Projects per year
Personal profile
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. 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.
Research Statement
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.
Achievements
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)
Other
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/
Teaching
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).
Expertise related to UN Sustainable Development 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):
Fingerprint
- 1 Similar Profiles
-
R2910CNR: Data-intensive discovery of resistance mechanisms and Achilles’ heels in genetically defined subtypes of Multiple Myeloma, towards more effective therapy
Overton, I. (PI), Crawford, L. (CoI) & Wappett, M. (CoI)
20/07/2022 → …
Project: Research
-
R5223CNR: CAST Studentship: Integrative Data-intensive Analytics to Discover Novel Therapies and Companion Diagnostics for Precision Oncology
Overton, I. (PI), Vandierendonck, H. (CoI) & Wappett, M. (CoI)
07/04/2022 → …
Project: Research
-
R1176CNR: Individualising Radiotherapy through Mechanistic Models
McMahon, S. (PI) & Overton, I. (CoI)
22/04/2020 → …
Project: Research
-
R5173CNR: Structural Bioinformatics for Vaccine Development
Overton, I. (PI)
29/04/2021 → 20/09/2023
Project: Research
-
R2643CNR: Bioinformatic stratification of stage II/III colorectal cancer patients
Overton, I. (PI), Coleman, H. (CoI) & Miller, P. (CoI)
06/10/2019 → 31/03/2024
Project: Research
-
QClique: optimizing performance and accuracy in maximum weighted clique
Abbas, Q., Koohi Esfahani, M., Overton, I. & Vandierendonck, H., 26 Aug 2024, 30th International European Conference on Parallel and Distributed Computing(Euro-Par 2024: Parallel Processing): proceedings, part III. Carretero, J., Shende, S., Garcia-Blas, J., Brandic, I., Olcoz, K. & Schreiber, M. (eds.). Springer, p. 88-102 15 p. (Lecture Notes in Computer Science; vol. 14803)(European Conference on Parallel Processing).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
Open AccessFile17 Downloads (Pure) -
Investigating the effects of chronic low-dose radiation exposure in the liver of a hypothermic zebrafish model
Cahill, T., da Silveira, W. A., Renaud, L., Wang, H., Williamson, T., Chung, D., Chan, S., Overton, I. & Hardiman, G., 17 Jan 2023, (Early online date) In: Scientific Reports. 13, 1, 918.Research output: Contribution to journal › Article › peer-review
Open AccessFile4 Citations (Scopus)68 Downloads (Pure) -
Strategies and techniques for quality control and semantic enrichment with multimodal data: a case study in colorectal cancer with eHDPrep
Toner, T. M., Pancholi, R., Miller, P., Forster, T., Coleman, H. G. & Overton, I. M., 12 May 2023, (Early online date) In: Gigascience. 12, 14 p., giad030.Research output: Contribution to journal › Article › peer-review
Open AccessFile87 Downloads (Pure) -
Transcriptome profiling reveals enhanced mitochondrial activity as a cold adaptive strategy to hypothermia in zebrafish muscle
Cahill, T., Chan, S., Overton, I. M. & Hardiman, G., 11 May 2023, In: Cells. 12, 10, 15 p., 1366.Research output: Contribution to journal › Article › peer-review
Open AccessFile1 Citation (Scopus)78 Downloads (Pure) -
Validation of in vitro trained transcriptomic radiosensitivity signatures in clinical cohorts
O'Connor, J. D., Overton, I. M. & McMahon, S. J., 05 Jul 2023, In: Cancers. 15, 13, 14 p., 3504.Research output: Contribution to journal › Article › peer-review
Open AccessFile1 Citation (Scopus)98 Downloads (Pure)
Prizes
-
CoronaHack AI vs COVID-19 prize winner
Overton, I. (Recipient), Apr 2019
Prize: Prize (including medals and awards)
-
-
Fellow of the Royal Society of Biology (FRSB)
Overton, I. (Recipient), 2022
Prize: Election to learned society
-
-
Member of the Royal Society of Edinburgh Young Academy of Scotland
Overton, I. (Recipient), 2011
Prize: Election to learned society
-
PhD external examiner University of Edinburgh
Overton, I. (Examiner)
09 Feb 2023 → 26 May 2023Activity: Examination types › PhD external examination
-
Celebrating Ada Lovelace Day: PGJCCR in partnership with QGI
Overton, I. (Organiser), Coleman, H. (Invited speaker), Allott, E. (Invited speaker), McCulloch, K. (Invited speaker), McCloskey, K. (Contributor) & Forsythe, K.-A. (Contributor)
11 Oct 2022Activity: Participating in or organising an event types › Participation in workshop, seminar, course
-
PhD external examiner University of Melbourne
Overton, I. (Examiner)
21 Apr 2022 → 03 Jun 2022Activity: Examination types › PhD external examination
-
PhD external examiner, University of Birmingham
Overton, I. (Examiner)
08 Apr 2022 → 27 Jul 2022Activity: Examination types › PhD external examination
Press/Media
-
-
-
Media Coverage of 'hibernating zebrafish' and radiation exposure risk for space travel research article
Overton, I., Cahill, T. & Hardiman, G.
25/05/2021 → 29/05/2021
2 items of Media coverage
Press/Media: Public Engagement Activities
-
Media coverage of SynLeGG cancer 'Achilles' heels' research article
Overton, I., Wappett, M. & McDade, S.
19/05/2021
4 items of Media coverage
Press/Media: Research
-
Short Research Video for PGJCCR Public Engagement
12/11/2020
1 Media contribution
Press/Media: Public Engagement Activities