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Genetic and genomic markers of anti-TNF treatment response in rheumatoid arthritis

Despite the success of anti-TNF drugs in the treatment of rheumatoid arthritis, a significant rate of nonresponse remains. Current clinical factors confer little power for predicting response and, in current practice, an unsatisfactory ‘trial and error’ approach governs therapeutic decisions. Candidate gene and unbiased genomewide investigations have sought to identify genetic biomarkers that predict who will respond to anti-TNF drugs before the drug is administered. To date, few studies have yielded robust associations; herein, we discuss currently identified associations and the issues that need to be addressed in future investigations including insufficient power and an inadequate measure of disease activity. The potential for alternative predictors of anti-TNF therapy response from transcriptomic and epigenetic data will also be explored. Keywords: anti-TNF • biologics • biomarker • GWAS • prediction • rheumatoid arthritis • treatment response

Background Rheumatoid arthritis (RA) is a chronic inflammatory disorder affecting 0.5–1% of the adult population worldwide [1] . Primarily manifesting as inflammation in synovial joints, this systemic autoimmune disease can also affect other organs including the eyes (scleritis/episcleritis), lungs (interstitial fibrosis) and blood vessels (vasculitis). In the joints, uncontrolled inflammation can progress to structural damage of synovial cartilage and bone with associated disability. At the cellular level, the disease is associated with synovial hyperplasia and the recruitment of immune cells to affected joints. As a result, RA is a painful and debilitating condition, significantly associated with co-morbidity and excess mortality, costing the UK economy up to GBP£4.8 billion per year [2] . RA aetiology is influenced by both genetic and environmental factors, resulting in an uncontrolled autoimmune response. Twin studies have estimated that the genetic heritability underpinning RA approaches 60% but less than 50% of this is accounted for by genetic

10.2217/BMM.15.18 © 2015 Future Medicine Ltd

James Oliver1, Darren Plant2, Amy P Webster1 & Anne Barton*,1,2 Arthritis Research UK Centre for Genetics & Genomics, Centre for Musculoskeletal Research, Institute of Inflammation & Repair, University Of Manchester, Manchester, M13 9PL, UK 2 NIHR Manchester Musculoskeletal Biomedical Research Unit, Manchester Academy of Health Sciences, Manchester, M13 9PL, UK *Author for correspondence: Tel.: +44 161 275 1638 anne.barton@ manchester.ac.uk 1

variants identified thus far [3] . Furthermore, RA may not be a single disease and heritability appears to be dependent on the presence or absence of anticitrullinated protein antibodies (ACPA) as measured by anticyclic citrullinated peptide status; ACPA-positive patients have a more severe disease progression and a poorer prognosis [4,5] . To date, more than 100 loci have been associated with RA susceptibility [6] . The two most significant associations reported are the PTPN22 and HLA-DRB1 loci; HLA-DRB1 is carried by 80% of RA patients in population-specific hospital-based series and is associated with a more severe systemic disease phenotype [7] . In recent years, the introduction of biologic drugs has revolutionized the treatment and management of RA. Complementing the use of previously established synthetic disease modifying antirheumatic drugs (DMARDs), biologics suppress the inflammation pivotal in disease progression (Figure 1) . The drugs improve long-term effects of RA such as joint destruction, co-morbidity, disability and reduced life expectancy [8,9] . One of the most

