Replication of association of the PTPRC gene with response to anti-tumor necrosis factor therapy in a large UK cohort

Towards Personalized Medicine in Rheumatoid Arthritis

Rheumatoid arthritis (RA) is a chronic, incurable, multisystem, inflammatory disease characterized by synovitis and extra-articular features. Although several advanced therapies targeting inflammatory mechanisms underlying the disease are available, no advanced therapy is universally effective. Therefore, a ceiling of treatment response is currently accepted where no advanced therapy is superior to another. The current challenge for medical research is the discovery and integration of predictive markers of drug response that can be used to personalize medicine so that the patient is started on "the right drug at the right time". This review article summarizes our current understanding of predicting response to anti-rheumatic drugs in RA, obstacles impeding the development of personalized medicine approaches and future research priorities to overcome these barriers.

Single Nucleotide Polymorphisms Associated with Rheumatoid Arthritis in Saudi Patients

Rheumatoid arthritis (RA) is a complex, multifactorial disorder with an autoimmune etiology. RA is highly heritable and is associated with both human leucocyte antigen (HLA) and non-HLA genes. We investigated the associations of 33 single nucleotide polymorphisms (SNPs) with RA in the Saudi population.

METHODS

This study included 105 patients with RA and an equal number of age- and sex-matched controls. The patients with RA attended outpatient clinics at King Khalid University Hospital in Riyadh, Saudi Arabia. Blood samples were collected, and DNA was extracted using Qiagen kits. Primers were designed for the 33 selected SNPs using the MassEXTEND primers program, and samples were genotyped on the Sequenom MassARRAY iPLEX platform. The allele frequencies and genotypes were determined for each SNP, and the results obtained for the patients were compared to those for the controls.

RESULTS

The allele and genotype frequencies of six SNPs were significantly associated with RA: rs1188934, rs10919563, rs3087243, rs1980422, rs10499194, and rs629326. The minor alleles of rs1188934, rs10919563, rs10499194, and rs629326 were protective, with odds ratios of 0.542, 0.597, 0.589, and 0.625, and p-values of 0.002, 0.023, 0.013 and 0.036, respectively. In addition, the heterozygote frequencies of two SNPs (rs6859219 and rs11586238) were significantly higher in the controls than in the patients.

CONCLUSIONS

There is considerable heterogeneity in the genetics of RA in different populations, and the SNPs that are associated with RA in some populations are not in others. We studied 33 SNPs and only eight were associated with RA. The remaining SNPs showed no allelic or genotypic associations with RA.

DNA Methylation Signatures of Response to Conventional Synthetic and Biologic Disease-Modifying Antirheumatic Drugs (DMARDs) in Rheumatoid Arthritis

Rheumatoid arthritis (RA) is a complex condition that displays heterogeneity in disease severity and response to standard treatments between patients. Failure rates for conventional, target synthetic, and biologic disease-modifying rheumatic drugs (DMARDs) are significant. Although there are models for predicting patient response, they have limited accuracy, require replication/validation, or for samples to be obtained through a synovial biopsy. Thus, currently, there are no prediction methods approved for routine clinical use. Previous research has shown that genetics and environmental factors alone cannot explain the differences in response between patients. Recent studies have demonstrated that deoxyribonucleic acid (DNA) methylation plays an important role in the pathogenesis and disease progression of RA. Importantly, specific DNA methylation profiles associated with response to conventional, target synthetic, and biologic DMARDs have been found in the blood of RA patients and could potentially function as predictive biomarkers. This review will summarize and evaluate the evidence for DNA methylation signatures in treatment response mainly in blood but also learn from the progress made in the diseased tissue in cancer in comparison to RA and autoimmune diseases. We will discuss the benefits and challenges of using DNA methylation signatures as predictive markers and the potential for future progress in this area.

MIF Variant rs755622 Is Associated with Severe Crohn's Disease and Better Response to Anti-TNF Adalimumab Therapy

Crohn's disease (CD), rheumatoid arthritis, psoriatic arthritis and other inflammatory diseases comprise a group of chronic diseases with immune-mediated pathogenesis which share common pathological pathways, as well as treatment strategies including anti-TNF biologic therapy. However, the response rate to anti-TNF therapy among those diseases varies, and approximately one third of patients do not respond. Since pharmacogenetic studies for anti-TNF therapy have been more frequent for other related diseases and are rare in CD, the aim of our study was to further explore markers associated with anti-TNF response in other inflammatory diseases in Slovenian CD patients treated with the anti-TNF drug adalimumab (ADA). We enrolled 102 CD patients on ADA, for which the response was defined after 4, 12, 20 and 30 weeks of treatment, using an IBDQ questionnaire and blood CRP value. We genotyped 41 SNPs significantly associated with response to anti-TNF treatment in other diseases. We found novel pharmacogenetic association between SNP rs755622 in the gene MIF (macrophage migration inhibitory factor) and SNP rs3740691 in the gene ARFGAP2 in CD patients treated with ADA. The strongest and most consistent association with treatment response was found for the variant rs2275913 in gene IL17A (p = 9.73 × 10-3).

Biomarkers (mRNAs and non-coding RNAs) for the diagnosis and prognosis of rheumatoid arthritis

In recent years, diagnostic and therapeutic approaches for rheumatoid arthritis (RA) have continued to improve. However, in the advanced stages of the disease, patients are unable to achieve long-term clinical remission and often suffer from systemic multi-organ damage and severe complications. Patients with RA usually have no overt clinical manifestations in the early stages, and by the time a definitive diagnosis is made, the disease is already at an advanced stage. RA is diagnosed clinically and with laboratory tests, including the blood markers C-reactive protein (CRP) and erythrocyte sedimentation rate (ESR) and the autoantibodies rheumatoid factor (RF) and anticitrullinated protein antibodies (ACPA). However, the presence of RF and ACPA autoantibodies is associated with aggravated disease, joint damage, and increased mortality, and these autoantibodies have low specificity and sensitivity. The etiology of RA is unknown, with the pathogenesis involving multiple factors and clinical heterogeneity. The early diagnosis, subtype classification, and prognosis of RA remain challenging, and studies to develop minimally invasive or non-invasive biomarkers in the form of biofluid biopsies are becoming more common. Non-coding RNA (ncRNA) molecules are composed of long non-coding RNAs, small nucleolar RNAs, microRNAs, and circular RNAs, which play an essential role in disease onset and progression and can be used in the early diagnosis and prognosis of RA. In this review of the diagnostic and prognostic approaches to RA disease, we provide an overview of the current knowledge on the subject, focusing on recent advances in mRNA-ncRNA as diagnostic and prognostic biomarkers from the biofluid to the tissue level.

