TNFAIP3 Gene Polymorphisms Are Associated with Response to TNF Blockade in Psoriasis

INTRODUCTION

Psoriasis is a common immune mediated disorder of the skin, nails, scalp and joints, with a multifactorial genetic basis. Recent association studies have identified 25 susceptibility loci reaching genome-wide significance for psoriasis (Ellinghaus et al., 2010; Huffmeier et al., 2010; Nair et al., 2009; Strange et al., 2010; Stuart et al., 2010; Sun et al., 2010). The HLA-C region on chromosome 6p21.3 emerged as the strongest associated locus in all populations studied, accompanied by several genes involved in the immune and inflammatory pathways (Elder et al., 2010). Biological drugs targeting tumor necrosis factor-α (TNF-α, encoded by the TNF gene which happens to be located 303 kb centromeric of HLA-C), have been highly successful in treating severe psoriasis vulgaris (PsV), as well as the subset of approximately 25% of PsV patients (Gottlieb et al., 2003; Leonardi et al., 2003; Papp et al., 2005) diagnosed with psoriatic arthritis (PsA). Nevertheless, there is marked heterogeneity in treatment response to these medications. Multiple studies evaluating the antipsoriatic efficacy of anti-TNF agents have demonstrated efficacy of ≥ 75% reduction in Psoriasis Area Severity Index (PASI, a composite score that measures erythema, induration and scaling weighted for the body surface involved) in 45–70% patients depending on the dosage and duration of treatment (Sobell et al., 2009).

Compared with traditional systemic therapies for psoriasis such as phototherapy and methotrexate, treatment with the anti-TNF drugs poses a considerable financial burden. A year of biologic therapy can cost between US $10,000–$25,000 based on the dosage and treatment regimen prescribed (Cordoro and Feldman, 2007) while methotrexate and narrow-band ultraviolet B (NB-UVB) phototherapy would cost $623 and $3,692 respectively (Pearce et al., 2006). Given these costs, accurate predictors of therapeutic response to anti-TNF agents would be of great value in making decisions on treatment options.

Previous studies addressing genetic predictors of therapeutic response have investigated promoter region polymorphisms in the TNF gene in rheumatoid arthritis (RA), ankylosing spondylitis (AS), Crohn’s disease and ulcerative colitis (Kooloos et al., 2007; Maxwell et al., 2008; Musone et al., 2008; O’Rielly et al., 2009; Pierik et al., 2006; Seitz et al., 2007). We carried out a survey-based evaluation of TNF promoter region polymorphisms in 343 psoriasis patients treated with TNF antagonists, and found an association with the TNF-238 G/A polymorphism (OR=2.03, p=0.044) (Tejasvi et al., 2008). However, this association was only nominally significant, prompting evaluation of additional patients as well as additional potential genetic loci.

TNFAIP3 was originally identified as a TNF inducible gene which, at least in some settings, functions as a negative feedback inhibitor of TNF signaling (Opipari et al., 1990; Werner et al., 2008). Recent genome-wide association studies (GWAS) have revealed associations between TNFAIP3 and psoriasis (Nair et al., 2009; Strange et al., 2010), rheumatoid arthritis (RA) (Plenge et al., 2007; Thomson et al., 2007), type 1 diabetes mellitus (Fung et al., 2009), systemic lupus erythematosus (SLE) (Graham et al., 2008), celiac disease (Trynka et al., 2009), and chronic sinusitis (Cormier et al., 2009). Its protein product, A20, functions as a dual enzyme: initially it removes Lys⁶³-linked ubiquitin chains from RIP1, an essential mediator of the proximal TNFR signaling complex. Later, it functions as E3 ligase, leading to polyubiquitinylation of RIP1 via Lys⁴⁸ that targets RIP1 for proteasomal degradation, resulting in termination of TNF-induced NF-κB signaling (Liu et al., 2005; Wertz et al., 2004). A20 can also remove K63-linked polyubiquitin chains from TRAF6 and RIP2, inhibiting NF-kB activation by TLR4 and NOD2, respectively (Vereecke et al., 2009). A20 also blocks NF-kB activation induced by TAK1 overexpression, which signals downstream of RIP1, RIP2 and TRAF6 (Zetoune et al., 2001). It can also target TRAF2 for lysosomal degradation, independent of its ubiquitin modifying properties (Li et al., 2009). Thus A20 acts at multiple steps in the NF-kB signaling pathway (Vereecke et al., 2009).

