Genome-wide association study of response to methotrexate in early rheumatoid arthritis patients

Genome-wide investigation of persistence with methotrexate treatment in early rheumatoid arthritis

OBJECTIVES

To investigate the influence of genetic factors on persistence with treatment of early RA with MTX monotherapy.

METHODS

We conducted a genome-wide association study (GWAS) in a sample of 3902 Swedish early-RA patients initiating MTX in DMARD monotherapy as their first-ever DMARD. The outcome, short- and long-term MTX treatment persistence, was defined as remaining on MTX at 1 and at 3 years, respectively, with no additional DMARDs added. As genetic predictors, we investigated individual SNPs, and then calculated a polygenic risk score (PRS) based on SNPs associated with RA risk. The SNP-based heritability of persistence was estimated overall and by RA serostatus.

RESULTS

No individual SNP reached genome-wide significance (P < 5 × 10-8), either for persistence at 1 year or at 3 years. The RA PRS was not significantly associated with MTX treatment persistence at 1 year [relative risk (RR) = 0.98 (0.96-1.01)] or at 3 years [RR = 0.96 (0.93-1.00)]. The heritability of MTX treatment persistence was estimated to be 0.45 (0.15-0.75) at 1 year and 0.14 (0-0.40) at 3 years. The results in seropositive RA were comparable with those in the analysis of RA overall, while heritability estimates and PRS RRs were attenuated towards the null in seronegative RA.

CONCLUSION

Despite being the largest GWAS on an MTX treatment outcome to date, no genome-wide significant associations were detected. The modest heritability observed, coupled with the broad spread of suggestively associated loci, indicate that genetic influence is of polygenic nature. Nevertheless, MTX monotherapy persistence was lower in patients with a greater genetic disposition, per the PRS, towards RA.

Methotrexate Safety and Efficacy in Combination Therapies in Patients With Early Rheumatoid Arthritis: A Post Hoc Analysis of a Randomized Controlled Trial

OBJECTIVE

We investigated methotrexate safety and the influence of dose on efficacy outcomes in combination with three different biologic treatments and with active conventional treatment (ACT) in early rheumatoid arthritis (RA).

METHODS

This post hoc analysis included 812 treatment-naïve patients with early RA who were randomized (1:1:1:1) in the NORD-STAR trial to receive methotrexate in combination with ACT, certolizumab-pegol, abatacept, or tocilizumab. Methotrexate safety, doses, and dose effects on Clinical Disease Activity Index (CDAI) remission were assessed after 24 weeks of treatment.

RESULTS

Compared with ACT, the prevalence of methotrexate-associated side effects was higher when methotrexate was combined with tocilizumab (hazard ratio [HR] 1.48, 95% confidence interval [CI] 1.20-1.84) but not with certolizumab-pegol (HR 0.99, 95% CI 0.79-1.23) or with abatacept (HR 0.93, 95% CI 0.75-1.16). With ACT as the reference, the methotrexate dose was significantly lower when used in combination with tocilizumab (β -4.65, 95% CI -5.83 to -3.46; P < 0.001) or abatacept (β -1.15, 95% CI -2.27 to -0.03; P = 0.04), and it was numerically lower in combination with certolizumab-pegol (β -1.07, 95% CI -2.21 to 0.07; P = 0.07). Methotrexate dose reductions were not associated with decreased CDAI remission rates within any of the treatment combinations.

CONCLUSION

Methotrexate was generally well tolerated in combination therapies, but adverse events were a limiting factor in receiving the target dose of 25 mg/wk, and these were more frequent in combination with tocilizumab versus ACT. On the other hand, methotrexate dose reductions were not associated with decreased CDAI remission rates within any of the four treatment combinations at 24 weeks.

Adenosine metabolic signature in circulating CD4+ T cells predicts remission in rheumatoid arthritis

OBJECTIVES

Long-term outcomes in rheumatoid arthritis (RA) depend on early and effective disease control. Methotrexate (MTX) remains the first-line disease modifying therapy, however there are no biomarkers with which to identify those most likely to achieve remission. To address this unmet need we explored metabolic pathways involved in MTX mechanism of action within circulating CD4+T cells in a cohort of treatment naive patients with early RA.

METHODS

Purified CD4+T cells were isolated from peripheral blood of 68 patients with early RA commencing MTX. The expression of a range of putative MTX metabolism and mechanism of action targets were explored by flow-cytometry and transcriptional analysis. From these data significant predictors of Disease Activity Score 28-C reactive protein (DAS28-CRP) remission (<2.4 at 6 months) were determined by logistic regression (clinical; flow-cytometry data) and linear modelling (gene expression data).

