Abstract 6113: Spatially resolved biomarker detection on single cells in TRACERx using multiplex imaging

Abstract

Abstract Introduction. Studying cell-to-cell variation within the tumour’s native context is crucial for fully understanding tumour heterogeneity and its impact on disease progression and therapy response. Tumour cells and their surrounding stromal cells can be spatially profiled using multiplex imaging with a growing number of detectable proteins. The methods currently used for identification of cell phenotypes from multiplex imaging data have been initially developed for cells in suspension. Therefore, more accurate and robust approaches are required to account for spatial signal overlap and to automatically detect the presence of biomarkers in spatial proteomics. Methods. We performed imaging mass cytometry on 221 tumour and adjacent normal regions from 81 untreated, non-small cell lung cancer patients in the TRACERx study. Two antibody panels with 35 markers were applied to include a range of phenotypic, functional, and structural biomarkers. Single cells were segmented using deep learning-based approaches, and the measured biomarker intensities per cell were further used to identify cell types and functional states. Results. We developed a computational approach optimised for spatial proteomics for identification of cell types and functional states and automated detection of the presence of biomarkers on single cells. First, we demonstrated that current approaches for cell phenotyping lack reproducibility with subsampling and can produce ambiguous clusters of cells from different cell types. Using a multi-tiered analytical approach in combination with statistical tools, we showed reproducibility between technical and biological replicates. We estimated the performance using orthogonal measurements from flow cytometry and immunohistochemistry from matched tumour samples. This approach has been automated with a scalable and portable Nextflow-based implementation. Conclusions. We developed a streamlined approach that overcame limitations of current methods and quantified single-cell biomarker presence using multiplex imaging with TRACERx. Implemented as open-source, portable and customisable, this pipeline for spatially resolved TME characterisation will allow the study of drug targets expression within their spatial context for a better understanding of therapy efficacy. Citation Format: Mihaela Angelova, Katey Enfield, Alastair Magness, Emma Colliver, Masako Shimato, Claudia Lee, David A. Moore, Febe Van Maldegem, Philip Hobson, Dina Levi, James L. Reading, Crispin T. Hiley, Julian Downward, Erik Sahai, Charles Swanton. Spatially resolved biomarker detection on single cells in TRACERx using multiplex imaging [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 6113.