An image informatics pipeline for imaging mass cytometry to characterize the immune landscape in pre- and on-treatment immune therapy and its application in recurrent platinium-resistant epithelial ovarian cancer

摘要

Imaging mass cytometry (IMC) visualizes thirty or more protein markers simultaneously at subcellular resolution in the spatial context of the tissue microenvironment, enabling comprehensive analysis of cellular phenotypes and their interrelationships. There is, however, a lack of robust data analytics pipelines for integrating spatial information of complex IMC data. To fill this gap, we developed an image informatics pipeline to analyze the immune landscape and spatial interactions between different cell types of the tumor tissues of pre- and on-treatment cancer patients and applied the technology to study tissue samples of advanced epithelial ovarian cancer (EOC) patients. Immunotherapy targeting CTLA4 and PD1 immune checkpoint pathways provides new strategies for EOC. We analyzed tissue samples from a clinical trial testing Durvalumab and Tremelimumab administered in combination vs. Tremelimumab alone in recurrent platinum-resistant EOC patients. Our results show that IMC reveals the immune cell diversity of the EOC tumor ecosystem. The numbers of CD8+ T cells increased while a subtype of tumor cells decreased in on-treatment samples. CD8+ T cells and FoxP3+ cells increased most strongly in the patients who had best response to the treatment. We also developed algorithms to visualize the overall proximity and spatial correlation between any two cell types in the patient tissue.