Independent Candidate Serum Protein Biomarkers of Response to Adalimumab and to Infliximab in Rheumatoid Arthritis: An Exploratory Study

Naïve Inflammatory Proteome Profiles of Glucocorticoid Responsive Polymyalgia Rheumatica and Rheumatic Arthritis Patients-Links to Triggers and Proteomic Manifestations

Polymyalgia rheumatica (PMR) is an inflammatory disorder of unknown etiology, sharing symptoms with giant cell arthritis (GCA) and rheumatoid arthritis (RA). The pathogenic inflammatory roots are still not well understood, and there is a lack of extensive biomarker studies to explain the disease debut and post-acute phase. This study aimed to deeply analyze the serum proteome and inflammatory response of PMR patients before and after glucocorticoid treatment. We included treatment-naïve PMR patients, collecting samples before and after 3 months of treatment. For comparison, disease-modifying antirheumatic drug (DMARD)-naïve RA patients were included and matched to healthy controls (CTL). The serum proteome was examined using label-free quantitative mass spectrometry, while inflammation levels were assessed using multiplex inflammatory cytokine and cell-free DNA assays. The serum proteomes of the four groups comprised acute phase reactants, coagulation factors, complement proteins, immunoglobulins, and apolipoproteins. Serum amyloid A (SAA1) was significantly reduced by active PMR treatment. Cell-free DNA levels in PMR and RA groups were significantly higher than in healthy controls due to acute inflammation. Complement factors had minimal changes post-treatment. The individual serum proteome in PMR patients showed over 100 abundantly variable proteins, emphasizing the systemic impact of PMR disease debut and the effect of treatment. Interleukin (IL)-6 and interferon-gamma (IFN-γ) were significantly impacted by glucocorticoid treatment. Our study defines the PMR serum proteome during glucocorticoid treatment and highlights the role of SAA1, IL-6, and IFN-γ in treatment responses. An involvement of PGLYRP2 in acute PMR could indicate a response to bacterial infection, highlighting its role in the acute phase of the immune response. The results suggest that PMR may be an aberrant response to a bacterial infection with an exacerbated IL-6 and acute phase inflammatory response and molecular attempts to limit the inflammation.

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.

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.

ATRPred: A machine learning based tool for clinical decision making of anti-TNF treatment in rheumatoid arthritis patients

Rheumatoid arthritis (RA) is a chronic autoimmune condition, characterised by joint pain, damage and disability, which can be addressed in a high proportion of patients by timely use of targeted biologic treatments. However, the patients, non-responsive to the treatments often suffer from refractoriness of the disease, leading to poor quality of life. Additionally, the biologic treatments are expensive. We obtained plasma samples from N = 144 participants with RA, who were about to commence anti-tumour necrosis factor (anti-TNF) therapy. These samples were sent to Olink Proteomics, Uppsala, Sweden, where proximity extension assays of 4 panels, containing 92 proteins each, were performed. A total of n = 89 samples of patients passed the quality control of anti-TNF treatment response data. The preliminary analysis of plasma protein expression values suggested that the RA population could be divided into two distinct molecular sub-groups (endotypes). However, these broad groups did not predict response to anti-TNF treatment, but were significantly different in terms of gender and their disease activity. We then labelled these patients as responders (n = 60) and non-responders (n = 29) based on the change in disease activity score (DAS) after 6 months of anti-TNF treatment and applied machine learning (ML) with a rigorous 5-fold nested cross-validation scheme to filter 17 proteins that were significantly associated with the treatment response. We have developed a ML based classifier ATRPred (anti-TNF treatment response predictor), which can predict anti-TNF treatment response in RA patients with 81% accuracy, 75% sensitivity and 86% specificity. ATRPred may aid clinicians to direct anti-TNF therapy to patients most likely to receive benefit, thus save cost as well as prevent non-responsive patients from refractory consequences. ATRPred is implemented in R.

