Ian Overton

Navigating from molecular measurements to phenotype implies understanding gene function. Many genes are poorly characterised, but coordinately regulated, for example in differentiation, and new functions continue to be discovered even for deeply studied coding genes. Most noncoding genes (e.g. lncRNA, miRNA) are not well understood, nor isoforms arising from alternate splicing. 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 current application in the group is the design 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.

Cancer cells sustain many loss (or gain) of gene function events; these mutations drive tumour progression but also may result in weak points that may be exploited by carefully targeted inhibitors. For example, cells that have lost BRCA1 are vulnerable to PARP inhibitors. We have developed an integrative approach to predict candidate genetic dependency networks integrating gene expression, CRISPR and mutation data and are applying this for cancer drug discovery. 

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. A wide range of techniques are employed, including supervised and unsupervised learning as well as information-theoretic approaches such as conditional mutual information. 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 drug response in renal and prostate cancers. Tools developed in the group are made widely accessible (e.g. here). We collaborate closely with clinical colleagues and aim to translate results into medical practice.

Fellow of the Royal Society of Biology (2022)

University of Edinburgh Chancellor's Fellowship (2015)

Marie Curie funded sabbatical visits (6 months total) at Harvard Medical School dept Systems Biology (2012 to 2013) and Vanderbilt Medical School Vanderbilt-Ingram Cancer Centre (2013).

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

Scottish Crucible Fellow (2010)

Royal Society of Edinburgh Scottish Government Fellowship (2009)

Websites:

SynLeGG: Synthetic Lethality with Genetics and Genomics - www.overton-lab.uk/synlegg

Royal Society of Edinburgh Young Academy of Scotland Open Data working group - https://www.youngacademyofscotland.org.uk/our-work/smarter/

Contributions to Postgraduate Taught and Undergraduate programmes at Queen's University Belfast:

1) Coordinator, assessment lead and delivery of Lectures/Tutorials for modules within the Bioinformatics and Computational Genomics MSc programme:

- 'Dissertation Research Project' (2017-present)

- 'Systems Medicine: From Molecules to Populations' (2019-present)

- 'Scientific Programming & Statistical Computing' (2021-22)

2) Supervision and assessment of postgraduate (2017-present) and undergraduate (2018-present) dissertation research projects and internships.

3) Delivery of lectures, a tutorial and  contributing to assessment for the module 'Bioinformatics and Systems Biology', part of the Molecular Biology and Biotechnology MSc programme. (2021-22 to present).

4) Lecture ‘Introduction to Systems Thinking in Biology’ within the 'Introductory Cell Biology and Computational Analysis' module (2017-20), shared across the following MSc programmes: i) Bioinformatics and Computational Genomics, ii) Molecular Pathology of Cancer, iii) Cancer Medicine & Oncology Drug Discovery.

5) Tutor for Medical Undergraduate Personal & Professional Development Portfolio (2018-2020).

6) Statistics seminar for the iENGAGE summer programme (2021).