The Pharmacogenomics Journal (2015), 1–4 © 2015 Macmillan Publishers Limited All rights reserved 1470-269X/15 www.nature.com/tpj
ORIGINAL ARTICLE
Replication of PTPRC as genetic biomarker of response to TNF inhibitors in patients with rheumatoid arthritis A Ferreiro-Iglesias1, A Montes1, E Perez-Pampin1, JD Cañete2, E Raya3, C Magro-Checa3, Y Vasilopoulos4, T Sarafidou4, R Caliz5, MA Ferrer5, B Joven6, P Carreira6, A Balsa7, D Pascual-Salcedo8, FJ Blanco9,10, MJ Moreno-Ramos11, A Fernández-Nebro12, MC Ordóñez12, JJ Alegre-Sancho13, J Narváez14, F Navarro-Sarabia15, V Moreira15, L Valor16, R García-Portales17, A Marquez18, J Martin18, JJ Gómez-Reino1,10 and A Gonzalez1 Genetic biomarkers could be useful for orienting treatment of patients with rheumatoid arthritis (RA), but none has been convincingly validated yet. Putative biomarkers include 14 single nucleotide polymorphisms that have shown association with response to TNF inhibitors (TNFi) in candidate gene studies and that we assayed here in 755 RA patients. Three of them, in the PTPRC, IL10 and CHUK genes, were significantly associated with response to TNFi. The most significant result was obtained with rs10919563 in PTPRC, which is a confirmed RA susceptibility locus. Its RA risk allele was associated with improved response (B = 0.33, P = 0.006). This is the second independent replication of this biomarker (P = 9.08 × 10 − 8 in the combined 3003 RA patients). In this way, PTPRC has become the most replicated genetic biomarker of response to TNFi. In addition, the positive but weaker replication of IL10 and CHUK should stimulate further validation studies. The Pharmacogenomics Journal advance online publication, 21 April 2015; doi:10.1038/tpj.2015.29
INTRODUCTION One of the main challenges in the treatment of rheumatoid arthritis (RA) is the election of biologic Disease Modifying AntiRheumatic Drugs.1–3 These drugs show a comparable level of efficacy, similar contraindications and low rate of adverse events. However, individual patients show large variability in response. The causes of this variability are unknown, and only a serum antibody biomarker of response to a biologic Disease Modifying Anti-Rheumatic Drugs (rituximab) has been widely validated yet.4–6 Other putative biomarkers, specially genetic biomarkers for response to the three most commonly used TNF inhibitors (TNFi): infliximab, etanercept (ETC) and adalimumab have been proposed.4,7 Two of these putative biomarkers were replicated in a second study. The first one, in the IL10 gene, was identified in an early study addressing selected single nucleotide polymorphisms (SNPs) in cytokine genes.8 It was replicated in the first genomewide association study (GWAS) of response to TNFi,9 but not in subsequent GWAS on this phenotype.10–13 The second replicated biomarker, in the PTPRC gene, was identified in a study considering the RA susceptibility loci as candidate genes.14 The RA risk allele of this locus was associated with improved response to TNFi. This result was confirmed in a replication study.15 Other putative biomarkers of response to TNFi have been reported only in a 1
study. They include three SNPs in RA susceptibility loci,16,17 two SNPs in candidate genes of the TLR and NFκB pathways18 and seven SNPs of the p38 MAPK signaling pathway.19 Here, we show that three of the 14 putative biomarkers analyzed were associated with response to TNFi in our collection of 755 patients with RA. These results define the PTPRC locus as a well-validated biomarker of response to TNFi treatment and encourage further research in this field. MATERIALS AND METHODS Patients Biologic-naive patients with RA according to the 1987 classification criteria20 were included. They were either of self-reported Spanish Caucasian ancestry (n = 731) or of Greek Caucasian ancestry (n = 57). All the patients provided their informed written consent and the study was approved by the local ethics committees. Health care of the patients was performed with independence of this study. Evaluations included Disease Activity Score 28 (DAS28) at the start of treatment and at 3, 6 and 12 months.21 Patients with baseline DAS28o3.2 (that is, showing low activity, n = 13), and samples failing most genotypes (n = 20) were excluded from further analysis. The remaining 755 patients were distributed as: 397 with infliximab, 155 with ETC and 203 with adalimumab. Their clinical characteristics are detailed in Table 1.
