Abstract

Anti-tumour necrosis factor (TNF) agents have revolutionized the treatment of patients with rheumatoid arthritis (RA). These therapies are, however, expensive and 30% of patients fail to respond. In a large cohort of Caucasian RA patients treated with anti-TNF medications (total n = 1050, etanercept n = 455, infliximab n = 450), we investigated whether genotypes of eight single nucleotide polymorphisms in the region containing the TNF gene were associated with response to anti-TNF therapy. Linear regression analyses adjusted for baseline 28 joint disease activity score (DAS28), baseline health assessment questionnaire score, gender and concurrent disease modifying anti-rheumatic drug treatment were used to assess association of these polymorphisms with treatment response, defined by change in DAS28 after 6 months. Analyses were performed in the entire cohort, and also stratified by anti-TNF agent. Association between DAS28 response and TNF-308 (rs1800629) genotype (P = 0.001) was detected across the whole cohort. After stratification by anti-TNF agent, the rare TNF-308AA genotype was associated with a significantly poorer response compared with TNF-308GG in etanercept (P = 0.001, n = 7) but not infliximab (P = 0.8, n = 17) treated patients. Conversely, the GA genotype at TNF-238 (rs361525) was associated with a poorer response to infliximab (P = 0.028, n = 40), but not etanercept (P = 0.6, n = 33). Owing to the small numbers of patients in some of the genotype groups examined, our data must be regarded as preliminary and will require replication in further large cohorts of anti-TNF-treated patients. If confirmed, our findings suggest the potential for genotype at these markers to aid selection of anti-TNF agent in patients with RA.

INTRODUCTION

Rheumatoid arthritis (RA) is the most common form of inflammatory arthritis and is characterized by inflammation of the synovial membrane, progressive joint damage and significant disability. Patients have traditionally been treated with disease modifying anti-rheumatic drugs (DMARDs), including methotrexate, sulphasalazine and leflunomide singly or in combination, but trials have consistently shown that only 40–60% of patients have a sustained response (1,2).

Evidence for the central role of tumour necrosis factor (TNF) in the pathogenesis of RA led to the introduction of the anti-TNF medications and their use has been a significant breakthrough in the treatment of severe RA. Three anti-TNF agents are currently in widespread use: infliximab, an IgG1 murine-human anti-TNF antibody (mAb); adalimumab, a humanized mAb and etanercept, a fusion protein of the human p75 TNF receptor and part of the human IgG1 that binds to both TNF and lymphotoxin. Despite the overall clinical efficacy of these agents, only 70% of patients achieve an American College of Rheumatology (ACR) 20 response, and 40% achieve an ACR50 (3,4). Thus, many patients are treated at considerable expense and risk of serious toxicity without significant clinical benefits. As a result, studies have been performed investigating the influence of demographic, clinical and immunological variables on treatment outcome. In a study of patients enrolled in the British Society of Rheumatology Biologics Register (BSRBR), baseline health assessment questionnaire (HAQ) score, female sex and current smoking status (in infliximab, but not in etanercept-treated patients) were identified as predictors of poor outcome in biological treatment of RA (5). In a subcohort of these patients (n = 642) also included in the present study, carriage of rheumatoid factor (RF) and anti-cyclic citrullinated (CCP) antibodies was associated with a reduced response to anti-TNF drugs (6), whereas concurrent use of methotrexate is associated with improved outcome (5,7). Other factors, including age, disease duration and the number of previous DMARDs, did not predict response to either infliximab or etanercept (5).

