Methotrexate Treatment Response Research Articles

Characteristics of T-Cell Receptor Repertoire for Differential Response to Methotrexate Treatment for Rheumatoid Arthritis

ABSTRACT Background Methotrexate (MTX) serves as the initial treatment for rheumatoid arthritis (RA). However, a substantial proportion of RA patients, estimated between 30% and 50%, do not respond positively to MTX. While the T-cell receptor (TCR) is crucial for the immune response during RA, its role in differentiating MTX responsiveness has not been thoroughly investigated. Methods This study used next-generation sequencing to analyze the TCR β-chain complementary determining region sequences in peripheral blood mononuclear cells obtained from RA patients before MTX treatment. This study aimed to compare the characteristics of the TCR repertoire between the MTX responder and non-responder groups. Results The study identified a significant difference in the TRBV6–6 gene (p = .003) concerning MTX treatment response. Additionally, a significant difference was found in the number of “3” nucleotide deletions at the 5‘Jdels site (p = .023) in the VDJ rearrangement. Conclusion These findings suggest distinct TCR repertoire characteristics between MTX responder and non-responder groups among RA patients. This discovery offers new insights into understanding the variable responses of RA patients to MTX therapy.

P096 Pathway directionality across different immune cell lineages contributes to methotrexate response in juvenile idiopathic arthritis

Abstract Background/Aims Juvenile idiopathic arthritis (JIA) is the most common autoimmune rheumatic disease in children with methotrexate (MTX) as the first line treatment. However, about 50% of JIA patients will not respond well to MTX yet still experience drug side effects. Early prediction to MTX treatment response would be beneficial for patients and families to avoid incurring unnecessary MTX side effects and ongoing uncontrolled inflammation: such validated tools or biomarkers are currently not available. Methods Transcriptional analysis was performed on peripheral blood mononuclear cells (PBMC) from pre-MTX-treated JIA patients (n = 97) of all ILAR JIA subtypes, excluding systemic JIA. RNAseq was performed on total PBMC and sorted immune cell populations: CD4+ T cells, CD8+ T cells, CD19+ B cells, and CD14+ monocytes. Clinical data collected at the time of sampling (baseline) and at the follow-up time (between 3-12 months) were used to define outcomes, measured as change in active joint count (AJC), change in Physician VAS (PhysVAS), and change in cJADAS10. After batch normalisation with ComBat-seq, differential gene expression (DGE) analysis was performed using limma-voom with age, sex, ethnicity, and steroid status included as covariates. Log2 fold changes were utilised to rank genes to implement gene set enrichment analysis (GSEA) by fgsea. Results DGE analysis showed minimal significant differentially expressed (DE) genes that passed 5% false discovery rate (FDR). The greatest number of significant DE genes were observed in CD14+ monocytes, where baseline expression of 13 genes were significantly associated with change in PhysVAS. As alterations of gene expression for a heterogeneous disease such as JIA can be subtle and correlated between genes, GSEA was performed to investigate expression changes at pathway level. Using MSigDb Hallmark pathways, GSEA showed interferon-related and tumor-necrosis-factor pathways as significantly associated with MTX response in all cell types (5% FDR). However, the directionality (up/down regulation) differed between cell lineages, suggesting pathways divergence between T cell and non-T cell lineages. For example, in interferon gamma pathway, up-regulation associated with poor treatment response in T-cells and down-regulation in non-T cells. Preliminary analysis of the leading edge genes of the interferon gamma pathway in the non-T cell lineages showed that many of the genes are driven by interferon alpha, whilst within CD4+ and CD8+ T cells the majority of the leading edge genes are specific to interferon gamma. Conclusion Different directionality of pathways that might be relevant to JIA response to treatment with MTX is observed in different mononuclear cell lineages. This could potentially explain the difficulties of finding biomarkers which correlate with response to treatment from whole blood or PBMC. Shared genes across different interferon-related pathways also suggest that interaction between interferon pathways might be driving the different direction of interferon pathway expression in different cell lineages. Disclosure M. Kartawinata: None. W. Lin: None. B. Jebson: None. K. O'Brien: None. E. Ralph: None. R. Restuadi: None. G.T. Hall: None. S. Castellano: None. C. Wallace: None. L.R. Wedderburn: Grants/research support; LRW Declares in kind contributions to CLUSTER by AbbVie, GSK, UCB, Sobi and Pfizer inc and non renumerated collaborations with Lilly and Novartis.

Identification of Cell-Specific Differential DNA Methylation Associated With Methotrexate Treatment Response in Rheumatoid Arthritis.