RESULTS

Low baseline DAS28-CRP was associated with remission at 6 months (p=0.02). Expression of the ectonucleotidase CD39, involved in ATP-ADP conversion during adenosine synthesis, was higher on CD4+CD25 High regulatory T cells at baseline in those achieving remission (molecules of equivalent fluorescence 1264 vs 847; p=0.007). Expression of other adenosine signalling elements in CD4+T cells were also upregulated at baseline in patients achieving remission: AMPD1 (p<0.001), ADORA2b (p=0.039) and ADORA3 (p=0.047). When combined into a single predictive metric, a combination of these variables outperformed baseline DAS28-CRP in prediction of early remission (area under the curve 0.92 vs 0.67, p=0.001) CONCLUSIONS: Adenosine signalling is important in the achievement of early remission with MTX in RA and biomarkers of adenosine activity may hold utility for the stratification of therapy in early disease.

Functional implications of rs9373441 with FOXP3+Treg and Tr1 for the clinical effectiveness of csDMARDs in rheumatoid arthritis

Rheumatoid arthritis (RA) is characterized by a deficiency in regulatory T cells (Treg), which play a crucial role in immune regulation. While conventional synthetic disease-modifying antirheumatic drugs (csDMARDs) are widely used, there remains a challenge as efficacy varies among patients. In this genome-wide association study (GWAS) involving 410 RA patients, rs9373441 emerged as the most significantly linked single-nucleotide polymorphism (SNP) to csDMARDs response. This non-coding variant functions as a cis-acting regulatory element within the UTRN gene, which is associated with cortical erosion and osteoporosis. Particularly, individuals with the TT allele at rs9373441 exhibited a more favorable response, characterized by a significant increase in FOXP3 + Treg and Type 1 regulatory T cells (Tr1) (p = 0.04, 0.02) and a decrease in Effector T helper cells (Effector Th) (p = 0.03). The GATA3-GCM2-PTH and GATA3-FOXO1-FOXP3 pathways were implicated. RNA-sequencing (RNA-seq) analysis revealed increased expression levels of UTRN, PTH2R, FOXO1, and FOXO3 in good and moderate responders (p = 0.01, 0.03, 0.0005, and 0.02). Notably, the change in FOXP3 + Treg and Tr1 was positively correlated with UTRN expression (both p = 0.03). These findings underscore the critical link between rs9373441 and the response to csDMARDs, empowering clinicians to tailor treatments for enhanced outcomes in patients with RA.

Patients with Systemic Juvenile Idiopathic Arthritis (SJIA) Show Differences in Autoantibody Signatures Based on Disease Activity

Systemic juvenile idiopathic arthritis (SJIA) is a severe rheumatic disease in children. It is a subgroup of juvenile idiopathic arthritis (JIA; MIM #604302), which is the most common rheumatic disease in children. The diagnosis of SJIA often comes with a significant delay, and the classification between autoinflammatory and autoimmune disease is still discussed. In this study, we analyzed the immunological responses of patients with SJIA, using human proteome arrays presenting immobilized recombinantly expressed human proteins, to analyze the involvement of autoantibodies in SJIA. Results from group comparisons show several differentially reactive antigens involved in inflammatory processes. Intriguingly, many of the identified antigens had a high reactivity against proteins involved in the NF-κB pathway, and it is also notable that many of the detected DIRAGs are described as dysregulated in rheumatoid arthritis. Our data highlight novel proteins and pathways potentially dysregulated in SJIA and offer a unique approach to unraveling the underlying disease pathogenesis in this chronic arthropathy.

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.

-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.

Toward Individualized Prediction of Response to Methotrexate in Early Rheumatoid Arthritis: A Pharmacogenomics-Driven Machine Learning Approach

OBJECTIVE

To test the ability of machine learning (ML) approaches with clinical and genomic biomarkers to predict methotrexate treatment response in patients with early rheumatoid arthritis (RA).

METHODS

Demographic, clinical, and genomic data from 643 patients of European ancestry with early RA (mean age 54 years; 70% female) subdivided into a training (n = 336) and validation cohort (n = 307) were used. The genomic data comprised 160 single-nucleotide polymorphisms (SNPs) previously associated with RA or methotrexate metabolism. Response to methotrexate monotherapy was defined as good or moderate by the European Alliance of Associations for Rheumatology (EULAR) response criteria at the 3-month follow-up. Supervised ML methods were trained with 5 repeats and 10-fold cross-validation using the training cohort. Prediction performance was validated in the independent validation cohort.

