Haptoglobin-α1, -α2, vitamin D-binding protein and apolipoprotein C-III as predictors of etanercept drug response in rheumatoid arthritis

Serum proteomics analysis of biomarkers for evaluating clinical response to MTX/IGU therapy in early rheumatoid arthritis

Methotrexate (MTX) and iguratimod (IGU) are conventional synthetic disease modifying antirheumatic drugs widely used in the treatment of Rheumatoid arthritis (RA) in China. Although MTX combined with IGU can significantly inhibit the progression of RA, some patients do not respond to the treatment. The purpose of this study is to explore the difference of serum protein expression between RA patients with good and poor response to the combined therapy by label-free quantitative proteomic approach. From the proteomics data, a total of 782 proteins in the serum of RA patients were detected, and of which 9 were upregulated and 18 were downregulated in the good response group compared to poor response group. Among them, four significantly differentially expressed proteins (RELN, LDHA, MRC1 and TKT) were further validated by multiple reaction monitoring (MRM)-based quantification approach, and three of them (RELN, LDHA and MRC1) were confirmed to be correlated with the response to MTX/IGU therapy. Logistic regression and ROC analysis indicated that the combination of RELN, LDHA and MRC1 had good performance in evaluating the response. This result proved the different serum proteins signature fingerprint between response group and non-response group; and highlighted the potential of the label-free and mass spectrometry-based quantitative proteomic approach in screening biomarkers for evaluating clinical response to MTX/IGU therapy in RA.

Prediction of response to anti-TNF treatment using laboratory biomarkers in patients with rheumatoid arthritis: a systematic review

OBJECTIVES

In this systematic review, we aim to identify laboratory biomarkers that predict response to tumour necrosis factor inhibitors (TNFi) in patients with rheumatoid arthritis (RA).

METHODS

EMBASE, PubMed and Cochrane Library (CENTRAL) were searched for studies that presented predictive accuracy measures of laboratory biomarkers, or in which these were calculable. Likelihood ratios were calculated in order to determine whether a test result relevantly changed the probability of response. Likelihood ratios between 2-10 and 0.5-0.1 were considered weak predictors, respectively, and ratios above 10 or below 0.1 were considered strong predictors of response. Primary focus was on biomarkers studied ≥3 times.

RESULTS

From 41 included studies, data on 99 different biomarkers were extracted. Five biomarkers were studied ≥3 times, being (1) anti-cyclic citrullinated peptide (CCP), (2) rheumatoid factor, (3) -308 polymorphism in the TNF-α gene, (4) SE copies in the HLA-DRB1 gene and (5) FcGR2A polymorphism. No studies showed a strong predictive association and only one study on anti-CCP showed a weak positive association.

CONCLUSIONS

No biomarkers were found that consistently showed a (strong) predictive effect for response to TNFi in patients with RA. Given the disappointing yield of previous predictive biomarker research, future studies should focus on exploring, combining and validating the most promising laboratory biomarkers identified in this review, and searching for new predictors. Besides this, they should focus on contexts where prediction-aided decision-making can have a large impact (even with limited predictive value of markers/models).

PROSPERO REGISTRATION NUMBER

CRD42021278987.

α2-fraction and haptoglobin as biomarkers for disease activity in oligo- and polyarticular juvenile idiopathic arthritis

OBJECTIVES

Unlike in adult rheumatology, for most forms of juvenile idiopathic arthritis (JIA) no reliable biomarkers currently exist to assess joint and disease activity. However, electrophoresis is frequently found changed in active juvenile arthritis. The objective of this study was to evaluate the α2-fraction of serum electrophoresis and its main components as biomarkers for JIA, categories extended/persistent oligoarthritis and seronegative polyarthritis, in comparison with the conventionally used erythrocyte sedimentation rate and C-reactive protein.