We undertook this study to estimate changes in cell-specific DNA methylation (DNAm) associated with methotrexate (MTX) response using whole blood samples collected from rheumatoid arthritis (RA) patients before and after initiation of MTX treatment. Patients included in this study were from the Rheumatoid Arthritis Medication Study (n=66) and the University of California San Francisco Rheumatoid Arthritis study (n=11). All patients met the American College of Rheumatology RA classification criteria. Blood samples were collected at baseline and following treatment. Disease Activity Scores in 28 joints using the C-reactive protein level were collected at baseline and after 3-6 months of treatment with MTX. Methylation profiles were generated using the Illumina Infinium HumanMethylation450 and MethylationEPIC v1.0 BeadChip arrays using DNA from whole blood. MTX response was defined using the EULAR response criteria (responders showed good/moderate response; nonresponders showed no response). Differentially methylated positions were identified using the Limma software package and Tensor Composition Analysis, which is a method for identifying cell-specific differential DNAm at the CpG level from tissue-level ("bulk") data. Differentially methylated regions were identified using Comb-p software. We found evidence of differential global methylation between treatment response groups. Further, we found patterns of cell-specific differential global methylation associated with MTX response. After correction for multiple testing, 1 differentially methylated position was associated with differential DNAm between responders and nonresponders at baseline in CD4+ T cells, CD8+ T cells, and natural killer cells. Thirty-nine cell-specific differentially methylated regions associated with MTX treatment response were identified. There were no significant findings in analyses of whole blood samples. We identified cell-specific changes in DNAm that were associated with MTX treatment response in RA patients. Future studies of DNAm and MTX treatment response should include measurements of DNAm from sorted cells.

MTHFR and MTRR Genetic Polymorphism of Methotrexate Therapy Outcomes in Early Rheumatoid Arthritis.

Methotrexate (MTX) is used as an anchor drug for the treatment of rheumatoid arthritis (RA) and there may be differences in drug action between genotypes. The purpose of this study was to investigate the relationship between clinical efficacy response and disease activity of MTX monotherapy with methylenetetrahydrofolate reductase (MTHFR) and methionine synthase reductase (MTRR) polymorphisms. In the study, a population of 32 patients in East China with early RA fulfilling the diagnostic standards of the American College of Rheumatology (ACR) were enrolled, all of them received MTX monotherapy. Genotyping of patients MTHFR C677T and A1298C, MTRR A66G using tetra-primer ARMS-PCR method and sanger sequencing to verify its accuracy. The distribution of three polymorphic genotypes that were studied is in accordance with the Hardy-Weinberg genetic equilibrium. The patient pathology variables smoke (OR = 0.088, P = 0.037), drink alcohol (OR = 0.039, P = 0.016) and males (OR = 0.088, P = 0.037) were significantly associated with non-response to MTX. Genotype, allele distribution and genetic statistical models were not found to be related to MTX treatment response and disease activity in both the response groups and non-response groups. Our findings suggest that the MTHFR C677T, MTHFR A1298C and MTRR A66G polymorphisms may not predict MTX clinical treatment response and disease activity in patients with early RA. The study revealed that smoke, alcohol, and males were possible influential factors for MTX non-response.

P046 A multinomial approach to prediction of methotrexate treatment response and discontinuation due to adverse events in rheumatoid arthritis

Abstract Background/Aims As recommended by the NICE guidelines, methotrexate (MTX) is typically prescribed as the first line therapy for patients with rheumatoid arthritis (RA). However, around 40% of patients do not respond to MTX at 6 months and around 80% experience adverse events (AEs). Our previous systematic review identified that clinical prediction models of MTX outcomes suffered from methodological limitations, including a lack of validation, suboptimal handling of missing data, and no consideration of competing risks, such as patients discontinuing due to adverse events (AEs). These shortcomings resulted in high risk of bias and should be addressed to aid the implementation of outcome prediction in clinical practice. Therefore, this study aimed to (i) develop a multinomial prediction model for estimating an individual’s risks of not achieving low disease activity (LDA) and discontinuing due to AEs at 6 months after starting MTX, using information observable at the starting point of treatment, (ii) update prognosis at 3 months, and (iii) internally validate these models to assess performance. Methods Data came from the national multi-centre (n = 38) Rheumatoid Arthritis Medication Study (RAMS), which recruited patients with a diagnosis of RA or undifferentiated polyarthritis starting MTX for the first time. We conducted a Patient and Public Involvement focus group with past and current MTX users, which informed our outcome choice of LDA (DAS28 ≤3.2). A multinomial logistic regression (MLR) was used to develop a prediction model that estimated the probabilities of three outcomes: 1) not in LDA, 2) achieved LDA, and 3) discontinued due to AEs. The updated model at 3 months was conditional on patients not having achieved LDA. Missing data was handled using multiple imputation and models were internally validated through bootstrapping. Calibration, discrimination, and variance explained was assessed to quantify model performance. Results A total of 1632 patients were included in the analysis. At 6 months, 756 (46%) patients achieved LDA, 730 (45%) were not in LDA, and 146 (9%) discontinued due to AEs. The updated model at 3 months included 1179 (72%) patients as they had not achieved LDA. For the LDA outcome pair, the c-statistic was 0.73 (0.70, 0.75), while it was 0.54 (0.49, 0.59) for the AEs outcome. The calibration slope was 0.99 (0.85, 1.12) and 0.74 (0.23, 1.23), respectively. Predictive performance at 3 months was similar to baseline. Conclusion Our MLR models handled outcomes of disease activity and AEs simultaneously. We were able to predict outcomes of LDA more accurately than AEs and performance was better in terms of calibration, which is crucial for the clinical utility of a prediction model. This modelling approach could provide more clinically useful and realistic predictions of MTX outcomes, as competing risks such as AEs have previously been ignored. Disclosure C.K. Gehringer: None. G.P. Martin: None. K.L. Hyrich: Honoraria; Abbvie. Grants/research support; BMS and Pfizer. S.M.M. Verstappen: None. J.C. Sergeant: None.

