Breast Cancer Risk Evaluation Research Articles

Abstract PO2-25-09: The hidden costs of beauty: An evaluation of breast cancer risk for Black women

Abstract The Black identity, hair product use, and breast cancer scale (BHBS) was developed and previously validated to evaluate the relationship between identity, hair product use, and breast cancer risk for Black women. Black women are disproportionately affected by breast cancer compared to other racial/ethnic groups, with a mortality rate of 40% higher than all other groups. Endocrine Disrupting Chemicals (EDCs) are present in many hair care products and have been linked to several health issues including breast cancer. Due to these health inequities and the complexity of how cultural norms shape Black women’s hair product usage, the purpose of this study was to assess the reliability of the BHBS for intervention planning and evaluate the hazard scores of hair products used by Black women. Methods: Participants were recruited using purposive convenience sampling techniques. The 11-item BHBS includes two subscales: five items measuring sociocultural perspectives about hair and identity (S1) and six items assessing perceived breast cancer risk related to hair product use (S2). To determine reliability, the BHBS was administered twice over a three-week period with sociodemographic factors obtained in the initial data collection phase. All statistical analyses, including descriptive statistics and test-retest reliability (Pearson correlations) were conducted using SPSS version 26. The Environmental Working Group's (EWG's) Skin Deep® database was used to determine hazard scores of hair products used by participants. Products are rated on a 10-point scale with low (0-2), moderate (3-6), and high hazard (7-10) scores. Results: Twenty-three (86.9% African, African American & Caribbean; 13% multiracial) female participants completed the study. Their age ranged from 18-63 years (30.78, SD= 15.40), 47.8% reported an income of less than $25,000, and 34.8% completed some college education. Both subscales demonstrated highly significant and strong reliability (S1: r=.82, p< .001; S2: r=.88, p< .001). Participants primarily used conditioners (91.3% used wash out and 60.9% used leave-in), and professional hair dyes (34.8%). There was also a high frequency of non-use (91.3%) of do-it-yourself relaxers and hair dye kits. We evaluated 40 products across three categories including wash out conditioners, leave-in conditioners and other hair products (e.g., styling gels, shampoo, and hair oils), of which most had a moderate hazard rating of 5 denoting ingredients of health concerns (e.g., parabens, fragrance, and phenoxyethanol). Additionally, of the 22 company brands analyzed, only 10 (45%) are Black owned or founded. Conclusions: The BHBS is a valid and reliable instrument to assess sociocultural perspectives about hair, identity and perceived breast cancer risk. Black women use hair products including relaxers, hair dyes, and conditioners that contain EDCs that may be harmful to their health including risk for breast cancer. The BHBS will be used to evaluate psychosocial interventions that address the unique needs of Black women including exposure to EDCs from hair product use and breast cancer risk. Citation Format: Nicole Sarkaria, Marissa Ericson, Abigail Lopez, Dede Teteh. The hidden costs of beauty: An evaluation of breast cancer risk for Black women [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-25-09.

Abstract PO4-09-03: Environmental Metal Exposures and Breast Cancer Risk: A Prospective Study of Nationally Representative Canadian Data

Abstract Introduction: The impact of metal exposure on breast cancer risk remains unclear. Studies have explored metals independently with limited investigation into chronic exposures and mixture analyses. This project describes exposure to eight heavy and essential metals and evaluates the association between metals and breast cancer risk, independently and in a mixture, among Canadian women. Methods: Demographic information and concentrations of urinary or blood metal biomarkers from 2007-2017 of the Canadian Health Measures Survey (CHMS) were analyzed. Incident breast cancers were ascertained through linkage to the Canadian Cancer Registry. Metal exposure was described using weighted percentiles and categorized by tertiles. Cox proportional hazards regression was used to estimate hazard ratios (HR) and 95% confidence intervals (CI) for metal exposure and breast cancer risk. Quantile g-computation was used to estimate the joint association between metal exposure and breast cancer risk. Results: This analysis included 5,100 women (mean age 44.6 years) with an average follow-up of 6.6 years. Higher urinary arsenic (> 13.0 µg/L) and cadmium (> 10.0 µg/L) had a significant increased risk of breast cancer (HR Arsenic T3 vs. T1 = 2.05; 95%CI 1.05-3.94; HR Cadmium T3 vs. T1 = 1.71; 95%CI 1.01 – 5.87). Analyses into the joint association between the metal exposure mixture and breast cancer risk are ongoing, and matrix results will be presented. Conclusion: This represents the first evaluation of metal exposure and breast cancer risk in a nationally representative cohort. Our findings suggest that arsenic and cadmium, even at low levels, may be associated with an increased risk of breast cancer. These findings can inform population-level interventions to reduce the burden of cancer in Canada. Citation Format: Katherine Pullella, Jan Lubinski, Anthony Hanley, Shelley Harris, Steven Narod, Joanne Kotsopoulos. Environmental Metal Exposures and Breast Cancer Risk: A Prospective Study of Nationally Representative Canadian Data [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-09-03.

