Department of Pathology, Seoul National University Hospital, Seoul, Korea; Department of Pathology, Seoul National University College of Medicine, Seoul, Korea.
Histopathology. 2014 Dec;65(6):868-78. doi: 10.1111/his.12507. Epub 2014 Oct 6.
Need for accurate histologic subtyping of non-small cell lung carcinomas (NSCLCs) is growing. IHC patterns may be ambiguous in some cases, rendering it difficult to determine subtypes.
Tissue microarrays composed of 184 resected NSCLCs were stained for TTF-1, Napsin A, CK7, p40, p63, CK5/6, and mucicarmine. TTF-1 and Napsin A were chosen as the most accurate adenocarcinoma (ADC) marker (ACM), and p40 as squamous cell carcinoma (SCC) marker (SCM). We then prospectively performed IHC using these markers (TTF-1, Napsin A, and p40) in a cohort of small NSCLC biopsies (n = 186) with ambiguous morphology. Of these biopsies, 82.8% (154/186) were classifiable into either ADC or SCC by applying '3-marker IHC panel'. Additional CK7, p63, and CK5/6 were applied in 30 biopsies with equivocal IHC patterns, including 18 ACM-/SCM- (double-negative) and 12 ACM+/SCM+ (double-positive) cases. Decision tree and support vector machine models revealed that TTF-1 was a critical single marker for ADC in double-positive cases (91.7% accuracy), whereas p63 and/or CK5/6 helped to subtype double-negative cases (72.2% accuracy).
We propose a novel comprehensive algorithm for subtyping NSCLCs using a 3-marker IHC panel and additional p63 and CK5/6 that would be useful for subtyping small NSCLC biopsies.
对非小细胞肺癌（NSCLC）进行准确的组织学亚型分类的需求日益增长。在某些情况下，免疫组织化学（IHC）模式可能存在歧义，导致难以确定亚型。
本研究构建了由 184 例 NSCLC 切除标本组成的组织微阵列，用于检测 TTF-1、Napsin A、CK7、p40、p63、CK5/6 和粘卡红。选择 TTF-1 和 Napsin A 作为最准确的腺癌（ADC）标志物（ACM），p40 作为鳞状细胞癌（SCC）标志物（SCM）。然后，我们在一组具有形态学不确定的小 NSCLC 活检标本（n=186）中，前瞻性地使用这些标志物（TTF-1、Napsin A 和 p40）进行 IHC 检测。这些活检标本中，82.8%（154/186）通过应用“3 标志物 IHC 面板”可分为 ADC 或 SCC。在 30 例 IHC 模式不确定的活检标本中进一步应用 CK7、p63 和 CK5/6，包括 18 例 ACM-/SCM-（双阴性）和 12 例 ACM+/SCM+（双阳性）病例。决策树和支持向量机模型显示，TTF-1 是双阳性病例中 ADC 的关键单一标志物（准确率为 91.7%），而 p63 和/或 CK5/6 有助于对双阴性病例进行亚型分类（准确率为 72.2%）。
我们提出了一种使用 3 标志物 IHC 面板和额外的 p63 和 CK5/6 对 NSCLC 进行综合亚型分类的新算法，该算法对小 NSCLC 活检标本的亚型分类有用。
