Personalized Breast Cancer Screening Research Articles

Implementing Multifactorial Risk Assessment with Polygenic Risk Scores for Personalized Breast Cancer Screening in the Population Setting: Challenges and Opportunities.

Risk-stratified breast screening has been proposed as a strategy to overcome the limitations of age-based screening. A prospective cohort study was undertaken within the PERSPECTIVE I&I project, which will generate the first Canadian evidence on multifactorial breast cancer risk assessment in the population setting to inform the implementation of risk-stratified screening. Recruited females aged 40-69 unaffected by breast cancer, with a previous mammogram, underwent multifactorial breast cancer risk assessment. The adoption of multifactorial risk assessment, the effectiveness of methods for collecting risk factor information and the costs of risk assessment were examined. Associations between participant characteristics and study sites, as well as data collection methods, were assessed using logistic regression; all p-values are two-sided. Of the 4246 participants recruited, 88.4% completed a risk assessment, with 79.8%, 15.7% and 4.4% estimated at average, higher than average and high risk, respectively. The total per-participant cost for risk assessment was CAD 315. Participants who chose to provide risk factor information on paper/telephone (27.2%) vs. online were more likely to be older (p = 0.021), not born in Canada (p = 0.043), visible minorities (p = 0.01) and have a lower attained education (p < 0.0001) and perceived fair/poor health (p < 0.001). The 34.4% of participants requiring risk factor verification for missing/unusual values were more likely to be visible minorities (p = 0.009) and have a lower attained education (p ≤ 0.006). This study demonstrates the feasibility of risk assessment for risk-stratified screening at the population level. Implementation should incorporate an equity lens to ensure cancer-screening disparities are not widened.

Unlocking the complete blood count as a risk stratification tool for breast cancer using machine learning: a large scale retrospective study

Optimizing early breast cancer (BC) detection requires effective risk assessment tools. This retrospective study from Brazil showcases the efficacy of machine learning in discerning complex patterns within routine blood tests, presenting a globally accessible and cost-effective approach for risk evaluation. We analyzed complete blood count (CBC) tests from 396,848 women aged 40–70, who underwent breast imaging or biopsies within six months after their CBC test. Of these, 2861 (0.72%) were identified as cases: 1882 with BC confirmed by anatomopathological tests, and 979 with highly suspicious imaging (BI-RADS 5). The remaining 393,987 participants (99.28%), with BI-RADS 1 or 2 results, were classified as controls. The database was divided into modeling (including training and validation) and testing sets based on diagnostic certainty. The testing set comprised cases confirmed by anatomopathology and controls cancer-free for 4.5–6.5 years post-CBC. Our ridge regression model, incorporating neutrophil–lymphocyte ratio, red blood cells, and age, achieved an AUC of 0.64 (95% CI 0.64–0.65). We also demonstrate that these results are slightly better than those from a boosting machine learning model, LightGBM, plus having the benefit of being fully interpretable. Using the probabilistic output from this model, we divided the study population into four risk groups: high, moderate, average, and low risk, which obtained relative ratios of BC of 1.99, 1.32, 1.02, and 0.42, respectively. The aim of this stratification was to streamline prioritization, potentially improving the early detection of breast cancer, particularly in resource-limited environments. As a risk stratification tool, this model offers the potential for personalized breast cancer screening by prioritizing women based on their individual risk, thereby indicating a shift from a broad population strategy.

The promise of AI in personalized breast cancer screening: are we there yet?

The promise of AI in personalized breast cancer screening: are we there yet?

The psychological and environment risk factors of development of breast cancer in women residing in industrial megalopolis and rural locality

The breast cancer hold leading position in the structure of oncological morbidity of women worldwide. The purpose of the study is to analyze contribution of psychological and environmental factors to risk of development of breast cancer in women residing in industrial metropolis and rural locality. The actuality of the study is conditioned by acquisition of new knowledge about risk factors of breast cancer. The study covered psychological factors (basic beliefs, life orientations, locus of control, coping behavior strategies, subjective assessment of quality of life, subjective age indicator, personal helplessness-independence, resilience) and environmental factor (place of urban of rural residence of women with breast cancer). The study established that in women residing in industrial metropolis the psychological risk factors are reduced indicators of basic beliefs, of quality of life and of resilience, rare application of coping strategy "Escape-Avoidance", external locus of control. Alternatively, in women residing in rural areas, psychological risk factors for breast cancer are rare application of coping strategies, reduced quality of life indicators, increased vital activity, internal level of subjective control and personal helplessness. The study results can be included in development of personalized breast cancer screening protocols and as well as considered in assessing risk of development of disease when classifying women by breast cancer risk groups.

