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BackgroundBOADICEA (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.MethodsBOADICEA 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.ResultsBARD1, 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.ConclusionsThese 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.

Background BOADICEA for breast cancer and the epithelial ovarian cancer (EOC) models included in the CanRisk tool (www.canrisk.org) provide future cancer risks based on rare 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 NICE guidelines. Including height as continuous, increased the BC 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.

Background: Before the era of targeted therapies, cytokines were the main therapy for metastatic renal cell carcinoma (mRCC). Our aim was to analyze the changes in treatments and overall survival (OS) of all mRCC patients in Estonia in relation to the introduction of new medications. Methods: All patients with mRCC who started medical therapy in Estonia during the years 2004-2012 were identified using the database of the Estonian Health Insurance Fund. Tumor and treatment data were gathered from medical records. Vital status data were obtained from the Estonian Population Registry. The only available therapy before 2008 was interferon alpha-2A (INFa2A), targeted agents added from 2008. For survival analysis, patients were divided into 2 groups: INFa therapy only (group 1) and INFa followed by targeted agents or targeted agents therapy only (group 2). Results: Out of 416 identified patients, 380 were eligible for analysis. The most common 1st-line treatments were INFa (55%), sunitinib (32%) and INFa+bevacizumab (13%). 28% of patients received 2nd-line therapies and 15% 3rdline treatments. Median survival of all patients was 13.7 months [95% confidence interval (CI) 11.3-16.2]; 7.6 months (CI 6.4-8.6) for group 1 and 19.8 months (CI 15.6-22.9) for group 2. In multivariate analysis, group 1 had nearly four times higher risk of dying than group 2 [hazard ration (HR) 3.88, 95% CI 2.64-5.72]. Conclusions: The implementation of targeted therapies significantly changed the outcomes of mRCC in Estonia: it prolonged median survival, reduced the risk of death and also enlarged the proportion of patients who received medical therapy.