Genome-wide association analysis implicates the involvement of eight loci with response to tocilizumab for the treatment of rheumatoid arthritis

Associations of genetic polymorphisms with clinical response to tocilizumab in Chinese rheumatoid arthritis patients

OBJECTIVE

To examine the associations of single-nucleotide polymorphisms (SNPs) within interleukin-6 (IL6) and IL-6 receptor (IL6R) as well as several potential SNPs revealed in a genome-wide association study (GWAS) with clinical response to tocilizumab (TCZ) in Chinese rheumatoid arthritis (RA) patients.

METHODS

A total of 23 SNPs were genotyped in 68 RA patients receiving intravenous TCZ, who were prospectively followed for 6 months to determine the clinical response based on several criteria, including clinical disease activity index (CDAI) low disease activity (LDA) and remission, disease activity score in 28 joint counts - erythrocyte sedimentation rate (DAS28-ESR) LDA and remission, European League Against Rheumatism (EULAR) good response and change in DAS28-ESR (ΔDAS28-ESR).

RESULTS

The patients were on average 51.16 (13.23) years, and 55 were female (80.88%) and 47 were bDMARD-naïve (69.12%). After adjusting potential confounding factors, IL6R rs35717427 and GALNT18 rs4910008 were significantly associated with CDAI LDA, and IL6R rs11265621, rs6690230 were significantly associated with EULAR good response, DAS28-ESR LDA and remission. In addition, IL6 2069840 was found to be significantly associated with DAS28-ESR remission, and rs10108210, CLEC2D rs1560011 and KCNMB1 rs703505 were found to be significantly associated with ΔDAS28-ESR.

CONCLUSION

Genetic polymorphisms hold the potential as predictive biomarkers for clinical response to TCZ, which might aid in individualized treatment with TCZ in Chinese patients with RA.

Genetics of rheumatoid arthritis

In the past four decades, a plethora of genetic association studies have been carried out in cohorts of patients with rheumatoid arthritis. These studies have highlighted key aspects of disease pathogenesis and suggested causal mechanisms. In this review, we discuss major advances in our understanding of the genetic architecture of rheumatoid arthritis susceptibility, severity and treatment response and explain how genetics supports current models of disease pathogenesis and outcome. We outline future research directions, like Mendelian randomisation, and present a number of potential avenues for clinical translation, including risk and outcome prediction, patient stratification into treatment response groups and pharmacological applications.

Impact of FCGR2A rs1801274 and IL-6R rs2228145 polymorphisms on tocilizumab response in the Iranian population with severe COVID-19

BACKGROUND

Although several genetic biomarkers have been reported in the tocilizumab (TCZ) response in rheumatoid arthritis, no studies have addressed the pharmacogenomics effect of TCZ in COVID-19.

METHODS

In this prospective longitudinal study, 95 individuals with severe COVID-19 were selected between 2020-2022. The recovery process was measured at 24 h, 48 h, and 10 days before and after taking TCZ. All participants were genotyped using RFLP-PCR. Different genotypes of FCGR2A rs1801274 and IL-6R rs2228145 were compared in terms of the recovery process.

RESULTS

43.2% of patients were male and 56.8% were female with an average age of 58.20(± 16.214) years. The GA genotype for FCGR2A rs1801274 increased the risk of death (OR = 2.83, P = 0.038) and ventilation (OR = 2.71, P = 0.047) in TCZ-treated individuals. However, there was no risk of death and ventilation with IL-6R rs2228145 (P > 0.05). Additionally, docking analysis showed that not only IL6R but also FCGR2A can be a ligand for TCZ.

CONCLUSION

This study provides valuable insights into the impact of genetic variations on the response rate of TCZ in COVID-19 patients. The GA genotype for FCGR2A rs1801274 was associated with poor treatment outcomes.

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.

Impact of IL6R genetic variants on treatment efficacy and toxicity response to sarilumab in rheumatoid arthritis

BACKGROUND

Sarilumab, an IL-6 receptor antagonist, is a first-line biologic disease-modifying anti-rheumatic drug for rheumatoid arthritis. The identification of genetic biomarkers as predictors of response to sarilumab could allow for a personalized treatment strategy to improve clinical outcomes.

METHODS

We conducted a retrospective cohort study of 62 patients treated with sarilumab to determine whether single-nucleotide polymorphisms (SNP) in the IL6R gene could predict efficacy and toxicity responses. Six SNPs previously described in the IL6R gene (rs12083537, rs11265618, rs4329505, rs2228145, rs4537545, and rs4845625) were genotyped in DNA samples obtained from these patients. Using parametric tests, we evaluated the association between these polymorphisms and clinicopathological features. Treatment response was assessed six months after treatment initiation. Satisfactory response was based on EULAR criteria. Low disease activity was determined according to DAS28 and CDAI and quantitative improvements in DAS28 and CDAI scores.

