Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
Section for Epidemiology, Department of Public Health, Aarhus University, Aarhus, Denmark.
Clin Cancer Res. 2017 Aug 1;23(15):4181-4189. doi: 10.1158/1078-0432.CCR-16-3011. Epub 2017 Feb 28.
Circulating hormone concentrations are associated with breast cancer risk, with well-established associations for postmenopausal women. Biomarkers may represent minimally invasive measures to improve risk prediction models. We evaluated improvements in discrimination gained by adding serum biomarker concentrations to risk estimates derived from risk prediction models developed by Gail and colleagues and Pfeiffer and colleagues using a nested case-control study within the EPIC cohort, including 1,217 breast cancer cases and 1,976 matched controls. Participants were pre- or postmenopausal at blood collection. Circulating sex steroids, prolactin, insulin-like growth factor (IGF) I, IGF-binding protein 3, and sex hormone-binding globulin (SHBG) were evaluated using backward elimination separately in women pre- and postmenopausal at blood collection. Improvement in discrimination was evaluated as the change in concordance statistic (C-statistic) from a modified Gail or Pfeiffer risk score alone versus models, including the biomarkers and risk score. Internal validation with bootstrapping (1,000-fold) was used to adjust for overfitting. Among women postmenopausal at blood collection, estradiol, testosterone, and SHBG were selected into the prediction models. For breast cancer overall, model discrimination after including biomarkers was 5.3 percentage points higher than the modified Gail model alone, and 3.4 percentage points higher than the Pfeiffer model alone, after accounting for overfitting. Discrimination was more markedly improved for estrogen receptor-positive disease (percentage point change in C-statistic: 7.2, Gail; 4.8, Pfeiffer). We observed no improvement in discrimination among women premenopausal at blood collection. Integration of hormone measurements in clinical risk prediction models may represent a strategy to improve breast cancer risk stratification. .
循环激素浓度与乳腺癌风险相关，绝经后妇女的相关性已得到充分证实。生物标志物可能代表了改善风险预测模型的微创措施。我们使用 EPIC 队列中的巢式病例对照研究，评估了通过添加血清生物标志物浓度来改善 Gail 和 Pfeiffer 同事开发的风险预测模型得出的风险估计值的判别能力。该研究包括 1217 例乳腺癌病例和 1976 例匹配对照，参与者在采血时处于绝经前或绝经后。使用回溯消除法分别评估绝经前和绝经后女性的循环性激素、催乳素、胰岛素样生长因子（IGF）I、IGF 结合蛋白 3 和性激素结合球蛋白（SHBG）。通过改良 Gail 或 Pfeiffer 风险评分与包括生物标志物和风险评分的模型的一致性统计量（C 统计量）变化来评估判别力的改善。使用 bootstrap（1000 倍）内部验证来调整过度拟合。在绝经后女性中，雌二醇、睾酮和 SHBG 被选入预测模型。对于乳腺癌总体，包括生物标志物后模型的判别力比改良 Gail 模型单独提高了 5.3 个百分点，比 Pfeiffer 模型单独提高了 3.4 个百分点，这考虑了过度拟合的影响。对于雌激素受体阳性疾病，判别力的改善更为明显（C 统计量的百分点变化：Gail 为 7.2，Pfeiffer 为 4.8）。我们没有观察到绝经前女性的判别力有所提高。将激素测量值整合到临床风险预测模型中可能是改善乳腺癌风险分层的一种策略。
