Abstract 4620: Automated multiplex immunofluorescence workflow to interrogate the cellular composition of the tumor microenvironment

Abstract

Abstract Background: The tumor microenvironment (TME) is a constantly changing niche due to dynamic interactions between tumor cells and their surroundings. Recently, it became evident that a better understanding of the TME is needed to target the disease more accurately (Binnewies et al., Nat Med 2018; Vitale et al., Nat Med 2021). Spatial hyperplex immunofluorescence enables the visualization of cellular and non-cellular tissue components simultaneously. However, the increasing number of biomarkers detected by spatial proteomics quickly escalates the complexity of images and renders their interpretation challenging. Extracting quantitative data from images remains a challenge as it requires extensive training and experience in data analysis. Additionally, inferring cellular interactions and dependencies from spatial assays is an important milestone that needs to be overcome with image analysis approaches. Here, we present an end-to-end solution that combines automated hyperplex execution with image data extraction to study the spatial composition of TME. Methods: Sequential hyperplex immunofluorescence was performed on COMET™ on an FFPE Breast Tissue Microarray (TMA), generating images containing 42 layers: nuclear DAPI, tissue autofluorescence, and 39 single layers for biomarkers. All layers were delivered as a single ome.tiff file and processed using HORIZON™ image analysis software in combination with downstream statistical analysis. The tissue composition was interrogated with the use of an in-house trained nuclei detection algorithm (Schmid et al., MICCAI 2018; Weigert et al., WACV 2020), in-house developed image artifact exclusion approach, and spatial analysis based on a Squidpy workflow (Palla G. et al., Nat Meth 2022). Results: We interrogated the composition of breast tumor tissues (n=9). Our in-house developed pipeline allows for filtering out both autofluorescent objects and segmentation artifacts based on the tissue autofluorescence and morphological features. Once the quality of segmented cell detection was assured, over 20 different cell phenotypes were identified by unsupervised Leiden clustering and visualized using UMAP dimensionality reduction method. It revealed several myeloid cell subsets highlighting their heterogeneous phenotypes including, but not limited to, immunosuppressive neutrophils and macrophages. Contrastingly, the uniform presence of T regulatory lymphocytes was detected in majority of cores and their co-occurrence with CD14+CD163+ macrophages was observed. Conclusion: Here, we showcased an innovative automated workflow that highlights the ease of adoption of multiplex imaging to explore TME composition at single-cell resolution using simple workflow: slide in-data out. This workflow is easily transferrable to various cohorts of specimens to provide a toolset for spatial cellular dissection of the cancer milieu. Citation Format: Vytautas Navikas, Quentin Juppet, Samuel Aubert, Benjamin Pelz, Joanna Kowal, Diego Dupouy. Automated multiplex immunofluorescence workflow to interrogate the cellular composition of the tumor microenvironment. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4620.