Gail Model Breast Cancer Risk Research Articles

Abstract PD1-07: Mammographic density and SNPs add to Tyrer-Cuzick and Gail model breast cancer risk in a UK screening cohort

Abstract Background: The predicting risk of cancer at screening study (PROCAS) in Manchester UK is a prospective study of breast cancer risk estimation. This article considers whether mammographic density and SNPs in PROCAS may help refine breast cancer risk estimation using the Gail (BCRAT) and Tyrer-Cuzick (TC, or IBIS) models, based on incident and prevalent breast cancers identified between two three-yearly screening rounds. Methods: Mammographic density was measured at entry as a percentage using the average visual assessment from two trained readers. Tyrer-Cuzick and Gail risks were based on a questionnaire completed at the same time. The contribution of density to risk models was assessed after adjustment for age and body mass index (BMI) using odds ratios (ORs) and profile likelihood confidence intervals (CIs). A secondary analysis compared cancer pathology characteristics using a two-sided Wilcoxon test. Eighteen breast cancer risk variants (SNP18) were assessed with a polygenic risk score (PRS) alongside TC and density in a subset of 8870 women with 341 prospective cancers. Results: Analysis included 50628 women of routine screening age (47-73 yrs), recruited between Aug 2009 and Jul 2014. 697 had a breast cancer diagnosed after enrolment. Median follow-up was 3.2 years. Visually assessed percentage breast density (inter-quartile range odds ratio (IQR OR) 1.48 (95%CI 1.34-1.63)) was a slightly stronger univariate risk factor than TC (IQR OR 1.36 (1.25-1.48)) or Gail (IQR OR 1.22 (1.12-1.33)). It continued to add information after allowing for TC (IQR OR 1.47 (1.33-1.62)) or Gail (IQR OR 1.45 (1.32-1.60)). 36472/50628 (72%) women had less than 3.5% 10-yr risk from the TC model and breast density combined. Women with dense breasts were more likely to have a higher stage breast cancer (P <.001). SNP18 showed a 2 fold relative breast cancer risk between the top and bottom quintile. Using a combined analysis of SNP18, TC and density there was a 5.6 fold risk between those identified at NICE defined high risk compared to low risk. Conclusion: Breast density and SNPs when combined with the TC or Gail risk model identify a larger number of high risk women at screening, and it is associated with higher stage of disease. Approximately 70% of women are identified with a combined TC and density risk assessment of less than 3.5% 10-yr risk (average or less than average risk), for whom three-yearly screening might be effective. SNP18 adds further precision and addition of further newly identified SNPs is likely to add greater discrimination. Citation Format: Evans DG, Astley SM, Brentnall A, Howell A, Cuzick J. Mammographic density and SNPs add to Tyrer-Cuzick and Gail model breast cancer risk in a UK screening cohort. [abstract]. In: Proceedings of the Thirty-Eighth Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2015 Dec 8-12; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2016;76(4 Suppl):Abstract nr PD1-07.

Abstract P1-11-02: Identifying women at high-risk for breast cancer in the primary care setting

Abstract Background: Breast cancer risk assessment and available interventions for prevention, such as chemoprevention, are underutilized in the U.S. Reasons for low uptake include inability to routinely identify high-risk women in the primary care setting, inadequate time for counseling, and insufficient knowledge about risk-reducing strategies among primary care providers (PCPs) and patients. Our goal is to expand breast cancer risk counseling to a broader population of high-risk women identified in the primary care setting by developing a novel breast cancer risk navigation (BNAV) tool integrated into the electronic health record (EHR). Methods: We propose to design the BNAV tool for PCPs, by integrating the Gail breast cancer risk assessment tool into the EHR. Our goal is to facilitate clinic workflow for the identification of high-risk women (5-year risk ≥1.7% or lifetime risk ≥20%), who may be referred for specialized risk counseling. We conducted recorded focus groups and individual interviews of PCPs (N = 20-25) at Columbia University Medical Center (CUMC), including internists, family practitioners, and gynecologists who use an EHR and see female patients, age 40-70 years, in the outpatient setting. We performed user analyses of PCPs on the characteristics of their practice and their clinic workflow. Information about the aims of BNAV and the development process were provided and PCPs were given an opportunity to ask questions and discuss the relative merits of BNAV and its potential application to clinical practice. Providers also completed a questionnaire to provide quantitative and qualitative feedback on BNAV. Verbal and written qualitative responses were condensed into themes using a qualitative approach based on grounded theory. Results: In terms of breast cancer risk assessment, few providers routinely assessed for breast cancer risk factors apart from family history. Although some were familiar with the Gail model, no one used the risk calculator in their practice. Many PCPs were concerned about the added burden of incorporating the Gail model into the clinic visit. Potential solutions included screening for high-risk women during mammography and having patients complete the Gail model while in the waiting rooms. Most PCPs preferred referring high-risk women for specialized risk counseling, rather than directly discussing chemoprevention with their patients. Results from our interviews informed the selection of electronic resources to configure the BNAV tool. Using an open application programming interface within the EHR, the BNAV tool will incorporate the following approaches to workflow integration: 1) external decision support plug-ins for risk calculation; 2) dashboards with informatics-enabled summaries of patient history and breast cancer risk factors; 3) extracting data already available in the EHR for the Gail model breast cancer risk calculation; 4) alerts indicating high-risk patients should be referred for specialized risk counseling; 5) semi-structured referral orders for high-risk consultations. Discussion: PCPs are on the front lines of preventive medicine and initiating the appropriate high-risk referrals. We propose to use health information technology methods to overcome barriers to breast cancer chemoprevention in the primary care setting. Citation Information: Cancer Res 2013;73(24 Suppl): Abstract nr P1-11-02.