Laboratorio de Investigacion 10 and Rheumatology Unit, Instituto de Investigacion Sanitaria—Hospital Clinico Universitario de Santiago, Santiago de Compostela, Spain; Rheumatology Unit, Hospital Clinic, IDIBAPS, Barcelona, Spain; 3Department of Rheumatology, Hospital Clínico San Cecilio, Granada, Spain; 4Department of Biochemistry and Biotechnology, University of Thessaly, Larissa, Greece; 5Rheumatology Unit, Hospital Universitario Virgen de las Nieves, Granada, Spain; 6Reumatology Department, Hospital 12 de Octubre, Madrid, Spain; 7Department of Rheumatology and Institute for Health Research (IdiPAZ), University Hospital La Paz. Madrid, Spain; 8Immunology Unit, Instituto de Investigación Hospital Universitario La Paz, Hospital Universitario La Paz, Madrid, Spain; 9Servicio de Reumatología. Instituto de Investigación Biomédica de A Coruña (INIBIC), Complexo Hospitalario Universitario de A Coruña (CHUAC), Sergas, Universidade da Coruña (UDC), A Coruña, Spain; 10Department of Medicine, University of Santiago de Compostela, Santiago de Compostela, Spain; 11Department of Rheumatology, Hospital Virgen de la Arrixaca, Murcia, Spain; 12UGC de Reumatología, Instituto deInvestigación Biomédica de Málaga (IBIMA), HRU de Málaga, Universidad de Málaga, Málaga, Spain; 13Department of Rheumatology, Hospital Doctor Peset, Valencia, Spain; 14Department of Rheumatology, Hospital Universitario de Bellvitge, Barcelona, Spain; 15Rheumatology Unit, Hospital Universitario Virgen Macarena, Sevilla, Spain; 16Rheumatology Unit, Hospital General Universitario Gregorio Marañón, Madrid, Spain; 17Department of Rheumatology, Hospital Virgen de la Victoria, Málaga, Spain and 18Instituto de Parasitología y Biomedicina López-Neyra, CSIC, Granada, Spain. Correspondence: Dr A Gonzalez, Laboratorio Investigacion 10, Hospital Clinico Universitario de Santiago, Edificio de consultas, planta -2, Travesia de Choupana, sn, Santiago de Compostela 15706, Spain. E-mail: [email protected] Received 9 October 2014; revised 16 February 2015; accepted 2 March 2015 2
PTPRC genetic biomarker of response to TNFi A Ferreiro-Iglesias et al
2 Genotyping Fourteen SNPs were selected because of previous evidence of association with the response to TNFi in patients with RA (Supplementary Table 1).8,14,16–19 An additional PTPRC SNP (rs6683595) was selected as the putative causal polymorphism of that RA locus.22 The 15 SNPs were genotyped by single-base extension with the SNaPshot Multiplex Kit (Applied Biosystems, Foster City, CA, USA). Primers and probes used for these analyses are available from the authors upon request.
Statistical analyses We have used duplicate genotypes of 10% of the samples, genotype call rate, the Hardy–Weinberg equilibrium and SNP frequencies in the HapMap Toscani in Italy collection23 for quality control. We have evaluated the response to TNFi primarily as change in DAS28 at 3, 6 and 12 months of follow-up (ΔDAS28 = DAS28baseline − DAS28follow-up). In addition, we have also considered the responder (good+moderate) or non-responder classification according to the European League Against Rheumatism (EULAR) criteria.24 Generalized linear models for ΔDAS28 and logistic regression models for EULAR response were fitted. Genotypes were considered according with an additive genetic model of minor allele counts (0, 1 or 2). Therefore, positive regression coefficients indicate a better response associated with minor allele additive effects. Covariates included in the models were baseline DAS28, gender, the specific TNFi and
Table 1. Clinical characteristics of the 755 patients with RA included in this study Characteristic
Value
Age, mean ± s.d. years Women, N (%) Age at diagnosis, mean ± s.d. years Diagnosis to TNFi treatment, mean ± s.d. years Rheumatoid factor positive, N (%) Erosive arthritis, N (%)a Anti-CCP positive, N (%)a Smoking habit, N (%)a Previous cDMARDS, mean ± s.d. Baseline DAS28, mean ± s.d. Baseline HAQ, mean ± s.d.a Baseline ESR, mean ± s.d.a Baseline CRP, mean ± s.d. a Concomitant cDMARDs, N (%)
60.7 ± 13.4 624 (82.7) 43.2 ± 14.1 8.0 ± 7.5 560 (74.4) 422 (70.7) 423 (70.6) 100 (20.0) 2.64 ± 1.3 5.8 ± 1.1 1.5 ± 0.7 39.3 ± 24.6 13.8 ± 22.5 547 (95.0)
TNFi, N (%) Infliximab Etanercept Adalimumab
397 (52.6) 155 (20.5) 203 (26.9)
EULAR response, N (%) 3 months (N = 452) Good responder Moderate responder Non responder 6 months (N = 689) Good responder Moderate responder Non responder 12 months (N = 531) Good responder Moderate responder Non responder
137 (30.3) 221 (48.9) 94 (20.8) 262 (38.0) 291 (42.2) 136 (19.7) 242 (45.6) 193 (36.4) 96 (18.1)
Abbreviations: cDMARD, conventional disease-modifying anti-rheumatic drug; CRP, C-reactive protein; DAS28, Disease Activity Score 28 joints; ESR, erythrocyte sedimentation rate; EULAR, The European League Against Rheumatism; HAQ, Health Assessment Questionnaire; SD, standard deviation; TNFi, TNF inhibitor; anti-CCP, anti-cyclic citrullinated peptide antibodies. aData from o85% of the patients were available: 599 for antiCCP, 501 for smoking, 576 for concomitant cDMARDs, 482 for baseline CRP, 526 for baseline ESR, 528 patients for baseline HAQ and 597 for erosive arthritis.
The Pharmacogenomics Journal (2015), 1 – 4
the Spanish or Greek origin. Statistica 7.0 (Statsoft, Tulsa, OK, USA) software was used to perform these analyses.
