Pharmacogenomic study in patients with multiple sclerosis

Abstract

Objectives:

We aimed to investigate the association between polymorphisms located in type I interferon (IFN)-induced genes, genes belonging to the toll-like receptor (TLR) pathway, and genes encoding neurotransmitter receptors and the response to IFN-β treatment in patients with multiple sclerosis (MS).

Methods:

In a first or screening phase of the study, 384 polymorphisms were genotyped in 830 patients with MS classified into IFN-β responders (n = 416) and nonresponders (n = 414) according to clinical criteria. In a second or validation phase, the most significant polymorphisms associated with IFN-β response were genotyped in an independent validation cohort of 555 patients with MS (281 IFN-β responders and 274 nonresponders).

Results:

Seven single nucleotide polymorphisms (SNPs) were selected from the screening phase for further validation: rs832032 (GABRR3; p = 0.0006), rs6597 (STUB1; p = 0.019), rs3747517 (IFIH1; p = 0.010), rs2277302 (PELI3; p = 0.017), rs10958713 (IKBKB; p = 0.003), rs2834202 (IFNAR1; p = 0.030), and rs4422395 (CXCL1; p = 0.017). None of these SNPs were significantly associated with IFN-β response when genotyped in an independent cohort of patients. Combined analysis of these SNPs in all patients with MS (N = 1,385) revealed 2 polymorphisms associated with IFN-β response: rs2277302 (PELI3; p = 0.008) and rs832032 (GABRR3; p = 0.006).

Conclusions:

These findings do not support an association between polymorphisms located in genes related to the type I IFN or TLR pathways or genes encoding neurotransmitter receptors and the clinical response to IFN-β. Nevertheless, additional genetic and functional studies of PELI3 and GABRR3 are warranted.

Interferon β (IFN-β) is one of the most widely prescribed disease-modifying therapies for patients with relapsing-remitting multiple sclerosis (MS) and has demonstrated positive effects on reducing disease activity.¹ However, IFN-β is only partially effective, and a significant proportion of patients with MS do not respond to this treatment.² Despite many efforts to identify allelic variants influencing the response to IFN-β,³ to date there is a lack of genetic biomarkers reliably associated with therapeutic response.

A previous IFN-β transcriptomic study conducted by our group (Cemcat) implicated genes belonging to the type I IFN pathway and genes related to the toll-like receptor (TLR) pathway in the clinical response to IFN-β.⁴ The 2 genome-wide pharmacogenomic studies published thus far in relation to the response to IFN-β^5,6 reported the involvement of type I IFN– responsive genes⁵ and the overrepresentation of genes coding for glutamate and γ-aminobutyric acid (GABA) receptors among the polymorphisms that best discriminated between IFN-β responders and nonresponders, suggesting a relationship between neuronal excitation and response to IFN-β.^5,6

Building on these observations, in the present study we aimed to genotype an extensive panel of polymorphisms located in genes related to the type I IFN and TLR pathways and genes coding for GABA and glutamate receptors in a multicenter cohort of IFN-β responders and nonresponders in order to investigate their potential association with the response to this treatment.

METHODS

Definition of response to IFN-β therapy.

Patients with relapse-onset MS treated with IFN-β and with a follow-up of at least 2 years were classified as responders and nonresponders. Responders were patients with no relapses and no increase in the Expanded Disability Status Scale (EDSS) score over the follow-up period. Nonresponders were patients having 1 or more relapses during the follow-up period and an increase in the EDSS score of at least 1 point confirmed at 6 months.⁷ We further classified the response of a subgroup of patients according to the number of relapses during the first 2 years of treatment without considering EDSS progression. Responders had no relapses during the follow-up period. Two different criteria were considered for nonresponders: (1) 1 or more relapses during the 2-year follow-up (criteria 1), and (2) 2 or more relapses over the follow-up period (for this criterion, patients with only 1 relapse were not considered in the analysis; criteria 2).

