Abstract 2732: Multiplexed immunohistochemistry and deep learning: a new approach to spatially map the tumor microenvironment

Abstract

Abstract The usual standard of care for assessing if a patient has cancer, its stage and its likely future biological behavior is visual examination of one or more H&E and/or Immunohistochemistry(IHC) stained sections. The paradigm of digital pathology has changed, moving from single-marker IHC towards multiplexed labeling, increasing the need for more advanced techniques that can be easily integrated in routine clinical pathology. 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 procedure combining multiple labels per round, enabling analysis of complex immune cell population's on a single slide through consecutive cycles of staining, destaining, hyperspectral imaging and spectral unmixing of the chromogen biomarkers in each round. Not only does this allow us to completely visualize and spatially map the tumor microenvironment(TME), but through the application of AI we can further recognize common histological features present in the tissue. Robust, accurate, segmentation of cell nuclei for overlapping nuclei is one of the most significant unsolved issues in digital pathology. Analyzing the cell-cell interactions between immune and tumor cells and identifying clinically relevant patterns may improve patient outcomes while informing on the likelihood of success of possible treatments. 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 improve the accuracy of tissue classification based upon the measured characteristics of these cells and their spatial organization. The two techniques joined can be scaled up to the entire tissue section level, providing an accurate spatial cell level representation of the tissue. Citation Format: Kouther Noureddine, Paul Gallagher, Martial Guillaud, Calum MacAulay. Multiplexed immunohistochemistry and deep learning: a new approach to spatially map the tumor microenvironment [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 2732.