Healthy individuals show a wide variation in the level of TNF production with high concordance having been demonstrated in monozygotic twins, suggesting a substantial genetic contribution to TNF regulation (8). No significant genetic variation has been discovered in the coding regions of the TNF gene, and the association of promoter polymorphisms with a wide variety of disease states relating particularly to infection and autoimmunity has therefore been extensively examined (9). The TNF-308 G/A polymorphism (rs1800629) has been most studied, with the rare A allele associating with the high TNF producing major histocompatability complex haplotype HLA-A1, B8 DR3. Carriage of the A allele has also been associated with a poorer outcome in leishmaniasis (10) and cerebral malaria (11), which are characterized by high TNF production. The results of functional studies examining the effect of genotype at TNF-308 have been variable according to which specific cell lines, promoter constructs and stimulation have been used. For example, no difference in transcriptional activity was seen between constructs carrying the A and G alleles in Jurkat or Raji cells in constructs containing the TNF 3'-untranslated region (UTR) (12), whereas in a construct lacking the 3′-UTR in Raji B cells, the A allele construct had an 8-fold greater activity compared with the G allele construct (13). Interestingly, the most reproducible differential expression by TNF-308 genotype has been demonstrated in macrophage-like cells (14), which are most relevant to the diseases associated with carriage of the A allele at this marker. It is likely therefore, in some cell types, under specific conditions that the TNF-308 polymorphism is capable of influencing TNF expression. In the context of anti-TNF treatment for RA, a number of relatively small studies have been performed investigating TNF-308 as a response marker. Although some authors have demonstrated a positive association between the G allele and the treatment outcome (15–17), others have found no association (18–20). A meta-analysis including 311 patients of mixed ethnicity and with varying response criteria suggested that patients carrying the A allele at TNF-308 have a poorer response to anti-TNF treatment than homozygotes for the G allele (21). Small patient numbers in these studies may have contributed to the conflicting results, and high statistical power is therefore of paramount importance in uncovering potential clinical effects of these genetic variants. In this study, we have used a large British Caucasian population of RA patients treated with anti-TNF agents to examine association between genetic variation around the TNF gene cluster and response to anti-TNF treatment.

RESULTS

Patient recruitment

Collaborations were established with 20 rheumatology centres across the UK from which 1485 patients receiving anti-TNF therapy for RA were eligible for recruitment based on the criteria outlined. Of these patients, 1292 (87%) responded to the invitation letter with 1195 (80%) willing to take part. DNA samples were extracted and available for a subgroup of 1050 patients, which were utilized in the current analysis.

Baseline patient characteristics

The baseline characteristics of these patients are presented in Table 1, and are comparable with those previously reported across the BSRBR data set, indicating that this cohort was representative of anti-TNF-treated RA patients in the UK. The proportion of females, mean age, disease duration, RF status, baseline DAS28 and HAQ scores were comparable across all drug types, but as would be expected in accordance with drug licences, a substantially greater proportion of patients taking infliximab were receiving concurrent DMARD therapy, compared with those receiving etanercept or adalimumab.

Table 1.

Baseline characteristics and EULAR response, by anti-TNF agent and in the whole cohort

Baseline characteristics Etanercept Infliximab Adalimumab Combined 
Number of cases 455 (43) 453 (43) 142 (14) 1050 
Age (years) 56 (11) 56 (11) 58 (12) 57 (11) 
Female 354 (78) 348 (77) 105 (74) 807 (77) 
Current smokers 79 (17) 81 (18) 18 (13) 178 (17) 
Ever smoked 279/450 (62) 254/450 (56) 62/138 (44) 613/1038 (59) 
Disease duration (years) 13.7 (9) 14.2 (10) 13.7 (9) 13.9 (10) 
Rheumatoid factor positive 314/346 (90) 270/310 (87) 92/108 (85) 676/764 (88) 
Anti-CCP positive 292/46 (84) 252/310 (81) 79/108 (73) 623/764 (82) 
Baseline DAS28 6.70 (1.0) 6.74 (1.0) 6.51 (1.0) 6.69 (1.0) 
Baseline HAQ 2.0 (0.6) 423 2.1 (0.6) 434 2.0 (0.5) 137 2.0 (0.6) 994 
Concurrent DMARD(s)a 235 (52) 409 (90) 81 (57) 725 (69) 
Concurrent steroids 174 (38) 196 (43) 50 (35) 420 (40) 
Previous biologica 30 (7) 17 (4) 16 (11) 63 (6) 
EULAR response     
 Non 79 (17) 105 (23) 25 (18) 208 (20) 
 Moderate 254 (56) 244 (54) 71 (50) 569 (54) 
 Good 122 (27) 104 (23) 46 (32) 272 (26) 
Baseline characteristics Etanercept Infliximab Adalimumab Combined 
Number of cases 455 (43) 453 (43) 142 (14) 1050 
Age (years) 56 (11) 56 (11) 58 (12) 57 (11) 
Female 354 (78) 348 (77) 105 (74) 807 (77) 
Current smokers 79 (17) 81 (18) 18 (13) 178 (17) 
Ever smoked 279/450 (62) 254/450 (56) 62/138 (44) 613/1038 (59) 
Disease duration (years) 13.7 (9) 14.2 (10) 13.7 (9) 13.9 (10) 
Rheumatoid factor positive 314/346 (90) 270/310 (87) 92/108 (85) 676/764 (88) 
Anti-CCP positive 292/46 (84) 252/310 (81) 79/108 (73) 623/764 (82) 
Baseline DAS28 6.70 (1.0) 6.74 (1.0) 6.51 (1.0) 6.69 (1.0) 
Baseline HAQ 2.0 (0.6) 423 2.1 (0.6) 434 2.0 (0.5) 137 2.0 (0.6) 994 
Concurrent DMARD(s)a 235 (52) 409 (90) 81 (57) 725 (69) 
Concurrent steroids 174 (38) 196 (43) 50 (35) 420 (40) 
Previous biologica 30 (7) 17 (4) 16 (11) 63 (6) 
EULAR response     
 Non 79 (17) 105 (23) 25 (18) 208 (20) 
 Moderate 254 (56) 244 (54) 71 (50) 569 (54) 
 Good 122 (27) 104 (23) 46 (32) 272 (26) 

