Identification of therapeutically-relevant patient subgroups from clinical and biological data

The student appointed to this project will develop the skills to extract useful clinical and biological insights from high-dimensionality datasets. Background knowledge in biological sciences and statistics will be required. Training in computational methods and biological techniques will be provided. If successful, this project may identify new treatments and diagnostic tests that can be directly evaluated in clinical practice.

Medicine advances by identifying important similarities between patients. We treat a future patient by making a prediction based on similarity with past patients. Until recently, similarities between patients could only be identified using easily observable features, compiled into patterns in the memory of an observant clinician. Now we have the technology to record and analyse millions of patient measurements.

Many clinical syndromes are loose groupings of patients who have relatively little in common. Perhaps the best example is sepsis, a frequently fatal condition that accounts for 30% of admissions to intensive care units in the UK. Sepsis is a final common pathway from severe infection. It can be caused by infection of any organ with any of an extremely wide range of pathogens. These infections are clearly different, but because the patients are clinically similar, they are all treated as a single disease. If we could stratify patients with sepsis, we could treat them better with drugs that already exist: targeted, narrow-spectrum antibiotics that would eliminate the causative organism without destroying commensals, whilst minimising evolution of antibiotic resistance.

The student will develop and evaluate network methods for the detection subgroups of patients sharing important biological similarities. Initially, analyses will focus on sepsis, before moving on to more generalisable analyses of clinical trial data.

Our previous work has employed sophisticated network analysis tools to detect biologically important subgroups of regulatory regions in the human genome(1), and clinically-distinct syndromes of acute mountain sickness(2), leading to a revision of the global consensus criteria for this condition.

We have extended this theme in unpublished work employing a novel method, exhaustive observation of network space (EONS). When applied to group of patients with various types of sepsis, for whom high-resolution biological data were available, EONS detects a clear separation of patients with sepsis caused by different types of bacteria (gram-positive bacteria vs. gram-negative). This signal was not detected by the authors of the original study(3).

In this project, the student will:

1. optimise and evaluate network analysis methods for detecting subgroups of patients with sepsis using existing datasets. This will be published during year 1.

2. Generate and analyse additional data from high-resolution phenotyping of confirmed bacteraemic patients in critical care. In addition to detailed clinical information, we will employ a high-resolution transcriptome sequencing methodology, CAGE, which we have recently shown is able to detect cell type-specific promoters and enhancers in numerous different cell types(4), thus enabling the detection of many additional biologically-important signals in patient samples.

3. Employ network methods to detect therapeutically-important subgroups in data from clinical trials, first in permuted data, then in data from completed clinical trials with various levels of biological phenotyping.

Application
This MRC DTP 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.

You can apply here via the University of Glasgow: http://www.gla.ac.uk/research/opportunities/howtoapplyforaresearchdegree/
Within the application, at the programme of study search field option, please select ‘MRC DTP in Precision Medicine’.

Please note that, in step 6 within the online application process, you are asked to detail supervisor/project title information. Please ensure that you clearly detail this information from the information provided within this abstract advert. Within the research area text box area, you can also add further details if necessary.

Please ensure that all of the following supporting documents are uploaded at point of application:
• CV/Resume
• Degree certificate (if you have graduated prior to 1 July 2016)
• Language test (if relevant)
• Passport
• Personal statement
• Reference 1 (should be from an academic who has a knowledge of your academic ability from your most recent study/programme)
• Reference 2 (should be from an academic who has a knowledge of your academic ability)
• Transcript

For more information about Precision Medicine at the University of Edinburgh, visit http://www.ed.ac.uk/medicine-vet-medicine/postgraduate/research-degrees/phds/precision-medicine