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Principal Investigators: Prof Andrew Hayward, Farr Institute London and UCL
Prof Paul Kellam, Imperial College London and Wellcome Trust Sanger Institute
Investigators: Prof Steven Morris, UCL , Dr Eleni Nastouli, UCLH, Dr Duncan Clark, Barts Health, Dr Zisis Kozlakidis, UCL
The Challenge
Virus infections represent a significant health service challenge. Viral disease outbreaks can lead to situations where health services cannot cope with large numbers of infections with associated high treatment costs. However, significant advances have been made in understanding how virus genes become highly variable through mutation to accelerate disease, drug resistance and transmission. If clinicians could have high resolution information about the gene variations in specific cases of infections more targeted and effective treatments could be selected.
The Research
The ICONIC project, hosted at the Farr Institute of Health Informatics Research, London, involves UCL, the Wellcome Trust Sanger Institute, five major NHS Trusts and Intel. Through a £3.4m award from the Department of Health and the Wellcome Trust, ICONIC researchers are using next-generation virus gene sequencing applied into treatment choices in routine healthcare. The research helps match best possible treatments to known genetic variations in viruses.
The Results
The researchers designed methods to process diagnostic clinical samples and a computer-based tool to quickly and accurately reconstruct entire sets of virus genes. During outbreaks very large amounts of genetic information can be processed and made available in time to help clinicians treating individual patients. By incorporating other clinical data, insights from patient outcomes having different treatments can be gained.
The Impact
The ICONIC process has been adopted by two large NHS Trusts. The team has analysed two influenza outbreaks at UCLH hospital, and has been applied in the Ebola and MERS outbreaks in West Africa and Saudi Arabia. Expertise from Intel has helped miniaturise the process and make it transferable to international disease hotspots.
For more information: Woolhouse et al. (2015) Science Translational Medicine. 7:307rv5; Hunt et al (2015) Bioinformatics 31:2374–2376.