Values are n (%) or mean (standard deviation). For smoking data, RF, anti-CCP and baseline HAQ, the number of patients in whom results/data are available is stated.

aStatistical differences (P < 0.05) for baseline characteristics between the treatment groups (χ2 test for categorical variables, ANOVA/Kruskal–Wallis test for comparison of means).

Effect of genetic variants on treatment response

One of the markers (TNF-863, rs1800630) was excluded from the analysis as the genotyping results significantly deviated from Hardy–Weinberg equilibrium (P ≤ 0.0002). Linear regression analyses adjusted for baseline DAS28, concurrent DMARD usage, baseline HAQ and gender, with change in DAS28 between baseline and 6 months as the dependent variable, were performed to assess the effects of genotype at each single nucleotide polymorphism (SNP) on treatment outcome. In the whole cohort, associations were demonstrated between genotype at TNF-238 and TNF-308 and response to anti-TNF treatment (Table 2). Responses to etanercept and infliximab in patients segregated according to TNF-308 and -238 were then examined with comparison of each genotype to the most common genotype (Table 3). Bonferroni's correction was made as two independent tests were performed. Patients homozygous for TNF-308A responded very poorly to etanercept with a mean DAS28 improvement of only 0.61 (Pc = 0.001) compared with improvements of 2.74 and 2.51 for TNF-308AG and GG genotypes, respectively. This association was demonstrated unchanged in the univariate linear regression analysis (P = 0.001). There was no association of TNF-308 genotype with response to infliximab therapy. A weaker association of heterozygosity at TNF-238 with poorer outcome was only found in infliximab-treated patients (Pc = 0.028), but was not maintained, in the univariate analysis (P = 0.09). Bonferroni's correction for the number of SNPs tested has not been applied as we believe this to be an overly conservative adjustment, but were this to be applied, the association between TNF-308AA genotype and response to etanercept treatment would remain significant (Pc = 0.007). In the secondary analysis according to European League Against Rheumatism (EULAR) response criteria (22), no SNP was associated with treatment response in the cohort as a whole, but when stratified by anti-TNF agent (Table 4), genotype at TNF-308 was associated with response to etanercept (P = 0.018), and genotype at TNF-238 with response to infliximab (P = 0.03). No associations were found for the remaining SNPs in the whole cohort, or in the stratified analysis with infliximab- and etanercept-treated patients analysed separately. The number of patients taking adalimumab (n = 142) was too small for meaningful analysis as a separate group. Haplotype analysis was also performed [SNPstats (23), data not shown], with no significant associations demonstrated between any haplotype and treatment response.

Table 2.