Higher peripheral blood mitochondrial DNA copy number and relative telomere length in under 48 years Indonesian breast cancer patients

ObjectiveBreast cancer is the leading cause of cancer incidence and mortality among Indonesian women. A comprehensive investigation is required to enhance the early detection of this disease. Mitochondrial DNA copy number (mtDNA-CN) and relative telomere length (RTL) have been proposed as potential biomarkers for several cancer risks, as they are linked through oxidative stress mechanisms. We conducted a case–control study to examine peripheral blood mtDNA-CN and RTL patterns in Indonesian breast cancer patients (n = 175) and healthy individuals (n = 181). The relative ratios of mtDNA-CN and RTL were determined using quantitative real-time PCR (qPCR).ResultsMedian values of mtDNA-CN and RTL were 1.62 and 0.70 in healthy subjects and 1.79 and 0.73 in breast cancer patients, respectively. We found a positive association between peripheral blood mtDNA-CN and RTL (p < 0.001). In under 48 years old breast cancer patients, higher peripheral blood mtDNA-CN (mtDNA-CN ≥ 1.73 (median), p = 0.009) and RTL (continuous variable, p = 0.010) were observed, compared to the corresponding healthy subjects. We also found a significantly higher ‘High-High’ pattern of mtDNA-CN and RTL in breast cancer patients under 48 years old (p = 0.011). Our findings suggest that peripheral blood mtDNA-CN and RTL could serve as additional minimally invasive biomarkers for breast cancer risk evaluation.

Are better AI algorithms for breast cancer detection also better at predicting risk? A paired case–control study

BackgroundThere is increasing evidence that artificial intelligence (AI) breast cancer risk evaluation tools using digital mammograms are highly informative for 1–6 years following a negative screening examination. We hypothesized that algorithms that have previously been shown to work well for cancer detection will also work well for risk assessment and that performance of algorithms for detection and risk assessment is correlated.MethodsTo evaluate our hypothesis, we designed a case-control study using paired mammograms at diagnosis and at the previous screening visit. The study included n = 3386 women from the OPTIMAM registry, that includes mammograms from women diagnosed with breast cancer in the English breast screening program 2010–2019. Cases were diagnosed with invasive breast cancer or ductal carcinoma in situ at screening and were selected if they had a mammogram available at the screening examination that led to detection, and a paired mammogram at their previous screening visit 3y prior to detection when no cancer was detected. Controls without cancer were matched 1:1 to cases based on age (year), screening site, and mammography machine type. Risk assessment was conducted using a deep-learning model designed for breast cancer risk assessment (Mirai), and three open-source deep-learning algorithms designed for breast cancer detection. Discrimination was assessed using a matched area under the curve (AUC) statistic.ResultsOverall performance using the paired mammograms followed the same order by algorithm for risk assessment (AUC range 0.59–0.67) and detection (AUC 0.81–0.89), with Mirai performing best for both. There was also a correlation in performance for risk and detection within algorithms by cancer size, with much greater accuracy for large cancers (30 mm+, detection AUC: 0.88–0.92; risk AUC: 0.64–0.74) than smaller cancers (0 to < 10 mm, detection AUC: 0.73–0.86, risk AUC: 0.54–0.64). Mirai was relatively strong for risk assessment of smaller cancers (0 to < 10 mm, risk, Mirai AUC: 0.64 (95% CI 0.57 to 0.70); other algorithms AUC 0.54–0.56).ConclusionsImprovements in risk assessment could stem from enhancing cancer detection capabilities of smaller cancers. Other state-of-the-art AI detection algorithms with high performance for smaller cancers might achieve relatively high performance for risk assessment.

