Breast Cancer Surveillance Consortium Risk Research Articles

Performance of Supplemental US Screening in Women with Dense Breasts and Varying Breast Cancer Risk: Results from the Breast Cancer Surveillance Consortium.

Background It is unclear whether breast US screening outcomes for women with dense breasts vary with levels of breast cancer risk. Purpose To evaluate US screening outcomes for female patients with dense breasts and different estimated breast cancer risk levels. Materials and Methods This retrospective observational study used data from US screening examinations in female patients with heterogeneously or extremely dense breasts conducted from January 2014 to October 2020 at 24 radiology facilities within three Breast Cancer Surveillance Consortium (BCSC) registries. The primary outcomes were the cancer detection rate, false-positive biopsy recommendation rate, and positive predictive value of biopsies performed (PPV3). Risk classification of participants was performed using established BCSC risk prediction models of estimated 6-year advanced breast cancer risk and 5-year invasive breast cancer risk. Differences in high- versus low- or average-risk categories were assessed using a generalized linear model. Results In total, 34 791 US screening examinations from 26 489 female patients (mean age at screening, 53.9 years ± 9.0 [SD]) were included. The overall cancer detection rate per 1000 examinations was 2.0 (95% CI: 1.6, 2.4) and was higher in patients with high versus low or average risk of 6-year advanced breast cancer (5.5 [95% CI: 3.5, 8.6] vs 1.3 [95% CI: 1.0, 1.8], respectively; P = .003). The overall false-positive biopsy recommendation rate per 1000 examinations was 29.6 (95% CI: 22.6, 38.6) and was higher in patients with high versus low or average 6-year advanced breast cancer risk (37.0 [95% CI: 28.2, 48.4] vs 28.1 [95% CI: 20.9, 37.8], respectively; P = .04). The overall PPV3 was 6.9% (67 of 975; 95% CI: 5.3, 8.9) and was higher in patients with high versus low or average 6-year advanced cancer risk (15.0% [15 of 100; 95% CI: 9.9, 22.2] vs 4.9% [30 of 615; 95% CI: 3.3, 7.2]; P = .01). Similar patterns in outcomes were observed by 5-year invasive breast cancer risk. Conclusion The cancer detection rate and PPV3 of supplemental US screening increased with the estimated risk of advanced and invasive breast cancer. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Helbich and Kapetas in this issue.

Variability in Breast Density Estimation and Its Impact on Breast Cancer Risk Assessment.

Breast density is an independent risk factor for breast cancer, although variability exists in measurements. This study sought to evaluate the agreement between radiologists and automated breast density assessment software and assess the impact of breast density measures on breast cancer risk estimates using the Breast Cancer Surveillance Consortium (BCSC) model (v.2). A retrospective database search identified women who had undergone mammography between December 2021 and June 2022. The Breast Imaging Reporting and Data System (BI-RADS) breast composition index assigned by a radiologist (R) was recorded and analyzed using three commercially available software programs (S1, S2, and S3). The agreement rate and Cohen's kappa (κ) were used to evaluate inter-rater agreements concerning breast density measures. The 5-year risk of invasive breast cancer in women was calculated using the BCSC model (v.2) with breast density inputs from various density estimation methods. Absolute differences in risk between various density measurements were evaluated. Overall, 1,949 women (mean age, 53.2 years) were included. The inter-rater agreement between R, S1, and S2 was 75.0-75.6%, while that between S3 and the others was 60.2%-63.3%. Kappa was substantial between R, S1, and S2 (0.66-0.68), and moderate (0.49-0.50) between S3 and the others. S3 placed fewer women in mammographic density d (14.9%) than R, S1, and S2 (40.5%-44.0%). In BCSC risk assessment (v.2), S3 assessed fewer women with a high 5-year risk of invasive breast cancer than the other methods, resulting in an absolute difference of 0% between R, S1, and S2 in 75.0%-75.6% of cases, whereas the difference between S3 and the other methods occurs in 60.2%-63.3% of cases. Breast density assessment using various methods showed moderate-to-substantial agreement, potentially affecting risk assessments. Precise and consistent breast density measurements may lead to personalized and effective strategies for breast cancer prevention.

Abstract PD14-07: PD14-07 Associations of Breast Cancer Risk Level and Prediction of Tumor Aggressiveness in the Athena Breast Health Network

