Validation of a breast cancer risk assessment model in women with a positive family history

Assessment of breast cancer risk among Iraqi women in 2019

Background

Breast cancer is one of the most common cancers among women worldwide and the leading cause of death among Iraqi women. Breast cancer cases in Iraq were found to have increased from 26.6/100,000 in 2000 to 31.5/100,000 in 2009. The present study aims to assess the established risk factors of breast cancer among Iraqi women and to highlight strategies that can aid in reducing the incidence.

Methods

1093 Iraqi females were enrolled in this cross-sectional study by purposive sampling methods. Data collection occurred from July 2019 to September 2019. 1500 women participated in the study, and 407 women were ultimately excluded. The questionnaire was conducted as a self-administrated form in an online survey. Ethical approval was obtained from the College of Medicine in the University of Baghdad. The Gail Model risk was calculated for each woman by the Breast Cancer Risk Assessment Tool (BCRAT), an interactive model developed by Mitchell Gail that was designed to estimate a woman’s absolute risk of developing breast cancer in the upcoming five years of her life and in her lifetime.

Results

The ages of the participants ranged from 35 to 84 years old. The mean 5–year risk of breast cancer was found to be 1.3, with 75.3% of women at low risk and 24.7% of women at high risk. The mean lifetime risk of breast cancer was found to be 13.4, with 64.7% of women at low risk, 30.3% at moderate risk, and 5.0% at high risk. The results show that geographically Baghdad presented the highest 5-year risk, followed by Dhi Qar, Maysan, and Nineveh. However, the highest lifetime risk was found in Najaf, followed by Dhi Qar, Baghdad, and Nineveh, successively.

Conclusion

Breast cancer is a wide-spreading problem in the world and particularly in Iraq, with Gail Model estimations of high risk in several governorates. Prevention programs need to be implemented and awareness campaigns organized in order to highlight the importance of early detection and treatment.

Development of Malignancy-Risk Gene Signature Assay for Predicting Breast Cancer Risk

BACKGROUND

Breast cancer (BC) risk assessment models are statistical estimates based on patient characteristics. We developed a gene expression assay to assess BC risk using benign breast biopsy tissue.

METHODS

A NanoString-based malignancy risk (MR) gene signature was validated for formalin-fixed paraffin-embedded (FFPE) tissue. It was applied to FFPE benign and BC specimens obtained from women who underwent breast biopsy, some of whom developed BC during follow-up to evaluate diagnostic capability of the MR signature. BC risk was calculated with MR score, Gail risk score, and both tests combined. Logistic regression and receiver operating characteristic curves were used to evaluate these 3 models.

RESULTS

NanoString MR demonstrated concordance between fresh frozen and FFPE malignant samples (r = 0.99). Within the validation set, 563 women with benign breast biopsies from 2007 to 2011 were identified and followed for at least 5 y; 50 women developed BC (affected) within 5 y from biopsy. Three groups were compared: benign tissue from unaffected and affected patients and malignant tissue from affected patients. Kruskal-Wallis test suggested difference between the groups (P = 0.09) with trend in higher predicted MR score for benign tissue from affected patients before development of BC. Neither the MR signature nor Gail risk score were statistically different between affected and unaffected patients; combining both tests demonstrated best predictive value (AUC = 0.71).

CONCLUSIONS

FFPE gene expression assays can be used to develop a predictive test for BC. Further investigation of the combined MR signature and Gail Model is required. Our assay was limited by scant cellularity of archived breast tissue.

Validation of the breast cancer surveillance consortium model of breast cancer risk

PURPOSE

In order to use a breast cancer prediction model in clinical practice to guide screening and prevention, it must be well calibrated and validated in samples independent from the one used for development. We assessed the accuracy of the breast cancer surveillance consortium (BCSC) model in a racially diverse population followed for up to 10 years.

METHODS

The BCSC model combines breast density with other risk factors to estimate a woman's 5- and 10-year risk of invasive breast cancer. We validated the model in an independent cohort of 252,997 women in the Chicago area. We evaluated calibration using the ratio of expected to observed (E/O) invasive breast cancers in the cohort and discrimination using the area under the receiver operating characteristic curve (AUROC).

RESULTS

In an independent cohort of 252,997 women (median age 50 years, 26% non-Hispanic Black), the BCSC model was well calibrated (E/O = 0.94, 95% confidence interval [CI] 0.90-0.98), but underestimated the incidence of invasive breast cancer in younger women and in women with low mammographic density. The AUROC was 0.633, similar to that observed in prior validation studies.

CONCLUSIONS

The BCSC model is a well-validated risk assessment tool for breast cancer that may be particularly useful when assessing the utility of supplemental screening in women with dense breasts.

Validation of a personalized risk prediction model for contralateral breast cancer

PURPOSE

Women diagnosed with unilateral breast cancer are increasingly choosing to remove their other unaffected breast through contralateral prophylactic mastectomy (CPM) to reduce the risk of contralateral breast cancer (CBC). Yet a large proportion of CPMs are believed to be medically unnecessary. Thus, there is a pressing need to educate patients effectively on their CBC risk. We had earlier developed a CBC risk prediction model called CBCRisk based on eight personal risk factors.

METHODS

In this study, we validate CBCRisk on independent clinical data from the Johns Hopkins University (JH) and MD Anderson Cancer Center (MDA). Women whose first breast cancer diagnosis was either invasive and/or ductal carcinoma in situ and whose age at first diagnosis was between 18 and 88 years were included in the cohorts because CBCRisk was developed specifically for these women. A woman who develops CBC is called a case whereas a woman who does not is called a control. The cohort sizes are 6035 (with 117 CBC cases) for JH and 5185 (with 111 CBC cases) for MDA. We computed the relevant calibration and validation measures for 3- and 5-year risk predictions.

RESULTS

We found that the model performs reasonably well for both cohorts. In particular, area under the receiver-operating characteristic curve for the two cohorts range from 0.61 to 0.65.

CONCLUSIONS

With this independent validation, CBCRisk can be used confidently in clinical settings for counseling BC patients by providing their individualized CBC risk. In turn, this may potentially help alleviate the rate of medically unnecessary CPMs.

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 .

