Improving reporting standards for polygenic scores in risk prediction studies.

Polygenic risk scores (PRSs), which often aggregate results from genome-wide association studies, can bridge the gap between initial discovery efforts and clinical applications for the estimation of disease risk using genetics. However, there is notable heterogeneity in the application and reporting of these risk scores, which hinders the translation of PRSs into clinical care. Here, in a collaboration between the Clinical Genome Resource (ClinGen) Complex Disease Working Group and the Polygenic Score (PGS) Catalog, we present the Polygenic Risk Score Reporting Standards (PRS-RS), in which we update the Genetic Risk Prediction Studies (GRIPS) Statement to reflect the present state of the field. Drawing on the input of experts in epidemiology, statistics, disease-specific applications, implementation and policy, this comprehensive reporting framework defines the minimal information that is needed to interpret and evaluate PRSs, especially with respect to downstream clinical applications. Items span detailed descriptions of study populations, statistical methods for the development and validation of PRSs and considerations for the potential limitations of these scores. In addition, we emphasize the need for data availability and transparency, and we encourage researchers to deposit and share PRSs through the PGS Catalog to facilitate reproducibility and comparative benchmarking. By providing these criteria in a structured format that builds on existing standards and ontologies, the use of this framework in publishing PRSs will facilitate translation into clinical care and progress towards defining best practice.

Segregation analysis of 17,425 population-based breast cancer families: Evidence for genetic susceptibility and risk prediction.

Rare pathogenic variants in known breast cancer-susceptibility genes and known common susceptibility variants do not fully explain the familial aggregation of breast cancer. To investigate plausible genetic models for the residual familial aggregation, we studied 17,425 families ascertained through population-based probands, 86% of whom were screened for pathogenic variants in BRCA1, BRCA2, PALB2, CHEK2, ATM, and TP53 via gene-panel sequencing. We conducted complex segregation analyses and fitted genetic models in which breast cancer incidence depended on the effects of known susceptibility genes and other unidentified major genes and a normally distributed polygenic component. The proportion of familial variance explained by the six genes was 46% at age 20-29 years and decreased steadily with age thereafter. After allowing for these genes, the best fitting model for the residual familial variance included a recessive risk component with a combined genotype frequency of 1.7% (95% CI: 0.3%-5.4%) and a penetrance to age 80 years of 69% (95% CI: 38%-95%) for homozygotes, which may reflect the combined effects of multiple variants acting in a recessive manner, and a polygenic variance of 1.27 (95% CI: 0.94%-1.65), which did not vary with age. The proportion of the residual familial variance explained by the recessive risk component was 40% at age 20-29 years and decreased with age thereafter. The model predicted age-specific familial relative risks consistent with those observed by large epidemiological studies. The findings have implications for strategies to identify new breast cancer-susceptibility genes and improve disease-risk prediction, especially at a young age.

Recommendations for laboratory workflow that better support centralised amalgamation of genomic variant data: findings from CanVIG-UK national molecular laboratory survey.

BACKGROUND: National and international amalgamation of genomic data offers opportunity for research and audit, including analyses enabling improved classification of variants of uncertain significance. Review of individual-level data from National Health Service (NHS) testing of cancer susceptibility genes (2002-2023) submitted to the National Disease Registration Service revealed heterogeneity across participating laboratories regarding (1) the structure, quality and completeness of submitted data, and (2) the ease with which that data could be assembled locally for submission. METHODS: In May 2023, we undertook a closed online survey of 51 clinical scientists who provided consensus responses representing all 17 of 17 NHS molecular genetic laboratories in England and Wales which undertake NHS diagnostic analyses of cancer susceptibility genes. The survey included 18 questions relating to 'next-generation sequencing workflow' (11), 'variant classification' (3) and 'phenotypical context' (4). RESULTS: Widely differing processes were reported for transfer of variant data into their local LIMS (Laboratory Information Management System), for the formatting in which the variants are stored in the LIMS and which classes of variants are retained in the local LIMS. Differing local provisions and workflow for variant classifications were also reported, including the resources provided and the mechanisms by which classifications are stored. CONCLUSION: The survey responses illustrate heterogeneous laboratory workflow for preparation of genomic variant data from local LIMS for centralised submission. Workflow is often labour-intensive and inefficient, involving multiple manual steps which introduce opportunities for error. These survey findings and adoption of the concomitant recommendations may support improvement in laboratory dataflows, better facilitating submission of data for central amalgamation.

