Personalized biological treatment for rheumatoid arthritis: a systematic review with a focus on clinical applicability

Impact of age and cardiovascular risk factors on the incidence of adverse events in patients with rheumatoid arthritis treated with Janus Kinase inhibitors: data from a real-life multicentric cohort

Inhibiting Janus Kinases (JAK) is a crucial therapeutic strategy in rheumatoid arthritis (RA). However, the use of JAK inhibitors has recently raised serious safety concerns. The study aims to evaluate the safety profile of JAKi in patients with RA and identify potential risk factors (RFs) for adverse events (AEs). Data of RA patients treated with JAKi in three Italian centers from January 2017 to December 2022 were retrospectively analyzed. 182 subjects (F:117, 64.3%) underwent 193 treatment courses. 78.6% had at least one RF, including age ≥ 65 years, obesity, smoking habit, hypertension, dyslipidemia, hyperuricemia, diabetes, previous VTE or cancer, and severe mobility impairment. We identified 70 AEs (28/100 patients/year), among which 15 were serious (6/100 patients/year). A high disease activity was associated with AEs occurrence (p = 0.03 for CDAI at T0 and T6; p = 0.04 for SDAI at T0 and T6; p = 0.01 and p = 0.04 for DAS28ESR at T6 and T12, respectively). No significant differences in AEs occurrence were observed after stratification by JAKi molecules (p = 0.44), age groups (p = 0.08) nor presence of RFs (p > 0.05 for all of them). Neither the presence of any RFs, nor the cumulative number of RFs shown by the patient, nor age ≥ 65 did predict AEs occurrence. Although limited by the small sample size and the limited number of cardiovascular events, our data do not support the correlation between cardiovascular RFs-including age-and a higher incidence of AEs during JAKi therapy. The role of uncontrolled disease activity in AEs occurrence should by emphasized.

Patterns and determinants of response to novel therapies in juvenile and adult-onset polyarthritis

Biologic and targeted synthetic DMARDs (b/tsDMARDs) have revolutionized the management of multiple rheumatic inflammatory conditions. Among these, polyarticular JIA (pJIA) and RA display similarities in terms of disease pathophysiology and response pattern to b/tsDMARDs. Indeed, the therapeutic efficacy of novel targeted drugs is variable among individual patients, in both RA and pJIA. The mechanisms and determinants of this heterogeneous response are diverse and complex, such that the development of true 'precision'-medicine strategies has proven highly challenging. In this review, we will discuss pathophysiological, patient-specific, drug-specific and environmental factors contributing to individual therapeutic response in pJIA in comparison with what is known in RA. Although some biomarkers have been identified that stratify with respect to the likelihood of either therapeutic response or non-response, few have proved useful in clinical practice so far, likely due to the complexity of treatment-response mechanisms. Consequently, we propose a pragmatic, patient-centred and clinically based approach, i.e. personalized instead of biomarker-based precision medicine in JIA.

Anti-citrullinated protein antibody profiles predict changes in disease activity in patients with rheumatoid arthritis initiating biologics

OBJECTIVES

To determine whether an expanded antigen-specific ACPA profile predicts changes in disease activity in patients with RA initiating biologics.

METHODS

The study included participants from a prospective, non-randomized, observational RA cohort. For this sub-study, treatment groups of interest included biologic-naïve initiating anti-TNF, biologic-exposed initiating non-TNF, and biologic-naïve initiating abatacept. ACPAs to 25 citrullinated peptides were measured using banked enrolment serum. Principal component analysis (PCA) was performed and associations of resulting principal component (PC) scores (in quartiles) and anti-CCP3 antibody (≤15, 16-250 or >250 U/ml) with EULAR (good/moderate/none) treatment response at 6 months were examined using adjusted ordinal regression models.

RESULTS

Participants (n = 1092) had a mean age of 57 (13) years and 79% were women. At 6 months, 68.5% achieved a moderate/good EULAR response. There were three PCs that cumulatively explained 70% of variation in ACPA values. In models including the three components and anti-CCP3 antibody category, only PC1 and PC2 were associated with treatment response. The highest quartile for PC1 (odds ratio [OR] 1.76; 95% CI: 1.22, 2.53) and for PC2 (OR 1.74; 95% CI: 1.23, 2.46) were associated with treatment response after multivariable adjustment. There was no evidence of interaction between PCs and treatment group in EULAR responses (P-value for interaction >0.1).

CONCLUSION

An expanded ACPA profile appears to be more strongly associated with biologic treatment response in RA than commercially available anti-CCP3 antibody levels. However, further enhancements to PCA will be needed to effectively prioritize between different biologics available for the treatment of RA.

[68 Ga]Ga-FAPI-04 PET/CT may be a predictor for early treatment response in rheumatoid arthritis

BACKGROUND

The identification of biomarkers predicting the treatment response of rheumatoid arthritis (RA) is important. [68 Ga]Ga-FAPI-04 showed markedly increased uptake in the joints of patients with RA. The purpose of this study is to investigate whether [68 Ga]Ga-FAPI-04 PET/CT can be a predictor of treatment response in RA.

RESULTS

Nineteen patients diagnosed with RA in the prospective cohort study were finally enrolled. Both total synovitis uptake (TSU) and metabolic synovitis volume (MSV) in [68 Ga]Ga-FAPI-04 and [18F]FDG PET/CT of the responders were significantly higher than those in non-responders according to Clinical Disease Activity Index (CDAI) and Simplified Disease Activity Index (SDAI) response criteria at 3-months' follow-up (P < 0.05). The PET joint count (PJC) detected in [68 Ga]Ga-FAPI-04 and [18F]FDG PET/CT were also significantly higher in CDAI responders than non-responders (P = 0.016 and 0.045, respectively). The clinical characteristics of disease activity at baseline did not show significant difference between the responders and non-responders, except CRP (P = 0.035 and 0.033 in CDAI and SDAI response criteria, respectively). The baseline PJCFAPI, TSUFAPI and MSVFAPI > cutoff values in [68 Ga]Ga-FAPI-04 PET/CT successfully discriminated CDAI and SDAI responders and non-responders at 3-months' follow-up.

CONCLUSION

[68 Ga]Ga-FAPI-04 uptake at baseline were significantly higher in early responders than those in non-responders. Trial registration ClinicalTrials. NCT04514614. Registered 13 August 2020, https://register.

CLINICALTRIALS

gov/prs/app/action/SelectProtocol?sid=S000A4PN&selectaction=Edit&uid=U0001JRW&ts=2&cx=-x9t7cp.

Novel DNA methylome biomarkers associated with adalimumab response in rheumatoid arthritis patients

Background and aims

Rheumatoid arthritis (RA) patients are currently treated with biological agents mostly aimed at cytokine blockade, such as tumor necrosis factor-alpha (TNFα). Currently, there are no biomarkers to predict therapy response to these agents. Here, we aimed to predict response to adalimumab (ADA) treatment in RA patients using DNA methylation in peripheral blood (PBL).

Methods

DNA methylation profiling on whole peripheral blood from 92 RA patients before the start of ADA treatment was determined using Illumina HumanMethylationEPIC BeadChip array. After 6 months, treatment response was assessed according to the European Alliance of Associations for Rheumatology (EULAR) criteria for disease activity. Patients were classified as responders (Disease Activity Score in 28 Joints (DAS28) < 3.2 or decrease of 1.2 points) or as non-responders (DAS28 > 5.1 or decrease of less than 0.6 points). Machine learning models were built through stability-selected gradient boosting to predict response prior to ADA treatment with predictor DNA methylation markers.

Results

Of the 94 RA patients, we classified 49 and 43 patients as responders and non-responders, respectively. We were capable of differentiating responders from non-responders with a high performance (area under the curve (AUC) 0.76) using a panel of 27 CpGs. These classifier CpGs are annotated to genes involved in immunological and pathophysiological pathways related to RA such as T-cell signaling, B-cell pathology, and angiogenesis.

Conclusion

Our findings indicate that the DNA methylome of PBL provides discriminative capabilities in discerning responders and non-responders to ADA treatment and may therefore serve as a tool for therapy prediction.

The added value of predictive biomarkers in treat-to-target strategies for rheumatoid arthritis patients: a conceptual modelling study

OBJECTIVES

To quantify the additional value of a hypothetical biomarker predicting response to treatment for RA regarding efficacy and costs by using a modelling design.

METHODS

A Markov model was built comparing a usual care T2T strategy with a biomarker-steered strategy for RA patients starting biologic therapy. Outcome measures include time spent in remission or low disease activity (LDA) and costs. Four additional scenario analyses were performed by varying biomarker or clinical care characteristics: (i) costs of the biomarker; (ii) sensitivity and specificity of the biomarker; (iii) proportion of eligible patients tapering; and (iv) medication costs.