RESULTS The 755 patients with RA showed characteristics of a severe disease (Table 1). The selected SNPs (Supplementary Table 1) included 14 that have been reported as biomarkers of RA response to TNFi in candidate gene studies8,14,16–19 and the putative causal SNP of the PTPRC RA susceptibility locus.22 Genotypes of the 15 SNPs passed our quality control filters on call rate (499%), reproducibility (100%), fit to Hardy–Weinberg equilibrium (P40.05), and consistency with HapMap frequencies (all pair-wise comparisons P40.05). Three of the response biomarkers were replicated when the ΔDAS28 outcome was considered (Table 2). The first significant result was association of the minor allele of IL10 rs1800896 with improved ΔDAS28 response at 3 months of follow-up (Table 2). This result replicated an early study of selected cytokine polymorphisms in patients treated with ETC,8 and the first genome-wide association study (GWAS) of response to TNFi.9 The two studies found association with the same direction of change and at the same time of follow-up as we have found here. However, drug stratified analysis did not show an effect of this SNP in the subset of our patients treated with ETC (B = 0.04 ± 0.18; P = 0.8). The next replication was obtained with rs11591741 located in CHUK that was associated with response to TNFi at 3 months in our analysis (Table 2). The direction of change matched the previous described,18 with the minor allele G linked to less ΔDAS28 improvement. However, two other particularities of the initial report were not reproduced. Namely, the CHUK association was initially identified as driven by patients treated with ETC and evaluated only at 6 months of follow-up;18 in our study, it was driven by patients treated with infliximab or adalimumab and evaluated at 3 months. The third significant association with response was the clearest one. It was obtained with the PTPRC rs10919563 SNP (Table 2). This association was observed at 6 months of follow-up and showed improved response with the RA risk allele. This was also the direction of change observed in previous studies,14,15 which were performed either at 6 months of follow-up,15 or at the 3–12 months range of follow-up times.14 In addition, the putative causal polymorphism for the PTPRC RA locus, rs6683595, showed a stronger association than rs10919563 in the analysis of ΔDAS28 at 6 months of follow-up (Table 2). In addition, it showed a significant difference between responder and non-responder patients (P = 0.03) not observed with rs10919563, the other PTPRC SNP (Table 2). None of the other SNPs was associated at any of the times analyzed or with the EULAR response criteria (Table 2). In addition, we did not replicate association of the multivariate model including five SNPs of the p38 MAPK pathway19 (not shown). DISCUSSION Our results contribute to the validation of three biomarkers of response to treatment in RA. The PTPRC and IL10 associations are specially remarkable, because they were already the most replicated biomarkers of the 14 explored.8,9,14,15 In addition, the three biomarkers have reproduced the same genotype/ response relationships previously reported.8,9,14,15,18 Therefore, these promising results should encourage further studies in this field, including those aiming to their confirmation in additional samples. Support for the three replicated biomarkers is not equivalent. The PTPRC locus stands out as the most convincing. It is supported by the large size of the three studies showing significant association with rs10919563 (3003 patients in total), and their © 2015 Macmillan Publishers Limited
© 2015 Macmillan Publishers Limited
Logistic regression. bLinear regression were adjusted for DAS28 at baseline, gender, TNFi drug and ancestry. cResults at 12 months of follow-up are not shown. dAbbreviations: CI, confidence interval; B, regression coefficient of the linear regression; NR, non responder: OR, odds ratio that is presented relative to the minor allele; R, responder (good+moderate responders); SE, standard error. a
1 0.9 0.9 1 0.4 0.05 0.6 0.9 0.5 0.08 0.007 0.5 0.3 0.4 0.0003 (0.07) (80.10) (0.07) (0.07) (0.09) (0.08) (0.11) (0.07) (0.08) (0.08) (0.12) (0.07) (0.07) (0.11) (0.11) 0.002 − 0.02 − 0.01 − 0.004 − 0.08 − 0.16 0.06 − 0.01 0.06 − 0.13 − 0.33 − 0.04 0.07 − 0.09 −0.40 0.3 0.3 0.5 0.3 0.9 0.2 0.4 0.6 0.12 0.4 0.11 0.5 0.5 0.9 0.03 (0.7–1.1) (0.8–1.9) (0.7–1.2) (0.9–1.5) (0.7–1.5) (0.6–1.1) (0.8–2.0) (0.7–1.2) (0.9–1.8) (0.7–1.2) (0.4–1.1) (0.7–1.2) (0.8–1.5) (0.6–1.5) (0.4–0.97) 0.86 1.26 0.9 1.15 1.03 0.82 1.24 0.94 1.29 0.89 0.69 0.91 1.1 0.96 0.64 42.2 12.5 47.4 45.9 15.9 32.2 8.9 45.1 26.1 32.1 12.9 48.5 45.2 11.4 15.8 38.6 14.4 44.1 49 17.2 26.9 10.6 44.4 30.2 28.5 9.2 45.5 47.4 11.5 10.7 0.01 0.6 0.041 0.4 0.3 0.4 0.3 0.8 0.4 0.3 0.7 0.8 0.8 0.8 0.8 (0.09) (0.12) (0.09) (0.09) (0.11) (0.10) (0.14) (0.09) (0.09) (0.09) (0.15) (0.09) (0.09) (0.13) (0.14) 0.22 0.06 − 0.18 0.08 0.11 − 0.09 0.15 0.02 0.08 − 0.09 0.06 − 0.02 − 0.02 − 0.03 0.03 0.11 0.05 0.3 0.5 0.14 0.9 0.6 0.4 0.7 0.7 0.3 0.5 0.6 0.7 0.5 (0.9–1.9) (0.99–2.8) (0.6–1.2) (0.8–1.6) (0.9–2.3) (0.7–1.5) (0.7–2.0) (0.8–1.6) (0.8–1.6) (0.7–1.3) (0.5–1.3) (0.8–1.6) (0.7–1.3) (0.6–1.5) (0.5–1.4) 1.33 1.65 0.85 1.11 1.42 1.04 1.16 1.14 1.08 0.94 0.77 1.13 0.91 0.91 0.84 41.5 16.9 44.4 49.4 19 26.8 10 48.3 29.7 27.9 9.7 49 47.3 12.1 11.5 IL-10 MYD88 CHUK MAP2K6 MAPKAPK2 RPS6KA5 MAPK14 MAP2K6 RPS6KA4 RPS6KA5 PTPRC AFF3 CD226 TRAF6 PTPRC
rs1800896 rs7744 rs11591741 rs2716191 rs4240847 rs1286076 rs916344 rs11656130 rs475032 rs1286112 rs10919563 rs10865035 rs763361 rs540386 rs6683595
34.9 11.2 48.9 47.3 13.6 26.9 8.7 45.2 28 30.1 12.2 45.7 49.5 12.8 13.3
P-value R P-value B (SE)
3 months
P-value OR (95% CI ) NR d
R SNP Locus
Table 2.
