title: Blood autoantibody and cytokine profiles predict response to anti-tumor necrosis factor therapy in rheumatoid arthritis.
abstract: BACKGROUND
  Anti-TNF therapies have revolutionized the treatment of rheumatoid arthritis (RA), a common systemic autoimmune disease involving destruction of the synovial joints. However, in the practice of rheumatology approximately one-third of patients demonstrate no clinical improvement in response to treatment with anti-TNF therapies, while another third demonstrate a partial response, and one-third an excellent and sustained response. Since no clinical or laboratory tests are available to predict response to anti-TNF therapies, great need exists for predictive biomarkers.
  
  
  METHODS
  Here we present a multi-step proteomics approach using arthritis antigen arrays, a multiplex cytokine assay, and conventional ELISA, with the objective to identify a biomarker signature in three ethnically diverse cohorts of RA patients treated with the anti-TNF therapy etanercept.
  
  
  RESULTS
  We identified a 24-biomarker signature that enabled prediction of a positive clinical response to etanercept in all three cohorts (positive predictive values 58 to 72%; negative predictive values 63 to 78%).
  
  
  CONCLUSIONS
  We identified a multi-parameter protein biomarker that enables pretreatment classification and prediction of etanercept responders, and tested this biomarker using three independent cohorts of RA patients. Although further validation in prospective and larger cohorts is needed, our observations demonstrate that multiplex characterization of autoantibodies and cytokines provides clinical utility for predicting response to the anti-TNF therapy etanercept in RA patients.
authors.affiliation: Department of Medicine, Division of Immunology & Rheumatology, Stanford University, Stanford, CA 94305, USA. whueber@stanford.edu
authors.forename: Wolfgang
authors.initials: W
authors.lastname: Hueber
authors.forename: Beren H
authors.initials: BH
authors.lastname: Tomooka
authors.forename: Franak
authors.initials: F
authors.lastname: Batliwalla
authors.forename: Wentian
authors.initials: W
authors.lastname: Li
authors.forename: Paul A
authors.initials: PA
authors.lastname: Monach
authors.forename: Robert J
authors.initials: RJ
authors.lastname: Tibshirani
authors.forename: Ronald F
authors.initials: RF
authors.lastname: Van Vollenhoven
authors.forename: Jon
authors.initials: J
authors.lastname: Lampa
authors.forename: Kazuyoshi
authors.initials: K
authors.lastname: Saito
authors.forename: Yoshiya
authors.initials: Y
authors.lastname: Tanaka
authors.forename: Mark C
authors.initials: MC
authors.lastname: Genovese
authors.forename: Lars
authors.initials: L
authors.lastname: Klareskog
authors.forename: Peter K
authors.initials: PK
authors.lastname: Gregersen
authors.forename: William H
authors.initials: WH
authors.lastname: Robinson
mesh_terms: D000328:Adult; D000368:Aged; D018501:Antirheumatic Agents / Q000494:pharmacology / Q000627:therapeutic use*; D001172:Arthritis, Rheumatoid / Q000097:blood / Q000188:drug therapy* / Q000276:immunology*; D001323:Autoantibodies / Q000097:blood*; D015415:Biomarkers / Q000097:blood; D015331:Cohort Studies; D016207:Cytokines / Q000097:blood*; D000068800:Etanercept; D005260:Female; D006801:Humans; D007074:Immunoglobulin G / Q000627:therapeutic use; D008297:Male; D008875:Middle Aged; D011237:Predictive Value of Tests; D018124:Receptors, Tumor Necrosis Factor / Q000627:therapeutic use; D014409:Tumor Necrosis Factor-alpha / Q000037:antagonists & inhibitors* / Q000097:blood
chemical_list: D018501:Antirheumatic Agents; D001323:Autoantibodies; D015415:Biomarkers; D016207:Cytokines; D007074:Immunoglobulin G; D018124:Receptors, Tumor Necrosis Factor; D014409:Tumor Necrosis Factor-alpha; D000068800:Etanercept