MICA: Development and Validation of a Transcriptomic-Based Model for Classifying and Predicting Treatment Response in Rheumatoid Arthritis (TRACT-RA)

Lead Research Organisation: Queen Mary University of London
Department Name: William Harvey Research Institute

Abstract

Rheumatoid arthritis (RA) is the most common form of inflammatory arthritis affecting 1% of the population worldwide and around 500,000 people in the UK. It affects all ages and is characterised by inflammation of the joint lining (synovium) and destruction of cartilage and bone. In approximately 40% of patients current medications are ineffective leading to uncontrolled disease progression, loss of productivity and disability, causing patients suffering and costing ~£4.8billion/year in the UK.

RA is clinically a very diverse disease and at the time patients are diagnosed we can't predict what the likely outcome of the disease will be, or why in some patients treatments are effective whereas in others they are not. This lack of predictive markers leads to: delays in disease control; unnecessary exposure to potentially toxic drugs; large waste of valuable NHS/societal resources.
There are several ways of classifying patients using observable characteristics (known as the phenotype) and/or defining their detailed functional or molecular make-up (known as the endotype). We have a large, unique collection of samples (synovial tissue and blood) from over 750 patients at three distinctive stages of the disease and treated with different drugs: patients have early disease, patient with more advanced disease and patients with "resistant RA" in whom current medication are not effective.

The aim of this research is to develop a method to classify patients using a coding product of the DNA called RNA and this leads to the production of proteins the building blocks of our bodies. Looking at the RNA, known as the transcriptome, indicates which genes are active and in RA helps to identify those that cause inflammation and joint damage. We have already analysed (sequenced) the RNA in the disease tissue in our large collections and in the patients with early disease we have identified different patterns (referred to as signatures) associated with different sub-types of disease and how it progresses.

Our objectives are:

1) To find out whether the signatures we have found in early arthritis are also present at different disease stages i.e. in the established and the resistant RA patient groups, and whether these signatures are modified by treatment. Namely, whether they are conserved, so that we would only need to classify a patient once or whether they change over time and repeating testing is required.

2) To evaluate whether matching the way a medicine works to the signature identified in the patients tissue means that treatment is more effective than when they are not matched, so that we can work out which treatments work best for individual patients.

3) The sequencing of RNA to identify the signatures is complicated, expensive and time consuming so we will work with a company (NanoString) which has developed methods for doing similar tests in other diseases that can be used routinely in hospitals.

4) If we find important signatures we will look to see whether we can also identify these in blood samples which are easier to take than a biopsy.

5) We will apply mathematical modelling using the signature data and clinical information with the aim of developing a process to improve the care patients receive.

If we can identify patients who are more likely to develop aggressive forms of the disease this could be highlighted to their clinical team so that they are followed more closely and quickly offered new treatments to stop the disease deteriorating. Also, being able to predict the treatment likely to work best for a patient may save many years of 'trial and error' until the right treatment is found, this has the potential to greatly improve the quality of life of RA patients, prevent disability and save money to the NHS and society.

Technical Summary

Rheumatoid Arthritis (RA) represents a huge public health problem. It is one of the most important chronic inflammatory disorders, leading to increased morbidity/mortality and a heavy health-economic burden. Although current medications have revolutionized the treatment of RA, they are not effective in ~40% of patients, the mechanisms of no-response remain unknown and there are no current predictors of treatment response. With MRC/Versus Arthritis support, by RNA-seq/NanoString analyses we have identified predictive signatures of prognosis and treatment response in the disease tissue (synovium) of early arthritis patients (PEAC) (Ref:6,7,8 in CfS) and delivered the first two biopsy-driven RCTs (STRAP and R4RA) worldwide.

The aim of the proposed project is to develop and validate a transcriptomic-based model building on the PEAC signatures to be evaluated in STRAP & R4RA.

The main hypothesis to be tested is that specific signatures in the synovium define disease and treatment-response subtypes (endotypes) that can predict prognosis and response to current targeted biologic therapies.

We will test this hypothesis by applying robust analytical pipelines to our unique RNA-seq datasets developed by our team (Ref:6,14 in CfS). Given that RNA-seq is not practical or economical for translation into clinical practice, we will evaluate the portability of synovial RNA-seq signatures into NanoString panels also at blood level in collaboration with two of their most senior scientists.

