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

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS
Lucília Lovane, Carla Carrilho, Christina Karlsson
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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.

宫颈癌研究中的组织芯片验证。一种方法论途径。
组织芯片(TMA)是临床研究中研究生物标记物的一种经济有效的工具。宫颈癌(CC)是全球妇女的高发病之一,由于缺乏有组织的筛查,中低收入国家的发病率最高。宫颈癌与持续的高危人类乳头瘤病毒感染有关。目前已研究出多种用于诊断、治疗和预后的生物标志物。我们的目的是评估和验证 TMA 与全切片图像(WSs)相比在 CC 中的有效性。我们选取了 20 例 CC 病例并对其进行了匿名处理。对 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 的表达进行了免疫组化检测。扫描所有 WS,并为每个样本选择 10 个直径为 0.6 毫米的代表性虚拟 TMA 核心。对每个生物标记物评估了每个病例 1-5 个圆柱的 10 个随机组合。TMA 和 WS 之间的评分一致性通过卡帕统计进行评估。我们发现,在我们的病例中,TMA 上三个 0.6 毫米的核心可准确代表 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 取样问题的方法,这种方法可以推广到基于 TMA 的研究中,无论感兴趣的组织和生物标记物是什么。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
ACS Applied Bio Materials
ACS Applied Bio Materials Chemistry-Chemistry (all)
CiteScore
9.40
自引率
2.10%
发文量
464
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