Abstract 4169: A deep learning approach to guide acquisition region selection for imaging mass cytometry

Abstract The growth in cancer immunotherapy agents requires an understanding of the immune contexture of the tumor microenvironment. This can be aided by high-plex imaging and analysis to obtain phenotypes of specific cells and study their biodistribution and interactions. Imaging Mass Cytometry™ (IMC) is the method of choice for single-step staining and high-plex imaging of tissues, avoiding the complications of autofluorescence and cyclic imaging. IMC has expanded its capabilities with three distinct imaging modes: Preview, Cell, and Tissue. The Preview Mode is a rapid scanning system that captures a comprehensive overview of the stained tissue, mapping out the distribution of over 40 markers and revealing tissue heterogeneity. This enables researchers to make informed decisions about which areas warrant closer examination on the same. Building on this, Cell Mode offers high-resolution imaging for detailed analysis of the Regions of Interest (ROI) identified during Preview, all using the same slide. Tissue Mode complements these by providing a fast acquisition of the entire tissue at a lower resolution, which is optimal for quantitative pixel-based analysis of tissue biology. These modes support automated, continuous imaging of more than 40 large tissue samples (400 mm2) weekly. Following Preview Mode, the selection of ROIs for high-resolution imaging is a critical step, enhanced by automated AI algorithms to ensure it is informed by biomarker expression. Tissue sections of colon adenocarcinoma were stained with a 30-marker IMC panel of structural, tumor, stroma, immune cell, and immune activation markers. Images were acquired on Hyperion XTi™ (Standard BioTools), first in Preview Mode, then in Cell Mode with automatic selection of ROIs using Phenoplex™ (Visiopharm). ROIs were automatically selected based on two criteria: 1) actively proliferating and non-proliferating tumor regions; 2) cold and hot tumor regions as identified by hotspots of lymphoid and myeloid immune markers within stromal or epithelial tumor regions. Single cell analysis of the high-resolution images obtained in Cell Mode, was performed as a multi-step workflow using Phenoplex. Tissue segmentation was used to divide the tissue into epithelial tumor (E-Cadherin/panCK), stromal, and tumor margin regions; cell segmentation was based on Iridium DNA channels; and phenotyping was performed using the guided highplex workflow. These data were used to compare the immune contexture through a series of t-SNE plots partitioned by spatial region and clinical variables. This work demonstrates that the multi-modal features of Hyperion XTi can greatly accelerate the ability of IMC users to gain useful insights from complex biological samples. Phenoplex enables a comprehensive workflow for the analysis of this complex data, providing automated ROI selection, and phenotyping and spatial analyses of high-resolution IMC images for biological assessment. Citation Format: Fabian Schneider, Smriti Kala, Sam Lim, Clinton Hupple, Nina Lane, James Robert Mansfield. A deep learning approach to guide acquisition region selection for imaging mass cytometry abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts) ; 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84 (6Suppl): Abstract nr 4169.