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Review  Oliver, Plant, Webster & Barton successful groups of biologic drugs targets the TNF cytokine, borne from an appreciation of the pivotal role that TNF plays in RA pathogenesis. Currently, there are five clinically approved anti-TNF agents although three of these (adalimumab, etanercept and infliximab) were the first to be licensed and have dominated RA management and research into the underlying mechanism of anti-TNF therapy. Adalimumab and infliximab are both monoclonal antibodies that target membrane-bound TNF. Etanercept is a soluble TNF-receptor 2 fusion protein engineered from two p75 TNF-α receptors and an IgG1 Fc moiety. Etanercept inhibits both soluble TNF-α and lymphotoxin LT-α, also expressed by T helper cells thought to be involved in RA pathogenesis [10,11] . The drugs function by blocking interaction between TNF and cognate receptors, inhibiting stimulation of downstream inflammatory signaling pathways. Despite their success, anti-TNF agents exhibit a significant inadequate response rate of 30–40% of RA patients treated [12] . Their use is also infrequently associated with adverse effects, often attributed to an enhanced susceptibility to infection, including tuberculosis and herpes simplex infections [13] . Anti-TNF agents are manufactured through expensive recombinant DNA methodologies and administered subcutaneously, meaning that the cost of anti-TNF agents is approximately £10,000 per patient per year in the UK. Indeed, it was estimated that the total cost of biologics to the NHS in 2009 was £160 million [2] . Since it is well established that treatment prescribed early in disease progression is the best predictor of long-term prognosis  [8,9,14,15] , identifying predictors of response is of huge importance in both clinical and economic terms. Variability in response to anti-TNF therapy is not fully understood; currently, there is no clinically established procedure for predicting biologic drug efficacy and toxicity. A limited number of clinical and serological factors have been correlated with response to anti-TNF agents including age, gender, baseline health assessment questionnaire (a measure of disability) score, the presence of ACPA and rheumatoid factor and concurrent DMARD therapy. Together these factors account for approximately 17% of the variance observed in therapeutic response [12,16] and even in the most optimum configuration, lack the reliability to inform critical decisions on treatment selection. Thus, currently, therapies are administered on an essentially ‘trial and error’ basis. In most countries, the relatively cheaper synthetic DMARDs such as methotrexate (priced at ∼£40 per patient per year) are administered prior to anti-TNF agents. Since use of methotrexate is discontinued after 2 years in approximately 45% of patients, it is clear that

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current first-line treatments exhibit significant rates of nonresponse and adverse effects [17] . In current clinical practice, disease activity and response to drugs is monitored using a composite score that integrates a variety of quantifiable RA-associated components. The most commonly utilized iteration is the 28-joint count disease activity score (DAS28) which incorporates both objective and subjective criteria to give an overall estimate of RA activity. Subjective components include a tender joint count in 28 specified joints and a patient-reported global health assessment on a visual analog scale. The objective components include a swollen joint count in the same 28 prespecified joints and blood-based acute-phase reactant (APR) levels, which act as a marker of inflammation. At regular intervals during a treatment regimen, the score is used to assess response. In the UK, guidance issued by the National Institute for Health and Care Excellence governs the eligibility for anti-TNF drug prescription, stipulating that patients must present with a DAS28 score over 5.1 on two separate occasions 1 month apart, indicative of active disease. They must also have shown incompatibility with two previous DMARDs. Patients are then reassessed at 6 months and treatment should be withdrawn in the absence of an adequate response  [18–21] . However, for patients destined not to respond to a particular therapy, the delay in switching to an effective treatment means continued disease activity, with consequent effects on quality of life and accumulation of joint damage due to uncontrolled inflammation, and exposure to the risk of side effects. Ideally, a panel of molecular biomarkers that are significantly associated with specific pharmacological outcomes would be used in combination with clinical predictors to select treatments to stratify patients into treatment groups according to the therapies they are most likely to respond to (Figure 2) . With access to a rich source of disease-associated factors in the blood and a large range of targeted drugs at the clinician’s disposal, RA is an ideal candidate to implement a precision-medicine approach to treatment. An informed treatment choice based on the molecular characteristics of the RA subtype and stage of disease could enhance drug efficacy, reduce toxicity and ensure that scarce medical resources are responsibly utilized. Genetic studies of anti-TNF response

Genetic variation has been successfully used to predict response in the treatment of other conditions such as the administration of warfarin in anticoagulation. The CYP2C9 and VKORC1 variant genotypes account for 30–50% of the risk underlying warfarin-induced overanticoagulation and may be tested to direct the dosage  [22,23] . However, recent clinical trials suggest that

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Genetic & genomic markers to inform treatment of rheumatoid arthritis with anti-TNF drugs 

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Autoantigens on dendritic costimulate T cells

RF ACPA

Antigen-presenting B cells produce RF and ACPA

T-cell costimulation inhibitors Cytokine production and inflammation

T cell

B-cell inhibitors

Activated macrophages produce TNF

IL-1 receptor antagonists

TNF antagonists

TNF induces proinflammatory cytokines (IL-1 and IL-6)

IL-1

IL-6

Synovial inflammation

IL-6 inhibitors

Figure 1. Schematic diagram to show how the different classes of biologic drugs interact with specific targets in the pathogenesis of rheumatoid arthritis.  ACPA: Anticitrullinated protein antibody; RF: Rheumatoid factor. 

the importance of pharmacogenetics in warfarin dosing may be less than first estimated [24] . Nevertheless, genetic markers are attractive since they offer temporal and spatial stability, remaining constant prior to disease onset, throughout treatment and across heterogeneous cell types. Since genetic factors participate in RA causality, they may also be important in anti-TNF treatment outcomes. Historically, many studies have employed a candidate gene approach, utilizing knowledge of RA causal genes to search for single-nucleotide polymorphisms (SNPs) associated with drug response.

