Professor Stephen Finn
Consultant Histopathologist and Principal Investigator
- Research Institute:
Translational Oncology Research Group, Trinity Translational Medicine Institute (TTMI)
- Contact e-mail:
- Research Area(s):
Lung, Prostate, Ovarian, Drug Resistance, Therapeutics, Biomarkers, non-coding RNA, circRNA, Digital Pathology, Molecular Patholog.
Research Description:
This group consists of a multi-disciplinary team, which bridge clinical and translational research. Currently there are a number of under and post graduate students including an Irish Clinical Academic Training (ICAT) Programme Fellow and a Centre for Research Training in Genomics Data Science (CRT) PhD candidate. The group have several ongoing national and international collaborations including the Harvard School of Public Health and VUMC, Amsterdam. Clinical and translational studies are undertaken in conjunction with academic and industry partners such as Cancer Trials Ireland and the European Thoracic Oncology Platform (ETOP). The programme within the group comprises of three main research themes – Biomarker Discovery (prognostic/predictive), Targeted Therapies and Liquid Biopsy. The group have several ongoing studies:
(i) Non-coding RNA signatures as diagnostic and prognostic tools in prostate cancer: The use of blood and tissue based miRNA (microRNA), lncRNA (long non-coding RNA) and circRNA (circularRNA) signatures as markers for guiding clinical decision making and stratifying people for therapy.
(ii) Exploring and modelling the resistance phenotype in PARPi treated ovarian cancer: Identifying novel mechanisms of acquired resistance in ovarian cancer, with a specific interest in assessing the circRNA-mRNA-miRNA network.
(iii) Real world application of liquid biopsies as a means to track the natural evolution of cancer and identify mechanisms of drug resistance: Detecting blood based molecular markers in cell free DNA (cfDNA) and circulating tumour cells (CTCs), to further our understanding of the development of drug resistance at a genomic level using Next Generation Sequencing (NGS).
(iv) Digital Pathology and artificial intelligence (AI) in Cancer Genomics: This project applies machine and deep learning to histopathological, genomic and clinical data to identify specific cancer subtypes and assess their utility to inform personalised treatment strategies for people with cancer.