Talking about Familial Breast and Ovarian Cancer Risk-Evaluation of a Psychosocial Training Module for Gynecologists in Germany.

Primary care gynecologists are increasingly integrated into the care of patients with hereditary breast and ovarian cancer (HBOC) risks. These physicians should not only have basic genetic knowledge; they should also feel able to sensitively address an increased HBOC risk and deal with emotional, stressful situations in this context. Our project aimed at developing a training module, 'iKNOWgynetics', addressing psychosocial challenges in the context of HBOC care for primary care gynecologists. We developed the psychosocial training module in three phases: first, we conducted an online survey with n = 35 women with a family history of breast or ovarian cancer to assess patients' experiences and needs. Second, based on the results of the needs assessment, we developed the training module. Third, we evaluated the training by assessing physicians' (n = 109) self-efficacy with regard to communication skills in the context of HBOC before and after the training. In the needs assessment, seven psychosocial themes emerged. These themes, complementing a review of the literature, informed the training curriculum. The training was divided into two parts: (1) communicating with women before genetic testing and (2) care co-management for women with HBOC after genetic testing. After the training, participants reported a significant increase in self-efficacy in three domains: communicating empathetically, educating patients in a comprehensible way and dealing with emotionally challenging situations. Our results highlight the relevance of psychosocial issues for patients with HBOC. A genetic literacy training module that integrates aspects of psychosocial care increases physicians' confidence in dealing with emotionally challenging situations before and after their patients' genetic testing. Thus, such trainings may improve the care of women with hereditary cancer risks.

Deep Learning Approaches with Digital Mammography for Evaluating Breast Cancer Risk, a Narrative Review.

Breast cancer remains the leading cause of cancer-related deaths in women worldwide. Current screening regimens and clinical breast cancer risk assessment models use risk factors such as demographics and patient history to guide policy and assess risk. Applications of artificial intelligence methods (AI) such as deep learning (DL) and convolutional neural networks (CNNs) to evaluate individual patient information and imaging showed promise as personalized risk models. We reviewed the current literature for studies related to deep learning and convolutional neural networks with digital mammography for assessing breast cancer risk. We discussed the literature and examined the ongoing and future applications of deep learning techniques in breast cancer risk modeling.

Evaluation of Breast Cancer Risk, Fear and Knowledge Levels and their Effect on Examination Behaviors

This study is aimed to evaluate the risk of breast cancer (BC) by using the Gail risk model (GRM), assess the fear of BC, determine the knowledge level of BC and show how they affect examination behaviors. This study is a descriptive cross-sectional study carried out at the Istanbul Faculty of Medicine. Survey forms were filled out by 241 women who were 35 years or older. Survey forms include general information about the participant, the BC fear scale (BCFS) to assess the fear of BC, GRM-related questions to evaluate BC risk and the comprehensive breast cancer knowledge level test (CBCKLT) to determine the knowledge level of BC. According to the GRM score, 79 (33%) of the participants were found to have a high 5-year risk of BC. There was no statistically significant correlation between the GRM score and the BC fear or knowledge level of the participants (p&gt;0.05). According to BCFS classification, the fear level of 47 (20%) participants was low, 51 (21%) moderate, and 143 (59%) high, and as the knowledge level of the participants increased, their fear of BC statistically significantly increased (r=0.139; p=0.031). The total score of CBCKLT was 7.39±1.75, the general knowledge sub-dimension score was 5.63±1.60, treatability sub-dimension score was 13.02±2.52. As the education level of the participants increased, their BC knowledge level increased (p=0.003). Moreover, participants who had clinical breast examination (CBE) were statistically significant and had higher BC knowledge levels than those who did not (p=0.030). In this study, there was no statistically significant correlation between the GRM score and BC fear levels or BC knowledge levels. Additionally, as the BC knowledge level of the participants increased, their fear of BC increased. Moreover, as it is expected, as the education level of the participants increased, their BC knowledge level increased, and the knowledge levels of BC have a role in increasing CBE.

Effects of Hormones on Breast Development and Breast Cancer Risk in Transgender Women.