Small extracellular vesicles have distinct CD81 and CD9 tetraspanin expression profiles in plasma from rheumatoid arthritis patients.

Extracellular vesicles (EVs) are implicated in the pathogenesis of rheumatoid arthritis (RA) but little is known about the composition of specific small EV (sEV) subpopulations. This study aimed to characterize the CD63, CD81 and CD9 tetraspanin profile in the membrane of single EVs in plasma from treatment naïve RA patients and assess potential discrepancies between methotrexate (MTX) responder groups. EVs isolated from plasma were characterized using transmission electron microscopy, and detection of surface markers (CD63, CD81 and CD9) on single EVs was performed on the ExoView platform. All RA patients (N = 8) were newly diagnosed, treatment naïve, females, ACPA positive and former smokers. The controls (N = 5) were matched for age and gender. After three months of MTX treatment, responders (N = 4) were defined as those with ΔDAS28 > 1.2 and DAS28 ≤ 3.2 post-treatment. The isolated EVs were 50-200nm in size. The RA patients had a higher proportion of both CD9 and CD81 single positive sEVs compared to healthy controls, while there was a decrease in CD81/CD9 double positive sEVs in patients. Stratification of RA patients into MTXresponders and non-responders revealed a distinctly higher proportion of CD81 single positive sEVs in the responder group. The proportion of CD81/CD9 double positive sEVs (anti-CD9 captured) was lower in the non-responders, but increased upon 3months of MTXtreatment. Our exploratory study revealed distinct tetraspanin profiles in RA patients suggesting their implication in RA pathophysiology and MTX treatment response.

Toward Individualized Prediction of Response to Methotrexate in Early Rheumatoid Arthritis: A Pharmacogenomics-Driven Machine Learning Approach.

To test the ability of machine learning (ML) approaches with clinical and genomic biomarkers to predict methotrexate treatment response in patients with early rheumatoid arthritis (RA). Demographic, clinical, and genomic data from 643 patients of European ancestry with early RA (mean age 54 years; 70% female) subdivided into a training (n = 336) and validation cohort (n = 307) were used. The genomic data comprised 160 single-nucleotide polymorphisms (SNPs) previously associated with RA or methotrexate metabolism. Response to methotrexate monotherapy was defined as good or moderate by the European Alliance of Associations for Rheumatology (EULAR) response criteria at the 3-month follow-up. Supervised ML methods were trained with 5 repeats and 10-fold cross-validation using the training cohort. Prediction performance was validated in the independent validation cohort. Supervised ML methods combining age, sex, smoking, rheumatoid factor, baseline Disease Activity Score in 28 joints (DAS28) scores and 160 SNPs predicted EULAR response at 3 months with the area under the receiver operating curve of 0.84 (P = 0.05) in the training cohort and achieved a prediction accuracy of 76% (P = 0.05) in the validation cohort (sensitivity 72%, specificity 77%). Intergenic SNPs rs12446816, rs13385025, rs113798271, and ATIC (rs2372536) had variable importance above 60.0 and along with baseline DAS28 scores were among the top predictors of methotrexate response. Pharmacogenomic biomarkers combined with baseline DAS28 scores can be useful in predicting response to methotrexate in patients with early RA. Applying ML to predict treatment response holds promise for guiding effective RA treatment choices, including timely escalation of RA therapies.

HLA-G 3'UTR polymorphisms & response to a yoga-based lifestyle intervention in rheumatoid arthritis: A randomized controlled trial.