Information on the IFN-β neutralizing antibody status was not available for patients included in the study.

Single nucleotide polymorphism selection.

Genes related to the type I IFN and TLR pathways and genes coding for GABA and glutamate receptors were selected through bibliographical searches, and a total of 384 biologically informative single nucleotide polymorphisms (SNPs) were selected for genotyping using Select Your SNPs, an online tool for selecting tagSNPs from a roster of candidate genes.⁸

Study design.

In a first or screening phase of the study (phase I), 384 SNPs were genotyped in 830 patients with MS classified according to their response to IFN-β. There were 416 responders and 414 nonresponders. Genomic DNA samples were obtained using standard methods and genotyping was performed using an Illumina GoldenGate assay at Progenika Biopharma SA (Bizkaia, Spain).

Overall genotyping success rate was 95%. The full list of SNPs and probes is provided as table e-1 at Neurology.org/nn.

In a second or validation phase of the study (phase II), the most significant SNPs obtained in phase I were genotyped in an independent cohort of 555 patients with MS (281 responders and 274 nonresponders to IFN-β). Genotyping was performed using TaqMan assays at the Cemcat (Barcelona, Spain). Genotyping success rate was 97%.

A summary of demographic and baseline clinical characteristics of all patient cohorts is shown in table 1.

Table 1.

Summary of demographic and baseline clinical characteristics for all the cohorts of patients with MS (responders and nonresponders to IFN-β treatment)

Statistical analysis.

Data processing, missingness, Hardy-Weinberg analysis, and allele frequency comparisons between responders and nonresponders to IFN-β were performed with SNPator (www.snpator.org).⁹ Possible stratification owing to different population origin was assessed using the Cochran-Mantel-Haenszel test implemented in PLINK (http://pngu.mgh.harvard.edu/∼purcell/plink/).

Standard protocol approvals, registrations, and patient consents.

The study was approved by the corresponding local ethics committee, and all patients gave their informed consent.

RESULTS

In the first phase of the study, a total of 384 SNPs were genotyped in 830 patients with MS classified according to their clinical response to IFN-β treatment. The results of the allelic association analysis for all the study cohorts (combined and stratified by MS center) are shown in table e-2. The most significant SNPs associated with the response to IFNβ were selected for further validation (table 2): (1) rs832032 (GABRR3; p = 0.0006), (2) rs6597 (STUB1; p = 0.019), (3) rs3747517 (IFIH1; p = 0.010), (4) rs2277302 (PELI3; p = 0.017), (5) rs10958713 (IKBKB; p = 0.003), (6) rs2834202 (IFNAR1; p = 0.030), and (7) rs4422395 (CXCL1; p = 0.017).

Table 2.

Summary of results of allele association analysis

In a second phase of the study, these 7 SNPs were genotyped in an independent cohort of 555 patients with MS classified according to the same clinical criteria as in phase I. As depicted in table 2, allelic analysis in this validation cohort showed a lack of statistically significant associations between selected polymorphisms from phase I and the response to IFN-β (figure e-1). Combined analysis in the whole cohort of patients with MS (N = 1,385; table 2) revealed 2 SNPs associated with the response to IFN-β with p values below 0.01: rs2277302, a synonymous polymorphism located in the PELI3 gene; and rs832032, a polymorphism located in the GABRR3 gene that results in a premature stop codon and protein truncation.

A classification of the response to IFN-β based only on the number of relapses during the first 2 years of treatment was performed in a subgroup of 526 patients with MS from the screening cohort. It did not result in stronger associations with the response to IFN-β relative to the combined classification including relapses and EDSS progression (table e-3 for criteria 1; table e-4 for criteria 2).

