Abstract Implementing population-wide risk-stratified breast screening (RSBS) requires coordinated planning across diverse stakeholders and adaptation within complex healthcare systems. We convened a multi-stakeholder workshop with 34 policymakers, breast screening program leaders and healthcare professionals across Canada. Participants co-envisioned an RSBS program and focused on defining care pathways, the implementation roadmap, stakeholder engagement, and workforce training. A national, flexible RSBS strategy was proposed with multiple entry points and timings for risk assessment, interoperable information technology systems, phased roll-out, and an embedded learning healthcare system. The resulting actionable plans support RSBS implementation in Canada and offer transferable insights for planning RSBS in varied international settings.

Polygenic risk scores (PRSs), which quantify inherited susceptibility to complex traits and diseases, have emerged as valuable tools for risk stratification and precision medicine. Despite their promise, PRS developed on European cohorts often demonstrate substantially reduced predictive accuracy in non-European populations, due to differences in genetic architecture. The disproportionate representation of European ancestry cohorts in genome-wide association studies (GWAS) leads to inequitable deployment of PRS technologies across diverse populations. Here, we introduce PRANA (Polygenic Risk Adaptation via Neural-network Architecture), a deep learning framework that adapts an existing PRS developed on one population to other ancestries. Unlike methods that require large-scale GWAS in the target population, PRANA leverages pre-trained PRS models derived from European cohorts and adapts them using modestly sized cohorts from the target population. We evaluated PRANA on seven complex traits in South Asian, East Asian and Ashkenazi Jewish populations, as well as in selected smaller East Asian subpopulations where the scarcity of training data poses a particular challenge. PRANA mostly improved predictive performance of the baseline PRS models by 5%-20% in terms of effect size (β) and Nagelkerke's R2, and, in most cases, outperformed existing cross-ancestry multi-PRS approaches. These results highlight PRANA as a scalable and practical strategy to reduce disparities in genomic risk prediction and advance the equitable application of PRS in diverse populations.

BACKGROUND/OBJECTIVES:Risk-based breast cancer (BC) screening can provide tailored recommendations based on individual risk. We aimed to identify key predictors for BC risk stratification to inform implementation in screening programs. METHODS:We estimated 10-year BC risks using BOADICEA v.6 (CanRisk) in 3753 women aged 40-70 with no cancer history from the PERSPECTIVE I&I cohort. The primary endpoint was risk reclassification, assessed as the proportion of women whose assigned 10-year risk category changed when using different risk factor combinations against a full multifactorial model including questionnaire-based risk factors (QRFs), polygenic score (PGS), mammographic density (MD), and pedigree-structured first- and second-degree family history (FH) of breast, ovarian, pancreatic and prostate cancer, including both affected and unaffected relatives. Relative risk thresholds were set as <1.5 (average), 1.5-2.7 (higher-than-average), and ≥2.7 (high), equivalent to the remaining lifetime risk categories of <15%, 15-25% and ≥25% for women aged 30 (the anchor) to age 80. We quantified individual-level reclassification flows by direction and magnitude. RESULTS:Excluding PGS from risk calculations led to the highest overall reclassification. Using only the BC status in first- and second-degree relatives produced comparable risk classification to that of the full FH data that included breast, ovarian, prostate and pancreatic cancer (reclassification = 0.5%). However, collecting only affected relatives led to overestimation of risk. Excluding either PGS, MD or FH resulted in a greater proportion of reclassification among younger women. Adding the PGS to risk factors already collected in provincial screening programs reduced reclassification from 23% to ~13%. CONCLUSIONS:PGS, MD, QRFs and FH of BC in affected and unaffected first- and second-degree relatives are key for refining risk stratification. These findings provide real-world evidence on how incorporating different sets of risk factors, both those routinely collected in screening programs and those requiring additional data collection, affect individual-level risk classification amongst a population-based cohort, and how the impacts differ across age groups. While risk classification reflects model-based changes in estimated risk categories rather than direct evidence of mis-screening or clinical outcomes, comparison with the current eligibility criteria used to identify women at higher-than-average risk highlights the potential clinical value of a multifactorial risk assessment approach in ensuring more appropriate screening strategies.

In the context of limited resources and growing demand, patients access genetic testing for hereditary breast and ovarian cancer (HBOC) through various service models, some of which include genetic counseling sessions. This study assessed the impact of these service models and participation in genetic counseling on patients’ experiences and satisfaction with the genetic testing process. A total of 501 patients undergoing genetic testing for HBOC completed a 35-item survey, which included the Genetic Counseling Satisfaction Scale, the Decision Regret Scale, and a modified Royal Marsden Satisfaction Questionnaire. Additional information was gathered from the medical records. Descriptive statistics and Fisher’s exact tests were employed for the analysis. Four aspects of the genetic testing experience differed between service models and attendance to genetic counseling: i) receipt of informational materials prior to testing, ii) information that additional discussions with the genetic team were possible, iii) clarity regarding the timeline for receiving results, and iv) explanation of how the results would be delivered. The service model and participation in genetic counseling seem to influence patients’ experiences with genetic testing for HBOC. However, satisfaction was generally high and decision regret was low across all service models, highlighting the promise of care models designed to enhance accessibility.

