用于在内镜超声引导下细针穿刺抽吸中鉴别胰腺导管癌与胃肠道污染及良性胰腺导管上皮的免疫组织化学抗体组合

Immunohistochemical Antibody Panel for the Differential Diagnosis of Pancreatic Ductal Carcinoma From Gastrointestinal Contamination and Benign Pancreatic Duct Epithelium in Endoscopic Ultrasound-Guided Fine-Needle Aspiration.

作者信息

Furuhata Ayako, Minamiguchi Sachiko, Shirahase Hiroyuki, Kodama Yuzo, Adachi Souichi, Sakurai Takaki, Haga Hironori

机构信息

From the *Department of Diagnostic Pathology, †Human Health Sciences, and ‡Department of Gastroenterology and Hepatology, Kyoto University Graduate School of Medicine, Kyoto, Japan.

出版信息

Pancreas. 2017 Apr;46(4):531-538. doi: 10.1097/MPA.0000000000000774.

DOI:10.1097/MPA.0000000000000774

PMID:28099249

Abstract

OBJECTIVES

The diagnosis of pancreatic ductal adenocarcinoma (PDAC) by endoscopic ultrasound-guided fine-needle aspiration (EUS-FNA) can be challenging to distinguish tumor cells from benign epithelium (BE). The aim of the present study was to set a minimal antibody panel to differentiate PDAC from contaminated BE in EUS-FNA specimens.

METHODS

Immunohistochemistry using claudin 4, EZH2, Ki-67, maspin, p53, and S100P was performed on tissue microarray sections containing 53 PDACs and 33 BE as well as cell blocks of EUS-FNA including 53 PDACs and 22 BE. The positive rate was scored as 0 to 4+. The receiver operating characteristic curve was applied to determine a cutoff point, and the Classification And Regression Trees method was used to obtain a classification tree of the best panel.

RESULTS

The cutoff point was 1+ for claudin 4, EZH2, Ki-67, p53, and S100P and 2+ for maspin. All BE scored 0 for p53. The classification tree revealed using p53, S100P, and claudin 4 was the most powerful. The sensitivity and specificity of the tree were 96.2% and 100% in tissue microarrays and 100% and 95.5% in EUS-FNA, respectively.

CONCLUSIONS

The classification tree using p53, S100P, and claudin 4 seems to successfully distinguish PDAC from the accompanying BE.

摘要

目的

通过内镜超声引导下细针穿刺活检（EUS-FNA）诊断胰腺导管腺癌（PDAC）时，区分肿瘤细胞与良性上皮细胞（BE）可能具有挑战性。本研究的目的是确定一个最小抗体组合，以在EUS-FNA标本中区分PDAC与受污染的BE。

方法

对包含53例PDAC和33例BE的组织微阵列切片以及包含53例PDAC和22例BE的EUS-FNA细胞块进行免疫组织化学检测，使用紧密连接蛋白4（claudin 4）、EZH2、Ki-67、乳腺丝抑蛋白（maspin）、p53和S100P。阳性率评分为0至4+。应用受试者工作特征曲线确定临界值，并使用分类与回归树方法获得最佳抗体组合的分类树。

结果

claudin 4、EZH2、Ki-67、p53和S100P的临界值为1+，maspin为2+。所有BE的p53评分为0。分类树显示，使用p53、S100P和claudin 4的组合最有效。在组织微阵列中，该分类树的敏感性和特异性分别为96.2%和100%，在EUS-FNA中分别为100%和95.5%。