Obstetrics and Gynecology Epidemiology Center, Brigham and Women's Hospital, Boston, Massachusetts.
Harvard Medical School, Boston, Massachusetts.
Cancer Epidemiol Biomarkers Prev. 2019 Jun;28(6):1076-1085. doi: 10.1158/1055-9965.EPI-18-1120. Epub 2019 Apr 4.
Cancer antigen 125 (CA125) is the most promising ovarian cancer screening biomarker to date. Multiple studies reported CA125 levels vary by personal characteristics, which could inform personalized CA125 thresholds. However, this has not been well described in premenopausal women.
We evaluated predictors of CA125 levels among 815 premenopausal women from the New England Case Control Study (NEC). We developed linear and dichotomous (≥35 U/mL) CA125 prediction models and externally validated an abridged model restricting to available predictors among 473 premenopausal women in the European Prospective Investigation into Cancer and Nutrition Study (EPIC).
The final linear CA125 prediction model included age, race, tubal ligation, endometriosis, menstrual phase at blood draw, and fibroids, which explained 7% of the total variance of CA125. The correlation between observed and predicted CA125 levels based on the abridged model (including age, race, and menstrual phase at blood draw) had similar correlation coefficients in NEC ( = 0.22) and in EPIC ( = 0.22). The dichotomous CA125 prediction model included age, tubal ligation, endometriosis, prior personal cancer diagnosis, family history of ovarian cancer, number of miscarriages, menstrual phase at blood draw, and smoking status with AUC of 0.83. The abridged dichotomous model (including age, number of miscarriages, menstrual phase at blood draw, and smoking status) showed similar AUCs in NEC (0.73) and in EPIC (0.78).
We identified a combination of factors associated with CA125 levels in premenopausal women.
Our model could be valuable in identifying healthy women likely to have elevated CA125 and consequently improve its specificity for ovarian cancer screening.
癌抗原 125(CA125)是迄今为止最有前途的卵巢癌筛查生物标志物。多项研究报告称,CA125 水平因个人特征而异,这可以为个性化 CA125 阈值提供信息。然而,这在绝经前妇女中尚未得到很好的描述。
我们评估了来自新英格兰病例对照研究(NEC)的 815 名绝经前妇女中 CA125 水平的预测因素。我们开发了线性和二项式(≥35U/mL)CA125 预测模型,并在欧洲癌症与营养前瞻性研究(EPIC)中的 473 名绝经前妇女中对简化模型进行了外部验证,该模型仅限于可用的预测因素。
最终的线性 CA125 预测模型包括年龄、种族、输卵管结扎术、子宫内膜异位症、采血时的月经阶段和子宫肌瘤,该模型解释了 CA125 总方差的 7%。基于简化模型(包括年龄、种族和采血时的月经阶段)观察到的和预测的 CA125 水平之间的相关性在 NEC(r=0.22)和 EPIC(r=0.22)中具有相似的相关系数。二项式 CA125 预测模型包括年龄、输卵管结扎术、子宫内膜异位症、既往个人癌症诊断、卵巢癌家族史、流产次数、采血时的月经阶段和吸烟状态,AUC 为 0.83。简化的二项式模型(包括年龄、流产次数、采血时的月经阶段和吸烟状态)在 NEC(0.73)和 EPIC(0.78)中显示出相似的 AUC。
我们确定了与绝经前妇女 CA125 水平相关的一系列因素。
我们的模型可以有效地识别可能具有升高 CA125 的健康女性,从而提高其对卵巢癌筛查的特异性。