Personalized Breast Cancer Screening: A Risk Prediction Model Based on Women Attending BreastScreen Norway.

We aimed to develop and validate a model predicting breast cancer risk for women targeted by breast cancer screening. This retrospective cohort study included 57,411 women screened at least once in BreastScreen Norway during the period from 2007 to 2019. The prediction model included information about age, mammographic density, family history of breast cancer, body mass index, age at menarche, alcohol consumption, exercise, pregnancy, hormone replacement therapy, and benign breast disease. We calculated a 4-year absolute breast cancer risk estimates for women and in risk groups by quartiles. The Bootstrap resampling method was used for internal validation of the model (E/O ratio). The area under the curve (AUC) was estimated with a 95% confidence interval (CI). The 4-year predicted risk of breast cancer ranged from 0.22-7.33%, while 95% of the population had a risk of 0.55-2.31%. The thresholds for the quartiles of the risk groups, with 25% of the population in each group, were 0.82%, 1.10%, and 1.47%. Overall, the model slightly overestimated the risk with an E/O ratio of 1.10 (95% CI: 1.09-1.11) and the AUC was 62.6% (95% CI: 60.5-65.0%). This 4-year risk prediction model showed differences in the risk of breast cancer, supporting personalized screening for breast cancer in women aged 50-69 years.

Personalized Breast Cancer Screening.

Abundant literature and clinical trials indicate that routine cancer screenings decrease patient mortality for several common cancers. However, current national cancer screening guidelines heavily rely on patient age as the predominant factor in deciding cancer screening timing, neglecting other important medical characteristics of individual patients. This approach either delays screening or prescribes excessive screenings. Another disadvantage of the current approach is its inability to combine information across hospital systems because of the lack of a coherent records system. We propose to use claims data and medical insurance transactions that use consistent and pre-established sets of codes for diagnosis, procedures, and medications to develop a clinical support tool to supply supplemental insights and precautions for physicians to make more informed decisions. Furthermore, we propose a novel machine learning framework to recommend personalized, data-driven, and dynamic screening decisions. We apply this new method to the study of breast cancer mammograms using claims data from 378,840 female patients to demonstrate that across different risk populations, personalized screening reduces the average delay in a cancer diagnosis by 2-3 months with statistical significance, with even stronger benefits for individual patients up to 10 months. Incorporating personal medical characteristics using claims data and novel machine learning methodologies into breast cancer screening improves screening delay by more dynamically considering changing patient risks. Future incorporation of the proposed methodology in health care settings could be provided as a potential support tool for clinicians.

Inequities in referrals to a breast cancer risk assessment and prevention clinic: a mixed methods study

BackgroundInequitable access to personalized breast cancer screening and prevention may compound racial and ethnic disparities in outcomes. The Breast Cancer Personalized Risk Assessment, Education and Prevention (B-PREP) program, located within the Brigham and Women’s Hospital (BWH) Comprehensive Breast Health Center (BHC), provides care to patients at high risk for developing breast cancer. We sought to characterize the differences between BWH primary care patients referred specifically to B-PREP for risk evaluation and those referred to the BHC for benign breast conditions. Through interviews with primary care clinicians, we sought to explore contributors to potentially inequitable B-PREP referral patterns.MethodsWe used electronic health record data and the B-PREP clinical database to identify patients referred by primary care clinicians to the BHC or B-PREP between 2017 and 2020. We examined associations with likelihood of referral to B-PREP for risk assessment. Semi-structured interviews were conducted with nine primary care clinicians from six clinics to explore referral patterns.ResultsOf 1789 patients, 78.0% were referred for benign breast conditions, and 21.5% for risk assessment. In multivariable analyses, Black individuals were less likely to be referred for risk than for benign conditions (OR 0.38, 95% CI:0.23–0.63) as were those with Medicaid/Medicare (OR 0.72, 95% CI:0.53–0.98; OR 0.52, 95% CI:0.27–0.99) and those whose preferred language was not English (OR 0.26, 95% CI:0.12–0.57). Interviewed clinicians described inconsistent approaches to risk assessment and variable B-PREP awareness.ConclusionsIn this single-site evaluation, among individuals referred by primary care clinicians for specialized breast care, Black, publicly-insured patients, and those whose preferred language was not English were less likely to be referred for risk assessment. Larger studies are needed to confirm these findings. Interventions to standardize breast cancer risk assessment in primary care may improve equity.

