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Research in rheumatoid arthritis (RA) is increasingly focused on the discovery of biomarkers that could enable personalized treatments. The genetic biomarkers associated with the response to TNF inhibitors (TNFi) are among the most studied. They include 12 SNPs exhibiting promising results in the three largest genome-wide association studies (GWAS). However, they still require further validation. With this aim, we assessed their association with response to TNFi in a replication study, and a meta-analysis summarizing all non-redundant data. The replication involved 755 patients with RA that were treated for the first time with a biologic drug, which was either infliximab (n = 397), etanercept (n = 155) or adalimumab (n = 203). Their DNA samples were successfully genotyped with a single-base extension multiplex method. Lamentably, none of the 12 SNPs was associated with response to the TNFi in the replication study (p > 0.05). However, a drug-stratified exploratory analysis revealed a significant association of the NUBPL rs2378945 SNP with a poor response to etanercept (B = -0.50, 95% CI = -0.82, -0.17, p = 0.003). In addition, the meta-analysis reinforced the previous association of three SNPs: rs2378945, rs12142623, and rs4651370. In contrast, five of the remaining SNPs were less associated than before, and the other four SNPs were no longer associated with the response to treatment. In summary, our results highlight the complexity of the pharmacogenetics of TNFi in RA showing that it could involve a drug-specific component and clarifying the status of the 12 GWAS-drawn SNPs., Competing Interests: The authors have declared that no competing interests exist.

The aim of this case-control study was to evaluate whether 47 single-nucleotide polymorphisms (SNPs) in steroid hormone-related genes are associated with the risk of RA and anti-TNF drug response. We conducted a case-control study in 3 European populations including 2936 RA patients and 2197 healthy controls. Of those, a total of 1985 RA patients were treated with anti-TNF blockers. The association of potentially interesting markers in the discovery population was validated through meta-analysis with data from DREAM and DANBIO registries. Although none of the selected variants had a relevant role in modulating RA risk, the meta-analysis of the linear regression data with those from the DREAM and DANBIO registries showed a significant correlation of the CYP3A4 rs11773597 and CYP2C9 rs1799853 variants with changes in DAS28 after the administration of anti-TNF drugs (P = 0.00074 and P = 0.006, respectively). An overall haplotype analysis also showed that the ESR2 GGG haplotype significantly associated with a reduced chance of having poor response to anti-TNF drugs (P = 0.0009). Finally, a ROC curve analysis confirmed that a model built with eight steroid hormone-related variants significantly improved the ability to predict drug response compared with the reference model including demographic and clinical variables (AUC = 0.633 vs. AUC = 0.556; P LR_test  = 1.52 × 10 -6 ). These data together with those reporting that the CYP3A4 and ESR2 SNPs correlate with the expression of TRIM4 and ESR2 mRNAs in PBMCs (ranging from P = 1.98 × 10 - 6 to P = 2.0 × 10 -35 ), and that the CYP2C9 rs1799853 SNP modulates the efficiency of multiple drugs, suggest that steroid hormone-related genes may have a role in determining the response to anti-TNF drugs.KEY POINTS• Polymorphisms within the CYP3A4 and CYP2C9 loci correlate with changes in DAS28 after treatment with anti-TNF drugs.• A haplotype including eQTL SNPs within the ESR2 gene associates with better response to anti-TNF drugs.• A genetic model built with eight steroid hormone-related variants significantly improved the ability to predict drug response.

Genetic biomarkers are sought to personalize treatment of patients with rheumatoid arthritis (RA), given their variable response to TNF inhibitors (TNFi). However, no genetic biomaker is yet sufficiently validated. Here, we report a validation study of 18 previously reported genetic biomarkers, including 11 from GWAS of response to TNFi. The validation was attempted in 581 patients with RA that had not been treated with biologic antirheumatic drugs previously. Their response to TNFi was evaluated at 3, 6 and 12 months in two ways: change in the DAS28 measure of disease activity, and according to the EULAR criteria for response to antirheumatic drugs. Association of these parameters with the genotypes, obtained by PCR amplification followed by single-base extension, was tested with regression analysis. These analyses were adjusted for baseline DAS28, sex, and the specific TNFi. However, none of the proposed biomarkers was validated, as none showed association with response to TNFi in our study, even at the time of assessment and with the outcome that showed the most significant result in previous studies. These negative results are notable because this was the first independent validation study for 12 of the biomarkers, and because they indicate that prudence is needed in the interpretation of the proposed biomarkers of response to TNFi even when they are supported by very low p values. The results also emphasize the requirement of independent replication for validation, and the need to search protocols that could increase reproducibility of the biomarkers of response to TNFi.

