Transcriptomic network analysis reveals key drivers of response to anti-TNF biologics in patients with rheumatoid arthritis

OBJECTIVE

Anti-TNF biologics have been widely used to ameliorate disease activity in patients with RA. However, a large fraction of patients show a poor response to these agents. Moreover, no clinically applicable predictive biomarkers have been established. This study aimed to identify response-associated biomarkers using longitudinal transcriptomic data in two independent RA cohorts.

METHODS

RNA sequencing data from peripheral blood cell samples of Korean and Caucasian RA cohorts before and after initial treatment with anti-TNF biologics were analysed to assess treatment-induced expression changes that differed between highly reliable excellent responders and null responders. Weighted correlation network, immune cell composition, and key driver analyses were performed to understand response-associated transcriptomic networks and cell types and their correlation with disease activity indices.

RESULTS

In total, 305 response-associated genes showed significantly different treatment-induced expression changes between excellent and null responders. Co-expression network construction and subsequent key driver analysis revealed that 41 response-associated genes played a crucial role as key drivers of transcriptomic alteration in four response-associated networks involved in various immune pathways: type I IFN signalling, myeloid leucocyte activation, B cell activation, and NK cell/lymphocyte-mediated cytotoxicity. Transcriptomic response scores that we developed to estimate the individual-level degree of expression changes in the response-associated key driver genes were significantly correlated with the changes in clinical indices in independent patients with moderate or ambiguous response outcomes.

CONCLUSION

This study provides response-specific treatment-induced transcriptomic signatures by comparing the transcriptomic landscape between patients with excellent and null responses to anti-TNF drugs at both gene and network levels.

The implication of long non-coding RNA expression profile in rheumatoid arthritis: Correlation with treatment response to tumor necrosis factor inhibitor

OBJECTIVE

This study aimed to investigate the linkage of long non-coding RNA (lncRNA) expression profile with etanercept response in rheumatoid arthritis (RA) patients.

METHODS

Peripheral blood mononuclear cell (PBMC) samples were collected from 80 RA patients prior to etanercept treatment. Samples from eight responders and eight non-responders at week 24 (W24) were proposed to RNA-sequencing, then 10 candidate lncRNAs were sorted and their PBMC expressions were validated by reverse transcription quantitative chain reaction (RT-qPCR) in 80 RA patients. Subsequently, clinical response by lncRNA (CRLnc) prediction model was established.

RESULTS

RNA-sequencing identified 254 up-regulated and 265 down-regulated lncRNAs in W24 responders compared with non-responders, which were enriched in immune or joint related pathways such as B-cell receptor signaling, osteoclast differentiation and T-cell receptor signaling pathways, etc. By reverse transcription quantitative chain reaction (RT-qPCR) validation: Two lncRNAs were correlated with W4 response, three lncRNAs were correlated with W12 response, seven lncRNAs were correlated with W24 response. Subsequently, to construct and validate CRLnc prediction model, 80 RA patients were randomly divided into test set (n = 40) and validation set (n = 40). In the test set, lncRNA RP3-466P17.2 (OR = 9.743, P = .028), RP11-20D14.6 (OR = 10.935, P = .007), RP11-844P9.2 (OR = 0.075, P = .022), and TAS2R64P (OR = 0.044, P = .016) independently related to W24 etanercept response; then CRLnc prediction model integrating these four lncRNAs presented a good value in predicting W24 etanercept response (Area Under Curve (AUC): 0.956, 95%CI: 0.896-1.000). However, in the validation set, the CRLnc prediction model only exhibited a certain value in predicting W24 etanercept response (AUC: 0.753, 95%CI: 0.536-0.969).

CONCLUSIONS

CRLnc prediction model is potentially a useful tool to instruct etanercept treatment in RA patients.

-Omic Approaches and Treatment Response in Rheumatoid Arthritis

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

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

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

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

Background

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

Methods

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

Findings

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

Interpretation

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

Funding

The Instituto de Salud Carlos III.

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.

Plasma interferon-alpha is associated with double-positivity for autoantibodies but is not a predictor of remission in early rheumatoid arthritis-a spin-off study of the NORD-STAR randomized clinical trial

BACKGROUND

The type I interferon (IFN) gene signature is present in a subgroup of patients with early rheumatoid arthritis (RA). Protein levels of IFNα have not been measured in RA and it is unknown whether they associate with clinical characteristics or treatment effect.

METHODS

Patients with early untreated RA (n = 347) were randomized to methotrexate combined with prednisone, certolizumab-pegol, abatacept, or tocilizumab. Plasma IFNα protein levels were determined by single molecular array (Simoa) before and 24 weeks after treatment initiation and were related to demographic and clinical factors including clinical disease activity index, disease activity score in 28 joints, swollen and tender joint counts, and patient global assessment.

RESULTS

IFNα protein positivity was found in 26% of the patients, and of these, 92% were double-positive for rheumatoid factor (RF) and anti-citrullinated protein antibodies (ACPA). IFNα protein levels were reduced 24 weeks after treatment initiation, and the absolute change was similar irrespective of treatment. IFNα protein positivity was associated neither with disease activity nor with achievement of CDAI remission 24 weeks after randomization.

CONCLUSION

IFNα protein positivity is present in a subgroup of patients with early RA and associates with double-positivity for autoantibodies but not with disease activity. Pre-treatment IFNα positivity did not predict remission in any of the treatment arms, suggesting that the IFNα system is distinct from the pathways of TNF, IL-6, and T-cell activation in early RA. A spin-off study of the NORD-STAR randomized clinical trial, NCT01491815 (ClinicalTrials), registered 12/08/2011, https://clinicaltrials.gov/ct2/show/NCT01491815 .

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.

Clinical Utility and Cost Savings in Predicting Inadequate Response to Anti-TNF Therapies in Rheumatoid Arthritis

INTRODUCTION

The PrismRA® test identifies rheumatoid arthritis (RA) patients who are unlikely to respond to anti-tumor necrosis factor (anti-TNF) therapies. This study evaluated the clinical and financial outcomes of incorporating PrismRA into routine clinical care of RA patients.

