Longitudinal Spatial Deconstruction and Computational Modelling of Morphology Evolution in Hepatocellular Carcinoma
Supervisors:
Prof Ramanuj DasGupta, School of Cancer Sciences
Dr Ke Yuan, School of Cancer Sciences
Dr Paul Henderson, School of Computing Science
Summary:
Liver cancer is one of the deadliest cancers, with an abysmal 5 year median survival of <15%. This is largely due to its ability to rapidly evolve under therapeutic selection into refractory, metastatic disease. Our research team has recently developed an AI methodology that can automatically detect key morphological patterns from tumour pathology slides. What remains unknown is how these patterns, and the communities of cells within them, change when tumours are exposed to therapy.
This PhD project aims to investigate how liver cancer morphology evolves under treatment, over time. Working with a unique collection of patient samples taken both before and after therapy, you will apply our state-of-the-art AI methodologies to chart how tumour structures evolve over time. You will then integrate these findings with spatial transcriptomics, a powerful technology that measures which genes are actively expressed in different regions of the tumour while preserving their spatial context.
By combining AI with spatial biology, you will uncover the molecular programs that drive therapy resistance and develop the first computational model to simulate this evolutionary process. This interdisciplinary project brings together cancer biology, pathology, and AI with the ultimate goal of identifying new ways to predict treatment failure and design more effective therapies (see image below).