DISCUSSION

Previous pharmacogenomic studies have suggested a role for genes responsive to type I IFNs, genes related to the TLR pathway, and genes coding for neurotransmitter receptors in the response to IFN-β treatment in patients with MS.^4–6 Prompted by these observations, we genotyped an extensive panel of polymorphisms positioned in genes within these pathways in a large multicenter cohort—the largest thus far published in the field—of responders and nonresponders to IFN-β. Classification of patients with MS into IFN-β responders and nonresponders was conducted after 2 years of follow-up according to common stringent criteria based on the presence of relapses and progression of the EDSS score. These clinical criteria were similar to those used in previous studies.^4–6

Despite the statistically significant associations with the response to IFN-β observed for some of the SNPs genotyped in the screening phase of the study, none of these associations remained significant following genotyping in an independent cohort of responders and nonresponders to IFN-β. While the reasons for this lack of association observed between selected polymorphisms from the screening phase and the response to IFN-β are manifold, a lack of statistical power to detect statistically significant associations in the validation cohort cannot totally be ruled out. In fact, combined analysis in the whole cohort of 1,385 patients with MS revealed significant associations for 2 polymorphisms, rs832032 and rs2277302.

Rs832032 is located in the GABRR3 gene and encodes a subunit of the GABA(C) receptor. It is interesting that this polymorphism results in a premature stop codon and subsequent protein truncation. At present, there is no evidence in the literature of a functional relationship between GABA(C) receptors and IFN-β mechanism of action.

Rs2277302 is a synonymous SNP located in the PELI3 gene, which codes for an E3 ubiquitin protein ligase involved in TLR signaling. In a recent publication,¹⁰ Peli3-deficient mice expressed increased levels of endogenous IFN-β in response to TLR3 activation, and an autoregulation mechanism was proposed in which PELI3 inhibited type I IFNs by targeting IFN regulatory factor 7. These findings are in agreement with previous data from our group revealing increased baseline levels of endogenous IFN-β in peripheral blood cells from IFN-β nonresponders.¹¹

Overall, results from this study do not support an association between polymorphisms positioned in genes belonging to the type I IFN or TLR pathways or genes coding for GABA or glutamate receptors and the clinical response to IFN-β. Findings with rs832032 and rs2277302 polymorphisms warrant further studies exploring the role of GABRR3 and PELI3 in the response to IFN-β in larger cohorts of patients with MS.

GLOSSARY

EDSS

Expanded Disability Status Scale

GABA

γ-aminobutyric acid

IFN

interferon

MS

multiple sclerosis

SNP

single nucleotide polymorphism

TLR

toll-like receptor

AUTHOR CONTRIBUTIONS

M.F. Bustamante: study concept and design, manuscript drafting and revision, genotyping, analysis and interpretation of the data. C. Morcillo-Suárez: manuscript drafting and revision, statistical analysis, analysis and interpretation of the data. S. Malhotra: manuscript revision, genotyping. J. Rio: manuscript revision, contribution of patients and clinical data. L. Leyva: manuscript revision, contribution of patients and clinical data. O. Fernández: manuscript revision, contribution of patients and clinical data. U.K. Zettl: manuscript revision, contribution of patients and clinical data. J. Killestein: manuscript revision, contribution of patients and clinical data. D. Brassat: manuscript revision, contribution of patients and clinical data. J.A. García-Merino: manuscript revision, contribution of patients and clinical data. A.J. Sánchez: manuscript revision, contribution of patients and clinical data. E. Urcelay: manuscript revision, contribution of patients and clinical data. R. Alvarez-Lafuente: manuscript revision, contribution of patients and clinical data. L.M. Villar: manuscript revision, contribution of patients and clinical data. J.C. Álvarez-Cermeño: manuscript revision, contribution of patients and clinical data. X. Farré manuscript revision, statistical analysis supervision, analysis and interpretation of the data. J. Lechner-Scott: manuscript revision, contribution of patients and clinical data. K. Vandenbroeck: manuscript revision, contribution of patients and clinical data. A. Rodríguez-Antigüedad: manuscript revision, contribution of patients and clinical data. J.S. Drulovic: manuscript revision, contribution of patients and clinical data. F. Martinelli Boneschi: manuscript revision, contribution of patients and clinical data. A. Chan: manuscript revision, contribution of patients and clinical data. J. Oksenberg: manuscript revision, contribution of patients and clinical data. A. Navarro: manuscript revision, statistical analysis supervision, analysis and interpretation of the data. X. Montalban: manuscript revision, contribution of patients and clinical data, study concept and design, analysis and interpretation of the data. M. Comabella: manuscript drafting and revision, study concept and design, analysis and interpretation of the data, study supervision.