The development, validation and replication of our transcriptomic-based model using NanoString technologies, represent important steps toward future development of a clinical assay with potential major impact on patient care.

This may enable a new molecular definition of RA and the definition of drug target modules in the disease tissue as predictors of response, which may transform current management of RA from "trial and error" drug-prescribing to rational therapy allocation.

Publications

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Lewis MJ (2023) Progress continues in prediction of the response to treatment of RA. in Nature reviews. Rheumatology

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Schmidt M (2022) Transcriptomic and proteomic profiling of NaV1.8-expressing mouse nociceptors. in Frontiers in molecular neuroscience

 
Description Invitation to Join QMUL/EMR's Musculoskeletal (MSK) Precision Medicine Patient and Public Involvement Group
Geographic Reach Local/Municipal/Regional 
Policy Influence Type Participation in a guidance/advisory committee
 
Description (DEMORA) Deep Spatial Characterisation of Synovial Macrophages in Rheumatoid Arthritis (MSCA PF 2022)
Amount £200,510 (GBP)
Funding ID 101109903 
Organisation United Kingdom Research and Innovation 
Sector Public
Country United Kingdom
Start 08/2023 
End 08/2025
 
Description BRC 2022-27 - Precision Musculoskeletal Care
Amount £21,000,000 (GBP)
Funding ID NIHR203330 
Organisation National Institute for Health Research 
Sector Public
Country United Kingdom
Start 12/2022 
End 11/2027
 
Description Prediction Of Clinical Response To TNF Inhibitor Using Both Gene Expression And Epigenetic Profile
Amount £248,654 (GBP)
Organisation The Medical College of Saint Bartholomew's Hospital Trust 
Sector Charity/Non Profit
Country United Kingdom
Start 03/2021 
End 07/2024
 
Description SQUEEZE - Maximizing Impact of Prescription Drugs in Rheumatoid Arthritis
Amount € 10,216,000 (EUR)
Funding ID 101095052 
Organisation European Commission 
Sector Public
Country European Union (EU)
Start 12/2022 
End 11/2027
 
Title R4RA molecular paper: RNAseq and metadata 
Description This dataset has been used in our study that was recently accepted by Nature Medicine: "Rituximab versus tocilizumab in rheumatoid arthritis: Synovial biopsy-based biomarker analysis of the phase 4 R4RA randomized trial". Sample collection protocol:A minimum of 6 samples (ultrasound-guided synovial biopsies) per patient were immediately immersed in RNA-Later. All patients provided written informed consent before the synovial tissue biopsy procedure. They were fulfilling 2010 ACR/EULAR classification criteria for RA and were eligible for treatment with rituximab therapy according to UK NICE guidelines, i.e. failing or intolerant to csDMARD therapy and at least one biologic therapy (excluding trial IMPs) were recruited when fulfilling the trial inclusion/exclusion criteria (for the full study protocol and baseline patient characteristics see Humby et al). The patients were recruited under R4RA clinical trial. RNAseq: Image analysis and base calling were conducted by the HiSeq Control Software (HCS). Raw sequence data (.bcl files) generated from Illumina HiSeq was converted into fastq files and de-multiplexed using Illumina's bcl2fastq 2.17 software. One mismatch was allowed for index sequence identification. Fastq files were trimmed to remove the Illumina adaptors using bbduk from the BBMap package version 37.93 using the default parameters. Transcripts were then quantified using Salmon version 0.13.1 and an index generated from the Gencode release 29 transcriptome following the standard operating procedure. Tximport version 1.13.10 was used to aggregate the transcript level expression data to genes, counts were then subject to variance stabilizing transformation (VST) and transcripts per million (TPM) using the DESeq2 version 1.25.9 package and in-house script, respectively. 
Type Of Material Database/Collection of data 
Year Produced 2022 
Provided To Others? Yes  
Impact Average unique users per month: 25 Total in 2022: 340 (329 new users) Demonstrating its utility for the scientific community. 
URL https://figshare.com/articles/dataset/R4RA_molecular_paper_RNAseq_and_metadata/19336679
 
