Abstract 370: Understanding the complexity of the tumor microenvironment by 3-dimensional multiplexed imaging and artificial intelligence
Authors: Menietti Elena , Stoltzfus Caleb , Olin Brandy , Speziale Dario , Kunz Leo , Poeshinger Thomas , Colombetti Sara , Gerner Michael , Perro Mario 
Published: September 2020

Abstract
Cancer Immunotherapy with checkpoint inhibitors (CPI) has been a major breakthrough in cancer treatment and it has been proven effective in around 30% of inflamed tumors. However, why the majority of solid tumors do not respond to CPIs is not well understood, and knowledge of resistance mechanisms (primary or acquired) and associated biomarkers could provide great benefit for more successful application of CPIs and other immunotherapies. Recent advances in highly multiplexed imaging techniques is starting to reveal that the organization of immune cells within tumors can be an important prognostic indicator of disease outcome. However, the complexity and heterogeneity of cancer microenvironments, as well as the paucity of tools to study such 3-dimensional spatial data, still limits our understanding of immune cell organization and function within tumors. Here, we developed and implemented a workflow to image resected tumors in two or three dimensions, using up to 13 fluorescent parameters. To resolve local cellular networks and reveal fundamental features of tissue organization within tumors, we utilized a newly-developed spatial analysis toolbox (CytoMAP), that incorporates machine learning, clustering, positional correlations, dimensionality reduction and region reconstruction. We employed this multiplexed imaging approach to explore the organization of immune cells in several syngeneic murine cancer models, representing inflamed and not-inflamed subtypes. We identified hallmark features of baseline tumor architecture, such as interaction between immune cells and blood vessels, and baseline organization and localization of innate and adaptive immune subsets, even across datasets with extensive intra- and inter- tumor heterogeneity. We, additionally, applied CytoMAP to understand how immunotherapies change tumor microenvironment. Treatment of MC38 tumors with CPI lead to an increased area of the tumor mass occupied by regions containing high number of activated CD8+ T cell and MHC-II+ macrophages but low number of Tregs and scattered tumor cells, identifying areas of active tumor killing. The relative area occupied by these ‘immune-control' regions, compared to that found at baseline, might better define responder and non-responder tumors. In conclusion, the combination of highly multiplexed confocal imaging and quantitative image analysis with CytoMAP allows deconvolution of the heterogeneity of the tumors into functional regions, providing a predictive tool to assess the response to different immune-therapies.
Citation Format: Elena Menietti, Caleb Stoltzfus, Brandy Olin, Dario Speziale, Leo Kunz, Thomas Poeshinger, Sara Colombetti, Michael Gerner, Mario Perro. Understanding the complexity of the tumor microenvironment by 3-dimensional multiplexed imaging and artificial intelligence [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 370.