Abstract Background: The Athena Breast Health Network (Athena) is a University of California (UC) initiative integrating clinical care and research to drive improvements in breast cancer screening. Through standardized, self-reported clinical intake forms, a patient’s breast health status and information regarding their breast cancer risk is captured before each mammography appointment. Breast cancer risk models provide a level of risk to develop breast cancer but do not take into account aggressiveness of breast cancer. Here, we evaluated outcome data of a large screening cohort for association between risk level and aggressiveness at diagnosis. Methods: We calculated Breast Cancer Surveillance Consortium (BCSC) risk scores for a cohort of 8,923 consented UCSF Athena participants from the years 2012-2018 with a median follow-up of 5-years. To identify those who developed invasive breast cancer on or after completing an Athena intake form, we performed a cancer linkage with the San Francisco Mammography Registry (SFMR), a local registry that regularly collects cancer data from the California Cancer Registry. We classified tumors as aggressive if they met one or more of the following criteria: hormone receptor (HR)-negative, HER2-positive, grade 3. All other tumors were classified as non-aggressive. We used student’s t-tests to examine associations between BCSC 5-year risk score, the development of invasive breast cancer, and tumor aggressiveness among cases. To account for the association between older age and higher BCSC risk score (as well as HR-positive subtypes), we stratified by percentiles of BCSC risk by age (top 2.5% vs. bottom 97.5%). The top 2.5% by age threshold consistently identifies women with lifetime risk of 23–28% and was chosen as high-risk threshold to trigger annual screening in the WISDOM study (Dreher: PMID34843026). Results: Of 8,923 participants, 170 (2%) developed breast cancer during the follow-up period. The average 5-year BCSC risk score for women with breast cancer was 1.81% and 1.47% for those without (p< 0.001). Among women with breast cancer, 123 (72%) developed non-aggressive cancers and 47 (28%) developed aggressive cancers. The average 5-year BCSC risk score for women with non-aggressive and aggressive cancers was 1.89% and 1.60%, respectively (p=0.13). In analyses stratified by percentile of BCSC risk by age, 521 (6%) participants had a BCSC 5-year risk score in the top 2.5% by age and 8,402 (94%) participants had a BCSC 5-year risk score in the bottom 97.5%. A higher percentage of women with non-aggressive cancers vs healthy women (controls) were in the top 2.5% by age (p = 0.001), but the percentage of aggressive cancers vs healthy women in the top 2.5% by age was similar (p = 0.61). Conclusion: Through this study we confirmed that higher 5-year BCSC risk scores are associated with higher overall breast cancer development. Interestingly, participants with the highest 5-year BCSC risk scores (top 2.5% by age), are more likely to develop cancers with non-aggressive features (low grade, hormone positive). This suggests that the BCSC model may preferentially predict less aggressive tumors, and those with the highest 5-year BCSC risk may be more likely to benefit from endocrine risk reduction therapy. There remains a gap in our ability to identity those at risk for aggressive cancers. Our findings highlight the need for screening programs to better understand who is at risk for what type of breast cancer. Current work is focused on developing models tailored to risk prediction of aggressive cancers. Citation Format: Katherine Leggat-Barr, Tomiyuri Lewis, Rosalyn Sayaman, Paige Warner, Kathy Malvin, Leah Sabacan, Elene Tsopurashvili, WISDOM Study and Athena Breast Health Network Investigators and Advocate Partners, Allison Stover Fiscalini, Jeffrey Tice, Karla Kerlikowske, Yiwey Shieh, Laura J. Esserman, Laura Van ’t Veer. PD14-07 Associations of Breast Cancer Risk Level and Prediction of Tumor Aggressiveness in the Athena Breast Health Network [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr PD14-07.

Reassessing the Benefits and Harms of Risk-Reducing Medication Considering the Persistent Risk of Breast Cancer Mortality in Estrogen Receptor-Positive Breast Cancer.

Recent studies, including a meta-analysis of 88 trials, have shown higher than expected rates of recurrence and death in hormone receptor-positive breast cancer. These new findings suggest a need to re-evaluate the use of risk-reducing medication to avoid invasive breast cancer and breast cancer death in high-risk women. We adapted an established Cancer Intervention and Surveillance Modeling Network model to evaluate the lifetime benefits and harms of risk-reducing medication in women with a ≥ 3% 5-year risk of developing breast cancer according to the Breast Cancer Surveillance Consortium risk calculator. Model input parameters were derived from meta-analyses, clinical trials, and large observational data. We evaluated the effects of 5 years of risk-reducing medication (tamoxifen/aromatase inhibitors) with annual screening mammography ± magnetic resonance imaging (MRI) compared with no screening, MRI, or risk-reducing medication. The modeled outcomes included invasive breast cancer, breast cancer death, side effects, false positives, and overdiagnosis. We conducted subgroup analyses for individual risk factors such as age, family history, and prior biopsy. Risk-reducing tamoxifen with annual screening (± MRI) decreased the risk of invasive breast cancer by 40% and breast cancer death by 57%, compared with no tamoxifen or screening. This is equivalent to an absolute reduction of 95 invasive breast cancers, and 42 breast cancer deaths per 1,000 high-risk women. However, these drugs are associated with side effects. For example, tamoxifen could increase the number of endometrial cancers up to 11 per 1,000 high-risk women. Benefits and harms varied by individual characteristics. The addition of risk-reducing medication to screening could further decrease the risk of breast cancer death. Clinical guidelines for high-risk women should consider integrating shared decision making for risk-reducing medication and screening on the basis of individual risk factors.

Feasibility and Acceptability of Personalized Breast Cancer Screening (DECIDO Study): A Single-Arm Proof-of-Concept Trial.