RESULTS

Three SNPs (rs4845625, rs4329505 and rs11265618) were significantly associated with response outcomes. All of the SNPs, except for rs12083537, had at least one significant association with dyslipidemia or hepatotoxicity.

CONCLUSIONS

These findings support the potential clinical value of SNPs, particularly rs4845625, as potentially useful biomarkers to predict response to sarilumab in patients with RA.

Gomariz R, Muñoz-Calleja C, Fernández-Ruiz E, González-Álvaro I, Cardeñoso L. Occurrence of SARS-CoV-2 viremia is associated with genetic variants of genes related to COVID-19 pathogenesis

Introduction

SARS-CoV-2 viral load has been related to COVID-19 severity. The main aim of this study was to evaluate the relationship between SARS-CoV-2 viremia and SNPs in genes previously studied by our group as predictors of COVID-19 severity.

Materials and methods

Retrospective observational study including 340 patients hospitalized for COVID-19 in the University Hospital La Princesa between March 2020 and December 2021, with at least one viremia determination. Positive viremia was considered when viral load was above the quantifiable threshold (20 copies/ml). A total of 38 SNPs were genotyped. To study their association with viremia a multivariate logistic regression was performed.

Results

The mean age of the studied population was 64.5 years (SD 16.6), 60.9% patients were male and 79.4% white non-Hispanic. Only 126 patients (37.1%) had at least one positive viremia. After adjustment by confounders, the presence of the minor alleles of rs2071746 (HMOX1; T/T genotype OR 9.9 p < 0.0001), rs78958998 (probably associated with SERPING1 expression; A/T genotype OR 2.3, p = 0.04 and T/T genotype OR 12.9, p < 0.0001), and rs713400 (eQTL for TMPRSS2; C/T + T/T genotype OR 1.86, p = 0.10) were associated with higher risk of viremia, whereas the minor alleles of rs11052877 (CD69; A/G genotype OR 0.5, p = 0.04 and G/G genotype OR 0.3, p = 0.01), rs2660 (OAS1; A/G genotype OR 0.6, p = 0.08), rs896 (VIPR1; T/T genotype OR 0.4, p = 0.02) and rs33980500 (TRAF3IP2; C/T + T/T genotype OR 0.3, p = 0.01) were associated with lower risk of viremia.

Conclusion

Genetic variants in HMOX1 (rs2071746), SERPING1 (rs78958998), TMPRSS2 (rs713400), CD69 (rs11052877), TRAF3IP2 (rs33980500), OAS1 (rs2660) and VIPR1 (rs896) could explain heterogeneity in SARS-CoV-2 viremia in our population.

Clinical Value of IL6R Gene Variants as Predictive Biomarkers for Toxicity to Tocilizumab in Patients with Rheumatoid Arthritis

Tocilizumab is a first-line biologic disease-modifying anti-rheumatic drug (bDMARD) that inhibits the interleukin-6 (IL-6) pathway by antagonizing the IL-6 receptor (IL-6R). Tocilizumab is widely used to treat rheumatoid arthritis (RA), a prevalent autoimmune disease that can cause irreversible joint damage and disability. Although many bDMARDs have been developed for RA, there is a lack of validated biomarkers which could guide personalized medicine strategies. To evaluate whether single-nucleotide polymorphisms (SNPs) in the IL6R gene could predict tocilizumab toxicity in patients with RA, we conducted a retrospective cohort study of 88 patients treated with tocilizumab. Six SNPs previously described in the IL6R gene were genotyped (rs12083537, rs11265618, rs4329505, rs2228145, rs4537545, and rs4845625). Using parametric tests, we studied the association between the SNPs and hepatotoxicity, infection, hypersensitivity, gastrointestinal, hematological, and dyslipidemia adverse events (AEs). We found associations between dyslipidemia and rs4845625 and between hematological AEs and rs11265618 and rs4329505. No further associations were found for the remaining SNPs and other AEs. Our findings support the potential clinical value of SNPs in the IL6R gene as predictive biomarkers for toxicity to tocilizumab in patients with RA.

Role of IL6R Genetic Variants in Predicting Response to Tocilizumab in Patients with Rheumatoid Arthritis

Rheumatoid arthritis (RA) is a prevalent autoimmune disease characterized by chronic arthritis that may lead to irreversible joint damage and significant disability. Patients with RA are commonly treated with Tocilizumab (TCZ), an IL-6 receptor (IL-6R) antagonist, but many patients refractorily respond to this therapy. Identifying genetic biomarkers as predictors of TCZ response could be a key to providing a personalized medicine strategy. We aimed to evaluate whether functional single nucleotide polymorphisms (SNPs) in the IL6R gene could predict TCZ response in patients with RA. We retrospectively included 88 RA patients treated with TCZ. Six SNPs previously described in the IL6R gene (rs12083537, rs11265618, rs4329505, rs2228145, rs4537545, and rs4845625) were genotyped in DNA samples from these patients. Using parametric tests, we evaluated the association between these polymorphisms and clinicopathological features. Responses to treatments were assessed at six months using three variables: a quantitative improvement in Disease activity score including 28 joints (DAS28), a satisfactory European League Against Rheumatism (EULAR) response, and low disease activity (LDA) achievement. The three response variables studied were associated with genetic variant rs4845625, and no association was found with the other five SNPs. Our findings support the potential clinical value of SNPs in the IL6R gene as predictive biomarkers for TCZ response.

