Abstract 1677: Understanding the TME: Advanced analysis and visualization of multiplexed fluorescence images

Abstract

Abstract Background: Multiplexed immunofluorescence (mIF) has the potential to revolutionize immuno-oncology and pathology research as it enables the identification of complex cell phenotypes and their potential interactions in the tumor microenvironment (TME). But for a whole slide image with millions of cells, as we increase the number of biomarkers imaged for every cell, the complexity of the data analysis and visualization task increases exponentially. For n markers, a total of 2n phenotypes are possible (e.g. 10 markers have 1024 potential phenotypes). To address this problem and reveal the biologically relevant information embedded in the data, we have developed software tools to reduce the complexity, visualize, and quantify spatial distributions of cells across the full spectrum of possible phenotypes. Methods: Here we present results using two different methods. The first is an image processing technique called Phenotypic Surface Density Mapping (PSDM), that produces not only true surface density images of each phenotype (cells/µm2), but also surface density images that quantify a variety of other statistics such as the level of expression (intensity) of key markers, inter-phenotype nearest neighbor distance maps, and maps of cell size/morphology. Some important features of these surface density maps are that they are quantitatively robust, have real physical units (e.g. cells/µm2 or intensity/µm2), and they are generated in an unbiased fashion to reveal information about every possible phenotype. The second analysis method, dimensionality reduction, exploits a new technique called Uniform Manifold Approximation and Projection (UMAP), reducing dozens or even hundreds of dimensions for millions of cells to a simple 2D scatter plot. We have developed interactive software that displays the UMAP for a slide and allows the user to select a given cell or region of cells to view closeup images of each cell and statistics about the collection. Results: Examples of the surface density maps provide insights into mapping the complexity of the TME. We assess the results on deidentified samples by comparison with both human generated labels (pathology review) for individual cells and with automatically generated labels (software review). We show how these tools can be used to both identify tumors and quantify the level of activity in different tumor regions. We demonstrate how increasing the level of multiplexing allows one to differentiate subtle variability and separate subclasses of cells from each other. Conclusions: Multiplexed data brings valuable information about the TME but much of this information is inaccessible by simply viewing the captured images or performing simple cell counting alone. To address this problem, we demonstrate two new software tools, PSDM and UMAP that preserve and quantify the spatial information of the underlying biology and provide this analysis for all possible phenotypes. Citation Format: Douglas Wood, Bonnie Phillips, Courtney Hebert, Aditi Sharma, Jamie Buell, Sean Downing. Understanding the TME: Advanced analysis and visualization of multiplexed fluorescence images [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 1677.