Predicting Impact of Myeloid Cells on T Cells Effector Function from Spatial Organization of TiME in CRC

Recent developments in digital computational pathology support not only classical cell density based tumor characterization, but also a more comprehensive analysis of the spatial cell organization in the tumor immune microenvironment (TiME). Leveraging that methodology in the current study, we tried to address the question of how the distribution of myeloid cells in TiME of primary colorectal cancer (CRC) affects the function and location of cytotoxic T cells. We applied multicolored immunohistochemistry to identify monocytic (CD11b/CD14) and neutrophilic (CD11b/CD15) myeloid cell populations together with proliferating and non-proliferating cytotoxic T cells (Ki67/CD8). Through automated object detection and image registration using HALO software (IndicaLab), we applied dedicated spatial statistics to measure the extent of overlap between the areas occupied by myeloid and T cells. With this approach, we observed distinct spatial organizational patterns of immune cells in tumors obtained from 74 treatment-naive CRC patients. Detailed analysis of inter-cell distances and myeloid-T cell spatial overlap combined with integrated gene expression data allowed to stratify patients irrespective of their mismatch repair (MMR) status or consensus molecular subgroups (CMS) classification. In addition, generation of cell distance-derived gene signatures and their mapping to the TCGA data set revealed associations between spatial immune cell distribution in TiME and certain subsets of CD8 and CD4 T cells. Presented study sheds a new light on myeloid and T cell interactions in TiME in CRC patients. Our results show that CRC tumors present distinct distribution patterns of not only T effector cells but also tumor resident myeloid cells, stressing out the necessity of more comprehensive characterization of TiME in order to better predict cancer prognosis. This research emphasizes the importance of multimodal approach combining computational pathology with its detailed spatial statistics and gene expression profiling. Finally, our study presents a novel approach to cancer patients characterization that can potentially be used to develop new immunotherapy strategies, not based on classical biomarkers related to CRC biology.