Suhayl Dhib-Jalbut1, Reuben M Valenzuela2, Kouichi Ito3, Michael Kaufman4, Mary Ann Picone5, Steve Buyske6. 1. Department of Neurology, UMDNJ-Robert Wood Johnson Medical School, 683 Hoes lane, Piscataway, NJ 08554, USA. Electronic address: jalbutsu@umdnj.edu. 2. Department of Neurology, UMDNJ-Robert Wood Johnson Medical School, 683 Hoes lane, Piscataway, NJ 08554, USA. Electronic address: valenzru@gmail.com. 3. Department of Neurology, UMDNJ-Robert Wood Johnson Medical School, 683 Hoes lane, Piscataway, NJ 08554, USA. Electronic address: itoko@umdnj.edu. 4. Carolina Medical Center, Multiple Sclerosis Center, Charlotte, NC, USA. Electronic address: Michael.Kaufman@carolinashealthcare.org. 5. Multiple Sclerosis Center at Holy Name Hospital, Teaneck, NJ, USA. Electronic address: piconerametta@optonline.net. 6. Rutgers University, Department of Statistics and Biostatistics, Piscataway, USA. Electronic address: buyske@stat.rutgers.edu.
Abstract
OBJECTIVE: Clinical response to immunomodulatory therapies in multiple sclerosis (MS) is variable among patients. Currently, there are no validated biomarkers of clinical response to any of the approved treatments for MS. The objective of this study was to determine if HLA-class II alleles predict the clinical response to glatiramer acetate (GA). METHODS: This was a prospective study of 64 MS patients with relapsing-remitting disease. Patients were HLA-typed and classified as GA-responders or non-responders after 2 years of treatment based on a clinical criterion. Statistical models were used to determine whether HLA-DR and DQ alleles and haplotypes predict the clinical response to GA. RESULTS: Tests of association of response singled out four alleles and two haplotypes with nominal p<0.01. The presence of alleles DR15 or DQ6 or the absence of DR17 and DQ2 alleles was associated with favorable clinical response. The presence of the DR15-DQ6 haplotype and the absence of the DR17-DQ2 haplotype were also associated with favorable treatment response. A best fitting two-haplotype model resulted in the identification of three prognostic categories (good, neutral, and poor). A DR15-DQ6 positive but DR17-DQ2 negative combination was strongly predictive of a favorable clinical response (71%). Conversely, a DR15-DQ6 negative but DR17-DQ2 positive combination was strongly predictive of poor clinical response to GA (17%). CONCLUSION: HLA-DR and DQ typing may prove to be useful biomarkers of predicting response to GA in MS and may help select patients appropriate for this treatment.
OBJECTIVE: Clinical response to immunomodulatory therapies in multiple sclerosis (MS) is variable among patients. Currently, there are no validated biomarkers of clinical response to any of the approved treatments for MS. The objective of this study was to determine if HLA-class II alleles predict the clinical response to glatiramer acetate (GA). METHODS: This was a prospective study of 64 MSpatients with relapsing-remitting disease. Patients were HLA-typed and classified as GA-responders or non-responders after 2 years of treatment based on a clinical criterion. Statistical models were used to determine whether HLA-DR and DQ alleles and haplotypes predict the clinical response to GA. RESULTS: Tests of association of response singled out four alleles and two haplotypes with nominal p<0.01. The presence of alleles DR15 or DQ6 or the absence of DR17 and DQ2 alleles was associated with favorable clinical response. The presence of the DR15-DQ6 haplotype and the absence of the DR17-DQ2 haplotype were also associated with favorable treatment response. A best fitting two-haplotype model resulted in the identification of three prognostic categories (good, neutral, and poor). A DR15-DQ6 positive but DR17-DQ2 negative combination was strongly predictive of a favorable clinical response (71%). Conversely, a DR15-DQ6 negative but DR17-DQ2 positive combination was strongly predictive of poor clinical response to GA (17%). CONCLUSION: HLA-DR and DQ typing may prove to be useful biomarkers of predicting response to GA in MS and may help select patients appropriate for this treatment.
Authors: Colin J Ross; Fadi Towfic; Jyoti Shankar; Daphna Laifenfeld; Mathis Thoma; Matthew Davis; Brian Weiner; Rebecca Kusko; Ben Zeskind; Volker Knappertz; Iris Grossman; Michael R Hayden Journal: Genome Med Date: 2017-05-31 Impact factor: 11.117