-Omic Approaches and Treatment Response in Rheumatoid Arthritis

Rheumatoid arthritis (RA) is an inflammatory disorder characterized by an aberrant activation of innate and adaptive immune cells. There are different drugs used for the management of RA, including disease-modifying antirheumatic drugs (DMARDs). However, a significant percentage of RA patients do not initially respond to DMARDs. This interindividual variation in drug response is caused by a combination of environmental, genetic and epigenetic factors. In this sense, recent -omic studies have evidenced different molecular signatures involved in this lack of response. The aim of this review is to provide an updated overview of the potential role of -omic approaches, specifically genomics, epigenomics, transcriptomics, and proteomics, to identify molecular biomarkers to predict the clinical efficacy of therapies currently used in this disorder. Despite the great effort carried out in recent years, to date, there are still no validated biomarkers of response to the drugs currently used in RA. -Omic studies have evidenced significant differences in the molecular profiles associated with treatment response for the different drugs used in RA as well as for different cell types. Therefore, global and cell type-specific -omic studies analyzing response to the complete therapeutical arsenal used in RA, including less studied therapies, such as sarilumab and JAK inhibitors, are greatly needed.

Gene Ontology Analysis Highlights Biological Processes Influencing Non-Response to Anti-TNF Therapy in Rheumatoid Arthritis

Anti-TNF therapy has significantly improved disease control in rheumatoid arthritis, but a fraction of rheumatoid arthritis patients do not respond to anti-TNF therapy or lose response over time. Moreover, the mechanisms underlying non-response to anti-TNF therapy remain largely unknown. To date, many single biomarkers of response to anti-TNF therapy have been published but they have not yet been analyzed as a system of interacting nodes. The aim of our study is to systematically elucidate the biological processes underlying non-response to anti-TNF therapy in rheumatoid arthritis using the gene ontologies of previously published predictive biomarkers. Gene networks were constructed based on published biomarkers and then enriched gene ontology terms were elucidated in subgroups using gene ontology software tools. Our results highlight the novel role of proteasome-mediated protein catabolic processes (p = 2.91 × 10⁻¹⁵) and plasma lipoproteins (p = 4.55 × 10⁻¹¹) in anti-TNF therapy response. The results of our gene ontology analysis help elucidate the biological processes underlying non-response to anti-TNF therapy in rheumatoid arthritis and encourage further study of the highlighted processes.

Longitudinal analysis of blood DNA methylation identifies mechanisms of response to tumor necrosis factor inhibitor therapy in rheumatoid arthritis

Background

Rheumatoid arthritis (RA) is a chronic, immune-mediated inflammatory disease of the joints that has been associated with variation in the peripheral blood methylome. In this study, we aim to identify epigenetic variation that is associated with the response to tumor necrosis factor inhibitor (TNFi) therapy.

Methods

Peripheral blood genome-wide DNA methylation profiles were analyzed in a discovery cohort of 62 RA patients at baseline and at week 12 of TNFi therapy. DNA methylation of individual CpG sites and enrichment of biological pathways were evaluated for their association with drug response. Using a novel cell deconvolution approach, altered DNA methylation associated with TNFi response was also tested in the six main immune cell types in blood. Validation of the results was performed in an independent longitudinal cohort of 60 RA patients.

Findings

Treatment with TNFi was associated with significant longitudinal peripheral blood methylation changes in biological pathways related to RA (FDR<0.05). 139 biological functions were modified by therapy, with methylation levels changing systematically towards a signature similar to that of healthy controls. Differences in the methylation profile of T cell activation and differentiation, GTPase-mediated signaling, and actin filament organization pathways were associated with the clinical response to therapy. Cell type deconvolution analysis identified CpG sites in CD4+T, NK, neutrophils and monocytes that were significantly associated with the response to TNFi.

Interpretation

Our results show that treatment with TNFi restores homeostatic blood methylation in RA. The clinical response to TNFi is associated to methylation variation in specific biological pathways, and it involves cells from both the innate and adaptive immune systems.

Funding

The Instituto de Salud Carlos III.

Pharmacogenetics of Drug Therapies in Rheumatoid Arthritis

Rheumatoid arthritis (RA) is a chronic systemic inflammatory disorder that can lead to severe joint damage and is often associated with a high morbidity and disability. Disease-modifying anti-rheumatic drugs (DMARDs) are the mainstay of treatment in RA. DMARDs not only relieve the clinical signs and symptoms of RA but also inhibit the radiographic progression of disease and reduce the effects of chronic systemic inflammation. Since the introduction of biologic DMARDs in the late 1990s, the therapeutic range of options for the management of RA has significantly expanded. However, patients' response to these agents is not uniform with considerable variability in both efficacy and toxicity. There are no reliable means of predicting an individual patient's response to a given DMARD prior to initiation of therapy. In this chapter, the current published literature on the pharmacogenetics of traditional DMARDS and the newer biologic DMARDs in RA is highlighted. Pharmacogenetics may help individualize drug therapy in patients with RA by providing reliable biomarkers to predict medication toxicity and efficacy.

Molecular and Cellular Heterogeneity in Rheumatoid Arthritis: Mechanisms and Clinical Implications

Rheumatoid arthritis is an autoimmune disease that exhibits significant clinical heterogeneity. There are various treatments for rheumatoid arthritis, including disease-modifying anti-rheumatic drugs (DMARDs), glucocorticoids, non-steroidal anti-inflammatory drugs (NSAIDs), and inflammatory cytokine inhibitors (ICI), typically associated with differentiated clinical effects and characteristics. Personalized responsiveness is observed to the standard treatment due to the pathophysiological heterogeneity in rheumatoid arthritis, resulting in an overall poor prognosis. Understanding the role of individual variation in cellular and molecular mechanisms related to rheumatoid arthritis will considerably improve clinical care and patient outcomes. In this review, we discuss the source of pathophysiological heterogeneity derived from genetic, molecular, and cellular heterogeneity and their possible impact on precision medicine and personalized treatment of rheumatoid arthritis. We provide emphasized description of the heterogeneity derived from mast cells, monocyte cell, macrophage fibroblast-like synoviocytes and, interactions within immune cells and with inflammatory cytokines, as well as the potential as a new therapeutic target to develop a novel treatment approach. Finally, we summarize the latest clinical trials of treatment options for rheumatoid arthritis and provide a suggestive framework for implementing preclinical and clinical experimental results into clinical practice.

Adding value to real-world data: the role of biomarkers

Adding biomarker information to real world datasets (e.g. biomarker data collected into disease/drug registries) can enhance mechanistic understanding of intra-patient differences in disease trajectories and differences in important clinical outcomes. Biomarkers can detect pathologies present early in disease potentially paving the way for preventative intervention strategies, which may help patients to avoid disability, poor treatment outcome, disease sequelae and premature mortality. However, adding biomarker data to real world datasets comes with a number of important challenges including sample collection and storage, study design and data analysis and interpretation. In this narrative review we will consider the benefits and challenges of adding biomarker data to real world datasets and discuss how biomarker data have added to our understanding of complex diseases, focusing on rheumatoid arthritis.