RESULTS

In the base model, patients spent 2.9 months extra in LDA or remission in the biomarker strategy compared with usual care T2T over 48 months. Total costs were €43 301 and €42 568 for, respectively, the usual care and biomarker strategy, and treatment costs accounted for 91% of total costs in both scenarios. Cost savings were driven due to patients in the biomarker strategy experiencing remission or LDA earlier, and starting tapering sooner. Cost-effectiveness was not so much driven by costs or test characteristics of the biomarker (scenario 1/2), but rather by the level of early and proactive tapering and drug costs (scenarios 3/4).

CONCLUSIONS

The use of a biomarker for prediction of response to b/tsDMARD treatment in RA can be of added value to current treat-to-target clinical care. However, gains in efficacy are modest and cost gains are depending on a combination of early proactive tapering and high medication costs.

Biomarkers to guide immunomodulatory treatment: where do we stand?

INTRODUCTION

This review summarizes current progress in the development of biomarkers to guide immunotherapy in oncology, rheumatology, and critical illness.

AREAS COVERED

An extensive literature search was performed about biomarkers classifying patients' immune responses to guide immunotherapy in oncology, rheumatology, and critical illness. Surface markers, such as programmed death-ligand 1 (PD-L1), genetic biomarkers, such as tumor mutation load, and circulating tumor DNA are biomarkers associated with the effectiveness of immunotherapy in oncology. Genomics, metabolomics, and proteomics play a crucial role in selecting the most suitable therapeutic options for rheumatologic patients. Phenotypes and endotypes are a promising approach to detect critically ill patients with hyper- or hypo-inflammation. Sepsis trials using biomarkers such as ferritin, lymphopenia, HLA-DR expression on monocytes and PD-L1 to guide immunotherapy have been already conducted or are currently ongoing. Immunotherapy in COVID-19 pneumonia, guided by C-reactive protein and soluble urokinase plasminogen activator receptor (suPAR) has improved patient outcomes globally. More research is needed into immunotherapy in other critical conditions.

EXPERT OPINION

Targeted immunotherapy has improved outcomes in oncology and rheumatology, paving the way for precision medicine in the critically ill. Transcriptomics will play a crucial role in detecting the most suitable candidates for immunomodulation.

Patient groups in Rheumatoid arthritis identified by deep learning respond differently to biologic or targeted synthetic DMARDs

Cycling of biologic or targeted synthetic disease modifying antirheumatic drugs (b/tsDMARDs) in rheumatoid arthritis (RA) patients due to non-response is a problem preventing and delaying disease control. We aimed to assess and validate treatment response of b/tsDMARDs among clusters of RA patients identified by deep learning. We clustered RA patients clusters at first-time b/tsDMARD (cohort entry) in the Swiss Clinical Quality Management in Rheumatic Diseases registry (SCQM) [1999-2018]. We performed comparative effectiveness analyses of b/tsDMARDs (ref. adalimumab) using Cox proportional hazard regression. Within 15 months, we assessed b/tsDMARD stop due to non-response, and separately a ≥20% reduction in DAS28-esr as a response proxy. We validated results through stratified analyses according to most distinctive patient characteristics of clusters. Clusters comprised between 362 and 1481 patients (3516 unique patients). Stratified (validation) analyses confirmed comparative effectiveness results among clusters: Patients with ≥2 conventional synthetic DMARDs and prednisone at b/tsDMARD initiation, male patients, as well as patients with a lower disease burden responded better to tocilizumab than to adalimumab (hazard ratio [HR] 5.46, 95% confidence interval [CI] [1.76-16.94], and HR 8.44 [3.43-20.74], and HR 3.64 [2.04-6.49], respectively). Furthermore, seronegative women without use of prednisone at b/tsDMARD initiation as well as seropositive women with a higher disease burden and longer disease duration had a higher risk of non-response with golimumab (HR 2.36 [1.03-5.40] and HR 5.27 [2.10-13.21], respectively) than with adalimumab. Our results suggest that RA patient clusters identified by deep learning may have different responses to first-line b/tsDMARD. Thus, it may suggest optimal first-line b/tsDMARD for certain RA patients, which is a step forward towards personalizing treatment. However, further research in other cohorts is needed to verify our results.

Identification of ferroptosis related markers by integrated bioinformatics analysis and In vitro model experiments in rheumatoid arthritis

BACKGROUND

Rheumatoid arthritis (RA) is an autoimmune disease characterized by destructive and symmetrical joint diseases and synovitis. This research attempted to explore the mechanisms involving ferroptosis in RA, and find the biological markers by integrated analysis.

METHODS

Gene expression data (GSE55235 and GSE55457) of synovial tissues from healthy and RA individuals were downloaded. By filtering the differentially expressed genes (DEGs) and intersecting them with the 484 ferroptosis-related genes (FRGs), the overlapping genes were identified. After the enrichment analysis, the machine learning-based approaches were introduced to screen the potential biomarkers, which were further validated in other two datasets (GSE77298 and GSE93272) and cell samples. Besides, we also analyze the infiltrating immune cells in RA and their correlation with the biomarkers.

RESULTS

With the criteria, 635 DEGs in RA were included, and 29 of them overlapped in the reported 484 FRGs. The enrichments of the 29 differentially expressed ferroptosis-related genes indicated that they may involve in the FoxO signaling pathway and inherited metabolic disorder. RRM2, validating by the external datasets and western blot, were identified as the biomarker with the high diagnostic value, whose associated immune cells, such as Neutrophils and Macrophages M1, were also further evaluated.

CONCLUSION

We preliminary explored the mechanisms between ferroptosis and RA. These results may help us better comprehend the pathophysiological changes of RA in basic research, and provide new evidences for the clinical transformation.

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.

Differences in trajectory of disease activity according to biologic and targeted synthetic disease-modifying anti-rheumatic drug treatment in patients with rheumatoid arthritis

Background

The purpose of this study was to stratify patients with rheumatoid arthritis (RA) according to the trend of disease activity by trajectory-based clustering and to identify contributing factors for treatment response to biologic and targeted synthetic disease-modifying anti-rheumatic drugs (DMARDs) according to trajectory groups.

Methods

We analyzed the data from a nationwide RA cohort from the Korean College of Rheumatology Biologics and Targeted Therapy registry. Patients treated with second-line biologic and targeted synthetic DMARDs were included. Trajectory modeling for clustering was used to group the disease activity trend. The contributing factors using the machine learning model of SHAP (SHapley Additive exPlanations) values for each trajectory were investigated.

Results

The trends in the disease activity of 688 RA patients were clustered into 4 groups: rapid decrease and stable disease activity (group 1, n = 319), rapid decrease followed by an increase (group 2, n = 36), slow and continued decrease (group 3, n = 290), and no decrease in disease activity (group 4, n = 43). SHAP plots indicated that the most important features of group 2 compared to group 1 were the baseline erythrocyte sedimentation rate (ESR), prednisolone dose, and disease activity score with 28-joint assessment (DAS28) (SHAP value 0.308, 0.157, and 0.103, respectively). The most important features of group 3 compared to group 1 were the baseline ESR, DAS28, and estimated glomerular filtration rate (eGFR) (SHAP value 0.175, 0.164, 0.042, respectively). The most important features of group 4 compared to group 1 were the baseline DAS28, ESR, and blood urea nitrogen (BUN) (SHAP value 0.387, 0.153, 0.144, respectively).

Conclusions

The trajectory-based approach was useful for clustering the treatment response of biologic and targeted synthetic DMARDs in patients with RA. In addition, baseline DAS28, ESR, prednisolone dose, eGFR, and BUN were important contributing factors for 4-year trajectories.

Managing inadequate response to initial anti-TNF therapy in rheumatoid arthritis: optimising treatment outcomes

Anti-tumour necrosis factors (anti-TNFs) are established as first-line biological therapy for rheumatoid arthritis (RA) with over two decades of accumulated clinical experience. Anti-TNFs have well established efficacy/safety profiles along with additional benefits on various comorbidities. However, up to 40% of patients may respond inadequately to an initial anti-TNF treatment because of primary non-response, loss of response, or intolerance. Following inadequate response (IR) to anti-TNF treatment, clinicians can consider switching to an alternative anti-TNF (cycling) or to another class of targeted drug with a different mechanism of action, such as Janus kinase inhibitors, interleukin-6 receptor blockers, B-cell depletion agents, and co-stimulation inhibitors (swapping). While European League Against Rheumatism recommendations for pharmacotherapeutic management of RA, published in 2020, are widely regarded as helpful guides to clinical practice, they do not provide any clear recommendations on therapeutic choices following an IR to first-line anti-TNF. This suggests that both cycling and swapping treatment strategies are of equal value, but that the treating physician must take the patient’s individual characteristics into account. This article considers which patient characteristics influence clinical decision-making processes, including the reason for treatment failure, previous therapies, comorbidities, extra-articular manifestations, pregnancy, patient preference and cost-effectiveness, and what evidence is available to support decisions made by the physician.

