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

A Machine Learning Approach for Prediction of CDAI Remission with TNF Inhibitors: A Concept of Precision Medicine from the FIRST Registry

INTRODUCTION

This study aimed to develop low-cost models using machine learning approaches predicting the achievement of Clinical Disease Activity Index (CDAI) remission 6 months after initiation of tumor necrosis factor inhibitors (TNFi) as primary biologic/targeted synthetic disease-modifying antirheumatic drugs (b/tsDMARDs) for rheumatoid arthritis (RA).

METHODS

Data of patients with RA initiating TNFi as first b/tsDMARD after unsuccessful methotrexate treatment were collected from the FIRST registry (August 2003 to October 2022). Baseline characteristics and 6-month CDAI were collected. The analysis used various machine learning approaches including logistic regression with stepwise variable selection, decision tree, support vector machine, and lasso logistic regression (Lasso), with 48 factors accessible in routine clinical practice for the prediction model. Robustness was ensured by k-fold cross validation.

RESULTS

Among the approaches tested, Lasso showed the advantages in predicting CDAI remission: with a mean area under the curve 0.704, sensitivity 61.7%, and specificity 69.9%. Predicted TNFi responders achieved CDAI remission at an average rate of 53.2%, while only 26.4% of predicted TNFi non-responders achieved remission. Encouragingly, the models generated relied solely on patient-reported outcomes and quantitative parameters, excluding subjective physician input.

CONCLUSIONS

While external cohort validation is warranted for broader applicability, this study highlights the potential for a low-cost predictive model to predict CDAI remission following TNFi treatment. The approach of the study using only baseline data and 6-month CDAI measures, suggests the feasibility of establishing regional cohorts to generate low-cost models tailored to specific regions or institutions. This may facilitate the application of regional/in-house precision medicine strategies in RA management.

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.

A personalized network framework reveals predictive axis of anti-TNF response across diseases

Personalized treatment of complex diseases has been mostly predicated on biomarker identification of one drug-disease combination at a time. Here, we use a computational approach termed Disruption Networks to generate a data type, contextualized by cell-centered individual-level networks, that captures biology otherwise overlooked when performing standard statistics. This data type extends beyond the "feature level space", to the "relations space", by quantifying individual-level breaking or rewiring of cross-feature relations. Applying Disruption Networks to dissect high-dimensional blood data, we discover and validate that the RAC1-PAK1 axis is predictive of anti-TNF response in inflammatory bowel disease. Intermediate monocytes, which correlate with the inflammatory state, play a key role in the RAC1-PAK1 responses, supporting their modulation as a therapeutic target. This axis also predicts response in rheumatoid arthritis, validated in three public cohorts. Our findings support blood-based drug response diagnostics across immune-mediated diseases, implicating common mechanisms of non-response.

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.

Peripheral blood cellular dynamics of rheumatoid arthritis treatment informs about efficacy of response to disease modifying drugs

Rheumatoid arthritis (RA) is an autoimmune disease characterized by systemic inflammation and is mediated by multiple immune cell types. In this work, we aimed to determine the relevance of changes in cell proportions in peripheral blood mononuclear cells (PBMCs) during the development of disease and following treatment. Samples from healthy blood donors, newly diagnosed RA patients, and established RA patients that had an inadequate response to MTX and were about to start tumor necrosis factor inhibitors (TNFi) treatment were collected before and after 3 months of treatment. We used in parallel a computational deconvolution approach based on RNA expression and flow cytometry to determine the relative cell-type frequencies. Cell-type frequencies from deconvolution of gene expression indicate that monocytes (both classical and non-classical) and CD4⁺ cells (T_h1 and T_h2) were increased in RA patients compared to controls, while NK cells and B cells (naïve and mature) were significantly decreased in RA patients. Treatment with MTX caused a decrease in B cells (memory and plasma cell), and a decrease in CD4 T_h cells (T_h1 and T_h17), while treatment with TNFi resulted in a significant increase in the population of B cells. Characterization of the RNA expression patterns found that most of the differentially expressed genes in RA subjects after treatment can be explained by changes in cell frequencies (98% and 74% respectively for MTX and TNFi).

Predictors of response of rituximab in rheumatoid arthritis by weighted gene co-expression network analysis

PURPOSE

The purpose of this study was to identify a biomarker that can predict the efficacy of rituximab (RTX) in the treatment of rheumatoid arthritis (RA) patients.

METHODS

Utilized weighted gene co-expression network analysis (WGCNA) and LASSO regression analysis of whole blood transcriptome data (GSE15316 and GSE37107) related to RTX treatment for RA from the GEO database, the critical modules, and key genes related to the efficacy of RTX treatment for RA were found. The biological functions were further explored through enrichment analysis. The area under the ROC curve (AUC) was validated using the GSE54629 dataset.