Human leucocyte antigen (HLA)-G plays a vital role in immunomodulation in rheumatoid arthritis (RA). The mounting evidence suggests a link between HLA-G gene polymorphisms, disease susceptibility and methotrexate treatment response. Various environmental factors influence the onset and progression of RA and its treatment outcomes. The aim is to identify the treatment response of HLA-G 3' untranslated region polymorphisms to yoga-based lifestyle intervention (YBLI). In this eight-week single-blinded randomized controlled trial (CTRI/2017/05/008589), patients with RA (n=140) were randomized into two groups namely, yoga group or non-yoga group. Baseline genomic DNA was isolated using salting-out method. PCR-based methods were used for genotyping. The levels of soluble (s) HLA-G and disease activity were assessed by ELISA and disease activity score-28-erythrocyte sedimentation rate (DAS28-ESR), respectively, at baseline (day 0) and after eight weeks of intervention. Low-producing sHLA-G genotypes, i.e. +3142GG and 14 bp ins/ins, showed a significant increase in sHLA-G levels after YBLI. The association analysis between HLA-G polymorphisms and treatment for RA showed no considerable differential treatment remission in either of the groups (P>0.05). The percentages of improvement were higher in the yoga group as compared to the non-yoga group in both the HLA-G +3142G>C and 14 bp ins/del polymorphisms irrespective of their respective genotypes. No significant association was found between sHLA-G levels and disease activity with respect to genotypes. Yoga intervention results in improvement and reduced severity of RA in patients irrespective of the HLA-G 14 bp ins/del or +3142G>C polymorphisms. YBLI may be used as an adjunct therapy in RA independent of the genotypes.

HLA-Cw6 Allele Expression is Associated with Good Narrowband Ultraviolet B Response in Javanese-Indonesian Psoriasis Subjects

BACKGROUND: Psoriasis is an autoimmune disease involving genetic-environmental factors. Human Leukocyte Antigen (HLA)-Cw6 allele is the main genetic risk factor of psoriasis, but its prevalence varies widely. HLA-Cw6 allele correlates with psoriasis clinical type and treatment responses. Narrowband ultraviolet B (NB-UVB) and methotrexate (MTX) are effective psoriasis treatments but their association with HLA-Cw6 allele has not been identified. The study aims to determine the association between HLA-Cw6 allele expression, NB-UVB, and MTX treatment responses in Javanese-Indonesian psoriasis subjects.METHODS: Ninety Javanese-Indonesian psoriasis subjects were recruited in this study, 45 subjects were treated using NB UVB, while the other 45 subjects were treated using MTX, respectively, for 12 weeks. The psoriasis diagnosis and treatment responses were evaluated by dermatologists using Psoriasis Area Severity Index (PASI) score. The HLA-Cw6 allele was examined using the single-specific-primer polymerase chain reaction method. Fisher's Exact and Chi-square tests were employed, where p-value<0.05 was considered of significant association.RESULTS: The HLA-Cw6 allele positivity was identified in 23 psoriasis subjects (25.56%), while the other 67 subjects expressed HLA-Cw6 allele negative (74.44%). Female with HLA-Cw6 allele positivity who did not have comorbid disease show good response to NB-UVB than MTX. Meanwhile, subjects who were treated with MTX showed no association between therapeutic response and HLA-Cw6. HLA-Cw6 status was not correlated with the onset of psoriasis, family history, and comorbid diseases in all subjects.CONCLUSION: HLA-Cw6 allele expression is more associated with good NB-UVB treatment response than with MTX treatment response in female Javanese-Indonesian psoriasis subjects without comorbid disease.KEYWORDS: HLA-Cw6, narrowband ultraviolet B, methotrexate, Javanese, psoriasis

A Radiomic Machine Learning Model to Predict Treatment Response to Methotrexate and Survival Outcomes in Primary Central Nervous System Lymphoma (PCNSL)

