Department of Population Health, New York University School of Medicine, 650 First Avenue, New York, NY, 10016, USA.
Epidemiology and Prevention Unit, Fondazione IRCCS - Istituto Nazionale dei Tumori, Milan, Italy.
Breast Cancer Res. 2019 Mar 19;21(1):42. doi: 10.1186/s13058-019-1126-z.
Models that accurately predict risk of breast cancer are needed to help younger women make decisions about when to begin screening. Premenopausal concentrations of circulating anti-Müllerian hormone (AMH), a biomarker of ovarian reserve, and testosterone have been positively associated with breast cancer risk in prospective studies. We assessed whether adding AMH and/or testosterone to the Gail model improves its prediction performance for women aged 35-50.
In a nested case-control study including ten prospective cohorts (1762 invasive cases/1890 matched controls) with pre-diagnostic serum/plasma samples, we estimated relative risks (RR) for the biomarkers and Gail risk factors using conditional logistic regression and random-effects meta-analysis. Absolute risk models were developed using these RR estimates, attributable risk fractions calculated using the distributions of the risk factors in the cases from the consortium, and population-based incidence and mortality rates. The area under the receiver operating characteristic curve (AUC) was used to compare the discriminatory accuracy of the models with and without biomarkers.
The AUC for invasive breast cancer including only the Gail risk factor variables was 55.3 (95% CI 53.4, 57.1). The AUC increased moderately with the addition of AMH (AUC 57.6, 95% CI 55.7, 59.5), testosterone (AUC 56.2, 95% CI 54.4, 58.1), or both (AUC 58.1, 95% CI 56.2, 59.9). The largest AUC improvement (4.0) was among women without a family history of breast cancer.
AMH and testosterone moderately increase the discriminatory accuracy of the Gail model among women aged 35-50. We observed the largest AUC increase for women without a family history of breast cancer, the group that would benefit most from improved risk prediction because early screening is already recommended for women with a family history.
为了帮助年轻女性确定开始筛查的时间，需要有能够准确预测乳腺癌风险的模型。在前瞻性研究中，循环抗苗勒管激素（AMH）——一种卵巢储备的生物标志物——和睾丸酮的绝经前浓度与乳腺癌风险呈正相关。我们评估了在 Gail 模型中加入 AMH 和/或睾丸酮是否可以改善其对 35-50 岁女性的预测性能。
在包括十个前瞻性队列（1762 例浸润性病例/1890 例匹配对照）的嵌套病例对照研究中，我们使用条件逻辑回归和随机效应荟萃分析，根据生物标志物和 Gail 风险因素估计了相对风险（RR）。使用这些 RR 估计值、使用联盟中病例的风险因素分布计算的归因风险分数，以及基于人群的发病率和死亡率，开发了绝对风险模型。使用接收器操作特征曲线（ROC）下的面积（AUC）来比较包含和不包含生物标志物的模型的区分准确性。
仅包含 Gail 风险因素变量的浸润性乳腺癌 AUC 为 55.3（95%CI 53.4，57.1）。AUC 随着 AMH（AUC 57.6，95%CI 55.7，59.5）、睾丸酮（AUC 56.2，95%CI 54.4，58.1）或两者的添加而适度增加（AUC 58.1，95%CI 56.2，59.9）。最大的 AUC 改善（4.0）发生在没有乳腺癌家族史的女性中。
AMH 和睾丸酮适度提高了 Gail 模型在 35-50 岁女性中的区分准确性。我们观察到没有乳腺癌家族史的女性 AUC 增加最大，这是受益于风险预测改善最大的群体，因为已经建议有家族史的女性进行早期筛查。
