Abstract 4425: Extracellular matrix segmentation combined with cell classification as a novel method for detailed tumor tissue mapping

Abstract

Abstract Tumor microenvironment (TME) represents a dynamic niche that regulates cancer cell behavior contributing directly to disease outcome. Systemic approach to analysis of TME should uncover its complexity and facilitate discovery of mechanisms orchestrating tumor development and metastasis. Multiplexed fluorescent tissue stain followed by spatial analysis of tumor tissue architecture can provide insights to pivotal interactions of cellular and acellular components of TME. Extracellular matrix (ECM), one of constitutive of TME, is represented mainly by collagen deposition. Recently it is became increasingly recognized that ECM contribution to TME dynamics not only depend upon amount of accumulated collagen but its geometrical features and spatial orientation of fibers. These characteristics of collagen contribute directly to physical and mechanical properties of tissue and can contribute to tumor growth and metastasis. Current algorithms of tissue stain-based methods include estimation of ECM deposition by measuring percent of positive area and separate to it cell classification and cell count. The goal of current work was to develop a new computational tool to perform spatial distribution analysis based on geometrical features and orientation of collagen fibers in combination with cell class assessment aimed to achieve detailed tumor tissue mapping. To pursue this goal, we generated fluorescent images of human breast tumor tissue, stained for following markers: CD3 - marker of T-lymphocytes, PanCytokeratin - marker of epithelial/tumor cells, collagen hybridizing peptide (3Helix) - marker of collagen, DAPI - nuclear counterstain. To develop image analysis pipeline, we utilized open source graphical interface analytical platform KNIME, where we generated modular workflow. For ECM analysis, we integrated Python written code into KNIME node. Segmentation of collagen fibers was performed using skeletonization with subsequent calculation of geometrical properties (length, alignment, widths) and orientation of each fiber. Data, collected from single cell analysis and ECM architecture assessment, were combined and forwarded to downstream spatial analysis, where distances from cell to cell or cell to ECM were computed and neighborhood analysis was performed. We demonstrate different patterns in tumor microenvironment organization that correlate with cancer outcome. Developed image analysis algorithm provides additional dimensionality to fluorescent tissue stains and can reveal underrecognized patterns of tumor microenvironment that can contribute to better understanding of tumorigenesis and metastasis. Citation Format: GEORGII VASIUKOV, Tatiana Novitskaya, Sergey Gutor, Anna Menshikh, Andries Zijlstra, Sergey Novitskiy. Extracellular matrix segmentation combined with cell classification as a novel method for detailed tumor tissue mapping [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 4425.