Validation of the BOADICEA model in a prospective cohort of BRCA1/2 pathogenic variant carriers.

BACKGROUND: No validation has been conducted for the BOADICEA multifactorial breast cancer risk prediction model specifically in BRCA1/2 pathogenic variant (PV) carriers to date. Here, we evaluated the performance of BOADICEA in predicting 5-year breast cancer risks in a prospective cohort of BRCA1/2 PV carriers ascertained through clinical genetic centres. METHODS: We evaluated the model calibration and discriminatory ability in the prospective TRANsIBCCS cohort study comprising 1614 BRCA1 and 1365 BRCA2 PV carriers (209 incident cases). Study participants had lifestyle, reproductive, hormonal, anthropometric risk factor information, a polygenic risk score based on 313 SNPs and family history information. RESULTS: The full multifactorial model considering family history together with all other risk factors was well calibrated overall (E/O=1.07, 95% CI: 0.92 to 1.24) and in quintiles of predicted risk. Discrimination was maximised when all risk factors were considered (Harrell's C-index=0.70, 95% CI: 0.67 to 0.74; area under the curve=0.79, 95% CI: 0.76 to 0.82). The model performance was similar when evaluated separately in BRCA1 or BRCA2 PV carriers. The full model identified 5.8%, 12.9% and 24.0% of BRCA1/2 PV carriers with 5-year breast cancer risks of <1.65%, <3% and <5%, respectively, risk thresholds commonly used for different management and risk-reduction options. CONCLUSION: BOADICEA may be used to aid personalised cancer risk management and decision-making for BRCA1 and BRCA2 PV carriers. It is implemented in the free-access CanRisk tool (https://www.canrisk.org/).

Estimation of age of onset and progression of breast cancer by absolute risk dependent on polygenic risk score and other risk factors.

BACKGROUND: Genetic, lifestyle, reproductive, and anthropometric factors are associated with the risk of developing breast cancer. However, it is not yet known whether polygenic risk score (PRS) and absolute risk based on a combination of risk factors are associated with the risk of progression of breast cancer. This study aims to estimate the distribution of sojourn time (pre-clinical screen-detectable period) and mammographic sensitivity by absolute breast cancer risk derived from polygenic profile and the other risk factors. METHODS: The authors used data from a population-based case-control study. Six categories of 10-year absolute risk based on different combinations of risk factors were derived using the Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm. Women were classified into low, medium, and high-risk groups. The authors constructed a continuous-time multistate model. To calculate the sojourn time, they simulated the trajectories of subjects through the disease states. RESULTS: There was little difference in sojourn time with a large overlap in the 95% confidence interval (CI) between the risk groups across the six risk categories and PRS studied. However, the age of entry into the screen-detectable state varied by risk category, with the mean age of entry of 53.4 years (95% CI, 52.2-54.1) and 57.0 years (95% CI, 55.1-57.7) in the high-risk and low-risk women, respectively. CONCLUSION: In risk-stratified breast screening, the age at the start of screening, but not necessarily the frequency of screening, should be tailored to a woman's risk level. The optimal risk-stratified screening strategy that would improve the benefit-to-harm balance and the cost-effectiveness of the screening programs needs to be studied.

Joint ABS-UKCGG-CanGene-CanVar consensus regarding the use of CanRisk in clinical practice.

BACKGROUND: The CanRisk tool, which operationalises the Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) is used by Clinical Geneticists, Genetic Counsellors, Breast Oncologists, Surgeons and Family History Nurses for breast cancer risk assessments both nationally and internationally. There are currently no guidelines with respect to the day-to-day clinical application of CanRisk and differing inputs to the model can result in different recommendations for practice. METHODS: To address this gap, the UK Cancer Genetics Group in collaboration with the Association of Breast Surgery and the CanGene-CanVar programme held a workshop on 16th of May 2023, with the aim of establishing best practice guidelines. RESULTS: Using a pre-workshop survey followed by structured discussion and in-meeting polling, we achieved consensus for UK best practice in use of CanRisk in making recommendations for breast cancer surveillance, eligibility for genetic testing and the input of available information to undertake an individualised risk assessment. CONCLUSIONS: Whilst consensus recommendations were achieved, the meeting highlighted some of the barriers limiting the use of CanRisk in clinical practice and identified areas that require further work and collaboration with relevant national bodies and policy makers to incorporate wider use of CanRisk into routine breast cancer risk assessments.

