Discovery of serum proteomic biomarkers for prediction of response to infliximab (a monoclonal anti-TNF antibody) treatment in rheumatoid arthritis: An exploratory analysis

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.

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.

iTRAQ and PRM-Based Proteomic Analysis Provides New Insights into Mechanisms of Response to Triple Therapy in Patients with Rheumatoid Arthritis

Background

Approximately 30% of patients with rheumatoid arthritis (RA) respond poorly to combination therapy of multiple drugs. The molecular mechanisms of different responses to methotrexate + leflunomide + infliximab therapy in patients with RA were explored in this study.

Methods

Infliximab was administered to patients with RA whose disease activity score was higher than 5.1 after 1 month of combination therapy with methotrexate and leflunomide. After 14 weeks of undergoing triple therapy, patients with RA were classified as responders and non-responders. Protein profiles at baseline and 14th week were investigated via isobaric tags for relative and absolute quantification (iTRAQ), and proteins with significant differences ≥1.2 folds change or ≤0.8 folds change were defined as differentially expressed proteins (DEPs). Overlapping DEPs between responders and non-responders were confirmed by parallel reaction monitoring (PRM). Bioinformatic analyses were performed for DEPs.

Results

The results revealed 5 non-responders (NRs) and 15 responders (Rs). iTRAQ analysis indicated 13 overlapping DEPs and included 6 opposite change DEPs such as testicular tissue protein Li 70, cofilin 1, fibrinogen beta chain, galectin-10, serotransferrin (TF) and albumin. The difference in serotransferrin between responders and non-responders confirmed by PRM was significant. Verification by PRM indicated that TF was elevated in the Rs group and was reduced in the NRs group. Bioinformatic analysis indicated that serotransferrin was involved in the hypoxia-inducible factor-1 pathway and ferroptosis.

Conclusion

Serotransferrin-related molecular mechanism may be a new direction to study refractory RA.

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

INTRODUCTION

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Contribution of Multiplex Immunoassays to Rheumatoid Arthritis Management: From Biomarker Discovery to Personalized Medicine

Rheumatoid arthritis (RA) is a multifactorial, inflammatory and progressive autoimmune disease that affects approximately 1% of the population worldwide. RA primarily involves the joints and causes local inflammation and cartilage destruction. Immediate and effective therapies are crucial to control inflammation and prevent deterioration, functional disability and unfavourable progression in RA patients. Thus, early diagnosis is critical to prevent joint damage and physical disability, increasing the chance of achieving remission. A large number of biomarkers have been investigated in RA, although only a few have made it through the discovery and validation phases and reached the clinic. The single biomarker approach mostly used in clinical laboratories is not sufficiently accurate due to its low sensitivity and specificity. Multiplex immunoassays could provide a more complete picture of the disease and the pathways involved. In this review, we discuss the latest proposed protein biomarkers and the advantages of using protein panels for the clinical management of RA. Simultaneous analysis of multiple proteins could yield biomarker signatures of RA subtypes to enable patients to benefit from personalized medicine.

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.

Secretomics to Discover Regulators in Diseases

Secretory proteins play important roles in the cross-talk of individual functional units, including cells. Since secretory proteins are essential for signal transduction, they are closely related with disease development, including metabolic and neural diseases. In metabolic diseases, adipokines, myokines, and hepatokines are secreted from respective organs under specific environmental conditions, and play roles in glucose homeostasis, angiogenesis, and inflammation. In neural diseases, astrocytes and microglia cells secrete cytokines and chemokines that play roles in neurotoxic and neuroprotective responses. Mass spectrometry-based secretome profiling is a powerful strategy to identify and characterize secretory proteins. This strategy involves stepwise processes such as the collection of conditioned medium (CM) containing secretome proteins and concentration of the CM, peptide preparation, mass analysis, database search, and filtering of secretory proteins; each step requires certain conditions to obtain reliable results. Proteomic analysis of extracellular vesicles has become a new research focus for understanding the additional extracellular functions of intracellular proteins. Here, we provide a review of the insights obtained from secretome analyses with regard to disease mechanisms, and highlight the future prospects of this technology. Continued research in this field is expected to provide valuable information on cell-to-cell communication and uncover new pathological mechanisms.

