Supervisor Database Search
Guidance for ICAT Supervisors
The ICAT Supervisor list is reviewed annually by the partner universities and updated online in March/April each year.
You can read about the ICAT supervisor selection process and eligibility criteria below:
Terms of reference/guide to supervising ICAT Fellows.
You can read the terms of reference for supervisors actively supervising ICAT Fellows below:
Supervisor Database
Full NameDr Simon McDade
Patrick G Johnston Centre for Cancer Research
Queen's University Belfast
Webpage:qub.ac.uk
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- genetics, genomics and molecular biology
- cell and developmental biology/regenerative medicine
- cancer/oncology
- Medicine
- Surgery
- Pathology
- Dermatology
- Gastroenterology
- Oncology
Work in the functional genomics group (FGG) focusses on the use of cutting-edge genomics technologies (e.g., ChIP-Seq, RNA-seq, CRISPR-Screens) and aligned integrative bioinformatics strategies to investigate transcription factor de-regulation in disease with a particular interest in the p53 family. Through better understanding the genome-wide targets, mechanism of action and consequences of de-regulation of key transcription factors, we aim to identify altered genes or processes which could be exploited for diagnostic or therapeutic application.
My groups work is somewhat disease agnostic, with our project closely with large multidisciplinary translational teams with current funding from BBSRC, MRC, CRUK, PCUK-Movember, Bowel Research UK and Innovate UK. Our current funded work is focussed on Colorectal, Prostate and Squamous cancers as well an interest in homeostasis of Skin and other barrier tissues. We also have a burgeoning interest in the development novel genomics assays, analysis methodologies and easy to use analysis tools.
My group are actively involved in the CRUK ECMC-network with number of active Clinical-Translational studies of relevance to potential projects for the ICAT programme. Our translational work is focused on using functional genomics studies in cutting-edge models to identify targetable vulnerabilities induced by standard-of-care (SoC) treatments in genetically defined subsets of patients.