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Using computational biology and transcriptional data to identify signaling that contributes to cancer progression


   School of Medicine, Dentistry & Biomedical Sciences

  Dr Philip Dunne  Applications accepted all year round  Self-Funded PhD Students Only

About the Project

Our group is looking to recruit a PhD candidate, with experience of working with molecular datasets, with an interest in performing translational bioinformatics analyses to understand the nuanced and complex interactions between cancer cells and their microenvironment for major emerging clinical tumour subtypes. 

Solid tumours are complex ecosystems, dictated by the inter-dependence of distinct tissue compartments (tumour epithelium, immune system, stroma, and matrix). These compartments co-evolve, supporting tumour evolution, malignant progression, metastatic dissemination, and the adaptive response to therapy. Of all human diseases, cancer has one of the worst rates for converting insight from preclinical research models into clinically useful treatments. The genomic sequencing of hard-to-treat cancers, such as metastatic CRC, has yielded fewer clinically druggable mutations than expected.  

Our lab (https://dunne-lab.com/) is focussed on improving outcomes for patients with CRC, by increasing understanding of the biology underpinning aggressive traits in tumours and converting that new knowledge into new treatment options. The group employs 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. 

In this study we aim to: 

  • Utilise existing transcriptional cancer dataset to comprehensively characterise critical signalling cascades that contribute to cancer progression 
  • Perform in silico analyses to identify potential therapeutic vulnerabilities in clinically-relevant patient subtypes 
  • Work in partnership with our inter-disciplinary team to identify the mechanism of action from therapeutic interventions using in vitro and in vivo experiments 

Start Date: October 2022


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