Busch Evan L, Crous-Bou Marta, Prescott Jennifer, Chen Maxine M, Downing Michael J, Rosner Bernard A, Mutter George L, De Vivo Immaculata
Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts.
Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.
Cancer Epidemiol Biomarkers Prev. 2017 May;26(5):727-735. doi: 10.1158/1055-9965.EPI-16-0821. Epub 2017 Jan 4.
Endometrial tumors arise from a hormonally responsive tissue. Defining subtypes by hormone receptor expression might better inform etiology and prediction of patient outcomes. We evaluated the potential role of tumor estrogen receptor (ER) and progesterone receptor (PR) expression to define endometrial cancer subtypes. We measured semi-continuous ER and PR protein expression in tissue specimens from 360 endometrial primary tumors from the Nurses' Health Study. To explore the impact of different definitions of marker positivity, we dichotomized ER and PR expression at different cut points in increments of 5% positive cells. Logistic regression was used to estimate associations between endometrial cancer risk factors, such as body mass index, with dichotomous ER or PR status. Reclassification statistics were used to assess whether adding dichotomous ER or PR status to standard prognostic factors of stage, grade, and histologic type would improve endometrial cancer-specific mortality prediction. Compared with not being obese, obesity increased the odds of having an ER-positive tumor at cut points of 0% to 20% [maximum OR, 2.92; 95% confidence interval (CI), 1.34-6.33] as well as the odds of having a PR-positive tumor at cut points of 70% to 90% (maximum OR, 2.53; 95% CI, 1.36-4.68). Adding dichotomous tumor ER or PR status to the panel of standard predictors did not improve both model discrimination and calibration. Obesity may be associated with greater endometrial tumor expression of ER and PR. Adding either marker does not appear to improve mortality prediction beyond the standard predictors. Body mass index might explain some of the biological variation among endometrial tumors. .
子宫内膜肿瘤起源于对激素有反应的组织。通过激素受体表达来定义亚型可能更有助于了解病因和预测患者预后。我们评估了肿瘤雌激素受体(ER)和孕激素受体(PR)表达在定义子宫内膜癌亚型方面的潜在作用。我们测量了来自护士健康研究的360例子宫内膜原发性肿瘤组织标本中ER和PR蛋白的半连续表达。为了探讨标记物阳性不同定义的影响,我们以5%阳性细胞的增量在不同切点将ER和PR表达二分法。使用逻辑回归估计子宫内膜癌风险因素(如体重指数)与二分法ER或PR状态之间的关联。重新分类统计用于评估将二分法ER或PR状态添加到分期、分级和组织学类型等标准预后因素中是否会改善子宫内膜癌特异性死亡率预测。与非肥胖相比,肥胖在0%至20%的切点增加了ER阳性肿瘤的几率[最大比值比(OR),2.92;95%置信区间(CI),1.34 - 6.33],以及在70%至90%的切点增加了PR阳性肿瘤的几率(最大OR,2.53;95%CI,1.36 - 4.68)。将二分法肿瘤ER或PR状态添加到标准预测指标组中并没有改善模型的辨别力和校准。肥胖可能与子宫内膜肿瘤中ER和PR的更高表达相关。添加任何一种标记物似乎都不能在标准预测指标之外改善死亡率预测。体重指数可能解释了子宫内膜肿瘤之间的一些生物学差异。