The aim of this study was to assess the acceptability and feasibility of offering risk-based breast cancer screening and its integration into regular clinical practice. A single-arm proof-of-concept trial was conducted with a sample of 387 women aged 40–50 years residing in the city of Lleida (Spain). The study intervention consisted of breast cancer risk estimation, risk communication and screening recommendations, and a follow-up. A polygenic risk score with 83 single nucleotide polymorphisms was used to update the Breast Cancer Surveillance Consortium risk model and estimate the 5-year absolute risk of breast cancer. The women expressed a positive attitude towards varying the frequency of breast screening according to individual risk and, especially, more frequently inviting women at higher-than-average risk. A lower intensity screening for women at lower risk was not as welcome, although half of the participants would accept it. Knowledge of the benefits and harms of breast screening was low, especially with regard to false positives and overdiagnosis. The women expressed a high understanding of individual risk and screening recommendations. The participants’ intention to participate in risk-based screening and satisfaction at 1-year were very high.

Accuracy of the Breast Cancer Surveillance Consortium Model Among Women with LCIS.

The Breast Cancer Surveillance Consortium (BCSC) model predicts risk of invasive breast cancer risk based on age, race, family history, breast density, and history of benign breast disease, including lobular carcinoma in situ (LCIS). However, validation studies for this model included few women with LCIS. We sought to evaluate the accuracy of the BCSC model among this cohort. Women with LCIS diagnosed between 1983 and 2017 were identified from a prospectively maintained database. The BCSC score was calculated; those with prior breast cancer, unknown breast density, age < 35 years or > 74 years, or with history of chemoprevention use were excluded. The Kaplan-Meier method was used to estimate incidence rates. Time-dependent receiver operating characteristic (ROC) analysis was used to analyze the discriminative capacity of the model. 1302 women with LCIS were included. At a median follow-up of 7years, 152 women (12%) developed invasive cancer (6 with bilateral disease). Cumulative incidences of invasive breast cancer were 7.1% (95% CI 5.6-8.7) and 13.3% (95% CI 10.9-15.6), respectively, and the median BCSC risk scores were 4.9 and 10.4, respectively, at 5 and 10years. The median 10-year BCSC score was significantly lower than the 10-year Tyrer-Cuzick score (10.4 vs 20.8, p < 0.001). The ROC curve scores (AUC) for BCSC at 5 and 10years were 0.59 (95% CI 0.52-0.66) and 0.58 (95% CI 0.52-0.64), respectively. The BCSC model has moderate accuracy in predicting invasive breast cancer risk among women with LCIS with fair discrimination for risk prediction between individuals.

A case-case analysis of women with breast cancer: predictors of interval vs screen-detected cancer.

The Breast Cancer Surveillance Consortium (BCSC) model is a widely used risk model that predicts 5- and 10-year risk of developing invasive breast cancer for healthy women aged 35-74years. Women with high BCSC risk may also be at elevated risk to develop interval cancers, which present symptomatically in the year following a normal screening mammogram. We examined the association between high BCSC risk (defined as the top 2.5% by age) and breast cancers presenting as interval cancers. We conducted a case-case analysis among women with breast cancer in which we compared the mode of detection and tumor characteristics of patients in the top 2.5% BCSC risk by age with age-matched (1:2) patients in the lower 97.5% risk. We constructed logistic regression models to estimate the odds ratio (OR) of presenting with interval cancers, and poor prognosis tumor features, between women from the top 2.5% and bottom 97.5% of BCSC risk. Our analysis included 113 breast cancer patients in the top 2.5% of risk for their age and 226 breast cancer patients in the lower 97.5% of risk. High-risk patients were more likely to have presented with an interval cancer within one year of a normal screening, OR 6.62 (95% CI 3.28-13.4, p < 0.001). These interval cancers were also more likely to be larger, node positive, and higher stage than the screen-detected cancers. Breast cancer patients in the top 2.5% of BCSC risk for their age were more likely to present with interval cancers. The BCSC model could be used to identify healthy women who may benefit from intensified screening.

Background Parenchymal Uptake on Molecular Breast Imaging and Breast Cancer Risk: A Cohort Study.

