Feuchtinger Annette, Stiehler Tabitha, Jütting Uta, Marjanovic Goran, Luber Birgit, Langer Rupert, Walch Axel
Research Unit Analytical Pathology, German Research Center for Environmental Health, Institute of Pathology, Helmholtz Zentrum München, Ingolstädter Landstraße 1, 85764, Neuherberg, Germany,
Histochem Cell Biol. 2015 Jan;143(1):1-9. doi: 10.1007/s00418-014-1258-2. Epub 2014 Aug 26.
Quantification of protein expression based on immunohistochemistry (IHC) is an important step in clinical diagnoses and translational tissue-based research. Manual scoring systems are used in order to evaluate protein expression based on staining intensities and distribution patterns. However, visual scoring remains an inherently subjective approach. The aim of our study was to explore whether digital image analysis proves to be an alternative or even superior tool to quantify expression of membrane-bound proteins. We analyzed five membrane-binding biomarkers (HER2, EGFR, pEGFR, β-catenin, and E-cadherin) and performed IHC on tumor tissue microarrays from 153 esophageal adenocarcinomas patients from a single center study. The tissue cores were scored visually applying an established routine scoring system as well as by using digital image analysis obtaining a continuous spectrum of average staining intensity. Subsequently, we compared both assessments by survival analysis as an end point. There were no significant correlations with patient survival using visual scoring of β-catenin, E-cadherin, pEGFR, or HER2. In contrast, the results for digital image analysis approach indicated that there were significant associations with disease-free survival for β-catenin, E-cadherin, pEGFR, and HER2 (P = 0.0125, P = 0.0014, P = 0.0299, and P = 0.0096, respectively). For EGFR, there was a greater association with patient survival when digital image analysis was used compared to when visual scoring was (visual: P = 0.0045, image analysis: P < 0.0001). The results of this study indicated that digital image analysis was superior to visual scoring. Digital image analysis is more sensitive and, therefore, better able to detect biological differences within the tissues with greater accuracy. This increased sensitivity improves the quality of quantification.
基于免疫组织化学（IHC）的蛋白质表达定量是临床诊断和基于组织的转化研究中的重要步骤。为了基于染色强度和分布模式评估蛋白质表达，人们使用了手动评分系统。然而，视觉评分仍然是一种本质上主观的方法。我们研究的目的是探讨数字图像分析是否被证明是一种用于量化膜结合蛋白表达的替代工具，甚至是更优越的工具。我们分析了五种膜结合生物标志物（HER2、EGFR、pEGFR、β-连环蛋白和E-钙黏蛋白），并对来自一项单中心研究的153例食管腺癌患者的肿瘤组织微阵列进行了免疫组织化学检测。使用既定的常规评分系统对组织芯进行视觉评分，并通过数字图像分析获得平均染色强度的连续谱。随后，我们以生存分析作为终点比较了这两种评估方法。使用β-连环蛋白、E-钙黏蛋白、pEGFR或HER2的视觉评分与患者生存无显著相关性。相比之下，数字图像分析方法的结果表明，β-连环蛋白、E-钙黏蛋白、pEGFR和HER2与无病生存有显著关联（分别为P = 0.0125、P = 0.0014、P = 0.0299和P = 0.0096）。对于EGFR，与视觉评分相比，使用数字图像分析时与患者生存的关联更大（视觉评分：P = 0.0045，图像分析：P < 0.0001）。本研究结果表明，数字图像分析优于视觉评分。数字图像分析更敏感，因此能够更准确地检测组织内的生物学差异。这种提高的敏感性改善了定量的质量。
