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

Tailoring the treatment of inflammatory rheumatic diseases by a better stratification and characterization of the clinical patient heterogeneity. Findings from a systematic literature review and experts' consensus

Inflammatory rheumatic diseases are different pathologic conditions associated with a deregulated immune response, codified along a spectrum of disorders, with autoinflammatory and autoimmune diseases as two-end phenotypes of this continuum. Despite pathogenic differences, inflammatory rheumatic diseases are commonly managed with a limited number of immunosuppressive drugs, sometimes with partial evidence or transferring physicians' knowledge in different patients. In addition, several randomized clinical trials, enrolling these patients, did not meet the primary pre-established outcomes and these findings could be linked to the underlying molecular diversities along the spectrum of inflammatory rheumatic disorders. In fact, the resulting patient heterogeneity may be driven by differences in underlying molecular pathology also resulting in variable responses to immunosuppressive drugs. Thus, the identification of different clinical subsets may possibly overcome the major obstacles that limit the development more effective therapeutic strategies for these patients with inflammatory rheumatic diseases. This clinical heterogeneity could require a diverse therapeutic management to improve patient outcomes and increase the frequency of clinical remission. Therefore, the importance of better patient stratification and characterization is increasingly pointed out according to the precision medicine principles, also suggesting a new approach for disease treatment. In fact, based on a better proposed patient profiling, clinicians could more appropriately balance the therapeutic management. On these bases, we synthetized and discussed the available literature about the patient profiling in regard to therapy in the context of inflammatory rheumatic diseases, mainly focusing on randomized clinical trials. We provided an overview of the importance of a better stratification and characterization of the clinical heterogeneity of patients with inflammatory rheumatic diseases identifying this point as crucial in improving the management of these patients.

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.

Novel DNA methylome biomarkers associated with adalimumab response in rheumatoid arthritis patients

Background and aims

Rheumatoid arthritis (RA) patients are currently treated with biological agents mostly aimed at cytokine blockade, such as tumor necrosis factor-alpha (TNFα). Currently, there are no biomarkers to predict therapy response to these agents. Here, we aimed to predict response to adalimumab (ADA) treatment in RA patients using DNA methylation in peripheral blood (PBL).

Methods

DNA methylation profiling on whole peripheral blood from 92 RA patients before the start of ADA treatment was determined using Illumina HumanMethylationEPIC BeadChip array. After 6 months, treatment response was assessed according to the European Alliance of Associations for Rheumatology (EULAR) criteria for disease activity. Patients were classified as responders (Disease Activity Score in 28 Joints (DAS28) < 3.2 or decrease of 1.2 points) or as non-responders (DAS28 > 5.1 or decrease of less than 0.6 points). Machine learning models were built through stability-selected gradient boosting to predict response prior to ADA treatment with predictor DNA methylation markers.

Results

Of the 94 RA patients, we classified 49 and 43 patients as responders and non-responders, respectively. We were capable of differentiating responders from non-responders with a high performance (area under the curve (AUC) 0.76) using a panel of 27 CpGs. These classifier CpGs are annotated to genes involved in immunological and pathophysiological pathways related to RA such as T-cell signaling, B-cell pathology, and angiogenesis.

Conclusion

Our findings indicate that the DNA methylome of PBL provides discriminative capabilities in discerning responders and non-responders to ADA treatment and may therefore serve as a tool for therapy prediction.

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.

Association Study of Anticitrullinated Peptide Antibody Status with Clinical Manifestations and SNPs in Patients Affected with Rheumatoid Arthritis: A Pilot Study

Introduction. Rheumatoid arthritis (RA) is an autoimmune disease of unknown etiology that leads to disability due to articular and extra-articular damage. RA prevalence is variable. The disease is most common among females with a 3 : 1 ratio. The interaction of environmental and host factors contributes to RA development. Currently, the genome-wide association studies (GWAS) give the opportunity to uncover the RA genetic background. Anticitrullinated peptide antibody (ACPA) is a highly specific RA antibody, associated with poor prognosis and severe course of RA, and regulated by numerous genes. Our study is aimed at investigating whether there are any clinical and genetic aspects correlate with ACPA presence in Kazakhstani patients with RA. Indeed, the available studies on this subject are focused on Caucasian and East Asian populations (mainly Japanese and Chinese), and there are scarce data from Central Asia. Methods. Our study included 70 RA patients. Patients’ blood samples were collected and genotyped for 14 SNPs by real-time polymerase chain reaction (RT-PCR). General examination, anamnestic, and clinical and laboratory data collection were carried out. Statistical analysis was performed using R statistics. Results and Conclusion. Our study revealed a significant association of ACPA positivity with Fc receptor-like 3 (FCRL3) and ACPA negativity with signal transducer and activator of transcription 4 (STAT4) genes, but not with T cell activation Rho GTPase activating protein (TAGAP). In addition, ACPA positivity was associated with radiographic progression, rheumatoid factor (RF), erythrocyte sedimentation rate (ESR), age of RA onset, the patient global assessment, body mass index (BMI), and Gamma globulin. Conclusion. Remained 11 earlier identified significantly associated in Caucasian and Asian population SNPs were not replicated in our cohort. Further studies on larger cohorts are needed to confirm our findings with higher confidence levels and stronger statistical power.