BACKGROUND. Background parenchymal uptake (BPU) on molecular breast imaging (MBI) was identified in a case-control study as a breast cancer risk factor beyond mammographic density. To our knowledge, this finding has not yet been confirmed in a cohort study. OBJECTIVE. The objectives of this study were to examine the association of BPU with breast cancer and to estimate the absolute risk and discriminatory accuracy of BPU in a cohort study. METHODS. A retrospective cohort was established that included women without a history of breast cancer who underwent MBI from 2004 to 2015. Radiologists who were blinded to future breast cancer diagnoses assessed BPU on baseline MBI examinations as low (photopenic or minimal) or elevated (mild, moderate, or marked). Associations of BPU with breast cancer were estimated using multivariable Cox proportional hazards models of the time to diagnosis. The 5-year absolute risk was calculated for study subgroups. The discriminatory accuracy of BPU was also assessed. RESULTS. Among 2992 women (mean age, 56.3 years; SD, 10.6 years) who underwent MBI, breast cancer events occurred in 144 women (median follow-up, 7.3 years). Median time to diagnosis after MBI was 4.2 years (range, 0.5-11.6 years). Elevated BPU was associated with a greater breast cancer risk (hazard ratio [HR], 2.39; 95% CI, 1.68-3.41; p ≤ .001). This association remained in postmenopausal women (HR, 3.50; 95% CI, 2.31-5.31; p < .001) but was not significant in premenopausal women (HR, 1.29; 95% CI, 0.72-2.32; p = .39). The 5-year absolute risk of breast cancer was 4.3% (95% CI, 2.9-5.7%) for women with elevated BPU versus 2.5% (95% CI, 1.8-3.1%) for those with low BPU. Postmenopausal women with dense breasts and elevated BPU had a 5-year absolute risk of 8.1% (95% CI, 4.3-11.8%) versus 2.8% (1.8-3.8%) for those with low BPU. Among postmenopausal women, discriminatory accuracy for invasive cancer was improved with the addition of BPU versus use of the Gail risk score alone (C statistic, 65.1 vs 59.1; p = .04) or use of the Breast Cancer Surveillance Consortium risk score alone (C statistic, 66.4 vs 60.4; p = .04). CONCLUSION. BPU on MBI is an independent risk factor for breast cancer, with the strongest association observed among postmenopausal women with dense breasts. In postmenopausal women, BPU provides incremental discrimination in predicting breast cancer when combined with either the Gail model or the Breast Cancer Surveillance Consortium model. CLINICAL IMPACT. Observation of elevated BPU on MBI may identify a subset of women with dense breasts who would benefit most from supplemental screening or preventive options.

Abstract PS7-43: Validation study results for a personalized prevention education aid in breast cancer risk reduction

Abstract BackgroundOptions for breast cancer risk reduction include endocrine medications (tamoxifen, raloxifene, aromatase inhibitors) and lifestyle modifications (increasing exercise, reducing BMI, or alcohol intake). At present, there are limited patient-facing resources that provide information on personalized risk and prevention strategies. To address this unmet need, the Breast Health Decisions (BHD) Tool was designed to educate and empower women in the WISDOM (Women Informed to Screen Depending on Measures of risk) Study, a preference-tolerant randomized control trial comparing personalized risk-based screening to traditional annual screening. The tool supports Aim 4 of the WISDOM Study: test whether risk-based screening, including individualized risk assessment and targeted risk reduction education for those in the top 2.5% risk, enables higher uptake of preventive interventions. We conducted a study of the first 100 participants counseled using the BHD Tool to assess its impact on risk-reduction strategies. MethodsThe BHD Tool, built on the Salesforce platform, integrates WISDOM Study risk assessments to generate personalized education about risk and risk reduction, using concise wording, 8th-grade reading level or lower, and visual representations to support shared decision making for high-risk women. Changes to improve usability were incorporated from an initial pilot study. The study population was WISDOM Study participants in the top 2.5% 5-year risk by age, excluding mutation carriers. 5-year risk was calculated using the Breast Cancer Surveillance Consortium risk modified by a polygenic risk score. The tool was available through the participants’ online study portals. Study staff contacted these participants to schedule consultations via Zoom with a WISDOM Breast Health Specialist, who navigated the participant through the tool during an interactive 45-minute consultation. Participants could decline the consultation and use the tool independently. A survey was conducted afterward to assess the tool’s utility in motivating women to pursue risk-reducing options. ResultsWe surveyed 100 high-risk participants who used the BHD Tool. 65% found it very helpful in understanding their breast cancer risk. 27 participants listed additional lifestyle improvements that they were practicing or hoping to begin, including yoga, walking, breast exams, self-exams, yard work, dietary improvements, meditation, and stress reduction. 37% of participants agreed that the tool eased their breast cancer worries and anxiety, while 44% were neutral and 17% disagreed. At the time of presentation, we will present 3-month follow up data and report which preventive actions were actually taken and barriers encountered. ConclusionsThe BHD Tool synthesizes up-to-date chemoprevention literature in a patient-friendly interface to help educate women about their prevention options and to facilitate future discussions with a provider to empower informed decisions. Data from the first 100 high-risk women who used this tool suggest that the majority of women presented with information about their risk are interested in reducing it. More are considering lifestyle measures than medications. The BHD tool will be made available to all women in the personalized arm of the WISDOM Study. Future improvements include making the tool accessible to clinicians who counsel high-risk women. Survey questionNumber of participants (N=100)Interested in reducing chance of developing breast cancer97Currently participating in a breast cancer risk reducing activity*77Reducing alcohol intake35Losing weight60Increasing exercise27Risk reducing medications5Considering participating in a breast cancer risk reducing activity*72Reducing alcohol intake12Losing weight26Increasing exercise24Risk reducing medications22*Participants can choose more than one risk-reducing activity Citation Format: Tianyi Wang, Mandy Che, Yash Huilgol, Deborah Goodman, Holly Keane, Vivian Lee, Jeff Belkora, Allison Fiscalini, Laura Esserman. Validation study results for a personalized prevention education aid in breast cancer risk reduction [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS7-43.