A20 binds to ABIN-1, a protein encoded by the TNIP1 gene, which, interestingly, is also associated with psoriasis (Nair et al., 2009) and SLE (Gateva et al., 2009; Han et al., 2009). This interaction also temporally limits NF-κB activation (Heyninck et al., 1999), at least in part by binding of ABIN-1 to NEMO/IKK-γ (Mauro et al., 2006).

In a genome-wide association study (GWAS) of response to TNF blockers in rheumatoid arthritis (RA), 89 patients were analyzed using 283,348 single nucleotide polymorphisms (SNPs), yielding nine signals reaching nominal significance (p < 0.05) (Liu et al., 2008). In a very recent GWAS, a total of 1286 anti-TNF-treated patients were analyzed in a 3-stage design (Plant et al., 2011). No loci yielded statistical evidence reaching genome-wide significance, emphasizing the need for much larger cohorts for unbiased estimation of pharmacogenetic responses to TNF antagonists and indeed to other biologics and immunomodulators in general.

A recent pharmacogenomic study looking into the profile of early gene expression profile among responders and non-responders to anti-TNF therapy in RA revealed early downregulation of expression of TNFAIP3 levels secondary to TNF neutralization in responders (Koczan et al., 2008). However, to date there are no published pharmacogenetic studies of the relationship between the TNFAIP3 locus and responsiveness to anti-TNF agents in psoriasis. In this study, we evaluate the role of two polymorphisms in the TNFAIP3 gene as predictors of clinical response to TNF blockade in patients with psoriasis and/or PsA from two study sites.

RESULTS

We studied 433 psoriasis patients from Michigan and 199 patients from Toronto who received treatment with any of three different TNF blockers – etanercept, infliximab and adalimumab. We compared the two samples for 19 demographic and phenotypic variables (Table 1). Highly significant differences were observed between the means of three variables—the Toronto sample had more PsA patients (93.0% vs. 58.3%, p = 2.9 × 10⁻²⁰), while the Michigan sample had a higher mean age (51.4 yrs vs. 46.0 yrs, p = 8.6 × 10⁻⁷) and a higher percentage of patients with a family history of psoriasis (60.6% vs. 42.9 %, p = 4.5 × 10⁻⁵). The Michigan sample also had a later age at PsA onset (37.6 yrs vs. 33.6 yrs, p = 0.0028). A nominally significant difference (p = 0.019) in the percentage of patients with inflammatory bowel disease did not remain so after correction for multiple testing. No significant difference between the two samples was seen in the percentage of good response to treatment with any of the anti-TNF agents, either individually or combined. Furthermore, the frequencies of the allele imparting risk for psoriasis did not differ significantly between the two groups for either of the two analyzed TNFAIP3 SNPs.

Covariate analyses with age at onset, BMI and presence of PsA were performed in both cohorts, along with worst-ever total body surface area (TBSA) in the Michigan cohort. Age at onset was significantly and negatively associated with response to TNF blockers in the Michigan cohort (OR=0.973, p=0.00016). Although not reaching nominal significance, age at onset also trended towards negative association with all all anti-TNF responses in the Toronto sample (OR=0.982 for all TNF blockers combined).