Results of the association analysis using either EULAR criteriaa or ΔDAS28b as the outcome of response to TNFic
NR
OR (95% CI )
6 months
B (SE)
P-value
PTPRC genetic biomarker of response to TNFi A Ferreiro-Iglesias et al
combined P-value (P = 9.08 × 10 − 8), which approaches the GWAS significance threshold in fixed-effect meta-analysis. In contrast, previous studies showing association of the IL10 rs1800896 SNP were small (123 and 89 patients, respectively), and their P-values were modest (P = 0.04 and 0.01),8,9 as in the present study (P = 0.01). Even more limited is the support for the CHUK rs11591741 SNP, which is replicated here for the first time. Inclusion in the study of rs668395 has been rewarding because it showed a stronger association than rs10919563, with which it is in LD (r2 = 0.86). This result exemplifies the improvement in association due to causal polymorphisms in place of tagSNPs and indicates that replacing rs10919563 by rs668395 in future studies will increase their power. The absence of significant association of these biomarkers in the GWAS of response to TNFi is not a strong argument against them because of the insufficient power of current GWAS (the largest included 2706 patients).10 In addition, these loci seem to show time-specificity in their association with response to TNFi, and most GWAS have combined multiple follow-up times. The time-specificity is supported by the PTPRC and IL10 SNPs, which were only associated at the same follow-up time as in previous studies15,8,9 and by other two biomarkers of response to TNFi that have previously shown time-specificity25 (Montes A. et al. Arthritis Research and Therapy, in press). These caveats apply also to a study that did not replicate association of rs1091956 in 233 patients assessed at various times of follow-up.26 Another study did not replicate association of rs10919563 and rs11591741 in 183 patients.27 In this study, response to TNFi of all patients was assessed at 6 months, but only with the EULAR criteria, which leads to less powerful analyses than ΔDAS28 (as shown here and in other studies,28 and in agreement with their respective dichotomous/continuous nature29). The potential usefulness of the replicated biomarkers can already be envisaged by considering the difference in ΔDAS28 between the genotypes. For example, the differences between the TT genotype and the two other genotypes (CC+TC genotypes) at rs6683595 (1.23 and 0.71, respectively) are larger than the ΔDAS28 considered clinically significant in the EULAR criteria (0.6).24 These differences are also larger than the difference in ΔDAS28 between sero-negative and sero-positive patients treated with rituximab (0.35). Difference that is widely used to rule out rituximab for sero-negative patients.5,6 Therefore, it is tempting to suggest that the TT patients should not be treated with a TNFi. However, further replication is required before reaching any firm conclusion about the utility of this or the other two replicated biomarkers. In addition, the frequency of the TT genotype at rs6683595 is small (1.0%), limiting its clinical interest. The replicated biomarkers could also contribute to the knowledge of RA disease pathogenesis. In this regard, association of the same allele of PTPRC with risk to RA and with an improved response to TNFi is particularly intriguing. It suggests that unleashed immune reactivity predisposes to RA, but also leads to increased sensitivity to TNFi of the ensuing inflammation. The known function of PTPRC, also called as CD45, which is a receptortype protein tyrosine phosphatase critical for controlling signals mediated through TCR, BCR and cytokine receptors, is congruent with this hypothesis. In addition, identification of the putative causal SNP rs6683595 has focused attention to CD4 Tregs due to a cell-specific H3K4me3 epigenetic mark surrounding it.22 The Treg cells are suspected to have a critical involvement both in RA pathology and in the response to biologic Disease Modifying AntiRheumatic Drugs, but available studies with RA patients have been inconclusive.30 Therefore, investigation of the role of this biomarker could contribute to clarify these aspects of RA and to define the role of Tregs as therapeutic targets. The rs1800896 SNP in IL10 (also called − 1802A/G) is also a cis regulatory polymorphism, although its relevance for clinical phenotypes remains to be established.31 Its G allele determines high expression of IL10 owing The Pharmacogenomics Journal (2015), 1 – 4
3
PTPRC genetic biomarker of response to TNFi A Ferreiro-Iglesias et al
4
to preferential binding of the Sp1 transcription factor.32,33 This is the same allele associated with improved response to TNFi here and in the two previous studies.8,9 Therefore, the most direct interpretation of the results is that increased IL10 expression contributes to the action of TNFi, likely through suppressive effects. In contrast, the CHUK SNP rs11591741 has no known function. Therefore, experiments exploring the involvement of the three biomarkers in response to TNFi could increase our knowledge of RA. In conclusion, we have contributed to establish the PTPRC RA locus as the most widely replicated biomarker of response to TNFi to date. Our results also support rs6683595 as the putative causal SNP at this locus and reinforce the implication of the RA risk allele in a better response to TNFi. In addition, replication of two other proposed biomarkers, in IL10 and CHUK, should stimulate further efforts for their validation.
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CONFLICT OF INTEREST The authors declare no conflict of interest.
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ACKNOWLEDGMENTS We thank Carmen Pena-Pena for technical assistance and Manuel Calaza for help with data management. We also thank Arturo R de la Serna and Berta Magallares of the Hospital Santa Creu e San Pau (Barcelona, Spain) for providing samples. Funding was provided by grants PI11/01048, PI12/01909 and by RETICS Program, RD12/0009/0008 and RD12/0009/0004 (RIER) of the Instituto de Salud Carlos III (Spain) that are partially financed by the European Regional Development Fund of the European Union.
