Literature DB >> 22111603

Allelic combinations of immune-response genes associated with glatiramer acetate treatment response in Russian multiple sclerosis patients.

Ekaterina Y Tsareva1, Olga G Kulakova, Alexey N Boyko, Sergey G Shchur, Dmitrijs Lvovs, Alexander V Favorov, Evgeny I Gusev, Koen Vandenbroeck, Olga O Favorova.   

Abstract

BACKGROUND: Glatiramer acetate (GA) is widely used as a first-line disease-modifying treatment for multiple sclerosis (MS). However, a significant proportion of MS patient appears to experience modest benefit from GA-treatment. Genetic variants affecting the clinical response to GA are believed to be relevant as biomarkers of GA-treatment efficiency. PATIENTS &
METHODS: Nine polymorphisms in candidate genes were analyzed as possible determinants of GA response in 285 Russian MS patients. Special attention was given to identification of response-associated allelic combinations by means of the APSampler algorithm.
RESULTS: No significant associations were found for individual polymorphisms. Alleles DRB1*15, TGFB1*T, CCR5*d and IFNAR1*G were the components of the combinations, of which carriage was significantly higher in nonresponders than in responders. Carriers of the most significant combinations: DRB1*15 + TGFB1*T + CCR5*d + IFNAR1*G and DRB1*15 + TGFB1*T + CCR5*d (permutation p-values: 0.0056 and 0.013, respectively) had a 14 to 15-times increased risk of ineffective response to GA therapy. DISCUSSION: The results suggest that the influence of immune-response genes on GA-induced response has a polygenic nature. The data are interpreted as evidence of additive and epistatic influences of the genes on GA efficiency for MS treatment.

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Year:  2011        PMID: 22111603     DOI: 10.2217/pgs.11.136

Source DB:  PubMed          Journal:  Pharmacogenomics        ISSN: 1462-2416            Impact factor:   2.533


  8 in total

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Journal:  Mediators Inflamm       Date:  2014-01-02       Impact factor: 4.711

3.  Genomic correlates of glatiramer acetate adverse cardiovascular effects lead to a novel locus mediating coronary risk.

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Journal:  PLoS One       Date:  2017-08-22       Impact factor: 3.240

Review 4.  Pharmacogenetic Biomarkers to Predict Treatment Response in Multiple Sclerosis: Current and Future Perspectives.

Authors:  Patricia K Coyle
Journal:  Mult Scler Int       Date:  2017-07-19

Review 5.  Combinations of Genetic Variants Occurring Exclusively in Patients.

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Journal:  Comput Struct Biotechnol J       Date:  2017-03-10       Impact factor: 7.271

Review 6.  Pharmacogenomics of Multiple Sclerosis: A Systematic Review.

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Journal:  Front Neurol       Date:  2019-02-26       Impact factor: 4.003

7.  A Polygenic Approach to the Study 
of Polygenic Diseases.

Authors:  D Lvovs; O O Favorova; A V Favorov
Journal:  Acta Naturae       Date:  2012-07       Impact factor: 1.845

8.  A pharmacogenetic signature of high response to Copaxone in late-phase clinical-trial cohorts of multiple sclerosis.

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

  8 in total

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