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Applying machine learning techniques to Next Generation Sequencing Big Data to improve Prostate Cancer patient outcome (BREWERU19FMH )

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  • Full or part time
    Dr D Brewer
    Prof C Cooper
  • Application Deadline
    No more applications being accepted
  • Competition Funded PhD Project (Students Worldwide)
    Competition Funded PhD Project (Students Worldwide)

Project Description

Prostate cancer (PCa) is the second most common cancer in men worldwide and an estimated 307,000 men annually die from PCa worldwide. The progression of PCa is highly variable, with some cancers laying dormant for many years while others advance rapidly. Risk assessment at the time of diagnosis is a critical step in disease management, determining whether the cancer is simply monitored or there is radical intervention by prostatectomy or radiotherapy. Unfortunately, there is currently no completely reliable approach to predict which tumours will progress and kill the patient.

The last decade has seen an explosion in the amount of global in silico data, which has led to new tools and techniques being developed to optimally utilise it. In medical research, the amount of data available has rapidly increased with the introduction of next generation sequencing. The international Pan-Prostate Cancer Group (PPCG; http://panprostate.org) has produced an unprecedented set of data from over 2000 men with PCa. This consists of whole genome sequencing, methylation, transcriptome, clinical and histopathology information.

By applying cutting-edge machine learning techniques to the multiple layers of clinical and molecular data available from the PPCG you will help build an improved predictor of aggressive disease, reveal novel subtypes of PCa, and gain a greater understanding of PCa aetiology.

This is a bioinformatics/data analysis-based PhD. During the PhD you will gain knowledge on how to deal with “Big Data”, high performance computing, developing pipelines and statistical analyses. You will be part of the Cancer Genetics team at the Norwich Medical School, which is an interdisciplinary team comprising a mixture of bioinformaticians and lab-based scientists. We have a broad interest in translational cancer based molecular studies with the aim of improving patient care. Research includes urine-based biomarker development, whole genome sequencing studies, cancer-subtype detection and bacteria in cancer studies.

For more information on the supervisor for this project, please go here: https://people.uea.ac.uk/en/persons/d-brewer
The type of programme: PhD
The start date of the project: 01/10/2019
Mode of Study: Full time
Entry requirements: Acceptable first degree in Computer Science, Physics, Mathematics, Engineering, Biological Sciences, Biochemistry, Biomedical Science. The standard minimum entry requirement for the studentship competition is 2:1 (or equivalent) and minimum entry requirements is 2:1.

Funding Notes

This PhD project is in a Faculty of Medicine and Health Sciences competition for funded studentships. These studentships are funded for 3 years and comprise of Home/EU fees, a stipend of £15,009 and £1000 per annum to support research training. Overseas applicants may apply but are required to fund the difference between home/EU and overseas tuition fees (in 2019/20 the difference is £14,373 for lab based projects and £11,073 for non-lab based projects but fees are subject to an annual increase).

References

1. Wedge, D. C. et al. Sequencing of prostate cancers identifies new cancer genes, routes of progression and drug targets. Nat. Genet. 50, 682–692 (2018).
2. Luca, B. et al. DESNT: A Poor Prognosis Category of Human Prostate Cancer. Eur. Urol. Focus 4, 842–850 (2018).
3. Lalonde, E. et al. Tumour genomic and microenvironmental heterogeneity for integrated prediction of 5-year biochemical recurrence of prostate cancer: a retrospective cohort study. Lancet Oncol 15, 1521–1532 (2014).
4. Gerhauser, C. et al. Molecular evolution of early onset prostate cancer identifies novel molecular risk markers and clinical trajectories. Cancer Cell 996–1011 (2018). doi:10.1016/j.ccell.2018.10.016
5. Gundem, G. et al. The evolutionary history of lethal metastatic prostate cancer. Nature 520, 353–357 (2015).



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