An Immunohistochemical Algorithm for Ovarian Carcinoma Typing.
An Immunohistochemical Algorithm for Ovarian Carcinoma Typing.
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DOI：
10.1097/pgp.0000000000000274
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发表时间：
2016-09
期刊：
影响因子：
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通讯作者：
Mes Masson AM
中科院分区：
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作者：
Köbel M;Rahimi K;Rambau PF;Naugler C;Le Page C;Meunier L;de Ladurantaye M;Lee S;Leung S;Goode EL;Ramus SJ;Carlson JW;Li X;Ewanowich CA;Kelemen LE;Vanderhyden B;Provencher D;Huntsman D;Lee CH;Gilks CB;Mes Masson AM
补充数字内容可在文本中找到。卵巢癌有5种主要组织型。诊断分型标准随着时间的推移而发展，过去的队列可能会被当前的标准错误分类。我们的目标是重新分类最近组装的加拿大卵巢实验统一资源和阿尔伯塔卵巢肿瘤类型队列使用免疫组织化学（IHC）生物标志物，并开发一个IHC算法卵巢癌组织分型。通过比较原始组织型和预测组织型，对来自加拿大卵巢实验统一资源和阿尔伯塔卵巢肿瘤类型的总共1626个卵巢癌样本进行重新分类。使用先前重新分类的队列（N=784），采用8个IHC标志物的二进制输入，从标称逻辑回归模型中推导出组织型预测。与原始或预测组织型不一致的病例进行仲裁。重新分类后，所有队列的1762例病例均接受预测模型（χ2自动相互作用检测、递归分割和名义逻辑回归），其中包含可变IHC标记物输入。在加拿大卵巢实验统一资源和阿尔伯塔卵巢肿瘤类型队列的1521/1626（93.5%）例病例中确认了组织学类型。最高的错误分类发生在类胶质瘤型中，其中大多数变化涉及从类胶质瘤到高级别浆液性癌的重新分类，这也得到了突变数据和结果的支持。使用重新分类的组织型作为终点，4标记预测模型正确分类88%，6标记91%，8标记93%的1762例。这项研究提供了经统计学验证的廉价IHC算法，在研究，临床实践和临床试验中具有广泛的应用。
Supplemental Digital Content is available in the text. There are 5 major histotypes of ovarian carcinomas. Diagnostic typing criteria have evolved over time, and past cohorts may be misclassified by current standards. Our objective was to reclassify the recently assembled Canadian Ovarian Experimental Unified Resource and the Alberta Ovarian Tumor Type cohorts using immunohistochemical (IHC) biomarkers and to develop an IHC algorithm for ovarian carcinoma histotyping. A total of 1626 ovarian carcinoma samples from the Canadian Ovarian Experimental Unified Resource and the Alberta Ovarian Tumor Type were subjected to a reclassification by comparing the original with the predicted histotype. Histotype prediction was derived from a nominal logistic regression modeling using a previously reclassified cohort (N=784) with the binary input of 8 IHC markers. Cases with discordant original or predicted histotypes were subjected to arbitration. After reclassification, 1762 cases from all cohorts were subjected to prediction models (χ2 Automatic Interaction Detection, recursive partitioning, and nominal logistic regression) with a variable IHC marker input. The histologic type was confirmed in 1521/1626 (93.5%) cases of the Canadian Ovarian Experimental Unified Resource and the Alberta Ovarian Tumor Type cohorts. The highest misclassification occurred in the endometrioid type, where most of the changes involved reclassification from endometrioid to high-grade serous carcinoma, which was additionally supported by mutational data and outcome. Using the reclassified histotype as the endpoint, a 4-marker prediction model correctly classified 88%, a 6-marker 91%, and an 8-marker 93% of the 1762 cases. This study provides statistically validated, inexpensive IHC algorithms, which have versatile applications in research, clinical practice, and clinical trials.