Furuhata Ayako, Minamiguchi Sachiko, Shirahase Hiroyuki, Kodama Yuzo, Adachi Souichi, Sakurai Takaki, Haga Hironori
From the *Department of Diagnostic Pathology, †Human Health Sciences, and ‡Department of Gastroenterology and Hepatology, Kyoto University Graduate School of Medicine, Kyoto, Japan.
Pancreas. 2017 Apr;46(4):531-538. doi: 10.1097/MPA.0000000000000774.
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.
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.
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.
The classification tree using p53, S100P, and claudin 4 seems to successfully distinguish PDAC from the accompanying BE.
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。
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。
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%。
使用p53、S100P和claudin 4的分类树似乎能成功区分PDAC与伴随的BE。
