Publications by authors named "Darren Plant"

72 Publications

Machine learning in precision medicine: lessons to learn.

Nat Rev Rheumatol 2021 01;17(1):5-6

NIHR Manchester Biomedical Research Centre, Manchester University NHS Trust, Manchester Academic Health Science Centre, Manchester, UK.

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http://dx.doi.org/10.1038/s41584-020-00538-2DOI Listing
January 2021

Pharmacogenetics of TNF inhibitor response in rheumatoid arthritis utilizing the two-component disease activity score.

Pharmacogenomics 2020 11 30;21(16):1151-1156. Epub 2020 Oct 30.

NIHR Manchester Biomedical Research Center, Central Manchester NHS Foundation Trust, Manchester Academic Health Science Center, Manchester, M13 9WL, UK.

TNF inhibitor drugs are a treatment option for rheumatoid arthritis, but response is not universal. Response is typically measured using the composite 4-component (4C) disease activity score 28 (DAS28) which contains more subjective measures. This study used a validated 2-component (2C) DAS28 score to determine whether SNPs associated with response were replicated in the UK population. A literature review identified TNF inhibitor response SNPs. Linear regression was conducted to replicate associations with 4C or 2C-DAS28 response. Eighteen independent SNPs were analyzed in 1828 patients. One and four associations with 4C and 2C-DAS28 response respectively were identified (p ≤ 0.05). Further genetic associations were replicated using the 2C-DAS28 which may reflect the objective nature of 2C-AS28.
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http://dx.doi.org/10.2217/pgs-2020-0043DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7649675PMC
November 2020

Investigation of genetically regulated gene expression and response to treatment in rheumatoid arthritis highlights an association between expression and treatment response.

Ann Rheum Dis 2020 11 30;79(11):1446-1452. Epub 2020 Jul 30.

Population Health Sciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, Tyne and Wear, UK

Objectives: In this study, we sought to investigate whether there was any association between genetically regulated gene expression (as predicted using various reference panels) and anti-tumour necrosis factor (anti-TNF) treatment response (change in erythrocyte sedimentation rate (ESR)) using 3158 European ancestry patients with rheumatoid arthritis.

Methods: The genetically regulated portion of gene expression was estimated in the full cohort of 3158 subjects (as well as within a subcohort consisting of 1575 UK patients) using the PrediXcan software package with three different reference panels. Estimated expression was tested for association with anti-TNF treatment response. As a replication/validation experiment, we also investigated the correlation between change in ESR with measured gene expression at the () gene in whole blood and synovial tissue, using an independent replication data set of patients receiving conventional synthetic disease modifying anti-rheumatic drugs, with directly measured (via RNA sequencing) gene expression.

Results: We found that predicted expression of showed a consistent signal of association with treatment response across the reference panels. In our independent replication data set, expression in whole blood showed correlation with the change in ESR between baseline and follow-up (=0.35, p=0.0091). Change in ESR was also correlated with the expression of in synovial tissue (=0.28, p=0.02).

Conclusion: Our results suggest that expression is worthy of further investigation as a potential predictor of treatment response in rheumatoid arthritis that is not specific to a particular drug type.
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http://dx.doi.org/10.1136/annrheumdis-2020-217204DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7569378PMC
November 2020

Latent Class Trajectory Modeling of 2-Component Disease Activity Score in 28 Joints Identifies Multiple Rheumatoid Arthritis Phenotypes of Response to Biologic Disease-Modifying Antirheumatic Drugs.

Arthritis Rheumatol 2020 10 6;72(10):1632-1642. Epub 2020 Sep 6.

Versus Arthritis Centre for Genetics and Genomics, University of Manchester, NIHR Manchester Biomedical Research Centre, Manchester University NHS Foundation Trust, and Manchester Academic Health Science Centre, Manchester, UK.

Objective: To determine whether using a reweighted disease activity score that better reflects joint synovitis, i.e., the 2-component Disease Activity Score in 28 joints (DAS28) (based on swollen joint count and C-reactive protein level), produces more clinically relevant treatment outcome trajectories compared to the standard 4-component DAS28.

Methods: Latent class mixed modeling of response to biologic treatment was applied to 2,991 rheumatoid arthritis (RA) patients in whom treatment with a biologic disease-modifying antirheumatic drug was being initiated within the Biologics in Rheumatoid Arthritis Genetics and Genomics Study Syndicate cohort, using both 4-component and 2-component DAS28 scores as outcome measures. Patient groups with similar trajectories were compared in terms of pretreatment baseline characteristics (including disability and comorbidities) and follow-up characteristics (including antidrug antibody events, adherence to treatments, and blood drug levels). We compared the trajectories obtained using the 4- and 2-component scores to determine which characteristics were better captured by each.

Results: Using the 4-component DAS28, we identified 3 trajectory groups, which is consistent with previous findings. We showed that the 4-component DAS28 captures information relating to depression. Using the 2-component DAS28, 7 trajectory groups were identified; among them, distinct groups of nonresponders had a higher incidence of respiratory comorbidities and a higher proportion of antidrug antibody events. We also identified a group of patients for whom the 2-component DAS28 scores remained relatively low; this group included a high percentage of patients who were nonadherent to treatment. This highlights the utility of both the 4- and 2-component DAS28 for monitoring different components of disease activity.