Overcoming the limitations of screening mammography in Japan and Korea: a paradigm shift to personalized breast cancer screening based on ultrasonography.

Screening mammography programs have been implemented in numerous Western countries with the aim of reducing breast cancer mortality. However, despite over 20 years of population-based screening mammography, the mortality rates in Japan and Korea continue to rise. This may be due to the fact that screening mammography is not as effective for Japanese and Korean women, who often have dense breasts. This density decreases the sensitivity of mammography due to a masking effect. Therefore, the early detection of small invasive cancers requires more than just mammography, particularly for women in their 40s. This review discusses the limitations and challenges of screening mammography, as well as the keys to successful population-based breast cancer screening in Japan and Korea. This includes a focus on breast ultrasonography techniques, which are based on histopathologic anatomical knowledge, and personalized screening strategies that are based on risk assessments measured by glandular tissue components.

Personalized breast cancer screening with selective addition of digital breast tomosynthesis through artificial intelligence.

Breast cancer screening is predominantly performed using digital mammography (DM), but digital breast tomosynthesis (DBT) has higher sensitivity. DBT demands more resources than DM, and it might be more feasible to reserve DBT for women with a clear benefit from the technique. We explore if artificial intelligence (AI) can select women who would benefit from DBT imaging. We used data from Malmö Breast Tomosynthesis Screening Trial, where all women prospectively were examined with separately double read DM and DBT. We retrospectively analyzed DM examinations () with a breast cancer detection system and used the provided risk score (1 to 10) for risk stratification. We tested how different score thresholds for adding DBT to an initial DM affects the number of detected cancers, additional DBT examinations needed, detection rate, and false positives. If using a threshold of 9.0, 25 (26%) more cancers would be detected compared to using DM alone. Of the 41 cancers only detected on DBT, 61% would be detected, with only 1797 (12%) of the women examined with both DM and DBT. The detection rate for the added DBT would be 14/1000 women, whereas the false-positive recalls would be increased with 58 (21%). Using DBT only for selected high gain cases could be an alternative to complete DBT screening. AI can analyze initial DM images to identify high gain cases where DBT can be added during the same visit. There might be logistical challenges, and further studies in a prospective setting are necessary.

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.

Personalized Disease Screening Decisions Considering a Chronic Condition

Clinical practice guidelines do not sufficiently address the needs of patients with chronic conditions as these guidelines focus on single disease management and ignore unique patient-specific conditions. As a result, a nonpersonalized approach to the management of the patients with chronic conditions leads to adverse events and increases the financial burden on the healthcare system as over 150 million Americans experience chronic conditions. To this end, we develop a stochastic modeling framework to personalize the disease screening decisions for patients with or at risk for developing a chronic condition and provide an exact solution algorithm. We consider the optimal management of the screening decisions for an index disease (e.g., breast cancer, colorectal cancer, human immunodeficiency virus, etc.) while accounting for the existence of a chronic condition (e.g., hypertension, diabetes, Alzheimer’s disease, etc.). Our modeling framework is particularly useful for the cases where the chronic condition affects the risk of the index disease. In a case study using real breast cancer epidemiology data, we demonstrate how our modeling framework can be used to personalize breast cancer screening for women with type 2 diabetes. In addition to providing a personalized breast cancer screening schedule for women with diabetes, we find some important policy insights that were not previously recognized by the medical community. More specifically, we find that compared with women without diabetes, women with diabetes should be screened less aggressively, but screening should end at similar ages. We also find that adherence to the optimal screening policy is more crucial for women with diabetes compared with nondiabetic women. Our main insight on screening recommendations also has important resource implications as it leads to fewer screening mammograms. That is, compared with the current national breast cancer screening guidelines, the optimal breast cancer screening policy for women with diabetes could save the healthcare system approximately 2.6 million mammograms annually. This paper was accepted by Stefan Scholtes, healthcare management. Funding: This work was supported in part by the National Institutes of Health under the National Cancer Institute [Grants U01CA152958 and U01CA253911]. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2022.4336 .