Disentangling the relationships of body mass index and circulating sex hormone concentrations in mammographic density using Mendelian randomization.

PURPOSE: Mammographic density phenotypes, adjusted for age and body mass index (BMI), are strong predictors of breast cancer risk. BMI is associated with mammographic density measures, but the role of circulating sex hormone concentrations is less clear. We investigated the relationship between BMI, circulating sex hormone concentrations, and mammographic density phenotypes using Mendelian randomization (MR). METHODS: We applied two-sample MR approaches to assess the association between genetically predicted circulating concentrations of sex hormones [estradiol, testosterone, sex hormone-binding globulin (SHBG)], BMI, and mammographic density phenotypes (dense and non-dense area). We created instrumental variables from large European ancestry-based genome-wide association studies and applied estimates to mammographic density phenotypes in up to 14,000 women of European ancestry. We performed analyses overall and by menopausal status. RESULTS: Genetically predicted BMI was positively associated with non-dense area (IVW: ß = 1.79; 95% CI = 1.58, 2.00; p = 9.57 × 10-63) and inversely associated with dense area (IVW: ß = - 0.37; 95% CI = - 0.51,- 0.23; p = 4.7 × 10-7). We observed weak evidence for an association of circulating sex hormone concentrations with mammographic density phenotypes, specifically inverse associations between genetically predicted testosterone concentration and dense area (ß = - 0.22; 95% CI = - 0.38, - 0.053; p = 0.009) and between genetically predicted estradiol concentration and non-dense area (ß = - 3.32; 95% CI = - 5.83, - 0.82; p = 0.009), although results were not consistent across a range of MR approaches. CONCLUSION: Our findings support a positive causal association between BMI and mammographic non-dense area and an inverse association between BMI and dense area. Evidence was weaker and inconsistent for a causal effect of circulating sex hormone concentrations on mammographic density phenotypes. Based on our findings, associations between circulating sex hormone concentrations and mammographic density phenotypes are weak at best.

Long-term outcomes of hysterectomy with bilateral salpingo-oophorectomy: a systematic review and meta-analysis.

OBJECTIVE: This study aimed to provide an up-to-date systematic review of "the long-term outcomes of bilateral salpingo-oophorectomy at the time of hysterectomy" and perform a meta-analysis for the reported associations. DATA SOURCES: Our study updated a previous systematic review by searching the literature using PubMed, Web of Science, and Embase for publications between January 2015 and August 2022. STUDY ELIGIBILITY CRITERIA: Our study included studies of women who had a hysterectomy with bilateral salpingo-oophorectomy vs women who had a hysterectomy with ovarian conservation or no surgery. METHODS: The quality of the evidence was assessed using the Grading of Recommendations, Assessment, Development and Evaluations. Adjusted hazard ratios were extracted and combined to obtain fixed effect estimates. RESULTS: Compared with hysterectomy or no surgery, hysterectomy with bilateral salpingo-oophorectomy in young women was associated with decreased risk of breast cancer (hazard ratio, 0.78; 95% confidence interval, 0.73-0.84) but with an increased risk of colorectal cancer (hazard ratio, 1.27; 95% confidence interval, 1.10-1.47). In addition, it was associated with an increased risk of total cardiovascular diseases, coronary heart disease, and stroke with hazard ratios of 1.18 (95% confidence interval, 1.11-1.25), 1.17 (95% confidence interval, 1.10-1.25), and 1.20 (95% confidence interval, 1.10-1.31), respectively. Compared with no surgery, hysterectomy with bilateral salpingo-oophorectomy before the age of 50 years was associated with an increased risk of hyperlipidemia (hazard ratio, 1.44; 95% confidence interval, 1.25-1.65), diabetes mellitus (hazard ratio, 1.16; 95% confidence interval, 1.09-1.24), hypertension (hazard ratio, 1.13; 95% confidence interval, 1.06-1.20), dementia (hazard ratio, 1.70; 95% confidence interval, 1.07-2.69), and depression (hazard ratio, 1.39; 95% confidence interval, 1.22-1.60). The evidence on the association with all-cause mortality in young women showed substantial heterogeneity between the studies (I2=85%; P<.01). CONCLUSION: Hysterectomy with bilateral salpingo-oophorectomy was associated with multiple long-term outcomes. The benefits of the addition of bilateral salpingo-oophorectomy to hysterectomy should be balanced against the risks.