'Difficult to treat' rheumatoid arthritis: current position and considerations for next steps

The European Alliance of Associations for Rheumatology recently defined difficult to treat (D2T) rheumatoid arthritis (RA) and provided points to consider in its management. This review summarises the key concepts of D2T-RA that underpinned this recent guidance. D2T-RA is primarily characterised by failure of at least two different mechanism of action biologic/targeted synthetic disease-modifying antirheumatic drug (DMARDs) with evidence of active/progressive disease. The basis for progressive disease, however, is not limited to clear inflammatory joint pathology, capturing wider contributors to treatment cycling such as comorbidity, obesity and fibromyalgia. This means D2T-RA comprises a heterogeneous population, with a proportion within this exhibiting bona fide treatment-refractory disease. The management points to consider, however, emphasise the importance of checking for the presence of inflammatory pathology before further treatment change. This review suggests additional considerations in the definition of D2T-RA, the potential value in identifying D2T traits and intervening before the development of D2T-RA state and the need for real world evidence of targeted synthetic DMARD in this population to compare to recent trial data. Finally, the review asks whether the presence of D2T-RA implies a failure to treat effectively from the outset, and the need for pharmacological and non-pharmacological management approaches to address the wider D2T-RA population effectively.

New Druggable Targets for Rheumatoid Arthritis Based on Insights From Synovial Biology

Rheumatoid arthritis (RA) is a multifactorial autoimmune disease characterized by chronic inflammation and destruction of multiple small joints which may lead to systemic complications. Altered immunity via pathogenic autoantibodies pre-date clinical symptom development by several years. Incompletely understood range of mechanisms trigger joint-homing, leading to clinically evident articular disease. Advances in therapeutic approaches and understanding pathogenesis have improved prognosis and likely remission. However, partial/non-response to conventional and biologic therapies witnessed in a subset of patients highlights the need for new therapeutics. It is now evident that joint disease chronicity stems from recalcitrant inflammatory synovial environment, majorly maintained by epigenetically and metabolically reprogrammed synoviocytes. Therefore, interference with effector functions of activated cell types seems a rational strategy to reinstate synovial homeostasis and complement existing anti-inflammatory interventions to mitigate chronic RA. Presenting this newer aspect of fibroblast-like synoviocytes and myeloid cells underlying the altered synovial biology in RA and its potential for identification of new druggable targets is attempted in this review. Major leads from i) molecular insights of pathogenic cell types from hypothesis free OMICS approaches; ii) hierarchy of their dysregulated signaling pathways; and iii) knowledge of druggability of molecular nodes in these pathways are highlighted. Development of such synovial biology-directed therapeutics hold promise for an enriched drug repertoire for RA.

Clinical Academic Rheumatology: A Boon for Health Systems

BACKGROUND/PURPOSE

Finding a balance between clinical and scholarly productivity is a challenge for many academic clinician-educator (CE) rheumatologists. An examination of workload and downstream revenue determines if the financial value generated by services rendered by rheumatologists are proportionate to the financial value created for a health system. A 2005 study found that academic rheumatologists generate $10.02 for every $1.00 they receive for an office visit.² METHODS: A retrospective analysis of ordering practices of five full-time CE rheumatologists from August 2017 - February 2019 was done. Individual workload is defined as averaged full-time equivalents based on time spent on clinical and academic duties. Academic productivity was reviewed. Revenue generating activities that benefited the division directly and downstream revenue were collected. Revenue was extrapolated based on volumes of referrals, publicly available drug costs and estimated Medicare reimbursement values (average sales price) of representative drugs.

RESULTS

The total revenue by physician which benefited the division directly is $597,203 with evaluation and management codes accounting for $174,456. Downstream revenue by physician totaled $2,119,437. The largest contributor was from referrals to the hospital-based infusion center at $1,287,496. The downstream revenue generated by rheumatologist per dollar of evaluation and management services was found to be $12.14 ($9.37 in 2005 dollars).

CONCLUSIONS

For every $1 generated though office visits by five practicing academic rheumatologists at our institution $12.14 were generated through downstream revenue, which when adjusted for inflation shows stability in the value generated by academic rheumatologists ($10.02 vs. $9.37).

Drug levels, anti-drug antibodies and B-cell counts were not predictive of response in rheumatoid arthritis patients on low dose rituximab

OBJECTIVES

The REDO trial showed that ultra-low dose rituximab (500 mg or 200 mg) was similarly effective in the majority of rheumatoid arthritis (RA) patients. This pre-planned secondary analysis investigates 1) associations between rituximab dosage, drug levels, anti-drug antibodies (ADA) and B cell counts and 2) the predictive value of pharmacokinetic and -dynamic parameters, patient, disease and treatment characteristics in relation to response to ultra-low dose rituximab.

METHODS

For 140 RA patients from the REDO trial, differences in drug levels, ADA and B cell counts were examined at baseline, three and six months after dosing. Treatment response was defined as absence of flare and no extra rituximab or > 1 glucocorticoid injection received during follow-up. The association between potential predictors and response was investigated using logistic regression analyses.

RESULTS

Lower doses of rituximab resulted in lower drug levels but did not significantly affect ADA levels and B cell counts. 3 (10.7%), 12 (20.7%) and 7 (13.0%) patients failed to meet response-criteria in respectively the 1000 mg, 500 mg and 200 mg group. Drug levels, ADA and B cell counts as well as patient, disease and treatment characteristics were not predictive for response to ultra-low dose rituximab.

CONCLUSION

Results of this study further support that continued treatment with 500 or 200 mg rituximab is similarly effective as 1000 mg in RA patients doing well on rituximab. These results, combined with absence of clinical dose response relation in the original REDO study, suggest that 200 mg rituximab is not yet the lowest effective rituximab retreatment dose in RA.

Cellular and molecular diversity in Rheumatoid Arthritis

Rheumatoid Arthritis (RA) is a chronic systemic autoimmune disease. RA mainly affects synovial joints, with inflammation of the synovial membrane (synovitis), characterised by neo-angiogenesis, hyperplasia of lining layer, and immune cell infiltration that drive local inflammation and, if untreated, can lead to joint destruction and disability. In parallel to the well-known clinical heterogeneity, the underlying synovitis can also be significantly heterogeneous, both at cellular and molecular level, which can at least in part explain why despite the availability of highly effective treatment options, a large proportion of patients are resistant to some individual treatments. The assimilation of recent high-throughput data from analysis at the single-cell level with rigorous and high-quality clinical outcomes obtained from large randomised clinical trials support the definition of disease and treatment response endotypes. Looking ahead, the integration of histological and molecular signatures from the diseased tissue into clinical algorithms may help decision making in the management of patients with Rheumatoid Arthritis in clinical practice.

Torque Teno Virus Quantification for Monitoring of Immunomodulation with Biological Compounds in the Treatment of Rheumatoid Arthritis

OBJECTIVES

Rheumatoid arthritis (RA) patients who fail to respond to methotrexate (MTX) can receive biologic disease-modifying antirheumatic drugs (bDMARDs). The Torque Teno Virus (TTV) is a potential novel candidate for monitoring of immunosuppression. We explore TTV in these patients and association with clinical response to bDMARDs.

METHODS

The BioBio Study is a multicentre randomized open-label trial, including RA patients with insufficient response to MTX. Patients were randomized to either TNFi (infliximab, INF), anti-IL-6 (tocilizumab, TCZ), CTLA4-Ig (abatacept, ABA) or anti-CD20 (rituximab, RTX) in addition to MTX. PCR was used to quantify TTV in the peripheral blood.

RESULTS

TTV was measured in 95 patients (INF, n = 23; TCZ, n = 22; ABA, n = 27; RTX; n = 23). TTV increased by a median of 4.5*104 copies/ml (c/ml; inter quartile range [IQR] 0-7.5*105) after 3 months. TTV levels at month 3 were associated with SDAI (p= 0.03) and CDAI response (p= 0.026) at month 6. A TTV cut-off level of 1.2*106 c/ml at month 3 had a positive likelihood ratio of 2.7 for prediction of SDAI85% response at month 6.

CONCLUSION

Our data suggest that TTV levels increase upon TNF, CD20 and co-stimulation blockade and associate with clinical response to bDMARDs in RA patients.

TRIAL REGISTRATION

ClinicalTrials.gov; https://clinicaltrials.gov; NCT01638715.