METHODS

A decision-analytic model was created to evaluate clinical and economic outcomes in the 12-month period following first biologic treatment. Two treatment strategies were compared: (1) observed clinical decision-making based on a 175-patient cohort receiving an anti-TNF therapy as their first biologic after failure of conventional synthetic disease-modifying antirheumatic drugs (csDMARDs) and (2) modeled clinical decision-making of the same population using PrismRA results to inform first-line biologic treatment choice. Modeled costs include biologic drug pharmacy, non-biologic pharmacy, and total medical costs. The odds of inadequate response to anti-TNF therapies and various components of patient care were calculated based on PrismRA results.

RESULTS

Identifying predicted inadequate responders to anti-TNF therapies resulted in a modeled 38% increase in ACR50 response to first-line biologic therapies. The fraction of patients who achieved an ACR50 response to any therapy (TNFi and others) within the 12-month period was 33% higher in the PrismRA-stratified population than in the unstratified population (59 vs. 44%, respectively). When therapy prescriptions were modeled according to PrismRA results, cost savings were modeled for all financial variables: overall costs (4% decreased total, 19% decreased on ineffective treatments), total biologic drug pharmacy (4% total, 23% ineffective), non-biologic pharmacy (2% total, 19% ineffective), and medical costs (6% total, 19% ineffective). Female sex was the clinical metric that showed the greatest association with inadequate response to anti-TNF therapies (odds ratio 2.42, 95% confidence interval 1.20, 4.88).

CONCLUSIONS

If PrismRA is implemented into routine clinical care as modeled, predicting which RA patients will have an inadequate response to anti-TNF therapies could save > $7 million in overall ineffective healthcare costs per 1000 patients tested and increase targeted DMARD response rates in RA.

Effects of Biological Therapies on Molecular Features of Rheumatoid Arthritis

Rheumatoid arthritis (RA) is an autoimmune and chronic inflammatory disease primarily affecting the joints, and closely related to specific autoantibodies that mostly target modified self-epitopes. Relevant findings in the field of RA pathogenesis have been described. In particular, new insights come from studies on synovial fibroblasts and cells belonging to the innate and adaptive immune system, which documented the aberrant production of inflammatory mediators, oxidative stress and NETosis, along with relevant alterations of the genome and on the regulatory epigenetic mechanisms. In recent years, the advances in the understanding of RA pathogenesis by identifying key cells and cytokines allowed the development of new targeted disease-modifying antirheumatic drugs (DMARDs). These drugs considerably improved treatment outcomes for the majority of patients. Moreover, numerous studies demonstrated that the pharmacological therapy with biologic DMARDs (bDMARDs) promotes, in parallel to their clinical efficacy, significant improvement in all these altered molecular mechanisms. Thus, continuous updating of the knowledge of molecular processes associated with the pathogenesis of RA, and on the specific effects of bDMARDs in the correction of their dysregulation, are essential in the early and correct approach to the treatment of this complex autoimmune disorder. The present review details basic mechanisms related to the physiopathology of RA, along with the core mechanisms of response to bDMARDs.

The 2018 OMERACT Synovial Tissue Biopsy Special Interest Group Report on Standardization of Synovial Biopsy Analysis

OBJECTIVE

The Outcome Measures in Rheumatology (OMERACT) synovial tissue biopsy (STB) working group initiated an international effort to standardize STB analyses, define consensual items to inform treatment choices, and predict responses in rheumatoid arthritis (RA).

METHODS

(1) A Delphi survey to determine items for STB analyses. (2) A multicenter retrospective study of STB data in patients with RA posttreatment with biological disease-modifying antirheumatic drugs.

RESULTS

The Delphi survey identified 18 STB analyses items. Consensus on histological markers was achieved in the OMERACT 2018 SIG.

CONCLUSION

Six markers were identified for examination in a multicenter study designed to define an OMERACT-endorsed set of STB markers to predict responses to treatment.

Expression levels of selected genes can predict individual rheumatoid arthritis patient response to tumor necrosis factor alpha blocker treatment

OBJECTIVES

Rheumatoid arthritis (RA) patients have many therapeutic options; however, tools to predict individual patient response are limited. The Genefron personal diagnostic kit, developed by analyzing large datasets, utilizes selected interferon stimulated gene expressions to predict treatment response. This study evaluates the kit's prediction accuracy of individual RA patients' response to tumor necrosis alpha (TNFα) blockers.

METHODS

A retrospective analysis was performed on RA patients reported in published datasets. A prospective analysis assessed RA patients, before and 3 months after starting a TNFα blocker. Clinical response was evaluated according to EULAR response criteria. Blood samples were obtained before starting treatment and were analyzed utilizing the kit which measures expression levels of selected genes by quantitative real time polymerase chain reaction (PCR). ROC analysis was applied to the published datasets and the prospective data.

RESULTS

The Genefron kit analysis of retrospective data predicted the response to a TNFα blocker in 53 of 61 RA patients (86.8% accuracy). In the prospective analysis, the kit predicted the response in 16 of 18 patients (89% accuracy) achieving a EULAR moderate response, and in 15 of 18 patients achieving a EULAR good response (83.3% accuracy). ROC analysis applied to the two published datasets yielded an AUC of 0.89. ROC analysis applied to the prospective data yielded an AUC of 0.83 (sensitivity - 100%, specificity - 75%) The statistical power obtained in the prospective study was .9.

CONCLUSION

The diagnostic kit predicted the response to TNFα blockers in a high percentage of patients assessed retrospectively or prospectively. This personal kit may guide selection of a suitable biological drug for the individual RA patient.

[Upcoming value of gene expression analysis in rheumatology]

Rheumatoid arthritis (RA) is a chronic inflammatory disease of unknown etiology, which involves disturbance in immune system signaling pathway functions, damage of other tissues, pain and joint destruction. Modern treatment attempts to improve pathophysiological and biochemical mechanisms damaged by the disease. However, due to the RA patient heterogeneity personalized approach to treatment is required; the choice of personalized treatment is complicated by the variability of patient's response to treatment. Gene expression analysis might serve a tool for the disease control and therapy personification for inhibition of inflammation and pain as well as for prevention of joint destruction.

