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  Identifying DNA methylation signatures of prostate cancer progression and mortality among patients with clinically confirmed, localised disease at baseline in a large prospective clinical trial

   Bristol Medical School

  , , , ,  Applications accepted all year round  Self-Funded PhD Students Only

About the Project


Prostate Cancer (PCa) is a leading cause of male mortality, with 336,000 deaths worldwide each year (1). Although most PCa cases are indolent, slow-growing, and tend not to progress, a subset of PCa cases are more aggressive and will progress to metastases, treatment resistance and death. Aggressive cases are a major driver of PCa mortality, which is the second most frequent cause of UK male cancer deaths (2). Currently, performance of even NICE recommended post-diagnostic clinical prediction models of PCa progression (based on clinical variables and prostate biopsy) is limited, with C-index at 10 years ranging from 0.73 to 0.81. Tools that improve discrimination of PCa progression and reduce harm due to both under- and over-treatment are required to guide treatment clinically.

DNA methylation (DNAm) is an epigenetic modification that regulates tissue-specific gene expression and is disrupted in cancer development where it’s a hallmark of oncogenesis and progression pathophysiology (3). Attempts to identify DNAm differences in PCa have been underpowered and featured substantial design flaws (e.g. insufficient treatment adjustment, inappropriate follow-up window, etc.). However, blood DNAm is increasingly being appreciated as valuable for predicting cancer progression and prognosis (4), which merits further exploration with adequate sample size and sufficient methodology.

Aims & Objectives

Given the role of DNAm in tumorigenesis, the potential for DNAm to capture signal of early disease progression, and the poor performance of existing biomarkers, this project aims to improve discrimination of the rate and severity of PCa progression by:

(1) Identifying genome-wide blood DNAm patterns that differ prospectively between aggressive and indolent forms of PCa

(2) Developing a DNAm signature using machine learning techniques for predicting aggressive PCa among patients with confirmed localised disease that could inform the need for radical therapeutic intervention

(3) Evaluating whether such a DNAm signature can improve upon existing NICE-recommended clinical progression methods


This project will use participants from the Prostate Testing for Cancer and Treatment (ProtecT) trial which included 1,600 UK men with confirmed, localised PCa and prospectively evaluated two forms of radical PCa treatment (prostatectomy and radiotherapy) to active monitoring. Median follow-up was 10-years and PCa mortality was the primary outcome. Using DNA isolated from whole blood samples collected at baseline, genome-wide DNAm levels will be quantified by Illumina HumanMethylationEPIC BeadChip for N = 850 (125 aggressive cases, 725 indolent controls) age and treatment matched ProtecT participants.

An Epigenome Wide Association Study (EWAS) will be performed to identify where DNAm is differentially methylated between aggressive and indolent PCa cases at over >850k CpG sites measured on the Illumina HumanMethylationEPIC BeadChip. Analysis for identification of differentially methylated regions (DMRs) will also be performed. To determine the CpGs and CpG combinations most predictive of PCa progression, we will apply feature selection and engineering approaches, and a library of pre-specified supervised machine learning methods (e.g. elastic-net regression, tree-ensembles, etc.). All models will be evaluated by outcome-stratified repeated k-fold cross-validation to robustly assess out-of-sample predictive performance and tune relevant hyperparameters. C-statistic/AUC will be the primary performance metric, but several alternatives will be considered.

How to apply for this project

This project will be based in Bristol Medical School - Population Health Sciences in the Faculty of Health Sciences at the University of Bristol.

If you have secured your own sponsorship or can self-fund this PhD please visit our information page here for further information on the department of Population Health Science and how to apply.

Biological Sciences (4) Medicine (26)


1. Pernar CH, Ebot EM, Wilson KM, Mucci LA. The Epidemiology of Prostate Cancer. Cold Spring Harb Perspect Med TA - TT -. 2018;8(12):a030361.
2. Prostate Cancer incidence statistics: Cancer Research UK (CRUK) [Internet]. 2017. Available from:
3. Zhang J, Huang K. Pan-cancer analysis of frequent DNA co-methylation patterns reveals consistent epigenetic landscape changes in multiple cancers. BMC Genomics. 2017. Available from:
4. Yousefi PD, Suderman M, Langdon R, Whitehurst O, Davey Smith G, Relton CL. DNA methylation-based predictors of health: applications and statistical considerations. Nat Rev Genet. 2022 Mar 18;1–15.

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