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.

Background: Recent research suggests that genetic variants in the tumor necrosis factor receptor 2 (TNFRSF1B) gene may have an impact on susceptibility to rheumatoid arthritis (RA) and drug response. The present population-based case-control study was carried out to evaluate whether 5 tagging single-nucleotide polymorphisms (SNPs) within the TNFRSF1B gene are associated with the risk of RA and response to antitumor necrosis factor (TNF) drugs., Methods: The study population included 1412 RA patients and 1225 healthy controls. A subset of 596 anti-TNF-naive RA patients was selected to assess the association of TNFRSF1B SNPs and drug response according to the EULAR response criteria., Results: We found that carriers of the TNFRSF1Brs3397C allele had a significantly increased risk of developing RA (P=0.0006). Importantly, this association remained significant after correction for multiple testing. We also confirmed the lack of association of the TNFRSF1Brs1061622 SNP with the risk of RA in the single-SNP analysis (P=0.89), but also through well-powered meta-analyses (PDOM=0.67 and PREC=0.37, respectively). In addition, our study showed that carriers of the TNFRSF1Brs3397C/C, TNFRSF1Brs1061622G/G, and TNFRSF1Brs1061631A/A genotypes had an increased risk of having a worse response to anti-TNF drugs at the level of P less than 0.05 (P=0.014, 0.0085 and 0.028, respectively). We also observed that, according to a log-additive model, carriers of the TNFRSF1Brs3397C or TNFRSF1Brs1061622G alleles showed an increased risk of having worse response to anti-TNF medications (P=0.018 and 0.0059). However, the association of the TNFRSF1Brs1061622 SNP only reached marginal significance after correction for multiple testing according to a log-additive model (P=0.0059) and it was not confirmed through a meta-analysis (PDOM=0.12)., Conclusion: Our results suggest that the TNFRSF1Brs3397 variant may play a role in modulating the risk of RA, but does not provide strong evidence of an impact of TNFRSF1B variants in determining response to anti-TNF drugs.

Introduction: In this study, our aim was to elucidate the role of four polymorphisms identified in a prior large genome-wide association study (GWAS) in which the investigators analyzed the responses of patients with rheumatoid arthritis (RA) to treatment with tumor necrosis factor inhibitors (TNFi). The authors of that study reported that the four genetic variants were significantly associated. However, none of the associations reached GWAS significance, and two subsequent studies failed to replicate these associations., Methods: The four polymorphisms (rs12081765, rs1532269, rs17301249 and rs7305646) were genotyped in a total of 634 TNFi-treated RA patients of Spanish Caucasian origin. Four outcomes were evaluated: changes in the Disease Activity Score in 28 joints (DAS28) after 6 and 12 months of treatment and classification according to the European League Against Rheumatism (EULAR) response criteria at the same time points. Association with DAS28 changes was assessed by linear regression using an additive genetic model. Contingency tables of genotype and allele frequencies between EULAR responder and nonresponder patients were compared. In addition, we combined our data with those of previously reported studies in a meta-analysis including 2,998 RA patients., Results: None of the four genetic variants showed an association with response to TNFi in any of the four outcomes analyzed in our Spanish patients. In addition, only rs1532269 yielded a suggestive association (P = 0.0033) with the response to TNFi when available data from previous studies were combined in the meta-analysis., Conclusion: Our data suggest that the rs12081765, rs1532269, rs17301249 and rs7305646 genetic variants do not have a role as genetic predictors of TNFi treatment outcomes.