Description QMUL collaboration 
Organisation Queen Mary University of London
Department Centre for Inflammation and Therapeutic Innovation
Country United Kingdom 
Sector Hospitals 
PI Contribution Co-applicants on successful MRC and Versus Arthritis grant awards. On-going collaboration investigating novel outcome measures and genetic studies of treatment response.
Collaborator Contribution Lead applicants on MRC and Versus Arthritis grant awards; provided data for collaborative genetic / outcome studies
Impact MRC and Versus Arthritis grant awards Joint publications: 1. Stratified medicine in rheumatoid arthritis-the MATURA programme. Barton A, Pitzalis C. Rheumatology (Oxford). 2017 Aug 1;56(8):1247-1250. doi: 10.1093/rheumatology/kew369. PMID: 28165532 Free PMC article. No abstract available. 2 Biomarkers and personalised medicine in rheumatoid arthritis: a proposal for interactions between academia, industry and regulatory bodies. Miossec P, Verweij CL, Klareskog L, Pitzalis C, Barton A, Lekkerkerker F, Reiter S, Laslop A, Breedveld F, Abadie E, Flamion B, Dere W, Mpofu S, Goel N, Ethgen D, Mitlak B, Ormarsdóttir S, Rao R, Tsouderos Y, Reginster JY; Group for Respect of Ethics and Excellence in Science (GREES). Ann Rheum Dis. 2011 Oct;70(10):1713-8. doi: 10.1136/ard.2011.154252. Epub 2011 Jul 22. PMID: 21784723 Free article. Review. 3 Expression quantitative trait loci analysis in rheumatoid arthritis identifies tissue specific variants associated with severity and outcome. Goldmann K, Spiliopoulou A, Iakovliev A, Plant D, Nair N, Cubuk C; MATURA Consortium; McKeigue P, Barnes MR, Barton A, Pitzalis C, Lewis MJ. Ann Rheum Dis. 2023 Nov 18:ard-2023-224540. doi: 10.1136/ard-2023-224540. Online ahead of print. PMID: 37979960 Free article. 4 Stratification of biological therapies by pathobiology in biologic-naive patients with rheumatoid arthritis (STRAP and STRAP-EU): two parallel, open-label, biopsy-driven, randomised trials. Rivellese F, Nerviani A, Giorli G, Warren L, Jaworska E, Bombardieri M, Lewis MJ, Humby F, Pratt AG, Filer A, Gendi N, Cauli A, Choy E, McInnes I, Durez P, Edwards CJ, Buch MH, Gremese E, Taylor PC, Ng N, Cañete JD, Raizada S, McKay ND, Jadon D, Sainaghi PP, Stratton R, Ehrenstein MR, Ho P, Pereira JP, Dasgupta B, Gorman C, Galloway J, Chinoy H, van der Heijde D, Sasieni P, Barton A, Pitzalis C; STRAP collaborative group. Lancet Rheumatol. 2023 Nov;5(11):e648-e659. doi: 10.1016/S2665-9913(23)00241-2. PMID: 38251532 Free article. Clinical Trial. 5 Genome-wide association study of response to methotrexate in early rheumatoid arthritis patients. Taylor JC, Bongartz T, Massey J, Mifsud B, Spiliopoulou A, Scott IC, Wang J, Morgan M, Plant D, Colombo M, Orchard P, Twigg S, McInnes IB, Porter D, Freeston JE, Nam JL, Cordell HJ, Isaacs JD, Strathdee JL, Arnett D, de Hair MJH, Tak PP, Aslibekyan S, van Vollenhoven RF, Padyukov L, Bridges SL, Pitzalis C, Cope AP, Verstappen SMM, Emery P, Barnes MR, Agakov F, McKeigue P, Mushiroda T, Kubo M, Weinshilboum R, Barton A, Morgan AW, Barrett JH; MATURA; and PAMERA; Consortia. Pharmacogenomics J. 2018 Jul;18(4):528-538. doi: 10.1038/s41397-018-0025-5. Epub 2018 May 25. PMID: 29795407 Free article. Review. 