The Potential Role of Genomic Medicine in the Therapeutic Management of Rheumatoid Arthritis

During the last decade, important advances have occurred regarding understanding of the pathogenesis and treatment of rheumatoid arthritis (RA). Nevertheless, response to treatment is not universal, and choosing among different therapies is currently based on a trial and error approach. The specific patient’s genetic background influences the response to therapy for many drugs: In this sense, genomic studies on RA have produced promising insights that could help us find an effective therapy for each patient. On the other hand, despite the great knowledge generated regarding the genetics of RA, most of the investigations performed to date have focused on identifying common variants associated with RA, which cannot explain the complete heritability of the disease. In this regard, rare variants could also contribute to this missing heritability as well as act as biomarkers that help in choosing the right therapy. In the present article, different aspects of genetics in the pathogenesis and treatment of RA are reviewed, from large-scale genomic studies to specific rare variant analyses. We also discuss the shared genetic architecture existing among autoimmune diseases and its implications for RA therapy, such as drug repositioning.

Anti-TNF Therapy in Spondyloarthritis and Related Diseases, Impact on the Immune System and Prediction of Treatment Responses

Immune-mediated inflammatory diseases (IMIDs), such as spondyloarthritis (SpA), psoriasis, Crohn's disease (CD), and rheumatoid arthritis (RA) remain challenging illnesses. They often strike at a young age and cause lifelong morbidity, representing a considerable burden for the affected individuals and society. Pioneering studies have revealed the presence of a TNF-dependent proinflammatory cytokine cascade in several IMIDs, and the introduction of anti-TNF therapy 20 years ago has proven effective to reduce inflammation and clinical symptoms in RA, SpA, and other IMID, providing unprecedented clinical benefits and a valid alternative in case of failure or intolerable adverse effects of conventional disease-modifying antirheumatic drugs (DMARDs, for RA) or non-steroidal anti-inflammatory drugs (NSAIDs, for SpA). However, our understanding of how TNF inhibitors (TNFi) affect the immune system in patients is limited. This question is relevant because anti-TNF therapy has been associated with infectious complications. Furthermore, clinical efficacy of TNFi is limited by a high rate of non-responsiveness (30–40%) in RA, SpA, and other IMID, exposing a substantial fraction of patients to side-effects without clinical benefit. Despite the extensive use of TNFi, it is still not possible to determine which patients will respond to TNFi before treatment initiation. The recent introduction of antibodies blocking IL-17 has expanded the therapeutic options for SpA, as well as psoriasis and psoriatic arthritis. It is therefore essential to develop tools to guide treatment decisions for patients affected by SpA and other IMID, both to optimize clinical care and contain health care costs. After a brief overview of the biology of TNF, its receptors and currently used TNFi in the clinics, we summarize the progress that has been made to increase our understanding of the action of TNFi on the immune system in patients. We then summarize efforts dedicated to identify biomarkers that can predict treatment responses to TNFi and we conclude with a section dedicated to the recently introduced inhibitors of IL-17A and IL-23 in SpA and related diseases. The focus of this review is on SpA, however, we also refer to RA on topics for which only limited information is available on SpA in the literature.

Evaluation of 12 GWAS-drawn SNPs as biomarkers of rheumatoid arthritis response to TNF inhibitors. A potential SNP association with response to etanercept

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.

Polymorphisms at phase I-metabolizing enzyme and hormone receptor loci influence the response to anti-TNF therapy in rheumatoid arthritis patients

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.

Genome-wide association study of response to tumour necrosis factor inhibitor therapy in rheumatoid arthritis

Rheumatoid arthritis (RA) is characterised by chronic synovial joint inflammation. Treatment has been revolutionised by tumour necrosis factor alpha inhibitors (TNFi) but each available drug shows a significant non-response rate. We conducted a genome-wide association study of 1752 UK RA TNFi-treated patients to identify predictors of change in the Disease Activity Score 28 (DAS28) and subcomponents over 3–6 months. The rs7195994 variant at the FTO gene locus was associated with infliximab response when looking at a change in the swollen joint count (SJC28) subcomponent (p = 9.74 × 10⁻⁹). Capture Hi-C data show chromatin interactions in GM12878 cells between rs2540767, in high linkage disequilibrium with rs7195994 (R² = 0.9) and IRX3, a neighbouring gene of FTO. IRX3 encodes a transcription factor involved in adipocyte remodelling and is regarded as the obesity gene at the FTO locus. Importantly, the rs7195994 association remained significantly associated following adjustment for BMI. In addition, using capture Hi-C data we showed interactions between TNFi-response associated variants and 16 RA susceptibility variants.

A cytokine protein-protein interaction network for identifying key molecules in rheumatoid arthritis

Rheumatoid arthritis (RA) is a chronic inflammatory disease of the synovial joints. Though the current RA therapeutics such as disease-modifying antirheumatic drugs (DMARDs), nonsteroidal anti-inflammatory drugs (NSAIDs) and biologics can halt the progression of the disease, none of these would either dramatically reduce or cure RA. So, the identification of potential therapeutic targets and new therapies for RA are active areas of research. Several studies have discovered the involvement of cytokines in the pathogenesis of this disease. These cytokines induce signal transduction pathways in RA synovial fibroblasts (RASF). These pathways share many signal transducers and their interacting proteins, resulting in the formation of a signaling network. In order to understand the involvement of this network in RA pathogenesis, it is essential to identify the key transducers and their interacting proteins that are part of this network. In this study, based on a detailed literature survey, we have identified a list of 12 cytokines that induce signal transduction pathways in RASF. For these cytokines, we have built a signaling network using the protein-protein interaction (PPI) data that was obtained from public repositories such as HPRD, BioGRID, MINT, IntAct and STRING. By combining the network centrality measures with the gene expression data from the RA related microarrays that are available in the open source Gene Expression Omnibus (GEO) database, we have identified 24 key proteins of this signaling network. Two of these 24 are already drug targets for RA, and of the remaining, 12 have direct PPI links to some of the current drug targets of RA. Therefore, these key proteins seem to be crucial in the pathogenesis of RA and hence might be treated as potential drug targets.

Validation study of genetic biomarkers of response to TNF inhibitors in rheumatoid arthritis

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.

Precision Medicine in Rheumatoid Arthritis

Treatment of rheumatoid arthritis (RA) has substantially improved in recent years because of the development of novel drugs. However, response is not universal for any of the treatment options, and selection of an effective therapy is currently based on a trial-and-error approach. Delayed treatment response increases the risk of progressive joint damage and resultant disability and also has a significant impact on quality of life for patients. For many drugs, the patient's genetic background influences response to therapy, and understanding the genetics of response to therapy in RA may allow for targeted personalized health care.

Genetic variants within the TNFRSF1B gene and susceptibility to rheumatoid arthritis and response to anti-TNF drugs: a multicenter study

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.

Right drug, right patient, right time: aspiration or future promise for biologics in rheumatoid arthritis?

