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Applying machine learning to sequencing data to predict prostate cancer outcome.


   Norwich Medical School

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  Prof D Brewer  No more applications being accepted  Funded PhD Project (UK Students Only)

About the Project

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 lying 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 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.


Funding Notes

This full-time PhD studentship is funded by Big C Cancer Charity and UEA. The studentship consists of tuition fees at Home fee rate and a doctoral stipend of £16,560 per annum for three years. International applicant may apply but they are required to fund the difference in costs between Home and International tuition fees, please visit the University’s Tuition fees website for details of 2022/23 International Postgraduate Research fees and note that tuition fees are subject to an annual increase.

References

1. Luca, B. et al. A novel stratification framework for predicting outcome in patients with prostate cancer. Br. J. Cancer 122, 1467–1476 (2020).
2. Connell, S. P. et al. A Four-Group Urine Risk Classifier for Predicting Outcome in Prostate Cancer Patients. BJU Int. (2019) doi:10.1111/bju.14811.
3. 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).
4. ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium. Pan-cancer analysis of whole genomes. Nature 578, 82–93 (2020).
5. Connell, S. P. et al. Integration of urinary EN2 protein & cell-free RNA data in the development of a multivariable risk model for the detection of prostate cancer prior to biopsy. Cancers (Basel). 13, (2021).
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