Potential clinical biomarkers in rheumatoid arthritis with an omic approach

Objective

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

Methods

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

Results

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

Conclusion

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

Anti-TROVE2 Antibody Determined by Immune-Related Array May Serve as a Predictive Marker for Adalimumab Immunogenicity and Effectiveness in RA

Anti-drug antibody (ADAb) development is associated with secondary therapeutic failure in biologic-treated rheumatoid arthritis (RA) patients. With a treat-to-target goal, we aimed to identify biomarkers for predicting ADAb development and therapeutic response in adalimumab-treated patients. Three independent cohorts were enrolled. In Cohort-1, 24 plasma samples (6 ADAb-positive and 6 ADAb-negative patients at baseline and week 24 of adalimumab therapy, respectively) were assayed with immune-related microarray containing 1,636 correctly folded functional proteins. Next, we executed statistically powered autoantibody profiling analysis of 50 samples in Cohort-2 (24 ADAb-positive and 26 ADAb-negative patients). Subsequently, immunofluorescence assay was performed on 48 samples in Cohort-3 to correlate with ADAb titers and drug levels. The biomarkers were identified for predicting ADAb development and therapeutic response using the immune-related microarray and machine learning approach. ADAb-positive patients had lower drug levels at week 24 (median = 0.024 μg/ml) compared with ADAb-negative patients (median = 6.38 μg/ml, p < 0.001). ROC analysis based on the ADAb status revealed the top 20 autoantibodies with AUC ≥ 0.7 in differentiating both groups in Cohort-1. Analysis of Cohort-2 dataset identified a panel of 8 biomarkers (TROVE2, SSB, NDE1, ZHX2, SH3GL1, CARD9, PTPN20, and KLHL12) with 80.6% specificity, 77.4% sensitivity, and 79.0% accuracy in discriminating poor from EULAR responders. Immunofluorescence assay validated that anti-TROVE2 antibody could highly predict ADAb development and poor EULAR response (AUC 0.79 and 0.89, respectively). Multivariate regression analysis proved anti-TROVE2 antibody to be an independent predictor for developing ADAb. Immune-related protein microarray and replication analysis identified anti-TROVE2 antibody as a useful biomarker for predicting ADAb development and therapeutic response in adalimumab-treated patients.

Multiple biomarker approach for the diagnosis and therapy of rheumatoid arthritis

The lack of specific clinical symptoms for patients in the early stage of rheumatoid arthritis (RA) has created strong interest in the laboratory diagnosis of RA. The main laboratory markers of RA, rheumatoid factor (RF) and anti-citrullinated protein antibodies (ACPAs), can be found in patients with other pathologies and in healthy donors. Even today, there is no single laboratory test that can diagnosis RA with high sensitivity and specificity. To improve the diagnosis and treatment of RA, alternative biomarkers, including 14-3-3η protein, connective tissue growth factor (CTGF), antibodies against PAD4, antibodies against BRAF, and anti-acetylated and anti-carbamylated protein antibodies have been studied extensively. The use of a multiple biomarker approach, the simultaneous measurement of a set of biomarkers, is an alternative strategy for the diagnosis of RA and for predicting the therapeutic effect of biological disease-modifying antirheumatic drugs (DMARDs). However, despite the large number of studies, only a few biomarker combinations have been validated and can be applied in clinical practice. In this article, results of studies focused on the multiple biomarker approach (both multiplex and combined single-analyte assays) to diagnose RA and to predict response to biological drug therapy are reviewed. Additionally, general factors limiting the use of multiplex analysis in RA diagnostics and therapy are discussed.

The Ulcerative Colitis Response Index for Detection of Mucosal Healing in Patients Treated With Anti-tumour Necrosis Factor

BACKGROUND

Surrogate markers that accurately detect mucosal healing [MH] in patients with ulcerative colitis [UC] are urgently needed. Several stool neutrophil-related proteins are currently used as biomarkers for MH. However, the sensitivity and specificity are not sufficient to avoid unnecessary endoscopic evaluations.