Combining quantitative and qualitative breast density measures to assess breast cancer risk

BackgroundAccurately identifying women with dense breasts (Breast Imaging Reporting and Data System [BI-RADS] heterogeneously or extremely dense) who are at high breast cancer risk will facilitate discussions of supplemental imaging and primary prevention. We examined the independent contribution of dense breast volume and BI-RADS breast density to predict invasive breast cancer and whether dense breast volume combined with Breast Cancer Surveillance Consortium (BCSC) risk model factors (age, race/ethnicity, family history of breast cancer, history of breast biopsy, and BI-RADS breast density) improves identifying women with dense breasts at high breast cancer risk.MethodsWe conducted a case-control study of 1720 women with invasive cancer and 3686 control subjects. We calculated ORs and 95% CIs for the effect of BI-RADS breast density and Volpara™ automated dense breast volume on invasive cancer risk, adjusting for other BCSC risk model factors plus body mass index (BMI), and we compared C-statistics between models. We calculated BCSC 5-year breast cancer risk, incorporating the adjusted ORs associated with dense breast volume.ResultsCompared with women with BI-RADS scattered fibroglandular densities and second-quartile dense breast volume, women with BI-RADS extremely dense breasts and third- or fourth-quartile dense breast volume (75% of women with extremely dense breasts) had high breast cancer risk (OR 2.87, 95% CI 1.84–4.47, and OR 2.56, 95% CI 1.87–3.52, respectively), whereas women with extremely dense breasts and first- or second-quartile dense breast volume were not at significantly increased breast cancer risk (OR 1.53, 95% CI 0.75–3.09, and OR 1.50, 95% CI 0.82–2.73, respectively). Adding continuous dense breast volume to a model with BCSC risk model factors and BMI increased discriminatory accuracy compared with a model with only BCSC risk model factors (C-statistic 0.639, 95% CI 0.623–0.654, vs. C-statistic 0.614, 95% CI 0.598–0.630, respectively; P < 0.001). Women with dense breasts and fourth-quartile dense breast volume had a BCSC 5-year risk of 2.5%, whereas women with dense breasts and first-quartile dense breast volume had a 5-year risk ≤ 1.8%.ConclusionsRisk models with automated dense breast volume combined with BI-RADS breast density may better identify women with dense breasts at high breast cancer risk than risk models with either measure alone.

Joint relative risks for estrogen receptor-positive breast cancer from a clinical model, polygenic risk score, and sex hormones.

Models that predict the risk of estrogen receptor (ER)-positive breast cancers may improve our ability to target chemoprevention. We investigated the contributions of sex hormones to the discrimination of the Breast Cancer Surveillance Consortium (BCSC) risk model and a polygenic risk score comprised of 83 single nucleotide polymorphisms. We conducted a nested case-control study of 110 women with ER-positive breast cancers and 214 matched controls within a mammography screening cohort. Participants were postmenopausal and not on hormonal therapy. The associations of estradiol, estrone, testosterone, and sex hormone binding globulin with ER-positive breast cancer were evaluated using conditional logistic regression. We assessed the individual and combined discrimination of estradiol, the BCSC risk score, and polygenic risk score using the area under the receiver operating characteristic curve (AUROC). Of the sex hormones assessed, estradiol (OR 3.64, 95% CI 1.64-8.06 for top vs bottom quartile), and to a lesser degree estrone, was most strongly associated with ER-positive breast cancer in unadjusted analysis. The BCSC risk score (OR 1.32, 95% CI 1.00-1.75 per 1% increase) and polygenic risk score (OR 1.58, 95% CI 1.06-2.36 per standard deviation) were also associated with ER-positive cancers. A model containing the BCSC risk score, polygenic risk score, and estradiol levels showed good discrimination for ER-positive cancers (AUROC 0.72, 95% CI 0.65-0.79), representing a significant improvement over the BCSC risk score (AUROC 0.58, 95% CI 0.50-0.65). Adding estradiol and a polygenic risk score to a clinical risk model improves discrimination for postmenopausal ER-positive breast cancers.

Identifying women at high-risk for breast cancer using data from the electronic health record compared to self-report.

e13044 Background: One of the barriers to chemoprevention uptake among high-risk women is the lack of routine breast cancer risk assessment in the primary care setting. We calculated breast cancer risk using the Breast Cancer Surveillance Consortium (BCSC) model, which accounts for age, race/ethnicity, first-degree family history of breast cancer, benign breast disease, and mammographic density, using data collected from the electronic health record (EHR) compared to self-report. Methods: Among women undergoing screening mammography, we collected breast cancer risk information from the EHR and a self-administered survey. Eligibility criteria for calculating 5-year invasive breast cancer risk using the BCSC model included age 35-74, no prior history of breast cancer, mastectomy, or breast augmentation. We extracted data on demographics, structured first-degree family history, breast radiology and pathology reports from the EHR. We assessed agreement in breast cancer risk information between the EHR and self-report data. Results: Among 13,735 women with EHR data for BCSC risk calculation, 2708 women (20%) met high-risk criteria, based upon a 5-year breast cancer risk ≥1.67%. Among high-risk women, 2% were age 40-49, 23% age 50-59, 48% age 60-69, and 26% age 70-74. From the EHR, data was missing on 31% for race/ethnicity and 85% for family history. Among 2320 women with both EHR and self-report data, more complete information was available for race/ethnicity, family history, and breast biopsies in the surveys. More first-degree family history (14% vs. 3%) and prior breast biopsies (18% vs. 11%) were identified by self-report vs. EHR, respectively. However, more women with atypia and lobular carcinoma in situwere identified from the EHR. More high-risk women (20% vs. 16%) were identified with EHR vs. survey data, respectively, with correlation of 0.82. Conclusions: Among women undergoing screening mammography, we identified 20% who met high-risk criteria according to the BCSC model based upon EHR data, despite missing information on race/ethnicity, family history, and prior breast biopsies. This may serve as an initial screen for identifying women eligible for breast cancer chemoprevention.