RESULTS

WGCNA screened 71 genes for a dark turquoise module that were correlated with the efficacy of RTX treatment for RA (r = 0.42, P < 0.05). Through the calculation of gene significance (GS) and module membership (MM), 12 important genes were identified; in addition, 21 important genes were screened by the LASSO regression model; two key genes were obtained from the intersection between the important genes. Then, BANK1 (AUC = 0.704, P < 0.05) was identified as a potential biomarker to predict the efficacy of RTX treatment for RA by ROC curve evaluation of the treatment and validation groups. BANK1 gene expression was significantly decreased after RTX treatment, and a statistically significant difference was found (log FC =  - 2.08, P < 0.05). Immune cell infiltration analysis revealed that the infiltration of CD4 + T cell memory subset was increased in the group with high BANK1 expression, and a statistically significant difference was found (P < 0.05).

CONCLUSIONS

BANK1 can be used as a potential biomarker to predict the response of RTX treatment in RA patients. Key Points • Identifying the hub genes BANK1 as a potential biomarker to predict the response of RTX treatment in RA patients and confirming it in validation data. • Using the WGCNA approach and LASSO analyses to identify the BANK1 in a data set consisting of two GEO data merged and assessing the correlations between BANK1 and immune infiltration by CIBERSORT algorithm.

Synovial Inflammatory Pathways Characterize Anti-TNF-Responsive Rheumatoid Arthritis Patients

OBJECTIVE

This study was undertaken to understand the mechanistic basis of response to anti-tumor necrosis factor (anti-TNF) therapies and to determine whether transcriptomic changes in the synovium are reflected in peripheral protein markers.

METHODS

Synovial tissue from 46 rheumatoid arthritis (RA) patients was profiled with RNA sequencing before and 12 weeks after treatment with anti-TNF therapies. Pathway and gene signature analyses were performed on RNA expression profiles of synovial biopsies to identify mechanisms that could discriminate among patients with a good response, a moderate response, or no response, according to the American College of Rheumatology (ACR)/EULAR response criteria. Serum proteins encoded by synovial genes that were differentially expressed between ACR/EULAR response groups were measured in the same patients.

RESULTS

Gene signatures predicted which patients would have good responses, and pathway analysis identified elevated immune pathways, including chemokine signaling, Th1/Th2 cell differentiation, and Toll-like receptor signaling, uniquely in good responders. These inflammatory pathways were correspondingly down-modulated by anti-TNF therapy only in good responders. Based on cell signature analysis, lymphocyte, myeloid, and fibroblast cell populations were elevated in good responders relative to nonresponders, consistent with the increased inflammatory pathways. Cell signatures that decreased following anti-TNF treatment were predominately associated with lymphocytes, and fewer were associated with myeloid and fibroblast populations. Following anti-TNF treatment, and only in good responders, several peripheral inflammatory proteins decreased in a manner that was consistent with corresponding synovial gene changes.

CONCLUSION

Collectively, these data suggest that RA patients with robust responses to anti-TNF therapies are characterized at baseline by immune pathway activation, which decreases following anti-TNF treatment. Understanding mechanisms that define patient responsiveness to anti-TNF treatment may assist in development of predictive markers of patient response and earlier treatment options.

-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.

Improvement in clinical disease activity index when treatment selection is informed by the tumor necrosis factor-ɑ inhibitor molecular signature response classifier: analysis from the study to accelerate information of molecular signatures in rheumatoid arthritis

BACKGROUND

A blood-based molecular signature response classifier (MSRC) predicts non-response to tumor necrosis factor-ɑ inhibitors (TNFi) in rheumatoid arthritis (RA).

RESEARCH DESIGN AND METHODS

This is an interim analysis of data collected in the Study to Accelerate Information of Molecular Signatures (AIMS) in RA from patients who received the MSRC test between September 2020 and November 2021. Absolute changes in clinical disease activity index (CDAI) scores from baseline were evaluated at 12 weeks (n = 470) and 24 weeks (n = 274).

RESULTS

Predicted TNFi non-responders who received a biologic or targeted synthetic disease-modifying antirheumatic drug (b/tsDMARD) with an alternative mechanism of action (altMOA) experienced up to 1.8-fold greater improvements in CDAI scores than those treated with a TNFi (12 weeks: 12.2 vs 8.0; p-value = 0.083; 24 weeks: 14.2 vs 7.8 p-value = 0.009). In patients with a molecular signature of non-response to TNFi in high disease activity at baseline, this corresponded to 43.2% relative improvement in achieving a lower CDAI disease activity level when likely TNFi non-responders were treated with a non-TNFi therapy (38.9% vs 55.7%). Commensurate improvements in efficiency of spend are expected when TNFi are avoided in favor of altMOA.

