Abstract 2756: A toolbox enabling a data-driven selection of regions of interest in tumor tissue sections for imaging mass cytometry analysis
Authors: Daniel Schulz, Stephanie Renaud-Tissot, Robin Liechti, Nils Eling, Michelle Daniel, Jonas Windhager, Yervand Karapetyan, Thomas Mrowiec, Sylvie Rusakiewicz, Henoch S. Hong, Marie Morfouace, Bernd Bodenmiller
Journal: Cancer Research (2021)

Abstract Highly multiplexed imaging technologies are increasingly applied in biomedical research to identify underlying patterns of disease. As part of an IMI-funded project, the IMMUcan (Integrated immunoprofiling of large adaptive cancer patients cohorts) consortium will generate single-cell resolved molecular profiles for thousands of cancer patients from hospitals across Europe. Patient tissues will be profiled using two imaging modalities, multispectral immunofluorescence (mIF) and Imaging Mass Cytometry (IMC), in junction with data from different omics techniques. IMC enables highly multiplexed tissue imaging, but its current throughput only enables to image a fraction of large/whole-slide tumor tissue sections. The selection of regions of interest for IMC analysis is of critical importance with respect to the cells present in an area and the antibody panel used for the analysis of such an area. To target areas of interest in tumor tissue sections, e.g. with immune cell infiltration or tertiary lymphoid structures, we have developed a data driven approach based on mIF imaging analyses from consecutive sections to select such regions of interest for analysis with IMC. A mIF panel was designed to stain T cells, B cells, macrophages, neutrophils, dendritic cells and tumor cells to have an overview of immune cell populations within the whole tissue. After quantification, mIF images and data are hosted on a webserver and regions that contain cell-types of interest are annotated with the help of a web-based tool. mIF images are used for alignment with brightfield whole slide scans of the consecutive FFPE sections which are used for IMC. After alignment, the annotated regions of interest are acquired in IMC. The mean positional offset from IF to IMC for regions of interest was roughly 120 µm and the overall cell-type numbers across both technologies were well correlated. The approach presented here has the great advantage to allow us to reproducibly target similar areas for IMC across thousands of samples based on mIF within the IMMUcan consortium and will enable single-cell analysis for whole tumor sections based on mIF and IMC to gain a deeper understanding of the underlying tumor biology. Funding: IMI2 JU grant agreement 821558, supported by EU's Horizon 2020 and EFPIA. Citation Format: Daniel Schulz, Stephanie Renaud-Tissot, Robin Liechti, Nils Eling, Michelle Daniel, Jonas Windhager, Yervand Karapetyan, Thomas Mrowiec, Sylvie Rusakiewicz, Henoch S. Hong, Marie Morfouace, Bernd Bodenmiller. A toolbox enabling a data-driven selection of regions of interest in tumor tissue sections for imaging mass cytometry analysis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2756.