Transgender women experience gender dysphoria due to a gender assignment at birth that is incongruent with their gender identity. Transgender people undergo different surgical procedures and receive sex steroids hormones to reduce psychological distress and to induce and maintain desired physical changes. These persons on feminizing hormones represent a unique population to study the hormonal effects on breast development, to evaluate the risk of breast cancer and perhaps to better understand the precise role played by different hormonal components. In MTF (male to female) patients, hormonal treatment usually consists of antiandrogens and estrogens. Exogenous hormones induce breast development with the formation of ducts and lobules and an increase in the deposition of fat. A search of the existing literature dedicated to hormone regimens for MTF patients, their impact on breast tissue (incidence and type of breast lesions) and breast cancer risk provided the available information for this review. The evaluation of breast cancer risk is currently complicated by the heterogeneity of administered treatments and a lack of long-term follow-up in the great majority of studies. Large studies with longer follow-up are required to better evaluate the breast cancer risk and to understand the precise mechanisms on breast development of each exogenous hormone.

Clinical tools and counseling considerations for breast cancer risk assessment and evaluation for hereditary cancer risk.

Breast cancer is a complex and heterogeneous disease. Unfortunately, it is the most common malignancy diagnosed in women in the USA, with 281,550 new cases of invasive breast cancer and 49,290 new cases of noninvasive breast cancer are diagnosed per year. In England, it is currently estimated that approximately 1 in 7 (14%) women will be diagnosed with breast cancer in their lifetime. In the UK in 2017, 54,700 women and 390 men were diagnosed with breast cancer. The risk of breast cancer is influenced by many factors, including but not limited to age, family history, reproductive history, hormonal exposure, proliferative breast lesions, physical activity, alcohol use, tobacco use, breast density, and environmental exposures. Breast cancer risk assessment is a critical part of public health. By identifying women at high risk for breast cancer, personalized recommendations can be deployed with regards to modes of screening, the age to initiate breast screening, and the frequency for completing such screens. In addition, breast cancer risk assessment can assist in determining a woman's eligibility for interventions to reduce risk, either through the use of chemoprevention medications or through surgical means with risk-reducing bilateral mastectomy. This chapter summarizes breast cancer risk assessment models and discusses interventions to reduce breast cancer risk to aid in reducing morbidity and mortality from breast cancer.

Applicability of a web app for breast cancer risk calculation and personalized recommendations for screening in Mexico.

1567 Background: A minority of women with breast cancer in Mexico are being diagnosed through a screening program, which translates into late diagnosis and a worse prognosis. It is imperative to develop easy access and low-cost interventions to increase the screening rate. Methods: We developed a web app tailored to guide patients to seek medical consultation if they were at high risk for breast cancer or standard screening recommendations based on the predicted risk for age (https://cuccuanl.com/calcula-tu-riesgo-de-cancer-de-mama/). The web app consisted of an 8-question survey designed using object-based programming with HTML5, the additional logic was programmed on JavaScript. The program logic automatically guided patients to medical consultation if they were at high risk for breast cancer, or standard screening recommendations based on their predicted risk for age. On each confirmation page, the contact information of the hospital appeared, also, consultation at the clinic could be scheduled within the web app. The web app was distributed via social media on International Breast Cancer Awareness Day. Results: A total of 1,012 persons answered the survey after a follow-up period of two weeks. The median age of respondents was 34 years. Among participants, 10.8% were considered at very high risk for breast cancer due to symptoms, 22% were classified as high risk based on family history or more than 5 years of contraceptive use, and 19% were considered as average-risk population for whom age-based screening tests were recommended. The remaining 48% of participants were considered at low risk for breast cancer development and were directed to educational information about breast cancer awareness. Among all persons that answered our survey, 21 requested a specialized medical appointment within the web app. Conclusions: This pilot study showed a high response of Mexican population seeking breast cancer risk evaluation, 11% of women that responded to our survey had symptoms highly suggestive of breast cancer. The use of web apps can result in mass diffusion which will help reach people with less medical access, a common scenario in many developing countries. Further analysis should be made to measure the real impact on breast cancer diagnosis and oncological outcomes. This study shows how effective social media is as a means of diffusion on health topics. [Table: see text]

Predictors of use of prevention strategies among women at high-risk for breast cancer.