Individualising biologic disease-modifying anti-rheumatic drugs (bDMARDs) to maximise outcomes and deliver safe and cost-effective care is a key goal in the management of rheumatoid arthritis (RA). Investigation to identify predictive tools of bDMARD response is a highly active and prolific area of research. In addition to clinical phenotyping, cellular and molecular characterisation of synovial tissue and blood in patients with RA, using different technologies, can facilitate predictive testing. This narrative review will summarise the literature for the available bDMARD classes and focus on where progress has been made. We will also look ahead and consider the increasing use of 'omics' technologies, the potential they hold as well as the challenges, and what is needed in the future to fully realise our ambition of personalised bDMARD treatment.

Systematic review and meta-analysis: pharmacogenetics of anti-TNF treatment response in rheumatoid arthritis

Rheumatoid arthritis (RA) is a chronic inflammatory disease that affects ~1% of the Caucasian population. Over the last decades, the availability of biological drugs targeting the proinflammatory cytokine tumour necrosis factor α, anti-TNF drugs, has improved the treatment of patients with RA. However, one-third of the patients do not respond to the treatment. We wanted to evaluate the status of pharmacogenomics of anti-TNF treatment. We performed a PubMed literature search and all studies reporting original data on associations between genetic variants and anti-TNF treatment response in RA patients were included and results evaluated by meta-analysis. In total, 25 single nucleotide polymorphisms were found to be associated with anti-TNF treatment response in RA (19 from genome-wide association studies and 6 from the meta-analyses), and these map to genes involved in T cell function, NFκB and TNF signalling pathways (including CTCN5, TEC, PTPRC, FCGR2A, NFKBIB, FCGR2A, IRAK3). Explorative prediction analyses found that biomarkers for clinical treatment selection are not yet available.

Genetics of rheumatoid arthritis susceptibility, severity, and treatment response

A decade after the first genome-wide association study in rheumatoid arthritis (RA), a plethora of genetic association studies have been published on RA and its clinical or serological subtypes. We review the major milestones in the study of the genetic architecture of RA susceptibility, severity, and response to treatment. We set the scientific context necessary for non-geneticists to understand the potential clinical applications of human genetics and its significance for a stratified approach to the management of RA in the future.

Polymorphisms in STAT4, PTPN2, PSORS1C1 and TRAF3IP2 Genes Are Associated with the Response to TNF Inhibitors in Patients with Rheumatoid Arthritis

Objective

Rheumatoid Arthritis (RA) is a progressive autoimmune disease characterized by chronic joint inflammation and structural damage. Remission or at least low disease activity (LDA) represent potentially desirable goals of RA treatment. Single nucleotide polymorphisms (SNPs) in several genes might be useful for prediction of response to therapy. We aimed at exploring 4 SNPs in candidate genes (STAT4, PTPN2, PSORS1C1 and TRAF3IP2) in order to investigate their potential role in the response to therapy with tumor necrosis factor inhibitors (TNF-i) in RA patients.

Methods

In 171 RA patients we investigated the following SNPs: rs7574865 (STAT4), rs2233945 (PSORS1C1), rs7234029 (PTPN2) and rs33980500 (TRAF3IP2). Remission, LDA, and EULAR response were registered at 6 months and 2 years after initiation of first line TNF-i [Adalimumab (ADA) and Etanercept (ETN)].

Results

STAT4 variant allele was associated with the absence of a good/moderate EULAR response at 2 years of treatment in the whole RA group and in ETN treated patients. The PTPN2 SNP was associated with no good/moderate EULAR response at 6 months in ADA treated patients. Patients carrying PSORS1C1 variant allele did not reach LDA at 6 months in both the whole RA group and ETN treated patients. TRAF3IP2 variant allele was associated with the lack of LDA and remission achievement at 6 months in all RA cohort while an association with no EULAR response at 2 years of treatment occurred only in ETN treated patients.

Conclusions

For the first time, we reported that SNPs in STAT4, PTPN2, PSORS1C1, and TRAF3IP2 are associated with response to TNF-i treatment in RA patients; however, these findings should be validated in a larger population.

A genetic risk score composed of rheumatoid arthritis risk alleles, HLA-DRB1 haplotypes, and response to TNFi therapy - results from a Swedish cohort study

Background

To prevent debilitating and irreversible joint damage, rheumatoid arthritis (RA) is often treated with tumor necrosis factor inhibitor (TNFi), but many patients do not respond to this costly therapy. Few predictors for response are known, and it has been proposed that genetic factors which influence the development of RA may also influence disease severity and response to therapy. Several previous studies have attempted to confirm this but results remain inconclusive. We expand on previous studies by including more RA risk alleles, and maximize power by combining them into a genetic risk score.

Method

We linked genotyped RA patients from the Epidemiological Investigation of Rheumatoid Arthritis study to the Swedish Rheumatology Quality Register, identifying patients who started a TNFi as their first biological disease-modifying anti-rheumatic drug, with a return visit within 2–8 months after treatment start (N = 867). We calculated risk scores from 76 established RA risk SNPs, and four HLA-DRB1 amino acid positions, and tested whether risk scores or individual genetic risk factors could predict the European League Against Rheumatism (EULAR) response.

Results

We found no association between any of the risk scores or HLA-DRB1 haplotypes and EULAR response, neither overall nor stratified by anti-citrullinated protein/peptide antibody (ACPA) status. When evaluating each of the 76 SNPs, we found that the number of SNPs presenting significant associations was not higher than expected by chance (5/76 SNPs had p < 0.05 in ACPA-positive RA, 4/76 in ACPA-negative RA).

Conclusion

Overall, known RA risk SNPs do not predict response to TNFi, either individually or when combined into a risk score. This does not support the hypothesis that genes influencing RA onset would also influence its prognosis and treatment response.

Electronic supplementary material

The online version of this article (doi:10.1186/s13075-016-1174-z) contains supplementary material, which is available to authorized users.

Confirmation of an IRAK3 polymorphism as a genetic marker predicting response to anti-TNF treatment in rheumatoid arthritis

Several genetic variants in Toll-like receptor (TLR) and nuclear factor (NF)-κB signalling pathways have been reported associated with responsiveness to tumour necrosis factor inhibitor (anti-TNF) treatment in rheumatoid arthritis (RA). The present study was undertaken to replicate these findings. In a retrospective case-case study including 1007 Danish anti-TNF-treated RA patients, we genotyped 7 previously reported associated single-nucleotide polymorphisms (SNPs) in these pathways. Furthermore, 5 SNPs previously reported by our group were genotyped in a subcohort (N=469). Primary analyses validated the IRAK3 rs11541076 variant as associated (odds ratio (OR)=1.33, 95% confidence interval (CI): 1.00-1.77, P-value=0.047) with a positive treatment response (EULAR (European League Against Rheumatism) good/moderate vs none response at 4±2 months), and found the NLRP3 rs461266 variant associated (OR=0.75, 95% CI: 0.60-0.94, P=0.014) with a negative treatment response. Meta-analyses combining data from previous studies suggested smaller effect sizes of associations between variant alleles of CHUK rs11591741, NFKBIB rs3136645 and rs9403 and a negative treatment response. In conclusion, this study validates rs11541076 in IRAK3, a negative regulator of TLR signalling, as a predictor of anti-TNF treatment response, and suggests true positive associations of previously reported SNPs within genes encoding activators/inhibitors of NF-κB (CHUK, MYD88, NFKBIB, and NLRP3).