Discovery and replication of microRNAs for breast cancer risk using genome-wide profiling

Background

Genome-wide miRNA expression may be useful for predicting breast cancer risk and/or for the early detection of breast cancer.

Results

A 41-miRNA model distinguished breast cancer risk in the discovery study (accuracy of 83.3%), which was replicated in the independent study (accuracy = 63.4%, P=0.09). Among the 41 miRNA, 20 miRNAs were detectable in serum, and predicted breast cancer occurrence within 18 months of blood draw (accuracy 53%, P=0.06). These risk-related miRNAs were enriched for HER-2 and estrogen-dependent breast cancer signaling.

Materials and Methods

MiRNAs were assessed in two cross-sectional studies of women without breast cancer and a nested case-control study of breast cancer. Using breast tissues, a multivariate analysis was used to model women with high and low breast cancer risk (based upon Gail risk model) in a discovery study of women without breast cancer (n=90), and applied to an independent replication study (n=71). The model was then assessed using serum samples from the nested case-control study (n=410).

Conclusions

Studying breast tissues of women without breast cancer revealed miRNAs correlated with breast cancer risk, which were then found to be altered in the serum of women who later developed breast cancer. These results serve as proof-of-principle that miRNAs in women without breast cancer may be useful for predicting breast cancer risk and/or as an adjunct for breast cancer early detection. The miRNAs identified herein may be involved in breast carcinogenic pathways because they were first identified in the breast tissues of healthy women.

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.

Germline DNA variations in breast cancer predisposition and prognosis: a systematic review of the literature

Breast cancer is the most common cancer and the second leading cause of death in women worldwide. The disease is caused by a combination of genetic, environmental, lifestyle, and reproductive risk factors. Linkage and family-based studies have identified many pathological germline mutations, which account for around 20% of the genetic risk of familial breast cancer. In recent years, single nucleotide polymorphism-based genetic association studies, especially genome-wide association studies (GWASs), have been very successful in uncovering low-penetrance common variants associated with breast cancer risk. These common variants alone may explain up to an additional 30% of the familial risk of breast cancer. With the advent of available genetic resources and growing collaborations among researchers across the globe, the much needed large sample size to capture variants with small effect sizes and low population frequencies is being addressed, and hence many more common variants are expected to be discovered in the coming days. Here, major GWASs conducted for breast cancer predisposition and prognosis until 2013 are summarized. Few studies investigating other forms of genetic variations contributing to breast cancer predisposition and disease outcomes are also discussed. Finally, the potential utility of the GWAS-identified variants in disease risk models and some future perspectives are presented.

A steroid metabolizing gene variant in a polyfactorial model improves risk prediction in a high incidence breast cancer population

Background

We have combined functional gene polymorphisms with clinical factors to improve prediction and understanding of sporadic breast cancer risk, particularly within a high incidence Caucasian population.

Methods

A polyfactorial risk model (PFRM) was built from both clinical data and functional single nucleotide polymorphism (SNP) gene candidates using multivariate logistic regression analysis on data from 5022 US Caucasian females (1671 breast cancer cases, 3351 controls), validated in an independent set of 1193 women (400 cases, 793 controls), and reassessed in a unique high incidence breast cancer population (165 cases, 173 controls) from Marin County, CA.

Results

The optimized PFRM consisted of 22 SNPs (19 genes, 6 regulating steroid metabolism) and 5 clinical risk factors, and its 5-year and lifetime risk prediction performance proved significantly superior (~ 2-fold) over the Gail model (Breast Cancer Risk Assessment Tool, BCRAT), whether assessed by odds (OR) or positive likelihood (PLR) ratios over increasing model risk levels. Improved performance of the PFRM in high risk Marin women was due in part to genotype enrichment by a CYP11B2 (-344T/C) variant.

Conclusions and general significance

Since the optimized PFRM consistently outperformed BCRAT in all Caucasian study populations, it represents an improved personalized risk assessment tool. The finding of higher Marin County risk linked to a CYP11B2 aldosterone synthase SNP associated with essential hypertension offers a new genetic clue to sporadic breast cancer predisposition.

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A polyfactorial breast cancer risk assessment model (PFRM) was built and validated.

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The optimized PFRM incorporates both genetic (22 SNPs/19 genes) and clinical risk factors.

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The PFRM was further validated in a high risk USA/Marin breast cancer population.

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This PFRM consistently performed significantly better than the BCRAT (Gail model).

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A functional aldosterone synthase SNP in PFRM improved predictive performance in Marin.

Breast cancer risk prediction accuracy in Jewish Israeli high-risk women using the BOADICEA and IBIS risk models

Several breast cancer risk prediction models have been validated in ethnically diverse populations, but none in Israeli high-risk women. To validate the accuracy of the IBIS and BOADICEA risk prediction models in Israeli high-risk women, the 10-year and lifetime risk for developing breast cancer were calculated using both BOADICEA and IBIS models for high-risk, cancer-free women, counselled at the Sheba Medical Center from 1 June 1996-31 May 2000. Women diagnosed with breast cancer by 31 May 2011 were identified from the Israeli National Cancer Registry. The observed to expected breast cancer ratios were calculated to evaluate the predictive value of both algorithms. Overall, 358 mostly (N = 205, 57·2%) Ashkenazi women, were eligible, age range at counselling was 20-75 years (mean 46·76 ± 9·8 years). Over 13·6 ± 1·45 years (range 11-16 years), 15 women (4·19%) were diagnosed with breast cancer, at a mean age of 57 ± 8·6 years. The 10-year risks assigned by BOADICEA and IBIS ranged from 0·2 to 12·6% and 0·89 to 21·7%, respectively. The observed:expected breast cancer ratio was 15/18·6 (0·8-95% CI 0·48-1·33) and 15/28·6 (0·52-95% CI 0·32-0·87), using both models, respectively. In Jewish Israeli high-risk women the BOADICEA model has a better predictive value and accuracy in determining 10-year breast cancer risk than the IBIS model.

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.

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.

Breast cancer risk prediction and mammography biopsy decisions: a model-based study

BACKGROUND

Controversy continues about screening mammography, in part because of the risk of false-negative and false-positive mammograms. Pre-test breast cancer risk factors may improve the positive and negative predictive value of screening.