6 BBmix: a Bayesian beta-binomial mixture model for accurate genotyping from RNA-sequencing. Vigorito E, Barton A, Pitzalis C, Lewis MJ, Wallace C. Bioinformatics. 2023 Jul 1;39(7):btad393. doi: 10.1093/bioinformatics/btad393. PMID: 37338536 Free PMC article. 7 Prediction of treatment response in rheumatoid arthritis patients using genome-wide SNP data. Cherlin S, Plant D, Taylor JC, Colombo M, Spiliopoulou A, Tzanis E, Morgan AW, Barnes MR, McKeigue P, Barrett JH, Pitzalis C, Barton A, Consortium M, Cordell HJ. Genet Epidemiol. 2018 Dec;42(8):754-771. doi: 10.1002/gepi.22159. Epub 2018 Oct 12. PMID: 30311271 Free PMC article. 8 Validity of a two-component imaging-derived disease activity score for improved assessment of synovitis in early rheumatoid arthritis. Hensor EMA, McKeigue P, Ling SF, Colombo M, Barrett JH, Nam JL, Freeston J, Buch MH, Spiliopoulou A, Agakov F, Kelly S, Lewis MJ, Verstappen SMM, MacGregor AJ, Viatte S, Barton A, Pitzalis C, Emery P, Conaghan PG, Morgan AW. Rheumatology (Oxford). 2019 Mar 1;58(8):1400-9. doi: 10.1093/rheumatology/kez049. Online ahead of print. PMID: 30824919 Free PMC article. 9 Genome-wide association study of response to tumour necrosis factor inhibitor therapy in rheumatoid arthritis. Massey J, Plant D, Hyrich K, Morgan AW, Wilson AG, Spiliopoulou A, Colombo M, McKeigue P, Isaacs J, Cordell H, Pitzalis C, Barton A; BRAGGSS, MATURA Consortium. Pharmacogenomics J. 2018 Sep;18(5):657-664. doi: 10.1038/s41397-018-0040-6. Epub 2018 Aug 31. PMID: 30166627 Free PMC article. 10 Robust optimization of SWATH-MS workflow for human blood serum proteome analysis using a quality by design approach. Serrano-Blesa E, Porter A, Lendrem DW, Pitzalis C, Barton A, Treumann A, Isaacs JD. Clin Proteomics. 2021 Aug 12;18(1):20. doi: 10.1186/s12014-021-09323-z. PMID: 34384350 Free PMC article. 11 Investigation of genetically regulated gene expression and response to treatment in rheumatoid arthritis highlights an association between IL18RAP expression and treatment response. Cherlin S, Lewis MJ, Plant D, Nair N, Goldmann K, Tzanis E, Barnes MR, McKeigue P, Barrett JH, Pitzalis C, Barton A; MATURA Consortium; Cordell HJ. Ann Rheum Dis. 2020 Nov;79(11):1446-1452. doi: 10.1136/annrheumdis-2020-217204. Epub 2020 Jul 30. PMID: 32732242 Free PMC article. 12 Association of response to TNF inhibitors in rheumatoid arthritis with quantitative trait loci for CD40 and CD39. Spiliopoulou A, Colombo M, Plant D, Nair N, Cui J, Coenen MJ, Ikari K, Yamanaka H, Saevarsdottir S, Padyukov L, Bridges SL Jr, Kimberly RP, Okada Y, van Riel PLC, Wolbink G, van der Horst-Bruinsma IE, de Vries N, Tak PP, Ohmura K, Canhão H, Guchelaar HJ, Huizinga TW, Criswell LA, Raychaudhuri S, Weinblatt ME, Wilson AG, Mariette X, Isaacs JD, Morgan AW, Pitzalis C, Barton A, McKeigue P. Ann Rheum Dis. 2019 Aug;78(8):1055-1061. doi: 10.1136/annrheumdis-2018-214877. Epub 2019 Apr 29. PMID: 31036624 Free PMC article.
Start Year 2012
 