-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.

Pharmacogenomics of Monoclonal Antibodies for the Treatment of Rheumatoid Arthritis

Precision medicine refers to a highly individualized and personalized approach to patient care. Pharmacogenomics is the study of how an individual’s genomic profile affects their drug response, enabling stable and effective drug selection, minimizing side effects, and maximizing therapeutic efficacy. Rheumatoid arthritis (RA) is an autoimmune disease that causes chronic inflammation in the joints. It mainly starts in peripheral joints, such as the hands and feet, and progresses to large joints, which causes joint deformation and bone damage due to inflammation of the synovial membrane. Here, we review various pharmacogenetic studies investigating the association between clinical response to monoclonal antibody therapy and their target genetic polymorphisms. Numerous papers have reported that some single nucleotide polymorphisms (SNPs) are related to the therapeutic response of several monoclonal antibody drugs including adalimumab, infliximab, rituximab, and tocilizumab, which target tumor necrosis factor (TNF), CD20 of B-cells, and interleukin (IL)-6. Additionally, there are some pharmacogenomic studies reporting on the association between the clinical response of monoclonal antibodies having various mechanisms, such as IL-1, IL-17, IL-23, granulocyte-macrophage colony-stimulating factor (GM-CSF) and the receptor activator of nuclear factor-kappa B (RANK) inhibition. Biological therapies are currently prescribed on a “trial and error” basis for RA patients. If appropriate drug treatment is not started early, joints may deform, and long-term treatment outcomes may worsen. Pharmacogenomic approaches that predict therapeutic responses for RA patients have the potential to significantly improve patient quality of life and reduce treatment costs.

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.

Toward Overcoming Treatment Failure in Rheumatoid Arthritis

Rheumatoid arthritis (RA) is an autoimmune disorder characterized by inflammation and bone erosion. The exact mechanism of RA is still unknown, but various immune cytokines, signaling pathways and effector cells are involved. Disease-modifying antirheumatic drugs (DMARDs) are commonly used in RA treatment and classified into different categories. Nevertheless, RA treatment is based on a "trial-and-error" approach, and a substantial proportion of patients show failed therapy for each DMARD. Over the past decades, great efforts have been made to overcome treatment failure, including identification of biomarkers, exploration of the reasons for loss of efficacy, development of sequential or combinational DMARDs strategies and approval of new DMARDs. Here, we summarize these efforts, which would provide valuable insights for accurate RA clinical medication. While gratifying, researchers realize that these efforts are still far from enough to recommend specific DMARDs for individual patients. Precision medicine is an emerging medical model that proposes a highly individualized and tailored approach for disease management. In this review, we also discuss the potential of precision medicine for overcoming RA treatment failure, with the introduction of various cutting-edge technologies and big data.

COVID-19 infection and rheumatoid arthritis: mutual outburst cytokines and remedies

In March 2020, COVID-19 infection caused by SARS-CoV-2 has been declared to be a global pandemic, where its complications, severity and mortality are reported to be due to the released inflammatory cytokines or the so-called cytokine storm. This is quite similar to that observed in the autoimmune and chronic inflammatory rheumatic disease, rheumatoid arthritis (RA). It was hypothesized that RA patients are at a higher risk of acquiring COVID-19; however, recent studies reported that they are not when compared to the rest of the population. In this review, we aim to highlight the mutual pathological features, cytokine profiles and risk factors between COVID-19 and RA. Also, many researchers are currently working to explore therapeutic agents that could aid in the eradication of COVID-19 infection. Due to the similarity between the inflammation status in COVID-19 and RA, many anti-rheumatic drugs such as hydroxychloroquine, tocilizumab, baricitinib and anakinra were proposed to be therapeutic modalities for COVID-19 infection.