PURPOSE

To create a model that estimates the potential impact of pre-test risk prediction using clinical and genomic information on the reclassification of women with abnormal mammograms (BI-RADS3 and BI-RADS4 [Breast Imaging-Reporting and Data System]) above and below the threshold for breast biopsy.

METHODS

The current study modeled 1-year breast cancer risk in women with abnormal screening mammograms using existing data on breast cancer risk factors, 12 validated breast cancer single-nucleotide polymorphisms (SNPs), and probability of cancer given the BI-RADS category. Examination was made of reclassification of women above and below biopsy thresholds of 1%, 2%, and 3% risk. The Breast Cancer Surveillance Consortium data were collected from 1996 to 2002. Data analysis was conducted in 2010 and 2011.

RESULTS

Using a biopsy risk threshold of 2% and the standard risk factor model, 5% of women with a BI-RADS3 mammogram had a risk above the threshold, and 3% of women with BI-RADS4A mammograms had a risk below the threshold. The addition of 12 SNPs in the model resulted in 8% of women with a BI-RADS3 mammogram above the threshold for biopsy and 7% of women with BI-RADS4A mammograms below the threshold.

CONCLUSIONS

The incorporation of pre-test breast cancer risk factors could change biopsy decisions for a small proportion of women with abnormal mammograms. The greatest impact comes from standard breast cancer risk factors.

Breast cancer risk assessment across the risk continuum: genetic and nongenetic risk factors contributing to differential model performance

Introduction

Clinicians use different breast cancer risk models for patients considered at average and above-average risk, based largely on their family histories and genetic factors. We used longitudinal cohort data from women whose breast cancer risks span the full spectrum to determine the genetic and nongenetic covariates that differentiate the performance of two commonly used models that include nongenetic factors - BCRAT, also called Gail model, generally used for patients with average risk and IBIS, also called Tyrer Cuzick model, generally used for patients with above-average risk.

Methods

We evaluated the performance of the BCRAT and IBIS models as currently applied in clinical settings for 10-year absolute risk of breast cancer, using prospective data from 1,857 women over a mean follow-up length of 8.1 years, of whom 83 developed cancer. This cohort spans the continuum of breast cancer risk, with some subjects at lower than average population risk. Therefore, the wide variation in individual risk makes it an interesting population to examine model performance across subgroups of women. For model calibration, we divided the cohort into quartiles of model-assigned risk and compared differences between assigned and observed risks using the Hosmer-Lemeshow (HL) chi-squared statistic. For model discrimination, we computed the area under the receiver operator curve (AUC) and the case risk percentiles (CRPs).

Results

The 10-year risks assigned by BCRAT and IBIS differed (range of difference 0.001 to 79.5). The mean BCRAT- and IBIS-assigned risks of 3.18% and 5.49%, respectively, were lower than the cohort's 10-year cumulative probability of developing breast cancer (6.25%; 95% confidence interval (CI) = 5.0 to 7.8%). Agreement between assigned and observed risks was better for IBIS (HL X₄² = 7.2, P value 0.13) than BCRAT (HL X₄² = 22.0, P value <0.001). The IBIS model also showed better discrimination (AUC = 69.5%, CI = 63.8% to 75.2%) than did the BCRAT model (AUC = 63.2%, CI = 57.6% to 68.9%). In almost all covariate-specific subgroups, BCRAT mean risks were significantly lower than the observed risks, while IBIS risks showed generally good agreement with observed risks, even in the subgroups of women considered at average risk (for example, no family history of breast cancer, BRCA1/2 mutation negative).

Conclusions

Models developed using extended family history and genetic data, such as the IBIS model, also perform well in women considered at average risk (for example, no family history of breast cancer, BRCA1/2 mutation negative). Extending such models to include additional nongenetic information may improve performance in women across the breast cancer risk continuum.

Risk of developing breast cancer by utilizing Gail model

The Gail model has been widely used to quantify an individual woman's risk of developing breast cancer by using important clinical parameters, usually for clinical counselling purposes or to determine eligibility for mammography and genetic tests. The aim of the present study was to estimate the five-year and lifetime breast cancer risk among women in Rasht, Iran. In this cross-sectional study, 314 women were evaluated at Alzahra Women Hospital in 2007. Participants were ≥35 years of age without a history of breast cancer. Risk estimation was performed using the computerized Gail model. A five-year risk >1.66% was considered high-risk; 5.1% of women were high-risk. The mean five-year breast cancer risk was 0.8% (SD±1). Mean breast cancer risk up to the age of 90 years (lifetime risk) was 9.0% (SD±3.9%); 16.2% of the participants had a five-year risk higher than the average woman of the same age, and 18.2% had the same risk. Also for the lifetime risk, 11.1% of the women had higher risk and 1.6% had the same risk as the average woman. Routine use of the Gail model is recommended for identifying women at high average risk for increasing the survival of women from breast cancer.

Evaluating breast cancer risk projections for Hispanic women

For Hispanic women, the Breast Cancer Risk Assessment Tool (BCRAT; "Gail Model") combines 1990-1996 breast cancer incidence for Hispanic women with relative risks for breast cancer risk factors from non-Hispanic white (NHW) women. BCRAT risk projections have never been comprehensively evaluated for Hispanic women. We compared the relative risks and calibration of BCRAT risk projections for 6,353 Hispanic to 128,976 NHW postmenopausal participants aged 50 and older in the Women's Health Initiative (WHI). Calibration was assessed by the ratio of the number of breast cancers observed with that expected by the BCRAT (O/E). We re-evaluated calibration for an updated BCRAT that combined BCRAT relative risks with 1993-2007 breast cancer incidence that is contemporaneous with the WHI. Cox regression was used to estimate relative risks. Discriminatory accuracy was assessed using the concordance statistic (AUC). In the WHI Main Study, the BCRAT underestimated the number of breast cancers by 18% in both Hispanics (O/E = 1.18, P = 0.06) and NHWs (O/E = 1.18, P < 0.001). Updating the BCRAT improved calibration for Hispanic women (O/E = 1.08, P = 0.4) and NHW women (O/E = 0.98, P = 0.2). For Hispanic women, relative risks for number of breast biopsies (1.71 vs. 1.27, P = 0.03) and age at first birth (0.97 vs. 1.24, P = 0.02) differed between the WHI and BCRAT. The AUC was higher for Hispanic women than NHW women (0.63 vs. 0.58, P = 0.03). Updating the BCRAT with contemporaneous breast cancer incidence rates improved calibration in the WHI. The modest discriminatory accuracy of the BCRAT for Hispanic women might improve by using risk factor relative risks specific to Hispanic women.