Background The Breast and Ovarian Analysis of Disease Incidence Algorithm (BOADICEA) model predicts breast cancer risk using cancer family history, epidemiological, and genetic data. We evaluated its validity in a large prospective cohort.Methods We assessed model calibration, discrimination and risk classification ability in 217 885 women (6838 incident breast cancers) aged 40-70 years of self-reported White ethnicity with no previous cancer from the UK Biobank. Age-specific risk classification was assessed using relative risk thresholds equivalent to the absolute lifetime risk categories of less than 17%, 17%-30%, and 30% or more, recommended by the National Institute for Health and Care Excellence guidelines. We predicted 10-year risks using BOADICEA v.6 considering cancer family history, questionnaire-based risk factors, a 313-single nucleotide polymorphisms polygenic score, and pathogenic variants. Mammographic density data were not available.Results The polygenic risk score was the most discriminative risk factor (area under the curve [AUC] = 0.65). Discrimination was highest when considering all risk factors (AUC = 0.66). The model was well calibrated overall (expected-to-observed ratio = 0.99, 95% confidence interval [CI] = 0.97 to 1.02; calibration slope = 0.99, 95% CI = 0.99 to 1.00), and in deciles of predicted risks. Discrimination was similar in women aged younger and older than 50 years. There was some underprediction in women aged younger than 50 years (expected-to-observed ratio = 0.89, 95% CI = 0.84 to 0.94; calibration slope = 0.96, 95% CI = 0.94 to 0.97), which was explained by the higher breast cancer incidence in UK Biobank than the UK population incidence in this age group. The model classified 87.2%, 11.4%, and 1.4% of women in relative risk categories less than 1.6, 1.6-3.1, and at least 3.1, identifying 25.6% of incident breast cancer patients in category relative risk of at least 1.6.Conclusion BOADICEA, implemented in CanRisk (www.canrisk.org), provides valid 10-year breast cancer risk, which can facilitate risk-stratified screening and personalized breast cancer risk management.

BOADICEA is a widely used algorithm for predicting breast and ovarian cancer risks, using a combination of genetic and lifestyle, hormonal and reproductive risk factors. However, it has largely been developed using data from White/European individuals, limiting its applicability to other ethnicities. Here, we updated BOADICEA to provide ethnicity-specific risk estimates. We utilised data from multiple sources to derive estimates for the distributions and effect sizes of risk factors in major UK ethnic groups (White, Black, South Asian, East Asian, and Mixed), along with ethnicity-specific population cancer incidences. We also developed a method for deriving adjusted polygenic scores for individuals of mixed genetic ancestry. The predicted average absolute risks were smaller in all non-White ethnic groups than in Whites, and the risk distributions were narrower. The proportion of women classified as at moderate or high risk of breast or ovarian cancer, according to national guidelines, was considerably smaller in non-Whites. The updated BOADICEA, available in the CanRisk tool ( www.canrisk.org ), is based on more appropriate estimates for non-White women in the UK. Further validation of the model in prospective studies is required. Considering these findings, risk classification guidelines for non-White women may need to be revised.

Supplementary Table S3 shows SNPs and weights for the BCAC PRS313.

Background: Breast cancer polygenic risk scores (PRS) and traditional risk models (e.g., the Gail model [Gail]) are known to contribute largely independent information, but it is unclear how the overlap varies by ancestry, age, disease type (invasive breast cancer, DCIS), and risk threshold. Methods: In a retrospective case–control study, we evaluated risk prediction performance in 180,398 women (161,849 of European ancestry; 18,549 of Asian ancestry). Odds ratios (ORs) from logistic regression models and the area under the receiver operating characteristic curve (AUC) were estimated. Results: PRS for invasive disease showed a stronger association in younger (<50 years) women (OR = 2.51, AUC = 0.622) than in women ≥ 50 years (OR = 2.06, AUC = 0.653) of European ancestry. PRS performance in Asians was lower (OR range = 1.62–1.64, AUC = 0.551–0.600). Gail performance was modest across groups and poor in younger Asian women (OR = 0.94–0.99, AUC = 0.523–0.533). Age interactions were observed for both PRS (p < 0.001) and Gail (p < 0.001) in Europeans, whereas in Asians, age interaction was observed only for Gail (invasive: p < 0.001; DCIS: p = 0.002). PRS identified more high-risk individuals than Gail in Asian populations, especially ≥50 years, while Gail identified more in Europeans. Overlap between PRS, Gail, and family history was limited at higher thresholds. Calibration analysis, comparing empirical and model-based ROC curves, showed divergence for both PRS and Gail (p < 0.001), which indicates miscalibration. In Europeans, family history and prior biopsies drove Gail discrimination. In younger Asians, age at first live birth was influential. Conclusions: PRS adds value to risk stratification beyond traditional tools, especially in younger women and Asian ancestry populations.

