Investigating novel cominatory strategies for pancreatic cancer through integrated computational and experiemental approaches
Supervisors:
Dr Xiao Fu, School of Cancer Sciences
Prof Jennifer Morton, School of Cancer Sciences
Prof Huabing Yin, James Watt School of Engineering
Summary:
In pancreatic cancer, normal cells surrounding cancer cells are often hijacked to influence tumour progression and treatment outcomes. Cancer-associated fibroblasts (CAFs) represent a major cell population in pancreatic cancer and encompass heterogeneous subgroups of cell phenotypes, with tumour-supporting or -restricting functions. Improved mechanistic insights into the interactions between pancreatic cancer cells (PCCs) and CAFs are required to improve treatment outcomes and, due to the inherently complex and dynamic nature, necessitate interdisciplinary strategies integrating laboratory-based and computer-based approaches.
The project brings together the team of supervisors with inter-disciplinary expertise, including data sciences and computational models (XF), in vivo and in vitro experimental systems (JM), and single-cell Raman-based phenotyping (HY). Through the integration of multi-modal approaches, we aim to characterize phenotypic changes of PCCs and CAFs following various therapeutic challenges and to identify novel combinatory therapeutic strategies.
The project provides an exciting opportunity for a talented and motivated student to work with a multi-disciplinary supervisory team and receive training in diverse computational and experimental knowledge and skills. With training opportunities with supervisors’ teams across the School of Cancer Sciences and the School of Engineering, the student will interact with researchers with diverse backgrounds and expertise in a highly dynamic and collaborative environment.