Implementation and external validation of the Cambridge Multimorbidity Score in the UK Biobank cohort.

BACKGROUND: Patients with multiple conditions present a growing challenge for healthcare provision. Measures of multimorbidity may support clinical management, healthcare resource allocation and accounting for the health of participants in purpose-designed cohorts. The recently developed Cambridge Multimorbidity scores (CMS) have the potential to achieve these aims using primary care records, however, they have not yet been validated outside of their development cohort. METHODS: The CMS, developed in the Clinical Research Practice Dataset (CPRD), were validated in UK Biobank participants whose data is not available in CPRD (the cohort used for CMS development) with available primary care records (n = 111,898). This required mapping of the 37 pre-existing conditions used in the CMS to the coding frameworks used by UK Biobank data providers. We used calibration plots and measures of discrimination to validate the CMS for two of the three outcomes used in the development study (death and primary care consultation rate) and explored variation by age and sex. We also examined the predictive ability of the CMS for the outcome of cancer diagnosis. The results were compared to an unweighted count score of the 37 pre-existing conditions. RESULTS: For all three outcomes considered, the CMS were poorly calibrated in UK Biobank. We observed a similar discriminative ability for the outcome of primary care consultation rate to that reported in the development study (C-index: 0.67 (95%CI:0.66-0.68) for both, 5-year follow-up); however, we report lower discrimination for the outcome of death than the development study (0.69 (0.68-0.70) and 0.89 (0.88-0.90) respectively). Discrimination for cancer diagnosis was adequate (0.64 (0.63-0.65)). The CMS performs favourably to the unweighted count score for death, but not for the outcomes of primary care consultation rate or cancer diagnosis. CONCLUSIONS: In the UK Biobank, CMS discriminates reasonably for the outcomes of death, primary care consultation rate and cancer diagnosis and may be a valuable resource for clinicians, public health professionals and data scientists. However, recalibration will be required to make accurate predictions when cohort composition and risk levels differ substantially from the development cohort. The generated resources (including codelists for the conditions and code for CMS implementation in UK Biobank) are available online.

UK consensus recommendations for clinical management of cancer risk for women with germline pathogenic variants in cancer predisposition genes: RAD51C, RAD51D, BRIP1 and PALB2.

Germline pathogenic variants (GPVs) in the cancer predisposition genes BRCA1, BRCA2, MLH1, MSH2, MSH6, BRIP1, PALB2, RAD51D and RAD51C are identified in approximately 15% of patients with ovarian cancer (OC). While there are clear guidelines around clinical management of cancer risk in patients with GPV in BRCA1, BRCA2, MLH1, MSH2 and MSH6, there are few guidelines on how to manage the more moderate OC risk in patients with GPV in BRIP1, PALB2, RAD51D and RAD51C, with clinical questions about appropriateness and timing of risk-reducing gynaecological surgery. Furthermore, while recognition of RAD51C and RAD51D as OC predisposition genes has been established for several years, an association with breast cancer (BC) has only more recently been described and clinical management of this risk has been unclear. With expansion of genetic testing of these genes to all patients with non-mucinous OC, new data on BC risk and improved estimates of OC risk, the UK Cancer Genetics Group and CanGene-CanVar project convened a 2-day meeting to reach a national consensus on clinical management of BRIP1, PALB2, RAD51D and RAD51C carriers in clinical practice. In this paper, we present a summary of the processes used to reach and agree on a consensus, as well as the key recommendations from the meeting.

Evaluation of European-based polygenic risk score for breast cancer in Ashkenazi Jewish women in Israel.

