Department of Pathology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea.
Department of Pathology, Ajou University School of Medicine, Suwon, South Korea.
Pathology. 2023 Dec;55(7):945-957. doi: 10.1016/j.pathol.2023.05.009. Epub 2023 Jul 17.
Oral and laryngeal epithelial lesions are currently diagnosed using histological criteria based on the World Health Organization (WHO) classification, which can cause interobserver variability. An integrated diagnostic approach based on immunohistochemistry (IHC) would aid in the interpretation of ambiguous histological findings of epithelial lesions. In the present study, IHC was used to evaluate the expression of p53 and Ki-67 in 114 cases of oral and laryngeal epithelial lesions in 104 patients. Logistic regression analysis and decision tree algorithm were employed to develop a scoring system and predictive model for differentiating the epithelial lesions. Cohen's kappa coefficient was used to evaluate interobserver variability, and next-generation sequencing (NGS) and IHC were used to compare TP53 mutation and p53 expression patterns. Two expression patterns for p53, namely, diffuse expression type (pattern HI) and null type (pattern LS), and the pattern HI for Ki-67 were significantly associated with high-grade dysplasia (HGD) or squamous cell carcinoma (SqCC). With an accuracy and area under the receiver operating characteristic curve (AUC) of 84.6% and 0.85, respectively, the scoring system based on p53 and Ki-67 expression patterns classified epithelial lesions into two types: non-dysplasia (ND) or low-grade dysplasia (LGD) and SqCC or HGD. The decision tree model constructed using the p53 and Ki-67 expression patterns classified epithelial lesions into ND, LGD, and group 2, including HGD or SqCC, with an accuracy and AUC of 75% and 0.87, respectively. The integrated diagnosis had a better correlation with near perfect agreement (weighted kappa 0.92, unweighted kappa 0.88). The patterns HI and LS for p53 were confirmed to be correlated with missense mutations and nonsense/frameshift mutations, respectively. A predictive model for diagnosis was developed based on the correlation between TP53 mutation and p53 expression patterns. These results indicate that the scoring system based on p53 and Ki-67 expression patterns can differentiate epithelial lesions, especially in cases when the morphological features are ambiguous.
口腔和喉部上皮病变目前采用基于世界卫生组织（WHO）分类的组织学标准进行诊断，但这可能导致观察者间的变异性。基于免疫组织化学（IHC）的综合诊断方法将有助于解释上皮病变的组织学发现存在的歧义。在本研究中，我们使用 IHC 评估了 104 例患者的 114 例口腔和喉部上皮病变中 p53 和 Ki-67 的表达。采用逻辑回归分析和决策树算法建立评分系统和预测模型，以区分上皮病变。采用 Cohen's kappa 系数评估观察者间的变异性，并采用下一代测序（NGS）和 IHC 比较 TP53 突变和 p53 表达模式。p53 的两种表达模式，即弥漫表达型（模式 HI）和无表达型（模式 LS），以及 Ki-67 的模式 HI 与高级别发育不良（HGD）或鳞状细胞癌（SqCC）显著相关。基于 p53 和 Ki-67 表达模式的评分系统将上皮病变分为两种类型：非发育不良（ND）或低级别发育不良（LGD）和 SqCC 或 HGD，其准确性和受试者工作特征曲线下面积（AUC）分别为 84.6%和 0.85。使用 p53 和 Ki-67 表达模式构建的决策树模型将上皮病变分为 ND、LGD 和包括 HGD 或 SqCC 在内的 2 组，其准确性和 AUC 分别为 75%和 0.87。综合诊断与近乎完美的一致性具有更好的相关性（加权kappa 0.92，未加权 kappa 0.88）。p53 的 HI 和 LS 模式分别被证实与错义突变和无义/移码突变相关。基于 TP53 突变和 p53 表达模式之间的相关性，建立了诊断预测模型。这些结果表明，基于 p53 和 Ki-67 表达模式的评分系统可区分上皮病变，尤其是在形态特征不明确的情况下。
