Carsten Gram Hansen, carsten.g.hansen@ed.ac.uk
Yunjie Yang, y.yang@ed.ac.uk
Project Description
Cancers, as a result of distinct mutations, lead to the expansion of clonal cell populations that exhibit considerable heterogeneity in epigenetic, physical and transcriptome profiles. This pathogenic capacity enables their survival under nutrient-poor conditions, infiltration and metastasis and poses significant treatment challenges, particularly due to the emergence of therapy-resistant cell subpopulations. Therefore, an ability to analyse and predict cellular responses to therapeutics early, and in rare cell populations would be transformative. Our project aims to identify precisely why, and how cancer cells differentiate from healthy cells by integrating Artificial Intelligence (AI) with cutting-edge bioimaging modalities. Specifically, we will focus on pleural mesothelioma, a deadly cancer caused by asbestos exposure, characterised by mutations in key tumour suppressors [1, 2]. Using state-of-the-art label-free imaging platforms (Nanolive and Lifecyte), alongside confocal high content imaging (Opera Phenix Plus), we will perform high-resolution, multi-parametric analysis to understand the unique cellular dynamics and predict patient responses. This approach will leverage our recently developed isogenic cellular model that closely represents the disease, facilitating the development of personalised treatment strategies. Through continuous refinement and validation of our AI-driven model against a panel of promising drug candidates, this project aims to provide ground-breaking improvements in diagnosing and treating mesothelioma, potentially extending to other cancers with similar genetic disruptions. We anticipate that this project will enhance opportunities for stratification and early diagnosis and contribute to the development of future therapeutic strategies.