Differential Methylation as a Biomarker of Response to Etanercept in Patients With Rheumatoid Arthritis

Objective

Biologic drug therapies represent a huge advance in the treatment of rheumatoid arthritis (RA). However, very good disease control is achieved in only 30% of patients, making identification of biomarkers of response a research priority. We undertook this study to test our hypothesis that differential DNA methylation patterns may provide biomarkers predictive of response to tumor necrosis factor inhibitor (TNFi) therapy in patients with RA.

Methods

An epigenome‐wide association study was performed on pretreatment whole blood DNA from patients with RA. Patients who displayed good response (n = 36) or no response (n = 36) to etanercept therapy at 3 months were selected. Differentially methylated positions were identified using linear regression. Variance of methylation at differentially methylated positions was assessed for correlation with cis‐acting single‐nucleotide polymorphisms (SNPs). A replication experiment for prioritized SNPs was performed in an independent cohort of 1,204 RA patients.

Results

Five positions that were differentially methylated between responder groups were identified, with a false discovery rate of <5%. The top 2 differentially methylated positions mapped to exon 7 of the LRPAP1 gene on chromosome 4 (cg04857395, P = 1.39 × 10⁻⁸ and cg26401028, P = 1.69 × 10⁻⁸). The A allele of the SNP rs3468 was correlated with higher levels of methylation for both of the top 2 differentially methylated positions (P = 2.63 × 10⁻⁷ and P = 1.05 × 10⁻⁶, respectively). Furthermore, the A allele of rs3468 was correlated with European League Against Rheumatism nonresponse in the discovery cohort (P = 0.03; n = 56) and in the independent replication cohort (P = 0.003; n = 1,204).

Conclusion

We identify DNA methylation as a potential biomarker of response to TNFi therapy, and we report the association between response and the LRPAP1 gene, which encodes a chaperone of low‐density lipoprotein receptor–related protein 1. Additional replication experiments in independent sample collections are now needed.

Associations between PTPRC rs10919563 A/G and FCGR2A R131H polymorphisms and responsiveness to TNF blockers in rheumatoid arthritis: a meta-analysis

We aimed to investigate whether the PTPRC rs10919563 A/G and Fc gamma receptor 2A (FCGR2A) R131H polymorphisms can predict the response to anti-TNF therapy in rheumatoid arthritis (RA) patients. We conducted a meta-analysis of studies on the association between the PTPRC rs10919563 A/G or the FCGR2A R131H polymorphism and responsiveness to anti-TNF therapy in RA patients. Eighteen studies (twelve on PTPRC and six on FCGR2A) from eight articles involving 3058 patients were considered in this meta-analysis. The meta-analysis showed a significant association between the PTPRC rs10919563 A allele and response to TNF-α blockers in RA. The OR of the PTPRC A allele was significantly lower in responders (OR = 0.584, 95 % CI = 0.409-0.835, P = 0.003). Meta-analysis revealed no association between the FCGR2A HH + HR genotype and responsiveness to TNF blockers in all study subjects (OR = 0.762, 95 % CI = 0.543-1.068, P = 0.115). However, stratification by TNF inhibitor type showed that the FCGR2A HH + HR genotype was associated with responsiveness to adalimumab (OR = 0.591, 95 % CI = 0.369-0.947, P = 0.029), but not infliximab and etanercept (OR = 0.929, 95 % CI = 0.354-2.440, P = 0.881; OR = 0.804, 95 % CI = 0.293-2.207, P = 0.673). The PTPRC rs10919563 A allele shows a poor response to anti-TNF therapy, and the FCGR2A HH + HR genotype shows a poor response to adalimumab for RA. Genotyping for these polymorphisms may be useful for predicting the response to TNF-α blockers with respect to personalized medicine.

Genetic data: The new challenge of personalized medicine, insights for rheumatoid arthritis patients

Rapid advances in genotyping technology, analytical methods, and the establishment of large cohorts for population genetic studies have resulted in a large new body of information about the genetic basis of human rheumatoid arthritis (RA). Improved understanding of the root pathogenesis of the disease holds the promise of improved diagnostic and prognostic tools based upon this information. In this review, we summarize the nature of new genetic findings in human RA, including susceptibility loci and gene-gene and gene-environment interactions, as well as genetic loci associated with sub-groups of patients and those associated with response to therapy. Possible uses of these data are discussed, such as prediction of disease risk as well as personalized therapy and prediction of therapeutic response and risk of adverse events. While these applications are largely not refined to the point of clinical utility in RA, it seems likely that multi-parameter datasets including genetic, clinical, and biomarker data will be employed in the future care of RA patients.

Association between catechol-O-methyl transferase gene polymorphisms and fibromyalgia in a Korean population: A case-control study

BACKGROUND

Although polymorphisms of the catechol-O-methyl transferase (COMT) gene have been implicated in altered pain sensitivity, results concerning the association between COMT gene polymorphisms and fibromyalgia (FM) are equivocal. We assessed the associations between COMT single-nucleotide polymorphisms (SNP) and FM risk and symptom severity.

METHODS

In total, 409 FM patients and 423 controls were enrolled. Alleles and genotypes at five positions [rs6269 (A>G), rs4633 (C>T), rs4818 (C>G), rs4680 (C>G) and rs165599 (A>G)] in the COMT gene were genotyped from peripheral blood DNA.

RESULTS

Alleles and genotypes of the rs4818 COMT gene polymorphism were significantly associated with increased susceptibility to FM. The rs4818 GG genotype was more strongly associated with FM compared to the CC genotype (OR = 1.680, 95% CI: 1.057, 2.672, p = 0.027). Although allele and genotype frequencies did not differ among groups, the rs4633 CT genotype was not associated with the presence of FM following adjustment for age and sex (OR = 0.745; 95% CI: 0.558, 0.995; p = 0.046). However, no association was observed between clinical measures and individual COMT SNPs. In haplotype analysis, there was a significant association between ACG haplotype and FM susceptibility sex (OR = 2.960, 95% CI: 1.447, 6.056, p = 0.003) and the number of tender points (p = 0.046).

CONCLUSIONS

This large-scale study suggests that polymorphisms of the COMT gene may be associated with FM risk and pain sensitivity in Korean FM patients. However, our results differed to those of previous studies, suggesting ethnic variation in COMT gene polymorphisms in FM.

WHAT DOES THIS STUDY ADD

By contrast to Caucasian and Latin-American populations, the COMT gene polymorphisms are associated with FM risk and pain sensitivity in Korean FM patients, suggesting ethnic variation in COMT gene polymorphisms.