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.

Type I interferon-mediated autoimmune diseases: pathogenesis, diagnosis and targeted therapy

Type I interferons (IFN-Is) are a group of molecules with pleiotropic effects on the immune system forming a crucial link between innate and adaptive immune responses. Apart from their important role in antiviral immunity, IFN-Is are increasingly recognized as key players in autoimmune CTDs such as SLE. Novel therapies that target IFN-I appear effective in SLE in early trials, but effectiveness is related to the presence of IFN-I biomarkers. IFN-I biomarkers may also act as positive or negative predictors of response to other biologics. Despite the high failure rate of clinical trials in SLE, subgroups of patients often respond better. Fully optimizing the potential of these agents is therefore likely to require stratification of patients using IFN-I and other biomarkers. This suggests the unified concept of type I IFN-mediated autoimmune diseases as a grouping including patients with a variety of different traditional diagnoses.

The clinical utility of gene expression examination in rheumatology

Rheumatoid arthritis (RA) is a chronic inflammatory disease with unknown etiology that affects various pathways within the immune system, involves many other tissues and is associated with pain and joint destruction. Current treatments fail to address pathophysiological and biochemical mechanisms involved in joint degeneration and the induction of pain. Moreover, RA patients are extremely heterogeneous and require specific treatments, the choice of which is complicated by the fact that not all patients equally respond to therapy. Gene expression analysis offer tools for patient management and personalization of patient’s care to meet individual needs in controlling inflammation and pain and delaying joint destruction.

Application of omics in predicting anti-TNF efficacy in rheumatoid arthritis

Rheumatoid arthritis (RA) is a systemic autoimmune disease characterized by progressive joint erosion. Tumor necrosis factor (TNF) antagonists are the most widely used biological disease-modifying anti-rheumatic drug in RA. However, there continue to be one third of RA patients who have poor or no response to TNF antagonists. Following consideration of the uncertainty of therapeutic effects and the high price of TNF antagonists, it is worthy to predict the treatment responses before anti-TNF therapy. According to the comparisons between the responders and non-responders to TNF antagonists by omic technologies, such as genomics, transcriptomics, proteomics, and metabolomics, rheumatologists are eager to find significant biomarkers to predict the effect of TNF antagonists in order to optimize the personalized treatment in RA.

Can Synovial Pathobiology Integrate with Current Clinical and Imaging Prediction Models to Achieve Personalized Health Care in Rheumatoid Arthritis?

Although great progress has been made in the past decade toward understanding the pathogenesis of rheumatoid arthritis (RA), clinicians remain some distance from a goal of personalized health care. The capacity to diagnose RA early, predict prognosis, and moreover predict response to biologic therapies has been a research focus for many years. How currently available clinical prediction models can facilitate such goals is reviewed in this article. In addition, the role of current imaging techniques in this regard is also discussed. Finally, the authors review the current literature regarding synovial biomarkers and consider whether integration of synovial pathobiology into clinical prediction algorithms may enhance their predictive value.

Understanding Human Autoimmunity and Autoinflammation Through Transcriptomics

Transcriptomics, the high-throughput characterization of RNAs, has been instrumental in defining pathogenic signatures in human autoimmunity and autoinflammation. It enabled the identification of new therapeutic targets in IFN-, IL-1- and IL-17-mediated diseases. Applied to immunomonitoring, transcriptomics is starting to unravel diagnostic and prognostic signatures that stratify patients, track molecular changes associated with disease activity, define personalized treatment strategies, and generally inform clinical practice. Herein, we review the use of transcriptomics to define mechanistic, diagnostic, and predictive signatures in human autoimmunity and autoinflammation. We discuss some of the analytical approaches applied to extract biological knowledge from high-dimensional data sets. Finally, we touch upon emerging applications of transcriptomics to study eQTLs, B and T cell repertoire diversity, and isoform usage.

Circulating Biomarkers for Predicting Infliximab Response in Rheumatoid Arthritis: A Systematic Bioinformatics Analysis

Background

Infliximab shows good efficacy in treating refractory rheumatoid arthritis (RA). However, many patients responded poorly and related studies were inconsistent in predictive biomarkers. This study aimed to identify circulating biomarkers for predicting infliximab response in RA.

Material/Methods

Public databases of Gene Expression Omnibus (GEO) and ArrayExpress were searched for related microarray datasets, focused on the response to infliximab in RA. All peripheral blood samples were collected before infliximab treatment and gene expression profiles were measured using microarray. Differential genes associated with infliximab efficacy were analyzed. The genes recognized by half of the datasets were regarded as candidate biomarkers and validated by prospective datasets.

Results

Eight microarray datasets were identified with 374 blood samples of RA patients, among which 191 (51.1%) were diagnosed as non-responders in the subsequent infliximab treatment. Five genes (FKBP1A, FGF12, ANO1, LRRC31, and AKR1D1) were associated with the efficacy and recognized by half of the datasets. The 5-gene model showed a good predictive power in random- and prospective-designed studies, with AUC (area under receiver operating characteristic [ROC] curve)=0.963 and 1.000, and it was also applicable at the early phase of treatment (at week 2) for predicting the response at week 14 (AUC=1.000). In the placebo group, the model failed to predict the response (AUC=0.697), indicating the model’s specificity in infliximab treatment.

Conclusions

The model of FKBP1A, FGF12, ANO1, LRRC31, and AKR1D1 in peripheral blood is useful for efficiently predicting the response to infliximab treatment in rheumatoid arthritis.

Integration of known DNA, RNA and protein biomarkers provides prediction of anti-TNF response in rheumatoid arthritis: results from the COMBINE study

OBJECTIVE

In rheumatoid arthritis (RA) several recent efforts have sought to discover means of predicting which patients would benefit from treatment. However, results have been discrepant with few successful replications. Our objective was to build a biobank with DNA, RNA and protein measurements to test the claim that the current state-of-the-art precision medicine will benefit RA patients.