Association between SNP genotype and anti-TNF treatment outcome at 6 months

SNP (rs no.) Genotype No. (frequency) Mean baseline DAS28 (SD) Mean change in DAS28 (SD) Global P-value 
TNF-238 (rs361525) GG 960 (0.92) 6.69 (0.97) −2.52 (1.51) 0.028 
 GA 82 (0.08) 6.81 (1.02) −2.25 (1.74)  
 AA 2 (0.00) 5.36 (0.00) −3.40 (1.00)  
TNF-308 (rs1800629) GG 682 (0.65) 6.70 (0.97) −2.43 (1.50) 0.001 
 GA 332 (0.32) 6.69 (1.01) −2.69 (1.58)  
 AA 27 (0.03) 6.58 (0.77) −1.85 (1.39)  
TNF-1031 (rs1799964) CC 633 (0.61) 6.70 (0.98) −2.53 (1.50) 0.505 
 CT 365 (0.35) 6.71 (0.98) −2.44 (1.56)  
 TT 45 (0.04) 6.55 (0.97) −2.52 (1.69)  
LTA ex-1 (rs2239704) GG 439 (0.42) 6.64 (0.97) −2.51 (1.57) 0.239 
 GT 452 (0.44) 6.72 (0.99) −2.54 (1.48)  
 TT 147 (0.14) 6.78 (0.94) −2.39 (1.58)  
LTA int-1 (rs909253) CC 382 (0.37) 6.76 (0.92) −2.46 (1.61) 0.229 
 CT 499 (0.48) 6.68 (1.02) −2.51 (1.47)  
 TT 163 (0.16) 6.58 (0.95) −2.54 (1.51)  
LST-1 ex-5 (rs1052248) AA 542 (0.52) 6.68 (0.96) −2.52 (1.46) 0.419 
 AT 429 (0.41) 6.68 (1.00) −2.43 (1.58)  
 TT 71 (0.07) 6.82 (0.97) −2.66 (1.71)  
LST-1 int-2 (rs2256965) CC 428 (0.41) 6.65 (0.99) −2.53 (1.55) 0.338 
 CT 490 (0.47) 6.72 (1.00) −2.49 (1.53)  
 TT 125 (0.12) 6.74 (0.87) −2.42 (1.50)  
SNP (rs no.) Genotype No. (frequency) Mean baseline DAS28 (SD) Mean change in DAS28 (SD) Global P-value 
TNF-238 (rs361525) GG 960 (0.92) 6.69 (0.97) −2.52 (1.51) 0.028 
 GA 82 (0.08) 6.81 (1.02) −2.25 (1.74)  
 AA 2 (0.00) 5.36 (0.00) −3.40 (1.00)  
TNF-308 (rs1800629) GG 682 (0.65) 6.70 (0.97) −2.43 (1.50) 0.001 
 GA 332 (0.32) 6.69 (1.01) −2.69 (1.58)  
 AA 27 (0.03) 6.58 (0.77) −1.85 (1.39)  
TNF-1031 (rs1799964) CC 633 (0.61) 6.70 (0.98) −2.53 (1.50) 0.505 
 CT 365 (0.35) 6.71 (0.98) −2.44 (1.56)  
 TT 45 (0.04) 6.55 (0.97) −2.52 (1.69)  
LTA ex-1 (rs2239704) GG 439 (0.42) 6.64 (0.97) −2.51 (1.57) 0.239 
 GT 452 (0.44) 6.72 (0.99) −2.54 (1.48)  
 TT 147 (0.14) 6.78 (0.94) −2.39 (1.58)  
LTA int-1 (rs909253) CC 382 (0.37) 6.76 (0.92) −2.46 (1.61) 0.229 
 CT 499 (0.48) 6.68 (1.02) −2.51 (1.47)  
 TT 163 (0.16) 6.58 (0.95) −2.54 (1.51)  
LST-1 ex-5 (rs1052248) AA 542 (0.52) 6.68 (0.96) −2.52 (1.46) 0.419 
 AT 429 (0.41) 6.68 (1.00) −2.43 (1.58)  
 TT 71 (0.07) 6.82 (0.97) −2.66 (1.71)  
LST-1 int-2 (rs2256965) CC 428 (0.41) 6.65 (0.99) −2.53 (1.55) 0.338 
 CT 490 (0.47) 6.72 (1.00) −2.49 (1.53)  
 TT 125 (0.12) 6.74 (0.87) −2.42 (1.50)  

P-values stated are for linear regression, adjusted for baseline DAS28, HAQ score, gender and concurrent DMARD treatment comparing treatment outcome among the three genotype groups for each allele, in the whole cohort n = 1050. SD, standard deviation.