A longitudinal genome-wide association study of anti-tumor necrosis factor response among Japanese patients with rheumatoid arthritis

Background

Studies of Caucasian patients with rheumatoid arthritis (RA) to identify genetic biomarkers of anti-tumor necrosis factor (TNF) response have used response at a single time point as the phenotype with which single nucleotide polymorphism (SNP) associations have been tested. The findings have been inconsistent across studies. Among Japanese patients, only a few SNPs have been investigated. We report here the first genome-wide association study (GWAS) to identify genetic biomarkers of anti-TNF response among Japanese RA patients, using response at 2 time-points for a more reliable clinical phenotype over time.

Methods

Disease Activity Scores based on 28 joint counts (DAS28) were assessed at baseline (before initial therapy), and after 3 and 6 months in 487 Japanese RA patients starting anti-TNF therapy for the first time or switching to a new anti-TNF agent. A genome-wide panel of SNPs was genotyped and additional SNPs were imputed. Using change in DAS28 scores from baseline at both 3 (ΔDAS-3) and 6 months (ΔDAS-6) as the response phenotype, a longitudinal genome-wide association analysis was conducted using generalized estimating equations (GEE) models, adjusting for baseline DAS28, treatment duration, type of anti-TNF agent and concomitant methotrexate. Cross-sectional analyses were performed using multivariate linear regression models, with response from a single time point (ΔDAS-3 or ΔDAS-6) as phenotype; all other variables were the same as in the GEE models.

Results

In the GEE models, borderline significant association was observed at 3 chromosomal regions (6q15: rs284515, p = 6.6x10⁻⁷; 6q27: rs75908454, p = 6.3x10⁻⁷ and 10q25.3: rs1679568, p = 8.1x10⁻⁷), extending to numerous SNPs in linkage disequilibrium (LD) across each region. Potential candidate genes in these regions include MAP3K7, BACH2 (6q15), GFRA1 (10q25.3), and WDR27 (6q27). The association at GFRA1 replicates a previous finding from a Caucasian dataset. In the cross-sectional analyses, ΔDAS-6 was significantly associated with the 6q15 locus (rs284511, p = 2.5x10⁻⁸). No other significant or borderline significant associations were identified.

Conclusion

Three genomic regions demonstrated significant or borderline significant associations with anti-TNF response in our dataset of Japanese RA patients, including a locus previously associated among Caucasians. Using repeated measures of response as phenotype enhanced the power to detect these associations.

Electronic supplementary material

The online version of this article (doi:10.1186/s13075-016-0920-6) contains supplementary material, which is available to authorized users.

Genetic Variations in Pattern Recognition Receptor Loci Are Associated with Anti-TNF Response in Patients with Rheumatoid Arthritis

OBJECTIVES

To determine whether genetic variation within genes related to the Toll-like receptor, inflammasome and interferon-γ pathways contributes to the differences in treatment response to tumour necrosis factor inhibitors (anti-TNF) in patients with rheumatoid arthritis (RA).

METHODS

In a retrospective case-case study, we assessed 23 functional single nucleotide polymorphisms (SNPs) in 15 genes. We included 538 anti-TNF naïve Danish RA patients from the nationwide DANBIO database. Multivariable logistic regression analyses were performed to detect associations (p-value<0.05) between genotypes and European League Against Rheumatism (EULAR) treatment responses. False Discovery Rate corrections for multiple testing (q-value) and stratified analyses were performed to investigate association with individual therapies and IgM-rheumatoid factor (RF) status.

RESULTS

Six of twenty successfully genotyped polymorphisms were nominally associated with EULAR treatment response. Three of these were in weak to moderate linkage disequilibrium with polymorphisms previously reported associated with anti-TNF treatment response. TLR5(rs5744174) variant allele carriers (odds ratio(OR) = 1.7(1.1-2.5),p = 0.010,q = 0.46) and TLR1(rs4833095) homozygous variant carriers (OR = 2.8(1.1-7.4),p = 0.037,q = 0.46) had higher odds for a positive treatment response. NLRP3(rs10754558) variant allele carriers (odds ratio(OR) = 0.6(0.4-1.0),p = 0.045,q = 0.46) were more likely to have a negative treatment response. The association in TLR5(rs5744174) remained significant after correction for multiple comparisons among patients negative for RF (OR = 6.2(2.4-16.3),p = 0.0002,q = 0.024). No other association withstood correction for multiple testing. Post hoc analyses showed that change in Patient Global score on a visual analogue scale (VAS) and change in pain VAS were the main factors responsible for the association.

CONCLUSIONS

We reproduced previously reported associations between genetic variation in the TLR10/1/6 gene cluster, TLR5, and NLRP3 loci and response to anti-TNF treatment in RA. Changes in VAS pain and patient global scores were the main contributors to the association found for TLR5. Furthermore, we identified other candidate genes that require replication in independent cohorts.

Genetic and genomic markers of anti-TNF treatment response in rheumatoid arthritis

Despite the success of anti-TNF drugs in the treatment of rheumatoid arthritis, a significant rate of nonresponse remains. Current clinical factors confer little power for predicting response and, in current practice, an unsatisfactory ‘trial and error’ approach governs therapeutic decisions. Candidate gene and unbiased genome-wide investigations have sought to identify genetic biomarkers that predict who will respond to anti-TNF drugs before the drug is administered. To date, few studies have yielded robust associations; herein, we discuss currently identified associations and the issues that need to be addressed in future investigations including insufficient power and an inadequate measure of disease activity. The potential for alternative predictors of anti-TNF therapy response from transcriptomic and epigenetic data will also be explored.

Association of HLA-DRB1 haplotypes with rheumatoid arthritis severity, mortality, and treatment response

IMPORTANCE

Advances have been made in identifying genetic susceptibility loci for autoimmune diseases, but evidence is needed regarding their association with prognosis and treatment response.

OBJECTIVE

To assess whether specific HLA-DRB1 haplotypes associated with rheumatoid arthritis (RA) susceptibility are also associated with radiological severity, mortality, and response to tumor necrosis factor (TNF) inhibitor drugs.

DESIGN, SETTING, AND PARTICIPANTS

The Norfolk Arthritis Register (NOAR; 1691 patients and 2811 radiographs; recruitment: 1989-2008; 2008 as final follow-up) was used as a discovery cohort and the Early Rheumatoid Arthritis Study (421 patients and 3758 radiographs; recruitment: 1986-1999; 2005 as final follow-up) as an independent replication cohort for studies of radiographic outcome. Mortality studies were performed in the NOAR cohort (2432 patients; recruitment: 1990-2007; 2011 as final follow-up) and studies of treatment response in the Biologics in Rheumatoid Arthritis Genetics and Genomics Study Syndicate cohort (1846 patients enrolled at initiation of TNF inhibitor; recruitment: 2006-2010; 2011 as final follow-up). Longitudinal statistical modeling was performed to integrate multiple radiograph records per patient over time. All patients were from the United Kingdom and had self-reported white ancestry.

EXPOSURES

Sixteen HLA-DRB1 haplotypes defined by amino acids at positions 11, 71, and 74.

