Investigating intra-tumor heterogeneity in non-small cell lung cancer using multiplexed immunohistochemistry and deep learning

Abstract

Non-Small Cell Lung Cancer(NSCLC) is the most common cause of cancer death in Canada and worldwide. As the immune component of the NSCLC tumor microenvironment(TME) is highly prognostic of patient outcome, further understanding of TME and the spatial organization of the immune cells within the TME is needed for better patient prognosis and treatment planning. Current immunohistochemistry techniques quantify immune cell counts and density, but generally cannot asses the spatial relationship between tumour and immune cells. We have developed a multiplexed Immunohistochemistry(mIHC) procedure combining multiple labels per round with several rounds, enabling analysis of immune cell populations on a 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. Robust, accurate, segmentation of cell nuclei for overlapping nuclei is one of the most significant unsolved issues in digital pathology. We have trained a deep learning segmentation method to accurately segment individual cell nuclei within overlapping clusters of nuclei. By combining a mIHC technique which enables the detection of multiple markers 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 NSCLC’s.