Relation of hormones in ductal lavage fluid to age, tamoxifen treatment, and breast cancer risk

Breast ductal lavage provides a source of both cells and fluid from the breast in a non-invasive manner. The fluid contains freely diffusible steroid hormones and other factors that may be secreted or released from the epithelial cells that line the alveoli and ducts of the breast as well as from myoepithelial cells, stomal cells, and immune cells that populate the breast. The purpose of the present study was to determine how perturbations of the system are reflected in ductal lavage fluid (DLF). We examined changes associated with menopausal status, tamoxifen treatment, and Gail model breast cancer risk scores.

Tamoxifen or Raloxifene in Postmenopausal Women for Prevention of Breast Cancer: A Tale of Two Choices—Counterpoint

Postmenopausal women 50 years or older with an estimated 5-year Gail model breast cancer risk of >1.66% or prior history of lobular carcinoma in situ now have two choices of prevention drug therapy: approved by the Food and Drug Administration for breast cancer risk reduction tamoxifen and

Interest in breast cancer chemoprevention among older women

The study aim is to describe interest in breast cancer chemoprevention among older women without a history of breast cancer and to determine whether aging-related factors such as diminished life expectancy, increasing comorbidity and medication burden attenuate chemoprevention interest. Cross-sectional survey. University of Pennsylvania Health System. Four-hundred fifty-seven community-dwelling women aged 60-65 years old who were potentially eligible for breast cancer chemoprevention according to guidelines linking risk and eligibility to age. Interest in breast cancer chemoprevention, Gail model breast cancer risk, perceived breast cancer risk, breast cancer worry, self-reported health status and comorbidities, and self-reported perceived life expectancy. Of 457 participants, 11.2% reported being interested in taking chemoprevention, 40.9% reported no interest, and 47.9% reported being unsure about their interest in chemoprevention. Overall, interest in chemoprevention was not associated with individual Gail model breast cancer risk. In adjusted analysis, lack of interest among high-risk women was associated with low breast cancer worry and low perceived risk. Conversely, interest in chemoprevention among low risk women was associated with greater breast cancer worry. Age-related factors hypothesized to affect chemoprevention interest, including subjective life expectancy, increased comorbidity, and number of daily medications did not attenuate chemoprevention interest. Breast cancer worry and perceived breast cancer risk contribute to the lack of correlation between interest in breast cancer chemoprevention and objective breast cancer risk. Perceived life expectancy, increased comorbidity, and medication burden do not attenuate chemoprevention interest among older women.

Mammographic Density Correlation with Gail Model Breast Cancer Risk Estimates and Component Risk Factors

The Gail model is a validated breast cancer risk assessment tool that is primarily based on nonmodifiable breast cancer risk factors. Conversely, mammographic breast density is strongly correlated with breast cancer risk and responds to risk-modifying interventions. The purpose of our study was to correlate mammographic density with breast cancer risk as calculated by the Gail model and to examine the relative association of each of the model covariates to mammographic density. The study included 99 participants of the National Surgical Breast and Bowel Project P-1 trial, ages 36 to 74 years, all of whom had a mammogram and Gail model risk estimates done upon trial entry. Baseline mammograms were retrieved and digitized, and mammographic density was assessed by both subjective and computer-assisted objective measures. Mammographic density was 2-fold higher in women with a >15% lifetime risk of breast cancer compared with those with <15% risk, by all density assessment methods. This was equivalent to a 3% to 6% increase in density per 10% increase in risk. Gail model covariates that measured benign or premalignant breast tissue changes accounted for the majority (41%) of the relationship with increased mammographic density. Seven percent of density was not explained by risk factors included in the Gail model. The Gail model does not fully account for the association between breast density and calculated breast cancer risk. Because mammographic density is a modifiable marker, development of a breast cancer risk assessment tool that includes mammographic density could be beneficial for assessing individual risk.

Gail model breast cancer risk components are poor predictors of risk perception and screening behavior

The Gail model is being used increasingly to determine individual breast cancer risk and to tailor preventive health recommendations accordingly. Although widely known to the medical and biostatistical communities, the risk factors included in the model may not be salient to the women to whom the model is being applied. This study explored the relationship of the individual Gail model risk factors to perceived risk of breast cancer and prior breast cancer screening among women with a family history of breast cancer. Data from baseline interviews with 969 women found a striking disparity between the objective risk factors included in the model and the accuracy of perceived risk and screening behaviors of this population, particularly among women over the age of 50 years. Risk perception accuracy was unrelated to all of the Gail model risk factors for all age groups. Reported mammography adherence was only associated with having had a breast biopsy in both age groups. Breast self examination (BSE) practice was independent of all measured factors for both age groups. These findings support the need for further research to identify additional determinants of risk perception and motivators of screening behavior.