Validation of GWAS-Identified Variants for Anti-TNF Drug Response in Rheumatoid Arthritis: A Meta-Analysis of Two Large Cohorts

We aimed to validate the association of 28 GWAS-identified genetic variants for response to TNF inhibitors (TNFi) in a discovery cohort of 1361 rheumatoid arthritis (RA) patients monitored in routine care and ascertained through the REPAIR consortium and DANBIO registry. We genotyped selected markers and evaluated their association with response to TNFi after 6 months of treatment according to the change in disease activity score 28 (ΔDAS28). Next, we confirmed the most interesting results through meta-analysis of our data with those from the DREAM cohort that included 706 RA patients treated with TNFi. The meta-analysis of the discovery cohort and DREAM registry including 2067 RA patients revealed an overall association of the LINC02549_rs7767069 SNP with a lower improvement in DAS28 that remained significant after correction for multiple testing (per-allele OR_Meta=0.83, P_Meta=0.000077; P_Het=0.61). In addition, we found that each copy of the LRRC55_rs717117G allele was significantly associated with lower improvement in DAS28 in rheumatoid factor (RF)-positive patients (per-allele OR_Meta=0.67, P=0.00058; P_Het=0.06) whereas an opposite but not significant effect was detected in RF-negative subjects (per-allele OR_Meta=1.38, P=0.10; P_Het=0.45; P_Interaction=0.00028). Interestingly, although the identified associations did not survive multiple testing correction, the meta-analysis also showed overall and RF-specific associations for the MAFB_rs6071980 and CNTN5_rs1813443 SNPs with decreased changes in DAS28 (per-allele OR_{Meta_rs6071980} = 0.85, P=0.0059; P_Het=0.63 and OR_{Meta_rs1813443_RF+}=0.81, P=0.0059; P_Het=0.69 and OR_{Meta_rs1813443_RF-}=1.00, P=0.99; P_Het=0.12; P_Interaction=0.032). Mechanistically, we found that subjects carrying the LINC02549_rs7767069T allele had significantly increased numbers of CD45RO+CD45RA+ T cells (P=0.000025) whereas carriers of the LINC02549_rs7767069T/T genotype showed significantly increased levels of soluble scavengers CD5 and CD6 in serum (P=0.00037 and P=0.00041). In addition, carriers of the LRRC55_rs717117G allele showed decreased production of IL6 after stimulation of PBMCs with B burgdorferi and E coli bacteria (P=0.00046 and P=0.00044), which suggested a reduced IL6-mediated anti-inflammatory effect of this marker to worsen the response to TNFi. In conclusion, this study confirmed the influence of the LINC02549 and LRRC55 loci to determine the response to TNFi in RA patients and suggested a weak effect of the MAFB and CNTN5 loci that need to be further investigated.

Machine learning model for identifying important clinical features for predicting remission in patients with rheumatoid arthritis treated with biologics

Background

We developed a model to predict remissions in patients treated with biologic disease-modifying anti-rheumatic drugs (bDMARDs) and to identify important clinical features associated with remission using explainable artificial intelligence (XAI).

Methods

We gathered the follow-up data of 1204 patients treated with bDMARDs (etanercept, adalimumab, golimumab, infliximab, abatacept, and tocilizumab) from the Korean College of Rheumatology Biologics and Targeted Therapy Registry. Remission was predicted at 1-year follow-up using baseline clinical data obtained at the time of enrollment. Machine learning methods (e.g., lasso, ridge, support vector machine, random forest, and XGBoost) were used for the predictions. The Shapley additive explanation (SHAP) value was used for interpretability of the predictions.

Results

The ranges for accuracy and area under the receiver operating characteristic of the newly developed machine learning model for predicting remission were 52.8–72.9% and 0.511–0.694, respectively. The Shapley plot in XAI showed that the impacts of the variables on predicting remission differed for each bDMARD. The most important features were age for adalimumab, rheumatoid factor for etanercept, erythrocyte sedimentation rate for infliximab and golimumab, disease duration for abatacept, and C-reactive protein for tocilizumab, with mean SHAP values of − 0.250, − 0.234, − 0.514, − 0.227, − 0.804, and 0.135, respectively.

Conclusions

Our proposed machine learning model successfully identified clinical features that were predictive of remission in each of the bDMARDs. This approach may be useful for improving treatment outcomes by identifying clinical information related to remissions in patients with rheumatoid arthritis.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13075-021-02567-y.

Personalized prediction of disease activity in patients with rheumatoid arthritis using an adaptive deep neural network

Background

Deep neural networks learn from former experiences on a large scale and can be used to predict future disease activity as potential clinical decision support. AdaptiveNet is a novel adaptive recurrent neural network optimized to deal with heterogeneous and missing clinical data.

Objective

We investigate AdaptiveNet for the prediction of individual disease activity in patients from a rheumatoid arthritis (RA) registry.

Methods

Demographic and disease characteristics from over 9500 patients and 65.000 visits from the Swiss Quality Management (SCQM) database were used to train and evaluate the network. Patient characteristics, clinical and patient reported outcomes, laboratory values and medication were used as input features. DAS28-BSR served as a target to predict active RA and future numeric individual disease activity by classification and regression.

Results

AdaptiveNet predicted active disease defined as DAS28-BSR >2.6 at the next visit with an overall accuracy of 75.6% (SD +- 0.7%) and a sensitivity and specificity of 84.2% (SD +- 1.6%) and 61.5% (SD +- 3.6%), respectively. Prediction performance was significantly higher in patients with a disease duration >3 years and positive rheumatoid factor. Regression allowed forecasting individual DAS28-BSR values with a mean squared error (MSE) of 0.9 (SD +- 0.05). This corresponds to a 8% deviation between estimated and real DAS28-BSR values. Compared to linear regression, random forest and support vector machines, AdaptiveNet showed an increased performance of over 7% in MSE. Medication played a minor role in the prediction of RA disease activity.

Conclusion

AdaptiveNet has a superior capacity to predict numeric RA disease activity compared to classical machine learning architectures. All investigated models had limitations in low specificity.

Using adalimumab serum concentration to choose a subsequent biological DMARD in rheumatoid arthritis patients failing adalimumab treatment (ADDORA-switch): study protocol for a fully blinded randomised superiority test-treatment trial

Background

A substantial proportion of rheumatoid arthritis (RA) patients discontinues treatment with tumour necrosis factor inhibitors (TNFi) due to inefficacy or intolerance. After the failure of treatment with a TNFi, treatment can be switched to another TNFi or a bDMARD with a different mode of action (non-TNFi). Measurement of serum drug concentrations and/or anti-drug antibodies (therapeutic drug monitoring (TDM)) may help to inform the choice for the next step. However, the clinical utility of TDM to guide switching has not been investigated in a randomised test-treatment study.

Methods

ADDORA-switch is a 24-week, multi-centre, triple-blinded, superiority test-treatment randomised controlled trial. A total of 84 RA patients failing adalimumab treatment (treatment failure defined as DAS28-CRP > 2.9) will be randomised in a 1:1 ratio to a switching strategy to either TNFi or non-TNFi based on adalimumab serum trough level (intervention group) or random allocation (control group). The primary outcome is the between-group difference in mean time-weighted DAS28 over 24 weeks.

Discussion

The trial design differs in many aspects from previously published and ongoing TDM studies and is considered the first blinded test-treatment trial using TDM in RA. Several choices in the design of this trial are described, and overarching principles regarding test-treatment trials and clinical utility of TDM are discussed in further detail.

Trial registration

Dutch Trial Register NL8210. Registered on 3 December 2019 (CMO NL69841.091.19).

Supplementary Information

The online version contains supplementary material available at 10.1186/s13063-021-05358-7.

Schistosome Infection and Schistosome-Derived Products as Modulators for the Prevention and Alleviation of Immunological Disorders

Parasitic helminths, comprising the flatworms (tapeworms and flukes) and nematodes (roundworms), have plagued humans persistently over a considerable period of time. It is now known that the degree of exposure to these and other pathogens inversely correlates with the incidence of both T helper 1 (Th1)-mediated autoimmunity and Th2-mediated allergy. Accordingly, there has been recent increased interest in utilizing active helminth worm infections and helminth-derived products for the treatment of human autoimmune and inflammatory diseases and to alleviate disease severity. Indeed, there is an accumulating list of novel helminth derived molecules, including proteins, peptides, and microRNAs, that have been shown to exhibit therapeutic potential in a variety of disease models. Here we consider the blood-dwelling schistosome flukes, which have evolved subtle immune regulatory mechanisms that promote parasite survival but at the same time minimize host tissue immunopathology. We review and discuss the recent advances in using schistosome infection and schistosome-derived products as therapeutics to treat or mitigate human immune-related disorders, including allergic asthma, arthritis, colitis, diabetes, sepsis, cystitis, and cancer.

Broad proteomic screen reveals shared serum proteomic signature in patients with psoriatic arthritis and psoriasis without arthritis

OBJECTIVE

To identify novel serum proteins involved in the pathogenesis of PsA as compared with healthy controls, psoriasis (Pso) and AS, and to explore which proteins best correlated to major clinical features of the disease.