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Supplementary Information accompanies the paper on the The Pharmacogenomics Journal website (http://www.nature.com/tpj)
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© 2015 Macmillan Publishers Limited
ORIGINAL ARTICLE
Replication of PTPRC as genetic biomarker of response to TNF inhibitors in patients with rheumatoid arthritis A Ferreiro-Iglesias1, A Montes1, E Perez-Pampin1, JD Cañete2, E Raya3, C Magro-Checa3, Y Vasilopoulos4, T Sarafidou4, R Caliz5, MA Ferrer5, B Joven6, P Carreira6, A Balsa7, D Pascual-Salcedo8, FJ Blanco9,10, MJ Moreno-Ramos11, A Fernández-Nebro12, MC Ordóñez12, JJ Alegre-Sancho13, J Narváez14, F Navarro-Sarabia15, V Moreira15, L Valor16, R García-Portales17, A Marquez18, J Martin18, JJ Gómez-Reino1,10 and A Gonzalez1 Genetic biomarkers could be useful for orienting treatment of patients with rheumatoid arthritis (RA), but none has been convincingly validated yet. Putative biomarkers include 14 single nucleotide polymorphisms that have shown association with response to TNF inhibitors (TNFi) in candidate gene studies and that we assayed here in 755 RA patients. Three of them, in the PTPRC, IL10 and CHUK genes, were significantly associated with response to TNFi. The most significant result was obtained with rs10919563 in PTPRC, which is a confirmed RA susceptibility locus. Its RA risk allele was associated with improved response (B = 0.33, P = 0.006). This is the second independent replication of this biomarker (P = 9.08 × 10 − 8 in the combined 3003 RA patients). In this way, PTPRC has become the most replicated genetic biomarker of response to TNFi. In addition, the positive but weaker replication of IL10 and CHUK should stimulate further validation studies. The Pharmacogenomics Journal advance online publication, 21 April 2015; doi:10.1038/tpj.2015.29
INTRODUCTION One of the main challenges in the treatment of rheumatoid arthritis (RA) is the election of biologic Disease Modifying AntiRheumatic Drugs.1–3 These drugs show a comparable level of efficacy, similar contraindications and low rate of adverse events. However, individual patients show large variability in response. The causes of this variability are unknown, and only a serum antibody biomarker of response to a biologic Disease Modifying Anti-Rheumatic Drugs (rituximab) has been widely validated yet.4–6 Other putative biomarkers, specially genetic biomarkers for response to the three most commonly used TNF inhibitors (TNFi): infliximab, etanercept (ETC) and adalimumab have been proposed.4,7 Two of these putative biomarkers were replicated in a second study. The first one, in the IL10 gene, was identified in an early study addressing selected single nucleotide polymorphisms (SNPs) in cytokine genes.8 It was replicated in the first genomewide association study (GWAS) of response to TNFi,9 but not in subsequent GWAS on this phenotype.10–13 The second replicated biomarker, in the PTPRC gene, was identified in a study considering the RA susceptibility loci as candidate genes.14 The RA risk allele of this locus was associated with improved response to TNFi. This result was confirmed in a replication study.15 Other putative biomarkers of response to TNFi have been reported only in a 1
study. They include three SNPs in RA susceptibility loci,16,17 two SNPs in candidate genes of the TLR and NFκB pathways18 and seven SNPs of the p38 MAPK signaling pathway.19 Here, we show that three of the 14 putative biomarkers analyzed were associated with response to TNFi in our collection of 755 patients with RA. These results define the PTPRC locus as a well-validated biomarker of response to TNFi treatment and encourage further research in this field. MATERIALS AND METHODS Patients Biologic-naive patients with RA according to the 1987 classification criteria20 were included. They were either of self-reported Spanish Caucasian ancestry (n = 731) or of Greek Caucasian ancestry (n = 57). All the patients provided their informed written consent and the study was approved by the local ethics committees. Health care of the patients was performed with independence of this study. Evaluations included Disease Activity Score 28 (DAS28) at the start of treatment and at 3, 6 and 12 months.21 Patients with baseline DAS28o3.2 (that is, showing low activity, n = 13), and samples failing most genotypes (n = 20) were excluded from further analysis. The remaining 755 patients were distributed as: 397 with infliximab, 155 with ETC and 203 with adalimumab. Their clinical characteristics are detailed in Table 1.