Analysis of the plasma proteome using iTRAQ and TMT-based Isobaric labeling

Over the past decade, chemical labeling with isobaric tandem mass tags, such as isobaric tags for relative and absolute quantification reagents (iTRAQ) and tandem mass tag (TMT) reagents, has been employed in a wide range of different clinically orientated serum and plasma proteomics studies. In this review the scope of these works is presented with attention to the areas of research, methods employed and performance limitations. These applications have covered a wide range of diseases, disorders and infections, and have implemented a variety of different preparative and mass spectrometric approaches. In contrast to earlier works, which struggled to quantify more than a few hundred proteins, increasingly these studies have provided deeper insight into the plasma proteome extending the numbers of quantified proteins to over a thousand.

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.

Proteomics to predict the response to tumour necrosis factor-α inhibitors in rheumatoid arthritis using a supervised cluster-analysis based protein score

OBJECTIVE

In rheumatoid arthritis (RA), it is of major importance to identify non-responders to tumour necrosis factor-α inhibitors (TNFi) before starting treatment, to prevent a delay in effective treatment. We developed a protein score for the response to TNFi treatment in RA and investigated its predictive value.

METHOD

In RA patients eligible for biological treatment included in the BiOCURA registry, 53 inflammatory proteins were measured using xMAP® technology. A supervised cluster analysis method, partial least squares (PLS), was used to select the best combination of proteins. Using logistic regression, a predictive model containing readily available clinical parameters was developed and the potential of this model with and without the protein score to predict European League Against Rheumatism (EULAR) response was assessed using the area under the receiving operating characteristics curve (AUC-ROC) and the net reclassification index (NRI).

RESULTS

For the development step (n = 65 patient), PLS revealed 12 important proteins: CCL3 (macrophage inflammatory protein, MIP1a), CCL17 (thymus and activation-regulated chemokine), CCL19 (MIP3b), CCL22 (macrophage-derived chemokine), interleukin-4 (IL-4), IL-6, IL-7, IL-15, soluble cluster of differentiation 14 (sCD14), sCD74 (macrophage migration inhibitory factor), soluble IL-1 receptor I, and soluble tumour necrosis factor receptor II. The protein score scarcely improved the AUC-ROC (0.72 to 0.77) and the ability to improve classification and reclassification (NRI = 0.05). In validation (n = 185), the model including protein score did not improve the AUC-ROC (0.71 to 0.67) or the reclassification (NRI = -0.11).

CONCLUSION

No proteomic predictors were identified that were more suitable than clinical parameters in distinguishing TNFi non-responders from responders before the start of treatment. As the results of previous studies and this study are disparate, we currently have no proteomic predictors for the response to TNFi.

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.

A disintegrin and metalloproteinase (ADAM)-10 as a predictive factor for tocilizumab effectiveness in rheumatoid arthritis

OBJECTIVES

A disintegrin and metalloproteinase (ADAM)-10 is expressed in rheumatoid arthritis (RA). In this study, we focused on ADAM-10 as a predictive factor for the treatment with biologics in RA.

METHODS

The levels of ADAM-10 and fractalkine/CX3CL1 in RA and healthy controls serum were measured using enzyme-linked immunosorbent assays. Fifteen patients were treated with adalimumab (ADA), and 20 patients were treated with tocilizumab (TCZ).

RESULTS

ADAM-10 positively correlated with fractalkine/CX3CL1 in the sera of RA patients and was presented at a significantly higher level compared to that in normal serum (487 ± 80 pg/ml and 85 ± 33 pg/ml, respectively, p < 0.05). ADAM-10 highly correlates with fractalkine/CX3CL1 in the sera of RA patients. The level of ADAM-10 decreased after the treatment with TCZ but not with ADA. In addition, we found that the level of ADAM-10 in TCZ responders was significantly higher than that of the TCZ nonresponders at 24 weeks (619 ± 134 pg/ml and 109 ± 25 pg/ml, respectively). Multiple regression analysis showed that ADAM-10 was only identified as independent predictive variable for the improvement of DAS28 (ESR) at 24 weeks.

CONCLUSIONS

ADAM-10 may be a predictor of the effectiveness of TCZ in treating RA.

