Combined omics analysis of cerebrospinal fluid is able to predict conversion from clinically isolated syndrome to clinically definite MS within 4 years
ECTRIMS Online Library. Probert F. 09/11/19; 278946; P586
Fay Probert
Fay Probert

Abstract: P586

Type: Poster Sessions

Abstract Category: Pathology and pathogenesis of MS - Biomarkers

F. Probert1, T. Yeo1,2, M. Sealey1, J. Palace3, D. Leppert4, J. Kuhle4, D.C. Anthony1

1Department of Pharmacology, Oxford University, Oxford, United Kingdom, 2National Neuroscience Institute, Singapore, Singapore, 3Nuffield Department of Clinical Neuroscience, Oxford University, Oxford, United Kingdom, 4University of Basel, Basel, Switzerland

Introduction: MS presents with an initial neurological attack, termed clinically isolated syndrome (CIS), however not all patients will convert to clinically definite MS (CDMS) and time to conversion varies. While the revised McDonald criteria uses a combination of clinical and radiological data along with the presence of oligoclonal bands (OCB) to diagnose MS earlier, there is no prognostic measure able to identify fast converters. Indeed, only 59% of OCB+ve CIS patients convert within 4 years.
Objectives/Aims: To determine whether metabolomics and proteomics analysis of cerebrospinal fluid (CSF) can predict early conversion from CIS to CDMS.
Methods: CSF samples were collected from 41 patients with CDMS, 71 patients with CIS, and 64 non-MS controls and analysed using a multi-omics methodology. This approach used nuclear magnetic resonance spectroscopy to measure over 100 serum metabolite concentrations and an aptamer-based proteomics assay (SOMAscan®) to measure over 5000 serum protein levels combined with multivariate feature selection and pathway analysis.
Results: Metabolomics analysis was able to diagnose CDMS and CIS with accuracies of 71±4% and 66±2% respectively. Interestingly, removal of CIS patients who tested negative for OCBs resulted in an improved accuracy of 68±3%. Further integration of the discriminatory metabolites confirmed that the CSF metabolite profile of the OCB+ve CIS patients is already indistinguishable from those with CDMS. Proteomics analysis further validated this, distinguishing OCB+ve CIS and CDMS patients from controls with accuracies of 82±3% and 75±4% respectively. The OCB+ve CIS and CDMS proteomics profiles were again indistinguishable. While OCB positivity can be used to diagnose MS, it is not predictive of early conversion; the area under the curve (AUC) is only 0.67 in patients who converted within 4 years. In contrast, our multi-omics approach identified these high risk patients with an AUC of 0.84.
Conclusions: These results indicate that combined metabolomics and proteomics analysis could not only be used as an adjunct in diagnosis of CDMS but could be used as a prognostic test to identify CIS patients at high risk of a second clinical attack within 4 years of onset. To date, this is the only reported method able to offer such prognostic information and future work will determine if this method can be used routinely in a clinical setting.
Disclosure: Fay Probert: nothing to disclose.
Tianrong Yeo: nothing to disclose.
Megan Sealey: nothing to disclose.
Jacqueline Palace: Jacqueline Palace is partly funded by highly specialised services to run a national congenital myasthenia service and a neuromyelitis service. She has received support for scientific meetings and honorariums for advisory work from Merck Serono, Biogen Idec, Novartis, Teva, Chugai Pharma and Bayer Schering, Alexion, Roche, Genzyme, MedImmune, EuroImmun, MedDay, Abide and ARGENX, and grants from Merck Serono, Novartis, Biogen Idec, Teva, Abide, MEDIMMUNE and Bayer Schering. She has received grants from the MS society, Guthie Jackson Foundation, NIHR, Oxford Health Services Research Committee, EDEN, MRC, GMSI, and John Fell for research studies.
David Leppert: has been a Novartis employee until 2019/1.
Jens Kuhle: received and exclusively used for research support: consulting fees from Biogen, Novartis, Protagen AG, Roche, and Teva; speaker fees from the Swiss MS Society, Biogen, Genzyme, Merck, Novartis, Roche; travel expenses from Merck Serono, Novartis, and Roche; and grants from the ECTRIMS Research Fellowship Programme, University of Basel, Swiss MS Society, Swiss National Research Foundation (320030_160221), Bayer, Biogen, Genzyme, Merck, Novartis, and Roche.
Daniel C Anthony: nothing to disclose.

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