Host Genetics at the Intersection of Autoimmunity and COVID-19: A Potential Key for Heterogeneous COVID-19 Severity

COVID-19 presentation is very heterogeneous across cases, and host factors are at the forefront for the variables affecting the disease manifestation. The immune system has emerged as a key determinant in shaping the outcome of SARS-CoV-2 infection. It is mainly the deleterious unconstrained immune response, rather than the virus itself, which leads to severe cases of COVID-19 and the associated mortality. Genetic susceptibility to dysregulated immune response is highly likely to be among the host factors for adverse disease outcome. Given that such genetic susceptibility has also been observed in autoimmune diseases (ADs), a number of critical questions remain unanswered; whether individuals with ADs have a significantly different risk for COVID-19-related complications compared to the general population, and whether studies on the genetics of ADs can shed some light on the host factors in COVID-19. In this perspective, we discuss the host genetic factors, which have been under investigation in association with COVID-19 severity. We touch upon the intricate link between autoimmunity and COVID-19 pathophysiology. We put forth a number of autoimmune susceptibility genes, which have the potential to be additional host genetic factors for modifying the severity of COVID-19 presentation. In summary, host genetics at the intersection of ADs and COVID-19 may serve as a source for understanding the heterogeneity of COVID-19 severity, and hence, potentially holds a key in achieving effective strategies in risk group identification, as well as effective treatments.

Predicting treatment response to IL6R blockers in rheumatoid arthritis

Patients with severe, active RA who have not responded to conventional therapy may receive biological disease modifying anti-rheumatic drugs (bDMARDs). However, 40% of cases do not achieve complete disease control, resulting in a negative impact on patient quality of life and representing a waste of healthcare resources. Ongoing research seeks to establish biomarkers, which can be used to predict treatment response to biologics in RA to enable more targeted approaches to treatment. However, much of the work has focused on one class of biologic drug, the TNF inhibitors (TNFi). Here, we will review the current state of research to identify biomarkers predictive of response to the class of bDMARDs targeting the IL6R. While success has been limited thus far, serum drug and low ICAM1 levels have shown promise, with associations reported in independent studies. The challenges faced by researchers and lessons learned from studies of TNFi will be discussed.

An enhanced machine learning tool for cis-eQTL mapping with regularization and confounder adjustments

Many expression quantitative trait loci (eQTL) studies have been conducted to investigate the biological effects of variants in gene regulation. However, these eQTL studies may suffer from low or moderate statistical power and overly conservative false-discovery rate. In practice, most algorithms for eQTL identification do not model the joint effects of multiple genetic variants with weak or moderate influence. Here we present a novel machine-learning algorithm, lasso least-squares kernel machine (LSKM-LASSO) that model the association between multiple genetic variants and phenotypic traits simultaneously with the existence of nongenetic and genetic confounding. With a more general and flexible framework for the estimation of genetic confounding, LSKM-LASSO is able to provide a more accurate evaluation of the joint effects of multiple genetic variants. Our simulations demonstrate that our approach outperforms three state-of-the-art alternatives in terms of eQTL identification and phenotype prediction. We then apply our method to genotype and gene expression data of 11 tissues obtained from the Genotype-Tissue Expression project. Our algorithm was able to identify more genes with eQTL than other algorithms. By incorporating a regularization term and combining it with least-squares kernel machine, LSKM-LASSO provides a powerful tool for eQTL mapping and phenotype prediction.

Filgotinib, a JAK1 Inhibitor, Modulates Disease-Related Biomarkers in Rheumatoid Arthritis: Results from Two Randomized, Controlled Phase 2b Trials

INTRODUCTION

The Janus kinase (JAK) inhibitor therapeutic class has shown significant clinical benefit in the treatment of rheumatoid arthritis (RA). We sought to gain insight into the mode of action and immunological effects of filgotinib, a JAK1 selective inhibitor, in active RA by analyzing secreted and cell-based biomarkers key to RA pathophysiology in two phase 2b trials of filgotinib in active RA.

METHODS

Immune cell subsets and 34 serum biomarkers were analyzed longitudinally over 12 weeks using blood samples collected from patients with active RA receiving filgotinib (100 or 200 mg once daily) or placebo (PBO) in the two phase 2b trials (DARWIN 1, on a background of methotrexate, and DARWIN 2, as monotherapy).

RESULTS

Consistently across both studies, filgotinib treatment decreased multiple immune response biomarkers that have key roles in RA for immune response, and decreased markers that promote matrix degradation, angiogenesis, leukocyte adhesion, and recruitment. Filgotinib did not significantly modulate T and natural killer (NK) lymphoid subsets, but slightly increased B cell numbers after 12 weeks. Multiple correlations were observed for changes in biomarkers with disease activity score 28-CRP. MIP1β showed modest predictivity at baseline for ACR50 response at 12 weeks in the 100 mg filgotinib dose across both studies (AUROC, 0.65 and 0.67, p < 0.05).

CONCLUSIONS

Filgotinib regulates biomarkers from multiple pathways, indicative of direct and indirect network effects on the immune system and the stromal response. These effects were not associated with reductions of major circulating lymphoid populations.

TRIAL REGISTRATION

ClinicalTrials.gov, NCT01888874, NCT01894516.

