Application of omics in predicting anti-TNF efficacy in rheumatoid arthritis

Low Serum BAFF Concentration Is Associated with Response to TNF Inhibitors in Seropositive Patients with Rheumatoid Arthritis

We investigated B-cell-activating factor (BAFF) in relation to response to treatment with TNF inhibitors (TNFis) in rheumatoid arthritis (RA). This was a longitudinal study including 158 patients with RA treated with TNFis and followed up for 6 months. Clinical response at 6 months of treatment was defined according to the EULAR criteria for good responders (GRs). BAFF concentration was measured in serum samples, collected at baseline and at 6 months. Associations with EULAR response were evaluated using univariable and multivariable logistic regression models. ROC analysis was performed to determine the optimal threshold of serum BAFF concentration associated with good EULAR response to treatment. After 6 months of TNFi treatment, 24% of patients were GRs. They had a lower BMI, lower baseline DAS28 and lower baseline serum BAFF concentration than non-responders. After 6 months of TNFi treatment, autoantibody-positive patients who attained GR had significantly lower serum BAFF concentrations compared with patients who did not. Serum BAFF < 968 pg/mL at 6 months represented the concentration likely to best discriminate between GR and non-GR at 6 months of TNFi treatment. Autoantibody-seropositive patients who had serum BAFF < 968 pg/mL at 6 months demonstrated a more than four-fold increased probability to be GRs compared with patients with higher BAFF concentrations. In conclusion, serum BAFF concentrations were associated with response to TNFis in seropositive RA patients, corroborating the importance of the B-cell compartment in RA.

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.

Dynamics of Type I and Type II Interferon Signature Determines Responsiveness to Anti-TNF Therapy in Rheumatoid Arthritis

The factors influencing long-term responses to a tumor necrosis factor inhibitor (TNFi) in rheumatoid arthritis (RA) patients currently remain unknown. Therefore, we herein conducted a multi-omics analysis of TNFi responses in a Japanese RA cohort. Blood samples were collected from 27 biological disease-modifying antirheumatic drug (DMARD)-naive RA patients at the initiation of and after three months of treatment with TNFi. Treatment responses were evaluated at one year. Differences in gene expression levels in peripheral blood mononuclear cells (PBMCs), plasma protein levels, drug concentrations, and the presence/absence of anti-drug antibodies were investigated, and a cell phenotypic analysis of PBMCs was performed using flow cytometry. After one year of treatment, thirteen patients achieved clinical remission (responders), while the others did not or switched to other biologics (non-responders). Differentially expressed genes related to treatment responses were enriched for the interferon (IFN) pathway. The expression of type I IFN signaling-related genes was higher in non-responders than in responders before and after treatment (P = 0.03, 0.005, respectively). The expression of type II IFN signaling-related genes did not significantly differ before treatment; however, it increased in non-responders and decreased in responders, with a significant difference being observed after three months of treatment (P = 1.2×10^-3). The total number of lymphocytes and C-X-C Motif Chemokine Ligand 10 (CXCL10) protein levels were associated with the type I IFN signature (P = 6.7×10^-7, 6.4×10^-3, respectively). Hepatocyte growth factor (HGF) protein levels before treatment predicted fold increases in type II IFN (P = 0.03). These IFN signature-related indices (the number of lymphocytes, CXCL10, and HGF) significantly differed between responders and non-responders (P = 0.01, 0.01, and 0.04, respectively). A single-cell analysis revealed that the type I IFN signature was more highly enriched in monocytes than in other cell types. A deconvolution analysis of bulk-RNA sequence data identified CD4+ and CD8+ T cells as the main sources of the type II IFN signature in non-responders. Collectively, the present results demonstrated that the dynamics of the type I and II IFN pathways affected long-term responses to TNFi, providing information on its biological background and potential for clinical applications.

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.

A comprehensive database for integrated analysis of omics data in autoimmune diseases

BACKGROUND

Autoimmune diseases are heterogeneous pathologies with difficult diagnosis and few therapeutic options. In the last decade, several omics studies have provided significant insights into the molecular mechanisms of these diseases. Nevertheless, data from different cohorts and pathologies are stored independently in public repositories and a unified resource is imperative to assist researchers in this field.

RESULTS

Here, we present Autoimmune Diseases Explorer ( https://adex.genyo.es ), a database that integrates 82 curated transcriptomics and methylation studies covering 5609 samples for some of the most common autoimmune diseases. The database provides, in an easy-to-use environment, advanced data analysis and statistical methods for exploring omics datasets, including meta-analysis, differential expression or pathway analysis.