Conclusion: Here we show that the 2-component modified DAS28 defines important biologic and clinical phenotypes associated with treatment outcome in RA and characterizes important underlying response mechanisms to biologic drugs.
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http://dx.doi.org/10.1002/art.41379DOI Listing
October 2020

Proteomic analysis to define predictors of treatment response to adalimumab or methotrexate in rheumatoid arthritis patients.

Pharmacogenomics J 2020 06 10;20(3):516-523. Epub 2019 Dec 10.

Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, The University of Manchester, Manchester, UK.

Seropositivity for anti-citrullinated peptide antibodies (ACPA) in patients with rheumatoid arthritis (RA), a chronic autoimmune arthritis, is associated with worse long-term disease outcomes. ACPA is ubiquitously tested in RA patients, but other autoantibodies exist (in both citrullinated and non-citrullinated form) which may provide additional information on RA subtypes and/or treatment response. We used a multiplex bead-based assay of 376 autoantibodies to test associations between these autoantibodies and treatment response in RA patients. Clusters of patients with similar autoantibody expression were defined and cluster membership was associated with treatment response. Thirty-four autoantibodies were differentially expressed in RA patients compared with healthy controls; citrullinated vimentin was associated with treatment response. A selection of citrullinated autoantibodies was found to be associated with treatment response in a subanalysis of ACPA-negative RA patients. Finer ACPA specificities in ACPA-negative RA patients may be predictive of treatment response and could represent a rich vein of future study.
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http://dx.doi.org/10.1038/s41397-019-0139-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7253356PMC
June 2020

The use of missing values in proteomic data-independent acquisition mass spectrometry to enable disease activity discrimination.

Bioinformatics 2020 04;36(7):2217-2223

Division of Informatics, Imaging and Data Sciences, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, UK.

Motivation: Data-independent acquisition mass spectrometry allows for comprehensive peptide detection and relative quantification than standard data-dependent approaches. While less prone to missing values, these still exist. Current approaches for handling the so-called missingness have challenges. We hypothesized that non-random missingness is a useful biological measure and demonstrate the importance of analysing missingness for proteomic discovery within a longitudinal study of disease activity.

Results: The magnitude of missingness did not correlate with mean peptide concentration. The magnitude of missingness for each protein strongly correlated between collection time points (baseline, 3 months, 6 months; R = 0.95-0.97, confidence interval = 0.94-0.97) indicating little time-dependent effect. This allowed for the identification of proteins with outlier levels of missingness that differentiate between the patient groups characterized by different patterns of disease activity. The association of these proteins with disease activity was confirmed by machine learning techniques. Our novel approach complements analyses on complete observations and other missing value strategies in biomarker prediction of disease activity.

Supplementary Information: Supplementary data are available at Bioinformatics online.
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http://dx.doi.org/10.1093/bioinformatics/btz898DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7141869PMC
April 2020

Differential DNA methylation correlates with response to methotrexate in rheumatoid arthritis.

Rheumatology (Oxford) 2020 06;59(6):1364-1371

University College Dublin School of Medicine and Medical Science, University College Dublin, Dublin, Ireland.

Objectives: Identifying blood-based biomarkers that predict treatment response in RA is a clinical priority. We investigated differential DNA methylation as a candidate biomarker of response for the first-line drug used in RA, MTX.

Methods: DNA methylation was measured in DNA samples from individuals recruited to the Rheumatoid Arthritis Medication Study. Differentially methylated positions were compared between whole blood samples collected at baseline and at 4 weeks from patients who, by 6 months, had a good (n = 34) or poor response (n = 34) to MTX using linear modelling, adjusting for gender, age, cell composition, baseline 28-joint disease activity score (DAS28) and smoking status. Analyses also compared methylation with changes in DAS28 and changes in swollen joint count and tender joint count, and changes in CRP over the initial 6 months after MTX commencement. Differentially methylated positions showing significant differences with any response parameter were tested using pyrosequencing in an independent group of 100 patients from the Rheumatoid Arthritis Medication Study.

Results: In the discovery group, two CpG sites showed methylation changes at 4 weeks associated with clinical EULAR response by 6 months. Significant changes in methylation for three differentially methylated positions associated with change in tender joint counts, three with change in swollen joint count and a further four with change in CRP. Of the 12 CpGs, four showed replicated association in an independent dataset of samples from the Rheumatoid Arthritis Medication Study.

Conclusion: These data represent an advance on current practice by contributing to a personalized medicine strategy allowing an escalation or change in therapy as early as 4 weeks.
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http://dx.doi.org/10.1093/rheumatology/kez411DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7244777PMC
June 2020

A Dashboard for Latent Class Trajectory Modeling: Application in Rheumatoid Arthritis.

Stud Health Technol Inform 2019 Aug;264:911-915

Centre for Health Informatics, University of Manchester, Manchester, United Kingdom.

A key trend in current medical research is a shift from a one-size-fit-all to precision treatment strategies, where the focus is on identifying narrow subgroups of the population that would benefit from a given intervention. Precision medicine will greatly benefit from accessible tools that clinicians can use to identify such subgroups, and to generate novel inferences about the patient population they are treating. We present a novel dashboard app that enables clinician users to explore patient subgroups with varying longitudinal treatment response, using latent class mixed modeling. The dashboard was developed in R Shiny. We present results of our approach applied to an observational study of patients with moderate to severe rheumatoid arthritis (RA) on first-line biologic treatment.
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http://dx.doi.org/10.3233/SHTI190356DOI Listing
August 2019

Adding value to real-world data: the role of biomarkers.