Identification of pathological RA endotypes using blood-based biomarkers reflecting tissue metabolism. A retrospective and explorative analysis of two phase III RA studies

There is an increasing demand for accurate endotyping of patients according to their pathogenesis to allow more targeted treatment. We explore a combination of blood-based joint tissue metabolites (neoepitopes) to enable patient clustering through distinct disease profiles. We analysed data from two RA studies (LITHE (N = 574, follow-up 24 and 52 weeks), OSKIRA-1 (N = 131, follow-up 24 weeks)). Two osteoarthritis (OA) studies (SMC01 (N = 447), SMC02 (N = 81)) were included as non-RA comparators. Specific tissue-derived neoepitopes measured at baseline, included: C2M (cartilage degradation); CTX-I and PINP (bone turnover); C1M and C3M (interstitial matrix degradation); CRPM (CRP metabolite) and VICM (macrophage activity). Clustering was performed to identify putative endotypes. We identified five clusters (A-E). Clusters A and B were characterized by generally higher levels of biomarkers than other clusters, except VICM which was significantly higher in cluster B than in cluster A (p<0.001). Biomarker levels in Cluster C were all close to the median, whilst Cluster D was characterised by low levels of all biomarkers. Cluster E also had low levels of most biomarkers, but with significantly higher levels of CTX-I compared to cluster D. There was a significant difference in ΔSHP score observed at 52 weeks (p<0.05). We describe putative RA endotypes based on biomarkers reflecting joint tissue metabolism. These endotypes differ in their underlining pathogenesis, and may in the future have utility for patient treatment selection.

'RA and the microbiome: do host genetic factors provide the link?

Rheumatoid arthritis (RA) is a chronic autoimmune disease, characterised by painful synovium inflammation, bony erosions, immune activation and the circulation of autoantibodies. Despite recent advances in therapeutics enabling disease suppression, there is a considerable demand for alternative therapeutic strategies as well as optimising those available at present. The relatively low concordance rate between monozygotic twins, 20–30% contrasts with heritability estimates of ∼65%, indicating a substantive role of other risk factors in RA pathogenesis. There is established evidence that RA has an infective component to its aetiology. More recently, differences in the commensal microbiota in RA compared to controls have been identified. Studies have shown that the gut, oral and lung microbiota is different in new onset treatment naïve, and established RA patients, compared to controls. Key taxonomic associations are an increase in abundance of Porphyromonas gingivalis and Prevotella copri in RA patients, compared to healthy controls. Host genetics may provide the link between disease and the microbiome. Genetic influence may be mediated by the host immune system; a differential response to RA associated taxa is suggested. The gut microbiome contains elements which are as much as 30% heritable. A better understanding of the influence of host genetics will shed light onto the role of the microbiome in RA. Here we review the role of the microbiome in RA through the lens of host genetics, and consider future research areas addressing microbiome study design and bioinformatics approaches.

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Rheumatoid arthritis (RA) affects 1% of the population and is highly debilitating.

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RA is ~65% heritable, yet the concordance rate between monozygotic twins is just 20–30%, indicating a substantive role of other risk factors.

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Studies have shown that the gut, oral and lung microbiome is different in treatment naïve and established RA patients, compared to controls.

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Current findings suggest an important influence of host genetics on the microbiome, which may contribute to RA via the host immune system.

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Associations of the microbiome with RA described thus far are confounded by host genetics, and future studies need to take account of this.

FCGR2A/FCGR3A Gene Polymorphisms and Clinical Variables as Predictors of Response to Tocilizumab and Rituximab in Patients With Rheumatoid Arthritis

We evaluated the influence of clinical, biochemical, and genetic factors on response in 142 patients diagnosed with rheumatoid arthritis, of whom 87 patients were treated with tocilizumab (61.26%) and 55 patients were treated with rituximab (38.7%;) according to the variables European League Against Rheumatism (EULAR) response, remission, low disease activity, and improvement in Disease Activity Score, 28 joints (DAS28) at 6, 12, and 18 months. A retrospective prospective cohort study was conducted. Patients carrying the FCGR3A rs396991-TT genotype treated with tocilizumab showed higher EULAR response (OR, 5.075; 95%CI, 1.20-21.33; P = .027) at 12 months, those who were naive for biological disease-modifying antirheumatic drugs (bDMARDs) at the beginning of treatment showed satisfactory EULAR response, higher remission, and greater improvement in DAS28 at 6 months. Younger age at start of tocilizumab treatment was associated with satisfactory EULAR response at 18 months and greater remission at 6 and 18 months. Subcutaneous tocilizumab administration was associated with higher remission at 6 months and improved low disease activity rate at 12 months. In patients treated with rituximab, carriers of the FCGR2A rs1801274-TT genotype had higher EULAR response at 6 months (OR, 4.861; 95%CI, 1.11-21.12; P = .035), 12 months (OR, 4.667; p = 0.066, 95%CI, 0.90-24.12; P = .066), and 18 months (OR, 2.487; 95%CI, 0.35-17.31; P = .357), higher remission (OR: 10.625; p = 0.044, CI_95% : 1.07, 105.47) at 6 months, and greater improvement in DAS28 at 12 months (B = 0.782; 95%CI, -0.15 to 1.71; P = .098) and 18 months (B = 1.414; 95%CI, 0.19-2.63; P = .025). The FCGR3A rs396991-G allele was associated with improved low disease activity rate (OR, 4.904; 95%CI, 0.84-28.48; P = .077) and greater improvement in DAS28 (B = -1.083; 95%CI, -1.98 to -0.18; P = .021) at 18 months. Patients with a lower number of previous biological therapies had higher remission at 12 months. We suggest that the FCGR3A rs396991-TT genotype, higher baseline value of DAS28, subcutaneous tocilizumab administration, younger age at the beginning of treatment, and being bDMARD naive are associated with better response to tocilizumab. In patients treated with rituximab, we found better response in those patients with the FCGR2A rs1801274-TT genotype, the FCGR3A rs396991-G allele, and lower number of previous biological therapies.