A genome-wide screen for variants influencing certolizumab pegol response in a moderate to severe rheumatoid arthritis population

Certolizumab pegol (CZP) is a PEGylated Fc-free tumor necrosis factor (TNF) inhibitor antibody approved for use in the treatment of rheumatoid arthritis (RA), Crohn’s disease, psoriatic arthritis, axial spondyloarthritis and psoriasis. In a clinical trial of patients with severe RA, CZP improved disease symptoms in approximately half of patients. However, variability in CZP efficacy remains a problem for clinicians, thus, the aim of this study was to identify genetic variants predictive of CZP response. We performed a genome-wide association study (GWAS) of 302 RA patients treated with CZP in the REALISTIC trial to identify common single nucleotide polymorphisms (SNPs) associated with treatment response. Whole-exome sequencing was also performed for 74 CZP extreme responders and non-responders within the same population, as well as 1546 population controls. No common SNPs or rare functional variants were significantly associated with CZP response, though a non-significant enrichment in the RA-implicated KCNK5 gene was observed. Two SNPs near spondin-1 and semaphorin-4G approached genome-wide significance. The results of the current study did not provide an unambiguous predictor of CZP response.

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.

Deciphering the association of intronic single nucleotide polymorphisms of crystallin gene family with congenital cataract

Purpose

Introns play an important role in gene regulation and expression. Single nucleotide polymorphisms (SNPs) in introns have the potential to cause disease and alter the genotype-phenotype association. Hence, this study aimed to decipher the association of SNPs in the introns of the crystallin gene in congenital cataracts.

Methods

SNPs in the introns of crystallin gene family - CRYAA (rs3788059), CRYAB (rs2070894), CRYBA4 (rs2071861), and CRYBB2 (rs5752083, rs5996863) - were genotyped in 248 participants consisting of 141 congenital cataracts and 107 healthy controls by allele-specific oligonucleotide polymerase chain reaction method. Around 10% of samples for each SNPs were sequenced to confirm the genotypes. The allele, genotype, and haplotype frequency were evaluated by the SHEsis online tool.

Results

Using dominant model, the "A" allele of rs3788059 was found to have an increased risk toward congenital cataract development whereas the "G" allele was found to be protective (AA + AG vs. GG; odds ratio [OR] 95% confidence interval [CI] = 3.73 [1.71, 8.15], P = 0.0009). The "A" allele of both rs2070894 (AA + AG vs. GG; OR [95% CI] = 0.49 [0.29, 0.84], P = 0.012) and rs5752083 (AA + AC vs. CC; OR [95% CI] = 0.25 [0.08, 0.76], P = 0.016) were suggested to have a protective role by the dominant model. The A-C-T haplotype (rs2071861, rs5752083, and rs5996863) was found to be a significant risk factor for the development of congenital cataract.

Conclusion

Intronic SNPs in crystallin genes may play a role in the predisposition toward congenital cataract. However, the present findings need to be replicated in a large cohort with more number of samples.

Multi-omics approach to precision medicine for immune-mediated diseases

Recent innovation in high-throughput sequencing technologies has drastically empowered the scientific research. Consequently, now, it is possible to capture comprehensive profiles of samples at multiple levels including genome, epigenome, and transcriptome at a time. Applying these kinds of rich information to clinical settings is of great social significance. For some traits such as cardiovascular diseases, attempts to apply omics datasets in clinical practice for the prediction of the disease risk have already shown promising results, although still under way for immune-mediated diseases. Multiple studies have tried to predict treatment response in immune-mediated diseases using genomic, transcriptomic, or clinical information, showing various possible indicators. For better prediction of treatment response or disease outcome in immune-mediated diseases, combining multi-layer information together may increase the power. In addition, in order to efficiently pick up meaningful information from the massive data, high-quality annotation of genomic functions is also crucial. In this review, we discuss the achievement so far and the future direction of multi-omics approach to immune-mediated diseases.

A Molecular Signature Response Classifier to Predict Inadequate Response to Tumor Necrosis Factor-α Inhibitors: The NETWORK-004 Prospective Observational Study

Introduction

Timely matching of patients to beneficial targeted therapy is an unmet need in rheumatoid arthritis (RA). A molecular signature response classifier (MSRC) that predicts which patients with RA are unlikely to respond to tumor necrosis factor-α inhibitor (TNFi) therapy would have wide clinical utility.