METHODS

A high-throughput serum biomarker platform (Olink) was used to assess the level of 951 unique proteins in serum of patients with PsA (n = 20), Pso (n = 18) and AS (n = 19), as well as healthy controls (HC, n = 20). Pso and PsA were matched for Psoriasis Area and Severity Index (PASI) and other clinical parameters.

RESULTS

We found 68 differentially expressed proteins (DEPs) in PsA as compared with HC. Of those DEPs, 48 proteins (71%) were also dysregulated in Pso and/or AS. Strikingly, there were no DEPs when comparing PsA with Pso directly. On the contrary, hierarchical cluster analysis and multidimensional scaling revealed that HC clustered distinctly from all patients, and that PsA and Pso grouped together. The number of swollen joints had the strongest positive correlation to ICAM-1 (r = 0.81, P < 0.001) and CCL18 (0.76, P < 0.001). PASI score was best correlated to PI3 (r = 0.54, P < 0.001) and IL-17 receptor A (r = -0.51, P < 0.01). There were more proteins correlated to PASI score when analysing Pso and PsA patients separately, as compared with analysing Pso and PsA patients pooled together.

CONCLUSION

PsA and Pso patients share a serum proteomic signature, which supports the concept of a single psoriatic spectrum of disease. Future studies should target skin and synovial tissues to uncover differences in local factors driving arthritis development in Pso.

The role of ixekizumab in non-radiographic axial spondyloarthritis

Among patients with axial spondyloarthritis (axSpA), non-radiographic axial spondyloarthritis (nr-axSpA) is distinguished from ankylosing spondylitis (AS) by a lack of obvious radiographic changes in the sacroiliac joint. Tumor necrosis factor inhibitor (TNFi) has been used as a highly effective treatment in patients with AS and has shown good efficacy and safety in clinical trials in patients with nr-axSpA. As the pathophysiological mechanism for axSpA has started to become better recognized, various drugs other than TNFi, all of which are related to the interleukin-17 (IL-17) axis, are being evaluated in patients with axSpA. IL-17 inhibitors, such as secukinumab and ixekizumab, are effective drugs for patients with AS. A recent clinical trial reported that ixekizumab, a monoclonal antibody against IL-17A, was also effective in patients with nr-axSpA. In a COAST-X study, ixekizumab was superior to a placebo for improving signs and symptoms in patients with nr-axSpA at weeks 16 and 52. The adverse events were no different from those found in previous ixekizumab studies, and no new safety signals were identified. However, when considering several IL-17 inhibitors, it is necessary to obtain sufficient data to identify the exacerbation of extra-articular manifestation. In terms of effectiveness and safety, ixekizumab may be an appropriate alternative to TNFi in nr-axSpA patients.

Dissecting the clinical heterogeneity of adult-onset Still's disease, results from a multi-dimensional characterisation and stratification

OBJECTIVES

To stratify adult-onset Still's disease (AOSD) patients in distinct clinical subsets to be differently managed, by using a multi-dimensional characterisation.

METHODS

AOSD patients were evaluated by using a hierarchical unsupervised cluster analysis comprising age, laboratory markers systemic score, and outcomes. The squared Euclidean distances between each pair of patients were calculated and put into a distance matrix, which served as the input clustering algorithm. Derived clusters were descriptively analysed for any possible difference.

RESULTS

Four AOSD patients clusters have been identified. Disease onset in cluster 1 was characterised by fever (100%), skin rash (92%), and arthritis (83%) with the highest ferritin levels (14724 ± 6837 ng/ml). In cluster 2 the onset was characterised by fever (100%), arthritis (100%), and liver involvement (90%) together with the highest CRP levels (288.10 ± 46.01 mg/l). The patients in cluster 3 presented with fever (100%), myalgia (96%), and sore throat (92%). The highest systemic score values (8.88 ± 1.70) and the highest mortality rate (54.2%) defined cluster 3. Fever (100%) and arthritis (90%) were the symptoms at the onset in cluster 4, which was characterized by the lowest ferritin and CRP levels (1457 ± 1298 ng/ml; 54.98 ± 48.67 mg/l).

CONCLUSION

Four distinct phenotypic subgroups in AOSD could be suggested possibly associated with different genetic background and pathogenic mechanisms. Our results could provide the basis for a precision medicine approach in AOSD, trying to find a clinical and laboratory multidimensional stratification and characterisation, which would drive a tailored therapeutic approach in these patients.

Relationship of MDR1 gene polymorphism and P-glycoprotein expression in Chinese refractory lupus nephritis

To evaluate the association of multidrug resistance 1 (MDR1) polymorphism and the expression of P-glycoprotein (Pgp) in Chinese refractory lupus nephritis (LN) patients. Polymerase chain reaction-direct sequencing was used to analyze MDR1 polymorphism. The genotype distribution of MDR1 polymorphism in 132 SLE (systemic lupus erythematosus) patients was evaluated. ELISA was used to measure the expression of Pgp. Relationship among Pgp expression, MDR1 polymorphism, SLEDAI (SLE disease activity index), and kidney pathological score was analyzed by using One-way ANOVA and Pearson linear correlation. The frequency distribution of the MDR1 gene was consistent with the Hardy-Weinberg equilibrium. Compared with CT and CC, patients with T/T homozygote in MDR1 C3435T had significantly increased Pgp expression in the refractory group (p < 0.05). Additionally, SLEDAI score was positively correlated with Pgp expression (r = 0.481, p < 0.05). Also, Pgp expression was positively correlated with renal pathological activity index (r = 0.76, p < 0.05). MDR1 C3435T polymorphism is significantly associated with Pgp expression in patients with refractory LN. Pgp expression is closely related to SLEDAI and renal pathological score. Thus, Pgp may be useful in evaluation of the prognosis of patients with refractory LN.

Beyond the joints, the extra-articular manifestations in rheumatoid arthritis

Rheumatoid arthritis (RA) is an inflammatory disease typically affecting the joints, but the systemic inflammatory process may involve other tissues and organs. Many extra-articular manifestations are recognized, which are related to worse long outcomes. Rheumatoid nodules are the most common extra-articular feature, found in about 30% of patients. Secondary Sjögren's syndrome and pulmonary manifestations are observed in almost 10% of patients, also in the early disease. Active RA with high disease activity has been associated with an increased risk of such features. Male gender, smoking habit, severe joint disease, worse function, high pro-inflammatory markers levels, high titer of rheumatoid factor, and HLA-related shared epitope have been reported as clinical predictors of occurrence of these rheumatoid complications. In addition, there is a little evidence deriving from randomized controlled trials in this field, thus the therapeutic strategy is mainly empiric and based on small case series and retrospective studies. However, considering that these extra-articular manifestations are usually related to the more active and severe RA, an aggressive therapeutic strategy is usually employed in view of the poor outcomes of these patients. The extra-articular features of RA remain, despite the improvement of joint damage, a major diagnostic and therapeutic challenge, since these are associated with a poor prognosis and need to be early recognized and promptly managed.

Tuberculosis screening and management of latent tuberculosis infection prior to biologic treatment in patients with immune-mediated inflammatory diseases: A longitudinal population-based analysis using claims data

BACKGROUND AND AIM

Screening for tuberculosis before treating with biologic agents is recommended in patients with immune-mediated inflammatory diseases (IMIDs). We conducted this study to identify adherence to the recommended practice in a real-world setting in Japan.

METHODS

We used a community-based insurance claims database in a city in the Greater Tokyo Area in Japan. Between July 2012 and January 2019, we enrolled patients with IMIDs in the age range 15 to 74 years who had initiated biologic therapy. Tuberculosis screening was defined as (a) interferon-γ release assay and/or a tuberculin skin test (IGRA/TST) and (b) IGRA/TST and X-ray and/or CT scan (X-ray/CT) within 2 months before starting biologic agents. We analyzed the proportions of patients who underwent tuberculosis screening and their association with the patient- and treatment-related factors and treatment for latent tuberculosis infection (LTBI).

RESULTS

Of 421 patients presumed to have initiated biologic therapy, 202 (48%) underwent IGRA/TST and 169 (40%) underwent IGRA/TST and X-ray/CT. Patients aged 65 to 74 years were more likely to undergo tuberculosis screening than those aged 45 to 64 years. Compared to infliximab, IGRA/TST was less frequently performed in patients treated with etanercept, adalimumab, golimumab, abatacept, and tocilizumab. Treatment for LTBI was provided to 67 (16%) patients. Proportions of patients receiving LTBI treatment did not significantly differ according to the screening status.

CONCLUSION

There was low adherence to the recommendations for tuberculosis screening and prophylactic treatment before biologic therapy. It is necessary to continue alerting clinical practitioners to the importance of screening for tuberculosis and treatment for LTBI.

