Clinical Impact of the Spatial Organization of the Immune Tumor Microenvironment in Diffuse Large B-Cell Lymphoma

Abstract

Introduction: Recent analyses of diffuse large B-cell lymphoma (DLBCL) have highlighted the clinical importance of immune tumor microenvironment (iTME), and based on the composition of the iTME, different DLBCL subtypes have been proposed (Kotlov et al. Cancer Discov. 2021, Steen et al. Cancer Cell. 2021). However, studies have mainly focused on the impact of different cell type proportions, whereas the clinical importance of their spatial organization has remained unclear. Materials and Methods: We used 12-plex immunohistochemistry panel to characterize B cells (CD20), T cells (CD3, CD4, CD8, FOXP3), macrophages (CD68, CD163), and immune checkpoint molecules (PD-1, PD-L1, CD96) from FFPE samples of 107 DLBCL patients treated with rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP)- like immunochemotherapy. We performed image processing using the Ilastik and CellProfiler softwares, and segmented nuclei with a pretrained deep learning segmentation model. Using histoCAT software, we quantified marker intensities for each cell, phenotyped the cells with the Phenograph algorithm, and, finally, performed a neighborhood analysis to recognize the cell types that neighbor or avoid each other. We correlated the findings with patient demographics and survival. Results: In total,we analyzed 739 825 single cells (median 7127 per sample; range 1640 - 12705) and discovered 16 different metaclusters, which included various T helper cell, cytotoxic T cell, regulatory T cell (Treg), M1 and M2 like macrophage, and B cell subgroups. Samples varied greatly in their immune cell composition, with a median proportion of B cells, T cells and macrophages being 48.0 %, 19.9 %, and 10.2 %, respectively. We divided the samples according to their iTME constitution using K means clustering. As expected, samples were split into non-inflamed (37 %) and inflamed iTME subgroups, the latter dominated by T cells (22 %) and M2 macrophages (40 %). However, there was no significant difference in survival between the subgroups. Neighborhood analysis revealed several interaction patterns, such as lymphoma cells favoring neighboring with other lymphoma/B cells. Interestingly, when T cells and, especially cytotoxic T cells, in the inflamed iTME neighbored with PD-L1/PD-1 negative B cells, the outcome was favorable (OS; p < 0.05; Figure 1A), independent of the IPI and cell-of-origin. In contrast, when B cells expressed PD-L1 or PD-1, there was no association with survival. Within the iTME, CD4+ T helper cells often neighbored with other CD4+ T cells, including Tregs, as well as cytotoxic T cells, and to a lesser extent macrophages. Cytotoxic T cells commonly neighbored with other cytotoxic T cells, but also with macrophages and T helper cells, whereas M1 and M2 like macrophages frequently neighbored with other M1 and M2 like macrophages, respectively, as well as T cells. When we clustered the cases according to how often M2 macrophages neighbored other immune cells, we identified a group of cases, where M2 macrophages accumulated around T cells. Notably, this iTME pattern translated to unfavorable outcome (OS; p < 0.05; Figure 1B). Conclusions: Our data reveal clinically significant interaction patterns between B and T cells, as well as between macrophages and T cells in the iTME of DLBCL.