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      Chromosome conformation signatures define predictive markers of inadequate response to methotrexate in early rheumatoid arthritis

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          Abstract

          Background

          There is a pressing need in rheumatoid arthritis (RA) to identify patients who will not respond to first-line disease-modifying anti-rheumatic drugs (DMARD). We explored whether differences in genomic architecture represented by a chromosome conformation signature (CCS) in blood taken from early RA patients before methotrexate (MTX) treatment could assist in identifying non-response to DMARD and, whether there is an association between such a signature and RA specific expression quantitative trait loci (eQTL).

          Methods

          We looked for the presence of a CCS in blood from early RA patients commencing MTX using chromosome conformation capture by EpiSwitch™. Using blood samples from MTX responders, non-responders and healthy controls, a custom designed biomarker discovery array was refined to a 5-marker CCS that could discriminate between responders and non-responders to MTX. We cross-validated the predictive power of the CCS by generating 150 randomized groups of 59 early RA patients (30 responders and 29 non-responders) before MTX treatment. The CCS was validated using a blinded, independent cohort of 19 early RA patients (9 responders and 10 non-responders). Last, the loci of the CCS markers were mapped to RA-specific eQTL.

          Results

          We identified a 5-marker CCS that could identify, at baseline, responders and non-responders to MTX. The CCS consisted of binary chromosome conformations in the genomic regions of IFNAR1, IL-21R, IL-23, CXCL13 and IL-17A. When tested on a cohort of 59 RA patients, the CCS provided a negative predictive value of 90.0% for MTX response. When tested on a blinded independent validation cohort of 19 early RA patients, the signature demonstrated a true negative response rate of 86 and a 90% sensitivity for detection of non-responders to MTX. Only conformations in responders mapped to RA-specific eQTL.

          Conclusions

          Here we demonstrate that detection of a CCS in blood in early RA is able to predict inadequate response to MTX with a high degree of accuracy. Our results provide a proof of principle that a priori stratification of response to MTX is possible, offering a mechanism to provide alternative treatments for non-responders to MTX earlier in the course of the disease.

          Electronic supplementary material

          The online version of this article (10.1186/s12967-018-1387-9) contains supplementary material, which is available to authorized users.

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          Most cited references63

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          Genomic variation. Impact of regulatory variation from RNA to protein.

          The phenotypic consequences of expression quantitative trait loci (eQTLs) are presumably due to their effects on protein expression levels. Yet the impact of genetic variation, including eQTLs, on protein levels remains poorly understood. To address this, we mapped genetic variants that are associated with eQTLs, ribosome occupancy (rQTLs), or protein abundance (pQTLs). We found that most QTLs are associated with transcript expression levels, with consequent effects on ribosome and protein levels. However, eQTLs tend to have significantly reduced effect sizes on protein levels, which suggests that their potential impact on downstream phenotypes is often attenuated or buffered. Additionally, we identified a class of cis QTLs that affect protein abundance with little or no effect on messenger RNA or ribosome levels, which suggests that they may arise from differences in posttranslational regulation. Copyright © 2015, American Association for the Advancement of Science.
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            Guidelines for the management of rheumatoid arthritis: 2002 Update.

            (2002)
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              Variation and Genetic Control of Protein Abundance in Humans

              Gene expression differs among both individuals and populations and is thought to be a major determinant of phenotypic variation. Although variation and genetic loci responsible for RNA expression levels have been analyzed extensively in human populations 1–5 , our knowledge is limited regarding the differences in human protein abundance and their genetic basis. Variation in mRNA expression is not a perfect surrogate for protein expression because the latter is influenced by a battery of post-transcriptional regulatory mechanisms, and, empirically, the correlation between protein and mRNA levels is generally modest 6,7 . Here we used isobaric tandem mass tag (TMT)-based quantitative mass spectrometry to determine relative protein levels of 5953 genes in lymphoblastoid cell lines (LCLs) from 95 diverse individuals genotyped in the HapMap Project 8,9 . We found that protein levels are heritable molecular phenotypes that exhibit considerable variation between individuals, populations, and sexes. Levels of specific sets of proteins involved in the same biological process co-vary among individuals, indicating that these processes are tightly regulated at the protein level. We identified cis-pQTLs (protein quantitative trait loci), including variants not detected by previous transcriptome studies. This study demonstrates the feasibility of high throughput human proteome quantification which, when integrated with DNA variation and transcriptome information, adds a new dimension to the characterization of gene expression regulation.
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                Author and article information

                Contributors
                claudio.carini@kcl.ac.uk
                ewan.hunter@oxfordbiodynamics.com
                aroul.ramadass@oxfordbiodynamics.com
                jayne.green@oxfordbiodynamics.com
                alexandre.akoulitchev@oxfordbiodynamics.com
                Iain.McInnes@glasgow.ac.uk
                Carl.Goodyear@glasgow.ac.uk
                Journal
                J Transl Med
                J Transl Med
                Journal of Translational Medicine
                BioMed Central (London )
                1479-5876
                29 January 2018
                29 January 2018
                2018
                : 16
                : 18
                Affiliations
                [1 ]ISNI 0000 0000 8800 7493, GRID grid.410513.2, Pfizer Inc., ; Cambridge, USA
                [2 ]Oxford BioDynamics Plc, Oxford, UK
                [3 ]ISNI 0000 0001 2193 314X, GRID grid.8756.c, Institute of Infection, Immunity and Inflammation, , University of Glasgow, ; Glasgow, UK
                [4 ]ISNI 0000 0001 2322 6764, GRID grid.13097.3c, Department of Asthma, Allergy & Lung Biology, GSTT Campus, , King’s College School of Medicine, ; London, UK
                Author information
                http://orcid.org/0000-0002-2222-0296
                Article
                1387
                10.1186/s12967-018-1387-9
                5789697
                29378619
                d749ece8-79f6-4ab9-95f0-f298bb6ce2f6
                © The Author(s) 2018

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 30 November 2017
                : 13 January 2018
                Funding
                Funded by: Oxford BioDynamics
                Categories
                Research
                Custom metadata
                © The Author(s) 2018

                Medicine
                early rheumatoid arthritis,methotrexate,rheumatoid arthritis,dmards (synthetic),precision medicine drug response biomarkers,methotrexate (mtx),chromatin conformation signatures (ccs),expression quantitative trait loci (eqtl)

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