Barriers and Facilitators to the Implementation of a Personalized Breast Cancer Screening Program: Views of Spanish Health Professionals.

This study explored the barriers and facilitators to the implementation of a risk-based breast cancer screening program from the point of view of Spanish health professionals. A cross-sectional study with 220 Spanish health professionals was designed. Data were collected in 2020 via a web-based survey and included the advantages and disadvantages of risk-based screening and barriers and facilitators for the implementation of the program. Descriptive statistics and Likert scale responses analyzed as category-ordered data were obtained. The risk-based screening was considered important or very important to reduce breast cancer mortality and promote a more proactive role for women in breast cancer prevention, to increase coverage for women under 50 years, to promote a breast cancer prevention strategy for women at high risk, and to increase efficiency and effectiveness. Switching to a risk-based program from an age-based program was rated as important or very important by 85% of participants. As barriers for implementation, risk communication, the workload of health professionals, and limited human and financial resources were mentioned. Despite the barriers, there is good acceptance, and it seems feasible, from the perspective of health professionals, to implement a risk-based breast cancer screening program in Spain. However, this poses a number of organizational and resource challenges.

Abstract OT-21-01: Personalized breast cancer screening in a population-based study: Women informed to screen depending on measures of risk (WISDOM)

Abstract Background: WISDOM is a 100,000 healthy women preference-tolerant, pragmatic study comparing traditional annual screening to personalized risk-based breast screening. The novelty of WISDOM personalized screening is the integration of previously validated genetic and clinical risk factors (age, family history, breast biopsy results, ethnicity, mammographic density) into a single risk assessment model that directs the starting age, timing, and frequency of screening. The goal of WISDOM is to determine if personalized screening, compared to annual screening, is as safe, less morbid, enables prevention, and is more accepted by women. The study is registered on ClinicalTrials.gov, NCT02620852. Methods: Women aged 40-74 years with no history of breast cancer, DCIS or previous double mastectomy can join the study online at wisdomstudy.org. Participants can either elect randomization or self-select a study arm. Then, they provide electronic consent and sign the Release for Medical Information via DocuSign. For all participants, 5-year risk of developing breast cancer is calculated according to the Breast Cancer Surveillance Consortium (BCSC) model. Participants in the personalized arm undergo panel-based mutation testing (BRCA1, BRCA2, TP53, PTEN, STK11, CDH1, ATM, PALB2, and CHEK2), and their 5-year risk is calculated using the BCSC score combined with a Polygenic Risk Score (BCSC-PRS) that includes 229 single nucleotide polymorphisms (SNPs) known to increase breast cancer risk. The SNPs and mutations are assessed by saliva-based testing through Color Genomics. Five-year risk level thresholds are used to stratify participants as low-, moderate- and high risk. Risk stratification determines age to start, stop, and frequency of screening in the personalized arm. Accrual: As of July 2020 the WISDOM Study is open to all eligible women in the United States. To date, 38,762 eligible women have registered, and 28,706 women have consented to participate in the trial. The median age is 56 years. Seventy-seven percent of participants are Caucasian, 2% African-American, 5% Asian, and 8% of self-reported Hispanic ethnicity. WISDOM is partnering with Blue Cross Blue Shield Association for regional plan opt-in coverage, self-insured companies (Salesforce, Genentech, Qualcomm, CalPERS) and Medi-Cal (Inland Empire Health Plan) using a coverage with evidence progression approach. Accrual expansion and diversity: To ensure that resulting data are meaningful and potentially practice-changing for all populations of women, the WISDOM Study is enhancing the diversity of our participant population by establishing WISDOM sites in diverse areas with large African-American (Alabama, Louisiana, Illinois) and Latina (Florida) populations. These new recruitment sites, intentionally selected for the diverse communities they serve, have established partnerships with community organizations and outreach navigators. Additionally, we have translated the WISDOM Study to Spanish to facilitate access by Latina communities. With the engagement of patient advocates and community partnerships, expanding diversity in the study population will strengthen our scientific knowledge of breast cancer risk and improve access to personalized breast cancer screening recommendations for all women. Enrollment will continue through 2022. Conclusions: Results of 5 years follow-up will enable us to demonstrate whether personalized screening improves outcomes for future patients and it improves healthcare value by reducing screen volumes and costs without jeopardizing outcomes. Citation Format: Irene Acerbi, Allison Stover Fiscalini, Mandy Che, Yiwey Shieh, Lisa Madlensky, Jeffrey Tice, Elad Ziv, Martin Eklund, Amie Blanco, Barry Tong, Deborah Goodman, Lamees Nassereddine, Nancy Anderson, Heather Harvey, Steele Fors, Hannah L Park, Antonia Petruse, Skye Stewart, Janet Wernisch, Larissa Risty, Ian Hurley, Barbara Koenig, Celia Kaplan, Robert Hiatt, Neil Wenger, Vivian Lee, Diane Heditsian, Susie Brain, Leah Sabacan, Tianyi Wang, Barbara A Parker, Alexander Borowsky, Hoda Anton-Culver, Arash Naeim, Andrea Kaster, Melinda Talley, Laura van 't Veer, Andrea Z LaCroix, Olufunmilayo I Olopade, Deepa Sheth, Augustin Garcia, Rachel Lancaster, Wisdom Study and Athena Breast Health Network Investigators and Advocate Partners, Laura Esserman. Personalized breast cancer screening in a population-based study: Women informed to screen depending on measures of risk (WISDOM) [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 OT-21-01.