STUDY FUNDING

No targeted funding reported.

DISCLOSURE

M.F. Bustamante and C. Morcillo-Suárez report no disclosures. S. Malhotra received research suport from FIS, Agència de Gestió d'Ajuts Universitaris i de Recerca, and Marie Curie Initial Training Network. J. Rio served on the advisory boards of Biogen-Idec, Genzyme, and Novartis and received speaker honoraria from Schering-Bayer, Serono, Biogen, and Teva. L. Leyva reports no disclosures. O. Fernández served on the advisory boards for Biogen-Idec, Bayer-Schering, Merck-Serono, Teva, Novartis, Actelion, Almirall, and Allergan; has received travel funding and/or speaker honoraria from Biogen-Idec, Bayer-Schering, Merck-Serono, Teva, Novartis, Actelion, Almirall, and Allergan; and received research support from Biogen-Idec, Bayer-Schering, Merck-Serono, Teva, Novartis, Actelion, Almirall, and Allegan. U.K. Zettl received speaker honoraria and travel grants from Bayer, Aventis, Teva, Merck-Serono, and Biogen-Idec. J. Killestein served on the advisory boards for Novartis, Merck-Serono, Biogen-Idec, Genzyme, and Teva; received speaker honoraria from Biogen, Novartis, Teva, and Merck-Serono; has consulted for Novartis, Merck-Serono, Biogen, and Teva; and received research support from Schering AG, Biogen, Merck-Serono, Teva, Genzyme, and Novartis. D. Brassat received travel funding and/or speaker honoraria from Biogen, Sanofi, Genzyme, Teva, Merck-Serono, Bayer, and Almirall; served on the scientific advisory board for Chugai; and received research support from French Ministry of Health, French Multiple Sclerosis Society, and European Union. J.A. García-Merino is on the scientific advisory board for Biogen, Novartis, Roche, Genzyme, and Merck; received travel funding and/or speaker honoraria from Biogen, Novartis, Almirall, Genzyme, Roche, and Merck; and received research support from Novartis, Biogen, and FIS. A.J. Sánchez and E. Urcelay report no disclosures. R. Alvarez-Lafuente is on the scientific advisory board for HHV-6 Foundation; received travel funding from Novartis, Merck, Biogen, Bayer, Teva, and Sanofi; is on the editorial board for ISRN Infectious Diseases, Austin Virology and Retro Virology, and Austin Journal of Multiple Sclerosis & Neuroimmunology; is on the speakers' bureau for Novartis, Merck, and Biogen; and received research support from Novartis, Merck, Bayer, Teva, ISCIII-FEDER, ISCIII, RETICS-REEM, Fundacion Mutua Madrilena, Fundacion Lair, Fundacion Salud, and Fundacion Genzyme. L.M. Villar received travel funding and/or speaker honoraria from Biogen, Merck-Serono, Novartis, Genzyme, Teva, Bayer, and Roche; holds a patent for biomarker for selecting good responders to IFN beta in multiple sclerosis; has consulted for Bayer and Biogen; and received research support from Genzyme, Merck-Serono, Spanish Ministry of Economy and Competitiveness, and MS Research. J.C. Alvarez-Cermeño is on the scientific advisory board for Biogen-Idec, Novartis, Genzyme, Roche, and Merck-Serono; received speaker honoraria from Biogen-Idec, Novartis, Genzyme, and Merck-Serono; and received research support from Biogen-Idec, Novartis, Merck-Serono, Carlos III Institute, and Spanish Ministry of Economy. X. Farré reports no disclosures. J. Lechner-Scott served on the scientific advisory board for Bayer, Biogen, Novartis, and Teva; received travel funding and speaker honoraria