This interdisciplinary project connects clinical outcomes and biological mechanism in order to advance cancer therapy, applying data-intensive, systems medicine approaches. Genome-scale biochemical interaction networks will provide an interaction map for graph-theoretic analysis of existing drug response datasets. Multiple functional genomics datasets from drug-treated cells provide signatures of drug response and hence insight into drug mode of action. Transcriptome and rich clinical data from prostate cancer patients will provide for investigation of candidate biomarkers and response modules identified in analysis of the cell line data. Key areas of research are:
1. Modelling gene function in cancer drug resistance and response; towards systems-wide executable models.
2. Identification of candidate drug combinations to drive tumour cell networks towards therapeutic endpoints (e.g. apoptosis). Prostate cancer will be an exemplar, where significant in vitro and clinical data are available in collaboration with the Mills group and the Belfast-Manchester Movember National Centre of Excellence.
3. Development of novel algorithms, techniques and computational workflows; including tools to risk stratify patients by survival and response to treatment . For example, one strand will work towards developing a companion diagnostic linked to candidate combination therapy from computational analysis.
4. Training will be provided in a range of research competencies across multiple disciplines including: cancer biology, polyomics data integration, cluster computing, biomedical statistics, computer programming, network biology, genetics, systems pharmacology, precision medicine. Training will also include transferable skills for example in: scientific method, critical thinking, problem-solving, communicating results etc.. There will be opportunity for wet-laboratory work with prostate cancer cell lines and organoids (Mills), following up computational results.
5. This project involves collaboration with GlaxoSmithKline who will provide datasets and computational workflows; interactions will include regular visits and a six-month placement during the second year of the studentship.
|Lab||Queen's University Belfast|
|Keywords||Cancer; network biology; systems medicine; polypharmacology; patient stratification; prostate cancer;|
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