CONCLUSIONS

RA treatment selection informed by MSRC test results improves clinical outcomes in real-world care and offers improvements in efficiency of healthcare spending.

Uncovering Novel Pre-Treatment Molecular Biomarkers for Anti-TNF Therapeutic Response in Patients with Crohn’s Disease

Neutralising monoclonal antibodies for tumour necrosis factor (TNF) has been widely used to treat Crohn’s disease (CD) in clinical practice. However, differential individual response necessitates a therapeutic response assessment of anti-TNF agents in CD patients for optimizing therapeutic strategy. We aimed to predict anti-TNF therapy response in CD patients using transcriptome analyses. Transcriptome analyses were performed using data from the Gene Expression Omnibus, GeneCards, and Human Protein Atlas databases. The significantly mitigated biological functions associated with anti-TNF therapy resistance in CD patients encompassed immune pathways, including Interleukin-17 (IL-17) signaling, cytokine-cytokine receptor interaction, and rheumatoid arthritis. The scores of immune cell markers, including neutrophils, monocytes, and macrophages/monocytes were also significantly decreased in non-responders compared with that measured in anti-TNF therapy responders. The KAT2B gene, associated with IL-17 cytokine mediated neutrophil mobilization and activation, was significantly under-expressed in both tissue and peripheral blood mononuclear cells (PBMCs) in anti-TNF therapy-resistant CD patients. The reduced expression of several pro-inflammatory cytokines due to down-regulated IL-17 signaling, is suggestive of the primary non-response to anti-TNF agents in CD patients. Furthermore, the PBMC KAT2B gene signature may be a promising pre-treatment prognostic biomarker for anti-TNF drug response in CD patients.

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.

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.

Multiomics and Machine Learning Accurately Predict Clinical Response to Adalimumab and Etanercept Therapy in Patients With Rheumatoid Arthritis

OBJECTIVE

To predict response to anti-tumor necrosis factor (anti-TNF) prior to treatment in patients with rheumatoid arthritis (RA), and to comprehensively understand the mechanism of how different RA patients respond differently to anti-TNF treatment.

METHODS

Gene expression and/or DNA methylation profiling on peripheral blood mononuclear cells (PBMCs), monocytes, and CD4+ T cells obtained from 80 RA patients before they began either adalimumab (ADA) or etanercept (ETN) therapy was studied. After 6 months, treatment response was evaluated according to the European League Against Rheumatism criteria for disease response. Differential expression and methylation analyses were performed to identify the response-associated transcription and epigenetic signatures. Using these signatures, machine learning models were built by random forest algorithm to predict response prior to anti-TNF treatment, and were further validated by a follow-up study.

RESULTS

Transcription signatures in ADA and ETN responders were divergent in PBMCs, and this phenomenon was reproduced in monocytes and CD4+ T cells. The genes up-regulated in CD4+ T cells from ADA responders were enriched in the TNF signaling pathway, while very few pathways were differential in monocytes. Differentially methylated positions (DMPs) were strongly hypermethylated in responders to ETN but not to ADA. The machine learning models for the prediction of response to ADA and ETN using differential genes reached an overall accuracy of 85.9% and 79%, respectively. The models using DMPs reached an overall accuracy of 84.7% and 88% for ADA and ETN, respectively. A follow-up study validated the high performance of these models.

CONCLUSION

Our findings indicate that machine learning models based on molecular signatures accurately predict response before ADA and ETN treatment, paving the path toward personalized anti-TNF treatment.

Whole Transcription Profile of Responders to Anti-TNF Drugs in Pediatric Inflammatory Bowel Disease

Background: Up to 30% of patients with pediatric inflammatory bowel disease (IBD) do not respond to anti-Tumor Necrosis Factor (anti-TNF) therapy. The aim of this study was to identify pharmacogenomic markers that predict early response to anti-TNF drugs in pediatric patients with IBD. Methods: An observational, longitudinal, prospective cohort study was conducted. The study population comprised 38 patients with IBD aged < 18 years who started treatment with infliximab or adalimumab (29 responders and nine non-responders). Whole gene expression profiles from total RNA isolated from whole blood samples of six responders and six non-responders taken before administration of the biologic and after two weeks of therapy were analyzed using next-generation RNA sequencing. The expression of six selected genes was measured for purposes of validation in all of the 38 patients recruited using qPCR. Results: Genes were differentially expressed in non-responders and responders (32 before initiation of treatment and 44 after two weeks, Log2FC (Fold change) >0.6 or <−0.6 and p value < 0.05). After validation, FCGR1A, FCGR1B, and GBP1 were overexpressed in non-responders two weeks after initiation of anti-TNF treatment (Log2FC 1.05, 1.21, and 1.08, respectively, p value < 0.05). Conclusion: Expression of the FCGR1A, FCGR1B, and GBP1 genes is a pharmacogenomic biomarker of early response to anti-TNF agents in pediatric IBD.