Rheumatoid arthritis

Rheumatoid arthritis is a chronic inflammatory joint disease, which can cause cartilage and bone damage as well as disability. Early diagnosis is key to optimal therapeutic success, particularly in patients with well-characterised risk factors for poor outcomes such as high disease activity, presence of autoantibodies, and early joint damage. Treatment algorithms involve measuring disease activity with composite indices, applying a treatment-to-target strategy, and use of conventional, biological, and newz non-biological disease-modifying antirheumatic drugs. After the treatment target of stringent remission (or at least low disease activity) is maintained, dose reduction should be attempted. Although the prospects for most patients are now favourable, many still do not respond to current therapies. Accordingly, new therapies are urgently required. In this Seminar, we describe current insights into genetics and aetiology, pathophysiology, epidemiology, assessment, therapeutic agents, and treatment strategies together with unmet needs of patients with rheumatoid arthritis.

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

Response to treatment of rheumatoid arthritis shows large inter-individual variability. This heterogeneity is observed with all the anti-rheumatic drugs, including the commonly used TNF inhibitors. It seems that drug-specific and target-specific factors lead individual patients to respond or not to a given drug, although this point has been challenged. The search of biomarkers distinguishing responders from non-responders has included shotgun proteomics of serum, as a previous study of response to infliximab, an anti-TNF antibody. Here, we have used the same study design and technology to search biomarkers of response to a different anti-TNF antibody, adalimumab, and we have compared the results obtained for the two anti-TNF drugs. Search of biomarkers of response to adalimumab included depletion of the most abundant serum proteins, 8-plex isobaric tag for relative and absolute quantitation (iTRAQ) labeling, two-dimensional liquid chromatography fractionation and relative quantification with a hybrid Orbitrap mass spectrometer. With this approach, 264 proteins were identified in all the samples with at least 2 peptides and 95% confidence. Nine proteins showed differences between non-responders and responders (P < 0.05), representing putative biomarkers of response to adalimumab. These results were compared with the previous study of infliximab. Surprisingly, the non-responder/responder differences in the two studies were not correlated (rs = 0.07; P = 0.40). This overall independence with all the proteins showed two identifiable components. On one side, the putative biomarkers of response to either adalimumab or infliximab, which were not shared and showed an inverse correlation (rs = -0.69; P = 0.0023). On the other, eight proteins showing significant non-responder/responder differences in the analysis combining data of response to the two drugs. These results identify new putative biomarkers of response to treatment of rheumatoid arthritis and indicate that they are notably drug-specific.

Discovery of serum proteomic biomarkers for prediction of response to moxibustion treatment in rats with collagen-induced arthritis: an exploratory analysis

OBJECTIVE

To examine the possible impact of moxibustion on the serum proteome of the collagen-induced arthritis (CIA) rat model.

MATERIALS AND METHODS

Thirty-six male Sprague-Dawley rats were included in this experiment. The CIA animal model was prepared by injection of type II bovine collagen in Freund's adjuvant on the first and seventh day. The 36 rats were randomly divided into two groups: the untreated CIA group (control), and the CIA plus treatment with moxibustion (CIA+moxi) group. Moxibustion was administered daily at ST36 and BL23 for 7, 14 or 21 days (n=12 rats each). Arthritis score was used to assess the severity of arthritis. At the end of each 7 day treatment, blood samples from the control group and the CIA+moxi group were collected. After removal of high abundance proteins from serum samples, two-dimensional gel combined with matrix-assisted laser desorption ionisation time-of-flight MS/MS (MALDI-TOF-MS/MS) techniques were performed to examine serum protein expression patterns of the CIA rat model with and without moxibustion treatment. In addition, the relevant proteins were further analysed with the use of bioinformatics analysis.

RESULTS

Moxibustion significantly decreased arthritis severity in the rats in the CIA+moxi group, when compared with the rats in the CIA group 35 days after the first immunisation (p=0.001). Seventeen protein spots which changed >1.33 or <0.77 at p<0.05 using Bonferonni correction for multiple testing were found to be common to all three comparisons, and these proteins were used for classification of functions using the Gene Ontology method. Consequently, with the use of the Ingenuity Pathway Analysis, the top canonical pathways and a predicted proteomic network related to the moxibustion effect of CIA were established.

