), International Breast Intervention Study (IBIS; Tyrer-Cuzick), and BRCAPRO breast cancer risk assessment models using data from the Marin Women's Study, a cohort of women within Marin County, California, with high rates of breast cancer, nulliparity, and delayed childbirth. Existing models have not been well-validated in these high-risk populations.

METHODS

Discrimination was assessed using the area under the receiver operating characteristic curve (AUC) and calibration by estimating the ratio of expected-to-observed (E/O) cases. The models were assessed using data from 12,843 participants, of whom 203 had developed cancer during a 5-year period. All tests of statistical significance were 2-sided.

RESULTS

The IBIS model achieved an AUC of 0.65 (95% confidence interval [CI], 0.61-0.68) compared with 0.62 (95% CI, 0.59-0.66) for BCRAT and 0.60 (95% CI, 0.56-0.63) for BRCAPRO. The corresponding estimated E/O ratios for the models were 1.08 (95% CI, 0.95-1.25), 0.81 (95% CI, 0.71-0.93), and 0.59 (95% CI, 0.52-0.68). In women with age at first birth > 30 years, the AUC for the IBIS, BCRAT, and BRCAPRO models was 0.69 (95% CI, 0.62-0.75), 0.63 (95% CI, 0.56-0.70), and 0.62 (95% CI, 0.56-0.68) and the E/O ratio was 1.15 (95% CI, 0.89-1.47), 0.81 (95% CI, 0.63-1.05), and 0.53 (95% CI, 0.41-0.68), respectively.

CONCLUSIONS

The IBIS model was well calibrated for the high-risk Marin mammography population and demonstrated the best calibration of the 3 models in nulliparous women. The IBIS model also achieved the greatest overall discrimination and displayed superior discrimination for women with age at first birth > 30 years.

Development and validation of risk models and molecular diagnostics to permit personalized management of cancer

Despite the advances made in cancer management over the past few decades, improvements in cancer diagnosis and prognosis are still poor, highlighting the need for individualized strategies. Toward this goal, risk prediction models and molecular diagnostic tools have been developed, tailoring each step of risk assessment from diagnosis to treatment and clinical outcomes based on the individual's clinical, epidemiological, and molecular profiles. These approaches hold increasing promise for delivering a new paradigm to maximize the efficiency of cancer surveillance and efficacy of treatment. However, they require stringent study design, methodology development, comprehensive assessment of biomarkers and risk factors, and extensive validation to ensure their overall usefulness for clinical translation. In the current study, the authors conducted a systematic review using breast cancer as an example and provide general guidelines for risk prediction models and molecular diagnostic tools, including development, assessment, and validation.

Lipid metabolism genes in contralateral unaffected breast and estrogen receptor status of breast cancer

Risk biomarkers that are specific to estrogen receptor (ER) subtypes of breast cancer would aid the development and implementation of distinct prevention strategies. The contralateral unaffected breast of women with unilateral breast cancer (cases) is a good model for defining subtype-specific risk because women with ER-negative (ER-) index primaries are at high risk for subsequent ER-negative primary cancers. We conducted random fine needle aspiration of the unaffected breasts of cases. Samples from 30 subjects [15 ER-positive (ER+) and 15 ER- cases matched for age, race and menopausal status] were used for Illumina expression array analysis. Findings were confirmed using quantitative real-time PCR (qRT-PCR) in the same samples. A validation set consisting of 36 subjects (12 ER+, 12 ER- and 12 standard-risk healthy controls) was used to compare gene expression across groups. ER- case samples displayed significantly higher expression of 18 genes/transcripts, 8 of which were associated with lipid metabolism on gene ontology analysis (GO: 0006629). This pattern was confirmed by qRT-PCR in the same samples, and in the 24 cases of the validation set. When compared to the healthy controls in the validation set, significant overexpression of 4 genes (DHRS2, HMGCS2, HPGD and ACSL3) was observed in ER- cases, with significantly lower expression of UGT2B11 and APOD in ER+ cases, and decreased expression of UGT2B7 in both subtypes. These data suggest that differential expression of lipid metabolism genes may be involved in the risk for subtypes of breast cancer, and are potential biomarkers of ER-specific breast cancer risk.

Validation of a breast cancer risk prediction model developed for Black women

BACKGROUND

A breast cancer risk prediction model for black women, developed from data in the Women's Contraceptive and Reproductive Experiences (CARE) study, has been validated in women aged 50 years or older but not among younger women or for specific breast cancer subtypes.

METHODS

We assessed calibration and discrimination of the CARE model in the Black Women's Health Study (BWHS) with data from 45 942 women aged 30 to 69 years at baseline.

