Lee Sangjune Laurence, Cabanero Michael, Hyrcza Martin, Butler Marcus, Liu Fei-Fei, Hansen Aaron, Huang Shao Hui, Tsao Ming-Sound, Song Yuyao, Lu Lin, Xu Wei, Chepeha Douglas B, Goldstein David P, Weinreb Ilan, Bratman Scott V
Department of Radiation Oncology, University of Toronto, Canada.
Department of Laboratory Medicine and Pathobiology, University of Toronto, Canada.
Clin Transl Radiat Oncol. 2019 May 18;17:32-39. doi: 10.1016/j.ctro.2019.05.001. eCollection 2019 Jul.
Oral tongue squamous cell carcinoma (OTSCC) displays variable levels of immune cells within the tumor microenvironment. The quantity and localization of tumor infiltrating lymphocytes (TILs), specific functional TIL subsets (e.g., CD8+), and biomarker-expressing cells (e.g., PD-L1+) may have prognostic and predictive value. The purpose of this study was to evaluate the robustness and utility of computer-assisted image analysis tools to quantify and localize immunohistochemistry-based biomarkers within the tumor microenvironment on a tissue microarray (TMA). We stained a 91-patient OTSCC TMA with antibodies targeting CD3, CD4, CD8, FOXP3, IDO, and PD-L1. Cell populations were segmented into epithelial (tumor) or stromal compartments according to a mask derived from a pan-cytokeratin stain. Definiens Tissue Studio was used to enumerate marker-positive cells or to quantify the staining intensity. Automated methods were validated against manual tissue segmentation, cell count, and stain intensity quantification. Univariate associations of cell count and stain intensity with smoking status, stage, overall survival (OS), and disease-free survival (DFS) were determined. Our results revealed that the accuracy of automated tissue segmentation was dependent on the distance of the tissue section from the cytokeratin mask and the proportion of the tissue containing tumor vs. stroma. Automated and manual cell counts and stain intensities were highly correlated (Pearson coefficient range: 0.46-0.90; p < 0.001). Within this OTSCC cohort, smokers had significantly stronger PD-L1 stain intensity and higher numbers of CD3+, CD4+ and FOXP3+ TILs. In the subset of patients who had received adjuvant radiotherapy, a higher number of CD8+ TILs was associated with inferior OS and DFS. Taken together, this proof-of-principle study demonstrates the robustness and utility of computer-assisted image analysis for high-throughput assessment of multiple IHC markers on TMAs, with potential implications for studies on prognostic and predictive biomarkers.
口腔舌鳞状细胞癌（OTSCC）在肿瘤微环境中表现出不同水平的免疫细胞。肿瘤浸润淋巴细胞（TILs）的数量和定位、特定功能的TIL亚群（如CD8+）以及表达生物标志物的细胞（如PD-L1+）可能具有预后和预测价值。本研究的目的是评估计算机辅助图像分析工具在组织微阵列（TMA）上对肿瘤微环境中基于免疫组织化学的生物标志物进行定量和定位的稳健性和实用性。我们用靶向CD3、CD4、CD8、FOXP3、IDO和PD-L1的抗体对一个包含91例OTSCC患者的TMA进行染色。根据源自全细胞角蛋白染色的掩码，将细胞群体分为上皮（肿瘤）或基质区室。使用Definiens Tissue Studio对标记阳性细胞进行计数或对染色强度进行定量。针对手动组织分割、细胞计数和染色强度定量对自动化方法进行了验证。确定了细胞计数和染色强度与吸烟状态、分期、总生存期（OS）和无病生存期（DFS）的单变量关联。我们的结果显示，自动化组织分割的准确性取决于组织切片与细胞角蛋白掩码的距离以及肿瘤与基质组织的比例。自动化和手动细胞计数及染色强度高度相关（皮尔逊系数范围：0.46 - 0.90；p < 0.001）。在这个OTSCC队列中，吸烟者的PD-L1染色强度明显更强，CD3+、CD4+和FOXP3+ TILs数量更多。在接受辅助放疗的患者亚组中，较高数量的CD8+ TILs与较差的OS和DFS相关。综上所述，这项原理验证研究证明了计算机辅助图像分析在TMA上高通量评估多种免疫组化标记物的稳健性和实用性，对预后和预测生物标志物的研究具有潜在意义。
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