Gene Signatures of Early Response to Anti-TNF Drugs in Pediatric Inflammatory Bowel Disease

Around a 20–30% of inflammatory bowel disease (IBD) patients are diagnosed before they are 18 years old. Anti-TNF drugs can induce and maintain remission in IBD, however, up to 30% of patients do not respond. The aim of the work was to identify markers that would predict an early response to anti-TNF drugs in pediatric patients with IBD. The study population included 43 patients aged <18 years with IBD who started treatment with infliximab or adalimumab. Patients were classified into primary responders (n = 27) and non-responders to anti-TNF therapy (n = 6). Response to treatment could not be analyzed in 10 patients. Response was defined as a decrease in over 15 points in the disease activity indexes from week 0 to week 10 of infliximab treatment or from week 0 to week 26 of adalimumab treatment. The expression profiles of nine genes in total RNA isolated from the whole-blood of pediatric IBD patients taken before biologic administration and after 2 weeks were analyzed using qPCR and the 2^−∆∆Ct method. Before initiation and after 2 weeks of treatment the expression of SMAD7 was decreased in patients who were considered as non-responders (p value < 0.05). Changes in expression were also observed for TLR2 at T0 and T2, although that did not reach the level of statistical significance. In addition, the expression of DEFA5 decreased 1.75-fold during the first 2 weeks of anti-TNF treatment in responders, whereas no changes were observed in non-responders. Expression of the SMAD7 gene is a pharmacogenomic biomarker of early response to anti-TNF agents in pediatric IBD. TLR2 and DEFA5 need to be validated in larger studies.

Molecular profiling of rheumatoid arthritis patients reveals an association between innate and adaptive cell populations and response to anti-tumor necrosis factor

Background

The goal of this study is to use comprehensive molecular profiling to characterize clinical response to anti-TNF therapy in a real-world setting and identify reproducible markers differentiating good responders and non-responders in rheumatoid arthritis (RA).

Methods

Whole-blood mRNA, plasma proteins, and glycopeptides were measured in two cohorts of biologic-naïve RA patients (n = 40 and n = 36) from the Corrona CERTAIN (Comparative Effectiveness Registry to study Therapies for Arthritis and Inflammatory coNditions) registry at baseline and after 3 months of anti-TNF treatment. Response to treatment was categorized by EULAR criteria. A cell type-specific data analysis was conducted to evaluate the involvement of the most common immune cell sub-populations. Findings concordant between the two cohorts were further assessed for reproducibility using selected NCBI-GEO datasets and clinical laboratory measurements available in the CERTAIN database.

Results

A treatment-related signature suggesting a reduction in neutrophils, independent of the status of response, was indicated by a high level of correlation (ρ = 0.62; p < 0.01) between the two cohorts. A baseline, response signature of increased innate cell types in responders compared to increased adaptive cell types in non-responders was identified in both cohorts. This result was further assessed by applying the cell type-specific analysis to five other publicly available RA datasets. Evaluation of the neutrophil-to-lymphocyte ratio at baseline in the remaining patients (n = 1962) from the CERTAIN database confirmed the observation (odds ratio of good/moderate response = 1.20 [95% CI = 1.03–1.41, p = 0.02]).

Conclusion

Differences in innate/adaptive immune cell type composition at baseline may be a major contributor to response to anti-TNF treatment within the first 3 months of therapy.

A Combined Transcriptomic and Genomic Analysis Identifies a Gene Signature Associated With the Response to Anti-TNF Therapy in Rheumatoid Arthritis

Background: Rheumatoid arthritis (RA) is the most frequent autoimmune disease involving the joints. Although anti-TNF therapies have proven effective in the management of RA, approximately one third of patients do not show a significant clinical response. The objective of this study was to identify new genetic variation associated with the clinical response to anti-TNF therapy in RA.

Methods: We performed a sequential multi-omic analysis integrating different sources of molecular information. First, we extracted the RNA from synovial biopsies of 11 RA patients starting anti-TNF therapy to identify gene coexpression modules (GCMs) in the RA synovium. Second, we analyzed the transcriptomic association between each GCM and the clinical response to anti-TNF therapy. The clinical response was determined at week 14 using the EULAR criteria. Third, we analyzed the association between the GCMs and anti-TNF response at the genetic level. For this objective, we used genome-wide data from a cohort of 348 anti-TNF treated patients from Spain. The GCMs that were significantly associated with the anti-TNF response were then tested for validation in an independent cohort of 2,706 anti-TNF treated patients. Finally, the functional implication of the validated GCMs was evaluated via pathway and cell type epigenetic enrichment analyses.