Transforming clinical trials in rheumatology: towards patient-centric precision medicine

Despite the success of targeted therapies in the treatment of inflammatory arthritides, the lack of predictive biomarkers drives a 'trial and error' approach to treatment allocation, leading to variable and/or unsatisfactory responses. In-depth characterization of the synovial tissue in rheumatoid arthritis, as well as psoriatic arthritis and spondyloarthritis, is bringing new insights into the diverse cellular and molecular features of these diseases and their potential links with different clinical and treatment-response phenotypes. Such progress raises the tantalizing prospect of improving response rates by matching the use of specific agents to the cognate target pathways that might drive particular disease subtypes in specific patient groups. Innovative patient-centric, molecular pathology-driven clinical trial approaches are needed to achieve this goal. Whilst progress is clearly being made, it is important to emphasize that this field is still in its infancy and there are a number of potential barriers to realizing the premise of patient-centric clinical trials.

Efficacy and safety of sarilumab in combination with csDMARDs or as monotherapy in subpopulations of patients with moderately to severely active rheumatoid arthritis in three phase III randomized, controlled studies

Background

The interleukin-6 receptor inhibitor sarilumab demonstrated efficacy in combination with conventional synthetic disease-modifying antirheumatic drugs (csDMARDs) or as monotherapy in patients with moderately to severely active rheumatoid arthritis (RA) with an inadequate response (IR) or intolerant (INT) to methotrexate (MTX) or tumour necrosis factor (TNF)-α inhibitors. This analysis investigated the efficacy and safety of sarilumab in patient subgroups.

Methods

Data were included from phase III studies: two placebo-controlled studies of subcutaneous sarilumab 150/200 mg every 2 weeks (q2w) either + MTX in MTX-IR patients (52 weeks) or + csDMARDs in TNF-IR/INT patients (24 weeks), and a monotherapy study of sarilumab 200 mg q2w vs. adalimumab 40 mg q2w in MTX-IR/INT patients (24 weeks). Prespecified and post hoc subgroups included patient demographics, disease characteristics, and prior treatments. Prespecified and post hoc endpoints included clinical, radiographic, and physical function measures, and p values are considered nominal. Safety was assessed during double-blind treatment.

Results

The superiority of sarilumab (either as monotherapy vs. adalimumab or in combination with csDMARDs vs. placebo + csDMARDs) across clinical endpoints was generally consistent across subgroups defined by patient demographics, disease characteristics, and prior treatments, demonstrating the benefit of sarilumab treatment for a wide range of patient types. Interaction p values of < 0.05 were consistently observed across studies only for baseline anti-cyclic citrullinated peptide antibody (ACPA) status for American College of Rheumatology 20% response, but not American College of Rheumatology 50% or 70% response. Adverse events and worsening laboratory parameters occurred more frequently in sarilumab-treated vs. placebo-treated patients and were more frequent in the small number of patients ≥ 65 years (n = 289) vs. patients < 65 years (n = 1819). Serious infections occurred in six patients aged ≥ 65 years receiving sarilumab, although the incidence of serious infections was generally higher in patients aged ≥ 65 years regardless of treatment.

Conclusions

Apart from ACPA status, there were no consistent signals indicating differential effects of sarilumab in any of the subpopulations assessed. Sarilumab demonstrated consistent efficacy and safety across a wide range of patients with RA.

Trial registration

ClinicalTrials.gov NCT01061736, registered on February 03, 2010; ClinicalTrials.gov NCT01709578, registered on October 18, 2012; ClinicalTrials.gov NCT02332590, registered on January 07, 2015

A Pauci-Immune Synovial Pathotype Predicts Inadequate Response to TNFα-Blockade in Rheumatoid Arthritis Patients

Objectives: To assess whether the histopathological features of the synovium before starting treatment with the TNFi certolizumab-pegol could predict clinical outcome and examine the modulation of histopathology by treatment. Methods: Thirty-seven RA patients fulfilling UK NICE guidelines for biologic therapy were enrolled at Barts Health NHS trust and underwent synovial sampling of an actively inflamed joint using ultrasound-guided needle biopsy before commencing certolizumab-pegol and after 12-weeks. At 12-weeks, patients were categorized as responders if they had a DAS28 fall >1.2. A minimum of 6 samples was collected for histological analysis. Based on H&E and immunohistochemistry (IHC) staining for CD3 (T cells), CD20 (B cells), CD138 (plasma cells), and CD68 (macrophages) patients were categorized into three distinct synovial pathotypes (lympho-myeloid, diffuse-myeloid, and pauci-immune). Results: At baseline, as per inclusion criteria, DAS28 mean was 6.4 ± 0.9. 94.6% of the synovial tissue was retrieved from the wrist or a metacarpophalangeal joint. Histological pathotypes were distributed as follows: 58% lympho-myeloid, 19.4% diffuse-myeloid, and 22.6% pauci-immune. Patients with a pauci-immune pathotype had lower levels of CRP but higher VAS fatigue compared to lympho- and diffuse-myeloid. Based on DAS28 fall >1.2, 67.6% of patients were deemed as responders and 32.4% as non-responders. However, by categorizing patients according to the baseline synovial pathotype, we demonstrated that a significantly higher number of patients with a lympho-myeloid and diffuse-myeloid pathotype in comparison with pauci-immune pathotype [83.3% (15/18), 83.3 % (5/6) vs. 28.6% (2/7), p = 0.022) achieved clinical response to certolizumab-pegol. Furthermore, we observed a significantly higher level of post-treatment tender joint count and VAS scores for pain, fatigue and global health in pauci-immune in comparison with lympho- and diffuse-myeloid patients but no differences in the number of swollen joints, ESR and CRP. Finally, we confirmed a significant fall in the number of CD68+ sublining macrophages post-treatment in responders and a correlation between the reduction in the CD20+ B-cells score and the improvement in the DAS28 at 12-weeks. Conclusions: The analysis of the synovial histopathology may be a helpful tool to identify among clinically indistinguishable patients those with lower probability of response to TNFα-blockade.

Therapeutic drug monitoring of adalimumab in RA: no predictive value of adalimumab serum levels and anti-adalimumab antibodies for prediction of response to the next bDMARD

BACKGROUND

After adalimumab treatment failure, tumour necrosis factor inhibition (TNFi) and non-TNFi biological disease-modifying anti-rheumatic drugs (bDMARDs) are equally viable options on a group level as subsequent treatment in rheumatoid arthritis (RA) based on the current best evidence synthesis. However, preliminary data suggest that anti-adalimumab antibodies (anti-drug antibodies, ADA) and adalimumab serum levels (ADL) during treatment predict response to a TNFi as subsequent treatment.

OBJECTIVE

To validate the association of presence of ADA and/or low ADL with response to a subsequent TNFi bDMARD or non-TNFi bDMARD. Sub-analyses were performed for primary and secondary non-responders.

METHODS

A diagnostic test accuracy retrospective cohort study was done in consenting RA patients who discontinued adalimumab after >3 months of treatment due to inefficacy and started another bDMARD. Inclusion criteria included the availability of (random timed) serum samples between ≥8 weeks after start and ≤2 weeks after discontinuation of adalimumab, and clinical outcome measurements Disease Activity Score in 28 joints - C-reactive protein (DAS28-CRP) between 3 to 6 months after treatment switch. Test characteristics for EULAR (European League Against Rheumatism) good response (DAS28-CRP based) after treatment with the next (non-)TNFi bDMARD were assessed using area under the receiver operating characteristic and sensitivity/specificity.

RESULTS

137 patients were included. ADA presence was not predictive for response in switchers to a TNFi (sensitivity/specificity 18%/75%) or a non-TNFi (sensitivity/specificity 33%/70%). The same was true for ADL levels in patients that switched to a TNFi (sensitivity/specificity 50%/52%) and patients that switched to a non-TNFi (sensitivity/specificity 32%/69%). Predictive value of ADA and ADL were similar for both primary and secondary non-responders to adalimumab.

CONCLUSIONS

In contrast to earlier research, we could not find predictive value for response to a second TNFi or non-TNFi for either ADA or random timed ADL.

Biomarkers identified by serum metabolomic analysis to predict biologic treatment response in rheumatoid arthritis patients

OBJECTIVES

Biologic treatment has recently revolutionized the management of RA. Despite this success, ∼30-40% of the patients undergoing biologic treatment respond insufficiently. The aim of this study was to identify several specific reliable metabolites for predicting the response of RA patients to TNF-α inhibitors (TNFi) and abatacept (ABT), using capillary electrophoresis-time-of-flight mass spectrometry (CE-TOFMS).

METHODS

We collected serum from RA patients with moderate or high disease activity prior to biologic treatment, and obtained the serum metabolomic profiles of these samples using CE-TOFMS. The patients' response was determined 12 weeks after starting biologic treatment, according to the EULAR response criteria. We compared the metabolites between the response and non-response patient groups and analysed their discriminative ability.

