MRC - The University of Edinburgh DOCTORAL TRAINING PROGRAMME IN PRECISION MEDICINE PROJECT PROPOSALS March 2016 Information on the DTP in Precision Medicine: http://www.ed.ac.uk/medicine-vet-medicine/postgraduate/research- degrees/phds/precision-medicine Apply online: http://www.ed.ac.uk/studying/postgraduate/degrees/index.php?r=site/view&id= 919 Project title: Towards patient stratification at point of care using rapid microRNA detection Project description: Background Paracetamol (acetaminophen) overdose is one of the most common reasons for emergency hospital attendance and is the leading cause of acute liver failure in the Western world. Annually across the UK paracetamol overdose results in approximately 100,000 Emergency Department presentations and 50,000 acute hospital admissions. To put this workload in context, these numbers are larger than admissions due to heart attacks. The current approach to treatment is sub-optimal predominately due to an inability to stratify patients and identify those who stand to benefit from treatment. At present, too many patients are being admitted to hospital for unnecessary treatment with the antidote (NAC) because current blood tests cannot rule out liver damage in the emergency department. Therefore, there is an unmet clinical need for markers that can target treatment only to those who stand to benefit and rule out liver damage earlier than is currently possible. We have addressed these important limitations by identifying a new blood biomarker (microRNA-122 ‘miR-122’) that is more sensitive and specific for liver injury than all standard tests and accurately reports injury at first presentation to hospital, a time when current blood tests are often still in the normal range.1,2 The key roadblock to further clinical development - specifically using miR-122 in stratified clinical trials lead by our pharma partners - is the lack of a rapid point of care assay. Aims The proposed PhD project will expand an existing platform that uses electrochemical impedance spectroscopy (EIS) as a point of care test for liquid biopsies focussing on microRNA biomarkers.3,4 We have demonstrated that EIS detects synthetic miR-122 and reports paracetamol-induced liver injury in pigs and humans, which will be further developed in the current project including detection of wider miRNA panels. The EIS detection platform will be combined in a microfluidic device with an innovative, low complexity sample preparation and the direct, label-free detection approach will be developed during the PhD project. In the final phase of the project, the integrated assay will be tested using an unrivalled bank of samples from humans, and a range of animal samples including rodents, pigs and zebrafish. EIS detection of miR-122 has utility beyond our primary clinical indication, paracetamol overdose. Rapid detection of hepatotoxicity has great value in early phase drug development and our application is supported by our pharma partners who are important potential end- users. Hepatotoxicity is commonly caused by antimicrobial agents. As part of the project the student will perform RNA-Seq studies to identify microRNA markers for bacterial infection to complement our toxicity marker and allow targeted therapy. This work builds on our experience in antibiotic resistance and microRNA biology. Training outcomes The PhD student will be part of a multi-disciplinary team with world-leading expertise in molecular diagnostics, biosensors and clinical toxicology closely supported by our commercial 2 partner and the university technology transfer office. Specifically the student will learn RNA- Seq, bioinformatics analysis, microRNA manipulation and clinical study design. References: 1. Lewis, P. J. S.; Dear, J.; Platt, V.; Simpson, K. J.; Craig, D. G. N.; Antoine, D. J.; French, N. S.; Dhaun, N.; Webb, D. J.; Costello, E. M.; Neoptolemos, J. P.; Moggs, J.; Goldring, C. E.; Park, B. K. Circulating MicroRNAs as Potential Markers of Human Drug-Induced Liver Injury. Hepatology 2011, 54 (5), 1767-1776. 2. Vliegenthart, A. D. B.; Shaffer, J. M.; Clarke, J. I.; Peeters, L. E. J.; Caporali, A.; Bateman, D. N.; Wood, D. M.; Dargan, P. I.; Craig, D. G.; Moore, J. K.; Thompson, A. I.; Henderson, N. C.; Webb, D. J.; Sharkey, J.; Antoine, D. J.; Park, B. K.; Bailey, M. A.; Lader, E.; Simpson, K. J.; Dear, J. W. Comprehensive microRNA profiling in acetaminophen toxicity identifies novel circulating biomarkers for human liver and kidney injury. Scientific Reports 2015, 5. 3. Corrigan, D. K.; Schulze, H.; Henihan, G.; Hardie, A.; Ciani, I.; Giraud, G.; Terry, J. G.; Walton, A. J.; Pethig, R.; Ghazal, P.; Crain, J.; Campbell, C. J.; Templeton, K. E.; Mount, A. R.; Bachmann, T. T. Development of a PCR-free electrochemical point of care test for clinical detection of methicillin resistant Staphylococcus aureus (MRSA). Analyst 2013, 138 (22), 6997-7005. 4. Huang, J. M.; Henihan, G.; Macdonald, D.; Michalowski, A.; Templeton, K.; Gibb, A. P.; Schulze, H.; Bachmann, T. T. Rapid Electrochemical Detection of New Delhi Metallo-beta- lactamase Genes To Enable Point-of-Care Testing of Carbapenem-Resistant Enterobacteriaceae. Anal. Chem. 2015, 87 (15), 7738-7745. Contact email address(es): [email protected] [email protected] [email protected] Institute/Centre and/or other useful web addresses: The University of Edinburgh, College of Medicine and Veterinary Medicine, Edinburgh Medical School – Biomedical Sciences, Division of Infection and Pathway Medicine www.research.ed.ac.uk/portal/en/persons/till-bachmann(4c731049-5ce7-4f71-9984- fb216ee36fab).html www.ed.ac.uk/pathway-medicine www.cvs.ed.ac.uk/users/james-dear www.ed.ac.uk/pathway-medicine/our-staff/staff-profiles/drkatetempleton 3 Project title: Identification of therapeutically-relevant patient subgroups from clinical and biological data Project description: 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 4 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. References: 1. Forrest, A. R. R., Kawaji, H., Rehli, M., Baillie, J.K., et al. A promoter-level mammalian expression atlas. Nature 507, 462–470 (2014). 2. Hall, D. P. et al.