Blood autoantibody and cytokine profiles predict response to anti-tumor necrosis factor therapy in rheumatoid arthritis
1 Department of Medicine, Division of Immunology & Rheumatology, Stanford University, 269 Campus Drive, mail code 5166, Stanford, CA 94305, USA
2 GRECC, VA Palo Alto Health Care Systems, 3801 Miranda Ave, mailstop 154R, Palo Alto, CA 94304, USA
3 Feinstein Institute of Medical Research, North Shore LIJ Health System, 350 Community Drive, Manhasset, NY 11030, USA
4 Joslin Diabetes Center, One Joslin Place, Boston, MA 02215, USA
5 Brigham and Women's Hospital, 75 Francis Street, Boston, MA 02115 USA
6 Department of Statistics, 390 Serra Mall, Stanford University, Stanford, CA 94305, USA
7 Karolinska Institutet, Building D2:02, SE-171 76 Stockholm, Sweden
8 First Department of Internal Medicine, University of Occupational & Environmental Health, 1-1 Iseigaoka, Yahata-nishi, Kitakyushu 807-8555, Japan
Arthritis Research & Therapy 2009, 11:R76 doi:10.1186/ar2706
See related editorial by Verweij, http://arthritis-research.com/content/11/3/115Published: 21 May 2009
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.
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.
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%).
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.