Table 3.

Association of genotype at TNF-238 and TNF-308 stratified by anti-TNF agent

Group SNP Genotype No. (frequency) Mean baseline DAS28 (SD) Mean change in DAS28 (SD) Corrected P-value (compared with GG) 
Etanercept n=455 TNF-238 GG 419 (0.93) 6.72 (0.99) −2.59 (1.44) — 
GA 33 (0.07) 6.56 (0.93) −2.36 (1.78) 0.586 
AA — — — 
TNF-308 GG 283 (0.63) 6.71 (0.96) −2.51 (1.52) — 
GA 161 (0.35) 6.70 (1.05) −2.74 (1.37) 0.052 
AA 7 (0.02) 6.66 (0.74) −0.61 (0.71) 0.001 
Infliximab n=453 TNF-238 GG 410 (0.91) 6.72 (0.96) −2.42 (1.56) — 
GA 40 (0.09) 7.07 (0.99) −2.00 (1.56) 0.028 
AA 2 (0.00) 5.36 (0.00) −3.40 (1.00) 0.246 
TNF-308 GG 303 (0.67) 6.74 (0.98) −2.28 (1.48) — 
GA 129 (0.29) 6.76 (0.95) −2.61 (1.72) 0.108 
AA 17 (0.04) 6.62 (0.81) −2.28 (1.31) 0.769 
Group SNP Genotype No. (frequency) Mean baseline DAS28 (SD) Mean change in DAS28 (SD) Corrected P-value (compared with GG) 
Etanercept n=455 TNF-238 GG 419 (0.93) 6.72 (0.99) −2.59 (1.44) — 
GA 33 (0.07) 6.56 (0.93) −2.36 (1.78) 0.586 
AA — — — 
TNF-308 GG 283 (0.63) 6.71 (0.96) −2.51 (1.52) — 
GA 161 (0.35) 6.70 (1.05) −2.74 (1.37) 0.052 
AA 7 (0.02) 6.66 (0.74) −0.61 (0.71) 0.001 
Infliximab n=453 TNF-238 GG 410 (0.91) 6.72 (0.96) −2.42 (1.56) — 
GA 40 (0.09) 7.07 (0.99) −2.00 (1.56) 0.028 
AA 2 (0.00) 5.36 (0.00) −3.40 (1.00) 0.246 
TNF-308 GG 303 (0.67) 6.74 (0.98) −2.28 (1.48) — 
GA 129 (0.29) 6.76 (0.95) −2.61 (1.72) 0.108 
AA 17 (0.04) 6.62 (0.81) −2.28 (1.31) 0.769 

P-values stated are by linear regression, adjusted for baseline DAS28, HAQ score, gender and concurrent DMARD treatment, after Bonferroni's correction for two independent tests of drug type, comparing effect of GA and AA genotypes, with GG genotype. SD, standard deviation.

Table 4.

Association of genotype at TNF-238 and TNF-308 with EULAR response

Group SNP, genotype EULAR response
 
P-value 
  Non Good  
All TNF-308, GG 142 164 0.093 
 GA 57 97  
 AA  
Etanercept TNF-308, GG 51 73 0.018 
 GA 22 47  
 AA  
Infliximab TNF-308, GG 76 61 0.l79 
 GA 25 35  
 AA  
All TNF-238, GG 183 249 0.067 
 GA 24 18  
 AA  
Etanercept TNF-238, GG 69 113 0.424 
 GA  
 AA — —  
Infliximab TNF-238, GG 91 97 0.033 
 GA 14  
 AA  
Group SNP, genotype EULAR response
 
P-value 
  Non Good  
All TNF-308, GG 142 164 0.093 
 GA 57 97  
 AA  
Etanercept TNF-308, GG 51 73 0.018 
 GA 22 47  
 AA  
Infliximab TNF-308, GG 76 61 0.l79 
 GA 25 35  
 AA  
All TNF-238, GG 183 249 0.067 
 GA 24 18  
 AA  
Etanercept TNF-238, GG 69 113 0.424 
 GA  
 AA — —  
Infliximab TNF-238, GG 91 97 0.033 
 GA 14  
 AA  

P-values stated are for Fisher's exact test.