Overall assessment system of combined mammography and ultrasound for breast cancer screening in Japan

Mammography is the only screening method with evidence in support of reduced breast cancer mortality, but yields poor accuracy outcomes in women with dense breast tissue. The Japan Strategic Anti-cancer Randomized Trial (J-START) was conducted as part of the measures implemented to deal with women with dense breast tissue. Although the sensitivity was increased, the specificity was significantly lower in the intervention group because further examination was required in all positive cases classified by either mammography or ultrasound in the J-START. To address the issue, an overall assessment system of combined mammography and adjunctive ultrasound for breast cancer screening has been developed in Japan. The basic concept is based on a comprehensive assessment that includes a mammography assessment followed by an adjunctive assessment for ultrasound similar to the clinical setting. Currently, mammography alone is recommended for population-based breast cancer screening in Japan, but additional ultrasonography is extensively available for women, especially for women with dense breasts with shared decision-making for personalized breast cancer screening. The overall assessment system is recommended for use in Japan when breast cancer screening is conducted using both mammography and ultrasonography. In this article, we summarize the advantages of the overall assessment and the simultaneous mammography/ultrasound method, the basic approach used in Japan to assign the overall assessment category for breast cancer screening, and we outline the future directions of adjunctive screening ultrasound.

Abstract B078: [Advocate Abstract] Advocating for diversity in the WISDOM Breast Cancer Screening Study