Performing breast cancer risk assessments in a community setting

This article describes the implementation of a risk assessment program for women having screening mammography at a community center. The program used the National Cancer Institute's Breast Cancer Risk Assessment Tool to raise awareness in high-risk women. An evidence-based process is essential when implementing changes in clinical practice to overcome challenges and barriers.

Five-year and lifetime risk of breast cancer among U.S. subpopulations: implications for magnetic resonance imaging screening

BACKGROUND

Guidelines from the American Cancer Society recommend annual breast magnetic resonance imaging (MRI) screening for women with a projected lifetime risk of ≥20% based on risk models that use family history. Because MRI screening is costly and has limited specificity, estimates of the numbers of U.S. women with ≥20% breast cancer risk would be useful.

METHODS

We used data from the 2000 and 2005 National Health Interview Survey and the National Cancer Institute (NCI) Breast Cancer Risk Assessment Tool (i.e., Gail model 2 with a revision for African Americans) to calculate estimates of U.S. women by age and race/ethnicity categories with a lifetime absolute breast cancer risk of ≥20%. Distributions of 5-year and lifetime absolute risk of breast cancer were compared across demographic groups.

RESULTS

We estimated that 1.09% (95% confidence interval, 0.95-1.24%) of women age 30 to 84 years have a lifetime absolute breast cancer risk of ≥20%, which translates to 880,063 U.S. women eligible for MRI screening. The 5-year risks are highest for white non-Hispanics and lowest for Hispanics. The lifetime risks decrease with age and are generally highest for white non-Hispanics, lower for African American non-Hispanic, and lowest for Hispanics.

CONCLUSION

We provide national estimates of the number of U.S. women who would be eligible for MRI breast screening and distributions of 5-year and lifetime risks of breast cancer using the NCI Breast Cancer Risk Assessment Tool.

IMPACT

These estimates inform the potential resources and public health demand for MRI screening and chemopreventive interventions that might be required for U.S. women.

Assessing women at high risk of breast cancer: a review of risk assessment models

Women who are at high risk of breast cancer can be offered more intensive surveillance or prophylactic measures, such as surgery or chemoprevention. Central to decisions regarding the level of prevention is accurate and individualized risk assessment. This review aims to distill the diverse literature and provide practicing clinicians with an overview of the available risk assessment methods. Risk assessments fall into two groups: the risk of carrying a mutation in a high-risk gene such as BRCA1 or BRCA2 and the risk of developing breast cancer with or without such a mutation. Knowledge of breast cancer risks, taken together with the risks and benefits of the intervention, is needed to choose an appropriate disease management strategy. A number of models have been developed for assessing these risks, but independent validation of such models has produced variable results. Some models are able to predict both mutation carriage risks and breast cancer risk; however, to date, all are limited by only moderate discriminatory accuracy. Further improvements in the knowledge of how to best integrate both new risk factors and newly discovered genetic variants into these models will allow clinicians to more accurately determine which women are most likely to develop breast cancer. These steady and incremental improvements in models will need to undergo revalidation.

Effect of changing breast cancer incidence rates on the calibration of the Gail model

PURPOSE

The Gail model combines relative risks (RRs) for five breast cancer risk factors with age-specific breast cancer incidence rates and competing mortality rates from the Surveillance, Epidemiology, and End Results (SEER) program from 1983 to 1987 to predict risk of invasive breast cancer over a given time period. Motivated by changes in breast cancer incidence during the 1990s, we evaluated the model's calibration in two recent cohorts.

METHODS

We included white, postmenopausal women from the National Institutes of Health (NIH) -AARP Diet and Health Study (NIH-AARP, 1995 to 2003), and the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial (PLCO, 1993 to 2006). Calibration was assessed by comparing the number of breast cancers expected from the Gail model with that observed. We then evaluated calibration by using an updated model that combined Gail model RRs with 1995 to 2003 SEER invasive breast cancer incidence rates.

RESULTS

Overall, the Gail model significantly underpredicted the number of invasive breast cancers in NIH-AARP, with an expected-to-observed ratio of 0.87 (95% CI, 0.85 to 0.89), and in PLCO, with an expected-to-observed ratio of 0.86 (95% CI, 0.82 to 0.90). The updated model was well-calibrated overall, with an expected-to-observed ratio of 1.03 (95% CI, 1.00 to 1.05) in NIH-AARP and an expected-to-observed ratio of 1.01 (95% CI: 0.97 to 1.06) in PLCO. Of women age 50 to 55 years at baseline, 13% to 14% had a projected Gail model 5-year risk lower than the recommended threshold of 1.66% for use of tamoxifen or raloxifene but >or= 1.66% when using the updated model. The Gail model was well calibrated in PLCO when the prediction period was restricted to 2003 to 2006.

CONCLUSION

This study highlights that model calibration is important to ensure the usefulness of risk prediction models for clinical decision making.

Prospective multicenter cohort study to refine management recommendations for women at elevated familial risk of breast cancer: the EVA trial

PURPOSE

We investigated the respective contribution (in terms of cancer yield and stage at diagnosis) of clinical breast examination (CBE), mammography, ultrasound, and quality-assured breast magnetic resonance imaging (MRI), used alone or in different combination, for screening women at elevated risk for breast cancer.

METHODS

Prospective multicenter observational cohort study. Six hundred eighty-seven asymptomatic women at elevated familial risk (> or = 20% lifetime) underwent 1,679 annual screening rounds consisting of CBE, mammography, ultrasound, and MRI, read independently and in different combinations. In a subgroup of 371 women, additional half-yearly ultrasound and CBE was performed more than 869 screening rounds. Mean and median follow-up was 29.18 and 29.09 months.