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.

The importance of genetic factors for the development of arthropathy: a longitudinal study of children and adolescents with haemophilia A

Haemophilia A is a congenital bleeding disorder characterised by recurrent haemorrhages into the major joints. Haemophilic arthropathy is a well-established outcome of recurrent joint bleeding; however, it is clear that multiple factors determine the extent and severity of its occurrence. We sought to identify genetic factors related to abnormalities in range of motion (ROM) in the knees, ankles and elbows in a cohort of children and adolescents with haemophilia A not treated primarily with regular prophylaxis. Using data from the Haemophilia Growth and Development Study, we examined associations between 13,342 genetic markers and ROM scores measured at six-month intervals for up to seven years. As a first step, ordered logistic regression models were fit for each joint separately. A subset of SNP markers showing significant effects (p<0.01) on the right and left sides for at least two joints were included in a full model fit using a multivariate generalised linear mixed model assuming an ordinal response. The models contained all ROM scores obtained at all visits. Twenty-five markers analysed in the full model showed either increased or decreased risk of ROM abnormalities at the p<0.001 level. Several genes identified at either the first or second stage of the analysis have been associated with arthritis in a variety of large studies. Our results support the likelihood that risk for haemophilic arthropathy is associated with genetic factors, the identification of which holds promise for further advancing the individualisation of treatment.

Influence of IL6R gene polymorphisms in the effectiveness to treatment with tocilizumab in rheumatoid arthritis

In the present study, we aimed to investigate the influence of clinical parameters and single-nucleotide polymorphisms of interleukin-6 receptor (rs12083537, rs2228145, rs4329505 and rs11265618) on response to tocilizumab, TCZ (European League Against Rheumatism (EULAR) response, remission, low disease activity (LDA) and improvement of DAS28). We performed a retrospective cohort study in patients with Rheumatoid Arthritis (RA) treated with TCZ for 12 months. Multivariable analysis showed that the only variable independently associated to satisfactory EULAR response (odds ratio (OR): 0.61; 95% of confidence interval (CI)_95%: 0.42, 0.88; P=0.008), remission (OR: 0.51; CI_95%: 0.35, 0.75; P=0.001), LDA (OR: 0.41; CI_95%: 0.24, 0.72; P=0.002) and improvement in DAS28 (B=-0.32; CI_95%): -0.47, -0.17; P=7.5 × 10^-5) at 12 months was lower number of previous biological therapy (BT). High baseline DAS28 was also associated with a greater decrease in DAS28 at 12 months of treatment (B=0.99; CI_95%: 0.79, 1.20; P=1.5 × 10^-14). Those patients who were carriers of AA genotypes for rs12083537 (OR: 13.0; CI_95%: 2.31, 72.91; P=0.004) and CC for rs11265618 (OR: 12.15; CI_95%: 2.18, 67.81; P=0.004) had better LDA response at 12 months of treatment with TCZ. In conclusion, RA patients treated with TCZ showed better EULAR response, remission, LDA and DAS28 improvement rates when a lower number of BT were previously administered. The AA genotype for rs12083537 and CC for rs11265618 polymorphisms for may act as predictors of good response LDA.

Genetic and clinical biomarkers of tocilizumab response in patients with rheumatoid arthritis