DISCUSSION

This is the largest study to date evaluating the association of pharmacogenomic markers with efficacy of anti-TNF treatment in RA. We have demonstrated a complex relationship between genotype at TNF-238 and TNF-308 and treatment response that appears to differ according to the agent used. In particular, patients carrying the rare TNF-308AA genotype responded poorly to etanercept, whereas genotype at this SNP did not associate with response to infliximab. Conversely, genotype at TNF-238 was associated with treatment response to infliximab, but not etanercept. These findings have been confirmed in a secondary analysis of the same patients, comparing the frequency of EULAR good and non-responders according to TNF-238 and -308 genotypes.

A meta-analysis of previous studies has suggested that carriage of the A allele at TNF-308 is associated with a poorer response to anti-TNF treatment in comparison with patients homozygous for the G allele (21), although the meta-analysis and previous studies have lacked the necessary power to examine the GA and AA genotypes as separate groups. In our cohort, however, only homozygotes and not heterozygotes for the A allele showed a poorer response to treatment, with this effect confined to etanercept and not infliximab-treated patients. Patient numbers in both of these groups were small, but the magnitude of the effect in the etanercept-treated patients was marked in comparison with those treated with infliximab. Owing to the low frequency of the TNF-308 AA genotype, few reports have studied the association of this genotype with disease or treatment outcome in isolation from heterozygotes. In a large case–control study in Gambian children, however, McGuire et al. (11) demonstrated that A allele homozygotes compared with common homozygotes at TNF-308 had an increased relative risk of 7 for death or severe neurological sequelae as a result of cerebral malaria.

The association of the TNF-308AA genotype with response to etanercept, but not infliximab, raises important questions about the mechanistic differences between these two drugs, and the potential for genotype to differentially influence treatment response. Uniquely among the TNF antagonists, etanercept binds lymphotoxin alpha (LTA) (24), and does so with similar affinity to soluble TNF (25). Although the role of this cytokine in the pathogenesis of RA remains uncertain, it has been demonstrated in inflammatory bowel disease that LTA production can be influenced by genotype at TNF-308, with the AA genotype correlating with a high secretor phenotype (26). It is possible therefore that in the presence of increased quantities of both TNF and LTA in patients carrying the TNF-308AA genotype, the potency of etanercept is insufficient to neutralize both cytokines, with resulting poor treatment response. There are also profound pharmacokinetic differences between etanercept (a soluble receptor) and infliximab (a chimeric human-murine mAb), with infliximab having higher peak concentrations and bioavailability, and a significantly longer half-life. As a result, the ratio of area under the curve/time over the normal dosing period in RA is significantly higher for infliximab (3 mg/kg, 8 weekly) compared with etanercept (50 mg weekly) (27), reinforcing the possibility of differential effects of genotype at TNF-308. Differences also exist in binding avidity, with infliximab having the capability to generate complexes of trans-membrane or soluble TNF, whereas etanercept binds trimeric soluble TNF as a monomer (28). In addition, infliximab, but not etanercept, has been demonstrated to fix complement (29) and there is controversy regarding the potential for infliximab, but perhaps not etanercept to induce reverse signalling by engagement with trans-membrane TNF (30), resulting in the transduction of apoptotic signals, and suppression of cytokine production.

Association between genotype at TNF-238 and response to anti-TNF treatment in RA has not previously been reported, although genotype at this marker has been associated with X-ray damage in RA, and increased severity of disease (31). In our cohort, TNF-238GA genotype was associated with a poorer response to infliximab. An association between baseline DAS28 and TNF-238 genotype in infliximab-treated patients (P = 0.01) but not the cohort as a whole (P = 0.09) was also noted, with the heterozygotes having a higher baseline DAS28. This was not seen in the etanercept-treated patients, in whom there was also no association between TNF-238 genotype and treatment response. It is possible therefore that the poorer response to infliximab relates to the chance event of this group having more severe disease (a known marker of poor treatment response) than the etanercept-treated patients of similar genotype.