Data were analyzed for linear trend of association between drug response and genotypes and haplotypes of one disease-associated SNP in intron 6 (rs610604, MAF = 0.338) (Nair et al., 2009) and another in exon 3 of TNFAIP3 (rs2230926, MAF = 0.029). The latter was the only tested exonic SNP that was polymorphic in our sample. In the Michigan sample, association was observed between dosage of the G allele of rs610604 and good response to all TNF blockers (OR = 1.74, p_nom = 0.0027, p_corr = 0.0084)(Table 2). Combined analysis of the two samples using a fixed-effects model showed association between dosage of the G allele of rs610604 and good response to all TNF blockers combined (OR= 1.50, p_nom= 0.010, p_corr =0.050). Despite a similar OR, this association was no longer significant when the combined analysis used a random-effects model (OR= 1.39, p_nom= 0.22, p_corr =0.77) (Table 2). Age at onset of disease was included as a covariate in all association models and the nested logistic regression models testing association for each combination of SNP and drug revealed that the associations of age at onset and SNP rs610604 with drug response were independent of each other (no significant interaction).

Haplotype analysis (Table 3) revealed that dosage of the rs2230926 T–rs610604 G haplotype was associated with good response to all TNF blockers (OR = 1.82, p_nom = 0.0012, p_corr = 0.0060) in the Michigan cohort. Combined analysis of both groups under a fixed effects model showed association between the rs2230926 T–rs610604 G haplotype and response to all TNF blockers (OR = 1.55, p_nom= 0.0051, p_corr =0.031). The association of haplotype rs2230926 T–rs610604 G with response to TNF blockers in the Michigan cohort is driven mainly by rs610604; conditional haplotype testing showed that the independent contribution of the rs610604 G allele to the association is significant (p_nom = 0.0016) whereas the independent contribution of the rs2230926 T allele is not (p_nom = 0.90).

DISCUSSION

The TNFAIP3 gene has been shown to be associated with psoriasis in a recent genome-wide association study (Nair et al., 2009), a finding recently confirmed strongly in Caucasian (Strange et al., 2010) and suggestively in Chinese (Sun et al., 2010) populations. Here we demonstrate that polymorphisms in TNFAIP3 are associated with response to anti-TNF agents in psoriasis patients. In the Michigan sample, good response to etanercept alone and all TNF blockers combined was positively associated with dosage of the G allele of rs610604, a psoriasis susceptibility allele (Nair et al., 2009). The proportion of good responders to anti-TNF treatment was substantially larger for people carrying two copies of this allele compared to carriers of no copies, both for etanercept (90.7% vs. 70.3%, p = 0.0027) and for all TNF blockers combined (88.1% vs. 70.7%, p = 0.0075). To a lesser degree, the same holds true for heterozygous carriers of the G risk allele compared to non-carriers (79.1% vs. 70.3%, p = 0.067 for etanercept and 80.1% vs. 70.7%, p = 0.034 for all TNF blockers). We also found an association of haplotype rs2230926 T–rs610604 G with good response to etanercept or all TNF blockers (Table 3).

Here we also report significant association between age at onset and treatment response that is independent of TNFAIP3 genotype; a later age at onset is associated with poorer response to treatment. The profile of genetic risk factors, especially the role of HLA-Cw6, differs between early and late onset psoriasis (Chen et al., 2011; Gudjonsson et al., 2002), which may influence the effectiveness of TNF blockers. The use of phenotypes as predictors of response to anti-TNF agents in RA showed that age of the patient was not associated with treatment response (Eder et al., 2010) and in AS age was negatively associated with response to treatment (Arends et al., 2011). Since we did not collect age at treatment data, a direct comparison was not possible. Other covariates analyzed did not show any association with treatment response.

