Abstract PO-082: Combining multiplex immunohistochemistry and deep learning: A new approach to tracing the tumour microenvironment
Authors: Noureddine Kouther , Gallagher Paul , Guillaud Martial , MacAulay Calum 
Published: December 2020

The tumour microenvironment(TME) is a highly complex mixture containing epithelium, stroma and a diverse network of immune cells and the spatial organization of these immune cells within the TME reflects a crucial process in anti-tumor immunity. Not all tumour cells interact similarly; micro-environmental features are often key indicators of treatment response and patient outcome. 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 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 with multiple sequential rounds, where multiple is 12-14 chromogen based antibodies on a single tissue section. Thus, 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. That the process presented is chromogen based (absorption microscopy) means that high throughput imaging of 12-14 markers across entire slides is feasible in a reasonable time frame. Robust, accurate, segmentation of cell nuclei for touching/overlapping nuclei is one of the most significant unsolved issues in digital pathology. Analyzing cell-cell interactions between immune and tumour cells and identifying clinically relevant patterns may improve patients outcome by 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 recognize/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. These two techniques joined can be scaled up to the entire tissue section level, improving our understanding of the biological aggressiveness of specific cancers and enabling an accurate spatial cell level representation of the tissue.

Citation Format: Kouther Noureddine, Paul Gallagher, Martial Guillaud, Calum MacAulay. Combining multiplex immunohistochemistry and deep learning: A new approach to tracing the tumour microenvironment [abstract]. In: Proceedings of the AACR Virtual Special Conference on Tumor Heterogeneity: From Single Cells to Clinical Impact; 2020 Sep 17-18. Philadelphia (PA): AACR; Cancer Res 2020;80(21 Suppl):Abstract nr PO-082.