Results: A total of 149 GCMs were identified in the RA synovium. From these, 13 GCMs were found to be significantly associated with anti-TNF response (P < 0.05). At the genetic level, we detected two of the 13 GCMs to be significantly associated with the response to adalimumab (P = 0.0015) and infliximab (P = 0.021) in the Spain cohort. Using the independent cohort of RA patients, we replicated the association of the GCM associated with the response to adalimumab (P = 0.0019). The validated module was found to be significantly enriched for genes involved in the nucleotide metabolism (P = 2.41e-5) and epigenetic marks from immune cells, including CD4+ regulatory T cells (P = 0.041).

Conclusions: These findings show the existence of a drug-specific genetic basis for anti-TNF response, thereby supporting treatment stratification in the search for response biomarkers in RA.

Utilizing a PTPN22 gene signature to predict response to targeted therapies in rheumatoid arthritis

Despite the development of several targeted therapies for rheumatoid arthritis (RA), there is still no reliable drug-specific predictor to assist rheumatologists in selecting the most effective targeted therapy for each patient. Recently, a gene signature caused by impaired induction of PTPN22 in anti-CD3 stimulated peripheral blood mononuclear cells (PBMC) was observed in healthy at-risk individuals. However, the downstream target genes of PTPN22 and the molecular mechanisms regulating its expression are still poorly understood. Here we report that the PTPN22 gene signature is also present in PBMC from patients with active RA and can be reversed after effective treatment. The expression of PTPN22 correlates with that of more than 1000 genes in Th cells of anti-CD3 stimulated PBMC of healthy donors and is inhibited by TNFα or CD28 signals, but not IL-6, through distinct mechanisms. In addition, the impaired induction of PTPN22 in PBMC of patients with active RA can be normalized in vitro by several targeted therapies. More importantly, the in vitro normalization of PTPN22 expression correlates with clinical response to the targeted therapies in a longitudinal RA cohort. Thus, in vitro normalization of PTPN22 expression by targeted therapies can potentially be used to predict clinical response.

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.

RNA sequencing to predict response to TNF-α inhibitors reveals possible mechanism for nonresponse in smokers

BACKGROUND

Several studies have employed microarray-based profiling to predict response to tumor necrosis factor-alpha inhibitors (TNFi) in rheumatoid arthritis (RA); yet efforts to validate these targets have failed to show predictive abilities acceptable for clinical practice.

METHODS

The eighty most extreme responders and nonresponders to TNFi therapy were selected from the observational BiOCURA cohort. RNA sequencing was performed on mRNA from peripheral blood mononuclear cells (PBMCs) collected before initiation of treatment. The expression of pathways as well as individual gene transcripts between responders and nonresponders was investigated. Promising targets were technically replicated and validated in n = 40 new patients using qPCR assays.

RESULTS

Before therapy initiation, nonresponders had lower expression of pathways related to interferon and cytokine signaling, while also showing higher levels of two genes, GPR15 and SEMA6B (p = 0.02). The two targets could be validated, however, additional analyses revealed that GPR15 and SEMA6B did not independently predict response, but were rather dose-dependent markers of smoking (p < 0.0001).

CONCLUSIONS

The study did not identify new transcripts ready to use in clinical practice, yet GPR15 and SEMA6B were recognized as candidate explanatory markers for the reduced treatment success in RA smokers.

A novel gene and pathway-level subtyping analysis scheme to understand biological mechanisms in complex disease: a case study in rheumatoid arthritis

Complex diseases have heterogeneous underlying molecular mechanisms. In order to improve the diagnosis and treatment of disease, it is vital to stratify patients into homogeneous subgroups that share a similar disease etiology. In this study, we performed gene-level subtyping analysis on two independent Rheumatoid Arthritis gene expression cohorts from different ethnic groups to discover the possible disease mechanisms associated with each subtype. Also, a novel pathway-level analysis is proposed to increase the subtyping robustness and facilitate biological interpretation. This approach could stratify RA patients into two robust and homogeneous groups with differing activation of central signal transduction pathways and pro-inflammatory cytokines in the pathogenesis of RA. Such a methodology can help understand disease mechanisms at play in different patient sub-populations and also potentially explain why some patients don't respond to anti-TNF treatment.

Theory of signs and statistical approach to big data in assessing the relevance of clinical biomarkers of inflammation and oxidative stress

Biomarkers are widely used not only as prognostic or diagnostic indicators, or as surrogate markers of disease in clinical trials, but also to formulate theories of pathogenesis. We identify two problems in the use of biomarkers in mechanistic studies. The first problem arises in the case of multifactorial diseases, where different combinations of multiple causes result in patient heterogeneity. The second problem arises when a pathogenic mediator is difficult to measure. This is the case of the oxidative stress (OS) theory of disease, where the causal components are reactive oxygen species (ROS) that have very short half-lives. In this case, it is usual to measure the traces left by the reaction of ROS with biological molecules, rather than the ROS themselves. Borrowing from the philosophical theories of signs, we look at the different facets of biomarkers and discuss their different value and meaning in multifactorial diseases and system medicine to inform their use in patient stratification in personalized medicine.