Methods

The protein–protein interaction map specific to the rheumatoid arthritis pathophysiology and gene expression data in blood patient samples was used to discover a molecular signature of non-response to TNFi therapy. Inadequate response predictions were validated in blood samples from the CERTAIN cohort and a multicenter blinded prospective observational clinical study (NETWORK-004) among 391 targeted therapy-naïve and 113 TNFi-exposed patient samples. The primary endpoint evaluated the ability of the MSRC to identify patients who inadequately responded to TNFi therapy at 6 months according to ACR50. Additional endpoints evaluated the prediction of inadequate response at 3 and 6 months by ACR70, DAS28-CRP, and CDAI.

Results

The 23-feature molecular signature considers pathways upstream and downstream of TNFα involvement in RA pathophysiology. Predictive performance was consistent between the CERTAIN cohort and NETWORK-004 study. The NETWORK-004 study met primary and secondary endpoints. A molecular signature of non-response was detected in 45% of targeted therapy-naïve patients. The MSRC had an area under the curve (AUC) of 0.64 and patients were unlikely to adequately respond to TNFi therapy according to ACR50 at 6 months with an odds ratio of 4.1 (95% confidence interval 2.0–8.3, p value 0.0001). Odds ratios (3.4–8.8) were significant (p value < 0.01) for additional endpoints at 3 and 6 months, with AUC values up to 0.74. Among TNFi-exposed patients, the MSRC had an AUC of up to 0.83 and was associated with significant odds ratios of 3.3–26.6 by ACR, DAS28-CRP, and CDAI metrics.

Conclusion

The MSRC stratifies patients according to likelihood of inadequate response to TNFi therapy and provides patient-specific data to guide therapy choice in RA for targeted therapy-naïve and TNFi-exposed patients.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40744-021-00330-y.

A blood-based molecular signature response classifier (MSRC) integrating next-generation RNA sequencing data with clinical features predicts the likelihood that a patient with rheumatoid arthritis will have an inadequate response to TNFi therapy. Treatment selection guided by test results, with likely inadequate responders appropriately redirected to a different therapy, could improve response rates to TNFi therapies, generate healthcare cost savings, and increase rheumatologists’ confidence in prescribing decisions and altered treatment choices. The MSRC described in this study predicts the likelihood of inadequate response to TNFi therapies among targeted therapy-naïve and TNFi-exposed patients in a multicenter, 24-week blinded prospective clinical study: NETWORK-004. Patients with a molecular signature of non-response are less likely to have an adequate response to TNFi therapies than those patients lacking the signature according to ACR50, ACR70, CDAI, and DAS28-CRP with significant odds ratios of 3.4–8.8 for targeted therapy-naïve patients and 3.3–26.6 for TNFi-exposed patients. This MSRC provides a solution to the long-standing need for precision medicine tools to predict drug response in rheumatoid arthritis—a heterogeneous and progressive disease with an abundance of therapeutic options. These data validate the performance of the MSRC in a blinded prospective clinical study of targeted therapy-naïve and TNFi therapy-exposed patients.

Potential clinical biomarkers in rheumatoid arthritis with an omic approach

Objective

To aid in the selection of the most suitable therapeutic option in patients with diagnosis of rheumatoid arthritis according to the phase of disease, through the review of articles that identify omics biological markers.

Methods

A systematic review in PubMed/Medline databases was performed. We searched articles from August 2014 to September 2019, in English and Spanish, filtered by title and full text; and using the terms "Biomarkers" AND “Rheumatoid arthritis".

Results

This article supplies an exhaustive review from research of objective measurement, omics biomarkers and how disease activity appraise decrease unpredictability in treatment determinations, and finally, economic, and clinical outcomes of treatment options by biomarkers’ potential influence. A total of 122 articles were included. Only 92 met the established criteria for review purposes and 17 relevant references about the topic were included as well. Therefore, it was possible to identify 196 potential clinical biomarkers: 22 non-omics, 20 epigenomics, 33 genomics, 21 transcriptomics, 78 proteomics, 4 glycomics, 1 lipidomics and 17 metabolomics.

Conclusion

A biomarker is a measurable indicator of some, biochemical, physiological, or morphological condition; evaluable at a molecular, biochemical, or cellular level. Biomarkers work as indicators of physiological or pathological processes, or as a result of a therapeutic management. In the last five years, new biomarkers have been identified, especially the omics, which are those that proceed from the investigation of genes (genomics), metabolites (metabolomics), and proteins (proteomics). These biomarkers contribute to the physician choosing the best therapeutic option in patients with rheumatoid arthritis.