METHODS

Novel serum neutrophil-related markers (neutrophil gelatinase B-associated lipocalin and matrix metalloproteinase-9 [NGAL-MMP-9 complex], cathelicidin LL-37 and chitinase 3-like 1 [CHI3L1]), together with C-reactive protein [CRP] and neutrophil counts were studied. Serum samples were obtained from 176 anti-tumour necrosis factor [anti-TNF]-treated UC patients (145 infliximab [IFX] and 31 adalimumab [ADM]) at baseline and after a median of 9.5 weeks. All patients had active disease prior to treatment (Mayo endoscopic subscore [MES] ≥ 2), and MH was defined as MES ≤ 1. Serum was also obtained from 75 healthy controls. Binary logistic regression analysis was used to generate the Ulcerative Colitis Response Index [UCRI]. The performance of individual markers and UCRI was tested with receiver operating characteristic analysis.

RESULTS

All neutrophil-related markers were significantly higher in active UC patients compared to healthy controls. In the IFX cohort, CRP, NGAL-MMP-9, CHI3L1 and neutrophil count decreased significantly after treatment and all marker levels were significantly lower in healers compared to non-healers following IFX. In the ADM cohort, CRP, NGAL-MMP-9, CHI3L1 and neutrophil count decreased significantly only in healers. UCRI [including CRP, CHI3L1, neutrophil count and LL-37] accurately detected MH in both IFX-treated (area under the curve [AUC] = 0.83) and ADM-treated [AUC = 0.79] patients.

CONCLUSIONS

The new UCRI index accurately detects MH after treatment with IFX and ADM. This panel is useful for monitoring MH in UC patients under anti-TNF treatment.

PODCAST

This article has an associated podcast which can be accessed at https://academic.oup.com/ecco-jcc/pages/podcast.

Using the Immunophenotype to Predict Response to Biologic Drugs in Rheumatoid Arthritis

Tumour necrosis factor (TNF)-α is a key mediator of inflammation in rheumatoid arthritis, and its discovery led to the development of highly successful anti-TNF therapy. Subsequently, other biologic drugs targeting immune pathways, namely interleukin-6 blockade, B cell depletion, and T cell co-stimulation blockade, have been developed. Not all patients respond to a biologic drug, leading to a knowledge gap between biologic therapies available and the confident prediction of response. So far, genetic studies have failed to uncover clinically informative biomarkers to predict response. Given that the targets of biologics are immune pathways, immunological study has become all the more pertinent. Furthermore, advances in single-cell technology have enabled the characterization of many leucocyte subsets. Studying the blood immunophenotype may therefore, define biomarker profiles relevant to each individual patient's disease and treatment outcome. This review summarises our current understanding of how immune biomarkers might be able to predict treatment response to biologic drugs.

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

Research in rheumatoid arthritis (RA) is increasingly focused on the discovery of biomarkers that could enable personalized treatments. The genetic biomarkers associated with the response to TNF inhibitors (TNFi) are among the most studied. They include 12 SNPs exhibiting promising results in the three largest genome-wide association studies (GWAS). However, they still require further validation. With this aim, we assessed their association with response to TNFi in a replication study, and a meta-analysis summarizing all non-redundant data. The replication involved 755 patients with RA that were treated for the first time with a biologic drug, which was either infliximab (n = 397), etanercept (n = 155) or adalimumab (n = 203). Their DNA samples were successfully genotyped with a single-base extension multiplex method. Lamentably, none of the 12 SNPs was associated with response to the TNFi in the replication study (p > 0.05). However, a drug-stratified exploratory analysis revealed a significant association of the NUBPL rs2378945 SNP with a poor response to etanercept (B = -0.50, 95% CI = -0.82, -0.17, p = 0.003). In addition, the meta-analysis reinforced the previous association of three SNPs: rs2378945, rs12142623, and rs4651370. In contrast, five of the remaining SNPs were less associated than before, and the other four SNPs were no longer associated with the response to treatment. In summary, our results highlight the complexity of the pharmacogenetics of TNFi in RA showing that it could involve a drug-specific component and clarifying the status of the 12 GWAS-drawn SNPs.

