Enhancing the BOADICEA cancer risk prediction model to incorporate new data on RAD51C, RAD51D, BARD1 updates to tumour pathology and cancer incidence. Issue 12 (26th September 2022)

• Record Type: Journal Article
• Authors: Lee, Andrew; Mavaddat, Nasim; Cunningham, Alex; Carver, Tim; Ficorella, Lorenzo; Archer, Stephanie; Walter, Fiona M; Tischkowitz, Marc; Roberts, Jonathan; Usher-Smith, Juliet; Simard, Jacques; Schmidt, Marjanka K; Devilee, Peter; Zadnik, Vesna; Jürgens, Hannes; Mouret-Fourme, Emmanuelle; De Pauw, Antoine; Rookus, Matti; Mooij, Thea M; Pharoah, Paul PD; Easton, Douglas F; Antoniou, Antonis C
• Is Part Of: Journal of medical genetics. Volume 59:Issue 12(2022)
• Page Start: 1206
• Page End: 1218
• Publication Date: 2022-09-26
• Subjects: genetic counseling; Medical genetics -- Periodicals; 616.042
• DOI: 10.1136/jmedgenet-2022-108471
• Languages: English
• ISSNs: 1468-6244

Abstract

Abstract : 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.