METHODS

We collected 451 blood samples from 61 healthy individuals and 185 RA patients initiating treatment, before treatment initiation and at a 3 month follow-up time. All samples were subjected to high-throughput RNA sequencing, DNA genotyping, extensive proteomics and flow cytometry measurements, as well as comprehensive clinical phenotyping. Literature review identified 2 proteins, 52 single-nucleotide polymorphisms (SNPs) and 72 gene-expression biomarkers that had previously been proposed as predictors of TNF inhibitor response (∆DAS28-CRP).

RESULTS

From these published TNFi biomarkers we found that 2 protein, 2 SNP and 8 mRNA biomarkers could be replicated in the 59 TNF initiating patients. Combining these replicated biomarkers into a single signature we found that we could explain 51% of the variation in ∆DAS28-CRP. This corresponds to a sensitivity of 0.73 and specificity of 0.78 for the prediction of three month ∆DAS28-CRP better than -1.2.

CONCLUSIONS

The COMBINE biobank is currently the largest collection of multi-omics data from RA patients with high potential for discovery and replication. Taking advantage of this we surveyed the current state-of-the-art of drug-response stratification in RA, and identified a small set of previously published biomarkers available in peripheral blood which predicts clinical response to TNF blockade in this independent cohort.

The type I interferon signature in leukocyte subsets from peripheral blood of patients with early arthritis: a major contribution by granulocytes

Background

The type I interferon (IFN) signature in rheumatoid arthritis (RA) has shown clinical relevance in relation to disease onset and therapeutic response. Identification of the cell type(s) contributing to this IFN signature could provide insight into the signature’s functional consequences. The aim of this study was to investigate the contribution of peripheral leukocyte subsets to the IFN signature in early arthritis.

Methods

Blood was collected from 26 patients with early arthritis and lysed directly or separated into peripheral blood mononuclear cells (PBMCs) and polymorphonuclear granulocytes (PMNs). PBMCs were sorted into CD4⁺ T cells, CD8⁺ T cells, CD19⁺ B cells, and CD14⁺ monocytes by flow cytometry. Messenger RNA expression of three interferon response genes (IRGs RSAD2, IFI44L, and MX1) and type I interferon receptors (IFNAR1 and IFNAR2) was determined in whole blood and blood cell subsets by quantitative polymerase chain reaction. IRG expression was averaged to calculate an IFN score for each sample.

Results

Patients were designated “IFN^high” (n = 8) or “IFN^low” (n = 18) on the basis of an IFN score cutoff in whole peripheral blood from healthy control subjects. The difference in IFN score between IFN^high and IFN^low patients was remarkably large for the PMN fraction (mean 25-fold) compared with the other subsets (mean 6- to 9-fold), indicating that PMNs are the main inducers of IRGs. Moreover, the relative contribution of the PMN fraction to the whole-blood IFN score was threefold higher than expected from its abundance in blood (p = 0.008), whereas it was three- to sixfold lower for the other subsets (p ≤ 0.063), implying that the PMNs are most sensitive to IFN signaling. Concordantly, IFNAR1 and IFNAR2 were upregulated compared with healthy controls selectively in patient PMNs (p ≤ 0.0077) but not in PBMCs.

Conclusions

PMNs are the main contributors to the whole-blood type I IFN signature in patients with early arthritis, which seems due to increased sensitivity of these cells to type I IFN signaling. Considering the well-established role of neutrophils in the pathology of arthritis, this suggests a role of type I IFN activity in the disease as well.

Electronic supplementary material

The online version of this article (doi:10.1186/s13075-016-1065-3) contains supplementary material, which is available to authorized users.

A novel method predicting clinical response using only background clinical data in RA patients before treatment with infliximab

OBJECTIVES

The aim of the present study was to generate a novel method for predicting the clinical response to infliximab (IFX), using a machine-learning algorithm with only clinical data obtained before the treatment in rheumatoid arthritis (RA) patients.

METHODS

We obtained 32 variables out of the clinical data on the patients from two independent hospitals. Next, we selected both clinical parameters and machine-learning algorithms and decided the candidates of prediction method. These candidates were verified by clinical variables on different patients from two other hospitals. Finally, we decided the prediction method to achieve the highest score.

RESULTS

The combination of multilayer perceptron algorithm (neural network) and nine clinical parameters shows the best accuracy performance. This method could predict the good or moderate response to IFX with 92% accuracy. The sensitivity of this method was 96.7%, while the specificity was 75%.

CONCLUSIONS

We have developed a novel method for predicting the clinical response using only background clinical data in RA patients before treatment with IFX. Our method for predicting the response to IFX in RA patients may have advantages over the other previous methods in several points including easy usability, cost-effectiveness and accuracy.

The type I interferons: Basic concepts and clinical relevance in immune-mediated inflammatory diseases

There is increasing scientific and clinical interest in elucidating the biology of type I Interferons, which began approximately 60 years ago with the concept of "viral interference", a property that reduces the ability of a virus to infect cells. Although our understanding of the multiple cellular and molecular functions of interferons has advanced significantly, much remains to be learned and type I Interferons remain an active and fascinating area of inquiry. In this review, we cover some general aspects of type I interferon genes, with emphasis on interferon-alpha, and various aspects of molecular mechanisms triggered by type I interferons and toll-like receptor signaling by the Janus activated kinase/signal transducer activation of transcription (JAK-STAT) pathway and interferon regulatory factor pathway. We will also describe the role of type I interferons in autoimmune and inflammatory diseases, and its potential use as therapeutic agent.

Blood-based identification of non-responders to anti-TNF therapy in rheumatoid arthritis

BACKGROUND

Faced with an increasing number of choices for biologic therapies, rheumatologists have a critical need for better tools to inform rheumatoid arthritis (RA) disease management. The ability to identify patients who are unlikely to respond to first-line biologic anti-TNF therapies prior to their treatment would allow these patients to seek alternative therapies, providing faster relief and avoiding complications of disease.

