Using biomarkers to predict progression from clinically isolated syndrome to multiple sclerosis
1 Research Department, ArthroChip, LLC, Franklin, TN, USA
2 Department of Mathematics, Vanderbilt University, Nashville, TN, USA
3 Department of Medicine, Vanderbilt University School of Medicine, MCN T3219, 1161 21st Avenue South, Nashville, TN, 37232-2681, USA
4 Department of Neurology, Vanderbilt University School of Medicine, Nashville, TN, USA
5 Department of Neurology, University of South Carolina, Columbia, SC, USA
6 Department of Pathology, VU University Medical Center, Amsterdam, The Netherlands
7 Department of Medicine, Penn State Hershey Medical Center, Hershey, PA, USA
8 Department of Pathology, Microbiology and Immunology, Vanderbilt University School of Medicine, Nashville, TN, USA
Journal of Clinical Bioinformatics 2013, 3:18 doi:10.1186/2043-9113-3-18Published: 3 October 2013
Detection of brain lesions disseminated in space and time by magnetic resonance imaging remains a cornerstone for the diagnosis of clinically definite multiple sclerosis. We have sought to determine if gene expression biomarkers could contribute to the clinical diagnosis of multiple sclerosis.
We employed expression levels of 30 genes in blood from 199 subjects with multiple sclerosis, 203 subjects with other neurologic disorders, and 114 healthy control subjects to train ratioscore and support vector machine algorithms. Blood samples were obtained from 46 subjects coincident with clinically isolated syndrome who progressed to clinically definite multiple sclerosis determined by conventional methods. Gene expression levels from these subjects were inputted into ratioscore and support vector machine algorithms to determine if these methods also predicted that these subjects would develop multiple sclerosis. Standard calculations of sensitivity and specificity were employed to determine accuracy of these predictions.
Our results demonstrate that ratioscore and support vector machine methods employing input gene transcript levels in blood can accurately identify subjects with clinically isolated syndrome that will progress to multiple sclerosis.
We conclude these approaches may be useful to predict progression from clinically isolated syndrome to multiple sclerosis.