BACKGROUND: Polygenic risk score (PRS), calculated based on genome-wide association studies (GWASs), can improve breast cancer (BC) risk assessment. To date, most BC GWASs have been performed in individuals of European (EUR) ancestry, and the generalisation of EUR-based PRS to other populations is a major challenge. In this study, we examined the performance of EUR-based BC PRS models in Ashkenazi Jewish (AJ) women. METHODS: We generated PRSs based on data on EUR women from the Breast Cancer Association Consortium (BCAC). We tested the performance of the PRSs in a cohort of 2161 AJ women from Israel (1437 cases and 724 controls) from BCAC (BCAC cohort from Israel (BCAC-IL)). In addition, we tested the performance of these EUR-based BC PRSs, as well as the established 313-SNP EUR BC PRS, in an independent cohort of 181 AJ women from Hadassah Medical Center (HMC) in Israel. RESULTS: In the BCAC-IL cohort, the highest OR per 1 SD was 1.56 (±0.09). The OR for AJ women at the top 10% of the PRS distribution compared with the middle quintile was 2.10 (±0.24). In the HMC cohort, the OR per 1 SD of the EUR-based PRS that performed best in the BCAC-IL cohort was 1.58±0.27. The OR per 1 SD of the commonly used 313-SNP BC PRS was 1.64 (±0.28). CONCLUSIONS: Extant EUR GWAS data can be used for generating PRSs that identify AJ women with markedly elevated risk of BC and therefore hold promise for improving BC risk assessment in AJ women.

Germline mismatch repair (MMR) gene analyses from English NHS regional molecular genomics laboratories 1996-2020: development of a national resource of patient-level genomics laboratory records.

OBJECTIVE: To describe national patterns of National Health Service (NHS) analysis of mismatch repair (MMR) genes in England using individual-level data submitted to the National Disease Registration Service (NDRS) by the NHS regional molecular genetics laboratories. DESIGN: Laboratories submitted individual-level patient data to NDRS against a prescribed data model, including (1) patient identifiers, (2) test episode data, (3) per-gene results and (4) detected sequence variants. Individualised per-laboratory algorithms were designed and applied in NDRS to extract and map the data to the common data model. Laboratory-level MMR activity audit data from the Clinical Molecular Genetics Society/Association of Clinical Genomic Science were used to assess early years' missing data. RESULTS: Individual-level data from patients undergoing NHS MMR germline genetic testing were submitted from all 13 English laboratories performing MMR analyses, comprising in total 16 722 patients (9649 full-gene, 7073 targeted), with the earliest submission from 2000. The NDRS dataset is estimated to comprise >60% of NHS MMR analyses performed since inception of NHS MMR analysis, with complete national data for full-gene analyses for 2016 onwards. Out of 9649 full-gene tests, 2724 had an abnormal result, approximately 70% of which were (likely) pathogenic. Data linkage to the National Cancer Registry demonstrated colorectal cancer was the most frequent cancer type in which full-gene analysis was performed. CONCLUSION: The NDRS MMR dataset is a unique national pan-laboratory amalgamation of individual-level clinical and genomic patient data with pseudonymised identifiers enabling linkage to other national datasets. This growing resource will enable longitudinal research and can form the basis of a live national genomic disease registry.

Risks of second non-breast primaries following breast cancer in women: a systematic review and meta-analysis.