METHODS

We identified a gene expression classifier to predict, pre-treatment, which RA patients are unlikely to respond to the anti-TNF infliximab. The classifier was trained and independently evaluated using four published whole blood gene expression data sets, in which RA patients (n = 116 = 44 + 15 + 30 + 27) were treated with infliximab, and their response assessed 14-16 months post treatment according to the European League Against Rheumatism (EULAR) response criteria. For each patient, prior knowledge was used to group gene expression measurements into disease-relevant biological signaling mechanisms that were used as the input features for regularized logistic regression.

RESULTS

The classifier produced a substantial enrichment of non-responders (59 %, given by the cross validated test precision) compared to the full population (27 % non-responders), while identifying nearly a third of non-responders. Given this classifier performance, treatment of predicted non-responders with alternative biologics would decrease their chance of non-response by between a third and a half, substantially improving their odds of effective treatment and stemming further disease progression. The classifier consisted of 18 signaling mechanisms, which together indicated that higher inflammatory signaling mediated by TNF and other cytokines was present pre-treatment in the blood of patients who responded to infliximab treatment. In contrast, non-responders were classified by relatively higher levels of specific metabolic activities in the blood prior to treatment.

CONCLUSIONS

We were able to successfully produce a classifier to identify a population of RA patients significantly enriched in anti-TNF non-responders across four different patient cohorts. Additional prospective studies are needed to validate and refine the classifier for clinical use.

Evaluation of Minimally Invasive, Ultrasound-guided Synovial Biopsy Techniques by the OMERACT Filter--Determining Validation Requirements

OBJECTIVE

Because limited data currently support the clinical utility of peripherally expressed biomarkers in guiding treatment decisions for patients with rheumatoid arthritis, the search has turned to the disease tissue. The strategic aim of the Outcome Measures in Rheumatology (OMERACT) synovitis working group over the years has been to develop novel diagnostic and prognostic synovial biomarkers. A critical step in this process is to refine and validate minimally invasive, technically simple, robust techniques to sample synovial tissue, for use both in clinical trials and routine clinical practice. The objective of the synovitis working group (SWG) at OMERACT 12 (2014) was to examine whether recently developed ultrasound (US)-guided synovial biopsy techniques could be validated according to the OMERACT filter for future clinical use recommendation.

METHODS

The SWG examined whether current data reporting US-guided synovial biopsy of both large and small joints addressed the OMERACT filters of truth, discrimination, and feasibility.

RESULTS

There are currently limited data examining the performance of US-guided synovial biopsy, mainly from observational studies. Thus, it remains critical to evaluate its performance, within the clinical trials context, against the current gold standard of arthroscopic biopsy, with particular reference to: (1) synovial tissue yield, (2) capacity to determine treatment response as measured by a validated synovial biomarker, and (3) tolerability of the procedure.

CONCLUSION

We summarize the discrete work packages agreed to as requirements to validate US-guided synovial biopsy and therefore lead to a global consensus on the use of synovial biopsy for research and clinical practice.

Effect of prednisone on type I interferon signature in rheumatoid arthritis: consequences for response prediction to rituximab

Introduction

Elevated type I interferon (IFN) response gene (IRG) expression has proven clinical relevance in predicting rituximab non-response in rheumatoid arthritis (RA). Interference between glucocorticoids (GCs) and type I IFN signaling has been demonstrated in vitro. Since GC use and dose are highly variable among patients before rituximab treatment, the aim of this study was to determine the effect of GC use on IRG expression in relation to rituximab response prediction in RA.

Methods

In two independent cohorts of 32 and 182 biologic-free RA patients and a third cohort of 40 rituximab-starting RA patients, peripheral blood expression of selected IRGs was determined by microarray or quantitative real-time polymerase chain reaction (qPCR), and an IFN-score was calculated. The baseline IFN-score was tested for its predictive value towards rituximab response in relation to GC use using receiver operating characteristics (ROC) analysis in the rituximab cohort. Patients with a decrease in disease activity score (∆DAS28) >1.2 after 6 months of rituximab were considered responders.

Results

We consistently observed suppression of IFN-score in prednisone users (PREDN⁺) compared to non-users (PREDN⁻). In the rituximab cohort, analysis on PREDN⁻ patients (n = 13) alone revealed improved prediction of rituximab non-response based on baseline IFN-score, with an area under the curve (AUC) of 0.975 compared to 0.848 in all patients (n = 40). Using a group-specific IFN-score cut-off for all patients and PREDN⁻ patients alone, sensitivity increased from 41% to 88%, respectively, combined with 100% specificity.

Conclusions

Because of prednisone-related suppression of IFN-score, higher accuracy of rituximab response prediction was achieved in PREDN⁻ patients. These results suggest that the IFN-score-based rituximab response prediction model could be improved upon implementation of prednisone use.

MRP8/14 serum levels as a strong predictor of response to biological treatments in patients with rheumatoid arthritis

BACKGROUND

One-third of rheumatoid arthritis (RA) patients treated with biological therapy show lack of response. The use of predictive biomarkers to identify responders to treatment may provide guidance in optimising treatment strategies and reduce unnecessary side effects and costs.

OBJECTIVE

To test the ability of myeloid-related proteins (MRP)8/14 protein complexes, an endogenous TLR-4 receptor agonist, to predict and monitor response to biologics in RA patients.

METHODS

170 RA patients treated with adalimumab (n=86), infliximab (n=60) or rituximab (n=24) were categorised into clinical responders (n=123) and non-responders (n=47). MRP8/14 serum complexes were measured at baseline, and 4 and 16 weeks after initiation of treatment and related to response outcome.