Laboratorio de Investigacion 10 and Rheumatology Unit, Instituto de Investigacion Sanitaria—Hospital Clinico Universitario de Santiago, Santiago de Compostela, Spain; Rheumatology Unit, Hospital Clinic, IDIBAPS, Barcelona, Spain; 3Department of Rheumatology, Hospital Clínico San Cecilio, Granada, Spain; 4Department of Biochemistry and Biotechnology, University of Thessaly, Larissa, Greece; 5Rheumatology Unit, Hospital Universitario Virgen de las Nieves, Granada, Spain; 6Reumatology Department, Hospital 12 de Octubre, Madrid, Spain; 7Department of Rheumatology and Institute for Health Research (IdiPAZ), University Hospital La Paz. Madrid, Spain; 8Immunology Unit, Instituto de Investigación Hospital Universitario La Paz, Hospital Universitario La Paz, Madrid, Spain; 9Servicio de Reumatología. Instituto de Investigación Biomédica de A Coruña (INIBIC), Complexo Hospitalario Universitario de A Coruña (CHUAC), Sergas, Universidade da Coruña (UDC), A Coruña, Spain; 10Department of Medicine, University of Santiago de Compostela, Santiago de Compostela, Spain; 11Department of Rheumatology, Hospital Virgen de la Arrixaca, Murcia, Spain; 12UGC de Reumatología, Instituto deInvestigación Biomédica de Málaga (IBIMA), HRU de Málaga, Universidad de Málaga, Málaga, Spain; 13Department of Rheumatology, Hospital Doctor Peset, Valencia, Spain; 14Department of Rheumatology, Hospital Universitario de Bellvitge, Barcelona, Spain; 15Rheumatology Unit, Hospital Universitario Virgen Macarena, Sevilla, Spain; 16Rheumatology Unit, Hospital General Universitario Gregorio Marañón, Madrid, Spain; 17Department of Rheumatology, Hospital Virgen de la Victoria, Málaga, Spain and 18Instituto de Parasitología y Biomedicina López-Neyra, CSIC, Granada, Spain. Correspondence: Dr A Gonzalez, Laboratorio Investigacion 10, Hospital Clinico Universitario de Santiago, Edificio de consultas, planta -2, Travesia de Choupana, sn, Santiago de Compostela 15706, Spain. E-mail: [email protected] Received 9 October 2014; revised 16 February 2015; accepted 2 March 2015 2
PTPRC genetic biomarker of response to TNFi A Ferreiro-Iglesias et al
2 Genotyping Fourteen SNPs were selected because of previous evidence of association with the response to TNFi in patients with RA (Supplementary Table 1).8,14,16–19 An additional PTPRC SNP (rs6683595) was selected as the putative causal polymorphism of that RA locus.22 The 15 SNPs were genotyped by single-base extension with the SNaPshot Multiplex Kit (Applied Biosystems, Foster City, CA, USA). Primers and probes used for these analyses are available from the authors upon request.
Statistical analyses We have used duplicate genotypes of 10% of the samples, genotype call rate, the Hardy–Weinberg equilibrium and SNP frequencies in the HapMap Toscani in Italy collection23 for quality control. We have evaluated the response to TNFi primarily as change in DAS28 at 3, 6 and 12 months of follow-up (ΔDAS28 = DAS28baseline − DAS28follow-up). In addition, we have also considered the responder (good+moderate) or non-responder classification according to the European League Against Rheumatism (EULAR) criteria.24 Generalized linear models for ΔDAS28 and logistic regression models for EULAR response were fitted. Genotypes were considered according with an additive genetic model of minor allele counts (0, 1 or 2). Therefore, positive regression coefficients indicate a better response associated with minor allele additive effects. Covariates included in the models were baseline DAS28, gender, the specific TNFi and
Table 1. Clinical characteristics of the 755 patients with RA included in this study Characteristic
Value
Age, mean ± s.d. years Women, N (%) Age at diagnosis, mean ± s.d. years Diagnosis to TNFi treatment, mean ± s.d. years Rheumatoid factor positive, N (%) Erosive arthritis, N (%)a Anti-CCP positive, N (%)a Smoking habit, N (%)a Previous cDMARDS, mean ± s.d. Baseline DAS28, mean ± s.d. Baseline HAQ, mean ± s.d.a Baseline ESR, mean ± s.d.a Baseline CRP, mean ± s.d. a Concomitant cDMARDs, N (%)
60.7 ± 13.4 624 (82.7) 43.2 ± 14.1 8.0 ± 7.5 560 (74.4) 422 (70.7) 423 (70.6) 100 (20.0) 2.64 ± 1.3 5.8 ± 1.1 1.5 ± 0.7 39.3 ± 24.6 13.8 ± 22.5 547 (95.0)
TNFi, N (%) Infliximab Etanercept Adalimumab
397 (52.6) 155 (20.5) 203 (26.9)
EULAR response, N (%) 3 months (N = 452) Good responder Moderate responder Non responder 6 months (N = 689) Good responder Moderate responder Non responder 12 months (N = 531) Good responder Moderate responder Non responder
137 (30.3) 221 (48.9) 94 (20.8) 262 (38.0) 291 (42.2) 136 (19.7) 242 (45.6) 193 (36.4) 96 (18.1)
Abbreviations: cDMARD, conventional disease-modifying anti-rheumatic drug; CRP, C-reactive protein; DAS28, Disease Activity Score 28 joints; ESR, erythrocyte sedimentation rate; EULAR, The European League Against Rheumatism; HAQ, Health Assessment Questionnaire; SD, standard deviation; TNFi, TNF inhibitor; anti-CCP, anti-cyclic citrullinated peptide antibodies. aData from o85% of the patients were available: 599 for antiCCP, 501 for smoking, 576 for concomitant cDMARDs, 482 for baseline CRP, 526 for baseline ESR, 528 patients for baseline HAQ and 597 for erosive arthritis.
The Pharmacogenomics Journal (2015), 1 – 4
the Spanish or Greek origin. Statistica 7.0 (Statsoft, Tulsa, OK, USA) software was used to perform these analyses.