CONCLUSIONS

Using the proteomics technique, we have identified novel candidate proteins that may be involved in the mechanisms of action underlying the beneficial effects of moxibustion in rats with CIA. Our findings suggest that immune responses and metabolic processes may be involved in mediating the effects of moxibustion. Moreover, periodxiredoxin I (PRDX1) and inositol 1,4,5-triphosphate receptor (IP3R) may be potential targets.

Identification of 7 Proteins in Sera of RA Patients with Potential to Predict ETA/MTX Treatment Response

Objective: The recent growth of innovating biologics has opened fascinating avenues for the management of patients. In rheumatoid arthritis, many biologics are currently available, the choice of which being mostly determined empirically. Importantly, a given biologic may not be active in a fraction of patients and may even provoke side effects. Here, we conducted a comparative proteomics study in attempt to identify a predictive theranostic signature of non-response in patients with rheumatoid arthritis treated by etanercept/methotrexate combination.

Methods: A serum sample was collected prior to treatment exposure from a cohort of 22 patients with active RA. A proteomic “label free” approach was then designed to quantitate protein biomarkers using mass spectrometry. To verify these results, a relative quantification followed by an absolute quantification of interesting protein candidates were performed on a second cohort. The criterion of judgment was the response to etanercept/methotrexate combination according to the EULAR criteria assessed at 6 months of treatment.

Results: These investigations led to the identification of a set of 12 biomarkers with capacity to predict treatment response. A targeted quantitative analysis allowed to confirm the potential of 7 proteins from the latter combination on a new cohort of 16 patients. Two highly discriminating proteins, PROS and CO7, were further evaluated by ELISA on this second cohort. By combining the concentration threshold of each protein associated to a right classification (responders vs non-responders), the sensitivity and specificity reached 88.9 % and 100 %, respectively.

Conclusion: Prior to methotrexate/etanercept treatment, abundance of several sera proteins, notably PROS and CO7, were associated to response status of RA patients 6 month after treatment initiation.

Pretreatment Prediction of Individual Rheumatoid Arthritis Patients' Response to Anti-Cytokine Therapy Using Serum Cytokine/Chemokine/Soluble Receptor Biomarkers

The inability to match rheumatoid arthritis (RA) patients with the anti-cytokine agent most efficacious for them is a major hindrance to patients’ speedy recovery and to the clinical use of anti-cytokine therapy. Identifying predictive biomarkers that can assist in matching RA patients with more suitable anti-cytokine treatment was our aim in this report. The sample consisted of 138 RA patients (naïve and non-naïve) who were administered tocilizumab or etanercept for a minimum of 16 weeks as a prescribed RA treatment. Pretreatment serum samples were obtained from patients and clinical measures of their disease activity were evaluated at baseline and 16 weeks after treatment commenced. Using patients’ pretreatment serum, we measured 31 cytokines/chemokines/soluble receptors and used multiple linear regression analysis to identify biomarkers that correlated with patients’ symptom levels (DAS28-CRP score) at week 16 and multiple logistic analyses for biomarkers that correlated with patients’ final outcome. The results revealed that sgp130, logIL-6, logIL-8, logEotaxin, logIP-10, logVEGF, logsTNFR-I and logsTNFR-II pretreatment serum levels were predictive of the week 16 DAS28-CRP score in naïve tocilizumab patients while sgp130, logGM-CSF and logIP-10 were predictive in non-naïve patients. Additionally, we found logIL-9, logVEGF and logTNF-α to be less reliable at predicting the week 16 DAS28-CRP score in naïve etanercept patients. Multiple linear regression and multiple logistic regression analyses identified biomarkers that were predictive of remission/non-remission in tocilizumab and etanercept therapy. Although less reliable than those for tocilizumab, we identified a few possible biomarkers for etanercept therapy. The biomarkers for these two therapies differ suggesting that their efficacy will vary for individual patients. We discovered biomarkers in RA pretreatment serum that predicted their week 16 DAS28-CRP score and clinical outcome to tocilizumab therapy. Most of these biomarkers, especially sgp130, are involved in RA pathogenesis and IL-6 signal transduction, which further suggests that they are highly reliable.

