Hsieh Anne M-Y, Polyakova Olena, Fu Guodong, Chazen Ronald S, MacMillan Christina, Witterick Ian J, Ralhan Ranju, Walfish Paul G
Alex and Simona Shnaider Research Laboratory in Molecular Oncology, Sinai Health System, Toronto, ON, Canada.
Department of Pathology and Laboratory Medicine, Sinai Health System, Toronto, Ontario, Canada.
Oncotarget. 2018 Apr 13;9(28):19767-19782. doi: 10.18632/oncotarget.24833.
Recognition of noninvasive follicular thyroid neoplasms with papillary-like nuclear features (NIFTP) that distinguishes them from invasive malignant encapsulated follicular variant of papillary thyroid carcinoma (EFVPTC) can prevent overtreatment of NIFTP patients. We and others have previously reported that programmed death-ligand 1 (PD-L1) is a useful biomarker in thyroid tumors; however, all reports to date have relied on manual scoring that is time consuming as well as subject to individual bias. Consequently, we developed a digital image analysis (DIA) protocol for cytoplasmic and membranous stain quantitation (ThyApp) and evaluated three tumor sampling methods [Systemic Uniform Random Sampling, hotspot nucleus, and hotspot nucleus/3,3'-Diaminobenzidine (DAB)]. A patient cohort of 153 cases consisting of 48 NIFTP, 44 EFVPTC, 26 benign nodules and 35 encapsulated follicular lesions/neoplasms with lymphocytic thyroiditis (LT) was studied. ThyApp quantitation of PD-L1 expression revealed a significant difference between invasive EFVPTC and NIFTP; but none between NIFTP and benign nodules. ThyApp integrated with hotspot nucleus tumor sampling method demonstrated to be most clinically relevant, consumed least processing time, and eliminated interobserver variance. In conclusion, the fully automatic DIA algorithm developed using a histomorphological approach objectively quantitated PD-L1 expression in encapsulated thyroid neoplasms and outperformed manual scoring in reproducibility and higher efficiency.
识别具有乳头状核特征的非侵袭性滤泡性甲状腺肿瘤（NIFTP）并将其与侵袭性恶性甲状腺乳头状癌滤泡变异型（EFVPTC）区分开来，可避免对NIFTP患者进行过度治疗。我们和其他人之前曾报道，程序性死亡配体1（PD-L1）是甲状腺肿瘤中的一种有用生物标志物；然而，迄今为止所有报告都依赖于人工评分，这种评分既耗时又容易受到个体偏差的影响。因此，我们开发了一种用于细胞质和膜染色定量的数字图像分析（DIA）方案（ThyApp），并评估了三种肿瘤采样方法[系统均匀随机采样、热点核以及热点核/3,3'-二氨基联苯胺（DAB）]。对一个由153例患者组成的队列进行了研究，其中包括48例NIFTP、44例EFVPTC、26例良性结节以及35例伴有淋巴细胞性甲状腺炎（LT）的包膜滤泡性病变/肿瘤。ThyApp对PD-L1表达的定量分析显示，侵袭性EFVPTC和NIFTP之间存在显著差异；但NIFTP与良性结节之间无差异。ThyApp与热点核肿瘤采样方法相结合被证明在临床上最具相关性，处理时间最短，并且消除了观察者间的差异。总之，使用组织形态学方法开发的全自动DIA算法客观地定量了包膜甲状腺肿瘤中PD-L1的表达，在可重复性和更高效率方面优于人工评分。
