Dr Philip Dunne is a Reader in Molecular Pathology at Queen’s University Belfast and a Group Leader at the Cancer Research UK (CRUK) Beatson Institute in Glasgow. His translational research programme primarily focusses on the identification, mechanistic interrogation and therapeutic targeting of the tumour-immune-stromal interactions underpinning disease relapse in early-stage colorectal cancer. Dr Dunne’s lab (www.dunne-lab.com) is focused on developing novel treatment strategies for patients with early stage colon and rectal cancer, based on biological mechanisms underlying disease progression, with a particular interest in signalling associated with early dissemination of tumours cells and their interaction with the tumour microenvironment. This work primarily uses transcriptional and histological data derived from tumour samples for patient/mouse-based biological discovery, which enables identification of molecular signalling, and resulting phenotypes, that regulate critical steps in the metastatic cascade. Using this improved understanding of disease, the Dunne group looks to optimise translation of therapeutic options using a wide range of laboratory techniques, both “wet-lab” and “dry-lab”, by combining in vivo and in vitro molecular biology, in situ molecular pathology and in silico translational bioinformatics.

Dr Dunne is the current Deputy Chair of the NCRI Colorectal Group, a central part of the UK’s cancer research infrastructure, through the development of a strategic portfolio of clinical trials. He is PI on a CRUK Early Detection consortium aimed at developing molecular risk stratification approaches for patients diagnosed with T1 tumours via bowel cancer screening, and leads translational research workpackages within the CRUK International CRC pre-clinical accelerator programme (ACRCelerate), the MRC-funded National Mouse Genetics Network Cancer Cluster and the MRC/CRUK joint-funded Stratification in colorectal cancer programme (S:CORT). His research programme has enabled the development of a number of novel resources to align biomarkers to patient data and the most appropriate model(s), which may help in improving the translation of mechanistic science into clinical trials