Introduction: Primary CNS lymphomas (PCNSL) are heterogeneous, aggressive, extra-nodal non-Hodgkin lymphomas limited to the neuraxis. Published response rates to high-dose methotrexate (MTX) based induction regimens for PCNSL range from 35-78%. However, >50% of patients relapse and have a median survival of 2 months without additional treatment. Our ability to prognosticate outcomes is limited to clinical models like the International Extranodal Lymphoma Study Group (IELSG) score and Memorial Sloan-Kettering Cancer Center (MSKCC) classifier. There is an urgent need to develop improved biologic and radiologic predictive models for PCNSL to facilitate therapeutic advances. We hypothesize that a machine learning model using advanced magnetic resonance imaging (MRI) tumor characteristics will improve the accuracy of clinical models to predict response to MTX and survival outcomes. Methods: Data from patients with PCNSL treated at UT Southwestern and Parkland Health and Hospital System hospitals from 2008-2020 (n=95) were collected. An analytical dataset of 61 patients was selected based on the availability of T1 postcontrast (T1c) and T2w FLAIR MR images. A subset of 47 patients was used to evaluate MTX treatment response. Expert neuroradiologists drew regions of interest (ROIs) on the multiparametric MR images including whole tumor (consisting of edema + enhancing tumor + necrosis), enhancing tumor and necrosis (Figure 1). Response to methotrexate-based induction was defined per the International Primary CNS Lymphoma Collaborative Group (IPCG) criteria. For overall- and progression-free survival (OS and PFS) analysis, short (≤1 year) and long-term (>1 year) survivor groups were defined. A support vector machine (SVM) network was used for predicting treatment response to MTX and for predicting the OS groups. A Multinomial Naive Bayes (MNB) network was used for predicting the PFS groups. PyRadiomics package was used to extract 106 texture-based features from the combination of each MR image and tumor ROI. A total of 642 features were extracted from the imaging parameters. Clinical features including age, race, performance status, MSKCC class, IELSG score, histology, delay from 1st MRI to start of treatment, induction and consolidation treatments used were included in the analysis. Feature reduction methodology based on the feature importance derived from the gradient boost model was applied to reduce the number of features. 17 features (imaging = 14, clinical = 3) were used for predicting OS/PFS and 7 features (imaging = 5, clinical = 2) were used for predicting treatment response to MTX. Networks utilizing only clinical features were analyzed for comparison. The sklearn package in python was used for the machine learning analysis. 5-Fold cross validation was performed to generalize the network performance. Results: Baseline wclinical characteristics of the study population is shown in Table 1. Table 2 lists the accuracy, F1 score, sensitivity, specificity, positive predictive value, negative predictive value and area under the curve (AUC) values averaged for the 5-fold cross validation. The SVM network achieved a mean testing accuracy of 81.1 ± 12.3% for predicting the treatment response to MTX-based induction. Sensitivity, specificity and AUC values were 90.5 ± 13.1%, 63.3 ± 22.1% and 0.81 ± 0.14 respectively. The SVM and the MNB network achieved mean testing accuracies of 80.3 ± 11.4% and 83.3 ± 11.8% for predicting the long and short survival groups in OS and PFS respectively. Sensitivity, specificity and AUC values for the SVM and MNB networks were 79.3 ± 6.5%, 80.5 ± 16.5% and 0.86 ± 0.12 and 85.3 ± 12.9%, 81.9 ± 11.8% and 0.86 ± 0.13 respectively. The accuracy values for predicting treatment response to MTX, OS and PFS using only the clinical features were 61.6 ± 9.2%, 59.1 ± 16.4% and 62.1 ± 17.5% respectively. Conclusion: This machine learning model boosted the accuracy (≥20%) over currently validated clinical models alone in predicting response to methotrexate-based therapies and survival outcomes in PCNSL. The current analysis is limited by the small sample size, and we plan to statistically test this model across a larger dataset and report results at the meeting. Our preliminary results suggest that machine learning based radiomic analysis may predict biologic aggressiveness in PCNSL and has the potential to be integrated in clinical predictive tools and design of clinical trials. Disclosures Awan: Blueprint medicines: Consultancy; Celgene: Consultancy; Sunesis: Consultancy; Karyopharm: Consultancy; MEI Pharma: Consultancy; Astrazeneca: Consultancy; Genentech: Consultancy; Dava Oncology: Consultancy; Kite Pharma: Consultancy; Gilead Sciences: Consultancy; Pharmacyclics: Consultancy; Janssen: Consultancy; Abbvie: Consultancy. Desai:Boston Scientific: Consultancy, Other: Trial Finding.

P17 Prediction of response of methotrexate in patients with rheumatoid arthritis using serum lipidomics