Rheumatology (Oxford) 2020 01;59(1):31-38

Manchester Academic Health Science Centre, The University of Manchester, Arthritis Research UK Centre for Genetics and Genomics, Centre for Musculoskeletal Research, UK.

Adding biomarker information to real world datasets (e.g. biomarker data collected into disease/drug registries) can enhance mechanistic understanding of intra-patient differences in disease trajectories and differences in important clinical outcomes. Biomarkers can detect pathologies present early in disease potentially paving the way for preventative intervention strategies, which may help patients to avoid disability, poor treatment outcome, disease sequelae and premature mortality. However, adding biomarker data to real world datasets comes with a number of important challenges including sample collection and storage, study design and data analysis and interpretation. In this narrative review we will consider the benefits and challenges of adding biomarker data to real world datasets and discuss how biomarker data have added to our understanding of complex diseases, focusing on rheumatoid arthritis.
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http://dx.doi.org/10.1093/rheumatology/kez113DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6909909PMC
January 2020

Association of response to TNF inhibitors in rheumatoid arthritis with quantitative trait loci for and CD39.

Ann Rheum Dis 2019 08 29;78(8):1055-1061. Epub 2019 Apr 29.

Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK

Objectives: We sought to investigate whether genetic effects on response to TNF inhibitors (TNFi) in rheumatoid arthritis (RA) could be localised by considering known genetic susceptibility loci for relevant traits and to evaluate the usefulness of these genetic loci for stratifying drug response.

Methods: We studied the relation of TNFi response, quantified by change in swollen joint counts ( Δ SJC) and erythrocyte sedimentation rate ( Δ ESR) with locus-specific scores constructed from genome-wide assocation study summary statistics in 2938 genotyped individuals: 37 scores for RA; scores for 19 immune cell traits; scores for expression or methylation of 93 genes with previously reported associations between transcript level and drug response. Multivariate associations were evaluated in penalised regression models by cross-validation.

Results: We detected a statistically significant association between Δ SJC and the RA score at the locus (p=0.0004) and an inverse association between Δ SJC and the score for expression of CD39 on CD4 T cells (p=0.00005). A previously reported association between CD39 expression on regulatory T cells and response to methotrexate was in the opposite direction. In stratified analysis by concomitant methotrexate treatment, the inverse association was stronger in the combination therapy group and dissipated in the TNFi monotherapy group. Overall, ability to predict TNFi response from genotypic scores was limited, with models explaining less than 1% of phenotypic variance.

Conclusions: The association with the CD39 trait is difficult to interpret because patients with RA are often prescribed TNFi after failing to respond to methotrexate. The CD39 and pathways could be relevant for targeting drug therapy.
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http://dx.doi.org/10.1136/annrheumdis-2018-214877DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6669378PMC
August 2019

Development of a High-Throughput Cytometric Screen to Identify Anti- Wolbachia Compounds: The Power of Public-Private Partnership.

SLAS Discov 2019 06 8;24(5):537-547. Epub 2019 Apr 8.

1 Centre for Drugs and Diagnostics Research, Department of Tropical Disease Biology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, UK.

The Anti- Wolbachia (A·WOL) consortium at the Liverpool School of Tropical Medicine (LSTM) has partnered with the Global High-Throughput Screening (HTS) Centre at AstraZeneca to create the first anthelmintic HTS for neglected tropical diseases (NTDs). The A·WOL consortium aims to identify novel macrofilaricidal drugs targeting the essential bacterial symbiont ( Wolbachia) of the filarial nematodes causing onchocerciasis and lymphatic filariasis. Working in collaboration, we have validated a robust high-throughput assay capable of identifying compounds that selectively kill Wolbachia over the host insect cell. We describe the development and validation process of this complex, phenotypic high-throughput assay and provide an overview of the primary outputs from screening the AstraZeneca library of 1.3 million compounds.
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http://dx.doi.org/10.1177/2472555219838341DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6537165PMC
June 2019

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

Arthritis Rheumatol 2019 05 19;71(5):678-684. Epub 2019 Mar 19.

Manchester University NHS Foundation Trust, Manchester, UK.

Objective: Approximately 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.

Methods: RA 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.

Results: Based on the ratio of transcript values (i.e., the difference in log -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 ) and at 4 weeks after treatment initiation (P = 4.9 × 10 ).

Conclusion: Testing 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.
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http://dx.doi.org/10.1002/art.40810DOI Listing
May 2019

Industrial scale high-throughput screening delivers multiple fast acting macrofilaricides.

Nat Commun 2019 01 2;10(1):11. Epub 2019 Jan 2.

Centre for Drugs and Diagnostics, Department of Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA, UK.