RESULTS

Twenty-seven women were diagnosed with breast cancer: 11 ductal carcinoma in situ (41%) and 16 invasive cancers (59%). Three (11%) of 27 were node positive. All cancers were detected during annual screening; no interval cancer occurred; no cancer was identified during half-yearly ultrasound. The cancer yield of ultrasound (6.0 of 1,000) and mammography (5.4 of 1,000) was equivalent; it increased nonsignificantly (7.7 of 1,000) if both methods were combined. Cancer yield achieved by MRI alone (14.9 of 1,000) was significantly higher; it was not significantly improved by adding mammography (MRI plus mammography: 16.0 of 1,000) and did not change by adding ultrasound (MRI plus ultrasound: 14.9 of 1,000). Positive predictive value was 39% for mammography, 36% for ultrasound, and 48% for MRI.

CONCLUSION

In women at elevated familial risk, quality-assured MRI screening shifts the distribution of screen-detected breast cancers toward the preinvasive stage. In women undergoing quality-assured MRI annually, neither mammography, nor annual or half-yearly ultrasound or CBE will add to the cancer yield achieved by MRI alone.

Breast cancer risk assessments comparing Gail and CARE models in African-American women

The Gail model has been used to predict invasive breast cancer risk in women using risk factors of age, age at menarche, age at first live birth, number of first-degree relatives with breast cancer, and number of previous benign breast biopsies. However, this model underestimates breast cancer risk in African-American women. The Contraceptive and Reproductive Experience (CARE) model has been developed to replace the Gail model in predicting breast cancer risk in African-American women. In a sample of 883 women who participated in the breast cancer screening program at Howard University Cancer Center, we compared the breast cancer risk estimates from the Gail model and the CARE model. The mean 5-year breast cancer risk was 0.88% (Range: 0.18-6.60%) for the Gail model and 1.29% (Range: 0.20-4.50%) for the CARE model. Using the usual cutoff-point of 1.67% or above for elevated risk, there is a significant difference in the proportion of women with elevated breast cancer risk between the Gail and the CARE models (McNemar's test, p < 0.0001). For both models, there was a significant mean risk difference between those with and without a family history of breast cancer (Wilcoxon rank-sum test, p < 0.0001). Our results confirm the need for validation of the Gail model in African-Americans and diversity in research. Although these findings are not perfect and perhaps not definitive, they are additive in the discussions during counseling and risk assessment in African-Americans. Furthermore, these findings will be complemented by new technologies such as genomics in refining our ability to assess risk.

Applicability of the Gail model for breast cancer risk assessment in Turkish female population and evaluation of breastfeeding as a risk factor

The Gail model is considered the best available means for estimating risk of breast cancer development, but it has not yet been applied systematically and validated in Turkish female population. This study was designed to evaluate the performance of the Gail model for Turkish female population. Additionally duration of breastfeeding was examined as a possible risk factor. Our analysis included 650 patients with invasive breast carcinoma (group 1) and 640 women with negative results who had undergone a screening mammography on visiting a mammary care unit (group 2). Two groups were compared with regard to individual risk factors included in the Gail model and also duration of breastfeeding. The Gail model was used to predict 5-year risk for each woman. Age and first live birth > or =30 years were associated with an increased relative risk for breast cancer development. Age at menarche, previous breast biopsy, atypical hyperplasia, and number of first degree relatives with breast cancer were found to be non-significant. The Gail model showed 13.3% sensitivity and 92% specificity in estimating the risk of breast cancer development in Turkish women. Positive predictive value was 63%, negative predictive value was 51.9%, and validity index was 53.1%. Duration of breastfeeding was significantly longer in group 1 than 2 (median 17 vs. 13 months). The proportion of parous women with no breastfed was higher in group 1 than 2. The currently used Gail model does not seem to be an appropriate breast cancer risk assessment tool for Turkish female population.

Overview of risk prediction models in cardiovascular disease research

Many risk prediction models have been developed for cardiovascular diseases in different countries during the past three decades. However, there has not been consistent agreement regarding how to appropriately assess a risk prediction model, especially when new markers are added to an established risk prediction model. Researchers often use the area under the receiver operating characteristic curve (ROC) to assess the discriminatory ability of a risk prediction model. However, recent studies suggest that this method has serious limitations and cannot be the sole approach to evaluate the usefulness of a new marker in clinical and epidemiological studies. To overcome the shortcomings of this traditional method, new assessment methods have been proposed. The aim of this article is to overview various risk prediction models for cardiovascular diseases, to describe the receiver operating characteristic curve method and discuss some new assessment methods proposed recently. Some of the methods were illustrated with figures from a cardiovascular disease study in Australia.

Predictors of contralateral breast cancer in patients with unilateral breast cancer undergoing contralateral prophylactic mastectomy

BACKGROUND

Although contralateral prophylactic mastectomy (CPM) reduced the risk of contralateral breast cancer in unilateral breast cancer patients, it was difficult to predict which patients were most likely to benefit from the procedure. The objective of this study was to identify the clinicopathologic factors that predict contralateral breast cancer and thereby inform decisions regarding performing CPM in unilateral breast cancer patients.

METHODS

A total of 542 unilateral breast cancer patients who underwent CPM at The University of Texas M. D. Anderson Cancer Center from January 2000 to April 2007 were included in the current study. A logistic regression analysis was used to identify clinicopathologic factors that predict contralateral breast cancer.

RESULTS

Of the 542 patients included in this study, 25 (5%) had an occult malignancy in the contralateral breast. Eighty-two patients (15%) had moderate-risk to high-risk histologic findings identified at final pathologic evaluation of the contralateral breast. Multivariate analysis revealed that 3 independent factors predicted malignancy in the contralateral breast: an ipsilateral invasive lobular histology, an ipsilateral multicentric tumor, and a 5-year Gail risk >or=1.67%. Multivariate analysis also revealed that an age >or=50 years at the time of the initial cancer diagnosis and an additional ipsilateral moderate-risk to high-risk pathology were independent predictors of moderate-risk to high-risk histologic findings in the contralateral breast.

CONCLUSIONS

The findings indicated that CPM may be a rational choice for breast cancer patients who have a 5-year Gail risk >or=1.67%, an additional ipsilateral moderate-risk to high-risk pathology, an ipsilateral multicentric tumor, or an ipsilateral tumor of invasive lobular histology.