Supplementary Table S1 shows studies and samples used in these analyses

Background: Genome-wide association studies (GWAS) have identified more than 200 susceptibility loci for breast cancer, but these variants explain less than a fifth of the disease risk. Although gene-environment interactions have been proposed to account for some of the remaining heritability, few studies have empirically assessed this. Methods: We obtained genotype and risk factor data from 46,060 cases and 47,929 controls of European ancestry from population-based studies within the Breast Cancer Association Consortium (BCAC). We built gene expression prediction models for 4,864 genes with a significant (P<0.01) heritable component using the transcriptome and genotype data from the Genotype-Tissue Expression (GTEx) project. We leveraged predicted gene expression information to investigate the interactions between gene-centric genetic variation and 14 established risk factors in association with breast cancer risk, using a mixed-effects score test. Results: After adjusting for number of tests using Bonferroni correction, no interaction remained statistically significant. The strongest interaction observed was between the predicted expression of the C13orf45 gene and age at first full-term pregnancy (PGXE=4.44×10-6). Conclusion: In this transcriptome-informed genome-wide gene-environment interaction study of breast cancer, we found no strong support for the role of gene expression in modifying the associations between established risk factors and breast cancer risk. Impact: Our study suggests a limited role of gene-environment interactions in breast cancer risk.

Supplementary Table S2 show the country in which the studies were conducted

Supplementary Methods provides details of methodology

BackgroundThe Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) incorporates the effects of common genetic variants, from polygenic risk scores, pathogenic variants in major breast cancer (BC) susceptibility genes, lifestyle/hormonal risk factors, mammographic density, and cancer family history to predict risk levels of developing breast and ovarian cancer. While offering multifactorial risk assessment to the population could be a promising avenue for early detection of BC, obstacles to its implementation including fear of genetic discrimination (GD), could prevent individuals from undergoing screening.MethodsThe aim of our study was two-fold: determine the extent of legal protection in Canada available to protect information generated by risk prediction models such as the BOADICEA algorithm through a literature review, and then, assess individuals’ knowledge of and concerns about GD in this context by collecting data through surveys.ResultsOur legal analysis highlighted that while Canadian employment and privacy laws provide a good level of protection against GD, it remains uncertain whether the Genetic Non-Discrimination Act (GNDA) would provide protection for BC risk levels generated by a risk prediction model. The survey results of 3,055 participants who consented to risk assessment in the PERSPECTIVE I&I project showed divergent perspectives of how the law would protect BC risk level in the context of employment and that a high number of participants did not feel that their risk level was protected from access and use by life insurers. Indeed, 49,1% of participants reckon that the level of breast cancer risk could have an impact on a woman’s ability to buy insurance and 58,9% of participants reckon that a woman’s insurance might be cancelled if important health information (including level of breast cancer risk) is not given when buying or renewing life or health insurance.ConclusionThe results indicate that much work needs to be done to improve and clarify the extent of protection against GD in Canada and to inform the population of how the legal framework applies to risk levels generated by risk prediction models.

BACKGROUND:Risk-stratified breast cancer screening has been proposed as an alternative to the age-based approach currently used by most screening programs. This study, part of the Canadian PERSPECTIVE I&I project, examined perceived advantages and disadvantages of learning your breast cancer risk category and associated screening plans. METHOD:Women aged 40 to 69 from Ontario and Quebec (N = 3319) had multifactorial risk assessments using the CanRisk tool. Risk categories (average [78.9%], higher than average [16.4%], high [4.6%]) were communicated along with screening plans. Participants completed questionnaires on attitudes toward learning their risk before, at the time of, and 1 year later risk communication. Participant characteristics associated with these attitudes were assessed using multinomial logistic regression. RESULTS:At the time of risk communication, most participants (72.9%) perceived ``Easing worry'' as an advantage of learning their risk. However, participants at higher risk were more likely to report that it did not ease their worry. Visible minority participants (OR = 1.86, 95% CI, 1.16, 2.98) and those with lower education attainment were more likely to view "complicated information" as a disadvantage (College/Apprenticeship/Trades: OR = 1.54, 95% CI, 1.24, 1.92; High School or below: OR = 1.77, 95% CI, 1.29, 2.42). Ontario participants were more likely to view risk communication as "information I do not want to know" (OR = 0.44, 95% CI, 0.32, 0.59) compared to Quebec participants. CONCLUSION:Most women responded positively to learning their breast cancer risk category and screening plan. Successful implementation of risk-stratified screening will require clear communication, healthcare provider support, and adaptation to regional resources.