RESULTS

Supervised ML methods combining age, sex, smoking, rheumatoid factor, baseline Disease Activity Score in 28 joints (DAS28) scores and 160 SNPs predicted EULAR response at 3 months with the area under the receiver operating curve of 0.84 (P = 0.05) in the training cohort and achieved a prediction accuracy of 76% (P = 0.05) in the validation cohort (sensitivity 72%, specificity 77%). Intergenic SNPs rs12446816, rs13385025, rs113798271, and ATIC (rs2372536) had variable importance above 60.0 and along with baseline DAS28 scores were among the top predictors of methotrexate response.

CONCLUSION

Pharmacogenomic biomarkers combined with baseline DAS28 scores can be useful in predicting response to methotrexate in patients with early RA. Applying ML to predict treatment response holds promise for guiding effective RA treatment choices, including timely escalation of RA therapies.

Functional coding haplotypes and machine-learning feature elimination identifies predictors of Methotrexate Response in Rheumatoid Arthritis patients

BACKGROUND

Major challenges in large scale genetic association studies include not only the identification of causative single nucleotide polymorphisms (SNPs), but also accounting for SNP-SNP interactions. This study thus proposes a novel feature engineering approach integrating potentially functional coding haplotypes (pfcHap) with machine-learning (ML) feature selection to identify biologically meaningful, possibly causative genetic factors, that take into consideration potential SNP-SNP interactions within the pfcHap, to best predict for methotrexate (MTX) response in rheumatoid arthritis (RA) patients.

METHODS

Exome sequencing from 349 RA patients were analysed, of which they were split into training and unseen test set. Inferred pfcHaps were combined with 30 non-genetic features to undergo ML recursive feature elimination with cross-validation using the training set. Predictive capacity and robustness of the selected features were assessed using six popular machine learning models through a train set cross-validation and evaluated in an unseen test set.

FINDINGS

Significantly, 100 features (95 pfcHaps, 5 non-genetic factors) were identified to have good predictive performance (AUC: 0.776-0.828; Sensitivity: 0.656-0.813; Specificity: 0.684-0.868) across all six ML models in an unseen test dataset for the prediction of MTX response in RA patients.

INTERPRETATION

Majority of the predictive pfcHap SNPs were predicted to be potentially functional and some of the genes in which the pfcHap resides in were identified to be associated with previously reported MTX/RA pathways.

FUNDING

Singapore Ministry of Health's National Medical Research Council (NMRC) [NMRC/CBRG/0095/2015; CG12Aug17; CGAug16M012; NMRC/CG/017/2013]; National Cancer Center Research Fund and block funding Duke-NUS Medical School.; Singapore Ministry of Education Academic Research Fund Tier 2 grant MOE2019-T2-1-138.

Methotrexate Treatment of Newly Diagnosed RA Patients Is Associated With DNA Methylation Differences at Genes Relevant for Disease Pathogenesis and Pharmacological Action

Background

Methotrexate (MTX) is the first line treatment of rheumatoid arthritis (RA), and methylation changes in bulk T cells have been reported after treatment with MTX. We have investigated cell-type specific DNA methylation changes across the genome in naïve and memory CD4⁺ T cells before and after MTX treatment of RA patients. DNA methylation profiles of newly diagnosed RA patients (N=9) were assessed by reduced representation bisulfite sequencing.

Results

We found that MTX treatment significantly influenced DNA methylation levels at multiple CpG sites in both cell populations. Interestingly, we identified differentially methylated sites annotated to two genes; TRIM15 and SORC2, previously reported to predict treatment outcome in RA patients when measured in bulk T cells. Furthermore, several of the genes, including STAT3, annotated to the significant CpG sites are relevant for RA susceptibility or the action of MTX.

Conclusion

We detected CpG sites that were associated with MTX treatment in CD4⁺ naïve and memory T cells isolated from RA patients. Several of these sites overlap genetic regions previously associated with RA risk and MTX treatment outcome.