RESULTS

Before initiation of treatment, responders showed significantly higher MRP8/14 protein complex levels compared with non-responders in each prospective cohort (p=0.010, p=0.001 and p<0.001, respectively). Logistic regression analysis showed that having high MRP8/14 baseline levels increased the odds of being a responder by 3.3 up to 55. In responders to adalimumab or infliximab treatment, MRP8/14 levels decreased after 4 weeks of treatment by 46% and 60% and after 16 weeks by 61% and 68%, respectively. In contrast, MRP8/14 levels were stable in non-responders. In patients treated with rituximab, MRP8/14 levels decreased by 59% after 16 weeks in responders and increased by 89% after 16 weeks in non-responders.

CONCLUSION

Serum concentrations of MRP8/14 protein complex are a promising biomarker to predict response to biological therapy in active RA patients at baseline and could be used to monitor response to treatment across different mechanisms of action.

Type I IFNs as biomarkers in rheumatoid arthritis: towards disease profiling and personalized medicine

RA (rheumatoid arthritis) is a chronic rheumatic condition hallmarked by joint inflammation and destruction by self-reactive immune responses. Clinical management of RA patients is often hampered by its heterogeneous nature in both clinical presentation and outcome, thereby highlighting the need for new predictive biomarkers. In this sense, several studies have recently revealed a role for type I IFNs (interferons), mainly IFNα, in the pathogenesis of a subset of RA patients. Genetic variants associated with the type I IFN pathway have been linked with RA development, as well as with clinical features. Moreover, a role for IFNα as a trigger for RA development has also been described. Additionally, a type I IFN signature has been associated with the early diagnosis of RA and clinical outcome prediction in patients undergoing biological drug treatment, two challenging issues for decision-making in the clinical setting. Moreover, these cytokines have been related to endothelial damage and vascular repair failure in different autoimmune disorders. Therefore, together with chronic inflammation and disease features, they could probably account for the increased cardiovascular disease morbidity and mortality of these patients. The main aim of the present review is to provide recent evidence supporting a role for type I IFNs in the immunopathology of RA, as well as to analyse their possible role as biomarkers for disease management.

Pre-treatment whole blood gene expression is associated with 14-week response assessed by dynamic contrast enhanced magnetic resonance imaging in infliximab-treated rheumatoid arthritis patients

Approximately 30% of rheumatoid arthritis patients achieve inadequate response to anti-TNF biologics. Attempts to identify molecular biomarkers predicting response have met with mixed success. This may be attributable, in part, to the variable and subjective disease assessment endpoints with large placebo effects typically used to classify patient response. Sixty-one patients with active RA despite methotrexate treatment, and with MRI-documented synovitis, were randomized to receive infliximab or placebo. Blood was collected at baseline and genome-wide transcription in whole blood was measured using microarrays. The primary endpoint in this study was determined by measuring the transfer rate constant (K_trans) of a gadolinium-based contrast agent from plasma to synovium using MRI. Secondary endpoints included repeated clinical assessments with DAS28(CRP), and assessments of osteitis and synovitis by the RAMRIS method. Infliximab showed greater decrease from baseline in DCE-MRI K_trans of wrist and MCP at all visits compared with placebo (P<0.001). Statistical analysis was performed to identify genes associated with treatment-specific 14-week change in K_trans. The 256 genes identified were used to derive a gene signature score by averaging their log expression within each patient. The resulting score correlated with improvement of K_trans in infliximab-treated patients and with deterioration of K_trans in placebo-treated subjects. Poor responders showed high expression of activated B-cell genes whereas good responders exhibited a gene expression pattern consistent with mobilization of neutrophils and monocytes and high levels of reticulated platelets. This gene signature was significantly associated with clinical response in two previously published whole blood gene expression studies using anti-TNF therapies. These data provide support for the hypothesis that anti-TNF inadequate responders comprise a distinct molecular subtype of RA characterized by differences in pre-treatment blood mRNA expression. They also highlight the importance of placebo controls and robust, objective endpoints in biomarker discovery.

Trial Registration: ClinicalTrials.gov NCT01313520

Gene expression analysis in RA: towards personalized medicine

Gene expression has recently been at the forefront of advance in personalized medicine, notably in the field of cancer and transplantation, providing a rational for a similar approach in rheumatoid arthritis (RA). RA is a prototypic inflammatory autoimmune disease with a poorly understood etiopathogenesis. Inflammation is the main feature of RA; however, many biological processes are involved at different stages of the disease. Gene expression signatures offer management tools to meet the current needs for personalization of RA patients' care. This review analyses currently available information with respect to RA diagnostic, prognostic and prediction of response to therapy with a view to highlight the abundance of data, whose comparison is often inconclusive due to the mixed use of material source, experimental methodologies and analysis tools, reinforcing the need for harmonization if gene expression signatures are to become a useful clinical tool in personalized medicine for RA patients.

Pharmacogenetics and pharmacogenomics in rheumatology

Pharmacogenetics and pharmacogenomics deal with possible associations of a single genetic polymorphism or those of multiple gene profiles with responses to drugs. In rheumatology, genes and gene signatures may be associated with altered efficacy and/or safety of anti-inflammatory drugs, disease-modifying antirheumatic drugs (DMARDs) and biologics. In brief, genes of cytochrome P450, other enzymes involved in drug metabolism, transporters and some cytokines have been associated with responses to and toxicity of non-steroidal anti-inflammatory drugs, corticosteroids and DMARDs. The efficacy of biologics may be related to alterations in cytokine, chemokine and FcγR genes. Numerous studies reported multiple genetic signatures in association with responses to biologics; however, data are inconclusive. More, focused studies carried out in larger patient cohorts, using pre-selected genes, may be needed in order to determine the future of pharmacogenetics and pharmacogenomics as tools for personalized medicine in rheumatology.