RESULTS The 755 patients with RA showed characteristics of a severe disease (Table 1). The selected SNPs (Supplementary Table 1) included 14 that have been reported as biomarkers of RA response to TNFi in candidate gene studies8,14,16–19 and the putative causal SNP of the PTPRC RA susceptibility locus.22 Genotypes of the 15 SNPs passed our quality control filters on call rate (499%), reproducibility (100%), fit to Hardy–Weinberg equilibrium (P40.05), and consistency with HapMap frequencies (all pair-wise comparisons P40.05). Three of the response biomarkers were replicated when the ΔDAS28 outcome was considered (Table 2). The first significant result was association of the minor allele of IL10 rs1800896 with improved ΔDAS28 response at 3 months of follow-up (Table 2). This result replicated an early study of selected cytokine polymorphisms in patients treated with ETC,8 and the first genome-wide association study (GWAS) of response to TNFi.9 The two studies found association with the same direction of change and at the same time of follow-up as we have found here. However, drug stratified analysis did not show an effect of this SNP in the subset of our patients treated with ETC (B = 0.04 ± 0.18; P = 0.8). The next replication was obtained with rs11591741 located in CHUK that was associated with response to TNFi at 3 months in our analysis (Table 2). The direction of change matched the previous described,18 with the minor allele G linked to less ΔDAS28 improvement. However, two other particularities of the initial report were not reproduced. Namely, the CHUK association was initially identified as driven by patients treated with ETC and evaluated only at 6 months of follow-up;18 in our study, it was driven by patients treated with infliximab or adalimumab and evaluated at 3 months. The third significant association with response was the clearest one. It was obtained with the PTPRC rs10919563 SNP (Table 2). This association was observed at 6 months of follow-up and showed improved response with the RA risk allele. This was also the direction of change observed in previous studies,14,15 which were performed either at 6 months of follow-up,15 or at the 3–12 months range of follow-up times.14 In addition, the putative causal polymorphism for the PTPRC RA locus, rs6683595, showed a stronger association than rs10919563 in the analysis of ΔDAS28 at 6 months of follow-up (Table 2). In addition, it showed a significant difference between responder and non-responder patients (P = 0.03) not observed with rs10919563, the other PTPRC SNP (Table 2). None of the other SNPs was associated at any of the times analyzed or with the EULAR response criteria (Table 2). In addition, we did not replicate association of the multivariate model including five SNPs of the p38 MAPK pathway19 (not shown). DISCUSSION Our results contribute to the validation of three biomarkers of response to treatment in RA. The PTPRC and IL10 associations are specially remarkable, because they were already the most replicated biomarkers of the 14 explored.8,9,14,15 In addition, the three biomarkers have reproduced the same genotype/ response relationships previously reported.8,9,14,15,18 Therefore, these promising results should encourage further studies in this field, including those aiming to their confirmation in additional samples. Support for the three replicated biomarkers is not equivalent. The PTPRC locus stands out as the most convincing. It is supported by the large size of the three studies showing significant association with rs10919563 (3003 patients in total), and their © 2015 Macmillan Publishers Limited
© 2015 Macmillan Publishers Limited
Logistic regression. bLinear regression were adjusted for DAS28 at baseline, gender, TNFi drug and ancestry. cResults at 12 months of follow-up are not shown. dAbbreviations: CI, confidence interval; B, regression coefficient of the linear regression; NR, non responder: OR, odds ratio that is presented relative to the minor allele; R, responder (good+moderate responders); SE, standard error. a
1 0.9 0.9 1 0.4 0.05 0.6 0.9 0.5 0.08 0.007 0.5 0.3 0.4 0.0003 (0.07) (80.10) (0.07) (0.07) (0.09) (0.08) (0.11) (0.07) (0.08) (0.08) (0.12) (0.07) (0.07) (0.11) (0.11) 0.002 − 0.02 − 0.01 − 0.004 − 0.08 − 0.16 0.06 − 0.01 0.06 − 0.13 − 0.33 − 0.04 0.07 − 0.09 −0.40 0.3 0.3 0.5 0.3 0.9 0.2 0.4 0.6 0.12 0.4 0.11 0.5 0.5 0.9 0.03 (0.7–1.1) (0.8–1.9) (0.7–1.2) (0.9–1.5) (0.7–1.5) (0.6–1.1) (0.8–2.0) (0.7–1.2) (0.9–1.8) (0.7–1.2) (0.4–1.1) (0.7–1.2) (0.8–1.5) (0.6–1.5) (0.4–0.97) 0.86 1.26 0.9 1.15 1.03 0.82 1.24 0.94 1.29 0.89 0.69 0.91 1.1 0.96 0.64 42.2 12.5 47.4 45.9 15.9 32.2 8.9 45.1 26.1 32.1 12.9 48.5 45.2 11.4 15.8 38.6 14.4 44.1 49 17.2 26.9 10.6 44.4 30.2 28.5 9.2 45.5 47.4 11.5 10.7 0.01 0.6 0.041 0.4 0.3 0.4 0.3 0.8 0.4 0.3 0.7 0.8 0.8 0.8 0.8 (0.09) (0.12) (0.09) (0.09) (0.11) (0.10) (0.14) (0.09) (0.09) (0.09) (0.15) (0.09) (0.09) (0.13) (0.14) 0.22 0.06 − 0.18 0.08 0.11 − 0.09 0.15 0.02 0.08 − 0.09 0.06 − 0.02 − 0.02 − 0.03 0.03 0.11 0.05 0.3 0.5 0.14 0.9 0.6 0.4 0.7 0.7 0.3 0.5 0.6 0.7 0.5 (0.9–1.9) (0.99–2.8) (0.6–1.2) (0.8–1.6) (0.9–2.3) (0.7–1.5) (0.7–2.0) (0.8–1.6) (0.8–1.6) (0.7–1.3) (0.5–1.3) (0.8–1.6) (0.7–1.3) (0.6–1.5) (0.5–1.4) 1.33 1.65 0.85 1.11 1.42 1.04 1.16 1.14 1.08 0.94 0.77 1.13 0.91 0.91 0.84 41.5 16.9 44.4 49.4 19 26.8 10 48.3 29.7 27.9 9.7 49 47.3 12.1 11.5 IL-10 MYD88 CHUK MAP2K6 MAPKAPK2 RPS6KA5 MAPK14 MAP2K6 RPS6KA4 RPS6KA5 PTPRC AFF3 CD226 TRAF6 PTPRC
rs1800896 rs7744 rs11591741 rs2716191 rs4240847 rs1286076 rs916344 rs11656130 rs475032 rs1286112 rs10919563 rs10865035 rs763361 rs540386 rs6683595
34.9 11.2 48.9 47.3 13.6 26.9 8.7 45.2 28 30.1 12.2 45.7 49.5 12.8 13.3
P-value R P-value B (SE)
3 months
P-value OR (95% CI ) NR d
R SNP Locus
Table 2.