Nematodes causing lymphatic filariasis and onchocerciasis rely on their bacterial endosymbiont, Wolbachia, for survival and fecundity, making Wolbachia a promising therapeutic target. Here we perform a high-throughput screen of AstraZeneca's 1.3 million in-house compound library and identify 5 novel chemotypes with faster in vitro kill rates (<2 days) than existing anti-Wolbachia drugs that cure onchocerciasis and lymphatic filariasis. This industrial scale anthelmintic neglected tropical disease (NTD) screening campaign is the result of a partnership between the Anti-Wolbachia consortium (A∙WOL) and AstraZeneca. The campaign was informed throughout by rational prioritisation and triage of compounds using cheminformatics to balance chemical diversity and drug like properties reducing the chance of attrition from the outset. Ongoing development of these multiple chemotypes, all with superior time-kill kinetics than registered antibiotics with anti-Wolbachia activity, has the potential to improve upon the current therapeutic options and deliver improved, safer and more selective macrofilaricidal drugs.
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http://dx.doi.org/10.1038/s41467-018-07826-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6315057PMC
January 2019

Prediction of treatment response in rheumatoid arthritis patients using genome-wide SNP data.

Genet Epidemiol 2018 12 12;42(8):754-771. Epub 2018 Oct 12.

NIHR Manchester Biomedical Research Centre, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK.

Although a number of treatments are available for rheumatoid arthritis (RA), each of them shows a significant nonresponse rate in patients. Therefore, predicting a priori the likelihood of treatment response would be of great patient benefit. Here, we conducted a comparison of a variety of statistical methods for predicting three measures of treatment response, between baseline and 3 or 6 months, using genome-wide SNP data from RA patients available from the MAximising Therapeutic Utility in Rheumatoid Arthritis (MATURA) consortium. Two different treatments and 11 different statistical methods were evaluated. We used 10-fold cross validation to assess predictive performance, with nested 10-fold cross validation used to tune the model hyperparameters when required. Overall, we found that SNPs added very little prediction information to that obtained using clinical characteristics only, such as baseline trait value. This observation can be explained by the lack of strong genetic effects and the relatively small sample sizes available; in analysis of simulated and real data, with larger effects and/or larger sample sizes, prediction performance was much improved. Overall, methods that were consistent with the genetic architecture of the trait were able to achieve better predictive ability than methods that were not. For treatment response in RA, methods that assumed a complex underlying genetic architecture achieved slightly better prediction performance than methods that assumed a simplified genetic architecture.
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http://dx.doi.org/10.1002/gepi.22159DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6334178PMC
December 2018

Assessing the Role of DNA Methylation-Derived Neutrophil-to-Lymphocyte Ratio in Rheumatoid Arthritis.

J Immunol Res 2018 14;2018:2624981. Epub 2018 Aug 14.

MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, UK.

Rheumatoid arthritis (RA) is a disease of chronic systemic inflammation (SI). In the present study, we used four datasets to explore whether methylation-derived neutrophil-to-lymphocyte ratio (mdNLR) might be a marker of SI in new onset, untreated, and treated prevalent RA cases and/or a marker of treatment response to the tumour necrosis factor inhibitor (TNFi) etanercept. mdNLR was associated with increased odds of being a new onset RA case (OR = 2.32, 95% CI = 1.95-2.80, < 2 × 10) and performed better in distinguishing new onset RA cases from controls compared to covariates: age, gender, and smoking status. In untreated preclinical RA cases and controls, mdNLR at baseline was associated with diagnosis of RA in later life after adjusting for batch (OR = 4.30, 95% CI = 1.52-21.71, = 0.029) although no association was observed before batch correction. When prevalent RA cases were treated, there was no association with mdNLR in samples before and after batch correction (OR = 0.34, 95% CI = 0.05-1.82, = 0.23), and mdNLR was not associated with treatment response to etanercept (OR = 1.10, 95% CI = 0.75-1.68, = 0.64). Our results indicate that SI measured by DNA methylation data is indicative of the recent onset of RA. Although preclinical RA was associated with mdNLR, there was no difference in the mean mdNLR between preclinical RA cases and controls. mdNLR was not associated with RA case status if treatment for RA has commenced, and it is not associated with treatment response. In the future, mdNLR estimates may be used as a valuable research tool to reliably estimate SI in the absence of freshly collected blood samples.
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http://dx.doi.org/10.1155/2018/2624981DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6112073PMC
November 2018

Increased DNA methylation variability in rheumatoid arthritis-discordant monozygotic twins.

Genome Med 2018 09 4;10(1):64. Epub 2018 Sep 4.

Arthritis Research UK Centre for Genetics and Genomics, Centre for Musculoskeletal Research, The University of Manchester, Manchester, UK.

Background: Rheumatoid arthritis is a common autoimmune disorder influenced by both genetic and environmental factors. Epigenome-wide association studies can identify environmentally mediated epigenetic changes such as altered DNA methylation, which may also be influenced by genetic factors. To investigate possible contributions of DNA methylation to the aetiology of rheumatoid arthritis with minimum confounding genetic heterogeneity, we investigated genome-wide DNA methylation in disease-discordant monozygotic twin pairs.

Methods: Genome-wide DNA methylation was assessed in 79 monozygotic twin pairs discordant for rheumatoid arthritis using the HumanMethylation450 BeadChip array (Illumina). Discordant twins were tested for both differential DNA methylation and methylation variability between rheumatoid arthritis and healthy twins. The methylation variability signature was then compared with methylation variants from studies of other autoimmune diseases and with an independent healthy population.

Results: We have identified a differentially variable DNA methylation signature that suggests multiple stress response pathways may be involved in the aetiology of the disease. This methylation variability signature also highlighted potential epigenetic disruption of multiple RUNX3 transcription factor binding sites as being associated with disease development. Comparison with previously performed epigenome-wide association studies of rheumatoid arthritis and type 1 diabetes identified shared pathways for autoimmune disorders, suggesting that epigenetics plays a role in autoimmunity and offering the possibility of identifying new targets for intervention.