METHODS

Serum samples and clinical data from 181 patients with JIA were collected. Serum electrophoresis and α2-fraction and its components were determined using standard methods. Relationship between calculated α2-fraction of serum electrophoresis (CA2F) and its components, acute-phase parameters and cJADAS27 was assessed using Pearson's correlation coefficient and linear regression modelling, adjusting for confounding effects. Results were confirmed in a second cohort with 223 serum samples from 37 patients, using a mixed model to account for repeated measures.

RESULTS

Compared to ESR and CRP, CA2F showed higher correlation to cJADAS27, in particular for persistent oligoarthritis. Of the three components of the α2-fraction, haptoglobin showed the highest correlation to cJADAS27. Regression analysis demonstrated higher ability to predict cJADAS27 for CA2F, and especially for haptoglobin as a component thereof, than for CRP and ESR.

CONCLUSION

Compared to conventional methods, α2-fraction of serum electrophoresis and specifically, haptoglobin show higher correlations with disease activity in common subtypes of JIA, representing excellent candidates as biomarkers for disease activity. Further studies are necessary to determine diagnostic value and correlations in other subtypes.

-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.

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.

Apolipoprotein C-III is linked to the insulin resistance and beta-cell dysfunction that are present in rheumatoid arthritis

Background

Insulin resistance and beta-cell dysfunction are manifestations of rheumatoid arthritis (RA). Apolipoprotein C-III (ApoC3) has been associated with such insulin resistance and beta-cell dysfunction in the general population. Our purpose was to study whether ApoC3 is also related to the insulin resistance and beta-cell dysfunction that are present in patients with RA.

Methods

Three hundred thirty-eight non-diabetic patients with RA who had a glycemia lower than 110 mg/dl were recruited. Insulin, C-peptide, and ApoC3 were assessed. Insulin resistance and beta-cell function were calculated using the Homeostasis Model Assessment (HOMA2) indices. A multivariable regression analysis was performed to study the relationship of ApoC3 with those molecules and indices adjusting for classic factors associated with insulin resistance that included glucocorticoids.

Results

ApoC3 was related to significant higher levels of circulating insulin (beta coef. 0.37 [95%CI 0.01–0.73] µU/ml, p = 0.044) and C-peptide (beta coef. 0.13 [95%CI 0.05–0.22] ng/ml, p = 0.003), and higher insulin resistance —HOMA2-IR— (beta coef. 0.05 [95%CI 0.00–0.09], p = 0.041) and beta-cell dysfunction —HOMA2-%B— (beta coef. 2.94 [95%CI 0.07–5.80], p = 0.044) indices. This was found after a fully multivariable analysis that included, among others, prednisone intake and the classic factors associated with carbohydrate metabolism such as triglycerides, waist circumference, and obesity.

Conclusion

ApoC3, insulin resistance, and beta-cell dysfunction are independently associated in patients RA.

LC-MS/MS-Based Serum Protein Profiling for Identification of Candidate Biomarkers in Pakistani Rheumatoid Arthritis Patients

Rheumatoid arthritis is an autoimmune disorder of complex disease etiology. Currently available serological diagnostic markers lack in terms of sensitivity and specificity and thus additional biomarkers are warranted for early disease diagnosis and management. We aimed to screen and compare serum proteome profiles of rheumatoid arthritis serotypes with healthy controls in the Pakistani population for identification of potential disease biomarkers. Serum samples from rheumatoid arthritis patients and healthy controls were enriched for low abundance proteins using ProteoMinerTM columns. Rheumatoid arthritis patients were assigned to one of the four serotypes based on anti-citrullinated peptide antibodies and rheumatoid factor. Serum protein profiles were analyzed via liquid chromatography-tandem mass spectrometry. The changes in the protein abundances were determined using label-free quantification software ProgenesisQITM followed by pathway analysis. Findings were validated in an independent cohort of patients and healthy controls using an enzyme-linked immunosorbent assay. A total of 213 proteins were identified. Comparative analysis of all groups (false discovery rate < 0.05, >2-fold change, and identified with ≥2 unique peptides) identified ten proteins that were differentially expressed between rheumatoid arthritis serotypes and healthy controls including pregnancy zone protein, selenoprotein P, C4b-binding protein beta chain, apolipoprotein M, N-acetylmuramoyl-L-alanine amidase, catalytic chain, oncoprotein-induced transcript 3 protein, Carboxypeptidase N subunit 2, Apolipoprotein C-I and Apolipoprotein C-III. Pathway analysis predicted inhibition of liver X receptor/retinoid X receptor activation pathway and production of nitric oxide and reactive oxygen species pathway in macrophages in all serotypes. A catalogue of potential serum biomarkers for rheumatoid arthritis were identified. These biomarkers can be further evaluated in larger cohorts from different populations for their diagnostic and prognostic potential.