BACKGROUND: Second primary cancer incidence is rising among breast cancer survivors. We examined the risks of non-breast second primaries, in combination and at specific cancer sites, through a systematic review and meta-analysis. METHODS: We conducted a systematic search of PubMed, Embase, and Web of Science, seeking studies published by March 2022. We included studies that reported standardized incidence ratios (SIRs), with associated standard errors, assessing the combined risk of second non-breast primaries following breast cancer. We performed meta-analyses of combined second primary risks, stratifying by age, follow-up duration, and geographic region. We also assessed second primary risks at several specific sites, stratifying by age. The inverse variance method with DerSimonian-Laird estimators was used in all meta-analyses, assuming a random-effects model. Associated biases and study quality were evaluated using the Newcastle-Ottawa scale. RESULTS: One prospective and twenty-seven retrospective cohort studies were identified. SIRs for second non-breast primaries combined ranged from 0.84 to 1.84. The summary SIR estimate was 1.24 (95% CI 1.14-1.36, I2: 99%). This varied by age: the estimate was 1.59 (95% CI 1.36-1.85) when breast cancer was diagnosed before age 50, which was significantly higher than in women first diagnosed at 50 or over (SIR: 1.13, 95% CI 1.01-1.36, p for difference: < 0.001). SPC risks were also significantly higher when based on Asian, rather than European, registries (Asia-SIR: 1.47, 95% CI 1.29-1.67. Europe-SIR: 1.16, 95% CI 1.04-1.28). There were significantly increased risks of second thyroid (SIR: 1.89, 95% CI 1.49-2.38), corpus uteri (SIR: 1.84, 95% CI 1.53-2.23), ovary (SIR: 1.53, 95% CI 1.35-1.73), kidney (SIR: 1.43, 95% CI 1.17-1.73), oesophagus (SIR: 1.39, 95% CI 1.26-1.55), skin (melanoma) (SIR: 1.34, 95% CI 1.18-1.52), blood (leukaemia) (SIR: 1.30, 95% CI 1.17-1.45), lung (SIR: 1.25, 95% CI 1.03-1.51), stomach (SIR: 1.23, 95% CI 1.12-1.36) and bladder (SIR: 1.15, 95% CI 1.05-1.26) primaries. CONCLUSIONS: Breast cancer survivors are at significantly increased risk of second primaries at many sites. Risks are higher for those diagnosed with breast cancer before age 50 and in Asian breast cancer survivors compared to European breast cancer survivors. This study is limited by a lack of data on potentially confounding variables. The conclusions may inform clinical management decisions following breast cancer, although specific clinical recommendations lie outside the scope of this review.

Associations of height, body mass index, and weight gain with breast cancer risk in carriers of a pathogenic variant in BRCA1 or BRCA2: the BRCA1 and BRCA2 Cohort Consortium.

INTRODUCTION: Height, body mass index (BMI), and weight gain are associated with breast cancer risk in the general population. It is unclear whether these associations also exist for carriers of pathogenic variants in the BRCA1 or BRCA2 genes. PATIENTS AND METHODS: An international pooled cohort of 8091 BRCA1/2 variant carriers was used for retrospective and prospective analyses separately for premenopausal and postmenopausal women. Cox regression was used to estimate breast cancer risk associations with height, BMI, and weight change. RESULTS: In the retrospective analysis, taller height was associated with risk of premenopausal breast cancer for BRCA2 variant carriers (HR 1.20 per 10 cm increase, 95% CI 1.04-1.38). Higher young-adult BMI was associated with lower premenopausal breast cancer risk for both BRCA1 (HR 0.75 per 5 kg/m2, 95% CI 0.66-0.84) and BRCA2 (HR 0.76, 95% CI 0.65-0.89) variant carriers in the retrospective analysis, with consistent, though not statistically significant, findings from the prospective analysis. In the prospective analysis, higher BMI and adult weight gain were associated with higher postmenopausal breast cancer risk for BRCA1 carriers (HR 1.20 per 5 kg/m2, 95% CI 1.02-1.42; and HR 1.10 per 5 kg weight gain, 95% CI 1.01-1.19, respectively). CONCLUSION: Anthropometric measures are associated with breast cancer risk for BRCA1 and BRCA2 variant carriers, with relative risk estimates that are generally consistent with those for women from the general population.

Proactive breast cancer risk assessment in primary care: a review based on the principles of screening.

In the UK, the National Institute for Health and Care Excellence (NICE) recommends that women at moderate or high risk of breast cancer be offered risk-reducing medication and enhanced breast screening/surveillance. In June 2022, NICE withdrew a statement recommending assessment of risk in primary care only when women present with concerns. This shift to the proactive assessment of risk substantially changes the role of primary care, in effect paving the way for a primary care-based screening programme to identify those at moderate or high risk of breast cancer. In this article, we review the literature surrounding proactive breast cancer risk assessment within primary care against the consolidated framework for screening. We find that risk assessment for women under 50 years currently satisfies many of the standard principles for screening. Most notably, there are large numbers of women at moderate or high risk currently unidentified, risk models exist that can identify those women with reasonable accuracy, and management options offer the opportunity to reduce breast cancer incidence and mortality in that group. However, there remain a number of uncertainties and research gaps, particularly around the programme/system requirements, that need to be addressed before these benefits can be realised.

Breast Cancer Polygenic Risk Score and Contralateral Breast Cancer Risk.