Epidemiology, genetics, and risk evaluation of postmenopausal women at risk of breast cancer

Breast cancer risk factors have been studied for the past three decades, and the single most important risk factor is age. Hormonally linked adult reproductive and anthropometric risk factors contribute to the etiology of postmenopausal breast cancer. The risk of breast cancer increases among women older than 50 years of age who have benign breast disease, especially those with atypical ductal or lobular hyperplasia. Lobular carcinoma in situ increases risk significantly, as do a family history of breast cancer in first-degree relatives and the presence of BRCA1 or BRCA2 mutations. Diet, exercise, and environmental factors play a very small role in overall risk. Mammographic breast density increases relative risk fivefold among women with the highest density, and breast cancer risk is two to three times greater in women with elevated serum levels of estradiol or testosterone. Multivariate risk models allow determination of composite relative risks and cumulative lifetime risk, although improved models for African American women are required. For postmenopausal women, newer risk models are being developed and validated that include age, breast density, race, ethnicity, family history of breast cancer, a previous breast biopsy, body mass index, age at onset of natural menopause, hormone therapy, and previous false-positive mammography. A simpler model that includes only age, breast cancer in first-degree relatives, and previous breast biopsy performs well for estrogen receptor-positive breast cancer in postmenopausal women. As many as 10 million women in the United States are at increased risk, and clinicians are obligated to identify these women and manage their risk appropriately.

Assessment of the accuracy of the Gail model in women with atypical hyperplasia

PURPOSE

An accurate estimate of a woman's breast cancer risk is essential for optimal patient counseling and management. Women with biopsy-confirmed atypical hyperplasia of the breast (atypia) are at high risk for breast cancer. The Gail model is widely used in these women, but has not been validated in them.

PATIENTS AND METHODS

Women with atypia were identified from the Mayo Benign Breast Disease (BBD) cohort (1967 to 1991). Their risk factors for breast cancer were obtained, and the Gail model was used to predict 5-year-and follow-up-specific risks for each woman. The predicted and observed numbers of breast cancers were compared, and the concordance between individual risk levels and outcomes was computed.

RESULTS

Of the 9,376 women in the BBD cohort, 331 women had atypia (3.5%). At a mean follow-up of 13.7 years, 58 of 331 (17.5%) patients had developed invasive breast cancer, 1.66 times more than the 34.9 predicted by the Gail model (95% CI, 1.29 to 2.15; P < .001). For individual women, the concordance between predicted and observed outcomes was low, with a concordance statistic of 0.50 (95% CI, 0.44 to 0.55).

CONCLUSION

The Gail model significantly underestimates the risk of breast cancer in women with atypia. Its ability to discriminate women with atypia into those who did and did not develop breast cancer is limited. Health care professionals should be cautious when using the Gail model to counsel individual patients with atypia.

Gail model risk factors: impact of adding an extended family history for breast cancer

An approach commonly used in estimating breast cancer risk is the Gail model. The objective of this study was to evaluate the feasibility and impact of adding extended family history as a new breast cancer risk factor into the Gail model. The data of the present study include cases with breast cancer and hospitalized controls recruited in the National Cancer Institute of Naples (southern Italy) between 1997 and 2000. We compared the first-degree relative (FDR) risk factor (standard Gail model) with the second-degree relative (SDR) information; and the FDR risk factor (standard Gail model) with the combination of FDR and SDR. We computed the c-statistic by comparing the risks found in our population to those in Gail-US population. The concordance for the model with FDR was 0.55 (95% CI 0.53-0.58), the model with SDR shows a modest but significant discriminatory accuracy (0.56, 95% CI 0.53-0.59), and the combination of FDR+SDR gave the concordance statistic of 0.57 (95% CI 0.54-0.60), indicating a good comparison between the two models. The results of our study show that extended family history information could be useful to improve the discriminatory power of the Gail model risk factors.

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

BACKGROUND

Strategies for estrogen receptor (ER)-positive breast cancer risk reduction in postmenopausal women require screening of large populations to identify those with potential benefit. We evaluated and attempted to improve the performance of the Breast Cancer Risk Assessment Tool (i.e., the Gail model) for estimating invasive breast cancer risk by receptor status in postmenopausal women.

METHODS

In The Women's Health Initiative cohort, breast cancer risk estimates from the Gail model and models incorporating additional or fewer risk factors and 5-year incidence of ER-positive and ER-negative invasive breast cancers were determined and compared by use of receiver operating characteristics and area under the curve (AUC) statistics. All statistical tests were two-sided.

RESULTS

Among 147,916 eligible women, 3236 were diagnosed with invasive breast cancer. The overall AUC for the Gail model was 0.58 (95% confidence interval [CI]=0.56 to 0.60). The Gail model underestimated 5-year invasive breast cancer incidence by approximately 20% (P<.001), mostly among those with a low estimated risk. Discriminatory performance was better for the risk of ER-positive cancer (AUC = 0.60, 95% CI = 0.58 to 0.62) than for the risk of ER-negative cancer (AUC = 0.50, 95% CI = 0.45 to 0.54). Age and age at menopause were statistically significantly associated with ER-positive but not ER-negative cancers (P=.05 and P=.04 for heterogeneity, respectively). For ER-positive cancers, no additional risk factors substantially improved the Gail model prediction. However, a simpler model that included only age, breast cancer in first-degree relatives, and previous breast biopsy examination performed similarly for ER-positive breast cancer prediction (AUC=0.58, 95% CI= 0.56 to 0.60); postmenopausal women who were 55 years or older with either a previous breast biopsy examination or a family history of breast cancer had a 5-year breast cancer risk of 1.8% or higher.

CONCLUSIONS

In postmenopausal women, the Gail model identified populations at increased risk for ER-positive but not ER-negative breast cancers. A model with fewer variables appears to provide a simpler approach for screening for breast cancer risk.