Title Current Status of the Search for Biomarkers for Optimal Therapeutic Drug Selection for Patients with Rheumatoid Arthritis

Rheumatoid arthritis (RA) is an autoimmune disease characterized by destructive synovitis. It is significantly associated with disability, impaired quality of life, and premature mortality. Recently, the development of biological agents (including tumor necrosis factor-α and interleukin-6 receptor inhibitors) and Janus kinase inhibitors have advanced the treatment of RA; however, it is still difficult to predict which drug will be effective for each patient. To break away from the current therapeutic approaches that could be described as a “lottery,” there is an urgent need to establish biomarkers that stratify patients in terms of expected therapeutic responsiveness. This review deals with recent progress from multi-faceted analyses of the synovial tissue in RA, which is now bringing new insights into diverse features at both the cellular and molecular levels and their potential links with particular clinical phenotypes.

The Pretreatment Gut Microbiome Is Associated With Lack of Response to Methotrexate in New-Onset Rheumatoid Arthritis

OBJECTIVE

Although oral methotrexate (MTX) remains the anchor drug for rheumatoid arthritis (RA), up to 50% of patients do not achieve a clinically adequate outcome. In addition, there is a lack of prognostic tools for treatment response prior to drug initiation. This study was undertaken to investigate whether interindividual differences in the human gut microbiome can aid in the prediction of MTX efficacy in new-onset RA.

METHODS

We performed 16S ribosomal RNA gene and shotgun metagenomic sequencing on the baseline gut microbiomes of drug-naive patients with new-onset RA (n = 26). Results were validated in an additional independent cohort (n = 21). To gain insight into potential microbial mechanisms, we conducted ex vivo experiments coupled with metabolomics analysis to evaluate the association between microbiome-driven MTX depletion and clinical response.

RESULTS

Our analysis revealed significant associations of the abundance of gut bacterial taxa and their genes with future clinical response (q < 0.05), including orthologs related to purine and MTX metabolism. Machine learning techniques were applied to the metagenomic data, resulting in a microbiome-based model that predicted lack of response to MTX in an independent group of patients. Finally, MTX levels remaining after ex vivo incubation with distal gut samples from pretreatment RA patients significantly correlated with the magnitude of future clinical response, suggesting a possible direct effect of the gut microbiome on MTX metabolism and treatment outcomes.

CONCLUSION

Taken together, these findings are the first step toward predicting lack of response to oral MTX in patients with new-onset RA and support the value of the gut microbiome as a possible prognostic tool and as a potential target in RA therapeutics.

The genetics of rheumatoid arthritis

RA is a chronic systemic inflammatory disease that primarily affects the small joints of the hands and feet, and results in a mean reduction in life expectancy of 3-10 years. RA is a multigene disorder with a substantial genetic component and a heritability estimate of 60%. Large-scale Genome-Wide Association Studies (GWAS) and meta-analyses have revealed common disease-associated variants in the population that may contribute cumulatively to RA pathogenesis. This review identifies the most significant genetic variants associated with RA susceptibility to date, with particular focus on the contribution of the HLA class II genes across different ethnic groups. Also discussed are the potential applications of pharmacogenomics to RA management by identifying polymorphisms associated with variation in treatment response or toxicity. The use of genetic variants to guide treatment strategy has the potential to not only reduce National Health Service costs, but also drastically improve patient experience and quality of life.

Rheumatology in the era of precision medicine: synovial tissue molecular patterns and treatment response in rheumatoid arthritis

PURPOSE OF REVIEW

A critical unmet need in rheumatoid arthritis (RA) is the identification of biomarkers that predict which of the available medications will be most effective for an individual in order to lower disease activity sooner than is afforded by the current treat-to-target approach. Here we will discuss recent reports examining the potential for synovial tissue molecular, cellular, and spatial profiling in defining objective measures of treatment response and therein developing personalized medicine for RA.

RECENT FINDINGS

Recent high-dimensional molecular profiling of RA synovium has provided unprecedented resolution of the cell types and pathways in tissues affected by rheumatic diseases. Heightened attention to tissue architecture is also emerging as a means to classify individual disease variation that may allow patients to be further stratified by therapeutic response. Although this wealth of data may have already pinpointed promising biomarkers, additional studies, likely including tissue-based functional drug response assays, will be required to demonstrate how the complex tissue environment responds.

SUMMARY

Molecular, cellular, and more recently spatial profiling of the RA synovium are uncovering fundamental features of the disease. Current investigations are examining whether this information will provide meaningful biomarkers for individualized medicine in RA.