Abstract This poster demonstrates that advocacy support enhances diversity recruitment in the WISDOM Study (Women Informed to Screen Depending on Measures of Risk). Initially funded by a grant from the Patient-Centered Outcomes Research Institute, with advocates having a key role in design and planning, WISDOM is a 100,000 healthy women preference-tolerant, pragmatic study comparing annual to personalized risk-based breast screening. The novelty of WISDOM personalized screening is the integration of previously validated genetics and clinical risk factors (age, family history, breast biopsy results, ethnicity, mammographic density) into a single risk-assessment model that directs the starting age, timing, and frequency of screening. Importantly, the genetic component of risk is calibrated by race/ethnicity (Caucasian, Asian, African American, Hispanic), to provide each woman the most tailored personalized risk assessment. The goal of WISDOM is to determine if personalized screening, compared to annual screening, is as safe, less morbid, enables prevention, and is preferred by women. The study is registered on ClinicalTrials.gov, NCT02620852. The study's “preference-tolerant design” encourages women to be randomized but also allows self-assignment (this component was recommended by advocates) for those with a strong personal preference for either annual or risk-based screening. WISDOM study researchers aim to help all women make better-informed decisions by comparing the outcomes of women who have annual mammograms with those women who received mammograms on a schedule determined by their personal risk. Enrollment and consent take place entirely online at www.wisdomstudy.org, which serves as a patient portal where participants register, complete electronic consent, take health-related questionnaires and receive screening recommendations and genetic test results. Currently the clinical sites and infrastructure associated with this study are part of the Athena Breast Health Network (Athena), a unique collaboration among the five University of California (UC) medical/cancer centers (UCSF, UC Davis, UCLA, UC Irvine, and UCSD) and the Sanford Health System (based in Sioux Falls, SD). Athena has fostered partnerships with advocates since its inception in 2009 and formed the Consumer and Community Advisory Committee (CCAC). The CCAC comprises patient advocate representatives from each Athena site and community members with expertise in public heath, breast health, and under-represented populations. In addition to participation at twice-yearly WISDOM Retreats, advocates participate on regular WebEx WISDOM Study calls with committee updates on advocacy involvement, marketing and outreach materials, recruitment, bioethics, protocol and adherence. Each advocate's skills and background inform all aspects of the study as we focus on addressing recruitment strategies for expanding and encouraging enrollment in traditionally under-represented populations including, but not limited to, racial/ethnic, underserved, older women, and women with little or no access to the internet. Advocates, partnering with study staff, are championing efforts to further WISDOM diversity outreach by identifying key community champions in women's organizations, local nonprofits and faith-based organizations; identifying people who do community-based research and have established community relations; working on strategies on what we can offer to the community such as presentations events and in-person discussions; and, finally, helping to bridge technology gaps and access. As of July 2018, the WISDOM study is open to all eligible women in California, North Dakota, South Dakota, Minnesota and Iowa. To date, 23,329 eligible women have registered and 14,393 women have consented to participate in the trial. The median age is 56 years, 82% Caucasian, 1% African-American, and 6% Asian, and 9% self-reporting as Hispanic. WISDOM Study data collected so far do not reflect the diversity of our potential participant population. We are partnering with health insurers and self-insured companies and expanding to other states with enrollment continuing past 2019. With the engagement of patient advocates, expanding diversity recruitment will help fill gaps in scientific knowledge, resulting in personalized breast cancer screening recommendations for all women. Citation Format: Susie Brain. [Advocate Abstract] Advocating for diversity in the WISDOM Breast Cancer Screening Study [abstract]. In: Proceedings of the Eleventh AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2018 Nov 2-5; New Orleans, LA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(6 Suppl):Abstract nr B078.

RE: The WISDOM Personalized Breast Cancer Screening Trial: Simulation Study to Assess Potential Bias and Analytic Approaches.

RE: The WISDOM Personalized Breast Cancer Screening Trial: Simulation Study to Assess Potential Bias and Analytic Approaches.

Abstract OT3-03-02: Personalized breast cancer screening in a population-based study: Women informed to screen depending on measures of risk (WISDOM)

Abstract Background: WISDOM is a 100,000 healthy women preference-tolerant, pragmatic study comparing traditional annual screening to personalized risk-based breast screening. The novelty of WISDOM personalized screening is the integration of previously validated genetic and clinical risk factors (age, family history, breast biopsy results, ethnicity, mammographic density) into a single risk assessment model that directs the starting age, timing, and frequency of screening. The goal of WISDOM is to determine if personalized screening, compared to annual screening, is as safe, less morbid, enables prevention, and is more accepted by women. The study is registered on ClinicalTrials.gov, NCT02620852. Methods: Women aged 40-74 years with no history of breast cancer or DCIS, and no previous double mastectomy can join the study online at wisdomstudy.org. Participants can either elect randomization or self-select a study arm. Then, they can provide electronic consent and sign the Release for Medical Information via DocuSign. For all participants, 5-year risk of developing breast cancer is calculated according to the Breast Cancer Screening Consortium (BCSC) model. Participants in the personalized arm undergo panel-based mutation testing (BRCA1, BRCA2, TP53, PTEN, STK11, CDH1, ATM, PALB2, and CHEK2), and their 5-year risk is calculated using the BCSC score combined with a Polygenic Risk Score (BCSC-PRS) that includes 75 single nucleotide polymorphisms (SNPs) known to increase breast cancer risk (will increase to 229). The SNPs and mutations are assessed by saliva-based testing through Color Genomics. 5-year risk level thresholds are used to stratify for low-, moderate- and high risk. Risk stratification determines age to start, stop, and frequency of screening. Accrual: As of July 2019, the WISDOM study is open to all eligible women in California, North Dakota, South Dakota, Minnesota, Iowa, Illinois, and New Jersey. To date, 30,392 eligible women have registered, and 21,392 women have consented to participate in the trial. The median age was 56 years. 85% of participants were Caucasian, 2% African-American, and 5% Asian. 6% self-reported Hispanic ethnicity. WISDOM is actively partnering with Blue Cross Blue Shield Association for national coverage, self-insured companies (Salesforce, Genentech, Qualcomm, CalPERS) and Medi-Cal (Inland Empire Health Plan) using a coverage with evidence progression approach. Accrual expansion and diversity: To strengthen generalizability, the WISDOM Study is enhancing the diversity of our potential participant population by expanding to other states (Alabama, Louisiana), and partnering with other health insurers and self-insured companies. Future expansion regions include Texas, Florida, South Carolina, Oklahoma, Montana, and New Mexico. Additionally, we have translated the whole study experience to Spanish to further reach Spanish-speaking communities. With the engagement of patient advocates and community partnerships, expanding diversity recruitment will strengthen our scientific knowledge of breast cancer risk and increase access to personalized breast cancer screening recommendations for all women. WISDOM enrollment will continue through 2020. Conclusions: Results at 5 years will enable us to demonstrate that personalized screening improves healthcare value by reducing screen volumes and costs without jeopardizing outcomes. Citation Format: Mandy Che, Allison Stover Fiscallini, Irene Acerbi, Yiweh Shieh, Lisa Madlensky, Jeffrey Tice, Elad Ziv, Martin Eklund, Amie Blanco, Barry Tong, Deborah Goodman, Lamees Nassereddine, Nancy Anderson, Heather Harvey, Steele Fors, Hannah L Park, Antonia Petruse, Skye Stewart, Janet Wernisch, Larissa Risty, Ian Hurley, Barbara Koenig, Celia Kaplan, Robert Hiatt, Neil Wenger, Vivian Lee, Diane Heditsian, Susie Brain, Leah Sabacan, Barbara Parker, Alexander Borowsky, Hoda Anton-Culver, Hoda Anton-Culver, Arash Naeim, Andrea Kaster, Melinda Talley, Laura van't Veer, Andrea LaCroix, Olufunmilayo I Olopade, Deepa Sheth, WISDOM Study and Athena Breast Health Network Investigators and Advocate Partners and Laura Esserman. Personalized breast cancer screening in a population-based study: Women informed to screen depending on measures of risk (WISDOM) [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr OT3-03-02.