RESULTS

During a mean follow-up of 9.5 years, we identified 852 invasive breast cancers. The CARE model predicted 749.6 breast cancers, yielding an expected-to-observed (E/O) ratio of 0.88 (95% confidence interval [CI] = 0.82 to 0.94). The E/O ratio did not appreciably differ between women aged less than 50 years and those aged 50 years or older. The model underpredicted risk to the greatest degree among women aged 25 years or older at birth of first child (E/O = 0.71, 95% CI = 0.63 to 0.81); the model was well calibrated among women aged less than 25 years at birth of first child. The prevalence of later age at birth of first child was higher in the BWHS than in the CARE study, and breast cancer incidence was higher in the BWHS compared with national rates used in the CARE model. With respect to discriminatory accuracy, the concordance statistic was 0.57 (95% CI = 0.55 to 0.59) for breast cancer overall, 0.59 (95% CI = 0.57 to 0.61) for estrogen receptor (ER)-positive breast cancer, and 0.54 (95% CI = 0.50 to 0.57) for ER-negative breast cancer.

CONCLUSIONS

The CARE model underpredicted breast cancer risk in the BWHS, at least in part because of older age at first birth in this cohort, which led to higher breast cancer incidence rates. Our results suggest that inclusion of age at first birth may improve model performance. Discriminatory accuracy was modest and worse for ER-negative breast cancer.

Recalibration of the Gail model for predicting invasive breast cancer risk in Spanish women: a population-based cohort study

The Gail model for predicting the absolute risk of invasive breast cancer has been validated extensively in US populations, but its performance in the international setting remains uncertain. We evaluated the predictive accuracy of the Gail model in 54,649 Spanish women aged 45-68 years who were free of breast cancer at the 1996-1998 baseline mammographic examination in the population-based Navarre Breast Cancer Screening Program. Incident cases of invasive breast cancer and competing deaths were ascertained until the end of 2005 (average follow-up of 7.7 years) through linkage with population-based cancer and mortality registries. The Gail model was tested for calibration and discrimination in its original form and after recalibration to the lower breast cancer incidence and risk factor prevalence in the study cohort, and compared through cross-validation with a Navarre model fully developed from this cohort. The original Gail model overpredicted significantly the 835 cases of invasive breast cancer observed in the cohort (ratio of expected to observed cases 1.46, 95 % CI 1.36-1.56). The recalibrated Gail model was well calibrated overall (expected-to-observed ratio 1.00, 95 % CI 0.94-1.07), but it tended to underestimate risk for women in low-risk quintiles and to overestimate risk in high-risk quintiles (P = 0.01). The Navarre model showed good cross-validated calibration overall (expected-to-observed ratio 0.98, 95 % CI 0.92-1.05) and in different cohort subsets. The Navarre and Gail models had modest cross-validated discrimination indexes of 0.542 (95 % CI 0.521-0.564) and 0.544 (95 % CI 0.523-0.565), respectively. Although the original Gail model cannot be applied directly to populations with different underlying rates of invasive breast cancer, it can readily be recalibrated to provide unbiased estimates of absolute risk in such populations. Nevertheless, its limited discrimination ability at the individual level highlights the need to develop extended models with additional strong risk factors.

Consumption of dairy and meat in relation to breast cancer risk in the Black Women's Health Study

PURPOSE

Dairy and meat consumption may impact breast cancer risk through modification of hormones (e.g., estrogen), through specific nutrients (e.g., vitamin D), or through products formed in processing/cooking (e.g., heterocyclic amines). Results relating meat and dairy intake to breast cancer risk have been conflicting. Thus, we examined the risk of breast cancer in relation to intake of dairy and meat in a large prospective cohort study.

METHODS

In the Black Women's Health Study, 1,268 incident breast cancer cases were identified among 52,062 women during 12 years of follow-up. Multivariable (MV) relative risks (RRs) and 95 % confidence intervals (CIs) were calculated using Cox proportional hazards models.

RESULTS

Null associations were observed for total milk (MV RR = 1.05, 95 % CI 0.74-1.46 comparing ≥1,000-0 g/week) and total meat (MV RR = 1.04, 95 % CI 0.85-1.28 comparing ≥1,000 < 400 g/week) intake and risk of breast cancer. Associations with intakes of specific types of dairy, specific types of meat, and dietary calcium and vitamin D were also null. The associations were not modified by reproductive (e.g., parity) or lifestyle factors (e.g., smoking). Associations with estrogen receptor (ER) positive (+), ER negative (-), progesterone receptor (PR) +, PR-, ER+/PR+, and ER-/PR- breast cancer were generally null.

CONCLUSIONS

This analysis of African-American women provides little support for associations of dairy and meat intake with breast cancer risk.