The principal shortcoming of this study is the lack of a replication cohort, and we acknowledge that due to the small numbers in some genotype groups in this study, the associations demonstrated must be regarded as preliminary. Replication will be required in further large cohorts of anti-TNF-treated RA patients for our results to be validated. In addition, it is possible that the change of DAS28 over 6 months under-represents the true change in the inflammatory component of joint disease in patients with advanced disease, as the joint swelling and tenderness measured may be as a consequence of irreversible structural damage, and not current inflammation, and therefore replication in patients with shorter disease duration is advised. The effect of TNF genotypes in a large cohort of adalimumab-treated patients is also worthy of further study, and may give further insight into the mechanistic differences between these agents.

In conclusion, we have shown that TNF-308 and TNF-238 genotypes associate with response to etanercept and infliximab, respectively, in the treatment of RA. If confirmed in replication cohorts, our data suggest that genotype at these markers may be a valuable tool to guide selection of anti-TNF agents in RA.

MATERIALS AND METHODS

Subjects

A UK wide multi-centre collaboration was established with the aim of recruiting a large cohort of patients treated with anti-TNF drugs for RA. Eligible patients from each centre were identified from the BSRBR (32). This register contains extensive clinical information on patients commencing treatment with an anti-TNF agent with data including DAS28 collected prospectively, on a 6 monthly basis for 3 years and then annually for 2 years. DAS28 is a validated measure of disease activity in RA (33), with a score <2.6 defined as disease remission, and >5.1 the current UK requirement for funding of anti-TNF treatment. All patients included in the study had a DAS28 >5.1 prior to commencing an anti-TNF agent, and had received prior treatment with at least two DMARDs, including methotrexate. British Caucasian patients with a physician diagnosis of RA, currently or previously treated with an anti-TNF biological agent, and enrolled on the BSRBR with at least 6 months of follow-up were enrolled in the study. Patients who stopped treatment temporarily during the first 6 months of therapy were excluded. Similarly, patients who discontinued therapy prior to the 6 month follow-up for any reason other than inefficacy were excluded from selection.

Patient recruitment and sample collection

Once identified from the BSRBR, patients were invited to participate in the study. Blood samples were obtained from consenting patients and DNA was isolated using a standard phenol/chloroform extraction method. Ethics approval was obtained from the UK Central Office of Research Ethics Committees (04/Q1403/37).

Clinical information

Clinical data held on the BRSBR database included gender, date of birth, year of disease onset, details of the American College of Rheumatology (ACR) classification criteria for RA (34) smoking status, HAQ score (35) and details of biological and non-biological anti-rheumatic drug therapy (including drug type, changes to therapy and reasons for discontinuation). Disease activity, measured by treating clinicians using the four variable 28-joint count disease activity score (DAS28), was extracted at baseline and at 6 months follow-up (33).

SNP selection

HapMap (36) was used to select a set of seven known SNPs surrounding the TNF locus, including the genes coding for LTA and leucocyte-specific transcript 1 (LST-1) by pairwise tagging, using the criteria of a minor allele frequency >0.15, and including an additional marker (TNF-238, rs361525) based on the published literature (31).

Genotyping

Genotyping was performed using TaqMan assays designed by Applied Biosystems (Foster City, CA, USA). Positive (sequenced templates) and negative controls (ultrapure water) were included in all genotyping plates and 10% of assays were repeated for quality control purposes with no discrepancies found. The total reaction volume was 5 µl, containing 10 ng of genomic DNA. Thermal cycling in 384-well plates was performed using a PTC-225 DNA Engine Tetrad thermal cycler (MJ Research, San Francisco, CA, USA), and genotypes were determined using an ABI Prism 7900HT Sequence Detection System (Applied Biosystems, Warrington, UK).

Analysis

The primary outcome measure was change in DAS28 between baseline and 6 months. Initial linear regression analyses were performed to investigate association between the baseline factors outlined in Table 1, and the primary outcome measure (Supplementary Material, Table S1). Of these, baseline DAS28, baseline HAQ score, concurrent DMARD therapy and gender (but not smoking status) were significantly associated with drug response at 6 months (P < 0.0062, P < 0.05 adjusted for eight tests performed). Further linear regression analyses, under a genotypic model, were then performed to investigate association between change in DAS28 and genotype at each SNP, adjusted for the significantly associated baseline factors. The regression analyses were also repeated without adjustment, so that the impact of this correction could be assessed. For the purposes of this analysis, the recorded 6 month DAS28 scores were used whether patients had discontinued therapy due to inefficacy or not, but patients who had discontinued therapy prior to 6 months due to adverse reactions were excluded from the analysis. Interaction analyses were performed to determine whether any observed effects were similar across the two major drug types, namely etanercept and infliximab. Bonferroni post-test correction was applied to the stratified analysis, correcting for two independent tests of drug type. A secondary analysis was also performed comparing the number of patients achieving EULAR good response with EULAR non-responders in the cohort as a whole and stratified by anti-TNF agent. Fisher's exact test was used to compare genotype groups.