Biomarkers to Personalize the Treatment of Rheumatoid Arthritis: Focus on Autoantibodies and Pharmacogenetics

Rheumatoid arthritis (RA) is a chronic inflammatory disease that is very complex and heterogeneous. If not adequately treated, RA patients are likely to manifest excess of morbidity and disability with an important impact on the quality of life. Pharmacological treatment is based on the administration of the disease-modifying antirheumatic drugs (DMARDs), subdivided into conventional synthetic (csDMARDs), targeted synthetic (tsDMARDs), and biological (bDMARDs). bDMARDs are now frequently administered in patients, both as alternative treatment and together with csDMARDs. Unfortunately, there is a therapeutic response variability both to old and new drugs. Therefore, to identify pre-therapeutic and on-treatment predictors of response is a priority. This review aims to summarize recent advances in understanding the causes of the variability in treatment response in RA, with particular attention to predictive potential of autoantibodies and DMARD pharmacogenetics. In recent years, several biomarkers have been proposed to personalize the therapy. Unfortunately, a magic bullet does not exist, as many factors concur to disease susceptibility and treatment outcomes, acting around the patient’s congenital background. Models integrating demographic, clinical, biochemical, and genetic data are needed to enhance the predictive capacity of specific factors singularly considered to optimize RA treatment in light of multidisciplinary patient management.

Validation of a Prognostic Multivariable Prediction Model for Insufficient Clinical Response to Methotrexate in Early Rheumatoid Arthritis and Its Clinical Application in Evidencio

INTRODUCTION

Methotrexate (MTX) constitutes the first-line therapy in rheumatoid arthritis (RA), yet approximately 30% of the patients do not benefit from MTX. Recently, we reported a prognostic multivariable prediction model for insufficient clinical response to MTX at 3 months of treatment in the treatment in the Rotterdam Early Arthritis Cohort (tREACH), including baseline predictors: Disease activity score 28 (DAS28), Health Assessment Questionnaire (HAQ), erythrocyte folate, single-nucleotide polymorphisms (SNPs; ABCB1, ABCC3), smoking, and BMI. The purpose of the current study was (1) to externally validate the model and (2) to enhance the model's clinical applicability.

METHODS

Erythrocyte folate and SNPs were assessed in 91 early disease-modifying antirheumatic drug (DMARD)-naïve RA patients starting MTX in the external validation cohort (U-Act-Early). Insufficient response (DAS28 > 3.2) was determined after 3 months and non-response after 6 months of therapy. The previously developed prediction model was considered successfully validated in the U-Act-Early (validation cohort) if the area under the curve (AUC) of the receiver operating characteristic (ROC) was not significantly lower than in the tREACH (derivation cohort).

RESULTS

The AUCs in U-Act-Early at three and 6 months were 0.75 (95% CI 0.64-0.85) and 0.71 (95% CI 0.60-0.82) respectively, similar to the tREACH. Baseline DAS28 > 5.1 and HAQ > 0.6 were the strongest predictors. The model was simplified by excluding the SNPs, while still classifying 73% correctly. Furthermore, interaction terms between BMI and HAQ and BMI and erythrocyte folate significantly improved the model increasing correct classification to 75%. Results were successfully implemented in Evidencio online platform assisting clinicians in shared decision-making to intensify treatment when appropriate.

CONCLUSIONS

We successfully externally validated our recently reported prediction model for MTX non-response and enhanced its clinical application thus enabling its evaluation in a clinical trial.

TRIAL REGISTRATION

The U-Act-Early is registered at ClinicalTrials.gov. number: NCT01034137. tREACH is registered retrospectively at ISRCTN registry, number: ISRCTN26791028 at 23 August 2007.

Genetic feature engineering enables characterisation of shared risk factors in immune-mediated diseases

BACKGROUND

Genome-wide association studies (GWAS) have identified pervasive sharing of genetic architectures across multiple immune-mediated diseases (IMD). By learning the genetic basis of IMD risk from common diseases, this sharing can be exploited to enable analysis of less frequent IMD where, due to limited sample size, traditional GWAS techniques are challenging.

METHODS

Exploiting ideas from Bayesian genetic fine-mapping, we developed a disease-focused shrinkage approach to allow us to distill genetic risk components from GWAS summary statistics for a set of related diseases. We applied this technique to 13 larger GWAS of common IMD, deriving a reduced dimension "basis" that summarised the multidimensional components of genetic risk. We used independent datasets including the UK Biobank to assess the performance of the basis and characterise individual axes. Finally, we projected summary GWAS data for smaller IMD studies, with less than 1000 cases, to assess whether the approach was able to provide additional insights into genetic architecture of less common IMD or IMD subtypes, where cohort collection is challenging.