C-reactive protein in nipple aspirate fluid associated with Gail model factors

BACKGROUND

The majority of breast cancers originate in the epithelial lining of the breast ductal system. Premalignant cell damage in this lining may produce biochemical signals that deliver inflammatory proteins to the site. The presence of C-reactive protein (CRP) in nipple aspirate fluid (NAF) may reflect an inflammatory state indicative of a premalignant breast microenvironment. This study ascertained CRP's presence in NAF and evaluated if risk factors, as identified by the Gail model, were associated with NAF CRP levels among healthy women.

DESIGN

NAF CRP levels were assayed in 59 women.

RESULTS

CRP was present in NAF and significantly (p = .04) and positively related to breast cancer risk as predicted by the Gail model.

CONCLUSION

CRP is differentially present in NAF and varies by Gail model risk factors. CRP in NAF holds promise as a noninvasive biomarker that detects a precarcinogenic breast ductal microenvironment and may contribute to the diagnosis of breast cancer early in the course of the disease when prognosis is most favorable.

Genetic susceptibility to breast cancer

Deleterious mutations in two breast and ovarian cancer susceptibility genes, BRCA1 and BRCA2 have been identified in breast and ovarian cancer families. Women with a BRCA1 or BRCA2 mutation are candidates for additional risk reduction measures such as intensive screening, prophylactic surgery or chemoprevention. Additional susceptibility genes have been identified, including PTEN, ATM, TP53, CHEK2, CASP8, PBRL and BRIP1. Yet, many women with a personal or family history suggestive of a hereditary susceptibility to breast cancer undergo genetic testing and no significant genetic alteration is found. Thus, there are other susceptibility genes that have not been identified, and it is likely that the remaining familial contribution to breast cancer will be explained by the presence of multiple low penetrance alleles that coexist to confer high penetrance risks (a polygenic model). The American Cancer Society has identified cancer prevention as a key component of cancer management and there is interest in developing individualized cancer prevention focused on identifying high risk individuals who are most likely to benefit from more aggressive risk reduction measures. Breast cancer risk assessment and genetic counseling are currently provided by genetic counselors, oncology nurse specialist, geneticists, medical and surgical oncologists, gynecologists and other health care professionals, often working within a multidisciplinary clinical setting. Current methods for risk assessment and predictive genetic testing have limitations and improvements in molecular testing and risk assessment tools is necessary to maximize individual breast cancer risk assessment and to fulfill the promise of cancer prevention.

Diagnostic accuracy of the Gail model in the Black Women's Health Study

The Gail model is used to predict the risk of breast cancer in women of diverse race/ethnic groups for clinical trial protocols. However, this model has only been validated in US white women. Using a nested case-control study design, we evaluated the diagnostic accuracy of the original Gail model (GM) and that of the revised Gail model algorithm for blacks/African-Americans (GM-B) in the Black Women's Health Study (BWHS). Risk profiles were derived via a self reported questionnaire at the time of enrollment into the BWHS in 1995. Biennial questionnaires were obtained from the participants to determine the incident cases of breast cancer. The study of 725 breast cancer cases and 725 controls revealed that the 5-year risk of breast cancer based on the GM ranged from 0.2% to 15.4% among cases and 0.2% to 13.6% among the controls. Based on the GM-B, the 5-year risk of breast cancer ranged from 0.2% to 8.7% among cases and 0.2% to 7.2% among the controls. The sensitivities of the GM and GM-B model with the standard cutoff of 1.7% were 17.9% (95% CI: 15.9-19.9%) and 4.1% (95% CI: 3.0-5.2), respectively. Both the original and the modified version of the Gail model underestimate the risk of developing breast cancer in African-American women. More importantly, the modified Gail Model (GM-B) does a worse job at predicting the development of breast cancer for blacks than the original model (GM).

Hormonal interventions to prevent hormonal cancers: breast and prostate cancers

In 1998, the concept of breast cancer prevention became a reality with the approval of tamoxifen to reduce the risk of developing breast cancer in women at increased risk for the disease. This approval was based on decades of research on selective estrogen receptor modulators providing an understanding of the role of the estrogen receptor in breast cell growth, and an appreciation of the carcinogenic process. Although results from the Breast Cancer Prevention Trial demonstrated a 49% reduction in breast cancer in women at increased risk, there were associated toxicities related to the estrogenic effects of tamoxifen; that is, deep vein thrombosis, pulmonary embolism, and endometrial cancer. In an effort to improve its benefit-risk profile, tamoxifen is now being compared with raloxifene, a selective estrogen receptor modulator approved for the treatment and prevention of osteoporosis. This equivalency prevention Study of Tamoxifen and Raloxifene completed accrual of 19 747 high-risk postmenopausal women in November 2004. Meanwhile, another class of estrogen-directed drugs, the aromatase inhibitors, have shown efficacy in breast cancer adjuvant trials, spawning a number of prevention trials that have recently been initiated. As with breast cancer the hormonal contribution to prostate carcinogenesis was the basis for the Prostate Cancer Prevention Trial which showed that finasteride, an androgen antagonist, reduces the incidence of prostate cancer compared to placebo.

Role of physical activity in modulating breast cancer risk as defined by APC and RASSF1A promoter hypermethylation in nonmalignant breast tissue

Physical activity reduces breast cancer risk. Promoter hypermethylation of the tumor suppressor genes APC and RASSF1A, which is potentially reversible, is associated with breast cancer risk. We conducted a cross-sectional study in 45 women without breast cancer to determine the association of physical activity with promoter hypermethylation of APC and RASSF1A in breast tissue. We used quantitative methylation-specific PCR to test the methylation status of APC and RASSF1A, and questionnaires to assess study covariates and physical activity (measured in metabolic equivalent hours per week). In univariate analyses, the study covariate, benign breast biopsy number, was positively associated with promoter hypermethylation of APC (P = 0.01) but not RASSF1A. Mulitvariate logistic regression indicated that, although not significant, physical activities for a lifetime [odds ratio (OR), 0.57; 95% confidence interval (95% CI), 0.22-1.45; P = 0.24], previous 5 years (OR, 0.62; 95% CI, 0.34-1.12; P = 0.11), and previous year (OR, 0.72; 95% CI, 0.43-1.22; P = 0.22) were inversely related to promoter hypermethylation of APC but not RASSF1A for all physical activity measures. Univariate logistic regression indicated that physical activities for a lifetime, previous 5 years, and previous year were inversely associated with benign breast biopsy number, and these results were approaching significance for lifetime physical activity (OR, 0.41; 95% CI, 0.16-1.01; P = 0.05) and significant for physical activity in the previous 5 years (OR, 0.57; 95% CI, 0.34-0.94; P = 0.03). The study provides indirect evidence supporting the hypothesis that physical activity is inversely associated with promoter hypermethylation of tumor suppressor genes, such as APC, in nonmalignant breast tissue.