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Currently no RA susceptibility associations with antiTNF response have been confirmed [16,25] . However, the PTPRC gene encoding receptor tyrosine protein phosphatase C or CD45, previously associated with RA susceptibility, is a promising candidate. Replicated by two large independent studies, albeit with modest p-values, the rs10919563 G>A polymorphism has been associated with reduced efficacy of adalimumab, etanercept and infliximab [26,27] , but three others were unable to replicate the association [25,28,29] . Other studies have investigated SNPs within the TNF promoter

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Anti-TNF treatment

Abatacept

Epigenetics Transcriptomics Antibodies

Statistical algorithm

Genetics

Rituximab

Clinical factors Tocilizumab

Synthetic DMARDs (including methotrexate) Figure 2. Identifying optimum therapies for the treatment of rheumatoid arthritis. Before selecting the most appropriate treatment, clinicians can test rheumatoid arthritis patients for a panel of drug response-associated biomarkers. This information can be integrated with clinical factors into a statistical algorithm to assess overall suitability for various rheumatoid arthritis-targeted treatments. Patients can then be placed on the drug with the highest chance of efficacy and lowest chance of toxicity in the first instance.

and gene itself with response. Despite the G308A (rs1800629) polymorphism showing the strongest evidence for an association [30–34] , such SNP associations lack sufficient validation, have not been replicated by other studies [35] and are subject to influence from the HLA region, which is difficult to exclude. More recently, genome-wide approaches have been used to perform a hypothesis-free global scan for associated biomarkers of response. Expedited by everimproving genotyping technology, thousands of SNPs can be analyzed simultaneously, with the potential to identify novel metabolic pathways associated with RA aetiology or anti-TNF drug response. There are currently five reported genome-wide association studies of response to anti-TNF drugs in RA [36–40] exhibiting a progressive increase in size and power (Table 1) . However, based on these and associated validation studies, only two loci have surpassed or approached genome-wide levels of significance (p < 5 × 10 -8). The strongest association has been observed for the PDE3A-SLCO1C1 locus which contains open reading frames for two genes encoding cGMP-inhibited 3′5′-cyclic phosphodiesterase A and 1C1, a member of the solute carrier organic anion transporter. The C>T polymorphism at the rs3794271 locus has been associated with reduced efficacy to adalimumab, etanercept and infliximab (p = 3.3 × 10 -11) surpassing accepted

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genome-wide levels of significance [41] . The second locus, which has approached genome-wide significance (p = 8 × 10 -8) for association with reduced efficacy to etanercept is the CD84 gene, encoding SLAM family member 5 (rs6427528 G>A SNP) [36] . Unfortunately, these loci are of little clinical significance alone and, individually, confer little predictive ability to clinicians treating RA patients. Barriers to genetic studies of anti-TNF response

Associations need to be validated in an independent population in order to be confirmed and potentially translated to clinical practice. To date, disappointingly few loci have been validated as markers of response to anti-TNF drugs and if genetics is a major contributor, the majority of this underlying heritability remains unknown. Unlike the heritability for response to warfarin in anticoagulation [22,23] , the genetic landscape underpinning anti-TNF treatment response is likely to be composed of many small-effect polymorphisms, each contributing toward a heterogeneous nonresponse phenotype. This makes associations difficult to detect within small sample sizes, particularly if the frequency of the causal allele is either very common or very rare within the population. Thus, the lack of consistency in associations reported by genome-wide association study (GWAS) is symptomatic of insufficient power,

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Genetic & genomic markers to inform treatment of rheumatoid arthritis with anti-TNF drugs 

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Table 1. Genome-wide association studies of therapeutic response to anti-TNF agents in rheumatoid arthritis. Study (year)

Patients (n)

SNPs analyzed (n)

Platform

Reported loci

Ref.

