Jacques Simard

Evaluating the performance of the Breast and Ovarian Analysis of Disease Incidence Algorithm model in predicting 10-year breast cancer risks in UK Biobank

Abstract 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.

Adapting the BOADICEA breast and ovarian cancer risk models for the ethnically diverse UK population

Abstract Background 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. Methods 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. Results 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. Discussion 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.