Abstract P2-10-05: A breast cancer multi-racial/ethnic polygenic risk score for improved personalized breast cancer screening

Abstract Background: Polygenic risk scores (PRS) integrate risk information from breast cancer associated SNPs (single nucleotide polymorphism). The risk scores have mostly been developed in populations of European ancestry, and have been shown to improve risk prediction over standard breast cancer risk models in these populations. The ability of the PRS to personalize screening is currently being studied. We included PRS as a component of breast cancer risk assessment in the WISDOM Study, a trial of personalized vs. annual breast cancer screening. In order to account for race/ethnicity in PRS risk assessment, we developed a race/ethnicity calibrated and inclusive PRS risk score that we incorporated here into the Gail model to determine impact on risk stratification. Methods: We constructed two different PRS for each race/ethnicity: For Caucasian populations, we constructed two PRS based on SNPs discovered in European-ancestry populations. One PRS was based on 167 SNPs (PRS-167) and the other based on 313 SNPs (PRS-313) from the Breast Cancer Association Consortium studies as previously published. For each of the Asian-, Hispanic- and African-ancestry populations we added additional ancestry specific SNPs to the PRS-167 or the PRS-313, that were literature curated or our own identified race/ethnicity SNPs that we validated to provide independent risk prediction for their ancestry group: Asian added 10 or 4 additional SNPs, Hispanic 2 SNPs, and African 8 and 12 SNPs, respectively to each model. We tested this approach using datasets from several case-control studies of multiple racial/ethnic populations and compared discrimination of the models using area under the receiver operating characteristic curve (AUROC). Furthermore, we applied our multi-racial/ethnic PRS-313 in a sample of ~3000 multi-racial/ethnic women from the Athena Breast Screening Registry, case-control sampled by Gail score to be at elevated (Gail &amp;gt;1.67) or average (Gail≤1.67) risk, to evaluate the impact of our multi-ethnic adjustment on risk stratification. Results: A multi-race/ethnicity adjusted PRS-313 and PRS-167 plus ethnicity specific SNPs has moderate-high discriminatory power with AUROCs of 0.65 and 0.64, respectively. The specificity of our PRS-167 in the different race/ethnicity ancestries performs relatively well in Asian (AUROC 0.59) and Hispanic (AUROC 0.63) populations, but less so in African-ancestry (AUROC 0.56). Incorporating multi-race/ethnicity PRS into Gail model selected women, resulted in 20% of average-risk women transitioning to risk above 1.67%, and conversely, 38% of elevated risk patients were reclassified to average risk. Conclusion: We constructed a PRS risk score that can be applied to multi-ethnic populations and found moderate-high discrimination. Additional work is needed for the African-ancestry population. The addition of a multi-race/ethnicity SNP model to risk classification based on the Gail model significantly changes risk stratification and clinical care recommendations due to down- or up-reclassification of women at average versus elevated risk. Citation Format: Sarah Theiner, Donglei Hu, Scott Huntsman, Yiwey Shieh, Laura Fejerman, Irene Acerbi, Sarah D Sawyer, Paige Kendall, Wei Zheng, Dezheng Huo, Olufunmilayo I Olopade, Christopher Haiman, Karla Kerlikowske, Steven Cummings, Ester John, Gabriela Torres-Mejia, Lawrence H Kushi, Denise Wolf, Jeffery A Tice, David A Pearce, Laura Esserman, Athena Breast Health Network Investigators and Advocate Partners, Laura J van ‘t Veer, Elad Ziv. A breast cancer multi-racial/ethnic polygenic risk score for improved personalized breast cancer screening [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P2-10-05.