Liu et al. (2008)

89

317,000

Illumina HapMap300 SNP chip

Identified 16 loci including MAFB and PON1 (p < 0.05)

[37]

Plant et al. (2011)

566

459,446

Affymetrix GeneChip 500K Mapping Array Set

EYA4, RXRG, PDZD2, ATF7IP, TEC, IDH3GL, BC118985 (p < 10 -3)

[38]

Krintel et al. (2012)

196

486,450

Illumina HumanHap550K

PDE3A-SLCO1C1 (3.5 × 10 -6)†

[39]

 

 

 

Duo array

 

Mirkov et al. (2013)

882

2,557,253

Illumina HumanHap550Duo Bead Chip or the Human660W-Quad

Eight loci identified through metaanalysis reached suggestive significance (p < 10 -4 )

 

 

 

BeadChips

 

Cui et al. (2013)

2706 (733, etanercept) from 13 collections

>2 million

Eleven batches processed separately by heterogeneous methods

CD84 (SLAM family member 5) approaching genome-wide significance with etanercept (p = 8 × 10 -8 )

  [40]

  [36]

Summary of the five genome-wide association studies of anti-TNF treatment response performed to date. No loci were identified at genome-wide levels of significance; the p-value thresholds indicated were suggested as interesting by the authors. For all studies 28-joint disease activity score (DAS28) was used as the outcome measure of response. † This locus was identified with putative significance in this study, and in a later study was found to reach genome-wide significance [41].

whereby too few responders and nonresponders are analyzed in order to make statistically significant and robust conclusions. Some studies have considered change in disease activity as the outcome which, as a continuous trait, can enhance power [30,36] . A further potential confounding issue is that, due to low sample availability, patients treated with different anti-TNF agents are often analyzed as one group, which may prevent studies from detecting associations specific to a single agent. This was illustrated by the largest meta-analysis of anti-TNF response reported, to date, in which association with the CD84 gene was only detected in the subgroup treated with etanercept. [36] Underpowered studies are not unique to RA; across the spectrum of disease-focused genome-wide association studies, success has often been hampered by a lack of power associated with low sample sizes. Individual loci may, alone, not account for the total heritability assigned to a trait, but instead act epistatically which can also hinder detection. A relatively inexpensive way to enhance the power to detect variants is to employ large-scale meta-analyses which statistically amalgamate the findings of multiple independent studies. In fact, most novel risk variants discovered in recent years have arisen from meta-analyses of GWAS [42] . However, meta-analyses can be negatively impacted by heterogeneity between integrated studies. It is critical that samples are well characterized for demographics (age, gender, ethnicity), coadministration with other DMARDs (such as methotrexate)

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and disease subtype attributes (ACPA and rheumatoid factor positivity), since these factors may act as confounders and contribute to the relative success of therapy. Inadequate adherence is also problematic; a recent study reported that 20% of a cohort of 286 anti-TNFtreated patients self reported nonadherence, confirming the correlation between nonadherence and poorer clinical outcomes [43] . It illustrates the importance of omitting patients who exhibit nonadherence from genetic and genomic studies of response; nonadherent patients could be classed as nonresponders when they could have responded, if the drug had been taken as advised, thereby confounding studies of prediction. In future studies, novel next-generation sequencing technologies should facilitate comprehensive genome-wide investigations of alternative splicing and the discovery of rare variants. Inadequacy of current composite score assessments of RA The outcome measure used to assess patient response to anti-TNF agents is critical to genetic studies for assessing accurate changes in disease activity between baseline and 3 months and classifying patients as good, moderate or nonresponders according to European League Against Rheumatism (EULAR) guidelines [20] . While DAS28 is very useful for comparing efficacy of drugs in a clinical trials setting, it may be less helpful in observational research studies where confounding is an issue since there is an inherent margin of error

