Petitjean Carmen, Wilcox Naomi, Ficorella Lorenzo, Dennis Joe, Tyrer Jonathan, Lush Michael, Simard Jacques, Easton Douglas, Antoniou Antonis C, Yang Xin
British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB2 0BB, United Kingdom.
Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge CB2 0BB, United Kingdom.
J Natl Cancer Inst. 2025 May 1;117(5):948-958. doi: 10.1093/jnci/djae335.
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.
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.
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.
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.
乳腺癌和卵巢癌疾病发病率分析算法（BOADICEA）模型利用癌症家族史、流行病学和基因数据预测乳腺癌风险。我们在一个大型前瞻性队列中评估了其有效性。
我们在英国生物银行中对217885名年龄在40 - 70岁、自我报告为白种人且既往无癌症的女性（6838例新发乳腺癌患者）评估了模型的校准、区分能力和风险分类能力。使用与英国国家卫生与临床优化研究所指南推荐的绝对终生风险类别（小于17%、17% - 30%和30%及以上）等效的相对风险阈值评估特定年龄的风险分类。我们使用BOADICEA v.6预测10年风险，考虑癌症家族史、基于问卷的风险因素、一个包含313个单核苷酸多态性的多基因评分以及致病变异。未获取到乳腺X线密度数据。
多基因风险评分是最具区分能力的风险因素（曲线下面积[AUC]=0.65）。考虑所有风险因素时区分能力最高（AUC = 0.66）。该模型总体校准良好（预期与观察比值 = 0.99，95%置信区间[CI]=0.97至1.02；校准斜率 = 0.99，95% CI = 0.99至1.00），在预测风险的十分位数中也是如此。50岁以下和50岁以上女性的区分能力相似。50岁以下女性存在一些预测不足（预期与观察比值 = 0.89，95% CI = 0.84至0.94；校准斜率 = 0.96，95% CI = 0.94至0.97），这可以用英国生物银行中该年龄组的乳腺癌发病率高于英国总体发病率来解释。该模型将87.2%、11.4%和1.4%的女性分类为相对风险类别小于1.6、1.6 - 3.1和至少3.1，在相对风险至少为1.6的类别中识别出25.6%的新发乳腺癌患者。
在CanRisk（www.canrisk.org）中实施的BOADICEA可提供有效的10年乳腺癌风险，这有助于进行风险分层筛查和个性化乳腺癌风险管理。