10554 Background: Uptake of chemoprevention and prophylactic surgery among women at high risk for breast cancer is low, despite proven efficacy. We evaluated breast cancer risk factors as predictors of uptake of prevention treatment (PT) to better understand potential associations with these decisions. Methods: An IRB approved registry established in 2003 at the University of Vermont of high-risk women was used to evaluate the association between both modifiable (obesity, sedentary lifestyle and alcohol use) and non-modifiable (age, history of benign breast disease [BDD], genetic predisposition) risk factors and uptake of PT. Women were eligible for inclusion in this analysis if they had one of the following risk factors (BDD, strong family history, &gt; 20% lifetime modeled risk or genetic predisposition) and completed questionnaires regarding diet and physical activity. Alcohol use was assessed using a health questionnaire and physical activity was assessed using a 7-Day Physical Activity Recall questionnaire. We used logistic regression to estimate odds ratios (OR). Results: 504 women were included and had been followed for median of 13 years. Mean age was 44.5 years (range 19 – 75), 98% were Caucasian, and mean BMI was 26.9 (range 17-57). 78% had a family history of breast cancer, 60% had &gt;20% lifetime modeled risk, 14% had a history of benign breast disease (BBD), and 9% of women had confirmed genetic risk in high or moderate risk genes. Women may have had more than one risk factor. 55% were physically active and 55%. 4.2% were sedentary and 12.3% consumed &gt; 1 alcoholic beverage daily. 20.8% of the cohort participated in PT (8.1% took chemoprevention, 2.0% underwent prophylactic bilateral total mastectomy and 10.7% underwent risk reducing salpingo-oophorectomy). Among non-modifiable risk factors, age, genetic predisposition (ORadj 8.66, 95% CI 2.30–32.33, p &lt; 0.0001), and history of benign breast disease (ORadj 4.09, 95% CI 1.89–8.86, p &lt; 0.001) were associated with uptake of PT. Increasing age was associated with increasing uptake (ptrend was &gt; 0.0001) and highest among women aged 60-69 years (ORadj8.19, 95% CI 2.87-23.37 relative to women aged &lt; 40). Women with a strong family history were significantly less likely to take up PT (p = 0.04). BMI, physical activity status and alcohol use were not associated with uptake of PT. Conclusions: Like other studies, we found a low uptake of PT among women at high risk for developing breast cancer. We found that modifiable risk factors were not associated with uptake of PT, which might suggest that high-risk women with modifiable risk factors could be targeted for interventions designed to modify risk (i.e. chemoprevention, weight reduction, etc). Interestingly, women with a strong family history may be less likely to take up PT, and studies to further examine this relationship may identify opportunities for interventions to improve uptake.

Breast cancer risk stratification in women of screening age: Incremental effects of adding mammographic density, polygenic risk, and a gene panel

There is great promise in breast cancer risk stratification to target screening and prevention. It is unclear whether adding gene panels to other risk tools improves breast cancer risk stratification and adds discriminatory benefit on a population basis. In total, 10,025 of 57,902 women aged 46 to 73 years in the Predicting Risk of Cancer at Screening study provided DNA samples. A case-control study was used to evaluate breast cancer risk assessment using polygenic risk scores (PRSs), cancer gene panel (n= 33), mammographic density (density residual [DR]), and risk factorscollected using a self-completed 2-page questionnaire (Tyrer-Cuzick [TC] model version 8). In total, 525 cases and 1410 controls underwent gene panel testing and PRS calculation (18, 143, and/or 313 single-nucleotide polymorphisms [SNPs]). Actionable pathogenic variants (PGVs) in BRCA1/2 were found in 1.7% of cases and 0.55% of controls, and overall PGVs were found in 6.1% of cases and 1.3% of controls. A combined assessment of TC8-DR-SNP313 and gene panel provided the best risk stratification with 26.1% of controls and 9.7% of cases identified at <1.4% 10-year risk and 9.01% of controls and 23.3% of cases at ≥8% 10-year risk. Because actionable PGVs were uncommon, discrimination was identical with/without gene panel (with/without: area under the curve= 0.67, 95% CI= 0.64-0.70). Only 7 of 17 PGVs in cases resulted in actionable risk category change. Extended case (n= 644)-control (n= 1779) series with TC8-DR-SNP143 identified 18.9% of controls and only 6.4% of stage 2+ cases at <1.4% 10-year risk and 20.7% of controls and 47.9% of stage 2+ cases at ≥5% 10-year risk. Further studies and economic analysis will determine whether adding panels to PRS is a cost-effective strategy for risk stratification.

