Gurevich Michael
Multiple Sclerosis Center, Sheba Medical Center, Israel
Title: Optimizing multiple sclerosis diagnosis using gene expression and genomic association data
Biography
Biography: Gurevich Michael
Abstract
The diagnosis of multiple sclerosis (MS) is based on the neurological symptomatology in combination with the presence of central nervous system lesions disseminated in time and space. However, the clinical, imaging and/or laboratory findings of patients with MS may mimic a wide array of other vascular, inflammatory and demyelinating diseases, hereby defined as NonMS. This overlap may pose a significant diagnostic challenge especially in the process of diagnosis at the early disease stage. We utilized findings of large-scale Genome Wide Association Studies (GWAS) to develop a blood gene expression based classification tool to assist in the diagnosis during the first demyelinating event suggestive of MS. We merged knowledge of 110 MS susceptibility genes gained from MS GWAS studies together with our experimental results of differential blood gene expression profiling between 80 MS patients and 31 NonMS patients. Multiple classification algorithms were applied to this cohort to construct a diagnostic classifier that correctly distinguished between MS and NonMS patients. The overall accuracy of the constructed 42-gene classifier was tested on an independent patients population consisting of diagnostically challenging cases including NonMS patients with positive MRI findings and achieved a correct classification rate of 76.0±3.5%. The presented diagnostic classification tool complements the existing diagnostic McDonald criteria by assisting in the accurate exclusion of other neurological diseases at presentation of the first demyelinating event suggestive of MS.
