Projects per year
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. Including to identify molecular weak points or "Achilles' heels" that may be exploited for personalised medicine.
2. Developing more effective approaches for cancer patient stratification.
3. Generation of novel algorithms, techniques and computational workflows to advance the above.
Here's a short video about my research interests.
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 practise.
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)
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/
TMA Navigator: network inference, patient stratification and survival analysis with tissue microarray data - www.tmanavigator.org
Teaching for postgraduate taught and undergraduate programmes at Queen's University Belfast:
1) Coordinator and assessment lead for the following Bioinformatics and Computational Genomics MSc Programme modules:
- 'Dissertation Research Project' (2017-present)
- 'Systems Medicine: From Molecules to Populations' (2019-present)
- 'Scientific Programming & Statistical Computing' (2021-present)
2) Supervision and assessment of postgraduate (2017-present) and undergraduate (2018-present) dissertation research projects and internships.
3) 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.
4) Tutor for Medical Undergraduate Personal & Professional Development Portfolio (2018-2020).
5) Statistics seminar for the iENGAGE summer programme (2021).
Expertise related to UN Sustainable Devlopment Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):
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Cahill, T., da Silveira, W. A., Renaud, L., Williamson, T., Wang, H., Chung, D., Overton, I., Chan, S. S. L. & Hardiman, G., 14 Apr 2021, In: Cells. 10, 4, 26 p., 906.
Research output: Contribution to journal › Article › peer-reviewOpen AccessFile54 Downloads (Pure)
Long non-coding RNAs and their potential impact on diagnosis, prognosis, and therapy in prostate cancer: Racial, ethnic, and geographical considerationsMorgan, R., da Silveira, W. A., Kelly, R. C., Overton, I., Allott, E. H. & Hardiman, G., 19 Oct 2021, (Early online date) In: Expert review of molecular diagnostics.
Research output: Contribution to journal › Article › peer-review
SynLeGG: analysis and visualization of multiomics data for discovery of cancer ‘Achilles Heels’ and gene function relationships: Analysis and visualization of multiomics data for discovery of cancer 'Achilles Heels' and gene function relationshipsWappett, M., Harris, A., Lubbock, A. L. R., Lobb, I., McDade, S. & Overton, IM., 02 Jul 2021, In: Nucleic Acids Research. p. W613-W618
Research output: Contribution to journal › Article › peer-reviewOpen AccessFile104 Downloads (Pure)
Overton, I. M., Sims, A. H., Owen, J. A., Heale, B. S. E., Ford, M. J., Lubbock, A. L. R., Pairo-Castineira, E. & Essafi, A., 30 Sep 2020, In: Cancers. 12, 10, 34 p., 2823.
Research output: Contribution to journal › Article › peer-reviewOpen AccessFile
Using Simple PID-inspired Controllers for Online Resilient Resource Management of Distributed Scientific WorkflowsFerreira da Silva, R., Filgueira, R., Deelman, E., Pairo-Castineira, E., Overton, I. & Atkinson, M., Jun 2019, In: Future Generation Computing Systems. 95, p. 615-628
Research output: Contribution to journal › Article › peer-reviewOpen AccessFile