Previous research has shown that polygenic risk scores (PRSs) can be used to stratify women according to their risk of developing primary invasive breast cancer. This study aimed to evaluate the association between a recently validated PRS of 313 germline variants (PRS313) and contralateral breast cancer (CBC) risk. We included 56,068 women of European ancestry diagnosed with first invasive breast cancer from 1990 onward with follow-up from the Breast Cancer Association Consortium. Metachronous CBC risk (N = 1,027) according to the distribution of PRS313 was quantified using Cox regression analyses. We assessed PRS313 interaction with age at first diagnosis, family history, morphology, ER status, PR status, and HER2 status, and (neo)adjuvant therapy. In studies of Asian women, with limited follow-up, CBC risk associated with PRS313 was assessed using logistic regression for 340 women with CBC compared with 12,133 women with unilateral breast cancer. Higher PRS313 was associated with increased CBC risk: hazard ratio per standard deviation (SD) = 1.25 (95%CI = 1.18-1.33) for Europeans, and an OR per SD = 1.15 (95%CI = 1.02-1.29) for Asians. The absolute lifetime risks of CBC, accounting for death as competing risk, were 12.4% for European women at the 10th percentile and 20.5% at the 90th percentile of PRS313. We found no evidence of confounding by or interaction with individual characteristics, characteristics of the primary tumor, or treatment. The C-index for the PRS313 alone was 0.563 (95%CI = 0.547-0.586). In conclusion, PRS313 is an independent factor associated with CBC risk and can be incorporated into CBC risk prediction models to help improve stratification and optimize surveillance and treatment strategies.

Prospective validation of the BOADICEA multifactorial breast cancer risk prediction model in a large prospective cohort study.

BACKGROUND: The multifactorial Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) breast cancer risk prediction model has been recently extended to consider all established breast cancer risk factors. We assessed the clinical validity of the model in a large independent prospective cohort. METHODS: We validated BOADICEA (V.6) in the Swedish KARolinska Mammography Project for Risk Prediction of Breast Cancer (KARMA) cohort including 66 415 women of European ancestry (median age 54 years, IQR 45-63; 816 incident breast cancers) without previous cancer diagnosis. We calculated 5-year risks on the basis of questionnaire-based risk factors, pedigree-structured first-degree family history, mammographic density (BI-RADS), a validated breast cancer polygenic risk score (PRS) based on 313-SNPs, and pathogenic variant status in 8 breast cancer susceptibility genes: BRCA1, BRCA2, PALB2, CHEK2, ATM, RAD51C, RAD51D and BARD1. Calibration was assessed by comparing observed and expected risks in deciles of predicted risk and the calibration slope. The discriminatory ability was assessed using the area under the curve (AUC). RESULTS: Among the individual model components, the PRS contributed most to breast cancer risk stratification. BOADICEA was well calibrated in predicting the risks for low-risk and high-risk women when all, or subsets of risk factors are included in the risk prediction. Discrimination was maximised when all risk factors are considered (AUC=0.70, 95% CI: 0.66 to 0.73; expected-to-observed ratio=0.88, 95% CI: 0.75 to 1.04; calibration slope=0.97, 95% CI: 0.95 to 0.99). The full multifactorial model classified 3.6% women as high risk (5-year risk ≥3%) and 11.1% as very low risk (5-year risk <0.33%). CONCLUSION: The multifactorial BOADICEA model provides valid breast cancer risk predictions and a basis for personalised decision-making on disease prevention and screening.

Comprehensive epithelial tubo-ovarian cancer risk prediction model incorporating genetic and epidemiological risk factors.