Conclusions: Through genome-wide analysis of DNA methylation in disease-discordant monozygotic twins, we have identified a differentially variable DNA methylation signature, in the absence of differential methylation in rheumatoid arthritis. This finding supports the importance of epigenetic variability as an emerging component in autoimmune disorders.
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http://dx.doi.org/10.1186/s13073-018-0575-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6122744PMC
September 2018

Genome-wide association study of response to tumour necrosis factor inhibitor therapy in rheumatoid arthritis.

Pharmacogenomics J 2018 09 31;18(5):657-664. Epub 2018 Aug 31.

Arthritis Research UK Centre for Genetics and Genomics, Centre for Musculoskeletal Research, The University of Manchester, Manchester, UK.

Rheumatoid arthritis (RA) is characterised by chronic synovial joint inflammation. Treatment has been revolutionised by tumour necrosis factor alpha inhibitors (TNFi) but each available drug shows a significant non-response rate. We conducted a genome-wide association study of 1752 UK RA TNFi-treated patients to identify predictors of change in the Disease Activity Score 28 (DAS28) and subcomponents over 3-6 months. The rs7195994 variant at the FTO gene locus was associated with infliximab response when looking at a change in the swollen joint count (SJC28) subcomponent (p = 9.74 × 10). Capture Hi-C data show chromatin interactions in GM12878 cells between rs2540767, in high linkage disequilibrium with rs7195994 (R = 0.9) and IRX3, a neighbouring gene of FTO. IRX3 encodes a transcription factor involved in adipocyte remodelling and is regarded as the obesity gene at the FTO locus. Importantly, the rs7195994 association remained significantly associated following adjustment for BMI. In addition, using capture Hi-C data we showed interactions between TNFi-response associated variants and 16 RA susceptibility variants.
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http://dx.doi.org/10.1038/s41397-018-0040-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6150911PMC
September 2018

Genome-wide association study of response to methotrexate in early rheumatoid arthritis patients.

Pharmacogenomics J 2018 07 25;18(4):528-538. Epub 2018 May 25.

Leeds Institute of Cancer and Pathology, University of Leeds, and NIHR Leeds Biomedical Research Centre, Leeds Teaching Hospitals NHS Trust, Leeds, UK.

Methotrexate (MTX) monotherapy is a common first treatment for rheumatoid arthritis (RA), but many patients do not respond adequately. In order to identify genetic predictors of response, we have combined data from two consortia to carry out a genome-wide study of response to MTX in 1424 early RA patients of European ancestry. Clinical endpoints were change from baseline to 6 months after starting treatment in swollen 28-joint count, tender 28-joint count, C-reactive protein and the overall 3-component disease activity score (DAS28). No single nucleotide polymorphism (SNP) reached genome-wide statistical significance for any outcome measure. The strongest evidence for association was with rs168201 in NRG3 (p = 10 for change in DAS28). Some support was also seen for association with ZMIZ1, previously highlighted in a study of response to MTX in juvenile idiopathic arthritis. Follow-up in two smaller cohorts of 429 and 177 RA patients did not support these findings, although these cohorts were more heterogeneous.
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http://dx.doi.org/10.1038/s41397-018-0025-5DOI Listing
July 2018

HLA-A 31:01 is not associated with the development of methotrexate pneumonitis in the UK population: results from a genome-wide association study.

Ann Rheum Dis 2017 12 12;76(12):e51. Epub 2017 May 12.

Arthritis Research UK Centre for Genetics and Genomics, Centre for Musculoskeletal Research, The University of Manchester, Manchester, UK.

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http://dx.doi.org/10.1136/annrheumdis-2017-211512DOI Listing
December 2017

The predictive value of serum S100A9 and response to etanercept is not confirmed in a large UK rheumatoid arthritis cohort.

Rheumatology (Oxford) 2017 06;56(6):1019-1024

Arthritis Research UK, Centre for Genetics and Genomics, Centre for Musculoskeletal Research, Division of Musculoskeletal and Dermatological Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Sciences Centre, The University of Manchester.

Objective: The aim was to correlate protein concentrations of S100A9 in pretreatment serum samples with response to the tumour-necrosis factor (TNF) inhibitor drugs etanercept in a large UK replication cohort.

Methods: Pretreatment serum samples from patients with RA (n = 236) about to commence treatment with etanercept had S100A9 serum concentration measured using an ELISA. Following the experimental procedure, S100A9 concentrations were analysed with respect to EULAR response.

Results: No evidence of association between S100A9 concentration and EULAR response to the TNF-inhibitor biologic drug etanercept was observed following multinomial logistic regression analysis (non-responder vs moderate responder, P = 0.957; and non-responder vs good responder, P = 0.316). Furthermore, no significant associations were observed when correlating pretreatment S100A9 concentrations with clinical parameters of disease activity (P > 0.05).

Conclusion: In the largest replication cohort conducted to date, no evidence for association was observed to support the use of S100A9 as a clinical biomarker predictive of response to the TNF-inhibitor biologic drug etanercept.
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http://dx.doi.org/10.1093/rheumatology/kew387DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5445600PMC
June 2017

Discovery of Imidazo[1,2-a]pyridine Ethers and Squaramides as Selective and Potent Inhibitors of Mycobacterial Adenosine Triphosphate (ATP) Synthesis.