Breast Cancer Risk Assessment and Risk Reduction

Until recently, the primary message of breast health awareness programs was that early detection is a woman's best protection against breast cancer, because there was no way to prevent it. Currently, however, tamoxifen is approved for chemoprevention of breast cancer in high-risk women, and studies are underway evaluating other medications that may decrease breast cancer risk. Data have also become available regarding the efficacy of surgical strategies to reduce breast cancer risk. Any prevention method, however, will have associated risk of complications or adverse effects, and determining the net risk/benefit ratio depends on the ability to accurately quantify a woman's baseline likelihood of developing breast cancer. This article reviews available methods for assessing and reducing breast cancer risk.

Gail model for prediction of absolute risk of invasive breast cancer: independent evaluation in the Florence-European Prospective Investigation Into Cancer and Nutrition cohort

BACKGROUND

The Gail model 2 (GM) for predicting the absolute risk of invasive breast cancer has been used for counseling and to design intervention studies. Although the GM has been validated in US populations, its performance in other populations is unclear because of the wide variation in international breast cancer rates.

METHODS

We used data from a multicenter case-control study in Italy and from Italian cancer registries to develop a model (IT-GM) that uses the same risk factors as the GM. We evaluated the accuracy of the IT-GM and the GM using independent data from the Florence-European Prospective Investigation Into Cancer and Nutrition (EPIC) cohort. To assess model calibration (i.e., how well the model predicts the observed numbers of events in subsets of the population), we compared the number of expected incident breast cancers (E) predicted by these models with the number of observed incident breast cancers (O), and we computed the concordance statistic to measure discriminatory accuracy.

RESULTS

The overall E/O ratios were 0.96 (95% confidence interval [CI] = 0.84 to 1.11) and 0.93 (95% CI = 0.81 to 1.08) for the IT-GM and the GM, respectively. The IT-GM was somewhat better calibrated than GM in women younger than 50 years, but the GM was better calibrated when age at first live birth categories were considered (e.g., 20- to 24-year age-at-first-birth category E/O = 0.68, 95% CI = 0.53 to 0.94 for the IT-GM and E/O = 0.75, 95% CI = 0.58 to 1.03 for the GM). The concordance statistic was approximately 59% for both models, with 95% confidence intervals indicating that the models perform statistically significantly better than pure chance (concordance statistic of 50%).

CONCLUSIONS

There was no statistically significant evidence of miscalibration overall for either the IT-GM or the GM, and the models had equivalent discriminatory accuracy. The good performance of the IT-GM when applied on the independent data from the Florence-EPIC cohort indicates that GM can be improved for use in populations other than US populations. Our findings suggest that the Italian data may be useful for revising the GM to include additional risk factors for breast cancer.

Personal care products that contain estrogens or xenoestrogens may increase breast cancer risk

Established models of breast cancer risk, such as the Gail model, do not account for patterns of the disease in women under the age of 35, especially in African Americans. With the possible exceptions of ionizing radiation or inheriting a known genetic mutation, most of the known risk factors for breast cancer are related to cumulative lifetime exposure to estrogens. Increased risk of breast cancer has been associated with earlier onset of menses or later age at menopause, nulliparity or late first parity, use of hormonal contraceptives or hormone replacement therapy, shorter lactation history, exposure to light at night, obesity, and regular ingestion of alcohol, all of which increase circulating levels of unbound estradiol. Among African Americans at all ages, use of hormone-containing personal care products (PCPs) is more common than among whites, as is premature appearance of secondary sexual characteristics among infants and toddlers. We hypothesize that the use of estrogen and other hormone-containing PCPs in young African American women accounts, in part, for their increased risk of breast cancer prior to menopause, by subjecting breast buds to elevated estrogen exposure during critical windows of vulnerability in utero and in early life. These early life and continuing exposures to estrogenic and xenoestrogenic agents may also contribute to the increased lethality of breast cancer in young women in general and in African American women of all ages. Public disclosure by manufacturers of proprietary hormonally active ingredients is required for this research to move forward.

Prospective breast cancer risk prediction model for women undergoing screening mammography

BACKGROUND

Risk prediction models for breast cancer can be improved by the addition of recently identified risk factors, including breast density and use of hormone therapy. We used prospective risk information to predict a diagnosis of breast cancer in a cohort of 1 million women undergoing screening mammography.

METHODS

There were 2,392,998 eligible screening mammograms from women without previously diagnosed breast cancer who had had a prior mammogram in the preceding 5 years. Within 1 year of the screening mammogram, 11,638 women were diagnosed with breast cancer. Separate logistic regression risk models were constructed for premenopausal and postmenopausal examinations by use of a stringent (P<.0001) criterion for the inclusion of risk factors. Risk models were constructed with 75% of the data and validated with the remaining 25%. Concordance of the predicted with the observed outcomes was assessed by a concordance (c) statistic after logistic regression model fit. All statistical tests were two-sided.

RESULTS

Statistically significant risk factors for breast cancer diagnosis among premenopausal women included age, breast density, family history of breast cancer, and a prior breast procedure. For postmenopausal women, the statistically significant factors included age, breast density, race, ethnicity, family history of breast cancer, a prior breast procedure, body mass index, natural menopause, hormone therapy, and a prior false-positive mammogram. The model may identify high-risk women better than the Gail model, although predictive accuracy was only moderate. The c statistics were 0.631 (95% confidence interval [CI] = 0.618 to 0.644) for premenopausal women and 0.624 (95% CI = 0.619 to 0.630) for postmenopausal women.

CONCLUSION

Breast density is a strong additional risk factor for breast cancer, although it is unknown whether reduction in breast density would reduce risk. Our risk model may be able to identify women at high risk for breast cancer for preventive interventions or more intensive surveillance.