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MRC Precision Medicine DTP: Using machine learning to dissect genetic effects on DNA methylation heterogeneity in cancer

Project Description

Background: Genetic and epigenetic heterogeneity are fundamental hallmarks of cancer. Advances in high-throughput sequencing have resulted in the extensive characterisation of intra-tumoural genetic heterogeneity. However, although epigenetic heterogeneity has been postulated to be important in cancer (Landau et al 2014) and can associate with the silencing of tumour suppressor genes such as BRCA1, it remains far less studied and understood.

Aims: We have shown that epigenetic marks are strongly programmed by the genome (Benveniste et al 2014). The nature of this programming remains largely uncharacterised but we hypothesise that it is due to the action of transcription factors. It also means that genetic heterogeneity in tumours is likely to be a powerful player in generating epigenetic heterogeneity.
In this project we will use using machine-learning approaches to understand how genetic heterogeneity in cancer might generate epigenetic heterogeneity. We will particularly focus on characterising genetic effects on local patterns of the repressive epigenetic mark DNA methylation. During the project, the student will develop machine-learning approaches towards predicting the effect of somatic mutations on DNA methylation. We have previously shown that machine-learning approaches are well suited to studying the complex, multifactorial relationship between the genome and epigenome (Benveniste et al 2014). This project will extend our previous work by making use of more complex computational representations of DNA methylation patterns (Kapourani and Sanguinetti 2018) and also to place these predictions within an evolutionary framework (Caravagna et al 2018).
The results of these computational analyses can be verified with laboratory-based experiments using CRISPR genome-editing to engineer synthetic sequence variants cancer cell lines. This powerful combination of state-of-the-art approaches will ensure we can robustly determine the impact of somatic mutations on epigenetic heterogeneity in cancer.

Training outcomes:
This project will provide the student with excellent interdisciplinary research training in both genetics and informatics. During the project the student will be embedded within Dr Duncan Sproul’s research laboratory at the MRC Institute of Genetics and Molecular Medicine and will spend part of their time at the University of Edinburgh School of Informatics with Prof Guido Sanguinetti who will provide expert supervision in machine learning approaches. In particular the project will provide the student with:
- Training in applying machine learning approaches to complex biological problems (in particular Bayesian regression and GLM methods).
- Expertise in modern genetic laboratory techniques, particularly genome editing and high-throughput sequencing.
- Extensive background in genetics, epigenetics and cancer research.
This MRC programme is joint between the Universities of Edinburgh and Glasgow. You will be registered at the host institution of the primary supervisor detailed in your project selection.

All applications should be made via the University of Edinburgh, irrespective of project location:

Please note, you must apply to one of the projects and you should contact the primary supervisor prior to making your application. Additional information on the application process if available from the link above.

For more information about Precision Medicine visit:

Funding Notes

Start: September 2019

Qualifications criteria: Applicants applying for a MRC DTP in Precision Medicine studentship must have obtained, or will soon obtain, a first or upper-second class UK honours degree or equivalent non-UK qualifications, in an appropriate science/technology area.

Residence criteria: The MRC DTP in Precision Medicine grant provides tuition fees and stipend of at least £14,777 (RCUK rate 2018/19) for UK and EU nationals that meet all required eligibility criteria.

Full eligibility details are available: View Website

Enquiries regarding programme:


Benveniste et al 2014 ‘Transcription factor binding predicts histone modifications in human cell lines.’ PNAS 111:13367-72, PMID: 25187560
Caravagna et al 2018 ‘Detecting repeated cancer evolution from multi-region tumor sequencing data.’ Nature Methods 5:707-714, PMID: 30171232
Kapourani and Sanguinetti 2018 ‘BPRMeth: a flexible Bioconductor package for modelling methylation profiles. Bioinformatics 34:2485-2486, PMID: 29522078
Landau et al 2014 ‘Locally disordered methylation forms the basis of intratumor methylome variation in chronic lymphocytic leukemia’. Cancer Cell 26:813-25, PMID: 25490447

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