Description TRACT-RA - NanoString 
Organisation NanoString Technologies
Country United States 
Sector Private 
PI Contribution Providing samples and data from previous cohorts (PEAC/STRAP/R4RA) in the context of the TRACT-RA award
Collaborator Contribution This collaboration will investigate biomarkers of response/non-response, pathways analysis which may results in developing a diagnostic tool.
Impact Too early
Start Year 2021
 
Description TRACT-RA - PrecisionLife 
Organisation Precisionlife
Country United Kingdom 
Sector Private 
PI Contribution Providing data from previous cohorts (PEAC/STRAP/R4RA) in the context of the TRACT-RA award
Collaborator Contribution This collaboration will investigate biomarkers of response/non-response, pathways analysis which may results in developing a diagnostic tool.
Impact Too early
Start Year 2021
 
Description TRACT-RA collaboration Universities 
Organisation University of Edinburgh
Country United Kingdom 
Sector Academic/University 
PI Contribution Provide data and samples from previous cohorts in the context of the TRACT-RA award
Collaborator Contribution Contribute to the analysis of the data in the context of the TRACT-RA award. UoM to perform serological and genotyping analysis.
Impact Too early
Start Year 2021
 
Description TRACT-RA collaboration Universities 
Organisation University of Manchester
Country United Kingdom 
Sector Academic/University 
PI Contribution Provide data and samples from previous cohorts in the context of the TRACT-RA award
Collaborator Contribution Contribute to the analysis of the data in the context of the TRACT-RA award. UoM to perform serological and genotyping analysis.
Impact Too early
Start Year 2021
 
Title METHOD FOR TREATING RHEUMATOID ARTHRITIS 
Description A method for determining whether a Rheumatoid Arthritis (RA) patient is susceptible to treatment with a B cell targeted therapy, the method comprising the steps: (a) determining the level of one or more biomarker in one or more sample obtained from the patient, wherein the one or more biomarker is selected from Table 1; and (b) comparing the level of the one or more biomarker to one or more corresponding reference value; wherein the level of the one or more biomarker compared to the corresponding reference value is indicative of the susceptibility to treatment with a B cell targeted therapy. 
IP Reference WO2022157506 
Protection Patent / Patent application
Year Protection Granted 2022
Licensed Yes
Impact Patent has been licensed by Exagen Inc and is being used to develop a novel prognostic biomarker for stratifying optimal treatments in rheumatoid arthritis
 
Title R4RA RNA-Seq shiny web portal 
Description Shiny web app for interrogating RNA-Sequencing data from synovial biopsies from the R4RA clinical trial. 
Type Of Technology Webtool/Application 
Year Produced 2021 
Open Source License? Yes  
Impact This web app makes the RNA-Seq data from R4RA publicly available with an easy to use interface allowing researchers to query genes of interest and correlate gene expression in synovial biopsies against clinical parameters and clinical outcomes. 
URL https://r4ra.hpc.qmul.ac.uk
 
Title STRAP shiny web app 
Description Website using R/shiny interface to allow interactive analysis of RNA-Seq data from the STRAP trial and comparison with clinical metadata. 
Type Of Technology Webtool/Application 
Year Produced 2023 
Open Source License? Yes  
Impact Allows public dissemination of data with simple and easy to use analysis of datasets. 
URL https://strap.hpc.qmul.ac.uk/
 
Title glmmSeq R package 
Description Using mixed effects models to analyse longitudinal gene expression can highlight differences between sample groups over time. The most widely used differential gene expression tools are unable to fit linear mixed effect models, and are less optimal for analysing longitudinal data. This package provides negative binomial and Gaussian mixed effects models to fit gene expression and other biological data across repeated samples. This is particularly useful for investigating changes in RNA-Sequencing gene expression between groups of individuals over time, as described in: Rivellese, F., Surace, A. E., Goldmann, K., Sciacca, E., Cubuk, C., Giorli, G., ... Lewis, M. J., & Pitzalis, C. (2022) Nature medicine . 
Type Of Technology Software 
Year Produced 2022 
Open Source License? Yes  
Impact Enabled analysis of longitudinal RNA-Seq data from synovial biopsies from the R4RA clinical trial. 
URL https://cran.r-project.org/package=glmmSeq
 
Title nestedcv R package 
Description Implements nested k*l-fold cross-validation for lasso and elastic-net regularised linear models via the 'glmnet' package and other machine learning models via the 'caret' package. Cross-validation of 'glmnet' alpha mixing parameter and embedded fast filter functions for feature selection are provided. 
Type Of Technology Software 
Year Produced 2022 
Open Source License? Yes  
Impact Has been used to build predictive models from RNA-Seq data from the STRAP biopsy-driven RCT. 
URL https://cran.r-project.org/package=nestedcv