Results of the association analysis using either EULAR criteriaa or ΔDAS28b as the outcome of response to TNFic
NR
OR (95% CI )
6 months
B (SE)
P-value
PTPRC genetic biomarker of response to TNFi A Ferreiro-Iglesias et al
combined P-value (P = 9.08 × 10 − 8), which approaches the GWAS significance threshold in fixed-effect meta-analysis. In contrast, previous studies showing association of the IL10 rs1800896 SNP were small (123 and 89 patients, respectively), and their P-values were modest (P = 0.04 and 0.01),8,9 as in the present study (P = 0.01). Even more limited is the support for the CHUK rs11591741 SNP, which is replicated here for the first time. Inclusion in the study of rs668395 has been rewarding because it showed a stronger association than rs10919563, with which it is in LD (r2 = 0.86). This result exemplifies the improvement in association due to causal polymorphisms in place of tagSNPs and indicates that replacing rs10919563 by rs668395 in future studies will increase their power. The absence of significant association of these biomarkers in the GWAS of response to TNFi is not a strong argument against them because of the insufficient power of current GWAS (the largest included 2706 patients).10 In addition, these loci seem to show time-specificity in their association with response to TNFi, and most GWAS have combined multiple follow-up times. The time-specificity is supported by the PTPRC and IL10 SNPs, which were only associated at the same follow-up time as in previous studies15,8,9 and by other two biomarkers of response to TNFi that have previously shown time-specificity25 (Montes A. et al. Arthritis Research and Therapy, in press). These caveats apply also to a study that did not replicate association of rs1091956 in 233 patients assessed at various times of follow-up.26 Another study did not replicate association of rs10919563 and rs11591741 in 183 patients.27 In this study, response to TNFi of all patients was assessed at 6 months, but only with the EULAR criteria, which leads to less powerful analyses than ΔDAS28 (as shown here and in other studies,28 and in agreement with their respective dichotomous/continuous nature29). The potential usefulness of the replicated biomarkers can already be envisaged by considering the difference in ΔDAS28 between the genotypes. For example, the differences between the TT genotype and the two other genotypes (CC+TC genotypes) at rs6683595 (1.23 and 0.71, respectively) are larger than the ΔDAS28 considered clinically significant in the EULAR criteria (0.6).24 These differences are also larger than the difference in ΔDAS28 between sero-negative and sero-positive patients treated with rituximab (0.35). Difference that is widely used to rule out rituximab for sero-negative patients.5,6 Therefore, it is tempting to suggest that the TT patients should not be treated with a TNFi. However, further replication is required before reaching any firm conclusion about the utility of this or the other two replicated biomarkers. In addition, the frequency of the TT genotype at rs6683595 is small (1.0%), limiting its clinical interest. The replicated biomarkers could also contribute to the knowledge of RA disease pathogenesis. In this regard, association of the same allele of PTPRC with risk to RA and with an improved response to TNFi is particularly intriguing. It suggests that unleashed immune reactivity predisposes to RA, but also leads to increased sensitivity to TNFi of the ensuing inflammation. The known function of PTPRC, also called as CD45, which is a receptortype protein tyrosine phosphatase critical for controlling signals mediated through TCR, BCR and cytokine receptors, is congruent with this hypothesis. In addition, identification of the putative causal SNP rs6683595 has focused attention to CD4 Tregs due to a cell-specific H3K4me3 epigenetic mark surrounding it.22 The Treg cells are suspected to have a critical involvement both in RA pathology and in the response to biologic Disease Modifying AntiRheumatic Drugs, but available studies with RA patients have been inconclusive.30 Therefore, investigation of the role of this biomarker could contribute to clarify these aspects of RA and to define the role of Tregs as therapeutic targets. The rs1800896 SNP in IL10 (also called − 1802A/G) is also a cis regulatory polymorphism, although its relevance for clinical phenotypes remains to be established.31 Its G allele determines high expression of IL10 owing The Pharmacogenomics Journal (2015), 1 – 4
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PTPRC genetic biomarker of response to TNFi A Ferreiro-Iglesias et al
4
to preferential binding of the Sp1 transcription factor.32,33 This is the same allele associated with improved response to TNFi here and in the two previous studies.8,9 Therefore, the most direct interpretation of the results is that increased IL10 expression contributes to the action of TNFi, likely through suppressive effects. In contrast, the CHUK SNP rs11591741 has no known function. Therefore, experiments exploring the involvement of the three biomarkers in response to TNFi could increase our knowledge of RA. In conclusion, we have contributed to establish the PTPRC RA locus as the most widely replicated biomarker of response to TNFi to date. Our results also support rs6683595 as the putative causal SNP at this locus and reinforce the implication of the RA risk allele in a better response to TNFi. In addition, replication of two other proposed biomarkers, in IL10 and CHUK, should stimulate further efforts for their validation.
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CONFLICT OF INTEREST The authors declare no conflict of interest.
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ACKNOWLEDGMENTS We thank Carmen Pena-Pena for technical assistance and Manuel Calaza for help with data management. We also thank Arturo R de la Serna and Berta Magallares of the Hospital Santa Creu e San Pau (Barcelona, Spain) for providing samples. Funding was provided by grants PI11/01048, PI12/01909 and by RETICS Program, RD12/0009/0008 and RD12/0009/0004 (RIER) of the Instituto de Salud Carlos III (Spain) that are partially financed by the European Regional Development Fund of the European Union.
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Supplementary Information accompanies the paper on the The Pharmacogenomics Journal website (http://www.nature.com/tpj)
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