Ian Overton

      Dr Ian Overton

      Senior Lecturer

      Phone: +44 (0)28 9097 2802

      For media contact email comms.office@qub.ac.uk
      or call +44(0)2890 973091.

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      Research Interests

      We work to understand how cancer cells can spread around the body (metastasis) and how they become resistant to treatment with drugs; these factors cause the overwhelming majority of cancer deaths. We also develop software to make sense of increasingly large datasets and to inform clinical decision-making, for example to predict which patients will respond to a particular treatment. Together, these approaches help to develop better and more effective cancer medicine.
      Cells are organised and controlled by complex interactions between many individual parts (molecules), and so inherently form intricate networks. The properties of these networks underlie virtually every aspect of cell function. We map and analyse the messages passed, or information flow, amongst molecules by integrating billions of data points that describe key components such as DNA and proteins. Statistical inference, including machine learning, lets the data do the talking in order to reveal the molecular logic that controls health and disease. Indeed, computers are vital to modern biology, which interprets large datasets to gain insight into complex systems.

      Main research areas:
      1. Understanding molecular control and consequences of cell phenotypic plasticity in metastasis and drug resistance.
      2. Developing more effective approaches for cancer patient stratification.
      3. Generation of novel algorithms, techniques and computational workflows to advance the above.

      Research Statement

      Navigating from molecular measurements to phenotype implies understanding gene function; including gene products and their products. However, many coding genes are poorly characterised, but coordinately regulated, for example in differentiation. New functions continue to be discovered even for deeply studied genes, and most noncoding genes are not well understood (e.g. lncRNA, miRNA). Thus, a substantial portion of gene function is uncharted. Data driven networks provide useful abstractions to fill these knowledge gaps, enabling testing and generation of mechanistic hypotheses. One example application is the discovery of combination therapies to overcome drug resistance.

      The spread of cells from a primary tumour to a secondary site remains one of the most life-threatening pathological events. Epithelial-Mesenchymal Transition (EMT) is a cell programme involving loss of cell-cell adhesion, gain of motility, invasiveness and survival; these properties are fundamental for metastasis. Epithelial remodelling is also crucial for development. Reactivation of a programme resembling EMT is a credible mechanism for key aspects of the invasion-metastasis cascade and an MET-like process may produce the differentiation frequently observed in secondary tumours. Indeed, oncofetal signalling pathways (e.g. Hedgehog, Wnt, TGF-beta) activate EMT, and promote metastasis in multiple cancers.

      We have generated probabilistic systems-wide gene networks and are using these to investigate aspects of EMT/MET in different contexts; including to identify new EMT players, pathway crosstalk and drivers of metastasis. We also infer small scale causal networks combining ex vivo immunohistochemical and clinical measurements. These models integrate carefully selected data to represent the specific biological/clinical context of interest, including multiple 'omics datasets. Therefore, our work involves integration of 'big data' with machine learning and graph theoretic/statistical analyses. Supervised as well as unsupervised techniques are employed, including support vector machine and information-theoretic approaches. Prediction performance is assessed by rigorous benchmarking with blind test data.


      Novel algorithms are developed where required to advance biomedical understanding, for example we are working on methods towards systems-wide dynamic modelling of renal cancer drug resistance. Tools developed in the group are made widely accessible (e.g. www.tmanavigator.org). We collaborate closely with clinical colleagues and aim to translate results into medical practise.

       

      Achievements and Distinctions

      University of Edinburgh Chancellor's Fellowship (2015)

      Member of the Royal Society of Edinburgh Young Academy of Scotland (2011)

      Scottish Crucible Fellow (2010)

      Royal Society of Edinburgh Scottish Government Fellowship (2009)

       

      Other

      Websites:
      Royal Society of Edinburgh Young Academy of Scotland Open Data working group - http://www.youngacademyofscotland.org.uk/our-work/open-data.html

      TMA Navigator: network inference, patient stratification and survival analysis with tissue microarray data - www.tmanavigator.org

      Teaching

      Bioinformatics and Computational Genomics MSc Research Project Module Coordinator

      Frequent Journals

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      ID: 131132641