Using Breast Cancer Risk Associated Polymorphisms to Identify Women for Breast Cancer Chemoprevention.

BackgroundBreast cancer can be prevented with selective estrogen receptor modifiers (SERMs) and aromatase inhibitors (AIs). The US Preventive Services Task Force recommends that women with a 5-year breast cancer risk ≥3% consider chemoprevention for breast cancer. More than 70 single nucleotide polymorphisms (SNPs) have been associated with breast cancer. We sought to determine how to best integrate risk information from SNPs with other risk factors to risk stratify women for chemoprevention.MethodsWe used the risk distribution among women ages 35–69 estimated by the Breast Cancer Surveillance Consortium (BCSC) risk model. We modeled the effect of adding 70 SNPs to the BCSC model and examined how this would affect how many women are reclassified above and below the threshold for chemoprevention.ResultsWe found that most of the benefit of SNP testing a population is achieved by testing a modest fraction of the population. For example, if women with a 5-year BCSC risk of >2.0% are tested (~21% of all women), ~75% of the benefit of testing all women (shifting women above or below 3% 5-year risk) would be derived. If women with a 5-year risk of >1.5% are tested (~36% of all women), ~90% of the benefit of testing all women would be derived.ConclusionSNP testing is effective for reclassification of women for chemoprevention, but is unlikely to reclassify women with <1.5% 5-year risk. These results can be used to implement an efficient two-step testing approach to identify high risk women who may benefit from chemoprevention.

Breast cancer risk prediction using a clinical risk model and polygenic risk score

Breast cancer risk assessment can inform the use of screening and prevention modalities. We investigated the performance of the Breast Cancer Surveillance Consortium (BCSC) risk model in combination with a polygenic risk score (PRS) comprised of 83 single nucleotide polymorphisms identified from genome-wide association studies. We conducted a nested case-control study of 486 cases and 495 matched controls within a screening cohort. The PRS was calculated using a Bayesian approach. The contributions of the PRS and variables in the BCSC model to breast cancer risk were tested using conditional logistic regression. Discriminatory accuracy of the models was compared using the area under the receiver operating characteristic curve (AUROC). Increasing quartiles of the PRS were positively associated with breast cancer risk, with OR 2.54 (95% CI 1.69-3.82) for breast cancer in the highest versus lowest quartile. In a multivariable model, the PRS, family history, and breast density remained strong risk factors. The AUROC of the PRS was 0.60 (95% CI 0.57-0.64), and an Asian-specific PRS had AUROC 0.64 (95% CI 0.53-0.74). A combined model including the BCSC risk factors and PRS had better discrimination than the BCSC model (AUROC 0.65 versus 0.62, p=0.01). The BCSC-PRS model classified 18% of cases as high-risk (5-year risk ≥3%), compared with 7% using the BCSC model. The PRS improved discrimination of the BCSC risk model and classified more cases as high-risk. Further consideration of the PRS's role in decision-making around screening and prevention strategies is merited.