Power calculation

A power calculation (37) suggests that for our sample size of 1050, and a minor allele frequency of 0.15, this study had 99.9% power to detect a change in DAS28 of 0.6 at the 5% significance level. In the stratified analysis, there was 98.8% power to detect the same difference in a sample size of 455, but <85% power for the sample size of 142 in the adalimumab-treated group.

SUPPLEMENTARY MATERIAL

Supplementary Material is available at HMG Online.

FUNDING

We thank the Arthritis Research Campaign for their support. arc grant reference no: 17552.

ACKNOWLEDGEMENTS

We would like to thank Hannah Donovan, Paul Gilbert and Catriona McWhirter for performing the DNA extractions, and all UK clinicians who have contributed information to the British Society of Rheumatology Biologics registry.

Conflicts of Interest statement. None declared.

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APPENDIX: MEMBERS OF THE BIOLOGICS IN RHEUMATOID ARTHRITIS GENETICS AND GENOMICS STUDY SYNDICATE (BRAGGSS)

Cannock Chase Hospital, Cannock (Dr S.V. Chalam, Dr D. Mulherin, Dr T. Price, Dr T. Sheeran).

Chapel Allerton Hospital, Leeds (Dr S. Bingham, Dr A.W.M., Prof. P. Emery).

Derbyshire Royal Infirmary, Derby (Dr L.J. Badcock, Dr C.M. Deighton, Dr S.C. O'Reilly, Dr M.R. Regan, Dr M. Snaith, Dr G.D. Summers, Dr R.A. Williams).

Doncaster Royal Infirmary, Doncaster (Dr J.R. Lambert).

Edith Cavell Hospital, Peterborough (Dr N.J. Sheehan, Dr N.E. Williams).

Freeman Hospital, Newcastle (Dr H.E. Foster, Dr B. Griffiths, Dr I.D. Griffiths, Dr M.L. Grove, Prof. J.D.I., Dr P.N. Platt, Dr D.J. Walker).

Haywood Hospital, Stoke-On-Trent (Dr E.H. Carpenter, Dr. P.T. Dawes, Dr A. Hassell, Dr E.M. Hay, Dr S. Kamath, Dr J. Packham, Dr M.F. Shadforth).

Hereford Hospitals, Hereford (Dr D.H. Rees, Dr R.B. Williams).

Norfolk & Norwich University Hospital, Norfolk (Dr K. Gaffney, Dr Macgregor, Dr Marshall, Dr P. Merry, Prof. D.G.I. Scott).

North Manchester General Hospital, Manchester (Dr B. Harrison, Dr M. Pattrick, Dr H.N. Snowden).

Queen Alexandra Hospital, Portsmouth (Dr R.G. Hull, Dr J.M. Ledingham, Dr F. Mccrae, Dr M.R. Shaban, Dr A.L. Thomas).

Queen Elizabeth Hospital, Gateshead (Dr J. Hamilton, Dr C.R. Heycock, Dr C.A. Kelly, Dr V. Saravanan).

Royal Hallamshire Hospital, Sheffield (Dr M. Akil, Dr R.S. Amos, Dr D.E. Bax, Dr S.H. Till, Dr A.G.W., Dr J. Winfield).

Royal Lancaster Infirmary, Lancaster (Dr M. Bukhari, Dr W.N. Dodds, Dr J.P. Halsey).

Sandwell General Hospital, West Bromwich (Dr K.A. Grindulis, Dr F. Khattak).

Selly Oak Hospital, Birmingham (Dr Bowman, Prof. C.D. Buckley, Dr P. Jobanputra, Dr R.W. Jubb, Dr E.C. Rankin).

Author notes

See Appendix for list of members.