MRC Precision Medicine DTP: Precision medicine in Crohn’s disease and ulcerative colitis: Predicting disease flare and disease progression

Secondary Supervisor: Dr Charlie Lees FRCP(Ed) PhD, Consultant Gastroenterologist

This project would suit a motivated student with statistics, data science, machine learning, bioinformatics or related backgrounds. In this cross-disciplinary project, the student will develop, implement and apply Bayesian statistical methodology that can efficiently combine lifestyle, clinical and molecular data in order to better stratify patients with inflammatory bowel disease (IBD). This is a unique opportunity in which data science research will be translated into new interventions that can substantially improve patient outcomes. As such, the student will be trained as a biomedical data scientist, developing analytical and computational skills that are in high demand both in academia and in industry.

Background: Crohn’s disease and ulcerative colitis are the common forms of inflammatory bowel disease affecting up to 1 in 100 people in the UK. Incidence rates are increasing sharply in the previously undeveloped world, driven by urbanisation and adoption of a Western lifestyle. IBD typically manifests in adolescence / early adulthood with disturbed bowel function, a robust inflammatory response, systemic upset, psycho-social disturbance and substantial health-economic burden. Recent years have seen massive biotech / pharma investment in IBD with multiple different drug modalities now approved for use. However, remission rates are hitting a ceiling at 1 year of 30-40%, and no reliable biomarkers exist to aid with drug positioning. Management of IBD is further complicated by our inability to prognosticate on treatment response, disease flare or disease progression.

The PREdiCCt study www.predicct.co.uk (Dr Lees is chief investigator) is presently recruiting 3100 people with IBD from across the UK (n=1121 on 20.09.18). Patients are recruited in clinical remission, and followed-up during two years. Detailed baseline data is collected for all patients. Among others, this includes electronic health records held by NHS, whole-genome and metabolomic sequencing, diet/lifestyle data recorded through a mobile app [1] (developed as the PREdiCCt data collection tool) as well as a full blood panel. Throughout the follow-up, the mobile app is also used to regularly collect patient self-reported disease outcomes.

Aims: The overarching goal of this project is to develop and apply computational strategies that can improve the stratification and subsequent treatment of IBD patients. We will focus on high-dimensional Bayesian hierarchical models as a natural and powerful framework to combine information from multiple data sources, whist appropriately quantifying statistical uncertainty. In particular, we seek to address the following research questions:

1. What aspects of disease phenotype, diet, lifestyle, genetics and the gut microbiota contribute to a) disease flare, b) disease progression & c) treatment response in IBD?
2. How can we stratify patients based on disease biology and risk of progression?
3. Based on this, how can we intervene to improve patient outcomes?

Training outcomes: In this project the student will be familiarized with the structure and the properties of several (high-dimensional) data types that often play a central role in biomedical research. This includes, among others, genetics, metabolomics and electronic health records. The student with learn how to process and integrate this rich information within a integrative Bayesian framework. Close collaboration with clinicians, will also enable the student to learn the challenges associated to the diagnosis and treatment of IBD, as well as to translate data science research into practical interventions.
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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:

http://www.ed.ac.uk/studying/postgraduate/degrees/index.php?r=site/view&id=919

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:

http://www.ed.ac.uk/usher/precision-medicine