Breast Cancer Risk Prediction in Korean Women: Review and Perspectives on Personalized Breast Cancer Screening.

Due to an increasing proportion of older individuals and the adoption of a westernized lifestyle, the incidence rate of breast cancer is expected to rapidly increase within the next 10 years in Korea. The National Cancer Screening Program (NCSP) of Korea recommends biennial breast cancer screening through mammography for women aged 40–69 years old and according to individual risk and preference for women above 70 years old. There is an ongoing debate on how to most effectively screen for breast cancer, with many proponents of personalized screening, or screening according to individual risk, for women under 70 years old as well. However, to accurately stratify women into risk categories, further study using more refined personalized characteristics, including potentially incorporating a polygenic risk score (PRS), may be needed. While most breast cancer risk prediction models were developed in Western countries, the Korean Breast Cancer Risk Assessment Tool (KoBCRAT) was developed in 2013, and several other risk models have been developed for Asian women specifically. This paper reviews these models compared to commonly used models developed using primarily Caucasian women, namely, the modified Gail, Breast Cancer Surveillance Consortium, Rosner and Colditz, and Tyrer-Cuzick models. In addition, this paper reviews studies in which PRS is included in risk prediction in Asian women. Finally, this paper discusses and explores strategies toward development and implementation of personalized screening for breast cancer in Korea.

Personalized breast cancer screening strategies: A systematic review and quality assessment.

BackgroundThe effectiveness of breast cancer screening is still under debate. Our objective was to systematically review studies assessing personalized breast cancer screening strategies based on women’s individual risk and to conduct a risk of bias assessment.MethodsWe followed the standard methods of The Cochrane Collaboration and PRISMA declaration and searched the MEDLINE, EMBASE and Clinical Trials databases for studies published in English. The quality of the studies was assessed using the ISPOR-AMCP-NPC Questionnaire and The Cochrane Risk of Bias Tool. Two independent reviewers screened full texts and evaluated the risk of bias.ResultsOut of the 1533 initially retrieved citations, we included 13 studies. Three studies were randomized controlled trials, while nine were mathematical modeling studies, and one was an observational pilot study. The trials are in the recruitment phase and have not yet reported their results. All three trials used breast density and age to define risk groups, and two of them included family history, previous biopsies, and genetic information. Among the mathematical modeling studies, the main risk factors used to define risk groups were breast density, age, family history, and previous biopsies. Six studies used genetic information to define risk groups. The most common outcome measures were the gain in quality-adjusted life years (QALY), absolute costs, and incremental cost-effectiveness ratio (ICER), while the main outcome in the observational study was the detection rate. In all models, personalized screening strategies were shown to be effective. The randomized trials were of good quality. The modeling studies showed moderate risk of bias but there was wide variability across studies. The observational study showed a low risk of bias but its utility was moderate due to its pilot design and its relatively small scale.ConclusionsThere is some evidence of the effectiveness of screening personalization in terms of QUALYs and ICER from the modeling studies and the observational study. However, evidence is lacking on feasibility and acceptance by the target population.Review registrationPROSPERO: CRD42018110483