Novel approaches to develop biomarkers predicting treatment responses to TNF-blockers

Introduction: Chronic inflammatory diseases (CIDs) cause significant morbidity and are a considerable burden for the patients in terms of pain, impaired function, and diminished quality of life. Important progress in CID treatment has been obtained with biological therapies, such as tumor-necrosis-factor blockers. However, more than a third of the patients fail to respond to these inhibitors and are exposed to the side effects of treatment, without the benefits. Therefore, there is a strong interest in developing tools to predict response of patients to biologics. Areas covered: The authors searched PubMed for recent studies on biomarkers for disease assessment and prediction of therapeutic responses, focusing on the effect of TNF blockers on immune responses in spondyloarthritis (SpA), and other CID, in particular rheumatoid arthritis and inflammatory bowel disease. Conclusions will be drawn about the possible development of predictive biomarkers for response to treatment. Expert opinion: No validated biomarker is currently available to predict treatment response in CID. New insight could be generated through the development of new bioinformatic modeling approaches to combine multidimensional biomarkers that explain the different genetic, immunological and environmental determinants of therapeutic responses.

Biologic Drugs for Rheumatoid Arthritis in the Context of Biosimilars, Genetics, Epigenetics and COVID-19 Treatment

Rheumatoid arthritis (RA) affects around 1.2% of the adult population. RA is one of the main reasons for work disability and premature retirement, thus substantially increasing social and economic burden. Biological disease-modifying antirheumatic drugs (bDMARDs) were shown to be an effective therapy especially in those rheumatoid arthritis (RA) patients, who did not adequately respond to conventional synthetic DMARD therapy. However, despite the proven efficacy, the high cost of the therapy resulted in limitation of the widespread use and unequal access to the care. The introduction of biosimilars, which are much cheaper relative to original drugs, may facilitate the achievement of the therapy by a much broader spectrum of patients. In this review we present the properties of original biologic agents based on cytokine-targeted (blockers of TNF, IL-6, IL-1, GM-CSF) and cell-targeted therapies (aimed to inhibit T cells and B cells properties) as well as biosimilars used in rheumatology. We also analyze the latest update of bDMARDs' possible influence on DNA methylation, miRNA expression and histone modification in RA patients, what might be the important factors toward precise and personalized RA treatment. In addition, during the COVID-19 outbreak, we discuss the usage of biologicals in context of effective and safe COVID-19 treatment. Therefore, early diagnosing along with therapeutic intervention based on personalized drugs targeting disease-specific genes is still needed to relieve symptoms and to improve the quality of life of RA patients.

Genetic Polymorphisms Associated with Rheumatoid Arthritis Development and Antirheumatic Therapy Response

Rheumatoid arthritis (RA) is the most common inflammatory arthropathy worldwide. Possible manifestations of RA can be represented by a wide variability of symptoms, clinical forms, and course options. This multifactorial disease is triggered by a genetic predisposition and environmental factors. Both clinical and genealogical studies have demonstrated disease case accumulation in families. Revealing the impact of candidate gene missense variants on the disease course elucidates understanding of RA molecular pathogenesis. A multivariate genomewide association study (GWAS) based analysis identified the genes and signalling pathways involved in the pathogenesis of the disease. However, these identified RA candidate gene variants only explain 30% of familial disease cases. The genetic causes for a significant proportion of familial RA have not been determined until now. Therefore, it is important to identify RA risk groups in different populations, as well as the possible prognostic value of some genetic variants for disease development, progression, and treatment. Our review has two purposes. First, to summarise the data on RA candidate genes and the increased disease risk associated with these alleles in various populations. Second, to describe how the genetic variants can be used in the selection of drugs for the treatment of RA.

Disease modifying drugs for rheumatological diseases: a brief history of everything

The rheumatological diseases are a group of chronic, painful, degenerative and debilitating conditions with an increasing prevalence across the globe. The pathogenesis of these disorders is complex, overlapping and not fully understood. As such, it is difficult and time consuming to achieve correct diagnosis and complete remission for an individual patient. In this review we describe the most common forms of inflammatory arthritis and discuss how the management and treatment options for these rheumatic diseases have developed over time. We outline the successes and the limitations of current treatment regimens and discuss the economic burden of the current options. With advancements in understanding of disease mechanisms, we discuss the importance of the biologics revolution in the context of rheumatological disease and how the development of biosimilars and small molecule inhibitors will impact current treatment options in order to alleviate some of the cost burden of biological therapies. The ideal treatment strategy for the future would involve personalized and predictive medicine where by treatments can be tailored to an individual patient's needs in order to achieve fast and successful remission with no adverse events.

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.