BACKGROUND: Epithelial tubo-ovarian cancer (EOC) has high mortality partly due to late diagnosis. Prevention is available but may be associated with adverse effects. A multifactorial risk model based on known genetic and epidemiological risk factors (RFs) for EOC can help identify women at higher risk who could benefit from targeted screening and prevention. METHODS: We developed a multifactorial EOC risk model for women of European ancestry incorporating the effects of pathogenic variants (PVs) in BRCA1, BRCA2, RAD51C, RAD51D and BRIP1, a Polygenic Risk Score (PRS) of arbitrary size, the effects of RFs and explicit family history (FH) using a synthetic model approach. The PRS, PV and RFs were assumed to act multiplicatively. RESULTS: Based on a currently available PRS for EOC that explains 5% of the EOC polygenic variance, the estimated lifetime risks under the multifactorial model in the general population vary from 0.5% to 4.6% for the first to 99th percentiles of the EOC risk distribution. The corresponding range for women with an affected first-degree relative is 1.9%-10.3%. Based on the combined risk distribution, 33% of RAD51D PV carriers are expected to have a lifetime EOC risk of less than 10%. RFs provided the widest distribution, followed by the PRS. In an independent partial model validation, absolute and relative 5-year risks were well calibrated in quintiles of predicted risk. CONCLUSION: This multifactorial risk model can facilitate stratification, in particular among women with FH of cancer and/or moderate-risk and high-risk PVs. The model is available via the CanRisk Tool (www.canrisk.org).

Enhancing the BOADICEA cancer risk prediction model to incorporate new data on RAD51C, RAD51D, BARD1 updates to tumour pathology and cancer incidence.

BACKGROUND: BOADICEA (Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm) for breast cancer and the epithelial tubo-ovarian cancer (EOC) models included in the CanRisk tool (www.canrisk.org) provide future cancer risks based on pathogenic variants in cancer-susceptibility genes, polygenic risk scores, breast density, questionnaire-based risk factors and family history. Here, we extend the models to include the effects of pathogenic variants in recently established breast cancer and EOC susceptibility genes, up-to-date age-specific pathology distributions and continuous risk factors. METHODS: BOADICEA was extended to further incorporate the associations of pathogenic variants in BARD1, RAD51C and RAD51D with breast cancer risk. The EOC model was extended to include the association of PALB2 pathogenic variants with EOC risk. Age-specific distributions of oestrogen-receptor-negative and triple-negative breast cancer status for pathogenic variant carriers in these genes and CHEK2 and ATM were also incorporated. A novel method to include continuous risk factors was developed, exemplified by including adult height as continuous. RESULTS: BARD1, RAD51C and RAD51D explain 0.31% of the breast cancer polygenic variance. When incorporated into the multifactorial model, 34%-44% of these carriers would be reclassified to the near-population and 15%-22% to the high-risk categories based on the UK National Institute for Health and Care Excellence guidelines. Under the EOC multifactorial model, 62%, 35% and 3% of PALB2 carriers have lifetime EOC risks of <5%, 5%-10% and >10%, respectively. Including height as continuous, increased the breast cancer relative risk variance from 0.002 to 0.010. CONCLUSIONS: These extensions will allow for better personalised risks for BARD1, RAD51C, RAD51D and PALB2 pathogenic variant carriers and more informed choices on screening, prevention, risk factor modification or other risk-reducing options.

BRCA1 and BRCA2 pathogenic variants and prostate cancer risk: systematic review and meta-analysis.

BACKGROUND: BRCA1 and BRCA2 pathogenic variants (PVs) are associated with prostate cancer (PCa) risk, but a wide range of relative risks (RRs) has been reported. METHODS: We systematically searched PubMed, Embase, MEDLINE and Cochrane Library in June 2021 for studies that estimated PCa RRs for male BRCA1/2 carriers, with no time or language restrictions. The literature search identified 27 studies (BRCA1: n = 20, BRCA2: n = 21). RESULTS: The heterogeneity between the published estimates was high (BRCA1: I2 = 30%, BRCA2: I2 = 83%); this could partly be explained by selection for age, family history or aggressive disease, and study-level differences in ethnicity composition, use of historical controls, and location of PVs within BRCA2. The pooled RRs were 2.08 (95% CI 1.38-3.12) for Ashkenazi Jewish BRCA2 carriers, 4.35 (95% CI 3.50-5.41) for non-Ashkenazi European ancestry BRCA2 carriers, and 1.18 (95% CI 0.95-1.47) for BRCA1 carriers. At ages <65 years, the RRs were 7.14 (95% CI 5.33-9.56) for non-Ashkenazi European ancestry BRCA2 and 1.78 (95% CI 1.09-2.91) for BRCA1 carriers. CONCLUSIONS: These PCa risk estimates will assist in guiding clinical management. The study-level subgroup analyses indicate that risks may be modified by age and ethnicity, and for BRCA2 carriers by PV location within the gene, which may guide future risk-estimation studies.