RESULTS

Among 43 total patients, 14 of 26 patients in the TNFi group and 6 of 17 patients in the ABT group responded to the biologic treatment. Of the metabolites separated by CE-TOFMS, 196 were identified as known substances. Using an orthogonal partial least-squares discriminant analysis, we identified five metabolites as potential predictors of TNFi responders and three as predictors of ABT responders. Receiver operating characteristic analyses for multiple biomarkers revealed an area under the curve (AUC) of 0.941, with a sensitivity of 85.7% and specificity of 100% for TNFi, and an AUC of 0.985, with a sensitivity of 100% and specificity of 90.9% for ABT.

CONCLUSION

By metabolomic analysis, we identified serum biomarkers that have a high ability to predict the response of RA patients to TNFi or ABT treatment.

Precision medicine and management of rheumatoid arthritis

Precision medicine (PM) is a very commonly used term that implies a highly individualized and tailored approach to patient management. There are, however, many layers of precision, as for example taking an appropriate patient history, or performing additional lab or imaging tests are already helping to better tailor treatments to the right patient. All this adds to the narrower definition of PM, which implies using the unique molecular characteristics of a patient for management decisions. Big data has become an essential part of PM, including as much information as possible to improve precision of disease management, although integration of multi-source data continues to be a challenge in practical application. In research big data can identify new (sub-)phenotypes in unsupervised analyses, which ultimately advance precision by allowing new targeted therapeutic approaches. We will discuss the current status of PM in rheumatoid arthritis (RA) in the management areas of diagnosis, prognosis, selection of therapy, and decision to reduce therapy. PM markers for diagnosis of RA are usually markers of RA classification rather than diagnosis, and subtypes of RA are potentially underrecognized. Prognostic precision is well established for RA, including markers of disease activity or structure, as well as autoantibodies and genetics. The choice of the right compound in a patient identified to have a poor prognosis, however, remains widely arbitrary. Finally and most recently, the most reliable markers for a safe withdrawal of therapy continue to be lower levels of disease activity and longer presence of remission.

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

Background

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

Methods

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

Results

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

Conclusion

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

Steroid hormone-related polymorphisms associate with the development of bone erosions in rheumatoid arthritis and help to predict disease progression: Results from the REPAIR consortium

Here, we assessed whether 41 SNPs within steroid hormone genes associated with erosive disease. The most relevant finding was the rheumatoid factor (RF)-specific effect of the CYP1B1, CYP2C9, ESR2, FcγR3A, and SHBG SNPs to modulate the risk of bone erosions (P = 0.004, 0.0007, 0.0002, 0.013 and 0.015) that was confirmed through meta-analysis of our data with those from the DREAM registry (P = 0.000081, 0.0022, 0.00074, 0.0067 and 0.0087, respectively). Mechanistically, we also found a gender-specific correlation of the CYP2C9_rs1799853T/T genotype with serum vitamin D3 levels (P = 0.00085) and a modest effect on IL1β levels after stimulation of PBMCs or blood with LPS and PHA (P = 0.0057 and P = 0.0058). An overall haplotype analysis also showed an association of 3 ESR1 haplotypes with a reduced risk of erosive arthritis (P = 0.009, P = 0.002, and P = 0.002). Furthermore, we observed that the ESR2, ESR1 and FcγR3A SNPs influenced the immune response after stimulation of PBMCs or macrophages with LPS or Pam3Cys (P = 0.002, 0.0008, 0.0011 and 1.97•10⁻⁷). Finally, we found that a model built with steroid hormone-related SNPs significantly improved the prediction of erosive disease in seropositive patients (P_RF+ = 2.46•10⁻⁸) whereas no prediction was detected in seronegative patients (P_RF− = 0.36). Although the predictive ability of the model was substantially lower in the replication population (P_RF+ = 0.014), we could confirm that CYP1B1 and CYP2C9 SNPs help to predict erosive disease in seropositive patients. These results are the first to suggest a RF-specific association of steroid hormone-related polymorphisms with erosive disease.

Disease Activity and Patient-Reported Health Measures in Relation to Cytokine Levels in Ankylosing Spondylitis

INTRODUCTION

Ankylosing spondylitis (AS) is a lifelong condition where spinal inflammation causes chronic back pain and restriction of spinal function. While proinflammatory cytokines participate in the disease process, their relation with disease activity, spinal function, and quality of life is less well understood.

METHODS

Cross-sectional study of serum levels of four inflammatory cytokines (IL-6, TNF, IL-23, and IL-17A) in AS patients not on biologics. Disease characteristics and simultaneous spinal function tests and patient-reported health measures (Bath Functional Index (BASFI), Dougados Functional Index (DFI), Modified Health Assessment Questionnaire (MHAQ), and routine laboratory parameters were recorded. The composite ASDAS-CRP score was used to classify disease activity as absent, low, or high.

RESULTS

In 164 AS patients (age 46 years, 70.1% males, 90.9% HLAB27 positive, ASDAS-CRP 1.8), disease activity was classified as inactive in 14%, low in 54%, and high in 31%. ASDAS-CRP correlated well with MHAQ, DFI, BASFI, and spinal mobility across patients with low and high disease activity (all p < 0.05). Cytokine levels did not correlate with ASDAS-CRP, ESR, BASFI, or spinal mobility scores and were comparable between patients with no, low, or high disease activity regardless of gender or disease duration (all p > 0.2).

CONCLUSIONS

A large proportion of AS not on biologics have active disease far into the disease course. This impacts negatively on quality of life, work ability, and spinal mobility. Serum cytokine levels are poor markers for these central disease features in AS management.

FUNDING

Abbott Norway AS and Arthritis Foundation of Western Australia.

Anti-interleukin-1 treatment in patients with rheumatoid arthritis and type 2 diabetes (TRACK): A multicentre, open-label, randomised controlled trial

BACKGROUND

The inflammatory contribution to type 2 diabetes (T2D) has suggested new therapeutic targets using biologic drugs designed for rheumatoid arthritis (RA). On this basis, we aimed at investigating whether interleukin-1 (IL-1) inhibition with anakinra, a recombinant human IL-1 receptor antagonist, could improve both glycaemic and inflammatory parameters in participants with RA and T2D compared with tumour necrosis factor (TNF) inhibitors (TNFis).

METHODS AND FINDINGS

This study, designed as a multicentre, open-label, randomised controlled trial, enrolled participants, followed up for 6 months, with RA and T2D in 12 Italian rheumatologic units between 2013 and 2016. Participants were randomised to anakinra or to a TNFi (i.e., adalimumab, certolizumab pegol, etanercept, infliximab, or golimumab), and the primary end point was the change in percentage of glycated haemoglobin (HbA1c%) (EudraCT: 2012-005370-62 ClinicalTrial.gov: NCT02236481). In total, 41 participants with RA and T2D were randomised, and 39 eligible participants were treated (age 62.72 ± 9.97 years, 74.4% female sex). The majority of participants had seropositive RA disease (rheumatoid factor and/or anticyclic citrullinated peptide antibody [ACPA] 70.2%) with active disease (Disease Activity Score-28 [DAS28]: 5.54 ± 1.03; C-reactive protein 11.84 ± 9.67 mg/L, respectively). All participants had T2D (HbA1c%: 7.77 ± 0.70, fasting plasma glucose: 139.13 ± 42.17 mg). When all the enrolled participants reached 6 months of follow-up, the important crude difference in the main end point, confirmed by an unplanned ad interim analysis showing the significant effects of anakinra, which were not observed in the other group, led to the study being stopped for early benefit. Participants in the anakinra group had a significant reduction of HbA1c%, in an unadjusted linear mixed model, after 3 months (β: -0.85, p < 0.001, 95% CI -1.28 to -0.42) and 6 months (β: -1.05, p < 0.001, 95% CI -1.50 to -0.59). Similar results were observed adjusting the model for relevant RA and T2D clinical confounders (male sex, age, ACPA positivity, use of corticosteroids, RA duration, T2D duration, use of oral antidiabetic drug, body mass index [BMI]) after 3 months (β: -1.04, p < 0.001, 95% CI -1.52 to -0.55) and 6 months (β: -1.24, p < 0.001, 95% CI -1.75 to -0.72). Participants in the TNFi group had a nonsignificant slight decrease of HbA1c%. Assuming the success threshold to be HbA1c% ≤ 7, we considered an absolute risk reduction (ARR) = 0.42 (experimental event rate = 0.54, control event rate = 0.12); thus, we estimated, rounding up, a number needed to treat (NNT) = 3. Concerning RA, a progressive reduction of disease activity was observed in both groups. No severe adverse events, hypoglycaemic episodes, or deaths were observed. Urticarial lesions at the injection site led to discontinuation in 4 (18%) anakinra-treated participants. Additionally, we observed nonsevere infections, including influenza, nasopharyngitis, upper respiratory tract infection, urinary tract infection, and diarrhoea in both groups. Our study has some limitations, including open-label design and previously unplanned ad interim analysis, small size, lack of some laboratory evaluations, and ongoing use of other drugs.