The aim of this study was to investigate the influence of clinical and genetic factors on response to tocilizumab (TCZ) response, remission, low disease activity (LDA) and DAS28 improvement. A retrospective cohort study in 79 RA patients treated with TCZ during 6/18 months of therapy was conducted. CD69(rs11052877), GALNT18(rs4910008), CLEC2D(rs1560011), KCNMB1(rs703505), ENOX1(rs9594987), rs10108210, and rs703297 gene polymorphisms, identified in a recent GWAS as putative predictors of TCZ response, were analysed. Variables independently associated to satisfactory EULAR response at 6 months were GALNT18-CC genotype (ORCC/T-:12.8; CI95%:1.5,108.7; p=0.02), CD69 gene polymorphism (ORAA/GG:17.2; CI95%:2.5,119.6; p=0.004) and lower number of previous biological therapy, BT (OR: 0.45; CI95%:0.3, 0.7; p=0.001). The factors independently associated to higher remission were lower number of previous BT (OR:0.56; CI95%:0.38, 0.82; p=0.003), and GALNT18 CC genotype (ORCT/CC:0.09; CI95%:0.02,0.45;p=0.004; ORTT/CC:0.14; CI95%:0.02,0.79; p=0.026). The A-allele of CD69 (ORA_/GG:6.68;CI95%:1.68,26.51;p=0.007) and lower number of previous BT (OR:0.50; CI95%:0.32,0.77; p=0.002) were independent factors capable to predict higher LDA rates at 6 months. Independent factors associated to higher improvement in DAS28 at 6 months were CD69-AA genotype (B=-0.56; CI95%:-1.09, -0.03; p=0.039), GALNT18-CC genotype (B=-0.88;CI95%:-1.49, -0.27; p=0.005), subcutaneous administration (B=1.03; CI95%:0.44,1.62; p=0.001) and higher baseline DAS28 (B=0.82; CI95%:0.59, 1.05; p=4.9×10(-10)). Lower number of previous BT was the only independent predictor of satisfactory EULAR response (OR:0.60; CI95%:0.34,0.88; p=0.010) and higher remission (OR:0.65; CI95%:0.46,0.93; p=0.018) at 18 months. The C-allele of GALNT18 (ORC-/TT:4.60; CI95%:1.16, 18.27; p=0.03) and lower number of previous BT (OR:0.47; CI95%:0.29,0.74; p=0.001) were independent factors capable to predict higher LDA rates at 18 months. In conclusion, RA patients treated with TCZ showed better EULAR response, remission, LDA and DAS28 improvement rates in patients carrying the GALNT18 C-allele or the CD69 A-allele, particularly when lower number of BT were previously administered.

Recent Advances in Defining the Genetic Basis of Rheumatoid Arthritis

Rheumatoid arthritis (RA) is the most common inflammatory arthritis and exhibits genetic overlap with other autoimmune and inflammatory disorders. Although predominant associations with the HLA-DRB1 locus have been known for decades, recent data have revealed additional insight into the likely causative variants within HLA-DRB1 as well as within other HLA loci that contribute to disease risk. In addition, more than 100 common variants in non-HLA loci have been implicated in disease susceptibility. Genetic factors are involved not only in the development of RA, but also with various disease subphenotypes, including production and circulating levels of autoantibodies and joint destruction. The major current challenge is to integrate these new data into a precise understanding of disease pathogenesis, including the critical cell types and molecular networks involved as well as interactions with environmental factors. We predict that delineating the functional effects of genetic variants is likely to drive new diagnostic and therapeutic approaches to the disease.

The effect of gene polymorphisms on patient responses to rheumatoid arthritis therapy

INTRODUCTION

Rheumatoid arthritis (RA) is a systemic disease leading to joint destruction. The therapy of RA is mainly based on disease-modifying anti-rheumatic drugs (DMARDs) and biological drugs. The response to treatment is different among patients. Therefore, we have searched for factors that may predict the efficacy and toxicity during therapy in individual patients.

AREAS COVERED

This review presents the role of genetic polymorphisms as predictors of the efficacy and toxicity during the therapy of RA patients with DMARDs (methotrexate, leflunomide, sulfasalazine) and biological drugs (anti-TNF-alpha antagonists, Tocilizumab, Rituximab).

EXPERT OPINION

Despite studies having shown an association between genetic polymorphisms and response to therapy in RA patients, the majority of these findings are still inconclusive and inconsistent. We are still far from applying pharmacogenetic tests in routine clinical practice that can predict the outcome of treatment. Several factors, such as small sample size with low statistical power, variability in the outcome definitions and the heterogeneity of the cohorts, limited number of tested single nucleotide polymorphisms (SNPs), small effect for the selected variant, and a lack of consideration of epigenetic factors, may contribute to the inconsistency observed and may lead to limited success in personalizing therapy.

Altered Gene Expression Associated with microRNA Binding Site Polymorphisms

Allele-specific gene expression associated with genetic variation in regulatory regions can play an important role in the development of complex traits. We hypothesized that polymorphisms in microRNA (miRNA) response elements (MRE-SNPs) that either disrupt a miRNA binding site or create a new miRNA binding site can affect the allele-specific expression of target genes. By integrating public expression quantitative trait locus (eQTL) data, miRNA binding site predictions, small RNA sequencing, and Argonaute crosslinking immunoprecipitation (AGO-CLIP) datasets, we identified genetic variants that can affect gene expression by modulating miRNA binding efficiency. We also identified MRE-SNPs located in regions associated with complex traits, indicating possible causative mechanisms associated with these loci. The results of this study expand the current understanding of gene expression regulation and help to interpret the mechanisms underlying eQTL effects.