MAIN OUTCOMES AND MEASURES

Radiological outcome using the Larsen score (range: 0 [none] to 200 [severe joint damage]) and erosions of the hands and feet on radiographs, all-cause mortality, and treatment response measured by change in Disease Activity Score based on 28 joint counts and European League Against Rheumatism (EULAR) response.

RESULTS

Patients with RA and valine at position 11 of HLA-DRB1 had the strongest association with radiological damage (OR, 1.75 [95% CI, 1.51-2.05], P = 4.6E-13). By year 5, the percentages of patients with erosions of the hands and feet were 48% of noncarriers (150/314) of valine at position 11, 61% of heterozygote carriers (130/213), and 74% of homozygote carriers (43/58). Valine at position 11 also was associated with higher all-cause mortality in patients with inflammatory polyarthritis (hazard ratio, 1.16 [95% CI, 1.03-1.31], P = .01) (noncarriers: 319 deaths in 1398 patients over 17,196 person-years, mortality rate of 1.9% per year; carriers: 324 deaths in 1116 patients in 13,208 person-years, mortality rate of 2.5% per year) and with better EULAR response to TNF inhibitor therapy (OR, 1.14 [95% CI, 1.01-1.30], P = .04) (noncarriers: 78% [439/561 patients] with moderate or good EULAR response; heterozygote carriers: 81% [698/866]; and homozygote carriers: 86% [277/322]). The risk hierarchy defined by HLA-DRB1 haplotypes was correlated between disease susceptibility, severity, and mortality, but inversely correlated with TNF inhibitor treatment response.

CONCLUSIONS AND RELEVANCE

Among patients with RA, the HLA-DRB1 locus, which is associated with disease susceptibility, was also associated with radiological severity, mortality, and treatment response. Replication of these findings in other cohorts is needed as a next step in evaluating the role of HLA-DRB1 haplotype analysis for management of RA.

Replication of PTPRC as genetic biomarker of response to TNF inhibitors in patients with rheumatoid arthritis

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.

Haptoglobin-α1, -α2, vitamin D-binding protein and apolipoprotein C-III as predictors of etanercept drug response in rheumatoid arthritis

Introduction

The introduction of tumor necrosis factor-alpha (TNF-α) antagonists has substantially improved patient’s clinical outcome in rheumatoid arthritis (RA). However, nearly 20% to 40% of RA patients do not respond to anti-TNF-α treatment strategies. To identify valid predictors of TNF-α antagonist response in RA, serum proteome profiles from responders (R) and non-responders (NR) to etanercept, a soluble recombinant TNF-α receptor/IgG Fc fusion protein receptor, were compared in a prospective cohort study.

Methods

In this clinical study 50 RA patients with inadequate response to conventional DMARDs were included and treated with etanercept. The primary efficacy endpoint was response according to the European League against Rheumatism (EULAR) improvement criteria. Serum samples collected prior to initiation and after six months of etanercept therapy were cleared of the most abundant major proteins by immunoaffinity chromatography. After separation by two-dimensional differential gel electrophoresis (2D-DIGE) and identification by mass spectrometry (MS) data were validated by Western blot analysis.

Results

After six months of etanercept treatment 62% (n = 31) of RA patients achieved response. Haptoglobin-α1 (Hp-α1) and -α2 (Hp-α2) and vitamin D-binding protein (VDBP) were found to be significantly upregulated in responder sera (P ≤0.02) at study entry. In contrast, apolipoprotein C-III (ApoC-III) showed significantly higher levels in non-responders (P = 0.0162). At study end ApoA-II, Hp-α1, Hp-α2 and VDBP were identified to be expressed at significantly higher levels (P <0.05) in responder sera.

Conclusions

By application of clinical proteomics in immunodepleted sera we could identify and validate for the first time Hp-α1, -α2, VDBP and ApoC-III as potential biomarkers for prediction of etanercept drug response in RA.

Electronic supplementary material

The online version of this article (doi:10.1186/s13075-015-0553-1) contains supplementary material, which is available to authorized users.

TRAF1/C5 but not PTPRC variants are potential predictors of rheumatoid arthritis response to anti-tumor necrosis factor therapy

Background. The aim of our work was to replicate, in a Southern European population, the association reported in Northern populations between PTPRC locus and response to anti-tumor necrosis factor (anti-TNF) treatment in rheumatoid arthritis (RA). We also looked at associations between five RA risk alleles and treatment response. Methods. We evaluated associations between anti-TNF treatment responses assessed by DAS28 change and by EULAR response at six months in 383 Portuguese patients. Univariate and multivariate linear and logistic regression analyses were performed. In a second step to confirm our findings, we pooled our population with 265 Spanish patients. Results. No association was found between PTPRC rs10919563 allele and anti-TNF treatment response, neither in Portuguese modeling for several clinical variables nor in the overall population combining Portuguese and Spanish patients. The minor allele for RA susceptibility, rs3761847 SNP in TRAF1/C5 region, was associated with a poor response in linear and logistic univariate and multivariate regression analyses. No association was observed with the other allellic variants. Results were confirmed in the pooled analysis. Conclusion. This study did not replicate the association between PTPRC and the response to anti-TNF treatment in our Southern European population. We found that TRAF1/C5 risk RA variants potentially influence anti-TNF treatment response.

Current and future trends in biomarker discovery and development of companion diagnostics for arthritis

Musculoskeletal diseases such as rheumatoid arthritis are complex multifactorial disorders that are chronic in nature and debilitating for patients. A number of drug families are available to clinicians to manage these disorders but few tests exist to target these to the most responsive patients. As a consequence, drug failure and switching to drugs with alternate modes of action is common. In parallel, a limited number of laboratory tests are available which measure biological indicators or 'biomarkers' of disease activity, autoimmune status, or joint damage. There is a growing awareness that assimilating the fields of drug selection and diagnostic tests into 'companion diagnostics' could greatly advance disease management and improve outcomes for patients. This review aims to highlight: the current applications of biomarkers in rheumatology with particular focus on companion diagnostics; developments in the fields of proteomics, genomics, microbiomics, imaging and bioinformatics and how integration of these technologies into clinical practice could support therapeutic decisions.

Analysis of associations between polymorphisms within genes coding for tumour necrosis factor (TNF)-alpha and TNF receptors and responsiveness to TNF-alpha blockers in patients with rheumatoid arthritis

INTRODUCTION

Despite the fact that therapy with TNF-α inhibitors constitutes a breakthrough in rheumatoid arthritis management, no improvement is still achieved in approximately 30% of cases. The aim of the study was to evaluate whether single nucleotide polymorphisms (SNPs) within the TNF-α and TNF receptor encoding genes affect the efficacy of therapy with TNF-α inhibitors in patients with RA.

METHODS

Five SNPs within the TNF-α and TNF receptor encoding genes (TNFA: G-308A, G-238A, C-857T; TNFR1A G36A; TNFR1B T676G) were determined in 280 RA patients who had been treated with TNF-α inhibitors for at least 6 months or they stop therapy because of adverse events. The association between the relative change in DAS28 and SNP genotypes was tested by linear regression.