Abstract Background For patients with rheumatoid arthritis (RA), introduction of early, effective therapy has consistently been shown to improve long-term outcomes. Low-dose methotrexate (MTX) is commonly prescribed as first-line treatment for RA. However, MTX is not effective for a large minority of patients and there is currently no way to determine ahead of therapy which patients are most likely to benefit. Metabolomics and lipidomics are emerging approaches for studying patient stratification in RA and have the potential to identify disease processes that underpin treatment outcomes. Here we apply state-of-the-art machine learning algorithms to predict MTX treatment response, by testing serum lipid levels measured at two time-points (pre-treatment and following 4 weeks on drug) to predict MTX response by 6 months. Methods This study included patients from the Rheumatoid Arthritis Medication Study (RAMS), a UK multi-centre one-year prospective observational study investigating predictors of response to MTX in patients with RA. Since 2008, patients who are about to start MTX for the first time are asked to provide demographic and clinical data, as well as blood samples to permit DNA, RNA and serum-based biomarker studies. Patients about to commence MTX treatment were followed longitudinally and those categorised as good or non-responders following 6 months on-drug using EULAR response criteria were analysed. Serum lipid levels were measured at pre-treatment and following 4 weeks on drug using ultra-performance liquid chromatography tailored for complex lipid analysis, coupled to mass spectrometry. State-of-the-art supervised machine learning methods were then applied to predict EULAR response at 6 months. Models including lipid levels were compared to models including clinical covariates (including: MTX start dose, steroid use at inclusion, BMI, number of swollen joints, number of tender joints, CRP levels, patients’ assessment of their overall wellbeing, gender, age-at-inclusion, age-at-onset, disease duration, HAQ score and pre-treatment smoking habits). Results Following quality control, 3,366 features (1,060 in negatively-charged mode and 2,306 in positive mode) were available for analysis at pre-treatment and 4 weeks from 100 RA patients categorised as good (GR, n = 50) or poor (NR, n = 50) responders to MTX following 6 months on drug. The best model performance for the classifier including clinical covariates was observed using L1/L2-regularised logistic regression (ROC AUC 0.68 ± 0.02). However, the clinical covariate model outperformed the classifier including lipid levels when either pre- or on-treatment time-points were investigated (ROC AUC 0.61 ± 0.02). Conclusion These data do not support the utility of early treatment lipidomic monitoring in routine clinical practice in patients started on MTX for their RA. Disclosures M. Maciejewski: Shareholder/stock ownership; owns stock or stock options in Pfizer. C. Sands None. N. Nair None. S. Ling None. S. Verstappen None. K. Hyrich None. A. Barton None. D. Ziemek Shareholder/stock ownership; owns stock or stock options in Pfizer. M. Lewis None. D. Plant None.

Profiling of Gene Expression Biomarkers as a Classifier of Methotrexate Nonresponse in Patients With Rheumatoid Arthritis.

ObjectiveApproximately 30–40% of rheumatoid arthritis (RA) patients who are initially started on low‐dose methotrexate (MTX) will not benefit from the treatment. To date, no reliable biomarkers of MTX inefficacy in RA have been identified. The aim of this study was to analyze whole blood samples from RA patients at 2 time points (pretreatment and 4 weeks following initiation of MTX), to identify gene expression biomarkers of the MTX response.MethodsRA patients who were about to commence treatment with MTX were selected from the Rheumatoid Arthritis Medication Study. Using European League Against Rheumatism (EULAR) response criteria, 42 patients were categorized as good responders and 43 as nonresponders at 6 months following the initation of MTX treatment. Data on whole blood transcript expression were generated, and supervised machine learning methods were used to predict a EULAR nonresponse. Models in which transcript levels were included were compared to models in which clinical covariates alone (e.g., baseline disease activity, sex) were included. Gene network and ontology analysis was also performed.ResultsBased on the ratio of transcript values (i.e., the difference in log2‐transformed expression values between 4 weeks of treatment and pretreatment), a highly predictive classifier of MTX nonresponse was developed using L2‐regularized logistic regression (mean ± SEM area under the receiver operating characteristic [ROC] curve [AUC] 0.78 ± 0.11). This classifier was superior to models that included clinical covariates (ROC AUC 0.63 ± 0.06). Pathway analysis of gene networks revealed significant overrepresentation of type I interferon signaling pathway genes in nonresponders at pretreatment (P = 2.8 × 10−25) and at 4 weeks after treatment initiation (P = 4.9 × 10−28).ConclusionTesting for changes in gene expression between pretreatment and 4 weeks post–treatment initiation may provide an early classifier of the MTX treatment response in RA patients who are unlikely to benefit from MTX over 6 months. Such patients should, therefore, have their treatment escalated more rapidly, which would thus potentially impact treatment pathways. These findings emphasize the importance of a role for early treatment biomarker monitoring in RA patients started on MTX.

CD39 positive regulatory T cell frequency as a biomarker of treatment response to methotrexate in rheumatoid arthritis.

Nearly one-third of patients with rheumatoid arthritis (RA) do not respond to Methotrexate (MTX), the first-line therapy in RA. CD39, an ectonucleotidase highly expressed on regulatory T cells (Tregs), is responsible for production of adenosine, an important anti-inflammatory mediator of MTX action. Higher expression of CD39 on Tregs improves their suppressive capacity. Therefore, we aimed to study the role of CD39+ Treg frequency as a biomarker for MTX treatment response in RA. Patients with active RA who were naive to disease-modifying anti-rheumatic drugs were enrolled. Frequencies of CD39+ Tregs (CD4+ CD25+ FoxP3+ CD39+ cells) and CD4+ CD25+ CD39+ cells were determined by flow cytometry in peripheral blood before the start of therapy. After 4months of MTX monotherapy, patients were classified into responders (European League Against Rheumatism [EULAR] good/moderate response) and non-responders (EULAR no response). All samples were genotyped for single nucleotide polymorphisms (SNPs) rs11188513 and rs7071836 in the ENTPD1 (CD39) gene. After 4months of MTX monotherapy, 54 patients were classified as responders and 16 as non-responders. The baseline CD39+ Treg and CD4+ CD25+ CD39+ cell frequencies were significantly higher in the responder group as compared with the non-responder group (P<0.05 and P<0.01, respectively). AA genotype at SNP rs7071836 was associated with poor response to MTX (P<0.05; odds ratio=5.67; 95% CI=1.12-28.75). Higher frequencies of CD39+ Tregs and CD4+ CD25+ CD39+ cells in the peripheral blood are associated with response to MTX in RA and hence, these could be considered as potential biomarkers for prediction of response to MTX treatment.