Unmet needs in autoimmunity and potential new tools

The term "autoimmunity" refers to a pathological condition in which the immunological tolerance of self-antigens is broken through, cross-reactive T cells are activated, and autoantibodies are produced by B cells. The intricate interplay among those aberrantly activated immune cells as well as inflammatory cytokines secreted by them contributes to the development of proinflammatory cascade which eventually leads to the occurrence of autoimmune diseases (AIDs) and organ damage. Autoimmune diseases occupy a broad spectrum of human diseases with more than 70 different disorders and afflict approximately 5-8 % of the world's population. AIDs can be categorized into organ-specific and systemic. Although the exact mechanism of AIDs remains elusive, it is generally believed that both genetic polymorphism and environmental exposure are involved in the development of AIDs. Aberrant epigenetic marks are also identified in patients with AIDs. In addition, dysregulation of innate immune system and molecular mimicry are indicated to play important roles in the initiation and maintenance of autoreactive inflammation. Based on the progress made in elucidating molecular mechanisms underlying AIDs, novel biomarkers for prediction, early diagnosis, prognosis and treatment response, and therapeutic strategies are proposed, which represents a promising future in the battle against AIDs. However, challenges remain regarding the clinical application of these potential new tools.

A novel multiplexed immunoassay identifies CEA, IL-8 and prolactin as prospective markers for Dukes' stages A-D colorectal cancers

Background

Current methods widely deployed for colorectal cancers (CRC) screening lack the necessary sensitivity and specificity required for population-based early disease detection. Cancer-specific protein biomarkers are thought to be produced either by the tumor itself or other tissues in response to the presence of cancers or associated conditions. Equally, known examples of cancer protein biomarkers (e.g., PSA, CA125, CA19-9, CEA, AFP) are frequently found in plasma at very low concentration (pg/mL-ng/mL). New sensitive and specific assays are therefore urgently required to detect the disease at an early stage when prognosis is good following surgical resection. This study was designed to meet the longstanding unmet clinical need for earlier CRC detection by measuring plasma candidate biomarkers of cancer onset and progression in a clinical stage-specific manner. EDTA plasma samples (1 μL) obtained from 75 patients with Dukes’ staged CRC or unaffected controls (age and sex matched with stringent inclusion/exclusion criteria) were assayed for expression of 92 human proteins employing the Proseek® Multiplex Oncology I proximity extension assay. An identical set of plasma samples were analyzed utilizing the Bio-Plex Pro™ human cytokine 27-plex immunoassay.

Results

Similar quantitative expression patterns for 13 plasma antigens common to both platforms endorsed the potential efficacy of Proseek as an immune-based multiplex assay for proteomic biomarker research. Proseek found that expression of Carcinoembryonic Antigen (CEA), IL-8 and prolactin are significantly correlated with CRC stage.

Conclusions

CEA, IL-8 and prolactin expression were found to identify between control (unaffected), non-malignant (Dukes’ A + B) and malignant (Dukes’ C + D) stages.

Electronic supplementary material

The online version of this article (doi:10.1186/s12014-015-9081-x) contains supplementary material, which is available to authorized users.

Quantitative Proteomic Analysis of Peripheral Blood Mononuclear Cells in Ankylosing Spondylitis by iTRAQ

This study was designed to identify and quantify the different proteins expression levels in ankylosing spondylitis (AS) and to explore the pathogenesis of AS. We performed isobaric tags for relative and absolute quantitation (iTRAQ) coupled with multiple chromatographic fractionation and tandem mass spectrometry to detect the proteins profiling in peripheral blood mononuclear cells (PBMCs) from AS patients and healthy controls. Mascot software and the International Protein Index and the Gene Ontology (GO) database were used to conduct the bioinformatics analysis. The differentially expressed proteins were validated by enzyme-linked immunosorbent assay (ELISA). A total of 1,232 proteins were identified by iTRAQ, of which 183 showed differential expression and 18 differentially expressed proteins were acute phase reactants. Upon mapping of the differentially expressed proteins to GO database, we found four differentially expressed proteins involved in the biological process of cell killing, including up-regulated cathepsin G (CTSG), neutrophil defensin3 (DEFA3), protein tyrosine phosphatase receptor type C (PTPRC), and down-regulated peroxiredoxin-1(PRDX1),which were consistent with the verified results of ELISA. Our proteomic analyses suggested that the proteins involved in the biological process of cell killing might play an important role in the pathogenesis of AS.