Genomic and systems approaches to translational biomarker discovery in immunological diseases

The high failure rate of new therapeutic mechanisms tested in clinical development has spurred an upsurge in research dedicated to discovering biomarker readouts that can improve decision-making. Increasingly, systems biology and genomic technologies, such as transcriptional profiling, are being leveraged to aid in the discovery of biomarker readouts. For inflammatory and immunological diseases, such as rheumatoid arthritis (RA) and asthma, progress has been made in developing biomarkers to monitor disease activity, prediction of response to therapy, and pharmacodynamic (PD) measurements. In this review, we discuss recent successes and challenges in these endeavors, highlighting the importance of human clinical studies of standard-of-care treatments in control subjects and patients with disease as the most direct path toward identifying useful translational biomarkers for clinical development.

Targeting monocytes/macrophages in the treatment of rheumatoid arthritis

Biotherapies have revolutionized the treatment of RA. However, much work is needed to understand all the mechanisms of these biotherapies, and alternatives are needed to circumvent adverse effects and the high cost of these long-lasting treatments. In this article we outline some of the approaches we have used to target monocytes/macrophages as major components of inflammation and bone homeostasis. We also discuss how anti-TNF-α antibodies target monocytes/macrophages in the complex mechanisms contributing to inhibition of inflammation.

Detection of gene expression signatures related to underlying disease and treatment in rheumatoid arthritis patients

OBJECTIVES

Gene expression signatures can provide an unbiased view into the molecular changes underlying biologically and medically interesting phenotypes. We therefore initiated this study to identify signatures that would be of utility in studying rheumatoid arthritis (RA).

METHODS

We used microarray profiling of peripheral blood mononuclear cells (PBMCs) in 30 RA patients to assess the effect of different biologic agent (biologics) treatments and to quantify the degree of a type-I interferon (IFN) signature in these patients. A numeric score was derived for the quantification step and applied to patients with RA. To further characterize the IFN response in our cohort, we employed type-I IFN treatment of PBMCs in vitro and in reporter assays.

RESULTS

Profiling identified a subset of RA patients with upregulation of type-I IFN-regulated transcripts, thereby corroborating previous reports showing RA to be heterogeneous for an IFN component. A comparison of individuals currently untreated with a biologic with those treated with infliximab, tocilizumab, or abatacept suggested that each biologic induces a specific gene signature in PBMCs.

CONCLUSIONS

It is possible to observe signs of type-I IFN pathway activation in a subset of clinically active RA patients without C-reactive protein elevation. Furthermore, biologics-specific gene signatures in patients with RA indicate that looking for a biologic-specific response pattern may be a potential future tool for predicting individual patient response.

Peripheral blood gene expression and IgG glycosylation profiles as markers of tocilizumab treatment in rheumatoid arthritis

OBJECTIVE

Tocilizumab, a humanized anti-interleukin-6 receptor monoclonal antibody, has recently been approved as a biological therapy for rheumatoid arthritis (RA) and other diseases. It is not known if there are characteristic changes in gene expression and immunoglobulin G glycosylation during therapy or in response to treatment.

METHODS

Global gene expression profiles from peripheral blood mononuclear cells of 13 patients with RA and active disease at Week 0 (baseline) and Week 4 following treatment were obtained together with clinical measures, serum cytokine levels using ELISA, and the degree of galactosylation of the IgG N-glycan chains. Gene sets separating responders and nonresponders were tested using canonical variates analysis. This approach also revealed important gene groups and pathways that differentiate responders from nonresponders.

RESULTS

Fifty-nine genes showed significant differences between baseline and Week 4 and thus correlated with treatment. Significantly, 4 genes determined responders after correction for multiple testing. Ten of the 12 genes with the most significant changes were validated using real-time quantitative polymerase chain reaction. An increase in the terminal galactose content of N-linked glycans of IgG was observed in responders versus nonresponders, as well as in treated samples versus samples obtained at baseline.

CONCLUSION

As a preliminary report, gene expression changes as a result of tocilizumab therapy in RA were examined, and gene sets discriminating between responders and nonresponders were found and validated. A significant increase in the degree of galactosylation of IgG N-glycans in patients with RA treated with tocilizumab was documented.

Validation study of existing gene expression signatures for anti-TNF treatment in patients with rheumatoid arthritis

So far, there are no means of identifying rheumatoid arthritis (RA) patients who will fail to respond to tumour necrosis factor blocking agents (anti-TNF), prior to treatment. We set out to validate eight previously reported gene expression signatures predicting therapy outcome. Genome-wide expression profiling using Affymetrix GeneChip Exon 1.0 ST arrays was performed on RNA isolated from whole blood of 42 RA patients starting treatment with infliximab or adalimumab. Clinical response according to EULAR criteria was determined at week 14 of therapy. Genes that have been reported to be associated with anti-TNF treatment were extracted from our dataset. K-means partition clustering was performed to assess the predictive value of the gene-sets. We performed a hypothesis-driven analysis of the dataset using eight existing gene sets predictive of anti-TNF treatment outcome. The set that performed best reached a sensitivity of 71% and a specificity of 61%, for classifying the patients in the current study. We successfully validated one of eight previously reported predictive expression profile. This replicated expression signature is a good starting point for developing a prediction model for anti-TNF treatment outcome that can be used in a daily clinical setting. Our results confirm that gene expression profiling prior to treatment is a useful tool to predict anti-TNF (non) response.

The triad of success in personalised medicine: pharmacogenomics, biotechnology and regulatory issues from a Central European perspective

The population of the world has recently passed the 7 billion milestone and as the cost of human genome sequencing is rapidly declining, sequence data of billions of people should be accessible much sooner than anyone would have predicted 10 years ago. This will form the basis of personalised medicine. However it is still not clear, even in principle, whether these data, combined with data of the expression of one's genome in various cells and tissues relevant to different diseases, could be used effectively in clinical medicine and healthcare, or in predicting responses to different therapies. Therefore this is an important issue which needs to be addressed before more resources are wasted on less than informative studies and surveys simply because technologies exist. As a typical example, we have selected and summarise here key studies from the biomedical literature that focus on gene expression profiling of the response to biologic therapies in peripheral blood and biopsy samples in autoimmune diseases such as rheumatoid arthritis, spondylarthropathy, inflammatory bowel diseases and psoriasis. We also present the state of the biotechnology market from a European perspective, discuss how spin-offs leverage the power of genomic technologies and describe how they might contribute to personalised medicine. As ethical, legal and social issues are essential in the area of genomics, we analysed these aspects and present here the European situation with a special focus on Hungary. We propose that the synergy of these three issues: pharmacogenomics, biotechnology and regulatory issues should be considered a triad necessary to succeed in personalised medicine.

