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MRC DiMeN Doctoral Training Partnership: Gene expression network analysis to identify candidate drivers of treatment resistance in glioblastoma multiforme


Project Description

Glioblastoma (GBM) is an incurable brain cancer. Patients survive an average 14 months post-diagnosis, despite receiving surgery and intensive chemoradiation because 100% of tumours grow back. Our inability to kill these tumours is likely because they consist of numerous distinct cancer cell populations defined by unique DNA and/or RNA profiles which confer some of them with the ability to resist treatment. It is these treatment resistant cell subsets that we must specifically identify, characterise and learn how to kill.

The aim of this project is to assess the transcriptional changes across GBM gene expression networks during therapy to highlight candidate molecules responsible for conferring (or facilitating transcriptional reprogramming to) a more treatment resistant state. This will be done using our existing data and supplemented with that being produced within a global consortium that we are part of. The project will make use of the high-performance computing facilities at Leeds and bioinformatics expertise of all three supervisors. The project is based within Leeds Institute of Data Analytics and, occasionally, at St James’s University hospital (free transport is provided between the two).

Project Objectives:

1. Build and optimise co-expression networks using primary and recurrent GBM gene expression data.
We have high coverage RNA sequencing data from pairs of primary and matched recurrent GBM tumours. These data are strand directional and include about 80,000 mRNA and non-coding transcripts. The student will use these data to create co-expression networks within primary and recurrent tumours separately, using existing network analysis methods and developing of novel methods appropriate for this vast dataset.
TRAINING: High-performance computing; RNAseq data analysis; Network analysis and method development.

2. Use network theory to identify perturbations in network structure through treatment.
After generating of primary and recurrent networks, the student will compare the two to determine changes in network structure and connectivity caused by treatment, and identify sets of transcripts key to these perturbations. This analysis will involve the identification of suitable techniques from the network analysis literature, as well as the development of computational tools to perform the analyses on the datasets.
TRAINING: Network theory; Computational method development.

3. Layer biological information onto the nodes of the network to identify candidate master regulators of therapy-driven transcriptional (re)programming.
Once a set of perturbations have been computationally derived, we will assess their likely impact and possible mechanisms of action using biological information. This will involve the use of multiple bioinformatic tools to integrate data across multiple online databases with our results.
TRAINING: Database and knowledge base mining and integration; Functional enrichment analysis; Cancer (computational) biology.

This highly computational project uses an exciting and novel dataset derived from primary patient data to help further our understanding and treatment of a disease that is currently invariably fatal. This project would suit a student with an interest in the application of computational and statistical techniques to a modern biological dataset. Aside from the specific training provided for each objective, listed above, the student will learn a wide range of techniques across bioinformatics, cancer biology and computational biology.

Benefits of being in the DiMeN DTP:
This project is part of the Discovery Medicine North Doctoral Training Partnership (DiMeN DTP), a diverse community of PhD students across the North of England researching the major health problems facing the world today. Our partner institutions (Universities of Leeds, Liverpool, Newcastle and Sheffield) are internationally recognised as centres of research excellence and can offer you access to state-of the-art facilities to deliver high impact research.
We are very proud of our student-centred ethos and committed to supporting you throughout your PhD. As part of the DTP, we offer bespoke training in key skills sought after in early career researchers, as well as opportunities to broaden your career horizons in a range of non-academic sectors.
Being funded by the MRC means you can access additional funding for research placements, international training opportunities or internships in science policy, science communication and beyond. See how our current DiMeN students have benefited from this funding here: http://www.dimen.org.uk/overview/student-profiles/flexible-supplement-awards
Further information on the programme can be found on our website:
http://www.dimen.org.uk/

Funding Notes

Studentships are fully funded by the Medical Research Council (MRC) for 3.5yrs
Includes:
Stipend at national UKRI standard rate
Tuition fees
Research training and support grant (RTSG)
Travel allowance
Studentships commence: 1st October 2019.

To qualify, you must be a UK or EU citizen who has been resident in the UK/EU for 3 years prior to commencement. Applicants must have obtained, or be about to obtain, at least a 2.1 honours degree (or equivalent) in a relevant subject. All applications are scored blindly based on merit. Please read additional guidance here: View Website
Good luck!

References

Relevant publications:
Glioma through the looking GLASS: molecular evolution of diffuse gliomas and the Glioma Longitudinal Analysis Consortium
The GLASS Consortium
Neuro-Oncology, Volume 20, Issue 7, 18 June 2018, Pages 873–884, https://doi.org/10.1093/neuonc/noy020

How to analyse the spatiotemporal tumour samples needed to investigate cancer evolution: A case study using paired primary and recurrent glioblastoma.
Droop A, Bruns A, Tanner G, Rippaus N, Morton R, Harrison S, King H, Ashton K, Syed K, Jenkinson MD, Brodbelt A, Chakrabarty A, Ismail A, Short S, Stead LF.
International Journal of Cancer. 2018; 142 (8):1620-1626. https://doi.org/10.1002/ijc.31184

Elucidating drivers of oral epithelial dysplasia formation and malignant transformation to cancer using RNAseq.
Conway C, Graham JL, Chengot P, Daly C, Chalkley R, Ross L, Droop A, Rabbitts P, Stead LF.
Oncotarget. 2015; 6 (37):40186-40201. https://doi.org/10.18632/oncotarget.5529

How good is research at University of Leeds in Clinical Medicine?

FTE Category A staff submitted: 94.20

Research output data provided by the Research Excellence Framework (REF)

Click here to see the results for all UK universities

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