Validation in the ESPOIR cohort of vitamin K-dependent protein S (PROS) as a potential biomarker capable of predicting response to the methotrexate/etanercept combination

Background

To validate the ability of PROS (vitamin K-dependent protein S) and CO7 (complement component C7) to predict response to the methotrexate (MTX)/etanercept (ETA) combination in rheumatoid arthritis (RA) patients who received this therapeutic combination in a well-documented cohort.

Method

From the ESPOIR cohort, RA patients having received the MTX/ETA or MTX/adalimumab (ADA) combination as a first-line biologic treatment were included. Serum concentrations of PROS and CO7 were measured by ELISA prior to the initiation of ETA or ADA, at a time where the disease was active (DAS28 ESR > 3.2). The clinical efficacy (response/non-response) of both combinations has been evaluated after at least 6 months of treatment, according to the EULAR response criteria with some modifications.

Results

Thirty-two were treated by MTX/ETA; the numbers of responders and non-responders were 24 and 8, respectively. Thirty-three patients received the MTX/ADA combination; 27 and 5 patients were respectively responders and non-responders. While there were no differences for demographic, clinical, biological, and X-rays data, as well as for CO7, serum levels of PROS tended to be significantly higher in responders to the MTX/ETA combination (p = 0.08) while no difference was observed in the group receiving MTX/ADA. For PROS, the best concentration threshold to differentiate both groups was calculated at 40 μg/ml using ROC curve. The theranostic performances of PROS appeared better for the ETA/MTX combination. When considering the response to this combination, analysis of pooled data from ESPOIR and SATRAPE (initially used to validate PROS and CO7 as potential theranostic biomarkers) cohorts led to a higher theranostic value of PROS that became significant (p = 0.009).

Conclusion

PROS might be one candidate of a combination of biomarkers capable of predicting the response to MTX/ETA combination in RA patients refractory to MTX.

Trial registration

ClinicalTrials.gov identifiers: NCT03666091 and NCT00234234.

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.

Precision Medicine for Rheumatoid Arthritis: The Right Drug for the Right Patient-Companion Diagnostics

Despite the growing number of biologic and JAK inhibitor therapeutic agents available to treat various systemic autoimmune illnesses, the lack of a validated companion diagnostic (CDx) to accurately predict drug responsiveness for an individual results in many patients being treated for years with expensive, ineffective, or toxic drugs. This review will focus primarily on rheumatoid arthritis (RA) therapeutics where the need is greatest due to poor patient outcomes if the optimum drug is delayed. We will review current FDA-approved biologic and small molecule drugs and why RA patients switch these medications. We will discuss the sampling of various tissues for potential CDx and review early results from studies investigating drug responsiveness utilizing advanced technologies including; multiplex testing of cytokines and proteins, autoantibody profiling, genomic analysis, proteomics, miRNA analysis, and metabolomics. By using these new technologies for CDx the goal is to improve RA patient outcomes and achieve similar successes like those seen in oncology using precision medicine guided therapeutics.

Protein array-based companion diagnostics in precision medicine

INTRODUCTION

The development of companion diagnostics (CDx) will increase efficacy and cost-benefit markedly, compared to the currently prevailing trial-and-error approach for treatment. Recent improvements in high-throughput protein technology have resulted in large amounts of predictive biomarkers that are potentially useful components of future CDx assays. Current high multiplex protein arrays are suitable for discovery-based approaches, while low-density and more simple arrays are suitable for use in point-of-care facilities.