CONCLUSIONS

This is the first omics database focused on autoimmune diseases. This resource incorporates homogeneously processed data to facilitate integrative analyses among studies.

Remission Induced by TNF Inhibitors Plus Methotrexate is Associated With Changes in Peripheral Naïve B Cells in Patients With Rheumatoid Arthritis

Biological therapies, such as TNF inhibitors (TNFi), are increasing remission (REM) rates in rheumatoid arthritis (RA) patients, although these are still limited. The aim of our study was to analyze changes in the profile of peripheral blood mononuclear cells (PBMC) in patients with RA treated with TNFi in relation to the clinical response. This is a prospective and observational study including 78 RA patients starting the first TNFi. PBMC were analyzed by flow cytometry both at baseline and at 6 months. Disease activity at the same time points was assessed by DAS28, establishing DAS28 ≤ 2.6 as the criteria for REM. Logistic regression models were employed to analyze the association between the changes in PBMC and REM. After 6 months of TNFi treatment, 37% patients achieved REM by DAS28. Patients who achieved REM showed a reduction in the percentage of naive B cells, but only when patients had received concomitant methotrexate (MTX) (OR: 0.59; 95% CI: 0.39–0.91). However, no association was found for patients who did not receive concomitant MTX (OR: 0.85; 95% CI: 0.63–1.16). In conclusion, PBMC, mainly the B-cell subsets, are modified in RA patients with TNFi who achieve clinical REM. A significant decrease in naive B-cell percentage is associated with achieving REM after 6 months of TNFi treatment in patients who received concomitant therapy with MTX.

Novel approaches to develop biomarkers predicting treatment responses to TNF-blockers

Introduction: Chronic inflammatory diseases (CIDs) cause significant morbidity and are a considerable burden for the patients in terms of pain, impaired function, and diminished quality of life. Important progress in CID treatment has been obtained with biological therapies, such as tumor-necrosis-factor blockers. However, more than a third of the patients fail to respond to these inhibitors and are exposed to the side effects of treatment, without the benefits. Therefore, there is a strong interest in developing tools to predict response of patients to biologics. Areas covered: The authors searched PubMed for recent studies on biomarkers for disease assessment and prediction of therapeutic responses, focusing on the effect of TNF blockers on immune responses in spondyloarthritis (SpA), and other CID, in particular rheumatoid arthritis and inflammatory bowel disease. Conclusions will be drawn about the possible development of predictive biomarkers for response to treatment. Expert opinion: No validated biomarker is currently available to predict treatment response in CID. New insight could be generated through the development of new bioinformatic modeling approaches to combine multidimensional biomarkers that explain the different genetic, immunological and environmental determinants of therapeutic responses.

TNF-α regulates the early development of avascular necrosis of the femoral head by mediating osteoblast autophagy and apoptosis via the p38 MAPK/NF-κB signaling pathway

Previous studies have shown that the tumor necrosis factor-α (TNF-α) levels in serum and bone tissues formed in avascular necrosis of femoral head (ANFH) patients were higher than those of normal individuals, indicating TNF-α might play a role in the pathogenesis of ANFH. However, the underlying mechanisms remain unclear. Hematoxylin and eosin staining was performed to show the pathological changes of ANFH bone tissues. TNF-α expression in normal and ANFH tissues was examined by quantitative real-time polymerase chain reaction and western blot analyses. Osteoblast autophagy and apoptosis, as well as signaling pathways activation, were measured by their corresponding marker proteins. Osteoblast proliferation, autophagy, and apoptosis were evaluated using cell counting kit-8, transmission electron microscopy, and flow cytometry. The structures of bone tissues of ANFH were obviously damaged. TNF-α expression was significantly upregulated in ANFH bone tissues compared to normal tissues. Autophagy and apoptosis were remarkably promoted, and p38 mitogen-activated protein kinase (MAPK)/nuclear factor-κB (NF-κB) signaling pathways were markedly activated in ANFH. Suppression of the p38 MAPK/NF-κB pathway significantly attenuated the TNF-α-induced autophagy, however, enhanced the TNF-α-induced apoptosis in osteoblasts. Increased TNF-α in ANFH regulated osteoblast autophagy and apoptosis by p38 MAPK/NF-κB signaling pathways, blocking the pathway by inhibitors exacerbated TNF-α-induced apoptosis through impairing autophagy flux.