J Med Chem 2017 02 3;60(4):1379-1399. Epub 2017 Feb 3.

Innovative Medicines, AstraZeneca India Pvt. Ltd. , Bellary Road, Hebbal, Bangalore 560024, India.

The approval of bedaquiline to treat tuberculosis has validated adenosine triphosphate (ATP) synthase as an attractive target to kill Mycobacterium tuberculosis (Mtb). Herein, we report the discovery of two diverse lead series imidazo[1,2-a]pyridine ethers (IPE) and squaramides (SQA) as inhibitors of mycobacterial ATP synthesis. Through medicinal chemistry exploration, we established a robust structure-activity relationship of these two scaffolds, resulting in nanomolar potencies in an ATP synthesis inhibition assay. A biochemical deconvolution cascade suggested cytochrome c oxidase as the potential target of IPE class of molecules, whereas characterization of spontaneous resistant mutants of SQAs unambiguously identified ATP synthase as its molecular target. Absence of cross resistance against bedaquiline resistant mutants suggested a different binding site for SQAs on ATP synthase. Furthermore, SQAs were found to be noncytotoxic and demonstrated efficacy in a mouse model of tuberculosis infection.
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http://dx.doi.org/10.1021/acs.jmedchem.6b01358DOI Listing
February 2017

Discovery and Optimization of Allosteric Inhibitors of Mutant Isocitrate Dehydrogenase 1 (R132H IDH1) Displaying Activity in Human Acute Myeloid Leukemia Cells.

J Med Chem 2016 12 5;59(24):11120-11137. Epub 2016 Dec 5.

Manchester Institute of Biotechnology, University of Manchester , Princess Street, Manchester, M1 7DN, U.K.

A collaborative high throughput screen of 1.35 million compounds against mutant (R132H) isocitrate dehydrogenase IDH1 led to the identification of a novel series of inhibitors. Elucidation of the bound ligand crystal structure showed that the inhibitors exhibited a novel binding mode in a previously identified allosteric site of IDH1 (R132H). This information guided the optimization of the series yielding submicromolar enzyme inhibitors with promising cellular activity. Encouragingly, one compound from this series was found to induce myeloid differentiation in primary human IDH1 R132H AML cells in vitro.
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http://dx.doi.org/10.1021/acs.jmedchem.6b01320DOI Listing
December 2016

Capture Hi-C identifies a novel causal gene, IL20RA, in the pan-autoimmune genetic susceptibility region 6q23.

Genome Biol 2016 11 1;17(1):212. Epub 2016 Nov 1.

Arthritis Research UK Centre for Genetics and Genomics, Division of Musculoskeletal and Dermatological Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Stopford Building, Oxford Road, Manchester, M13 9PT, UK.

Background: The identification of causal genes from genome-wide association studies (GWAS) is the next important step for the translation of genetic findings into biologically meaningful mechanisms of disease and potential therapeutic targets. Using novel chromatin interaction detection techniques and allele specific assays in T and B cell lines, we provide compelling evidence that redefines causal genes at the 6q23 locus, one of the most important loci that confers autoimmunity risk.

Results: Although the function of disease-associated non-coding single nucleotide polymorphisms (SNPs) at 6q23 is unknown, the association is generally assigned to TNFAIP3, the closest gene. However, the DNA fragment containing the associated SNPs interacts through chromatin looping not only with TNFAIP3, but also with IL20RA, located 680 kb upstream. The risk allele of the most likely causal SNP, rs6927172, is correlated with both a higher frequency of interactions and increased expression of IL20RA, along with a stronger binding of both the NFκB transcription factor and chromatin marks characteristic of active enhancers in T-cells.

Conclusions: Our results highlight the importance of gene assignment for translating GWAS findings into biologically meaningful mechanisms of disease and potential therapeutic targets; indeed, monoclonal antibody therapy targeting IL-20 is effective in the treatment of rheumatoid arthritis and psoriasis, both with strong GWAS associations to this region.
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http://dx.doi.org/10.1186/s13059-016-1078-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5088679PMC
November 2016

Detection of anti-drug antibodies using a bridging ELISA compared with radioimmunoassay in adalimumab-treated rheumatoid arthritis patients with random drug levels.

Rheumatology (Oxford) 2016 Nov 25;55(11):2050-2055. Epub 2016 Aug 25.

NIHR Manchester Musculoskeletal Biomedical Research Unit, Central Manchester University Hospitals NHS Foundation Trust, Manchester Academic Health Science Centre, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK

Objective: To determine the concordance between RIA and bridging ELISA at detecting anti-drug antibodies (ADAbs) in the context of random adalimumab levels and investigate the additional clinical utility of detecting ADAbs in RA patients who test ADAb positive by RIA and negative by ELISA.

Methods: ADAb levels were determined using RIA and bridging ELISA in 63 adalimumab-treated RA patients (159 samples). Immunogenicity concordance was determined using receiver operating characteristic curves. To determine the additional clinical value provided by a positive RIA in the presence of negative ELISA, association between treatment response (ΔDAS28), adalimumab drug levels and ADAbs was evaluated longitudinally using generalized estimating equation.