Identification of new biomarkers to promote personalised treatment of patients with inflammatory rheumatic disease: protocol for an open cohort study

INTRODUCTION

The introduction of biological disease-modifying antirheumatic drugs (bDMARDs) has improved the treatment of inflammatory rheumatic diseases dramatically. However, bDMARD treatment failure occurs in 30%-40% of patients due to lack of effect or adverse events, and the tools to predict treatment outcomes in individual patients are currently limited. The objective of the present study is to identify diagnostic, prognostic and predictive biomarkers, which can be used to (1) diagnose inflammatory rheumatic diseases early in the disease course with high sensitivity and specificity, (2) improve prognostication or (3) predict and monitor treatment effectiveness and tolerability for the individual patient.

METHODS AND ANALYSIS

The present study is an observational and translational open cohort study with prospective collection of clinical data and biological materials (primarily blood) in patients with inflammatory rheumatic diseases treated in routine care. Patients contribute with one cross-sectional blood sample and/or are enrolled for longitudinal follow-up on initiation of a new DMARD (blood sampling after 0, 3, 6, 12, 24, 36, 48, 60 months of treatment). Other biological materials will be collected when accessible and relevant. Demographics, disease characteristics, comorbidities and lifestyle factors are registered at inclusion; DMARD treatment and outcomes are collected repeatedly during follow-up. Currently (July 2017), >5000 samples from approximately 3000 patients have been collected. Data will be analysed using appropriate statistical analyses.

ETHICS AND DISSEMINATION

The protocol is approved by the Danish Ethics Committee and the Danish Data Protection Agency. Participants give written and oral informed consent. Biomarkers will be evaluated and published according to the Reporting Recommendations for Tumour Marker (REMARK) prognostic studies, Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) and the Standards for Reporting of Diagnostic Accuracy (STARD) guidelines. Results will be published in peer-reviewed scientific journals and presented at international conferences.

TRIAL REGISTRATION NUMBER

NCT03214263.

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.

Qualitative and Quantitative Analysis of Proteome and Peptidome of Human Follicular Fluid Using Multiple Samples from Single Donor with LC-MS and SWATH Methodology

Human follicular fluid (hFF) is a natural environment of oocyte maturation, and some components of hFF could be used to judge oocyte capability for fertilization and further development. In our pilot small-scale study three samples from four donors (12 samples in total) were analyzed to determine which hFF proteins/peptides could be used to differentiate individual oocytes and which are patient-specific. Ultrafiltration was used to fractionate hFF to high-molecular-weight (HMW) proteome (>10 kDa) and low-molecular-weight (LMW) peptidome (<10 kDa) fractions. HMW and LMW compositions were analyzed using LC-MS in SWATH data acquisition and processing methodology. In total we were able to identify 158 proteins, from which 59 were never reported before as hFF components. 55 (45 not reported before) proteins were found by analyzing LMW fraction, 67 (14 not reported before) were found by analyzing HMW fraction, and 36 were identified in both fractions of hFF. We were able to perform quantitative analysis for 72 proteins from HMW fraction of hFF. We found that concentrations of 11 proteins varied substantially among hFF samples from single donors, and those proteins are promising targets to identify biomarkers useful in oocyte quality assessment.

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.

Defining the proteomic landscape of rheumatoid arthritis: progress and prospective clinical applications

INTRODUCTION

The heterogeneity of Rheumatoid Arthritis (RA) and the absence of clinical tests accurate enough to identify the early stages of this disease have hampered its management. Therefore, proteomics research is increasingly focused on the discovery of novel biological markers, which would not only be able make an early diagnosis, but also to gain insight into the different pathological mechanisms underlying the heterogeneity of RA and also to stratify patients, which is critical to enabling effective treatments. Areas covered: The proteomic approaches that have been utilised to provide knowledge about RA pathogenesis, and to identify biomarkers for RA diagnosis, prognosis, disease monitoring and prediction of response to therapy, are summarized. Expert commentary: Although each proteomic study is unique in its design, all of them have contributed to the understanding of RA pathogenesis and the discovery of promising biomarkers for patient stratification, which would improve clinical care of RA patients. Still, efforts need to be made to validate these findings and translate them into clinical practice.