Strategies to Identify and Recruit Women at High Risk for Breast Cancer to a Randomized Controlled Trial of Web-based Decision Support Tools.

We describe five strategies to identify and successfully recruit a large cohort of racially/ethnically diverse high-risk women from multiple sources to a randomized controlled trial evaluating interventions to increase chemoprevention uptake. Findings could inform recruitment efforts for future breast cancer prevention trials to increase recruitment yield of high-risk women.

Breast cancer risk evaluation for the primary care physician.

Primary care physicians are typically the frontline clinicians who assess female patients for their risk of breast cancer, doing so by using a combination of risk algorithms and collecting personal and family medical histories. Patients found to be at increased risk of breast cancer, defined as > 20% overall lifetime risk, are candidates for enhanced screening. This review notes risk factors, determinants of risk, and a systematic approach for primary care physicians to assess and manage patients at risk of breast cancer.

Genetic Variation Interacts with Selenium Exposure Regarding Breast Cancer Risk: Assessing Dietary Intake, Serum Levels and Genetically Elevated Selenium Levels.

Selenium has been suggested to be protective regarding breast cancer risk but no overall effect has been established. Genetics may modify the effect. This study compares the effect of selenium exposure on breast cancer risk between women with different alleles in single-nucleotide polymorphisms (SNPs). The Malmö Cancer and Diet Study, a cohort including 17,035 women and >25 years of follow-up on breast cancer diagnosis, was used. Five promising SNPs regarding interaction with selenium exposure were selected from the literature: rs1050450, rs4880, rs3877899, rs7579, and rs71304. Selenium exposure was assessed in three ways: genetically elevated (n = 16,429), dietary intake (n = 15,891) and serum levels (n = 2037) at baseline. Cox regression and logistic regression analyses evaluated breast cancer risk from selenium exposure, stratified for the SNPs and adjusted for risk factors. A total of 1946 women were diagnosed with breast cancer. Women with T/T alleles in rs1050450 had lower breast cancer risk compared with C/C, HR 0.81 (0.68–0.96). Interaction by rs1050450 limited a protective effect of higher selenium intake to T/T carriers, HR 0.68 (0.43–1.08) for intermediate intake and HR 0.63 (0.40–1.00) for high intake. No interactions or risk differences were seen for other SNPs or for serum selenium or genetically elevated selenium. The results indicate that genetic variation in rs1050450 might affect breast cancer risk and that selenium exposure could be a possible modifiable risk factor for breast cancer among women with that variation.

Design and Development of an Intelligent Clinical Decision Support System Applied to the Evaluation of Breast Cancer Risk.

Breast cancer is currently one of the main causes of death and tumoral diseases in women. Even if early diagnosis processes have evolved in the last years thanks to the popularization of mammogram tests, nowadays, it is still a challenge to have available reliable diagnosis systems that are exempt of variability in their interpretation. To this end, in this work, the design and development of an intelligent clinical decision support system to be used in the preventive diagnosis of breast cancer is presented, aiming both to improve the accuracy in the evaluation and to reduce its uncertainty. Through the integration of expert systems (based on Mamdani-type fuzzy-logic inference engines) deployed in cascade, exploratory factorial analysis, data augmentation approaches, and classification algorithms such as k-neighbors and bagged trees, the system is able to learn and to interpret the patient’s medical-healthcare data, generating an alert level associated to the danger she has of suffering from cancer. For the system’s initial performance tests, a software implementation of it has been built that was used in the diagnosis of a series of patients contained into a 130-cases database provided by the School of Medicine and Public Health of the University of Wisconsin-Madison, which has been also used to create the knowledge base. The obtained results, characterized as areas under the ROC curves of 0.95–0.97 and high success rates, highlight the huge diagnosis and preventive potential of the developed system, and they allow forecasting, even when a detailed and contrasted validation is still pending, its relevance and applicability within the clinical field.

Optimization of prediction methods for risk assessment of pathogenic germline variants in the Japanese population.