Results: Of the 60 RIA samples (n = 31 patients), 19 (n = 10 patients) were also ELISA, corresponding to 31.7% of samples. Area under the curve for detecting ADAbs using ELISA (compared with RIA) using receiver operating characteristic curves was 0.65 (95% CI: 0.59, 0.71); this increased to 0.91 (95% CI: 0.81, 0.99) if ADAbs were ⩾100 AU/ml using RIA. In RIA/ELISA patients, adalimumab levels were associated with ΔDAS28 over 12 months [regression coefficient: 0.098 (95% CI: 0.043, 0.15), P < 0.0001] and while ADAbs were significantly associated with drug level, they were not directly associated with ΔDAS28 over 12 months [β coefficient: 0.00083 (95% CI: -0.0038 to 0.0054), P = 0.72].

Conclusion: ADAbs were detected using ELISA more frequently when present in high titres as measured by RIA. In RIA/ELISA patients, only drug levels were significantly associated with treatment response. Although ADAbs were not independently associated with treatment response, they may be helpful in determining the aetiology of low drug levels.
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http://dx.doi.org/10.1093/rheumatology/kew299DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5088626PMC
November 2016

High frequency of antidrug antibodies and association of random drug levels with efficacy in certolizumab pegol-treated patients with rheumatoid arthritis: results from the BRAGGSS cohort.

Ann Rheum Dis 2017 Jan 31;76(1):208-213. Epub 2016 May 31.

Arthritis Research UK Centre for Genetics and Genomics, Centre for Musculoskeletal Research, Institute of Inflammation and Repair, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK.

Objectives: To evaluate (i) the association between random certolizumab drug levels, antidrug antibodies (ADAbs) and treatment response in patients with rheumatoid arthritis (RA); (ii) longitudinal factors associated with ADAbs and certolizumab drug levels.

Methods: This prospective cohort included 115 patients with RA treated with certolizumab. Serum samples were collected at 3, 6 and 12 months following treatment initiation. Drug levels and ADAbs were measured using ELISA and radioimmunoassay, respectively, at 3, 6 and 12 months. Disease Activity Score in 28 joints (DAS28) were measured at each visit and 12 months European League Against Rheumatism (EULAR) response was calculated. Patient self-reported adherence was collected longitudinally. Ordinal logistic regression and generalised estimating equation were used to test the association: (i) between drug levels, from serum sampled and treatment response; (ii) between ADAbs and drug levels; (iii) patient-centred factors and drug levels.

Results: ADAbs were detected in 37% (42/112 patients by 12 months). The presence of ADAbs were significantly associated with lower drug levels over 12 months (β=-0.037, 95% CI -0.055 to 0.018, p<0.0001) but not independently with 12 months EULAR response (β=0.0013 (95% CI -0.0032 to 0.00061), p=0.18). Drug level was associated with 12 months EULAR response (β=0.032 (95% CI 0.0011 to 0.063), p=0.042). In the multivariate model, ADAb level and adherence were significantly associated with drug concentrations.

Conclusions: This is the first study to demonstrate that higher certolizumab drug levels are associated with better 12 months EULAR response. ADAbs in certolizumab-treated patients with RA were detected at higher levels than previous studies and help determine the aetiology of a low drug level.
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http://dx.doi.org/10.1136/annrheumdis-2015-208849DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5264213PMC
January 2017

Previously reported PDE3A-SLCO1C1 genetic variant does not correlate with anti-TNF response in a large UK rheumatoid arthritis cohort.

Pharmacogenomics 2016 05 16;17(7):715-20. Epub 2016 May 16.

Arthritis Research UK, Centre for Genetics & Genomics, Centre for Musculoskeletal Research, Manchester Academic Health Sciences Centre, The University of Manchester, Manchester, UK.

Aim: A genetic variant has recently reached genome-wide significance for association with TNF-inhibitor response in rheumatoid arthritis patients. Here we undertake a replication study in a UK Caucasian population to test for association with TNF-inhibitor response.

Materials & Methods: The genetic variant, rs3794271, located within the PDE3A-SLCO1C1 locus was analyzed for correlation with treatment response using both the EULAR classification criteria and absolute change in (Δ)DAS28 scores as outcome measures.

Results: Genotype data were available from 1750 TNF-inhibitor treated individuals. However, no evidence for association was observed (EULAR: p = 0.91 and ΔDAS28: p = 0.93). Furthermore, no significant associations were observed upon stratification by the anti-TNF received (p > 0.05).

Conclusion: In the largest replication cohort conducted to date, no evidence for association was observed.
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http://dx.doi.org/10.2217/pgs.16.16DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4996314PMC
May 2016

Major histocompatibility complex harbors widespread genotypic variability of non-additive risk of rheumatoid arthritis including epistasis.

Sci Rep 2016 04 25;6:25014. Epub 2016 Apr 25.

Arthritis Research UK Centre for Genetics and Genomics, Institute of Inflammation and Repair, Faculty of Medical and Human Sciences, Manchester Academic Health Science Centre, University of Manchester, Oxford Road, Manchester M13 9PT, UK.