RESULTS

We identified 13 IMD genetic risk components. The projection of independent UK Biobank data demonstrated the IMD specificity and accuracy of the basis even for traits with very limited case-size (e.g. vitiligo, 150 cases). Projection of additional IMD-relevant studies allowed us to add biological interpretation to specific components, e.g. related to raised eosinophil counts in blood and serum concentration of the chemokine CXCL10 (IP-10). On application to 22 rare IMD and IMD subtypes, we were able to not only highlight subtype-discriminating axes (e.g. for juvenile idiopathic arthritis) but also suggest eight novel genetic associations.

CONCLUSIONS

Requiring only summary-level data, our unsupervised approach allows the genetic architectures across any range of clinically related traits to be characterised in fewer dimensions. This facilitates the analysis of studies with modest sample size by matching shared axes of both genetic and biological risk across a wider disease domain, and provides an evidence base for possible therapeutic repurposing opportunities.

Hydrogen Peroxide Inducible JAK3 Covalent Inhibitor: Prodrug for the Treatment of RA with Enhanced Safety Profile

Selective inhibition of Janus kinases (JAKs) is an arising strategy in drug discovery. Covalent inhibitors targeting a unique cysteine in JAK3 exhibit ultraselectivity among JAK family members. However, safety and tissue specific concerns still remain. A prodrug of a known JAK3 covalent inhibitor sensitive to H₂O₂ was designed and synthesized and its therapeutic effect was evaluated in the CIA (collagen-induced arthritis) mice model of RA (rheumatoid arthritis). The prodrug strategy relied on the introduction of a hydrogen peroxide-sensitive borate trigger group to avoid random covalent binding to thiol functionalities in biomacromolecules. The results show that the prodrug can be activated and released under pathophysiological concentration of H₂O₂. In addition, the prodrug demonstrated stability to the physiological environment. In comparison to the parent compound, the prodrug showed a similar therapeutic effect in the CIA model but notably exhibited lower toxicity and a larger therapeutic window.

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.

Detection and Comparative Analysis of Methylomic Biomarkers of Rheumatoid Arthritis

Rheumatoid arthritis (RA) is a common autoimmune disorder influenced by both genetic and environmental factors. To investigate possible contributions of DNA methylation to the etiology of RA with minimum confounding genetic heterogeneity, we investigated genome-wide DNA methylation in disease-discordant monozygotic twin pairs. This study hypothesized that methylomic biomarkers might facilitate accurate RA detection. A comprehensive series of biomarker detection algorithms were utilized to find the best methylomic biomarkers for detecting RA patients using the methylomic data of the peripheral blood samples. The best model achieved 100.00% in accuracy (Acc) with 81 methylomic biomarkers and a 10-fold cross-validation (10FCV) strategy. Some of the methylomic biomarkers were experimentally confirmed to be associated with the onset or development of RA. It is also interesting to observe that many of the detected biomarkers were from chromosome Y, supporting the knowledge that RA has a significant gender discrepancy.

Methotrexate and its mechanisms of action in inflammatory arthritis

Despite the introduction of numerous biologic agents for the treatment of rheumatoid arthritis (RA) and other forms of inflammatory arthritis, low-dose methotrexate therapy remains the gold standard in RA therapy. Methotrexate is generally the first-line drug for the treatment of RA, psoriatic arthritis and other forms of inflammatory arthritis, and it enhances the effect of most biologic agents in RA. Understanding the mechanism of action of methotrexate could be instructive in the appropriate use of the drug and in the design of new regimens for the treatment of RA. Although methotrexate is one of the first examples of intelligent drug design, multiple mechanisms potentially contribute to the anti-inflammatory actions of methotrexate, including the inhibition of purine and pyrimidine synthesis, transmethylation reactions, translocation of nuclear factor-κB (NF-κB) to the nucleus, signalling via the Janus kinase (JAK)-signal transducer and activator of transcription (STAT) pathway and nitric oxide production, as well as the promotion of adenosine release and expression of certain long non-coding RNAs.

