Division of Cancer Prevention, National Cancer Institute, National Institute of Health, Bethesda, MD 20892-7346, USA.
Cancer Prev Res (Phila). 2011 Mar;4(3):375-83. doi: 10.1158/1940-6207.CAPR-10-0193.
A panel of biomarkers may improve predictive performance over individual markers. Although many biomarker panels have been described for ovarian cancer, few studies used prediagnostic samples to assess the potential of the panels for early detection. We conducted a multisite systematic evaluation of biomarker panels using prediagnostic serum samples from the Prostate, Lung, Colorectal, and Ovarian Cancer (PLCO) screening trial. Using a nested case-control design, levels of 28 biomarkers were measured laboratory-blinded in 118 serum samples obtained before cancer diagnosis and 951 serum samples from matched controls. Five predictive models, each containing 6 to 8 biomarkers, were evaluated according to a predetermined analysis plan. Three sequential analyses were conducted: blinded validation of previously established models (step 1); simultaneous split-sample discovery and validation of models (step 2); and exploratory discovery of new models (step 3). Sensitivity, specificity, sensitivity at 98% specificity, and AUC were computed for the models and CA125 alone among 67 cases diagnosed within one year of blood draw and 476 matched controls. In step 1, one model showed comparable performance to CA125, with sensitivity, specificity, and AUC at 69.2%, 96.6%, and 0.892, respectively. Remaining models had poorer performance than CA125 alone. In step 2, we observed a similar pattern. In step 3, a model derived from all 28 markers failed to show improvement over CA125. Thus, biomarker panels discovered in diagnostic samples may not validate in prediagnostic samples; utilizing prediagnostic samples for discovery may be helpful in developing validated early detection panels.
一个生物标志物面板可能比单个标志物具有更好的预测性能。虽然已经描述了许多卵巢癌的生物标志物面板，但很少有研究使用诊断前样本来评估面板用于早期检测的潜力。我们使用前列腺癌、肺癌、结直肠癌和卵巢癌（PLCO）筛查试验的诊断前血清样本进行了多站点系统的生物标志物面板评估。使用巢式病例对照设计，在癌症诊断前获得的 118 份血清样本和 951 份匹配对照血清样本中，实验室盲法测量了 28 种生物标志物的水平。根据预定的分析计划，评估了包含 6 到 8 种生物标志物的 5 个预测模型。进行了三个连续的分析：先前建立的模型的盲法验证（步骤 1）；模型的同时拆分样本发现和验证（步骤 2）；以及新模型的探索性发现（步骤 3）。在采血后一年内诊断出的 67 例病例和 476 例匹配对照中，计算了模型和 CA125 单独使用的模型的灵敏度、特异性、98%特异性时的灵敏度和 AUC。在步骤 1 中，一个模型的表现与 CA125 相当，灵敏度、特异性和 AUC 分别为 69.2%、96.6%和 0.892。其余模型的表现均逊于 CA125 单独使用。在步骤 2 中，我们观察到类似的模式。在步骤 3 中，一个来自所有 28 种标志物的模型未能显示出优于 CA125 的效果。因此，在诊断样本中发现的生物标志物面板可能无法在诊断前样本中验证；利用诊断前样本进行发现可能有助于开发经过验证的早期检测面板。