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Review  Oliver, Plant, Webster & Barton attributed to self-reported criteria. A recent study by Cordingley et al. sought to investigate the performance of individual DAS28 components. Linear regression was used to model the relationship between psychological factors and individual components of DAS28. The visual analog scale score exhibited high correlation with cognitive factors and depression [44] , which is important since depression is widespread amongst RA patients [45] . The authors conclude that it may be beneficial to report individual components of DAS28 to adjust for perceived impact of treatment based on the requirements of the individual patient [44] . Ultrasonographically identified synovitis is more closely correlated with objective components of DAS28 than subjective components [46] . In support, we have also shown that the objective criteria of DAS28 exhibit a higher heritability than subjective components and thus may prove more informative for stratifying patients in genetic comparison studies [47] . However, another study reported that both tender joint count and swollen joint count were the factors that possess the greatest genetic influence [48] . A third study has highlighted the discordance between APR levels and other components of DAS28; 58% of 9135 patients from the Consortium of Rheumatology Researchers of North America registry with active RA exhibited no elevation of C-reactive protein (CRP) or erythrocyte sedimentation rate (ESR) with the authors concluding that the use of APR as a criterion for anti-TNF therapy may exclude some patients with severe RA [49] . Two different APR markers can be measured and incorporated into DAS28: the CRP or the ESR. Both factors reflect systemic inflammation rather than being specific to RA disease activity. Although these markers possess higher heritability, they tend to exhibit basal variation in the healthy population and are influenced by factors independent of RA pathology [43,50] . For example, ESR shows variation associated with age, gender, pregnancy and anemia [51,52] . The potential for confounding effects associated with CRP and ESR on DAS28 has been investigated in multiple studies by us and others. Recently, it was shown that genetic variation within the CR1 gene affects ESR levels but not final DAS28-ESR or changes in DAS28ESR  [43] . Similarly, CRP genotype appears to have no effect on DAS28-CRP negating the requirement to adjust for underlying genotype in the final DAS28 ­calculation  [50] . Another recent study questioned the utility of CRP and ESR for assessing low disease activity [53] , reporting that remission is undetectable above specific basal levels of APR marker. In such cases, the clinical disease activity index or a modified version of DAS28 (mDAS28) [54] neither of which considers such mark-

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ers, may be employed as an alternative. Nevertheless, this leaves the final assessment under heavier influence from subjective criteria and so may be less suitable for genetic studies; this is particularly relevant for IL-6 studies in which the clinical disease activity index is used as an assessment outcome. In therapeutic investigations, the confounding effects of DAS28 can be somewhat alleviated by comparing only patients with the most extreme response outcomes. Nevertheless this is costly for GWAS, reducing the pool of available subjects and further diminishing the power of studies to reliably detect significant associations. This is exemplified in the first GWAS by Liu et al.  [37] in which 35 of the 89 patients studied were classified as EULAR moderate responders, leaving the study considerably underpowered. Alternative measures of disease activity in RA Ideally, disease activity for genetic studies would be assessed using a reliable panel of molecular biomarkers that identifies an intermediate phenotype, correlating with clinical symptoms and ultrasonographic (or other imaging) evidence of synovitis. An encouraging candidate is the multibiomarker disease activity score which assesses the presence of 12 serum biomarkers. One study reported that the score is better able to predict further radiological damage in patients who were otherwise thought to be in remission by DAS28-CRP [55] . Nevertheless, despite the score correlating well with DAS28 and change in DAS28 [56] , the improved correlation with ultrasonography over DAS28 is only ­modest  [57] . The protein complex calprotectin (S100A8/9) produced by monocytes, macrophages and granulocytes, seems to reflect inflammation specifically at the synovium. Levels of calprotectin correlate well with DAS28, levels of CRP/ESR [58,59] and ultrasonographic evidence of synovitis. Moreover they exhibit predictive capability for radiographic joint damage  [60,61] and are reduced in response to adalimumab and infliximab [62,63] . A study by Choi et al. supported these findings and suggested that calprotectin may also predict anti-TNF response at baseline (pretreatment) since responders to adalimumab and infliximab exhibited significantly higher levels of calprotectin prior to treatment. [63] . While calprotectin appears to be a promising tool for monitoring RA disease activity, the predictive capability remains to be validated. In light of the limitations of DAS28, a recent study employed MRI to objectively assess variable response to infliximab [64] . MRI-based methods uniquely measure inflammation of synovium and bone, which is ultimately responsible for the degradation of articular