Pharmacogenetic markers to predict the clinical response to methotrexate in south Indian Tamil patients with psoriasis.

Despite the advent of several new systemic therapies, methotrexate remains the gold standard for the treatment of moderate to severe psoriasis. However, there exists a significant heterogeneity in individual response to methotrexate. There are no consistently reliable markers to predict methotrexate treatment response till date. We aimed to demonstrate the association of certain genetic variants in the HLA (HLA-A2, HLA-B17, and HLA-Cw6) and the non-HLA genes including T-helper (Th)-1, Th-2, Th-17 cytokine genes (IFN-γ, IL-2, IL-4, IL-10, IL-12B, and IL-23R), and T-regulatory gene (FOXP3) with the methotrexate treatment response in South Indian Tamil patients with psoriasis. Of the 360 patients recruited, 189 patients with moderate to severe psoriasis were treated with methotrexate. Of the 189 patients, 132 patients responded to methotrexate and the remaining 57 patients were non-responders. We analyzed the association of aforesaid polymorphisms with the methotrexate treatment outcome using binary logistic regression. We observed that there were significant differences between genotype frequencies of HLA-Cw6 and FOXP3 (rs3761548) among the responders compared to non-responders, with conservative estimation. We observed that pro-inflammatory cytokines such as IFN-γ, IL-2, IL-12, and IL-23 were markedly reduced with the use of methotrexate, in comparison to the baseline levels, while the plasma IL-4 levels were increased posttreatment. Our results serve as preliminary evidence for the clinical use of genetic markers as predictors of response to methotrexate in psoriasis. This might aid in the future in the development of a point-of-care testing (POCT) gene chip, to predict optimal treatment response in patients with psoriasis, based on their individual genotypic profile.

In the Pursuit of Methotrexate Treatment Response Biomarker in Juvenile Idiopathic Arthritis-Are We Getting Closer to Personalised Medicine?

Methotrexate (MTX) is the most widely used disease-modifying antirheumatic drug (DMARD) in paediatric rheumatology and the mainstay in the therapy of juvenile idiopathic arthritis (JIA). Despite its common use, about 30% of children fail to respond to this medicine that results in potentially irreversible joint damage. No clinical biomarker that would predict the outcome of MTX therapy exists. Results of several studies focused on gene polymorphisms and outcome of this DMARD therapy have been published, but no reliable genetic marker useful to tailor the therapy has been discovered so far. The results of the first genome-wide association study in this field have recently revealed new genetic candidates from outside the metabolic pathway of MTX that may be associated with the efficacy of treatment. However promising, those outcomes need validation in independent prospective cohorts before we can claim that clinically useful biomarker predicting MTX treatment response is discovered.

ATIC 347C>G gene polymorphism may be associated with methotrexate-induced adverse events in south Indian Tamil rheumatoid arthritis.

To find the association of ATIC 347C>G gene polymorphism with methotrexate (MTX) treatment response and MTX-induced adverse events in south Indian Tamil patients with rheumatoid arthritis. A total of 319 rheumatoid arthritis and 310 healthy controls were recruited for the study and ATIC 347C>G gene polymorphism was analyzed by PCR-RFLP method. The genotype and allele frequencies of ATIC 347 C>G SNP did not differ between good and nonresponders and hence this SNP was not found to be associated with MTX treatment response. However, the ATIC 347 GG genotype (p = 0.02; odds ratio [OR]: 4.46; 95% CI: 1.28-15.52) and mutant G allele was associated with MTX-induced gastrointestinal adverse events (p = 0.01; OR: 2.60; 95% CI: 1.27-5.35). ATIC 347C>G gene polymorphism may be associated with the development of MTX induced gastrointestinal adverse events.

Pharmacogenomics of Methotrexate in Diverse Populations

Methotrexate (MTX) is a commonly used disease modifying antirheumatic drug (DMARD) for the treatment of RA because of its affordability, long-term efficacy and acceptable toxicity profile. MTX is also frequently used in the treatment of other forms of inflammatory arthritis. Although MTX is considered to be a well-tolerated DMARD, there is a significant inter-patient variability in the toxicity and efficacy of MTX treatment. MTX treatment response is often multi-factorial, resulting from complex interplay of various factors such as age, sex, ethnicity, disease duration, disease severity and activity, presence of Creactive protein and RF factor. It is also clear that genetic factors [1] also make an important contribution to treatment variability.

