Liu Xinyue, Kong Yan, Qian Youwen, Guo Haoyue, Zhao Lishu, Wang Hao, Xu Kandi, Ye Li, Liu Yujin, Lu Hui, He Yayi
Department of Medical Oncology, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai 200433, China; School of Medicine, Tongji University, Shanghai 200092, China.
SJTU-Yale Joint Center for Biostatistics and Data Science, Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.
Transl Oncol. 2024 Dec;50:102143. doi: 10.1016/j.tranon.2024.102143. Epub 2024 Oct 3.
Tumor-infiltrating lymphocytes (TILs) are essential components of the tumor microenvironment (TME) of non-small cell lung cancer (NSCLC). Still, it is difficult to describe due to their heterogeneity. In this study, five cell markers from NSCLC patients were analyzed. We segmented tumor cells (TCs) and TILs using Efficientnet-B3 and explored their quantitative information and spatial distribution. After that, we simulated multiplex immunohistochemistry (mIHC) by overlapping continuous single chromogenic IHCs slices. As a result, the proportion and the density of programmed cell death-ligand 1 (PD-L1)-positive TCs were the highest in the core. CD8+ T cells were the closest to the tumor (median distance: 41.71 μm), while PD-1+T cells were the most distant (median distance: 62.2μm), and our study found that most lymphocytes clustered together within the peritumoral range of 10-30 μm where cross-talk with TCs could be achieved. We also found that the classification of TME could be achieved using CD8+ T-cell density, which is correlated with the prognosis of patients. In addition, we achieved single chromogenic IHC slices overlap based on CD4-stained IHC slices. We explored the number and spatial distribution of cells in heterogeneous TME of NSCLC patients and achieved TME classification. We also found a way to show the co-expression of multiple molecules economically.
肿瘤浸润淋巴细胞(TILs)是非小细胞肺癌(NSCLC)肿瘤微环境(TME)的重要组成部分。然而,由于其异质性,很难对其进行描述。在本研究中,对NSCLC患者的五种细胞标志物进行了分析。我们使用Efficientnet-B3对肿瘤细胞(TCs)和TILs进行分割,并探索了它们的定量信息和空间分布。之后,我们通过重叠连续的单显色免疫组化(IHC)切片来模拟多重免疫组化(mIHC)。结果显示,程序性细胞死亡配体1(PD-L1)阳性TCs的比例和密度在肿瘤核心区域最高。CD8 + T细胞距离肿瘤最近(中位距离:41.71μm),而PD-1 + T细胞距离最远(中位距离:62.2μm),并且我们的研究发现大多数淋巴细胞聚集在10 - 30μm的肿瘤周围范围内,在此范围内可实现与TCs的相互作用。我们还发现,使用与患者预后相关的CD8 + T细胞密度可以实现TME的分类。此外,我们基于CD4染色的IHC切片实现了单显色IHC切片的重叠。我们探索了NSCLC患者异质性TME中细胞的数量和空间分布,并实现了TME分类。我们还找到了一种经济地展示多种分子共表达的方法。