We did not find any association of drug response and TNFAIP3 genotype in the Toronto replication sample. This outcome could be due to limited power. For the strongest effect observed in the Michigan sample (OR of 1.83 for etanercept response and G allele dosage at rs6010604), the Toronto sample has only 43% power to detect association at a nominal significance level of 0.05 and 29% power when significance is corrected for multiple testing. Because of the winner’s curse (Goring et al., 2001), the estimated effect size in the Michigan sample is likely biased upwards, and power for a more realistic OR of 1.50 (midway between 1.83 and the lower bound of its corrected 95% confidence interval), is only 22% and 13% at a nominal and corrected significance level of 0.05, respectively. To achieve 80% power to detect an OR of 1.83 at a nominal or corrected significance of 0.05, sample size for the Toronto cohort would need to be increased 2.5- or 3.2-fold; for an OR of 1.50 80% power requires 5.5- or 6.9-fold increases in sample size.

The lack of association in the Toronto sample could also be due to heterogeneity; i.e., response to anti-TNF drugs is genuinely more poorly associated with TNFAIP3 genotype in the Toronto population than in the Michigan population. The estimated I² coefficient of heterogeneity for association, of both etanercept response and combined TNF blocker response with r610604 genotype, is greater than zero (I² = 27.5% and 54.3%, respectively), leading to a reduced significance of the combined analysis for these two associations when using a random-effects model versus fixed-effects model (Table 2). However, neither of these I² coefficients is significant (95% confidence interval = 0.0–72.0% and 0.0–88.8%, respectively), nor is the related Cochran’s Q-test of heterogeneity (p = 0.24 and 0.14, respectively). It is important to note that these results are inconclusive, since the power of heterogeneity tests is known to be low when the number of strata is small.

Other potential limitations of this study could also be responsible for the different results for the two samples. We combined skin and joint responses that were measured individually in Toronto to approximate the undifferentiated self-evaluated response for the Michigan sample. Also, the response to medication in the Michigan cohort was assessed retrospectively, which may introduce recall error (though not recall bias since it is unlikely that recall accuracy is differentially affected by TNFAIP3 genotype). The similar proportion of good responders in the two samples to each of the anti-TNF therapies (Table 1) provides some confidence that the two response metrics, though measured in different ways, are reasonably similar. Nevertheless, the much higher proportion of PsA in the Toronto sample could contribute to the observed lack of association if the response of the joint symptoms of people with PsA to anti-TNF therapy is more poorly correlated with TNFAIP3 genotype than is response of the skin symptoms of people with PsA or purely cutaneous psoriasis (PsC). Table S1 compares skin-only and joint-only responses in the Toronto sample. Despite limited power, this analysis yields no indication that joint symptoms are more poorly associated with TNFAIP3 genotype than are skin symptoms—for rs610604, the OR of response of joint vs. skin phenotypes is 1.17 vs. 0.77 for etancercept and 1.08 vs. 0.65 for all TNF blockers combined. Unfortunately, a similar comparison in the Michigan sample is not possible because this information was not collected. In lieu of this, Table S2 compares response to TNF blockers in the Michigan sample for people with PsA versus those with PsC. The strength of association with SNP rs610604 is approximately equal for both phenotypes, being slightly lower in PsA (OR = 1.67 vs. 1.77 for all TNF blockers combined). Hence neither sample supports the notion that response of joint symptoms to anti-TNF therapy is less well associated with TNFAIP3 genotype than is response of skin symptoms, although the small sample sizes prevent any definitive conclusions.

In conclusion, we demonstrate in this study that the G allele of SNP rs610604 located in the TNFAIP3 gene and its haplotype with the T allele of rs2230926 are markers of good response to anti-TNF agents. The results of this study using a limited sample set calls for confirmation by additional studies with substantially larger number of samples, prospective patient and physician evaluated responses, and gene and protein expression levels measured before and after therapy. Such efforts would also provide phenotype data and material needed for a GWAS of anti-TNF agents. Further association studies on TNFAIP3 and other genetically associated loci could result in a panel of polymorphisms that can be routinely typed in a clinical setting to help the physician make an informed decision on administration of anti-TNF therapy.

MATERIALS AND METHODS

Supplementary Material