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Genetic & genomic markers to inform treatment of rheumatoid arthritis with anti-TNF drugs 

cartilage and bone. To eliminate placebo effects which can significantly influence DAS28 outcomes [65] , the study measured the pretreatment transcriptome in infliximab plus methotrexate (n = 30) and placebo plus methotrexate (n = 31) treated patients. By employing dynamic contrast-enhanced MRI, the transfer rate constant (Ktrans) of a gadolinium-based contrast agent between blood plasma and the synovium was measured, reflecting histologic measurements of inflammation [64,66] . In contrast to DAS28, disease activity as measured by dynamic contrast-enhanced MRI showed no improvement in the placebo. They reported a baseline 256-gene expression signature associated with improved Ktrans in response to infliximab at 14 weeks. Nevertheless, the association was not replicated when re-evaluated by the alternative RA MRI scoring system method: a semi-quantitative scoring system where MRI is used to assess bone erosion, synovitis and osteitis. Furthermore, the signature was unable to accurately distinguish between EULAR good and poor responders calling for larger expression studies employing MRI-based evaluations. Gene expression studies of anti-TNF response Potential genomic markers for treatment response are not limited to the raw nucleotide sequence; a complementary approach involves analyzing the transcriptome for gene expression signatures predictive of response outcome. The transcriptome measures global mRNA reflecting all expressed genes at any one time point for a specific cell type, tissue or organ. The use of gene expression-based biomarkers has already been utilized extensively within the field of cancer; oestrogen receptor ER gene expression levels correlate with response rates to tamoxifen [67,68] . Since tamoxifen increases the incidence of benign and malignant uterine lesions, it is important that use is justified prior to administration  [69] . More recently, microarray-based transcriptome studies were used to subcategorize lymphomas and gliomas [70] , while an RNA-seq approach aided the classification of heterogeneous breast cancer isoforms [71] . The transcriptome is both temporally and spatially dynamic, subject to cell type, stage of disease, circadian rhythm and a multitude of environmental influences. Thus, in contrast to genetic studies, the search for transcriptomic markers of anti-TNF response is complicated by fluctuations in response to irrelevant stimuli such as inflammation or synthetic DMARD exposure. RA patients are subject to an oscillating disease course such that fluctuation in DAS28 and the transcriptome impact the ability to identify associations; it has been estimated that if initial DAS28

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is measured at the apex of disease, this may account for a third of the improvement to DAS28 following anti-TNF therapy [72] . Nevertheless, in contrast to genetic studies which require thousands of responders and nonresponders, transcriptomic studies may retain power to detect associations with much more modest patient cohorts. This is particularly relevant for studying anti-TNF response where sample availability is limited. Historically, microarrays have dominated bloodbased gene expression studies of anti-TNF response, offering high throughput measurement of tens of thousands of genes simultaneously and to a high degree of accuracy and sensitivity with well-established workflows for subsequent analysis [73–76] . However, the results from these studies are largely inconsistent due to low sample numbers and a high false positive rate associated with multiple testing. The experimental protocols are also highly heterogeneous with differences in microarray platform, response criteria, data analysis, tissue type, patient demographics and control group hindering meta-analyses [77] . One study seeking to validate previously reported response-associated expression profiles [78] was able to validate a 20-transcript signature from a study by Lequerré et al. [79] with 71% sensitivity and 61% specificity. Another validation study was unable to confirm most previously reported associations but following a meta-analysis to enhance statistical power, suggested that the G0S2 gene may function as a biomarker for anti-TNF response [80] . Recently, a study reported that specific sets of genes coordinately expressed (modules) as defined by Chaussabel and colleagues [81,82] exhibited differential expression in EULAR-defined good and moderate responders compared with nonresponders following 14 weeks of adalimumab, etanercept or infliximab therapy. While there appears to be no predictive capability at baseline, the results were consistent across three independent cohorts and could provide a novel method for objectively measuring disease activity in response to anti-TNF therapies [83] . Future success in transcriptomics may benefit from RNA sequencing to measure transcript abundance through hypothesis-free sequencing of complementary DNA. This could expand investigations to novel transcripts including splice variants, miRNAs and pseudogenes that are not represented in microarraybased studies. The technique supersedes microarrays in terms of noise and dynamic range, enhancing the sensitivity further to quantify low and high abundance transcripts to a high degree of accuracy [84] . The cost of RNA sequencing is on a downward spiral, facilitating mainstream use of the technology in future studies.