Rheumatological manifestations in inborn errors of immunity

Rare monogenetic diseases serve as natural models to dissect the molecular pathophysiology of the complex disease traits. Rheumatologic disorders by their nature are considered complex diseases with partially genetic origin, as illustrated by their heterogeneous genetic background and variable phenotypic presentation. Recent advances in genetic technologies have helped uncover multiple variants associated with disease susceptibility; however, a precise understanding of genotype-phenotype relationships is still missing. Inborn errors of immunity (IEIs), in addition to recurrent infections, may also present with autoimmune and autoinflammatory rheumatologic manifestations and have provided insights for understanding the underlying the principles of immune system homeostasis and mechanisms of immune dysregulation. This review discusses the rheumatologic manifestations in IEIs with overlapping and differentiating features in immunodeficiencies and rheumatologic disorders.

Methotrexate pharmacogenetics in the treatment of rheumatoid arthritis

For many decades, methotrexate (MXT) has remained the drug of choice in the treatment of rheumatoid arthritis (RA). Unfortunately, a considerable number of patients do not achieve an appropriate therapeutic response. Pharmacogenetics studies do not give usable results regarding differences in MTX response among RA patients. The mechanism of MTX action in RA is not completely understood. We present and discuss data regarding the molecular basis of folate and adenosine pathways, the most obvious MTX targets, to explain possible causes of therapy failure. The molecular basis of the disease could also have an impact on therapy outcomes and in this review we explore this. Finally, we make a short review of available pharmacogenetics study results.

Methotrexate an Old Drug with New Tricks

Methotrexate (MTX) is the first line drug for the treatment of a number of rheumatic and non-rheumatic disorders. It is currently used as an anchor disease, modifying anti-rheumatic drug in the treatment of rheumatoid arthritis (RA). Despite the development of numerous new targeted therapies, MTX remains the backbone of RA therapy due to its potent efficacy and tolerability. There has been also a growing interest in the use of MTX in the treatment of chronic viral mediated arthritis. Many viruses—including old world alphaviruses, Parvovirus B19, hepatitis B/C virus, and human immunodeficiency virus—have been associated with arthritogenic diseases and reminiscent of RA. MTX may provide benefits although with the potential risk of attenuating patients’ immune surveillance capacities. In this review, we describe the emerging mechanisms of action of MTX as an anti-inflammatory drug and complementing its well-established immunomodulatory activity. The mechanisms involve adenosine signaling modulation, alteration of cytokine networks, generation of reactive oxygen species and HMGB1 alarmin suppression. We also provide a comprehensive understanding of the mechanisms of MTX toxic effects. Lastly, we discussed the efficacy, as well as the safety, of MTX used in the management of viral-related rheumatic syndromes.

Polymorphism in Organic Anion-Transporting Polypeptide Gene Related to Methotrexate Response in Rheumatoid Arthritis Treatment

BACKGROUND

Methotrexate (MTX) is still the first-choice drug for the treatment of rheumatoid arthritis (RA). In Japan, MTX doses of up to 16 mg/week were approved in 2011. In this study, we aimed to identify the gene polymorphisms that can predict therapeutic effects of MTX in Japanese patients in current clinical settings.

METHODS

This study involved 171 patients with RA (all Japanese nationals, age 63.5±10.0 years) who had been administered MTX. The analyzed polymorphisms included 82 single nucleotide polymorphisms (SNPs) involved in the MTX pharmacological pathway or in the pathogenesis of RA. Responders were patients who showed high sustained remission or low disease activity with MTX or conventional disease-modifying anti-rheumatic drugs (DMARDs) treatment beyond 6 months. Non-responders were patients who showed moderate or high disease activity, who were prescribed biological DMARDs. A logistic model was constructed with Responder/Non-responder as the target variable, and minor allele frequency was set as an explanatory variable.

RESULTS

None of the 82 SNPs targeted for analysis met the Bonferroni significance threshold of 6.098×10^-4. However, we identified SLCO1B1 rs11045879 as an SNP that might yield significant results if the number of patients were to be increased (P=0.015).

CONCLUSIONS

The rs11045879 minor allele in the SLCO1B1 gene is a potential predictor of non-responders to MTX treatment among Japanese RA patients. In future collaborative research, we will investigate whether the association with SLCO1B1 polymorphism is significant by performing statistical analysis with a larger study population.

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.

