PMID- 33193316 OWN - NLM STAT- MEDLINE VI - 11 TI - Analysis of Spatial Organization of Suppressive Myeloid Cells and Effector T Cells in Colorectal Cancer-A Potential Tool for Discovering Prognostic Biomarkers in Clinical Research. PG - 550250 CI - Copyright © 2020 Zwing, Failmezger, Ooi, Hibar, Cañamero, Gomes, Gaire and Korski. LA - eng PT - Journal Article PT - Research Support, Non-U.S. Gov't PL - Switzerland TA - Front Immunol JT - Frontiers in immunology JID - 101560960 IS - 1664-3224 (Electronic) LID - 10.3389/fimmu.2020.550250 [doi] FAU - Zwing, Natalie AU - Zwing N AD - Early Biomarker Development Oncology, pharma Research and Early Development (pRED), Roche Innovation Center Munich, Penzberg, Germany. FAU - Failmezger, Henrik AU - Failmezger H AD - pharma Research and Early Development Informatics (pREDi), Roche Innovation Center Munich, Penzberg, Germany. FAU - Ooi, Chia-Huey AU - Ooi CH AD - Pharmaceutical Sciences-Biomarkers, Bioinformatics and Omics (PS-BiOmics), pharma Research and Early Development (pRED), Roche Innovation Center Basel, Basel, Switzerland. FAU - Hibar, Derrek P AU - Hibar DP AD - Product Development, Personalized Healthcare Analytics, Genentech, Inc., South San Francisco, CA, United States. FAU - Cañamero, Marta AU - Cañamero M AD - Early Biomarker Development Oncology, pharma Research and Early Development (pRED), Roche Innovation Center Munich, Penzberg, Germany. FAU - Gomes, Bruno AU - Gomes B AD - Early Biomarker Development Oncology, pharma Research and Early Development (pRED), Roche Innovation Center Basel, Basel, Switzerland. FAU - Gaire, Fabien AU - Gaire F AD - Early Biomarker Development Oncology, pharma Research and Early Development (pRED), Roche Innovation Center Munich, Penzberg, Germany. AD - Product Development, Personalized Healthcare Data Science Imaging, Roche Pharma, Basel, Switzerland. FAU - Korski, Konstanty AU - Korski K AD - Early Biomarker Development Oncology, pharma Research and Early Development (pRED), Roche Innovation Center Munich, Penzberg, Germany. AD - Product Development, Personalized Healthcare Data Science Imaging, Roche Pharma, Basel, Switzerland. IS - 1664-3224 (Linking) RN - 0 (Biomarkers) SB - IM MH - Adult MH - Aged MH - Aged, 80 and over MH - Biomarkers MH - Colorectal Neoplasms/*etiology/*metabolism/pathology/surgery MH - Computational Biology/methods MH - Disease Susceptibility MH - Female MH - Gene Expression Profiling/methods MH - Humans MH - Immunohistochemistry MH - Immunomodulation MH - Intestinal Mucosa/metabolism/pathology MH - Lymphocytes, Tumor-Infiltrating/immunology/metabolism/pathology MH - Male MH - Middle Aged MH - Myeloid-Derived Suppressor Cells/*immunology/*metabolism MH - Neoplasm Grading MH - Neoplasm Metastasis MH - Neoplasm Staging MH - Prognosis MH - T-Lymphocytes/*immunology/*metabolism MH - Tumor Microenvironment/genetics/immunology OTO - NOTNLM OT - *T cells OT - *colorectal cancer OT - *computational pathology OT - *spatial statistics OT - *suppressive myeloid cells OT - *tumor immune microenvironment PMC - PMC7658632 DCOM- 20210514 LR - 20210514 DP - 2020 DEP - 20201029 AB - The development and progression of solid tumors such as colorectal cancer (CRC) are known to be affected by the immune system and cell types such as T cells, natural killer (NK) cells, and natural killer T (NKT) cells are emerging as interesting targets for immunotherapy and clinical biomarker research. In addition, CD3+ and CD8+ T cell distribution in tumors has shown positive prognostic value in stage I-III 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 derived suppressor cells in TiME of primary CRC affects the function and location of cytotoxic T cells. We applied multicolored immunohistochemistry to identify monocytic (CD11b+CD14+) and granulocytic (CD11b+CD15+) myeloid cell populations together with proliferating and non-proliferating cytotoxic T cells (CD8+Ki67+/-). Through automated object detection and image registration using HALO software (IndicaLabs), 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. The 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, thus stressing the necessity of more comprehensive characterization of TiME in order to better predict cancer prognosis. This research emphasizes the importance of a multimodal approach by 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.