Circulating miRNAs as potential biomarkers of therapy effectiveness in rheumatoid arthritis patients treated with anti-TNFα

Introduction

The advent of anti-tumor necrosis factor alpha (anti-TNFα) drugs has considerably improved medical management in rheumatoid arthritis (RA) patients, although it has been reported to be ineffective in a fraction of them. MicroRNAs (miRNAs) are small, non-coding RNAs that act as fine-tuning regulators of gene expression. Targeting miRNAs by gain or loss of function approaches have brought therapeutic effects in various disease models. The aim of this study was to investigate serum miRNA levels as predictive biomarkers of response to anti-TNFα therapy in RA patients.

Methods

In total, 95 RA patients undergoing anti-TNFα/disease-modifying antirheumatic drugs (anti-TNFα/DMARDs) combined treatments were enrolled. Serum samples were obtained at 0 and 6 months and therapeutic efficacy was assessed. miRNAs were isolated from the serum of 10 patients before and after anti-TNFα/DMARDs combination therapy, cDNA transcribed and pooled, and human serum miRNA polymerase chain reaction (PCR) arrays were performed. Subsequently, selected miRNAs were analyzed in a validation cohort consisting of 85 RA patients. Correlation studies with clinical and serological variables were also performed.

Results

Ninety percent of RA patients responded to anti-TNFα/DMARDs combination therapy according to European League Against Rheumatism (EULAR) criteria. Array analysis showed that 91% of miRNAS were overexpressed and 9% downregulated after therapy. Functional classification revealed a preponderance of target mRNAs involved in reduction of cells maturation - especially on chondrocytes - as well as in immune and inflammatory response, cardiovascular disease, connective tissue and musculoskeletal system. Six out of ten miRNAs selected for validation were found significantly upregulated by anti-TNFα/DMARDs combination therapy (miR-16-5p, miR-23-3p, miR125b-5p, miR-126-3p, miRN-146a-5p, miR-223-3p). Only responder patients showed an increase in those miRNAs after therapy, and paralleled the reduction of TNFα, interleukin (IL)-6, IL-17, rheumatoid factor (RF), and C-reactive protein (CRP). Correlation studies demonstrated associations between validated miRNAs and clinical and inflammatory parameters. Further, we identified a specific plasma miRNA signature (miR-23 and miR-223) that may serve both as predictor and biomarker of response to anti-TNFα/DMARDs combination therapy.

Conclusions

miRNA levels in the serum of RA patients before and after anti-TNFα/DMARDs combination therapy are potential novel biomarkers for predicting and monitoring therapy outcome.

Electronic supplementary material

The online version of this article (doi:10.1186/s13075-015-0555-z) contains supplementary material, which is available to authorized users.

Coupling enrichment methods with proteomics for understanding and treating disease

Owing to recent advances in proteomics analytical methods and bioinformatics capabilities there is a growing trend toward using these capabilities for the development of drugs to treat human disease, including target and drug evaluation, understanding mechanisms of drug action, and biomarker discovery. Currently, the genetic sequences of many major organisms are available, which have helped greatly in characterizing proteomes in model animal systems and humans. Through proteomics, global profiles of different disease states can be characterized (e.g. changes in types and relative levels as well as changes in PTMs such as glycosylation or phosphorylation). Although intracellular proteomics can provide a broad overview of physiology of cells and tissues, it has been difficult to quantify the low abundance proteins which can be important for understanding the diseased states and treatment progression. For this reason, there is increasing interest in coupling comparative proteomics methods with subcellular fractionation and enrichment techniques for membranes, nucleus, phosphoproteome, glycoproteome as well as low abundance serum proteins. In this review, we will provide examples of where the utilization of different proteomics-coupled enrichment techniques has aided target and biomarker discovery, understanding the drug targeting mechanism, and mAb discovery. Taken together, these improvements will help to provide a better understanding of the pathophysiology of various diseases including cancer, autoimmunity, inflammation, cardiovascular disease, and neurological conditions, and in the design and development of better medicines for treating these afflictions.

