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宫颈癌研究中的组织微阵列验证。一种方法学途径。

Tissue microarray validation in cervical carcinoma studies. A methodological approach.

作者信息

Lovane Lucília, Carrilho Carla, Karlsson Christina

机构信息

School of Medical Sciences, Faculty of Medicine and Health, Örebro University, Örebro, Sweden.

Maputo Central Hospital, Maputo, Mozambique.

出版信息

Histol Histopathol. 2025 Mar;40(3):317-325. doi: 10.14670/HH-18-796. Epub 2024 Jul 16.

Abstract

Tissue microarrays (TMAs) are a cost-effective tool to study biomarkers in clinical research. Cervical cancer (CC) is one of the most prevalent in women worldwide, with the highest prevalence in low-middle-income countries due to a lack of organized screening. CC is associated with persistent high-risk human papillomavirus infection. Several biomarkers have been studied for diagnostic, therapeutic, and prognostic purposes. We aimed to evaluate and validate the effectiveness of TMA in CC compared to whole slide images (WSs). We selected and anonymized twenty cases of CC. P16, cytokeratin 5 (CK5), cytokeratin 7 (CK7), programmed death-ligand 1 (PD-L1), and CD8 expression were immunohistochemically investigated. All WS were scanned and 10 representative virtual TMA cores with 0.6 mm diameter per sample were selected. Ten random combinations of 1-5 cylinders per case were assessed for each biomarker. The agreement of scoring between TMA and WS was evaluated by kappa statistics. We found that three cores of 0.6 mm on TMA can accurately represent WS in our setting. The Kappa value between TMA and WS varied from 1 for p16 to 0.61 for PD-L1. Our study presents an approach to address TMA sampling that could be generalized to TMA-based research, regardless of the tissue and biomarkers of interest.

摘要

组织微阵列(TMAs)是临床研究中一种经济高效的生物标志物研究工具。宫颈癌(CC)是全球女性中最常见的癌症之一,由于缺乏有组织的筛查,在中低收入国家的发病率最高。CC与持续性高危型人乳头瘤病毒感染有关。为了诊断、治疗和预后目的,已经对几种生物标志物进行了研究。我们旨在评估和验证与全切片图像(WSs)相比,TMA在CC中的有效性。我们选择了20例CC病例并进行匿名化处理。采用免疫组织化学方法研究P16、细胞角蛋白5(CK5)、细胞角蛋白7(CK7)、程序性死亡配体1(PD-L1)和CD8的表达。对所有WS进行扫描,每个样本选取10个直径为0.6mm的代表性虚拟TMA核心。对每个生物标志物评估每个病例1-5个芯块的10种随机组合。通过kappa统计评估TMA和WS评分之间的一致性。我们发现,在我们的研究中,TMA上0.6mm的三个芯块可以准确代表WS。TMA和WS之间的Kappa值从P16的1到PD-L1的0.61不等。我们的研究提出了一种解决TMA采样的方法,该方法可以推广到基于TMA的研究中,而不管所关注的组织和生物标志物是什么。

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