The role of germline genetics in adjuvant aromatase inhibitor (AI) treatment efficacy in ER-positive breast cancer is poorly understood. We employed a two-stage candidate gene approach to examine associations between survival endpoints and common germline variants in 753 endocrine resistance-related genes. For a discovery cohort, we screened the Breast Cancer Association Consortium database (n ≥ 90,000 cases) and retrieved 2789 AI-treated patients. Cox model-based analysis revealed 125 variants associated with overall, distant relapse-free, and relapse-free survival (p-value ≤ 1E-04). In validation analysis using five independent cohorts (n = 8857), none of the six selected candidates representing major linkage blocks at CELA2B/CASP9, NR1I2/GSK3B, LRP1B, and MIR143HG (CARMN) were validated. We discuss potential reasons for the failed validation and replication of published findings, including study/treatment heterogeneity and other limitations inherent to genomic treatment outcome studies. For the future, we envision prospective longitudinal studies with sufficiently long follow-up and endpoints that reflect the dynamic nature of endocrine resistance.

Supplementary Table S4shows SNPs and weights used in the alternative PRS.

Polygenic risk scores (PRS) have been shown to be predictive of breast cancer (BC) risk in European BRCA1 and BRCA2 pathogenic variant (PV) carriers, but their utility in Asian populations has not been evaluated. In this study, we evaluated the association of two breast cancer PRS developed for the East Asian general population and three versions of a PRS developed for the European general population in 604 BRCA1 (390 affected by breast cancer) and 785 BRCA2 (552 affected by breast cancer) PV female carriers of Asian ancestry. Only the Asian-based PRS, constructed using approximately 1 million single-nucleotide variations (SNVs), showed a significant association with breast cancer risk (Hazard Ratio per standard deviation (95% Confidence Interval) is 1.47 (1.10-1.95) for BRCA1 and 1.43 (1.04-1.95) for BRCA2). Incorporating this PRS into risk prediction models may improve cancer risk assessment among PV carriers of Asian ancestry.

Many jurisdictions are considering a shift to risk-stratified breast cancer screening; however, evidence on the feasibility of implementing it on a population scale is needed. We conducted a prospective cohort study in the PERSPECTIVE I&I project to produce evidence on risk-stratified breast screening and recruited 3753 participants to undergo multifactorial risk assessment from 2019–2021. This qualitative study explored the perspectives of study personnel on barriers and facilitators to delivering multifactorial risk assessment and risk communication. One focus group and three one-on-one interviews were conducted and a thematic analysis conducted which identified five themes: (1) barriers and facilitators to recruitment for multifactorial risk assessment, (2) barriers and facilitators to completion of the risk factor questionnaire, (3) additional resources required to implement multifactorial risk assessment, (4) the need for a person-centered approach, and (5) and risk literacy. While risk assessment and communication processes were successful overall, key barriers were identified including challenges with collecting comprehensive breast cancer risk factor information and limited resources to execute data collection and risk communication activities on a large scale. Risk assessment and communication processes will need to be optimized for large-scale implementation to ensure they are efficient but robust and person-centered.

Risk-stratified breast cancer screening has been proposed as an alternative to age-based screening programs, though its implementation may face challenges and requires support from stakeholders, particularly women. This study used structural equation modeling (SEM) to identify personal factors influencing women's attitudes, comfort level, and willingness towards risk-stratified screening. Factors analyzed included sociodemographic variables, general health, breast cancer risk perception, screening, and genetic testing history. Three models were tested to assess the direct and indirect effects of statistically significant factors. None of the outcomes were significantly associated with women's perceived health or history of genetic testing (all p > 0.05). A history of mammography was found to mediate the relationships between age, perceived risk, and personal breast cancer history with the outcomes. Income also mediated the relationships between education, employment, marital status, and the outcomes. A history of mammography and higher income were significantly associated with more favorable attitudes (β_mammo = 0.157; β_income = 0.098), greater comfort (β_mammo = 0.425; β_income = 0.134), and higher willingness (β_mammo = 0.471; β_income = 0.198) towards risk-stratified screening. In contrast, non-white ethnicity and older age were linked to less favorable attitudes (β_ethnicity =  - 0.117; β_age =  - 0.071), lower comfort (β_ethnicity =  - 0.104; β_age =  - 0.269), and decreased willingness (β_ethnicity =  - 0.142; β_age =  - 0.295). This study identified key factors influencing the acceptability of risk-stratified breast cancer screening that could be targeted to facilitate its implementation.