Predicting pathogenic germline variants (PGVs) in breast cancer patients is important for selecting optimal therapeutics and implementing risk reduction strategies. However, PGV risk factors and the performance of prediction methods in the Japanese population remain unclear. We investigated clinicopathological risk factors using the Tyrer‐Cuzick (TC) breast cancer risk evaluation tool to predict BRCA PGVs in unselected Japanese breast cancer patients (n = 1,995). Eleven breast cancer susceptibility genes were analyzed using target‐capture sequencing in a previous study; the PGV prevalence in BRCA1, BRCA2, and PALB2 was 0.75%, 3.1%, and 0.45%, respectively. Significant associations were found between the presence of BRCA PGVs and early disease onset, number of familial cancer cases (up to third‐degree relatives), triple‐negative breast cancer patients under the age of 60, and ovarian cancer history (all P < .0001). In total, 816 patients (40.9%) satisfied the National Comprehensive Cancer Network (NCCN) guidelines for recommending multigene testing. The sensitivity and specificity of the NCCN criteria for discriminating PGV carriers from noncarriers were 71.3% and 60.7%, respectively. The TC model showed good discrimination for predicting BRCA PGVs (area under the curve, 0.75; 95% confidence interval, 0.69‐0.81). Furthermore, use of the TC model with an optimized cutoff of TC score ≥0.16% in addition to the NCCN guidelines improved the predictive efficiency for high‐risk groups (sensitivity, 77.2%; specificity, 54.8%; about 11 genes). Given the influence of ethnic differences on prediction, we consider that further studies are warranted to elucidate the role of environmental and genetic factors for realizing precise prediction.

Abstract PD10-03: Triple-negative breast cancer (TNBC) risk with pathogenic variants (PV) in hereditary cancer predisposition genes

Abstract BACKGROUND: TNBC is among the most aggressive subtypes of invasive breast cancer (BC), and accounts for approximately 10-15% of incidental BC diagnoses. TNBC is associated with early age of onset (median age of diagnosis &amp;lt;50) and disproportionately affects African American women. Breast MRI is currently recommended to screen for BC in women with at least a moderate-to-high lifetime risk of BC (a 2-fold or higher increased risk), and may also be superior to mammogram to screen for TNBC. TNBC has been most closely associated with germline PVs in BRCA1. However, recent studies have suggested that PVs in other genes previously associated with invasive BC may specifically confer high risks of the TNBC subtype. METHODS: Results were analyzed from 627,219 women undergoing clinical multi-gene panel testing at a single US-based commercial laboratory between 5/2013 and 2/2020, including genes associated with hereditary BC and other cancers. Demographic and personal/family history data were collected on a test requisition form. Individuals who had single- or founder-site testing, or prior BRCA1 or BRCA2 testing, were excluded. Multivariable regression analysis was used to examine the association between PVs/suspected PVs and personal history (PHx) of TNBC. Models were adjusted for age, personal/family cancer history, and ancestry. Odds ratios (OR) with 95% confidence intervals (CI) excluding 1.0 were considered significant. RESULTS: In total, 22.4% (140,467/627,219) of women tested reported PHx of BC, of whom 12.8% (17,951/140,467) reported PHx of TNBC. Elevated risks of TNBC were identified in carriers of PVs in 10 genes (see Table). While the highest TNBC risk was associated with PVs in BRCA1 (OR 21.24, 95% CI 19.71-22.88), high risks were also seen for BARD1 (OR 7.05, 95% CI 5.71-8.71), TP53 (OR 5.64, 95% CI 3.08-10.33), PTEN (OR 5.52, 95% CI 2.35-13.00) and PALB2 (OR 5.27, 95% CI 4.55-6.10). Moderate-to-high risks (2-5-fold increased risk) of TNBC were also seen for carriers of PVs in RAD51C, RAD51D, BRCA2, and CDKN2A/P16. By contrast, PVs in NBN, ATM, and CHEK2 were all associated with an apparent decreased risk of TNBC. CONCLUSIONS: PVs in several hereditary cancer genes routinely tested on multi-gene panel tests are associated with high risks (OR&amp;gt;5.0) and moderate-to-high risks (OR 2.0-5.0) of TNBC. These findings can inform practice guidelines about which genes to test when evaluating breast cancer risk and which PV carriers may benefit from intensive breast screening with magnetic resonance imaging (MRI). Odds ratios for TNBC in germline carriers of PV in hereditary cancer risk genesRisk GenePV Positive with TNBCOR95% CIp-valueHigh RiskBRCA1119321.2419.71-22.88&amp;lt;0.001BARD11257.055.71-8.71&amp;lt;0.001TP53125.643.08-10.33&amp;lt;0.001PTEN65.522.35-13.00&amp;lt;0.001PALB22315.274.55-6.10&amp;lt;0.001Moderate-to-High RiskRAD51C864.923.86-6.26&amp;lt;0.001RAD51D454.643.34-6.45&amp;lt;0.001BRCA24884.434.02-4.89&amp;lt;0.001CDKN2A (p16)172.521.52-4.18&amp;lt;0.001Moderate-to-Low RiskBRIP1771.921.51-2.44&amp;lt;0.001Protective EffectNBN140.550.32-0.940.030ATM400.510.37-0.70&amp;lt;0.001CHEK2490.440.33-0.58&amp;lt;0.001HOXB1350.330.14-0.810.015 Citation Format: Michael J Hall, Eric Rosenthal, Susana San Roman, Ryan Bernhisel, John Kidd, Elisha Hughes, Thomas Slavin, Allison Kurian. Triple-negative breast cancer (TNBC) risk with pathogenic variants (PV) in hereditary cancer predisposition genes [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 PD10-03.