A quantitative proteomic approach to identify significantly altered protein networks in the serum of patients with lymphangioleiomyomatosis (LAM)

Lymphangioleiomyomatosis (LAM) is a rare and progressive cystic lung condition affecting approximately 3.4–7.5/million women, with an average lag time between symptom onset and diagnosis of upwards of 4 years. The aim of this work was to identify altered proteins in LAM serum which may be potential biomarkers of disease. Serum from LAM patient volunteers and healthy control volunteers were pooled and analysis carried out using quantitative 4-plex iTRAQ technology. Differentially expressed proteins were validated using ELISAs and pathway analysis was carried out using Ingenuity Pathway Analysis. Fourteen proteins were differentially expressed in LAM serum compared to control serum (p<0.05). Further screening validated the observed differences in extracellular matrix remodelling proteins including fibronectin (30% decrease in LAM, p = 0.03), von Willebrand Factor (40% reduction in LAM, p = 0.03) and Kallikrein III (25% increase in LAM, p = 0.03). Pathway networks elucidated the relationships between the ECM and cell trafficking in LAM. This study was the first to highlight an imbalance in networks important for remodelling in LAM, providing a set of novel potential biomarkers. These understandings may lead to a new effective treatment for LAM in the future.

Mitochondrial protein targets of radiosensitisation by 1,8-dihydroxy-3-acetyl-6-methyl-9,10 anthraquinone on nasopharyngeal carcinoma cells

In our preliminary study, 1,8-dihydroxy-3-acetyl-6-methyl-9,10 anthraquinone (GXHSWAQ-1), synthesised according to the basic structure of emodin, exhibited a 1.58-fold radiosensitisation on nasopharyngeal carcinoma CNE-1 cells. This study demonstrated that its radiosensitisation activity was achieved by altering the mitochondrial structure: swollen volume, fragmented crista, and decreasing transmembrane potential (P<0.01). Using isobaric tag for relative and absolute quantitation (iTRAQ) technology, 1396 proteins were identified, and the differentially expressed proteins were involved in metabolism, cell proliferation, angiogenesis, DNA repair process according to the biological process clustering results. Bioinformatic analysis showed that CDH1, RAC1, CDC42 proteins might be mostly mitochondrial targets in the radiosensitisation process. Western blotting analyses verified the differential expression of these proteins.

Pretreatment multi-biomarker disease activity score and radiographic progression in early RA: results from the SWEFOT trial

Objectives

Prediction of radiographic progression (RP) in early rheumatoid arthritis (eRA) would be very useful for optimal choice among available therapies. We evaluated a multi-biomarker disease activity (MBDA) score, based on 12 serum biomarkers as a baseline predictor for 1-year RP in eRA.

Methods

Baseline disease activity score based on erythrocyte sedimentation rate (DAS28-ESR), disease activity score based on C-reactive protein (DAS28-CRP), CRP, MBDA scores and DAS28-ESR at 3 months were analysed for 235 patients with eRA from the Swedish Farmacotherapy (SWEFOT) clinical trial. RP was defined as an increase in the Van der Heijde-modified Sharp score by more than five points over 1 year. Associations between baseline disease activity measures, the MBDA score, and 1-year RP were evaluated using univariate and multivariate logistic regression, adjusted for potential confounders.

Results

Among 235 patients with eRA, 5 had low and 29 moderate MBDA scores at baseline. None of the former and only one of the latter group (3.4%) had RP during 1 year, while the proportion of patients with RP among those with high MBDA score was 20.9% (p=0.021). Among patients with low/moderate CRP, moderate DAS28-CRP or moderate DAS28-ESR at baseline, progression occurred in 14%, 15%, 14% and 15%, respectively. MBDA score was an independent predictor of RP as a continuous (OR=1.05, 95% CI 1.02 to 1.08) and dichotomised variable (high versus low/moderate, OR=3.86, 95% CI 1.04 to 14.26).

Conclusions

In patients with eRA, the MBDA score at baseline was a strong independent predictor of 1-year RP. These results suggest that when choosing initial treatment in eRA the MBDA test may be clinically useful to identify a subgroup of patients at low risk of RP.

Trial registration number

WHO database at the Karolinska Institute: CT20080004; and clinicaltrials.gov: NCT00764725.

