Abstract LB-088: Exploratory multiplex tissue image analysis of the impact of heterogeneity in the microenvironment of primary colorectal cancer on apoptosis markers in patients
Authors: Lindner Andreas Urlich , Salvucci Manuela , Stachtea Xanthi , Carberry Steven , Dunne Philip D. , Sood Anup , McDonough Elizabeth , Cho Sanghee , Laurent-Puig Pierre , Schaeybroeck Sandra Van , Salto-Tellez Manuel , Graf John F. , Rehm Markus , Lawler Mark , Longley Daniel B. , Fiona Fiona Ginty , Prehn Jochen H. 
Published: August 2019

Apoptosis is essential for chemotherapy responses. We previously developed the mathematical models APOPTO-CELL and DR_MOMP to estimate the magnitude of tumor cells resilience to undergo and execute apoptosis in response to chemotherapy, and demonstrated that these models are prognostic markers of clinical responses in stage 3 colorectal cancer patients [Salvucci M et al., Clin Cancer Res 2017; Lindner AU et al., Gut 2018]. However, the heterogeneity of colorectal cancer tissue represents a hurdle that we have yet to overcome on our way to entirely understand differences in response and clinical outcome in patients. Quantification of protein levels as input for systems studies routinely reflects a local cell population average rather than protein levels in individual cells. In this study we aim to gain a better understanding of apoptosis resilience in tumors with respect to tumor cell heterogeneity and cell composition and local aspects of the tumor microenvironment.

Two to three core slices of formalin-fixed and paraffin-embedded primary colorectal cancer tissue were collected from 165 stage II-IV patients from a previously used cohort [Salvucci M et al. 2017; Lindner AU et al. 2018]. The levels of 25 protein markers for cell type and immune status, apoptosis, metabolism and cell proliferation, as well as cell segmentation markers, were measured on TMA slides using a multiplexed tissue immunofluorescence imaging platform (Cell DIVE [Gerdes et al., Proc Natl Acad Sci USA 2013]). Batch correction between slides was performed using linear regression (using markers for regulatory T cell in patient tissues as housekeeping cells. Formalin-fixed gene deficient cells were used in parallel for antibody validation.

The layout allowed us to create protein profiles for on average 13,079 (± 2,282 SD) cells per core per patient, and to categorize these as epithelial-like cells, regulatory T cells, T helper cells, cytotoxic T cells, other leukocytes and other cells. The data will be used to identify cells contributing most to the signal measured with traditional methods (such as reverse protein phase arrays) that were used to predict patients clinical response to therapy in our previous studies. Further, we will assess spatial differences in distribution and frequency of different cells and their contribution to clinical outcome in colorectal cancer patients. Specifically, we are investigating metrics to characterize the spatial features and layout found in each core in terms of textures, degree of lacunarity, neighboring cells composition and patterns of protein expression (diffuse vs. patchy) and co-expression among cell types. Further, we will characterize the degree of intra- and inter-patient spatial heterogeneity and will examine the association between the identified spatial motifs with macroscopical characteristics of the tumor and outcome.

Citation Format: Andreas Urlich Lindner, Manuela Salvucci, Xanthi Stachtea, Steven Carberry, Philip D. Dunne, Anup Sood, Elizabeth McDonough, Sanghee Cho, Pierre Laurent-Puig, Sandra Van Schaeybroeck, Manuel Salto-Tellez, John F. Graf, Markus Rehm, Mark Lawler, Daniel B. Longley, Fiona Ginty Fiona, Jochen H. Prehn. Exploratory multiplex tissue image analysis of the impact of heterogeneity in the microenvironment of primary colorectal cancer on apoptosis markers in patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr LB-088.