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Review  Oliver, Plant, Webster & Barton Epigenetic markers of anti-TNF response Epigenetic mechanisms including DNA methylation (DNAm) and covalent histone modifications regulate gene expression by modulating the accessibility of transcriptional machinery to DNA. The role that epigenetics play in anti-TNF treatment response is promising yet largely unexplored, to date. There appears to be a close biological interaction between genetic and epigenetic regulation; epigenetic mechanisms have the ability to mask and unveil genetic variants and control transcription rates. Epigenetic studies may thus have the power to reveal associations previously missed by genetic investigations [85–87] . For example, DNAm studies in breast cancer have reported that the ABCB1 and GSTP1 genes may be predictive of response to the drug, doxorubicin [88] . In another study, Narayan et al. reported how methylation-dependent inactivation of the PCDH10 gene conferred high resistance to methotrexate treatment in acute lymphoblastic leukemia cell lines [89] . Similar studies concerning methotrexate within the context of RA are yet to be conducted. DNAm has already been associated with RA causality  [90] and the TNF locus [91,92] indicating a potential role for DNAm in response to anti-TNF therapy. Covalent histone modifications have also been associated with RA [93–95] and TNF-producing cells [91] and thus may also be implicated in response to anti-TNF blockade in RA. DNAm is generally restricted to cytosine-guanine dinucleotides (CpGs) which are normally clustered at gene regulatory sites within the genome. A number of issues require consideration when designing DNAm studies of anti-TNF response. While array-based methods are currently the most widely employed technique for measuring genome-wide DNAm, whole genome bisulphite sequencing, which measures DNAm at every CpG site in the genome, is becoming increasingly common. However, it is prohibitively expensive and its value has been questioned by a recent study which reported that, postzygotically, only 22% of autosomal CpGs are dynamically methylated. In this study, 70–80% of sequencing reads from 42 datasets did not contain any valuable CpG methylation information [96] . Furthermore, unlike SNP variation, DNAm shows considerable variation between cell types and in response to exogenous stimuli. It is clear that discrete DNAm patterns exist in different RA-relevant cell types, with a recent study identifying heterogeneity between B- and T-cell subsets [97] . Investigations based on whole blood represent an average summation of DNAm across all cell types which may not produce the most robust associations. Despite the use of algorithms to correct for discrepancies in cell composition between stratified groups [98] , highly informative associations localized to

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a specific cell type of low abundance may be missed or undervalued in whole blood. Although a panel of biomarkers present in venous blood offers the simplest clinical application, cells localized at the synovium may be more enriched for potential biomarkers of antiTNF response. Synovial biopsies are reported to be well tolerated by patients [99] and while no studies have yet evaluated synovial DNAm and anti-TNF response, synovial expression studies have been conducted and reviewed elsewhere [100] . The issue of instability may be overcome by comparing epigenetic profiles over various time points in RA pathogenesis and anti-TNF treatment, including prior to the manifestation of clinical symptoms, when ACPA and other RA-associated autoantibodies are detectable  [85] . Confounding factors such as age [101] , smoking status [102,103] and administration of methotrexate  [104] have all demonstrated influence over DNAm and so need to be accounted for in data analysis. Nevertheless, DNAm exhibits more stability than histone modifications, mRNA and most proteins and so may be less subject to confounding [105] . Recently, a large RA aetiology study set the standard for future epigenetic studies by using statistical algorithms to estimate and correct for differential cell proportions as well as demographic heterogeneity. They also incorporated genetic information to differentiate between causative associations and those which are a consequence of inflammation or treatment exposure [106] . Conclusion To date, few genetic markers for anti-TNF response have been validated and GWAS have shown little consistency in reported associations. Multiple factors have hampered success including a lack of power due to the fact that only a subset of RA patients will be treated by anti-TNFs so sample availability is low compared with RA causation studies. It may only be possible to enhance this through international collaborative studies such as that performed in the discovery of the CD84 association [36] . Experimental protocols and data analyses need to be sufficiently streamlined to aid larger more powerful meta-analyses using wellcharacterized patient cohorts. It may also be beneficial to substitute DAS28 for a more objective assessment of disease activity that correlates well with ultrasonographic/MRI evidence of inflammation in order to identify predictive genetic and genomic biomarkers. Future perspective Since the mechanism underlying response to anti-TNF drugs is complex and multifaceted, the greatest hope for a reliable, clinically applicable method may lie in an integrated panel of signatures identified from vari-

