Details

Autor(en) / Beteiligte

• O'Neill, Brenna
• Kala, Smriti
• Lim, Sam
• Hupple, Clinton
• Lane, Nina
• Sorensen, Rasmus Norre
• Lyngby, Rasmus A.
• Massaro, Alessandro
• Hussing, Andreas
• Thagaard, Jeppe
• Dore-Hansen, Johan
• Mansfield, James Robert

Titel

Abstract 4625: A comprehensive guided workflow for highplex imaging, tissue segmentation, and multiplex cellular phenotyping for tumor microenvironment analysis

Ist Teil von

• Cancer research (Chicago, Ill.), 2023-04, Vol.83 (7_Supplement), p.4625-4625

Erscheinungsjahr

2023

Quelle

Free E-Journal (出版社公開部分のみ）

Beschreibungen/Notizen

• Abstract The growth in cancer immunotherapy agents requires an understanding of the immune contexture of the tumor microenvironment (TME). Understanding immune contexture requires multiplex staining, imaging, and analysis to obtain multi-marker phenotypes of specific cells and analyze their biodistribution in the TME. Imaging Mass Cytometry™ (IMC) is the method of choice for single-step staining and highplex imaging of FFPE tissues. FFPE tissue is autofluorescent, which limits the utility of immunofluorescence methods. Lung and colorectal tissue (and bone, skin, etc) are highly autofluorescent, and therefore good targets for IMC. However, developments in analysis software for highplex imagery have not kept pace with imaging advances. We present a comprehensive workflow designed specifically for highplex image analysis, covering tissue segmentation, cell segmentation based on IMC DNA images, cellular phenotyping, and spatial analyses. Lung and colorectal tissue sections with a 30-marker IMC panel of structural, tumor, stroma, immune cell, and immune activation markers were imaged (Hyperion+™, Standard BioTools). Highplex image analysis (Phenoplex™, Visiopharm) was performed as a multi-step workflow in a single software package that includes: conversion of IMC images to pyramidal format; easy visualization methods for displaying different marker subsets; a paint-to-train algorithm for tissue segmentation (into tumor, stroma, blood vessels, etc.); deep-learning-based nuclear segmentation pre-trained on IMC DNA channels; cellular phenotyping based on thresholds based on visual assessment of positivity; spatial biodistribution metrics for cell populations; and a flexible set of outputs for downstream analysis. Tissue segmentation was used to divide the tissue into tumor, stromal, and tumor margin regions, and these regions were used to compare the immune contexture through a series of t-SNE images partitioned by spatial region. We demonstrate that a simple analysis workflow can be used for highplex images of different tissue types by users with no programming knowledge. Visualization templates for the marker subsets and the pre-trained IMC nuclear segmentation are reusable. A new tissue segmentation algorithm for each tissue type is required, as are new thresholds for biomarker positivity. Spatial biodistribution metrics, heatmaps and partitioned t-SNE plots were generated for each tissue type with a minimum of work. Highplex IMC imaging of lung and colorectal tumor samples is a simple and effective means of obtaining high-parameter images without interfering autofluorescence. Having a comprehensive workflow for the analysis of this complex data makes obtaining useful results from highplex images more accessible to biologists and immunologists by circumventing the requirement for expert programming for each specific application. Citation Format: Brenna O'Neill, Smriti Kala, Sam Lim, Clinton Hupple, Nina Lane, Rasmus Norre Sorensen, Rasmus A. Lyngby, Alessandro Massaro, Andreas Hussing, Jeppe Thagaard, Johan Dore-Hansen, James Robert Mansfield. A comprehensive guided workflow for highplex imaging, tissue segmentation, and multiplex cellular phenotyping for tumor microenvironment analysis. [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 4625.