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Cancer Research UK funded 4 year studentship: Development and implementation of computational systems biology approaches for the identification of novel therapeutic targets in follicular lymphoma

Project Description

We are looking for a graduate with an interest in cancer genomics, evolution, bioinformatics and computational biology, with, or expecting, at least an upper second class honours degree in an associated biological / mathematical / physics discipline for this project involving the development and implementation of computational system biology approaches for the identification of novel therapeutic targets in follicular lymphoma.

The project will commence in September/October 2019 and has funding for four years.

The student will be based primarily at the Barts Cancer Institute, Barts and the London School of Medicine and Dentistry (SMD), Charterhouse Square in the City of London.

Project Outline:
Although huge progress has been made in the management of follicular lymphoma (FL), current treatment regimes that target FL at their fully developed stages are often complex and ineffective. FL has a well-defined precursor state, termed common progenitor cells (CPC), and disease progression is usually due to the clonal expansion of a precursor cell (sub)population. Thus, targeting FL in their CPC and minimal residual disease (MRD) states provides the best opportunity to prevent disease progression, offering novel targets to eradicate the disease.

This studentship is part of a large international research programme funded by Cancer Research UK Accelerator Award to study the pre-malignant and MRD states of blood cancers. We will apply a range of state-of-the-art technologies such as single cell RNA-seq, mass cytometry (CyTOF) and ATAC-seq to fully characterise CPC and MRD populations. In this PhD project, the successful candidate will develop analytical pipelines and algorithms to analyse high-throughput single-cell data sets generated from this programme. Omics data including genomic, transcriptional and epigenomic, as well as other biological and clinical data will be integrated using bioinformatic/biostatistic tools. In particular we will adopt machine learning methods, such as Bayesian networks, decision trees and SVM, to integrate data from different and heterogeneous sources to derive a new generation of biomarkers for early detection, risk stratification and novel targets to treat CPC. We will also construct comprehensive gene regulatory networks using a systems-level approach to highlight key and novel components associated with clinical outcome and tumour subtypes.

As this is a highly multidisciplinary project, the successful candidate will be exposed and trained in a wide range of molecular techniques and informatics skills, including single-cell profiling, machine learning and next generation flow. Furthermore, the student will work closely with scientists and clinicians on parallel molecular, computational and specialised mouse modelling studies as part of this ambitious programme and benefit from the rich collaborative nature of the programme across the participating institutions in the UK, Spain and Italy.

For more information, including how to apply, please see:

Funding Notes

This studentship includes the following funding for 4 years:
- An annual stipend of £21,000
- Tuition fees at the Home/EU rate
- Project consumables

How good is research at Queen Mary University of London in Clinical Medicine?

FTE Category A staff submitted: 144.11

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

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