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Potential of a unique antibody gene signature to predict conversion to clinically definite multiple sclerosis.

ABSTRACT: We identified a unique antibody gene mutation pattern (i.e. "signature") in cerebrospinal fluid (CSF) B cells from multiple sclerosis (MS) patients not present in control populations. Prevalence of the signature in CSF B cells of patients at risk to develop MS predicted conversion to MS with 91% accuracy in a small cohort of clinically isolated syndrome patients. If confirmed, signature prevalence would be a novel genetic diagnostic tool candidate for patients with early demyelinating disease of the central nervous system.

SUBMITTER: Cameron EM

PROVIDER: S-EPMC2785005 | biostudies-literature | 2009 Aug

REPOSITORIES: biostudies-literature

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Potential of a unique antibody gene signature to predict conversion to clinically definite multiple sclerosis.

Cameron Elizabeth M EM Spencer Sade S Lazarini Jonathan J Harp Christopher T CT Ward E Sally ES Burgoon Mark M Owens Gregory P GP Racke Michael K MK Bennett Jeffrey L JL Frohman Elliot M EM Monson Nancy L NL

Journal of neuroimmunology 20090724 1-2

We identified a unique antibody gene mutation pattern (i.e. "signature") in cerebrospinal fluid (CSF) B cells from multiple sclerosis (MS) patients not present in control populations. Prevalence of the signature in CSF B cells of patients at risk to develop MS predicted conversion to MS with 91% accuracy in a small cohort of clinically isolated syndrome patients. If confirmed, signature prevalence would be a novel genetic diagnostic tool candidate for patients with early demyelinating disease of ...[more]

PMID: 19631394

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Project description:Understanding the predictors of progression from a first to a second demyelinating event (and formerly, a diagnosis of clinically definite multiple sclerosis) is important clinically. Previous studies have focused on predictors within a single domain, e.g. radiological, lacking prospective data across multiple domains. We tested a comprehensive set of personal, environmental, neurological, MRI and genetic characteristics, considered together, as predictors of progression from a first demyelinating event to clinically definite multiple sclerosis. Participants were aged 18-59 years and had a first demyelinating event during the study recruitment period (1 November 2003-31 December 2006) for the Ausimmune Study (n = 216) and had follow-up data to 2-3 years post-initial interview. Detailed baseline data were available on a broad range of demographic and environmental factors, MRI, and genetic and viral studies. Follow-up data included confirmation of clinically definite multiple sclerosis (or not) and changes in environmental exposures during the follow-up period. We used multivariable logistic regression and Cox proportional hazards regression modelling to test predictors of, and time to, conversion to clinically definite multiple sclerosis. On review, one participant had an undiagnosed event prior to study recruitment and was excluded (n = 215). Data on progression to clinically definite multiple sclerosis were available for 91.2% (n = 196); 77% were diagnosed as clinically definite multiple sclerosis at follow-up. Mean (standard deviation) duration of follow-up was 2.7 (0.7) years. The set of predictors retained in the best predictive model for progression from a first demyelinating event to clinically definite multiple sclerosis were as follows: younger age at first demyelinating event [adjusted odds ratio (aOR) = 0.92, 95% confidence interval (CI) = 0.87-0.97, per additional year of age); being a smoker at baseline (versus not) (aOR = 2.55, 95% CI 0.85-7.69); lower sun exposure at age 6-18 years (aOR = 0.86, 95% CI 0.74-1.00, per 100 kJ/m2 increment in ultraviolet radiation dose), presence (versus absence) of infratentorial lesions on baseline magnetic resonance imaging (aOR = 7.41, 95% CI 2.08-26.41); and single nucleotide polymorphisms in human leukocyte antigen (HLA)-B (rs2523393, aOR = 0.25, 95% CI 0.09-0.68, for any G versus A:A), TNFRSF1A (rs1800693, aOR = 5.82, 95% CI 2.10-16.12, for any C versus T:T), and a vitamin D-binding protein gene (rs7041, aOR = 3.76, 95% CI 1.41-9.99, for any A versus C:C). The final model explained 36% of the variance. Predictors of more rapid progression to clinically definite multiple sclerosis (Cox proportional hazards regression) were similar. Genetic and magnetic resonance imaging characteristics as well as demographic and environmental factors predicted progression, and more rapid progression, from a first demyelinating event to a second event and clinically definite multiple sclerosis.

Project description:Eighty-five percent of multiple sclerosis cases begin with a discrete attack termed clinically isolated syndrome, but 37% of clinically isolated syndrome patients do not experience a relapse within 20 years of onset. Thus, the identification of biomarkers able to differentiate between individuals who are most likely to have a second clinical attack from those who remain in the clinically isolated syndrome stage is essential to apply a personalized medicine approach. We sought to identify biomarkers from biochemical, metabolic and proteomic screens that predict clinically defined conversion from clinically isolated syndrome to multiple sclerosis and generate a multi-omics-based algorithm with higher prognostic accuracy than any currently available test. An integrative multi-variate approach was applied to the analysis of cerebrospinal fluid samples taken from 54 individuals at the point of clinically isolated syndrome with 2-10 years of subsequent follow-up enabling stratification into clinical converters and non-converters. Leukocyte counts were significantly elevated at onset in the clinical converters and predict the occurrence of a second attack with 70% accuracy. Myo-inositol levels were significantly increased in clinical converters while glucose levels were decreased, predicting transition to multiple sclerosis with accuracies of 72% and 63%, respectively. Proteomics analysis identified 89 novel gene products related to conversion. The identified biochemical and protein biomarkers were combined to produce an algorithm with predictive accuracy of 83% for the transition to clinically defined multiple sclerosis, outperforming any individual biomarker in isolation including oligoclonal bands. The identified protein biomarkers are consistent with an exaggerated immune response, perturbed energy metabolism and multiple sclerosis pathology in the clinical converter group. The new biomarkers presented provide novel insight into the molecular pathways promoting disease while the multi-omics algorithm provides a means to more accurately predict whether an individual is likely to convert to clinically defined multiple sclerosis.

Dataset Information

Potential of a unique antibody gene signature to predict conversion to clinically definite multiple sclerosis.

Publications

Potential of a unique antibody gene signature to predict conversion to clinically definite multiple sclerosis.

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