Revolutionary change in rheumatoid arthritis management with biological therapy

Biological agents targeting a specific molecule have extraordinarily fine specificity and powerful functional capabilities. By the introduction of biological therapy, management of rheumatoid arthritis has undergone a revolution and a paradigm shift. In this review, I will summarize the role in the pathogenesis of rheumatoid arthritis of the molecules targeted by biological agents. Providing evidence obtained in clinical trials and investigator-initiated clinical studies in Japan, the effectiveness and safety of biological therapy in rheumatoid arthritis are discussed. Finally, studies aiming at a personalized strategy with biological agents are listed and the future perspectives toward tailor-made medicine in the field of rheumatology are discussed.

Correlation of antinuclear antibody and anti-double-stranded DNA antibody with clinical response to infliximab in patients with rheumatoid arthritis: a retrospective clinical study

INTRODUCTION

The induction of antinuclear antibodies (ANAs) or anti-double-stranded (ds) -DNA antibodies (Abs) after infliximab (IFX) therapy in rheumatoid arthritis (RA) is a well-known phenomenon, but the correlation of such Abs with the clinical response to IFX has not yet been determined. The aims of this retrospective observational study were to examine the prevalence of positive ANA and anti-ds-DNA Abs before and after IFX therapy in patients with RA and to investigate whether an increased titer of such Abs is associated with the clinical efficacy of IFX.

METHODS

One hundred eleven RA patients who had received IFX were studied. ANA (indirect immunofluorescence with HEp-2 cells) and anti-ds-DNA Abs (Farr assay) results were examined before and after IFX therapy.

RESULTS

The overall clinical response assessed by EULAR response criteria was as follows: good response in 55%, including remission in 38%; moderate response in 18%; and no response (NOR) in 27%. The positivity of ANA (≥ 1:160) and anti-ds-DNA Abs significantly increased from 25% to 40% (P = 0.03) and from 3% to 26% (P < 0.001) after IFX, respectively. EULAR response differed significantly according to the ANA titer before IFX (P = 0.001), and the efficacy of IFX became worse as the ANA titer before starting IFX increased. Furthermore, the differences in the clinical response of the ANA titer before IFX ≤ 1:80 and ≥ 1:160 were significant (good, moderate, and no response were 66%, 9%, and 25% in ≤ 1:80 group versus 26%, 33%, 41% in ≥ 1:160 group, respectively; P < 0.001). In 13 patients whose ANA had increased after IFX, 10 showed NOR, only one showed a good response, and none reached remission. These clinical responses were significantly different from ANA no-change patients. In 21 patients with positive anti-ds-DNA Abs after IFX, 16 showed NOR, only two showed a good response, and none reached remission.

CONCLUSIONS

The present study suggests that the ANA titer before starting IFX predicts the clinical response to IFX. The increased titers of ANA or anti-ds-DNA Abs after IFX may be useful markers of NOR.

Optimising treatment in rheumatoid arthritis: a review of potential biological markers of response

Following a greater understanding of the pathogenesis of rheumatoid arthritis (RA), the treatment of this chronic disease has improved with the availability of biological agents targeting key molecules. Despite this, initial treatment produces an inadequate response in many patients and guidance on the optimal treatment for these patients is needed. Research in specific patient populations aims to define predictive biomarkers of response to identify those patients most likely to benefit from treatment with specific agents. Although there have been conflicting results from studies of various genetic markers, single nucleotide polymorphisms in the tumour necrosis factor (TNF) -308 promoter region may play a role in response to specific TNF inhibitors. Microarray analysis of mRNA expression levels has identified unique sets of genes with differentially regulated expression in responders compared with non-responders to the TNF inhibitor infliximab. Of the various protein biomarkers studied, rheumatoid factor and/or anticitrullinated protein autoantibodies may have a future role in predicting response or guiding the order in which to use biological agents. Further research is needed with larger, well-designed studies to clarify the current understanding on the role of biomarkers in predicting treatment response in RA to help guide clinical decision-making. Individualised treatment has the potential to improve the therapeutic outcomes for patients.

Patients with systemic lupus erythematosus, myositis, rheumatoid arthritis and scleroderma share activation of a common type I interferon pathway

OBJECTIVE

To characterise activation of the type I interferon (IFN) pathway in patients with systemic lupus erythematosus (SLE), dermatomyositis (DM), polymyositis (PM), rheumatoid arthritis (RA) and systemic scleroderma (SSc) and to evaluate the potential to develop a molecular diagnostic tool from the peripheral blood that reflects this activation in disease-affected tissues.

METHODS

Overexpressed transcripts were identified in the whole blood (WB) of 262 patients with SLE, 44 with DM, 33 with PM, 28 with SSc and 89 with RA and compared with 24 healthy subjects using Affymetrix microarrays. A five gene type I IFN signature was assessed in these subjects to identify subpopulations showing both activation and concordance of the type I IFN pathway in the peripheral blood and disease-affected tissues of each disease and to correlate activation of this pathway in the WB with clinical measurements.

RESULTS

A common set of 36 type I IFN inducible transcripts were identified among the most overexpressed in the WB of all subjects. Significant activation of the type I IFN pathway in subgroups of each of the five diseases studied was observed. Baseline disease activity measurements correlated with a type I IFN gene signature in the WB of subjects with SLE, PM and SSc, as did various serum autoantibody levels in subjects with SLE and DM. This signature was also well correlated between disease-affected tissue and WB in subjects with SLE, DM, PM and SSc.

CONCLUSIONS

The results indicate that the type I IFN pathway is activated in patient subsets of five rheumatic diseases and suggest that these subsets may benefit from anti-IFN therapy.