AREA COVERED

This review discusses the technical platforms available for protein array focused CDx, explains the technical details of the platforms and provide examples of clinical use, ranging from multiplex arrays to low-density clinically applicable arrays. We thereafter highlight recent predictive biomarkers within different disease areas, such as oncology and autoimmune diseases. Lastly, we discuss some of the challenges connected to the implementation of CDx assays as point-of-care tests.

EXPERT OPINION

Recent advances in the field of protein arrays have enabled high-density arrays permitting large biomarker discovery studies, which are beneficial for future CDx assays. The density of protein arrays range from a single protein to proteome-wide arrays, allowing the discovery of protein signatures that may correlate with drug response. Protein arrays will undoubtedly play a key role in future CDx assays.

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.

Genetic and clinical markers for predicting treatment responsiveness in rheumatoid arthritis

Although many drugs and therapeutic strategies have been developed for rheumatoid arthritis (RA) treatment, numerous patients with RA fail to respond to currently available agents. In this review, we provide an overview of the complexity of this autoimmune disease by showing the rapidly increasing number of genes associated with RA.We then systematically review various factors that have a predictive value (predictors) for the response to different drugs in RA treatment, especially recent advances. These predictors include but are certainly not limited to genetic variations, clinical factors, and demographic factors. However, no clinical application is currently available. This review also describes the challenges in treating patients with RA and the need for personalized medicine. At the end of this review, we discuss possible strategies to enhance the prediction of drug responsiveness in patients with RA.

Development of a Novel Diagnostic Biomarker Set for Rheumatoid Arthritis Using a Proteomics Approach

Background

Rheumatoid arthritis (RA) is an autoimmune disease that starts with inflammation of the synovial membrane. Studies have been conducted to develop methods for efficient diagnosis of RA and to identify the mechanisms underlying RA development. Blood samples can be useful for detecting disturbance of homeostasis in patients with RA. Nanoliquid chromatography-tandem mass spectrometry (LC-MS/MS) is an efficient proteomics approach to analyze blood sample and quantify serum proteins.

Methods

Serum samples of 18 healthy controls and 18 patients with RA were analyzed by LC-MS/MS. Selected candidate biomarkers were validated by enzyme-linked immunosorbent assay (ELISA) using sera from 43 healthy controls and 44 patients with RA.

Results

Thirty-eight proteins were significantly differentially expressed by more than 2-fold in healthy controls and patients with RA. Based on a literature survey, we selected six candidate RA biomarkers. ELISA was used to evaluate whether these proteins effectively allow distinguishing patients with RA from healthy controls and monitoring drug efficacy. SAA4, gelsolin, and vitamin D-binding protein were validated as potential biomarkers of RA for screening and drug efficacy monitoring of RA.

Conclusions

We identified a panel of three biomarkers for RA which has potential for application in RA diagnosis and drug efficacy monitoring. Further, our findings will aid in understanding the pathogenesis of RA.

Proteomics in Chronic Arthritis-Will We Finally Have Useful Biomarkers?

PURPOSE OF REVIEW

Current technical advances enable the assessment of the complex changes in body fluid proteomes and thus allow for the discovery of biomarker signatures rather than just following differences of a single marker. In this review, we aim to summarize current approaches to discover and evaluate multi-biomarker panels for improved monitoring of chronic arthritis disease activity.

RECENT FINDINGS

Mass spectrometry and affinity proteomic methodologies have been used to identify biomarker panels in synovial fluid, serum, plasma, or urine of pediatric and adult chronic arthritis patients. Notably, despite the numerous efforts to develop new and better biomarker panels, very few have undergone extensive analytical and clinical validation and been adopted into routine use for patient benefit. There remains a significant gap between discovery of chronic arthritis biomarker signatures and their validation for clinical use.

