title: Proteomic pattern analysis discriminates among multiple sclerosis-related disorders.
abstract: OBJECTIVE
  To use a new, unbiased biomarker discovery strategy to obtain and assess proteomic data from cerebrospinal fluid (CSF) of patients with multiple sclerosis (MS)-related disorders.
  
  
  METHODS
  CSF protein profiles were analyzed from 107 patients with either MS-related disorders (including relapsing remitting MS [RRMS], primary progressive MS [PPMS], anti-aquaporin4 antibody seropositive-neuromyelitis optica spectrum disorder [SP-NMOSD], and seronegative-NMOSD with long cord lesions on spinal magnetic resonance imaging [SN-NMOSD]), amyotrophic lateral sclerosis (ALS), or other inflammatory neurological diseases (used as controls). CSF peptides/proteins were purified with magnetic beads, and directly measured by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. The obtained spectra were analyzed with multivariate statistics and pattern matching algorithms. These analyses were replicated in an independent sample set of 84 patients composed of those with MS-related disorders or with other neurological diseases (the second cohort).
  
  
  RESULTS
  MS-related disorders differed considerably in terms of CSF protein profiles. SP-NMOSD and SN-NMOSD, both of which fit within the NMO spectrum, were distinguishable from RRMS with high cross-validation accuracy on a support vector machine classifier, especially in relapse phases. Some peaks derived from samples of relapsed SP-NMOSD can discriminate RRMS with high area under curve scores (>0.95) and this was reproduced on the second cohort. The similarity of proteomic patterns between selected neurological diseases were demonstrated by pattern matching analysis. To our surprise, the spectral differences between RRMS and PPMS were much larger than those of PPMS and ALS.
  
  
  CONCLUSIONS
  Our findings suggest that CSF proteomic pattern analysis can increase the accuracy of disease diagnosis of MS-related disorders and will aid physicians in appropriate therapeutic decision-making.
authors.affiliation: Department of Neurology, Graduate School of Medicine, Kyoto University, Japan.
authors.forename: Mika
authors.initials: M
authors.lastname: Komori
authors.forename: Yumiko
authors.initials: Y
authors.lastname: Matsuyama
authors.forename: Takashi
authors.initials: T
authors.lastname: Nirasawa
authors.forename: Herbert
authors.initials: H
authors.lastname: Thiele
authors.forename: Michael
authors.initials: M
authors.lastname: Becker
authors.forename: Theodore
authors.initials: T
authors.lastname: Alexandrov
authors.forename: Takahiko
authors.initials: T
authors.lastname: Saida
authors.forename: Masami
authors.initials: M
authors.lastname: Tanaka
authors.forename: Hidenori
authors.initials: H
authors.lastname: Matsuo
authors.forename: Hidekazu
authors.initials: H
authors.lastname: Tomimoto
authors.forename: Ryosuke
authors.initials: R
authors.lastname: Takahashi
authors.forename: Kei
authors.initials: K
authors.lastname: Tashiro
authors.forename: Masaya
authors.initials: M
authors.lastname: Ikegawa
authors.forename: Takayuki
authors.initials: T
authors.lastname: Kondo
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D015415:Biomarkers / Q000134:cerebrospinal fluid*; D002556:Cerebrospinal Fluid Proteins; D003937:Diagnosis, Differential; D016002:Discriminant Analysis; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D020528:Multiple Sclerosis, Chronic Progressive / Q000134:cerebrospinal fluid* / Q000175:diagnosis; D020529:Multiple Sclerosis, Relapsing-Remitting / Q000134:cerebrospinal fluid* / Q000175:diagnosis; D009471:Neuromyelitis Optica / Q000134:cerebrospinal fluid* / Q000175:diagnosis; D040901:Proteomics / Q000379:methods*; D019032:Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization; D055815:Young Adult
chemical_list: D015415:Biomarkers; D002556:Cerebrospinal Fluid Proteins