Pre-silencing of genes involved in the electron transport chain (ETC) pathway is associated with responsiveness to abatacept in rheumatoid arthritis

Background

In the current context of personalized medicine, one of the major challenges in the management of rheumatoid arthritis (RA) is to identify biomarkers that predict drug responsiveness. From the European APPRAISE trial, our main objective was to identify a gene expression profile associated with responsiveness to abatacept (ABA) + methotrexate (MTX) and to understand the involvement of this signature in the pathophysiology of RA.

Methods

Whole human genome microarrays (4 × 44 K) were performed from a first subset of 36 patients with RA. Data validation by quantitative reverse-transcription (qRT)-PCR was performed from a second independent subset of 32 patients with RA. Gene Ontology and WikiPathways database allowed us to highlight the specific biological mechanisms involved in predicting response to ABA/MTX.

Results

From the first subset of 36 patients with RA, a combination including 87 transcripts allowed almost perfect separation between responders and non-responders to ABA/MTX. Next, the second subset of patients 32 with RA allowed validation by qRT-PCR of a minimal signature with only four genes. This latter signature categorized 81% of patients with RA with 75% sensitivity, 85% specificity and 85% negative predictive value. This combination showed a significant enrichment of genes involved in electron transport chain (ETC) pathways. Seven transcripts from ETC pathways (NDUFA6, NDUFA4, UQCRQ, ATP5J, COX7A2, COX7B, COX6A1) were significantly downregulated in responders versus non-responders to ABA/MTX. Moreover, dysregulation of these genes was independent of inflammation and was specific to ABA response.

Conclusion

Pre-silencing of ETC genes is associated with future response to ABA/MTX and might be a crucial key to susceptibility to ABA response.

Electronic supplementary material

The online version of this article (doi:10.1186/s13075-017-1319-8) contains supplementary material, which is available to authorized users.

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.

Transcriptional profiling of leukocytes from rheumatoid arthritis patients before and after anti-tumor necrosis factor therapy: A comparison of anti-nuclear antibody positive and negative subsets

Anti-nuclear antibodies (ANAs) may be induced in patients with rheumatoid arthritis (RA) receiving anti-tumor necrosis factor (TNF) therapy with TNF inhibitors (TNFi), etanercept, infliximab or adalimumab. In the present study, 11 patients who were TNFi drug naive were started on TNFi at a time of high disease activity. Of these, all cases were positive for rheumatoid factor and 9 cases tested were positive for anti-citrullinated peptide (anti-CCP) antibodies prior to TNFi treatment. Peripheral blood mononuclear cells (PBMCs) and serum were collected from all patients before and after TNFi therapy. Serum was assayed for ANAs over time. Total cellular RNA was extracted from PBMCs and assessed using Illumina arrays. Gene expression profiles were examined for alterations in key effector pathways. After 3 or more months on TNFi, 6 patients converted to ANA-positivity. Analysis of transcripts from patients with RA who converted to ANA-positivity after 3 months on TNFi identified complex gene expression profiles that reflected a reduction in cell adhesion, cell stress and lipid metabolism transcripts. In summary, unique transcriptional profiles in PBMCs from patients with RA were observed after TNFi therapy. This pilot study suggests that transcriptional profiling is a precise method of measuring the impact of TNFi therapies and reveals novel pathways that likely influence the immune response.

Genomic stratification by expression of HLA-DRB4 alleles identifies differential innate and adaptive immune transcriptional patterns - A strategy to detect predictors of methotrexate response in early rheumatoid arthritis

Effective drug selection is the current challenge in rheumatoid arthritis (RA). Treatment failure may follow different pathomechanisms and therefore require investigation of molecularly defined subgroups. In this exploratory study, whole blood transcriptomes of 68 treatment-naïve early RA patients were analyzed before initiating MTX. Subgroups were defined by serologic and genetic markers. Response related signatures were interpreted using reference transcriptomes of various cell types, cytokine stimulated conditions and bone marrow precursors. HLA-DRB4-negative patients exhibited most distinctive transcriptional differences. Preponderance of transcripts associated with phagocytes and bone marrow activation indicated response and transcripts of T- and B-lymphocytes non-response. HLA-DRB4-positive patients were more heterogeneous, but also linked failure to increased adaptive immune response. RT-qPCR confirmed reliable candidate selection and independent samples of responders and non-responders the functional patterning. In summary, genomic stratification identified different molecular pathomechanisms in early RA and preponderance of innate but not adaptive immune activation suggested response to MTX therapy.