Genetic determinants for methotrexate response in juvenile idiopathic arthritis

Juvenile idiopathic arthritis (JIAs) is the most common chronic rheumatic disease of childhood and is an important cause of disability. The folic acid analog methotrexate is the first choice disease-modifying anti-rheumatic drug in this disease, however, 35–45% of patients fail to respond. Molecular elements, such as variants in genes of pharmacological relevance, influencing response to methotrexate in JIA, would be important to individualize treatment strategies. Several studies have evaluated the effects of candidate genetic variants in the complex pathway of genes involved in methotrexate pharmacodynamics and pharmacokinetics, however, results are still contrasting and no definitive genetic marker of methotrexate response useful for the clinician to tailor therapy of children with JIA has been identified. Recently, genome-wide approaches have been applied, identifying new potential biological processes involved in methotrexate response in JIA such as TGF-beta signaling and calcium channels. If these genomic results are properly validated and integrated with innovative analyses comprising deep sequencing, epigenetics, and pharmacokinetics, they will greatly contribute to personalize therapy with methotrexate in children with JIA.

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.

Genome-wide data reveal novel genes for methotrexate response in a large cohort of juvenile idiopathic arthritis cases

Clinical response to methotrexate (MTX) treatment for children with juvenile idiopathic arthritis (JIA) displays considerable heterogeneity. Currently, there are no reliable predictors to identify non-responders: earlier identification could lead to targeted treatment. We genotyped 759 JIA cases from the UK, Netherlands and Czech Republic. Clinical variables were measured at baseline and 6 months after start of treatment. In Phase I analysis samples were analysed for association with MTX response using ordinal regression of ACR-pedi categories and linear regression of change in clinical variables, and identified 31 genetic regions (P<0.001). Phase II analysis increased SNP density in the most strongly associated regions, identifying 14 regions (P<1×10⁻⁵): three contain genes of particular biological interest (ZMIZ1, TGIF1 and CFTR). These data suggest a role for novel pathways in MTX response and further investigations within associated regions will help reach our goal of predicting response to MTX in JIA.

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.

Systemic lupus erythematosus diagnostics in the 'omics' era

Systemic lupus erythematosus is a complex autoimmune disease affecting multiple organ systems. Currently, diagnosis relies upon meeting at least four out of eleven criteria outlined by the ACR. The scientific community actively pursues discovery of novel diagnostics in the hope of better identifying susceptible individuals in early stages of disease. Comprehensive studies have been conducted at multiple biological levels including: DNA (or genomics), mRNA (or transcriptomics), protein (or proteomics) and metabolites (or metabolomics). The 'omics' platforms allow us to re-examine systemic lupus erythematosus at a greater degree of molecular resolution. More importantly, one is hopeful that these 'omics' platforms may yield newer biomarkers for systemic lupus erythematosus that can help clinicians track the disease course with greater sensitivity and specificity.

IL-6 pathway-driven investigation of response to IL-6 receptor inhibition in rheumatoid arthritis

OBJECTIVES

To determine whether heterogeneity in interleukin-6 (IL-6), IL-6 receptor and other components of the IL-6 signalling pathway/network, at the gene, transcript and protein levels, correlate with disease activity in patients with rheumatoid arthritis (RA) and with clinical response to tocilizumab.

DESIGN

Biomarker samples and clinical data for five phase 3 trials of tocilizumab were analysed using serum (3751 samples), genotype (927 samples) and transcript (217 samples) analyses. Linear regression was then used to assess the association between these markers and either baseline disease activity or treatment response.

RESULTS

Higher baseline serum IL-6 levels were significantly associated (p<0.0001) with higher baseline DAS28, erythrocyte sedimentation rate, C reactive protein and Health Assessment Questionnaire in patients whose responses to disease-modifying antirheumatic drugs (DMARD-IR) and to antitumour necrosis factor (aTNF-IR) were inadequate and patients who were naive/responders to methotrexate (MTX). Higher baseline serum IL-6 levels were also significantly associated with better clinical response to tocilizumab (versus placebo) measured by cDAS28 in the pooled DMARD-IR (p<0.0001) and MTX-naive populations (p=0.04). However, the association with treatment response was weak. A threefold difference in baseline IL-6 level corresponded to only a 0.17-unit difference in DAS28 at week 16. IL-6 pathway single nucleotide polymorphisms and RNA levels also were not strongly associated with treatment response.

CONCLUSIONS

Our analyses illustrate that the biological activity of a disease-associated molecular pathway may impact the benefit of a therapy targeting that pathway. However, the variation in pathway activity, as measured in blood, may not be a strong predictor. These data suggest that the major contribution to variability in clinical responsiveness to therapeutics in RA remains unknown.