Abstract 1719: Investigating intra-tumor heterogeneity using multiplexed immunohistochemistry & deep learning: A new approach to spatially map the tumor microenvironment

Abstract The tumor microenvironment (TME) is a highly complex mixture containing epithelium, stroma and a diverse network of immune cells & the spatial organization of these immune cells within the TME reflects a crucial process in anti-tumor immunity. At present, the usual standard of care for assessing if a patient has cancer, its stage and its likely future biological behaviour is visual examination of one or more stained sections. Although recent advances in multiple immunostaining have enabled characterization of several parameters on a single tissue section. For a higher dimensional chromogen based methodology, we have developed a multiplexed IHC (mIHC) procedure combining multiple labels per round with several sequential rounds, enabling analysis of complex immune cell populations on a single slide through consecutive cycles of staining, destaining & hyperspectral imaging. By integrating serial imaging, sequential labeling & image registration, we are able to spatially map the TME. The process presented is using absorption microscopy, enabling these images to be done in a reasonable time frame. Robust, accurate, segmentation of cell nuclei for overlapping nuclei is one of the most significant unsolved issues in digital pathology. Using the counterstain concentration images from our mIHC methodology, we have trained a deep learning segmentation method to accurately segment individual cell nuclei within overlapping clusters of nuclei. By combining a multiplexed IHC technique which enables the detection of multiple markers on a single slide with deep learning segmentation methods to segment every individual cell nuclei in tissue sections with an accuracy comparable to human annotation. We can analyze the cell-cell interactions between immune and tumour cells, enhancing our ability to perform molecularly based single cell analysis of multiple cell types simultaneously within the tissue. These two techniques joined can be scaled up to the entire tissue section level, improving our understanding of the biological aggressiveness of specific cancers & enabling an accurate spatial cell level representation of the tissue. Citation Format: Kouther Noureddine, Paul Gallagher, Martial Guillaud, Calum MacAulay. Investigating intra-tumor heterogeneity using multiplexed immunohistochemistry & deep learning: A new approach to spatially map the tumor microenvironment [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 1719.