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.

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.

Is there a role of synovial biopsy in drug development?

Rheumatoid arthritis (RA) is an autoimmune disease which causes significant pain, joint deformity, functional disability. The pathological hallmark of RA is inflammation of the synovium characterized by involvement of inflammatory and resident stromal cells, soluble mediators and signalling pathways leading to irreversible joint destruction. The treatment goal in RA has evolved over the last decade towards a target of disease remission that is achieved in less than a third of patients in clinical trials. The lack of therapeutic response to current treatments is suggestive of alternative drivers of RA pathogenesis that might serve as promising therapeutic targets. There are data to justify the use of synovial tissue in early drug development. Synovial tissue represents an appropriate compartment to be studied in patients with inflammatory arthritis and provides information that is distinct from peripheral blood. Modern techniques have made the procedure much more accessible and ultrasound guided biopsies represent a safe and acceptable option. Advances in analytic technologies allowing transcriptomic level of analysis can provide unique inside to target organ/tissue following the exposure to investigational medicinal product. However, there are still caveats with regard to both the choice of technique and analytical methods. Therefore the significance of synovial biopsy remains to be determined in future clinical trials. The aim of the current debate is to explore the potential for accessing and evaluating synovial tissue in early drug development, to summarize lessons we have learned from clinical trials and to discuss the challenges that have arisen so far.

Investigating CD11c expression as a potential genomic biomarker of response to TNF inhibitor biologics in whole blood rheumatoid arthritis samples

INTRODUCTION

Gene expression profiling is rapidly becoming a useful and informative tool in a much needed area of research. Identifying patients as to whether they will respond or not to a given treatment before prescription is not only essential to optimise treatment outcome but also to lessen the economic burden that such drugs can have on healthcare resources. In rheumatoid arthritis (RA), there is of yet no genetic/genomic biomarker which can accurately predict response to TNF inhibitor biologics prior to treatment, despite much interest in this area. Multiple studies have reported findings on potential candidate genes; however, due to relatively small sample sizes or lack of sufficient validation, results have been disappointingly inconsistent. The aim of this research was to further explore the predictive value of a previously reported association between CD11c expression and response to the TNF inhibitor biologics, adalimumab and etanercept.

METHODS

Real-time qPCR was performed using whole blood RNA samples obtained from seventy-five rheumatoid arthritis patients about to commence treatment with a TNF inhibitor biologic drug, whose response status was determined at 3-month follow-up using the EULAR classification criteria. Relative quantification of CD11c using the comparative CT method outputted differential expression between good-responders and non-responders as a fold-change.

RESULTS

Relative expression of CD11c in patients receiving TNF inhibitor biologics yielded a decrease of 1.025 fold in good-responders as compared to non-responders (p-value = 0.36). Upon stratification of patients dependent upon the specific drug administered, adalimumab or etanercept, similar findings to the full cohort were observed, decreases of 1.015 (p-value = 0.33) and 1.032 fold (p-value = 0.13) in good-responders compared to non-responders, respectively.

CONCLUSION

The results from this study reveal that CD11c expression does not correlate with response to TNF inhibitor biologics when tested for within pre-treatment whole blood samples of rheumatoid arthritis patients.

Combining genetic and nongenetic biomarkers to realize the promise of pharmacogenomics for inflammatory diseases

Many drugs used to treat inflammatory diseases are ineffective in a substantial proportion of patients. Identifying patients that are likely to respond to specific therapies would facilitate personalized treatment strategies that could improve outcomes while reducing costs and risks of adverse events. Despite these clear benefits, there are limited examples of predictive biomarkers of drug efficacy currently implemented into clinical practice for inflammatory diseases. We review efforts to identify genetic and nongenetic biomarkers of drug response in these diseases and consider potential benefits from combining multiple sources of biological data into multifeature predictive models.

Response to Infliximab in Crohn's Disease: Genetic Analysis Supporting Expression Profile

Substantial proportion of Crohn's disease (CD) patients shows no response or a limited response to treatment with infliximab (IFX) and to identify biomarkers of response would be of great clinical and economic benefit. The expression profile of five genes (S100A8-S100A9, G0S2, TNFAIP6, and IL11) reportedly predicted response to IFX and we aimed at investigating their etiologic role through genetic association analysis. Patients with active CD (350) who received at least three induction doses of IFX were included and classified according to IFX response. A tagging strategy was used to select genetic polymorphisms that cover the variability present in the chromosomal regions encoding the identified genes with altered expression. Following genotyping, differences between responders and nonresponders to IFX were observed in haplotypes of the studied regions: S100A8-S100A9 (rs11205276^*G/rs3014866^*C/rs724781^*C/rs3006488^*A; P = 0.05); G0S2 (rs4844486^*A/rs1473683^*T; P = 0.15); TNFAIP6 (rs11677200^*C/rs2342910^*A/rs3755480^*G/rs10432475^*A; P = 0.10); and IL11 (rs1126760^*C/rs1042506^*G; P = 0.07). These differences were amplified in patients with colonic and ileocolonic location for all but the TNFAIP6 haplotype, which evidenced significant difference in ileal CD patients. Our results support the role of the reported expression signature as predictive of anti-TNF outcome in CD patients and suggest an etiological role of those top-five genes in the IFX response pathway.