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Genetic & genomic markers to inform treatment of rheumatoid arthritis with anti-TNF drugs 

ous ‘omic’ studies alongside current clinical predictors. Integration of gene expression data from peripheral blood mononuclear cells with genotype data were successfully utilized to identify the CD84 association [36] . A study by Chen et al. has explored the prospect of creating a highly informative ‘personal omics’ profile; they followed one individual over a 14-month period through oscillations between healthy and disease states and successfully predicted the onset of Type 2 diabetes mellitus [107] . Their analysis, based on peripheral blood, spanned genetics, transcriptomics, epigenetics, proteomics, microRNAs and autoantibodies, giving insight into how these huge datasets might be integrated and interpreted when applied within disease context, such as anti-TNF response. MicroRNAs are small ssRNAs that function in post-transcriptional regulation of gene expression and RNA silencing and may also be associated with anti-TNF response. As next-generation sequencing technologies become more affordable, the prospect of measuring these data on a personal basis to inform treatment options is ­approaching reality. Accumulation of antidrug antibodies is one mechanism known to reduce efficacy to anti-TNF treatment  [108] , particularly with administration of adalimumab and infliximab [109,110] ; one study

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reported that approximately 53% of patients receiving adalimumab treatment developed antidrug antibodies to some extent [111] . It may be beneficial to integrate antidrug antibody information and patient drug levels into association studies. Nonresponse in patients with high antidrug antibody levels can then be attributed, at least partially to immunogenicity. It has also been postulated that Fcγ receptors may impact the half life and downstream immune effects of anti-TNF agents by binding to the Fc fragment of the drug. However, while two genetic studies have reported an association between the high affinity V158F variant of FCGR3A and reduced response to anti-TNF treatment [112,113] , two further studies including a meta-analysis [114,115] were unable to ­replicate the association. Metabolomic data can also be integrated into the ‘omics’ profile; inflammation exhibits systemic metabolic effects such that alterations to the metabolic fingerprint in response to anti-TNF treatment may be detectable. One recent study compared metabolic profiles of urine samples collected from 16 RA patients before and during infliximab and etanercept treatment using nuclear magnetic resonance spectroscopy. Three different data analyses on the same samples were employed, each finding a similar panel of metabolites

Executive summary Background • Anti-TNF agents have revolutionized the treatment of rheumatoid arthritis but exhibit incomplete response rates of 30–40% and are a large financial burden on the UK economy. • Currently, there is no clinically established methodology for predicting how patients will respond prior to initiation of therapy. • Anti-TNF agents are administered to patients with active disease as measured by a composite score such as the disease activity score 28, incorporating both subjective and objective assessments.

Genetic studies into anti-TNF response • Targeted candidate gene and hypothesis-free genome-wide approaches have been largely unsuccessful in identifying and validating genetic markers associated with specific anti-TNF response outcomes. • The two currently confirmed associations (PDE3A-SLCO1C1 and CD84) alone confer insufficient predictive capability for clinical applications.

Barriers to genetic studies of anti-TNF response • The issues of insufficient power, protocol heterogeneity and nonadherence to the anti-TNF agent need to be addressed in future genetic studies of anti-TNF response.

Inadequacy of current composite score assessments of rheumatoid arthritis • Multiple studies have reported that while disease activity score 28 is useful in a clinical trials setting, it may be one of the largest sources of error in studies of treatment response.

Novel molecular biomarkers to measure disease activity in rheumatoid arthritis • Ideally, a novel molecular biomarker of synovial inflammation that correlates well with ultrasonographic evidence is required. • Both the multibiomarker disease activity score and protein complex calprotectin are potential candidates but require further validation.

Future perspective • Future studies will seek to integrate the various lines of data together to give a deeper understanding of the mechanisms underlying the variability in anti-TNF response, with the hope of identifying a robust panel of predictive biomarkers.

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Review  Oliver, Plant, Webster & Barton that discriminated EULAR good and poor responders at 12 months but this requires validation in independent samples [116] . Nonetheless, it is likely that treatment response prediction will be enhanced using a combination of ‘omic’ approaches to profile individuals and that in the next few years robust predictors of response will be identified. Acknowledgements The views expressed in this publication are those of the author(s) and not necessarily those of the NHS, the National Institute for Health Research or the Department of Health.

Financial & competing interests disclosure This work was funded by Arthritis Research UK (grant ref. 20385). This report includes independent research funded by the National Institute for Health Research Manchester Musculoskeletal Biomedical Research Unit. A Barton has received consultancy and or research awards from Pfizer, AbbVie and Eli Lilly. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or ­materials discussed in the manuscript apart from those disclosed. No writing assistance was utilized in the production of this manuscript. gene polymorphisms in improving its efficacy. Expert Opin. Drug Metab.Toxicol. 10(12), 1703–1710 (2014).

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