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Cancer immunotherapy has led to significant therapeutic progress in the treatment of metastatic and formerly untreatable tumors. However, drug response rates are variable and often only a subgroup of patients will show durable response to a treatment. Biomarkers that help to select those patients that will benefit the most from immunotherapy are thus of crucial importance. Here, we aim to identify such biomarkers by investigating the tumor microenvironment, i.e., the interplay between different cell types like immune cells, stromal cells and malignant cells within the tumor and developed a computational method that determines spatial tumor infiltration phenotypes. Our method is based on spatial point pattern analysis of immunohistochemically stained colorectal cancer tumor tissue and accounts for the intra-tumor heterogeneity of immune infiltration. We show that, compared to base-line models, tumor infiltration phenotypes provide significant additional support for the prediction of established biomarkers in a colorectal cancer patient cohort (n = 80). Integration of tumor infiltration phenotypes with genetic and genomic data from the same patients furthermore revealed significant associations between spatial infiltration patterns and common mutations in colorectal cancer and gene expression signatures. Based on these associations, we computed novel gene signatures that allow one to predict spatial tumor infiltration patterns from gene expression data only and validated this approach in a separate dataset from the Cancer Genome Atlas.

Understanding the role of myeloid cells in tumor biology requires a systems approach combining phenotypic and functional biomarkers. As a model system, colorectal cancer (CRC) tumors represent well-described clinically relevant immune subtypes. Distinct immune patterns have been reported involving the myeloid cells acting as immune suppressors for CD8 cytotoxic T cells. We hypothesized that myeloid derived suppressor cells (MDSCs) and CD8 T cells co-localize due to their direct interactions. A cohort of 85 CRC primary tumors were analyzed by multiplexed immunohistochemistry/ immunofluorescence and subsequent quantitative and qualitative whole slide image analysis. To define the functional status of the identified immune cell populations, their corresponding signatures were analyzed by gene expression profiling. Combining density and distribution of both myeloid cells and T cells as well as their distance to epithelial cancer cells, specific patterns were observed. In both microsatellite stable (MSS) and instable (MSI) tumors, MDSCs showed a comparable stromal distribution. By contrast, in MSI cases only, effector T cells tended to accumulate in the tumor epithelium suggesting they overcome the immunosuppressive environment. Additionally, the assessment of CD8 T cell morphology revealed changes potentially reflecting their cancer cell eliminating properties. This multimodal analysis represents a novel approach to characterize and understand the role of the myeloid compartment through a combination of morphological and spatial characteristics of the T cell compartment. Citation Format: Natalie Zwing. Unravelling myeloid and T cell compartment interactions through a novel approach to tumor multimodal analysis combining whole slide multiplexed immunofluorescence and gene expression profiling [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 999.

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With the recent advances in cancer immunotherapy it is critical to identify innovative biomarkers, reflecting the interaction between the tumor and its immune microenvironment. As a model system, CRC tumors are considered as non-immunogenic cancers with minor exception of microsatellite instable (MSI) cases that, in contrast to microsatellite stable (MSS) tumors, are characterized by a high tumor mutational burden (TMB) due to defects in mismatch repair (MMR) genes. These characteristics make MSI tumors more visible for the immune system, causing higher lymphocytic influx. When analyzing T cell kinetics, live cell imaging studies shown that T cells elongate while trafficking and are rounded while interacting or resting in the tissue. Based on these initial observations, we hypothesized that by assessing the density, distribution and shape of CD8 cytotoxic T cells in tumor areas, we could find a surrogate combined digital image-derived biomarker reflecting tumor immune microenvironment dynamics. To visualize this fundamental correlation, multiplexed immunohistochemistry was performed on 74 selected cases of primary CRC from our tissue bank collection with subsequent whole slide image analysis trained by machine learning. The CD8 T cell shape was assessed by nuclear eccentricity, along with PD1 and Ki67 activation status of the detected cell. Spatial plots revealed a haphazard T cell distribution in the overall tumor area, locating round and elongated CD8 T cells in both, tumor epithelium and tumor stroma compartments. Further discrimination of CD8 T cells according to PD1 and Ki67 positivity, showed higher activation / exhaustion in the round shaped T lymphocytes. In addition, we correlated the T cell shape with the MMR status and TMB assigned to the patient. Finally, integration of differential gene expression analysis provided insights into the role of the T cell shape in distinct tumor immune microenvironment conditions. With this novel approach, we discovered a new potential digital biomarker associated with the T effector cell function. Our results warrant further validation on bigger CRC patient cohorts, other tumor indications and through expanding the image analysis scope. Citation Format: Natalie Zwing, Xiao Li, Derrek Hibar, Henrik Failmezger, Yao Nie, Fabien Gaire, Konstanty Korski. Effector T-cell shape is a potential digital biomarker for assessing CD8 lymphocyte activation in colorectal cancer (CRC) tumors [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2835.