Association of the Differences in Average Glandular Dose with Breast Cancer Risk

Objectives. To compare the differences in normalized average glandular dose (NAGD) between the breasts of healthy subjects and those of cancer patients and to determine if the NAGD difference is associated with breast cancer risk and improves breast cancer classification. Materials and Methods. Craniocaudal view and mediolateral view full-field digital mammography (FFDM) images were obtained from 1682 healthy subjects whose breasts were categorized as Breast Imaging-Reporting and Data System (BI-RADS) I or II and from 811 biopsy-confirmed unilateral breast cancer patients whose breasts on the contralateral side were category I or II. Both populations were randomized into training and test sets. Multivariate logistic regression analysis was used to build the breast cancer risk assessment model, and the area under the receiver operating characteristic curve ( A z ) was used to evaluate the model. Twenty-two breast cancer patients who were originally categorized as BI-RADS I or II for both breasts, but were diagnosed with unilateral biopsy-confirmed breast cancer subsequently, were included to validate the model. Results. The NAGD differences in both FFDM images between tumor-bearing breasts and the healthy breasts of patients were significantly higher than those in healthy subjects ( P &lt; 0.001 ). The model with NAGD differences had a higher A z value than the model without NAGD differences. While there was no NAGD differences between originally healthy breasts of breast cancer patients, significant NAGD differences between now tumor-bearing breasts and the then previously healthy breasts were found in both FFDM images. Conclusions. NAGD differences between both breasts can be included in the breast cancer risk assessment model to evaluate breast cancer risk.

Combination of phenotype and polygenic risk score in breast cancer risk evaluation in the Spanish population: a case \u2013control study

BackgroundIn recent years, the identification of genetic and phenotypic biomarkers of cancer for prevention, early diagnosis and patient stratification has been a main objective of research in the field. Different multivariable models that use biomarkers have been proposed for the evaluation of individual risk of developing breast cancer.MethodsThis is a case control study based on a population-based cohort. We describe and evaluate a multivariable model that incorporates 92 Single-nucleotide polymorphisms (SNPs) (Supplementary Table S1) and five different phenotypic variables and which was employed in a Spanish population of 642 healthy women and 455 breast cancer patients.ResultsOur model allowed us to stratify two groups: high and low risk of developing breast cancer. The 9th decile included 1% of controls vs 9% of cases, with an odds ratio (OR) of 12.9 and a p-value of 3.43E-07. The first decile presented an inverse proportion: 1% of cases and 9% of controls, with an OR of 0.097 and a p-value of 1.86E-08.ConclusionsThese results indicate the capacity of our multivariable model to stratify women according to their risk of developing breast cancer. The major limitation of our analysis is the small cohort size. However, despite the limitations, the results of our analysis provide proof of concept in a poorly studied population, and opens up the possibility of using this method in the routine screening of the Spanish population.