Alzheimer's disease biomarker discovery using in silico literature mining and clinical validation
1 King’s College London, Institute of Psychiatry, De Crespigny Park, London, SE5 8AF, UK
2 BioWisdom Ltd, Cambridge, UK (now Instem Scientific
3 University of Eastern Finland and University Hospital of Kuopio, Kuopio, Finland
4 Medical University of Lodz, Lodz, Poland
5 3rd Department of Neurology, "G. Papanicolaou" Hospital, Aristotle University of Thessaloniki, Thessaloniki, Greece
6 UMR INSERM 1027, Gerontopole, CHU Toulouse, University of Toulouse, Toulouse, France
7 Department of Clinical Science, Intervention and Technology, Karolinska Institutet, Stockholm, Sweden
8 Institute of Gerontology and Geriatrics, University of Perugia, Perugia, Italy
9 Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
10 Currently at Somaxa Ltd and Abcodia Ltd, London, UK
Journal of Translational Medicine 2012, 10:217 doi:10.1186/1479-5876-10-217Published: 31 October 2012
Alzheimer’s Disease (AD) is the most widespread form of dementia in the elderly but despite progress made in recent years towards a mechanistic understanding, there is still an urgent need for disease modification therapy and for early diagnostic tests. Substantial international efforts are being made to discover and validate biomarkers for AD using candidate analytes and various data-driven 'omics' approaches. Cerebrospinal fluid is in many ways the tissue of choice for biomarkers of brain disease but is limited by patient and clinician acceptability, and increasing attention is being paid to the search for blood-based biomarkers. The aim of this study was to use a novel in silico approach to discover a set of candidate biomarkers for AD.
We used an in silico literature mining approach to identify potential biomarkers by creating a summarized set of assertional metadata derived from relevant legacy information. We then assessed the validity of this approach using direct assays of the identified biomarkers in plasma by immunodetection methods.
Using this in silico approach, we identified 25 biomarker candidates, at least three of which have subsequently been reported to be altered in blood or CSF from AD patients. Two further candidate biomarkers, indicated from the in silico approach, were choline acetyltransferase and urokinase-type plasminogen activator receptor. Using immunodetection, we showed that, in a large sample set, these markers are either altered in disease or correlate with MRI markers of atrophy.
These data support as a proof of concept the use of data mining and in silico analyses to derive valid biomarker candidates for AD and, by extension, for other disorders.