CONCLUSIONS

In this study, we observed an apparent benefit of IL-1 inhibition in participants with RA and T2D, reaching the therapeutic targets of both diseases. Our results suggest the concept that IL-1 inhibition may be considered a targeted treatment for RA and T2D.

TRIAL REGISTRATION

The trial is registered with EU Clinical Trials Register, EudraCT Number: 2012-005370-62 and with ClinicalTrial.gov, number NCT02236481.

Candidate Markers for Stratification and Classification in Rheumatoid Arthritis

Rheumatoid arthritis (RA) is a chronic autoimmune, inflammatory disease, characterized by synovitis in small- and medium-sized joints and, if not treated early and efficiently, joint damage, and destruction. RA is a heterogeneous disease with a plethora of treatment options. The pro-inflammatory cytokine tumor necrosis factor (TNF) plays a central role in the pathogenesis of RA, and TNF inhibitors effectively repress inflammatory activity in RA. Currently, treatment decisions are primarily based on empirics and economic considerations. However, the considerable interpatient variability in response to treatment is a challenge. Markers for a more exact patient classification and stratification are lacking. The objective of this study was to identify markers in immune cell populations that distinguish RA patients from healthy donors with an emphasis on TNF signaling. We employed mass cytometry (CyTOF) with a panel of 13 phenotyping and 10 functional markers to explore signaling in unstimulated and TNF-stimulated peripheral blood mononuclear cells from 20 newly diagnosed, untreated RA patients and 20 healthy donors. The resulting high-dimensional data were analyzed in three independent analysis pipelines, characterized by differences in both data clean-up, identification of cell subsets/clustering and statistical approaches. All three analysis pipelines identified p-p38, IkBa, p-cJun, p-NFkB, and CD86 in cells of both the innate arm (myeloid dendritic cells and classical monocytes) and the adaptive arm (memory CD4⁺ T cells) of the immune system as markers for differentiation between RA patients and healthy donors. Inclusion of the markers p-Akt and CD120b resulted in the correct classification of 18 of 20 RA patients and 17 of 20 healthy donors in regression modeling based on a combined model of basal and TNF-induced signal. Expression patterns in a set of functional markers and specific immune cell subsets were distinct in RA patients compared to healthy individuals. These signatures may support studies of disease pathogenesis, provide candidate markers for response, and non-response to TNF inhibitor treatment, and aid the identification of future therapeutic targets.

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.

Validation of a clinical pharmacogenetic model to predict methotrexate nonresponse in rheumatoid arthritis patients

Aim: To study the performance of a clinical pharmacogenetic model for the prediction of nonresponse in rheumatoid arthritis (RA) patients treated with methotrexate (MTX) in combination with other synthetic or biologic disease-modifying anti-rheumatic drugs . This prediction model includes gender, smoking status, rheumatoid factor positivity and four genetic variants in AMPD1 (rs17602729), ATIC (rs2372536), ITPA (rs1127354) and MTHFD1 (rs17850560). Methods: A total of 314 RA patients from three Dutch studies were retrospectively included. Eligible patients were adults diagnosed with RA and had a treatment duration with MTX and follow-up for at least two study evaluation visits. Prediction model risk scores at the first and second evaluation were calculated and compared with the actual nonresponse (disease activity score >2.4). Regression and receiver operating characteristic curve analyses of the prediction model were performed. Also, the sensitivity, specificity and the positive and negative predictive values (PPV and NPV) were determined. Results: The receiver operating characteristic area under the curve was 75% at first and 70% after second evaluation. At the second evaluation, prediction nonresponse had a sensitivity of 67% (CI: 54–78%), specificity of 69% (CI: 60–77%), PPV of 52% (CI: 45–60%) and NPV of 80% (CI: 73–85%). Conclusions: This study demonstrates that the clinical pharmacogenetic model has an inadequate performance for the prediction of nonresponse to MTX in RA patients treated with combination therapies.

The multi-biomarker disease activity score tracks response to rituximab treatment in rheumatoid arthritis patients: a post hoc analysis of three cohort studies

BACKGROUND

A multi-biomarker disease activity (MBDA) score has been validated as an objective measure of disease activity in rheumatoid arthritis (RA) and shown to track response to treatment with several disease-modifying anti-rheumatic drugs (DMARDs). The objective of this study was to evaluate the ability of the MBDA score to track response to treatment with rituximab.

METHODS

Data were used from 57 RA patients from three cohorts treated with rituximab 1000 mg and methylprednisolone 100 mg at days 1 and 15. The MBDA score was assessed in serum samples obtained at baseline and 6 months. Spearman's rank correlation coefficients were calculated for baseline values, 6-month values, and change from baseline to 6 months (∆), between MBDA score and the following measures: disease activity score assessing 28 joints (DAS28) using erythrocyte sedimentation rate (ESR) or high-sensitivity C-reactive protein (hsCRP), ESR, (hs)CRP, swollen and tender joint counts assessing 28 joints (SJC28, TJC28), patient visual analogue scale for general health (VAS-GH), health assessment questionnaire (HAQ), and radiographic progression over 12 months using Sharp/van der Heijde score (SHS), as well as six bone turnover markers. Additionally, multivariable linear regression analyses were performed using these measures as dependent variable and the MBDA score as independent variable, with adjustment for relevant confounders. The association between ∆MBDA score and European League Against Rheumatism (EULAR) response at 6 months was assessed with adjustment for relevant confounders.

RESULTS

At baseline, the median MBDA score and DAS28-ESR were 54.0 (IQR 44.3-70.0) and 6.3 (IQR 5.4-7.1), respectively. MBDA scores correlated significantly with DAS28-ESR, DAS28-hsCRP, ESR and (hs)CRP at baseline and 6 months. ∆MBDA score correlated significantly with changes in these measures. ∆MBDA score was associated with EULAR good or moderate response (adjusted OR = 0.89, 95% CI = 0.81-0.98, p = 0.02). Neither baseline MBDA score nor ΔMBDA score correlated statistically significantly with ∆SHS (n = 11) or change in bone turnover markers (n = 23), although ∆SHS ≥ 5 was observed in 5 (56%) of nine patients with high MBDA scores.

CONCLUSIONS

We have shown, for the first time, that the MBDA score tracked disease activity in RA patients treated with rituximab and that change in MBDA score reflected the degree of treatment response.

Polymorphisms at phase I-metabolizing enzyme and hormone receptor loci influence the response to anti-TNF therapy in rheumatoid arthritis patients

The aim of this case-control study was to evaluate whether 47 single-nucleotide polymorphisms (SNPs) in steroid hormone-related genes are associated with the risk of RA and anti-TNF drug response. We conducted a case-control study in 3 European populations including 2936 RA patients and 2197 healthy controls. Of those, a total of 1985 RA patients were treated with anti-TNF blockers. The association of potentially interesting markers in the discovery population was validated through meta-analysis with data from DREAM and DANBIO registries. Although none of the selected variants had a relevant role in modulating RA risk, the meta-analysis of the linear regression data with those from the DREAM and DANBIO registries showed a significant correlation of the CYP3A4_rs11773597 and CYP2C9_rs1799853 variants with changes in DAS28 after the administration of anti-TNF drugs (P = 0.00074 and P = 0.006, respectively). An overall haplotype analysis also showed that the ESR2_GGG haplotype significantly associated with a reduced chance of having poor response to anti-TNF drugs (P = 0.0009). Finally, a ROC curve analysis confirmed that a model built with eight steroid hormone-related variants significantly improved the ability to predict drug response compared with the reference model including demographic and clinical variables (AUC = 0.633 vs. AUC = 0.556; P_{LR_test} = 1.52 × 10^-6). These data together with those reporting that the CYP3A4 and ESR2 SNPs correlate with the expression of TRIM4 and ESR2 mRNAs in PBMCs (ranging from P = 1.98 × 10^-6 to P = 2.0 × 10^-35), and that the CYP2C9_rs1799853 SNP modulates the efficiency of multiple drugs, suggest that steroid hormone-related genes may have a role in determining the response to anti-TNF drugs.KEY POINTS• Polymorphisms within the CYP3A4 and CYP2C9 loci correlate with changes in DAS28 after treatment with anti-TNF drugs.• A haplotype including eQTL SNPs within the ESR2 gene associates with better response to anti-TNF drugs.• A genetic model built with eight steroid hormone-related variants significantly improved the ability to predict drug response.

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

OBJECTIVES

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

METHODS

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

RESULTS

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

CONCLUSION

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