ACPA-positive and ACPA-negative rheumatoid arthritis differ in their requirements for combination DMARDs and corticosteroids: secondary analysis of a randomized controlled trial

INTRODUCTION

UK guidelines recommend that all early active rheumatoid arthritis (RA) patients are offered combination disease-modifying antirheumatic drugs (DMARDs) and short-term corticosteroids. Anti-citrullinated protein antibody (ACPA)-positive and ACPA-negative RA may differ in their treatment responses. We used data from a randomized controlled trial - the Combination Anti-Rheumatic Drugs in Early RA (CARDERA) trial - to examine whether responses to intensive combination treatments in early RA differ by ACPA status.

METHODS

The CARDERA trial randomized 467 early active RA patients to receive: (1) methotrexate, (2) methotrexate/ciclosporin, (3) methotrexate/prednisolone or (4) methotrexate/ciclosporin/prednisolone in a factorial-design. Patients were assessed every six months for two years. In this analysis we evaluated 431 patients with available ACPA status. To minimize multiple testing we used a mixed-effects repeated measures ANOVA model to test for an interaction between ACPA and treatment on mean changes from baseline for each outcome (Larsen, disease activity scores on a 28-joint count (DAS28), Health Assessment Questionnaire (HAQ), EuroQol, SF-36 physical component summary (PCS) and mental component summary (MCS) scores). When a significant interaction was present, mean changes in outcomes were compared by treatment group at each time point using t-tests stratified by ACPA status. Odds ratios (ORs) for the onset of new erosions with treatment were calculated stratified by ACPA.

RESULTS

ACPA status influenced the need for combination treatments to reduce radiological progression. ACPA-positive patients had significant reductions in Larsen score progression with all treatments. ACPA-positive patients receiving triple therapy had the greatest benefits: two-year mean Larsen score increases comprised 3.66 (95% confidence interval (CI) 2.27 to 5.05) with triple therapy and 9.58 (95% CI 6.76 to 12.39) with monotherapy; OR for new erosions with triple therapy versus monotherapy was 0.32 (95% CI 0.14 to 0.72; P = 0.003). ACPA-negative patients had minimal radiological progression irrespective of treatment. Corticosteroid's impact on improving DAS28/PCS scores was confined to ACPA-positive RA.

CONCLUSIONS

ACPA status influences the need for combination DMARDs and high-dose tapering corticosteroids in early RA. In CARDERA, combination therapy was only required to prevent radiological progression in ACPA-positive patients; corticosteroids only provided significant disease activity and physical health improvements in ACPA-positive disease. This suggests ACPA is an important biomarker for guiding treatment decisions in early RA.

TRIAL REGISTRATION

Current Controlled Trials ISRCTN32484878.

Screening for novel serum biomarker for monitoring disease activity in rheumatoid arthritis using iTRAQ technology-based quantitative proteomic approach

Useful biomarkers, which enable the prediction of drug susceptibility, identification of side effects, and/or evaluation of disease activity during drug treatment, are urgently needed to select adequate drugs for patients. Gene mutation status, protein expression levels in a biopsy, and serum proteins are often used as biomarkers. One of the methods to screen for protein biomarkers involves quantitative proteomic approaches using mass spectrometry. Owing to the development of quantitative proteomic approaches, the efficiency of identifying novel biomarkers from clinical samples has improved. In particular, isobaric tag for relative and absolute quantitation technology, which enables relative comparative analysis of up to eight samples, enables high-throughput analysis of screening for biomarkers at the protein level. Here, we describe the identification of a novel biomarker, which is useful for the evaluation of disease activity in patients with rheumatoid arthritis who were treated with anti-TNF-α therapy.

Personalized medicine: predicting responses to therapy in patients with RA

Personalized medicine where each patient receives the right drug and the right intensity of drug treatment for as long as needed or safe is the goal of medicine. The identification of predictors of response is the first step toward this. In rheumatoid arthritis (RA), several prediction matrices were designed to predict the risk of rapid radiological progression (RRP) in the first year of treatment, on either disease modifying anti-rheumatic drug (DMARD) monotherapy or combination therapy with prednisone or a biological agent. Both clinical markers and biomarkers of response to either anti-TNF or different mode of action biological agents, and of successful discontinuation of these agents once the treatment goal has been achieved, have been identified in different studies. Most of these markers need validation in other cohorts. Research into combining clinical markers and biomarkers of response could lead to identification of risk profiles resulting in a new step toward personalized medicine in RA.