T-cell exhaustion, co-stimulation and clinical outcome in autoimmunity and infection

The clinical course of autoimmune and infectious disease varies greatly, even between individuals with the same condition. An understanding of the molecular basis for this heterogeneity could lead to significant improvements in both monitoring and treatment. During chronic infection the process of T-cell exhaustion inhibits the immune response, facilitating viral persistence. Here we show that a transcriptional signature reflecting CD8 T-cell exhaustion is associated with poor clearance of chronic viral infection, but conversely predicts better prognosis in multiple autoimmune diseases. The development of CD8 T-cell exhaustion during chronic infection is driven both by persistence of antigen and by a lack of accessory 'help' signals. In autoimmunity, we find that where evidence of CD4 T-cell co-stimulation is pronounced, that of CD8 T-cell exhaustion is reduced. We can reproduce the exhaustion signature by modifying the balance of persistent stimulation of T-cell antigen receptors and specific CD2-induced co-stimulation provided to human CD8 T cells in vitro, suggesting that each process plays a role in dictating outcome in autoimmune disease. The 'non-exhausted' T-cell state driven by CD2-induced co-stimulation is reduced by signals through the exhaustion-associated inhibitory receptor PD-1, suggesting that induction of exhaustion may be a therapeutic strategy in autoimmune and inflammatory disease. Using expression of optimal surrogate markers of co-stimulation/exhaustion signatures in independent data sets, we confirm an association with good clinical outcome or response to therapy in infection (hepatitis C virus) and vaccination (yellow fever, malaria, influenza), but poor outcome in autoimmune and inflammatory disease (type 1 diabetes, anti-neutrophil cytoplasmic antibody-associated vasculitis, systemic lupus erythematosus, idiopathic pulmonary fibrosis and dengue haemorrhagic fever). Thus, T-cell exhaustion plays a central role in determining outcome in autoimmune disease and targeted manipulation of this process could lead to new therapeutic opportunities.

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.

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

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

Modular analysis of peripheral blood gene expression in rheumatoid arthritis captures reproducible gene expression changes in tumor necrosis factor responders

OBJECTIVE

To establish whether the analysis of whole-blood gene expression is useful in predicting or monitoring response to anti-tumor necrosis factor (anti-TNF) therapy in patients with rheumatoid arthritis (RA).

METHODS

Whole-blood RNA (using a PAXgene system to stabilize whole-blood RNA in the collection tube) was obtained at baseline and at 14 weeks from 3 independent cohorts, consisting of a combined total of 240 RA patients who were beginning therapy with anti-TNF. We used an approach to gene expression analysis that is based on modular patterns of gene expression, or modules.

RESULTS

Good and moderate responders according to the European League Against Rheumatism criteria exhibited highly significant and consistent changes in multiple gene expression modules after 14 weeks of therapy, as demonstrated by hypergeometric analysis. Strikingly, nonresponders exhibited very little change in any modules, despite exposure to TNF blockade. These patterns of change were highly consistent across all 3 cohorts, indicating that immunologic changes after TNF treatment are specific to the combination of both drug exposure and responder status. In contrast, modular patterns of gene expression did not exhibit consistent differences between responders and nonresponders at baseline in the 3 study cohorts.

CONCLUSION

These data provide evidence that using gene expression modules related to inflammatory disease may provide a valuable method for objective monitoring of the response of RA patients who are treated with TNF inhibitors.

Moving towards personalized medicine in rheumatoid arthritis

To develop personalized medicine strategies for improvement of patient management in rheumatoid arthritis, the clinical and molecular properties of the individual patients need to be well characterized. A crucial step in this approach is to discover subgroups of patients that are characterized by a good or poor treatment outcome. Dennis and colleagues have identified distinct pretreatment gene expression profiles in affected synovial tissue specimens and a tissue type-related systemic protein pattern which are associated with a positive or negative clinical outcome to monotherapy with adalumimab (anti-TNFα) and tocilizumab (anti-IL-6 receptor). These observations assign biological pathways associated with response outcome and provide evidence for the existence of systemic, easy-to-measure predictive biomarkers for clinical benefit of these biologics.

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

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

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

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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.

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.