Study of Correlation Between Hp α1 Expression of Haptoglobin 2-1 and Clinical Course in Aneurysmal Subarachnoid Hemorrhage

BACKGROUND

Haptoglobin (Hp) comprising 2 light (α) and 2 heavy (β) chains has an antioxidant effect following free hemoglobin binding. Among 3 phenotypes-Hp1-1 (two α1), Hp2-1 (α1 and α2), and Hp2-2 (two α2)-a greater protective effect for toxic-free hemoglobin was reported for Hp2-2 compared with Hp1-1. However, few studies have focused on the association of Hp2-1 with outcomes. This study examined α1 and α2 expression, and evaluated the correlation of Hp2-1 with outcomes of subarachnoid hemorrhage (SAH).

METHODS

Eighty-seven patients were enrolled prospectively, including 12 in the Hp1-1 group (13.8%), 36 in the Hp2-1 group (41.4%), and 39 in the Hp2-2 group (44.8%). Phenotypes were confirmed by Western blot analysis. The relative band intensities were measured as α subunit intensities normalized to albumin intensities and expressed as median (interquartile range). The differences in α intensities according to delayed cerebral ischemia (DCI), angiographic vasospasm (AV), and outcome were analyzed.

RESULTS

DCI and AV were more frequently associated with Hp2-2 than with Hp1-1 (DCI: 21 [53.8%] vs. 3 [25.0%]; AV: 22 [56.4%] vs. 3 [25.0%]). The α1 intensities of Hp2-1 without DCI and AV were significantly higher than those with DCI and AV (without DCI: 0.70 [interquartile range (IQR), 0.54-0.89]; with DCI: 0.24 [IQR, 0.14-0.32]; P < 0.001; without AV: 0.65 [IQR, 0.32-0.88]; with AV: 0.32 [IQR, 0.17-0.67]; P = 0.046). No significant difference was noted with α2 intensities. The α1 (P = 0.359) and α2 (P = 0.233) intensities did not differ significantly according to outcome.

CONCLUSIONS

Higher α1 intensities in Hp2-1 are associated with a lower risk of DCI and AV. The degree of α1 intensity may provide additional information about the individual risk of secondary injury following SAH in patients with Hp2-1.

Prealbumin, platelet factor 4 and S100A12 combination at baseline predicts good response to TNF alpha inhibitors in rheumatoid arthritis

OBJECTIVES

Tumour necrosis factor-alpha inhibitors (TNFi) are effective treatments for Rheumatoid Arthritis (RA). Responses to treatment are barely predictable. As these treatments are costly and may induce a number of side effects, we aimed at identifying a panel of protein biomarkers that could be used to predict clinical response to TNFi for RA patients.

METHODS

Baseline blood levels of C-reactive protein, platelet factor 4, apolipoprotein A1, prealbumin, α1-antitrypsin, haptoglobin, S100A8/A9 and S100A12 proteins in bDMARD naive patients at the time of TNFi treatment initiation were assessed in a multicentric prospective French cohort. Patients fulfilling good EULAR response at 6 months were considered as responders. Logistic regression was used to determine best biomarker set that could predict good clinical response to TNFi.

RESULTS

A combination of biomarkers (prealbumin, platelet factor 4 and S100A12) was identified and could predict response to TNFi in RA with sensitivity of 78%, specificity of 77%, positive predictive values (PPV) of 72%, negative predictive values (NPV) of 82%, positive likelihood ratio (LR+) of 3.35 and negative likelihood ratio (LR-) of 0.28. Lower levels of prealbumin and S100A12 and higher level of platelet factor 4 than the determined cutoff at baseline in RA patients are good predictors for response to TNFi treatment globally as well as to Infliximab, Etanercept and Adalimumab individually.

CONCLUSION

A multivariate model combining 3 biomarkers (prealbumin, platelet factor 4 and S100A12) accurately predicted response of RA patients to TNFi and has potential in a daily practice personalized treatment.

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.