from Biogen, Novartis, and Merck-Serono; and received research support from Biogen, Novartis, Hunter Medical Institute, University of Newcastle Australia, and John Hunter Charitable Trust. K. Vandenbroeck received royalties from Taylor & Francis Group and received research support from Ministerio de Economía y Competitividad (MINECO)—Convocatoria Proyectos de Investigación, Funciones intracelulares de tipo-no-citoquina de las subunidades de la familia IL-12, Ayudas para Grupos de Investigación del Sistema Universitario Vasco—Gobierno Vasco, Neurogenomiks Group. A. Rodríguez-Antigüedad reports no disclosures. J.S. Drulovic served on the scientific advisory board for Novartis, Merck, and Bayer; received travel funding and/or speaker honoraria from Novartis, Merck, Medis, and Bayer-Schering; and received research support from Ministry of Education and Science of Republic of Serbia. F. Martinelli Boneschi received travel funding and speaker honoraria from Genzyme, Teva, and Biogen; is on the speakers' bureau for Biogen, Teva, and Genzyme; and received research support from Teva, Biogen, Genzyme, GW-Pharma, and Ministero della Salute, Fondazione Cariplo, Foundazione Italiana Sclerosi Multipla. A. Chan served on the scientific advisory board for Bayer-Schering, Biogen, Genzyme, Merck-Serono, Novartis Pharma, Sanofi-Aventis, Teva, and UCB; received speaker honoraria from Allmirall, Bayer-Schering, Biogen, Genzyme, Merck-Serono, Novartis, Sanofi-Aventis, and Teva; holds a patent for proteomic profile of NMO; has consulted for Bayer-Schering, Biogen, Genzyme, Merck-Serono, Novartis, Sanofi-Aventis, and Teva; received research support from Biogen, Novartis, Genzyme, German Ministry for Education and Research, and Ruhr University Bochum; and was an expert consultant for Sanofi-Aventis. J. Oksenberg is on the editorial board for Genes & Immunity and received research support from NIH and National Multiple Sclerosis Society. A. Navarro is on the editorial board for Bioinformatics, Evolution, and Biology Letters and received research support from Red Española de Esclerosis Múltiple (REEM), Spanish Instituto de Salud Carlos III Instituto Nacional de Bioinformática, and Dirección General de Investigación Científica y Técnica (DGICYT), Spanish Government. X. Montalban served on the scientific advisory board for Novartis, Teva, Merck, Biogen, Bayer, Almirall, Actelion, Genzyme, Octapharma, Receptos, Roche, Sanofi, and Trophos; received travel funding from Novartis, Teva, Merck, Biogen, Bayer, Almirall, Acetelion, Genzyme, Octapharma, Receptos, Roche, Sanofi, and Trophos; is on the editorial board for Multiple Sclerosis, Journal of Neurology, The International MS Journal, Revista Neurologia, and Therapeutic Advances in Neurological Disorders; has consulted for Novartis, Teva, Merck, Biogen, Bayer, Almirall, Acetelion, Genzyme, Octapharma, Receptos, Roche, Sanofi, and Trophos; and received research support from Multiple Sclerosis Foundation of Barcelona. M. Comabella received speaker honoraria from Bayer-Schering, Merck-Serono, Teva, Sanofi-Aventis, Genzyme, and Novartis; is on the editorial board for Journal of Neuroimmunology, Multiple Sclerosis Journal, and PLoS ONE; and received research support from Red Española de Esclerosis Múltiple (REEM) and Fondo de Investigación Sanitaria. Go to Neurology.org/nn for full disclosure forms.