Association between ABCB1 C3435T polymorphism and methotrexate treatment outcomes in rheumatoid arthritis patients: a meta-analysis

Aim: This study was conducted to investigate the relationship between ABCB1 gene C3435T polymorphism and methotrexate treatment outcomes in rheumatoid arthritis patients. Methods: Seven electronic databases (PubMed, EMBASE, Web of Science, Cochrane, OVID, Chinese biomedical literature [CBM], China National Knowledge Infrastructure [CNKI] and Wanfang databases) were searched to select eligible publications until 18 July 2018. The references of relevant articles were also manually searched. The quality evaluation of the included studies was carried out according to the guidelines of the Newcastle–Ottawa Scale. Data were analyzed with Review Manager 5.3 and Stata 13.0 software. In total, 12 articles involving 2014 patients were included. Conclusion: Our results demonstrated that the ABCB1 gene C3435T polymorphism might be a reliable predictor of response to methotrexate in rheumatoid arthritis patients. However, well-designed, multicenter and large-scale prospective studies are required to further confirm the validity of our results.

VEGF-C Gene Polymorphisms Increase Susceptibility to Rheumatoid Arthritis

Vascular endothelial growth factor C (VEGF-C) promotes angiogenesis, a prominent feature in rheumatoid synovitis, contributing to the perpetuation of the global burden of rheumatoid arthritis (RA). VEGF-C gene polymorphisms predict the risk of developing various human diseases, such as urothelial cell carcinoma, oral cancer and coronary artery disease. We sought to determine whether single nucleotide polymorphisms (SNPs) of the VEGF-C gene can predict the risk of RA. Our study recruited 210 patients with RA and 373 healthy controls between 2007 and 2015, and performed comparative genotyping for SNPs rs7664413, rs11947611, rs1485766, rs2046463 and rs3775194. In analyses adjusted for potential covariates, we found that compared with subjects with the A/A genotype of SNP rs11947611, those with the A/G genotype were 40% more likely to develop RA (adjusted odds ratio [AOR] 0.61; 95% confidence interval [CI] 0.40 to 0.92; p = 0.02). In addition, subjects lacking the A/A genotype (A/G, G/G) of SNP rs2046463 were more than twice as likely as those with the A/A genotype to require methotrexate (AOR 2.23, 95% CI 1.25 to 3.98; p = 0.01), while those who lacked the G/G genotype (G/C, C/C) in the SNP rs3775194 had a significantly lower risk of requiring prednisolone as compared with those with the G/G genotype (AOR 0.39, 95% CI 0.19 to 0.79; p = 0.01). Our findings suggest that VEGF-C gene polymorphisms might serve as a diagnostic marker and therapeutic target for RA therapy. Pharmacotherapies that modulate the activity of the VEGF-C gene may be promising for RA treatment.

Prediction of treatment response in rheumatoid arthritis patients using genome-wide SNP data

Although a number of treatments are available for rheumatoid arthritis (RA), each of them shows a significant nonresponse rate in patients. Therefore, predicting a priori the likelihood of treatment response would be of great patient benefit. Here, we conducted a comparison of a variety of statistical methods for predicting three measures of treatment response, between baseline and 3 or 6 months, using genome-wide SNP data from RA patients available from the MAximising Therapeutic Utility in Rheumatoid Arthritis (MATURA) consortium. Two different treatments and 11 different statistical methods were evaluated. We used 10-fold cross validation to assess predictive performance, with nested 10-fold cross validation used to tune the model hyperparameters when required. Overall, we found that SNPs added very little prediction information to that obtained using clinical characteristics only, such as baseline trait value. This observation can be explained by the lack of strong genetic effects and the relatively small sample sizes available; in analysis of simulated and real data, with larger effects and/or larger sample sizes, prediction performance was much improved. Overall, methods that were consistent with the genetic architecture of the trait were able to achieve better predictive ability than methods that were not. For treatment response in RA, methods that assumed a complex underlying genetic architecture achieved slightly better prediction performance than methods that assumed a simplified genetic architecture.

Transcriptome analysis of peripheral blood from patients with rheumatoid arthritis: a systematic review

In the era of precision medicine, transcriptome analysis of whole gene expression is an essential technology. While DNA microarray has a limited dynamic range and a problem of background hybridization, RNA sequencing (RNA-seq) has a broader dynamic range and a lower background signal that increase the sensitivity and reproducibility. While transcriptome analyses in rheumatoid arthritis (RA) have generally focused on whole peripheral blood mononuclear cells (PBMC), analyses of detailed cell subsets have an increased need for understanding the pathophysiology of disease because the involvement of CD4⁺ T cells in the pathogenesis of RA has been established. Transcriptome analysis of detailed CD4⁺ T cell subsets or neutrophils shed new light on the pathophysiology of RA. There are several analyses about the effect of biological treatment. Many studies report the association between type I interferon signature gene expression and response to therapy.