Genotypic variability based genome-wide association studies (vGWASs) can identify potentially interacting loci without prior knowledge of the interacting factors. We report a two-stage approach to make vGWAS applicable to diseases: firstly using a mixed model approach to partition dichotomous phenotypes into additive risk and non-additive environmental residuals on the liability scale and secondly using the Levene's (Brown-Forsythe) test to assess equality of the residual variances across genotype groups per marker. We found widespread significant (P < 2.5e-05) vGWAS signals within the major histocompatibility complex (MHC) across all three study cohorts of rheumatoid arthritis. We further identified 10 epistatic interactions between the vGWAS signals independent of the MHC additive effects, each with a weak effect but jointly explained 1.9% of phenotypic variance. PTPN22 was also identified in the discovery cohort but replicated in only one independent cohort. Combining the three cohorts boosted power of vGWAS and additionally identified TYK2 and ANKRD55. Both PTPN22 and TYK2 had evidence of interactions reported elsewhere. We conclude that vGWAS can help discover interacting loci for complex diseases but require large samples to find additional signals.
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http://dx.doi.org/10.1038/srep25014DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4842957PMC
April 2016

Differential Methylation as a Biomarker of Response to Etanercept in Patients With Rheumatoid Arthritis.

Arthritis Rheumatol 2016 06 21;68(6):1353-60. Epub 2016 Apr 21.

NIHR Manchester Musculoskeletal Biomedical Research Unit, Manchester Academy of Health Sciences, and Central Manchester NHS Trust, and Arthritis Research UK Centre for Genetics and Genomics, University of Manchester, Manchester, UK.

Objective: Biologic drug therapies represent a huge advance in the treatment of rheumatoid arthritis (RA). However, very good disease control is achieved in only 30% of patients, making identification of biomarkers of response a research priority. We undertook this study to test our hypothesis that differential DNA methylation patterns may provide biomarkers predictive of response to tumor necrosis factor inhibitor (TNFi) therapy in patients with RA.

Methods: An epigenome-wide association study was performed on pretreatment whole blood DNA from patients with RA. Patients who displayed good response (n = 36) or no response (n = 36) to etanercept therapy at 3 months were selected. Differentially methylated positions were identified using linear regression. Variance of methylation at differentially methylated positions was assessed for correlation with cis-acting single-nucleotide polymorphisms (SNPs). A replication experiment for prioritized SNPs was performed in an independent cohort of 1,204 RA patients.

Results: Five positions that were differentially methylated between responder groups were identified, with a false discovery rate of <5%. The top 2 differentially methylated positions mapped to exon 7 of the LRPAP1 gene on chromosome 4 (cg04857395, P = 1.39 × 10(-8) and cg26401028, P = 1.69 × 10(-8) ). The A allele of the SNP rs3468 was correlated with higher levels of methylation for both of the top 2 differentially methylated positions (P = 2.63 × 10(-7) and P = 1.05 × 10(-6) , respectively). Furthermore, the A allele of rs3468 was correlated with European League Against Rheumatism nonresponse in the discovery cohort (P = 0.03; n = 56) and in the independent replication cohort (P = 0.003; n = 1,204).

Conclusion: We identify DNA methylation as a potential biomarker of response to TNFi therapy, and we report the association between response and the LRPAP1 gene, which encodes a chaperone of low-density lipoprotein receptor-related protein 1. Additional replication experiments in independent sample collections are now needed.
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http://dx.doi.org/10.1002/art.39590DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4914881PMC
June 2016

Investigating CD11c expression as a potential genomic biomarker of response to TNF inhibitor biologics in whole blood rheumatoid arthritis samples.

Arthritis Res Ther 2015 Dec 14;17:359. Epub 2015 Dec 14.

Arthritis Research UK, Centre for Genetics and Genomics, Centre for Musculoskeletal Research, Manchester Academic Health Sciences Centre, the University of Manchester, Manchester, UK.

Introduction: Gene expression profiling is rapidly becoming a useful and informative tool in a much needed area of research. Identifying patients as to whether they will respond or not to a given treatment before prescription is not only essential to optimise treatment outcome but also to lessen the economic burden that such drugs can have on healthcare resources. In rheumatoid arthritis (RA), there is of yet no genetic/genomic biomarker which can accurately predict response to TNF inhibitor biologics prior to treatment, despite much interest in this area. Multiple studies have reported findings on potential candidate genes; however, due to relatively small sample sizes or lack of sufficient validation, results have been disappointingly inconsistent. The aim of this research was to further explore the predictive value of a previously reported association between CD11c expression and response to the TNF inhibitor biologics, adalimumab and etanercept.

Methods: Real-time qPCR was performed using whole blood RNA samples obtained from seventy-five rheumatoid arthritis patients about to commence treatment with a TNF inhibitor biologic drug, whose response status was determined at 3-month follow-up using the EULAR classification criteria. Relative quantification of CD11c using the comparative CT method outputted differential expression between good-responders and non-responders as a fold-change.

Results: Relative expression of CD11c in patients receiving TNF inhibitor biologics yielded a decrease of 1.025 fold in good-responders as compared to non-responders (p-value = 0.36). Upon stratification of patients dependent upon the specific drug administered, adalimumab or etanercept, similar findings to the full cohort were observed, decreases of 1.015 (p-value = 0.33) and 1.032 fold (p-value = 0.13) in good-responders compared to non-responders, respectively.

Conclusion: The results from this study reveal that CD11c expression does not correlate with response to TNF inhibitor biologics when tested for within pre-treatment whole blood samples of rheumatoid arthritis patients.
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http://dx.doi.org/10.1186/s13075-015-0868-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4704535PMC
December 2015