Lokhande Lavanya, Nilsson Daniel, de Matos Rodrigues Joana, Hassan May, Olsson Lina M, Pyl Paul-Theodor, Vasquez Louella, Porwit Anna, Gerdtsson Anna Sandström, Jerkeman Mats, Ek Sara
Department of Immunotechnology, Lund University, 221 00 Lund, Sweden.
Department of Laboratory Medicine, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Lund University, 221 00 Lund, Sweden.
Cancers (Basel). 2024 Jun 21;16(13):2289. doi: 10.3390/cancers16132289.
With the aim to advance the understanding of immune regulation in MCL and to identify targetable T-cell subsets, we set out to combine image analysis and spatial omic technology focused on both early and late differentiation stages of T cells. MCL patient tissue ( = 102) was explored using image analysis and GeoMx spatial omics profiling of 69 proteins and 1812 mRNAs. Tumor cells, T helper (T) cells and cytotoxic (T) cells of early (CD57-) and late (CD57+) differentiation stage were analyzed. An image analysis workflow was developed based on fine-tuned Cellpose models for cell segmentation and classification. T and CD57+ subsets of T cells were enriched in tumor-rich compared to tumor-sparse regions. Tumor-sparse regions had a higher expression of several key immune suppressive proteins, tentatively controlling T-cell expansion in regions close to the tumor. We revealed that T cells in late differentiation stages (CD57+) are enriched among MCL infiltrating T cells and are predictive of an increased expression of immune suppressive markers. CD47, IDO1 and CTLA-4 were identified as potential targets for patients with T-cell-rich MCL TIME, while GITR might be a feasible target for MCL patients with sparse T-cell infiltration. In subgroups of patients with a high degree of CD57+ T-cell infiltration, several immune checkpoint inhibitors, including TIGIT, PD-L1 and LAG3 were increased, emphasizing the immune-suppressive features of this highly differentiated T-cell subset not previously described in MCL.
为了加深对套细胞淋巴瘤（MCL）免疫调节的理解并确定可靶向的T细胞亚群，我们着手将图像分析与专注于T细胞早期和晚期分化阶段的空间组学技术相结合。我们使用图像分析以及对69种蛋白质和1812种mRNA进行的GeoMx空间组学分析，对102例MCL患者组织进行了研究。分析了早期（CD57-）和晚期（CD57+）分化阶段的肿瘤细胞、辅助性T（Th）细胞和细胞毒性T（Tc）细胞。基于经过微调的用于细胞分割和分类的Cellpose模型，开发了一种图像分析工作流程。与肿瘤稀疏区域相比，T细胞和CD57+ T细胞亚群在肿瘤丰富区域更为富集。肿瘤稀疏区域中几种关键免疫抑制蛋白的表达较高，可能在肿瘤附近区域控制T细胞的扩增。我们发现，晚期分化阶段（CD57+）的T细胞在MCL浸润性T细胞中富集，并且可预测免疫抑制标志物的表达增加。CD47、吲哚胺2,3-双加氧酶1（IDO1）和细胞毒性T淋巴细胞相关蛋白4（CTLA-4）被确定为富含T细胞的MCL肿瘤微环境（TIME）患者的潜在靶点，而糖皮质激素诱导的肿瘤坏死因子受体（GITR）可能是T细胞浸润稀疏的MCL患者的可行靶点。在CD57+ T细胞浸润程度高的患者亚组中，包括T细胞免疫球蛋白和粘蛋白结构域分子3（TIGIT）、程序性死亡配体1（PD-L1）和淋巴细胞活化基因3蛋白（LAG3）在内的几种免疫检查点抑制剂增加，这突出了这种高度分化的T细胞亚群的免疫抑制特征，而这在MCL中此前尚未有描述。