Abstract P6-02-08: Breast cancer screening in the precision medicine era

Abstract We are entering the era of precision medicine in which cancer screening, prevention and treatment will be tailored to each individual. The progress made in this field is due, in part, to advances in our understanding of cancer risk and tumor biology. The challenge before us is to harness this knowledge and apply it in the clinical setting. Breast cancer screening provides an excellent opportunity to test the value of precision medicine in the real world. In this report we describe the process of designing a model of personalized breast cancer screening. Methods Risk factors were selected that have the greatest impact, have been validated and can be measured across a population. A risk model was selected that is highly calibrated, has been validated in a large screening cohort and is easy to apply in a large population of women. An expert committee was convened that set risk thresholds for stratifying women into groups that will be recommended to undergo biennial, annual or every six month screening. Risk thresholds and screening schedules are in accordance with the United States Preventive Services Task Force breast cancer screening recommendations. Results Risk factors: Age, race/ethnicity, personal history of breast biopsies and benign breast disease, family history, breast density and breast cancer-associated genetic mutations and single nucleotide polymorphisms (SNPs) were chosen as the risk factors that will be used to determine breast cancer risk. Risk model: The Breast Cancer Surveillance Consortium risk model will be used to calculate a woman's 5-year risk and will be modified by a polygenic risk score based on 81 SNPs. Risk thresholds: Women will be recommended to undergo biennial screening mammography when they reach the age of 50 or have the risk of an average 50 year-old woman (1.3% 5-year risk). Women will be advised to undergo annual screening if they are at increased risk of developing an interval cancer (women in their forties with extremely dense breasts and women at increased risk of developing estrogen receptor negative breast cancer based on their SNPs). Women will be recommended to undergo annual mammography and annual MRI if they are found to be gene mutation positive, have the risk of a BRCA1 mutation carrier (6% 5-year risk) or have a history of mantle radiation. Discussion Selecting the appropriate risk factors and risk model and determining risk thresholds are key components of designing a personalized breast cancer screening model. Personalized screening may be the way forward, but this can only be determined within the setting of a randomized controlled trial. We will conduct such a trial to determine if personalized screening is as safe as, less morbid than, more preferred by women than and enables prevention when compared to annual screening. The WISDOM (Women Informed to Screen Depending on Measures of risk) study will compare risk-based screening to annual screening within the Athena Breast Health Network with support from the Patient-Centered Outcomes Research Institute. Our intent is that this trial will provide us with the data that we need to determine the safest and most effective way to screen women for breast cancer in the era of precision medicine. Citation Format: Thompson CK, Fiscalini AS, Donnellan P, Kaplan CP, Madlensky L, Eklund M, Ziv E, van't Veer LJ, Tice JA, Esserman LJ. Breast cancer screening in the precision medicine era. [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 P6-02-08.

Abstract 940: The contribution of common breast cancer susceptibility loci to the breast density and breast cancer association and the Breast Cancer Surveillance Consortium (BCSC) risk model

Abstract Purpose: Mammographic breast density is an established and strong risk factor for breast cancer. Recently, a number of common genetic susceptibility loci have been identified as risk factors for breast cancer. We present the first report on the relative contribution of 76 validated breast cancer susceptibility loci, in the context of a polygenic risk score (PRS), to the breast density and breast cancer association. We also examine whether the PRS improves prediction of the Breast Cancer Surveillance Consortium (BCSC) 5-year risk model above and beyond breast density and clinical factors included in the model. Methods: The study population included 1643 cases and 2397 controls from three independent epidemiologic studies: the Mayo Mammography Health Study (MMHS), the Mayo Clinic Breast Cancer Study (MCBCS), and the Bavarian Breast Cancer Cases and Control Study (BBCC). Data collected on patients in each of the studies included clinical risk factor, BI-RADS breast density [a) almost entirely fat; b) scattered fibroglandular densities; c) heterogeneously dense; and d) extremely dense] and genotypes for the 76 breast cancer susceptibility loci known at the time of the study. We formed a PRS from the reported per-SNP odds ratios (OR) for 76 known breast cancer susceptibility loci, and evaluated whether BI-RADS density and the PRS were independent risk factors for breast cancer, when adjusted for age, 1/BMI and study. We also incorporated the PRS (OR) into the BCSC 5-year risk model and estimated 5-year risk for the MMHS nested case-control study of 339 invasive cases, 765 controls. Results: BI-RADS density (p&amp;lt;0.0001) and PRS (p&amp;lt;0.0001) were independent risk factors for breast cancer that together showed greater discrimination of risk (AUC=0.69) than density (AUC=0.66; ΔAUC=0.029) or PRS score alone (AUC=0.68; ΔAUC=0.013; p&amp;lt;0.001). Relative to those with scattered fibroglandular densities and average (2nd quartile) PRS, women with extremely dense breasts and in the highest PRS quartile, had a 2.7 fold (95%CI: 1.7-4.1) increased risk of breast cancer, while those with fatty breasts and in the lowest PRS quartile had a reduced risk (OR=0.30, 95%CI: 0.18-0.51). Incorporation of the PRS into the BCSC risk model improved discrimination (ΔAUC=0.031, p=0.001), for a net reclassification improvement of 20% (95%CI: 11%-28%), split equally among cases (9%) and controls (11%). Conclusion: BI-RADS density and the PRS are both important risk factors for breast cancer that can be included in breast cancer risk models to improve prediction of this disease. Using these models to identify high and low-risk risk groups will facilitate improved tailored screening and primary prevention interventions. Citation Format: Celine M. Vachon, V. Shane Pankratz, Christopher G. Scott, Lothar Haeberle, Elad Ziv, Matthew R. Jensen, Kathleen R. Brandt, Dana H. Whaley, Janet E. Olson, Katharina Heusinger, Carolin C. Hack, Sebastian M. Jud, Matthias W. Beckmann, Jeffrey A. Tice, Kristen S. Purrington, Thomas A. Sellers, Karla Kerlikowske, Peter A. Fasching, Fergus J. Couch. The contribution of common breast cancer susceptibility loci to the breast density and breast cancer association and the Breast Cancer Surveillance Consortium (BCSC) risk model. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 940. doi:10.1158/1538-7445.AM2014-940

A randomized, controlled trial to increase discussion of breast cancer in primary care.

