Synaptophysin, CD117, and GATA3 as a Diagnostic Immunohistochemical Panel for Small Cell Neuroendocrine Carcinoma of the Urinary Tract.
Synaptophysin, CD117, and GATA3 as a Diagnostic Immunohistochemical Panel for Small Cell Neuroendocrine Carcinoma of the Urinary Tract.
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在诊断泌尿道小细胞神经内分泌癌（SCNEC）时，我们发现以前的活检被误诊为尿路上皮癌（UC），因为仅检测嗜铬粒蛋白和突触素来定义神经内分泌分化，并且两项检测均为阴性。这一病例促使我们进行本研究，以确定一组神经内分泌标志物，以确保传统的神经内分泌标志物阴性SCNEC的诊断。我们采用决策树分类算法分析了17种免疫组织化学标记物的表达，发现突触素（>5%）和CD 117（>20%）的范围和GATA 3（阴性或弱）的强度是主要参数。由于SCNEC是一种侵袭性肿瘤类型，需要与UC不同的治疗方法，因此SCNEC的准确诊断至关重要，该模型可以帮助病理学家在日常实践中准确诊断SCNEC。虽然SCNEC是基于其特征性的组织学，免疫组织化学（IHC）通常用于确认神经内分泌分化（NED）。这里的挑战是，SCNEC可能会产生传统神经内分泌标志物的阴性结果。为了建立NED的IHC组，在作为发现队列的34名SCNEC患者的47例中产生的组织微阵列构建体上检查17种神经元、基底和管腔标志物。采用决策树算法分析免疫反应性的程度和强度，并建立诊断模型。一个外部队列的8例和透射电子显微镜（TEM）被用来验证该模型。在17个标记物中，决策树诊断模型选择了3个标记物对NED进行分类，分类准确率为98.4%。选择突触体素的范围（>5%）作为NED的初始参数，CD 117的范围（>20%）作为第二参数，然后GATA 3的强度（≤1.5，阴性或弱免疫反应性）作为第三参数。各变量的重要性分别为0.758、0.213和0.029。该模型通过TEM和使用外部队列进行验证。使用突触素、CD117和GATA 3的决策树模型可能有助于确认传统标记阴性SCNEC的NED。
While diagnosing a case of small cell neuroendocrine carcinoma (SCNEC) in the urinary tract, we found that the previous biopsy had been misdiagnosed as urothelial carcinoma (UC) because only chromogranin and synaptophysin were tested to define neuroendocrine differentiation and both tests were negative. This case led us to conduct this present study to define a panel of neuroendocrine markers to ensure the diagnosis of traditional neuroendocrine marker-negative SCNEC. We employed a decision tree classifier algorithm to analyze the expression of 17 immunohistochemical markers and found that the extent of synaptophysin (>5%) and CD117 (>20%) and the intensity of GATA3 (negative or weak) are major parameters. Since SCNEC is an aggressive tumor type and requires therapeutic approaches that differ from those used for UC, an accurate diagnosis of SCNEC is critical and this model may help pathologists accurately diagnose SCNEC in daily practice. Although SCNEC is based on its characteristic histology, immunohistochemistry (IHC) is commonly employed to confirm neuroendocrine differentiation (NED). The challenge here is that SCNEC may yield negative results for traditional neuroendocrine markers. To establish an IHC panel for NED, 17 neuronal, basal, and luminal markers were examined on a tissue microarray construct generated from 47 cases of 34 patients with SCNEC as a discovery cohort. A decision tree algorithm was employed to analyze the extent and intensity of immunoreactivity and to develop a diagnostic model. An external cohort of eight cases and transmission electron microscopy (TEM) were used to validate the model. Among the 17 markers, the decision tree diagnostic model selected 3 markers to classify NED with 98.4% accuracy in classification. The extent of synaptophysin (>5%) was selected as the initial parameter, the extent of CD117 (>20%) as the second, and then the intensity of GATA3 (≤1.5, negative or weak immunoreactivity) as the third for NED. The importance of each variable was 0.758, 0.213, and 0.029, respectively. The model was validated by the TEM and using the external cohort. The decision tree model using synaptophysin, CD117, and GATA3 may help confirm NED of traditional marker-negative SCNEC.
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DOI：
10.1097/pai.0000000000000824
发表时间：
2020-10-01
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