Taylor Clive R, Jadhav Anagha P, Gholap Abhi, Kamble Gurunath, Huang Jiaoti, Gown Allen, Doshi Isha, Rimm David L
Department of Pathology, University of Southern California, Los Angeles.
OptraSCAN, Pune, India.
Appl Immunohistochem Mol Morphol. 2019 Apr;27(4):263-269. doi: 10.1097/PAI.0000000000000737.
Assessment of programmed death-ligand 1 (PD-L1) expression is a critical part of patient management for immunotherapy. However, studies have shown that pathologist-based analysis lacks reproducibility, especially for immune cell expression. The purpose of this study was to validate reproducibility of the automated machine-based Optra image analysis for PD-L1 immunohistochemistry for both tumor cells (TCs) and immune cells. We compared conventional pathologists' scores for both tumor and immune cell positivity separately using 22c3 antibody on the Dako Link 48 platform for PD-L1 expression in non-small cell lung carcinoma. We assessed interpretation first by pathologists and second by PD-L1 image analysis scores. Lin's concordance correlation coefficients (LCCs) for each pathologist were measured to assess variability between pathologists and between pathologists and Optra automated quantitative scores in scoring both tumor and immune cells. Lin's LCCs to evaluate the correlation between pathologists for TC was 0.75 [95% confidence interval (CI), 0.64-0.81] and 0.40 (95% CI, 0.40-0.62) for immune cell scoring. Pathologists were highly concordant for tumor scoring, but not for immune cell scoring, which is similar to previously reported studies where agreement is higher in TCs than immune cells. The LCCs between conventional pathologists' read and the machine score were 0.80 (95% CI, 0.74-0.85) for TCs and 0.70 (95% CI, 0.60-0.76) for immune cell population. This is considered excellent agreement for TCs and good concordance for immune cells. The automated scoring methods showed concordance with the pathologists' average scores that were comparable to interpathologist scores. This suggests promise for Optra automated assessment of PD-L1 in non-small cell lung cancer.
程序性死亡配体1(PD-L1)表达评估是免疫治疗患者管理的关键部分。然而,研究表明,基于病理学家的分析缺乏可重复性,尤其是对于免疫细胞表达。本研究的目的是验证基于自动化机器的Optra图像分析在非小细胞肺癌中对肿瘤细胞(TCs)和免疫细胞进行PD-L1免疫组化分析的可重复性。我们在Dako Link 48平台上使用22c3抗体,分别比较了传统病理学家对肿瘤和免疫细胞阳性的评分,以评估非小细胞肺癌中PD-L1的表达。我们首先由病理学家进行解读,其次通过PD-L1图像分析评分进行评估。测量每位病理学家的林氏一致性相关系数(LCCs),以评估病理学家之间以及病理学家与Optra自动定量评分在对肿瘤和免疫细胞评分时的变异性。评估病理学家之间TC相关性的林氏LCCs为0.75[95%置信区间(CI),0.64 - 0.81],免疫细胞评分的林氏LCCs为0.40(95%CI,0.40 - 0.62)。病理学家在肿瘤评分方面高度一致,但在免疫细胞评分方面不一致,这与先前报道的研究相似,即TCs中的一致性高于免疫细胞。传统病理学家的读数与机器评分之间的LCCs,TCs为0.80(95%CI,0.74 - 0.85),免疫细胞群体为0.70(95%CI,0.60 - 0.76)。这被认为是TCs的极佳一致性和免疫细胞的良好一致性。自动评分方法与病理学家的平均评分显示出一致性,与病理学家之间的评分相当。这表明Optra在非小细胞肺癌中对PD-L1进行自动评估具有前景。