Assessment and discussion of individual risk for breast cancer within the primary care setting are crucial to discussion of risk reduction and timely referral. We conducted a randomized controlled trial of a multiethnic, multilingual sample of women ages 40 to 74 years from two primary care practices (one academic, one safety net) to test a breast cancer risk assessment and education intervention. Patients were randomly assigned to control or intervention group. All patients completed a baseline telephone survey and risk assessment (via telephone for controls, via tablet computer in clinic waiting room before visit for intervention). Intervention (BreastCARE) patients and their physicians received an individualized risk report to discuss during the visit. One-week follow-up telephone surveys with all patients assessed patient-physician discussion of family cancer history, personal breast cancer risk, high-risk clinics, and genetic counseling/testing. A total of 655 control and 580 intervention women completed the risk assessment and follow-up interview; 25% were high-risk by family history, Gail, or Breast Cancer Surveillance Consortium risk models. BreastCARE increased discussions of family cancer history [OR, 1.54; 95% confidence interval (CI), 1.25-1.91], personal breast cancer risk (OR, 4.15; 95% CI, 3.02-5.70), high-risk clinics (OR, 3.84; 95% CI, 2.13-6.95), and genetic counseling/testing (OR, 2.22; 95% CI, 1.34-3.68). Among high-risk women, all intervention effects were stronger. An intervention combining an easy-to-use, quick risk assessment tool with patient-centered risk reports at the point of care can successfully promote discussion of breast cancer risk reduction between patients and primary care physicians, particularly for high-risk women. Next steps include scaling and dissemination of BreastCARE with integration into electronic medical record systems.

Changes in breast cancer risk distribution among Vermont women using screening mammography.

Screening mammography utilization in Vermont has declined since 2009 during a time of changing screening guidelines and increased interest in personalized screening regimens. This study evaluates whether the breast cancer risk distribution of the state's screened population changed during the observed decline. We examined the breast cancer risk distribution among screened women between 2001 and 2012 using data from the Vermont Breast Cancer Surveillance System. We estimated each screened woman's 5-year risk of breast cancer using the Breast Cancer Surveillance Consortium risk calculator. Annual screening counts by risk group were normalized and age-adjusted to the Vermont female population by direct standardization. The normalized rate of low-risk (5-year breast cancer risk of <1%) women screened increased 8.3% per year (95% confidence interval [CI] = 4.8 to 11.9) between 2003 and 2008 and then declined by -5.4% per year (95% CI = -8.1 to -2.6) until 2012. When stratified by age group, the rate of low-risk women screened declined -4.4% per year (95% CI = -8.8 to 0.1; not statistically significant) for ages 40 to 49 years and declined a statistically significant -7.1% per year (95% CI = -12.1 to -2.0) for ages 50 to 74 years during 2008 to 2012. These declines represented the bulk of overall decreases in screening after 2008, with rates for women categorized in higher risk levels generally exhibiting small annual changes. The observed decline in women screened in Vermont in recent years is largely attributable to reductions in screening visits by women who are at low risk of developing breast cancer.

Changes in the Breast Cancer Risk Distribution among Women Utilizing Screening Mammography in Vermont Between 2001 and 2012

Abstract We previously reported a decline in overall breast cancer screening rates in Vermont following 2009. During this period, there has been debate regarding the role of patient context in decisions about when and how often to get screened, as well as increased interest in risk- based screening to optimize the balance between the potential benefits and harms of screening. The purpose of the current study was to evaluate whether the breast cancer risk distribution of the screened population in Vermont has changed during the observed decline in utilization rates. We examined the distribution of breast cancer risk among the screened population in Vermont from 2001 to 2012 using cross-sectional data from the statewide Vermont Breast Cancer Surveillance System. We employed the Breast Cancer Surveillance Consortium risk model to estimate each individual's risk of developing breast cancer within 5 years according to age, breast density, race/ethnicity, family history of breast cancer, and biopsy history. Among women ages 40 to 74 who received screening mammograms, the absolute number of visits dropped by 4,257, from 54,415 to 50,158 (−7.3%; 95% CI: −7.5, −7.1) between 2009 and 2012. Concurrently, the number of screened women who were estimated to be at low risk of developing breast cancer decreased by 4,240 (95% CI: 3,907, 4,573), representing the bulk of the overall decrease. There was no significant change in the aggregate number of women estimated to be at higher risk (−17 women; 95% CI: −350, 316). The outsized proportion of the decline attributed to women at low estimated risk held across younger and older age groups: among women ages 40 to 49, the absolute number screened dropped by 3,337, with 2,495 (95% CI: 2,389, 2,601) reflected by declines among women at low risk; among women ages 50 to 74, the absolute number screened dropped by only 920, however this value reflects a decrease of 1,763 (95% CI: 1,519, 2,007) for the low risk category, and gains totaling 843 (95% CI: 599, 1,087) among higher risk categories. We conclude that the observed decline in women screened in Vermont since 2009 is largely attributable to reductions in visits by women who are estimated to be at low risk of developing breast cancer, and that this trend generally holds across age groups.