RESULTS

At week 24, low disease activity or remission was achieved by 45% of the patients. After 6 months remission of the disease or low disease activity were more frequently observed among patients homozygous for the TNFR1A 36A allele than among those who were GG homozygotes (52% vs. 34%, P=0.04). At week 24 DAS28 was significantly lower in the subgroup of patients homozygous for the TNFA-857T variant compared to the C allele carriers (P=0.045). The other polymorphisms were not found to be significantly associated with EULAR response at week 12 and 24 of the anti-TNF treatment.

CONCLUSIONS

Homozygosity for the TNFR1A 36A allele and the TNFA-875T variant could act as a genetic factor associated with better response to anti-TNF treatment.

Anti-TNF treatment response in rheumatoid arthritis patients is associated with genetic variation in the NLRP3-inflammasome

OBJECTIVE

Many patients with rheumatoid arthritis (RA) benefit from tumor necrosis factor-α blocking treatment (anti-TNF), but about one third do not respond. The objective of this study was to replicate and extend previously found associations between anti-TNF treatment response and genetic variation in the TNF-, NF-κB- and pattern recognition receptor signalling pathways.

METHODS

Forty-one single nucleotide polymorphisms (SNPs), including 34 functional, in 28 genes involved in inflammatory pathways were assessed in 538 anti-TNF naive Danish RA patients with clinical data. Multivariable logistic regression analyses were performed to test associations between genotypes and treatment response at 3-6 months using the European League Against Rheumatism (EULAR) response criterion. American College of Rheumatology treatment response (ACR50) and relative change in 28-joint disease activity score (relDAS28) were used as secondary outcomes. Subgroup analyses were stratified according to smoking status, type of anti-TNF drug and IgM-Rheumatoid Factor (IgM-RF) status. False discovery rate (FDR) controlling was used to adjust for multiple testing.

RESULTS

Statistically significant associations with EULAR response were found for two SNPs in NLRP3(rs4612666) (OR (odds ratio) for good/moderate response = 0.64 (95% confidence interval: 0.44-0.95), p = 0.025, q = 0.95) and INFG(rs2430561) (OR = 0.40 (0.21-0.76), p = 0.005, q = 0.18) and among IgM-RF positive patients for TNFRS1A(rs4149570) (0.59 (0.36-0.98), p = 0.040, q = 0.76). Current smokers who carried the NLRP3(rs4612666) variant allele were less likely to benefit from anti-TNF treatment (OR = 0.24 (0.10-0.56), p = 0.001, q = 0.04).

CONCLUSIONS

In a population of Danish RA patients, we confirm the NLRP3 gene as associated with EULAR anti-TNF response as previously reported. The NLRP3 variant (T) allele is associated with lower treatment response, in particular among current smokers. Furthermore, we find that a functional polymorphism in the interferon-γ gene is associated with anti-TNF response. All findings should be tested by replication in independent validation cohorts and augmented by assessing cytokine levels and activities of the relevant gene products.

Lack of validation of genetic variants associated with anti-tumor necrosis factor therapy response in rheumatoid arthritis: a genome-wide association study replication and meta-analysis

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.

Genetic and epigenetic predictors of responsiveness to treatment in RA

Methotrexate and TNF-blocking agents are the DMARDs most commonly prescribed for the treatment of rheumatoid arthritis (RA). However, not all patients treated with these nonbiologic and biologic DMARDs respond satisfactorily and few predictors of treatment efficacy have been identified, despite the fact that these therapies have now been available for many years. Many studies have investigated genetic factors that might predict patient responsiveness to therapies used to treat RA, and epigenetic studies regarding response to treatment are expected to accumulate in the literature in the near future. Herein, we review the advances in identifying genetic and epigenetic predictors of therapeutic responses to methotrexate and/or TNF inhibitors in RA that have been made to date, and highlight important considerations for future studies, such as the need for an improved, preferably biological, outcome measure reflecting response to treatment.

Predictive factors of response to biological disease modifying antirheumatic drugs: towards personalized medicine

Many therapies are now available for patients with rheumatoid arthritis (RA) who have an inadequate response to methotrexate including tumor necrosis factor inhibitors, abatacept, tocilizumab, and rituximab. Clinical response to drugs varies widely between individuals. A part of this variability is due to the characteristics of the patient such as age, gender, concomitant therapies, body mass index, or smoking status. Clinical response also depends on disease characteristics including disease activity and severity and presence of autoantibodies. Genetic background, cytokine levels, and immune cell phenotypes could also influence biological therapy response. This review summarizes the impact of all those parameters on response to biological therapies.

Pharmacogenetics in rheumatoid arthritis

Rheumatoid arthritis (RA) is a systemic inflammatory arthritis leading to severe joint damage and associated with high morbidity and mortality. Disease-modifying antirheumatic drugs (DMARDs) are the mainstay of treatment in RA. DMARDs not only relieve the clinical signs and symptoms of RA but also inhibit the radiographic progression of disease. In the last decade, a new class of disease-modifying medications, the biologic agents, has been added to the existing spectrum of DMARDs in RA. However, patients' response to these agents is not uniform with considerable variability in both efficacy and toxicity. There are no reliable means of predicting an individual patient's response to a given DMARD prior to initiation of therapy. In this chapter, the current published literature on the pharmacogenetics of traditional DMARDS and the newer biologic DMARDs in RA is highlighted. Pharmacogenetics may help individualize drug therapy in patients with RA in the near future.

Lack of association of variants previously associated with anti-TNF medication response in rheumatoid arthritis patients: results from a homogeneous Greek population

Treatment strategies blocking tumor necrosis factor (anti-TNF) have proven very successful in patients with rheumatoid arthritis (RA), showing beneficial effects in approximately 50-60% of the patients. However, a significant subset of patients does not respond to anti-TNF agents, for reasons that are still unknown. The aim of this study was to validate five single nucleotide polymorphisms (SNPs) of PTPRC, CD226, AFF3, MyD88 and CHUK gene loci that have previously been reported to predict anti-TNF outcome. In addition, two markers of RA susceptibility, namely TRAF1/C5 and STAT4 were assessed, in a cohort of anti-TNF–treated RA patients, from the homogeneous Greek island of Crete, Greece. The RA patient cohort consisted of 183 patients treated with either of 3 anti-TNF biologic agents (infliximab, adalimumab and etanercept) from the Clinic of Rheumatology of the University Hospital of Crete. The SNPs were genotyped by TaqMan assays or following the Restriction Fragments Length Polymorphisms (RFLPs) approach. Disease activity score in 28 joints (DAS28) at baseline and after 6 months were available for all patients and analysis of good versus poor response at 6 months was performed for each SNP. None of the 7 genetic markers correlated with treatment response. We conclude that the gene polymorphisms under investigation are not strongly predictive of anti-TNF response in RA patients from Greece.