Survivin polymorphism is associated with disease activity in rheumatoid arthritis patients.

Survivin expression was associated with unfavorable and erosive course of rheumatoid arthritis (RA). This is the first study investigating association between BIRC5 polymorphisms, survivin plasma levels and disease activity in RA. A testing group of 123 and validation group of 150 RA patients initially treated with methotrexate monotherapy were genotyped for three BIRC5 promoter polymorphisms. Survivin plasma levels were determined in testing group. BIRC5 c.-31G>C was marginally associated with treatment response after 6 months of methotrexate treatment (p = 0.046) and with DAS28 at the time of inclusion in testing (p = 0.052) and in validation group (p = 0.057). Survivin plasma levels were not associated with BIRC5 polymorphisms or DAS28. BIRC5 -31C>G polymorphism could be useful pharmacogenetic marker for methotrexate treatment response in RA.

Multidrug resistance 1 (MDR1) 3435C>T gene polymorphism influences the clinical phenotype and methotrexate-induced adverse events in South Indian Tamil rheumatoid arthritis.

Rheumatoid arthritis (RA) is a chronic systemic autoimmune disease primarily targeting the synovial joints causing joint damage and significant functional impairment. Methotrexate (MTX) remains the mainstay for the treatment of RA, and approximately 10-30% of the patients fail to attain remission because of inefficacy of the drug or due to development of adverse events. Multidrug resistance 1 (MDR1) gene encodes for permeable glycoprotein (P-gp) which is an integral membrane protein for the transport of chemotherapeutic agents, immunosuppressive drugs etc. MDR1 3435C>T results in a wobble mutation in exon 26 but is associated with altered P-gp expression and reduced P-gp function. The present study was carried out to find the role of MDR1 3435C>T gene polymorphism with clinical phenotype, treatment response, and MTX adverse events in 336 RA and 329 healthy controls of South Indian Tamil ethnicity. MDR1 3435C>T gene polymorphism was analyzed by TaqMan 5' nuclease assay. We found MDR1 3435T allele as a risk allele for contributing to high EULAR disease activity [p = 0.02, OR 1.50, 95% CI (1.06-2.13)]. Also, MDR1 3435CT genotype was associated with deforming disease [p = 0.02, OR 1.79, 95% CI (1.11-2.88)]. However, this SNP did not influence the MTX treatment response in these patients. MDR1 3435CT genotype was associated with MTX-induced adverse events [p = 0.01, OR 2.01, 95% CI (1.15-3.52)], and the 3435 TT genotype remained protective for the development of adverse events [p = 0.009, OR 0.40, 95% CI (0.21-0.78)]. Also, the heterozygous 3435 CT genotype was associated with gastrointestinal events [p = 0.02, OR 3.62, 95% CI (1.25-10.47)], and CT genotype remained protective in patients developing infection [p = 0.002, OR 0.05, 95% CI (0.006-0.460)]. MDR1 3435C>T gene polymorphism influences the clinical phenotype and adverse events to MTX in the South Indian cohort of patients with RA.

Reduced folate carrier-1 80G > A gene polymorphism is not associated with methotrexate treatment response in South Indian Tamils with rheumatoid arthritis.

Methotrexate (MTX) is the most commonly used disease-modifying drug to treat rheumatoid arthritis (RA). Although there are no reliable molecular markers to predict the treatment response and adverse effects to MTX therapy, the polymorphisms in genes coding for MTX metabolizing enzymes and transporters may play a crucial role. The reduced folate carrier-1 (RFC-1) is a bidirectional anion exchanger which transports MTX and folinic acid. It is reported to influence MTX treatment response and adverse effects in some ethnic populations but not in others. It is also associated with susceptibility to various diseases including systemic lupus erythematosus (SLE). The present study was aimed at investigating the role of RFC-1 80G > A gene polymorphism in association with disease susceptibility, MTX treatment response and the MTX-induced adverse events in the South Indian Tamil patients with rheumatoid arthritis. The RFC-1 80G > A gene polymorphism was investigated in 327 patients with RA and in 322 healthy controls by PCR-RFLP method. It was found that the heterozygous RFC-1 80 GA genotype was associated with protection against RA [p = 0.02, odds ratio (OR) 0.69, 95% confidence interval (CI) 0.50-0.95]. However, it was not found to be associated with MTX treatment response. The RFC-1 G allele frequency was higher in patients with adverse effects, but the difference was not statistically significant (p = 0.08, OR 1.44, 95% CI 0.97-2.13). RFC-1 80G > A gene polymorphism confers protection for RA. However, it is not associated with MTX treatment response and MTX-induced adverse effects in South Indian Tamil patients with RA.