Despite significant advances in the understanding and treatment of rheumatoid arthritis (RA) in recent years, a substantial number of patients do not respond to certain therapies. For example, while tumor necrosis factor inhibitors (TNFis) represent the most commonly used first-line biologic treatment, findings show that these agents are ineffective in up to 30% of RA cases.1 One of the major challenges in treating RA is the lack of biomarkers that could guide an individualized treatment approach for each patient, necessitating a trial-and-error process to identify the optimal therapy.
With the increasing focus on the use of genomics to inform precision medicine in various specialties, emerging evidence shows promise in the realm of RA. “The use of genetic variants as biomarkers that could predict the response to a specific treatment has several advantages, as these variants are stable and would remain unaltered, unlike changes in gene expression and epigenetics, which are highly dependent on the environment,” according to a recent paper published in the Journal of Clinical Medicine.1
The authors reviewed studies pertaining to the use of pharmacogenomics in RA treatment. Findings from several studies support the validity of a clinical pharmacogenetics model to predict methotrexate treatment response in RA. The model is comprised of 4 clinical variables — disease activity score (DAS), sex, smoking status, and rheumatoid factor — and 4 polymorphisms in methotrexate-relevant genes, such as methylenetetrahydrofolate reductase 1, D1rs2236225; aminoimidazole carboxamide ribonucleotide transformylase, rs2372536; adenosine monophosphate deaminase 1 rs17602729; and inosine triphosphate pyrophosphatase rs1127354.2 “However, it is expected that many more genes and many more variants with modest effects contribute to response to treatment, and therefore the model could be further improved by including other clinical variables and updating the list of associated genetic variants,” the review authors wrote.1
Studies have demonstrated that a polymorphism rs1051266 in the solute carrier family 19 member 1 gene is associated with intracellular methotrexate levels and methotrexate treatment response.1 The aminoimidazole carboxamide ribonucleotide transformylase 347 C/G gene polymorphism has been linked to methotrexate toxicity and treatment response.1 The G308A polymorphism in the tumor necrosis factor, TNF gene has been associated with greater efficacy of adalimumab, etanercept, and infliximab.1 The phosphodiesterase 3A gene, PDE3A-SLCO1C1 genetic variant has been closely linked with TNFi response, reaching genome-wide significance in one study.1 Several studies have found associations between TNFi response and polymorphisms on the protein tyrosine phosphatase receptor type C gene, PTPRC.1
Although pharmacogenomics research in RA is still in its initial stages, the “integration of all of this information with clinical and environmental data will eventually help us to identify the biological mechanisms underlying the development of the disease, to establish accurate biomarkers for patient stratification, and to tailor treatment to genetic architecture, increasing the probability of obtaining an adequate response to a particular drug and eventually achieving disease remission,” the authors concluded.1
For additional insights regarding this topic, Rheumatology Advisor interviewed the following specialists: James Bluett, MBBS MRCP PhD, senior clinical lecturer and honorary consultant rheumatologist at the University of Manchester, United Kingdom; Harris Perlman, PhD, chief of rheumatology and professor of medicine at the Feinberg School of Medicine, Northwestern University, Illinois, and co-editor in chief of Arthritis Research and Therapy; and Tamiko Katsumoto, MD, rheumatologist and clinical assistant professor in the division of immunology and rheumatology at Stanford University, California.
Rheumatology Advisor: What are some ways in which pharmacogenomics could ultimately transform RA treatment?
Dr Bluett: Currently, a range of treatment options that target different aspects of the disease process are available, but none are universally effective. Treatment response is likely to be multifactorial and include clinical, psychological, and biological factors. Using pharmacogenomics to predict response has huge implications. If we were able to tell which patients were more likely to respond to which drug, this would lead to a stratified medicine approach, which would be more cost effective and reduce the current trial-and-error approach to prescribing.
Dr Perlman: Over the past several years, we have tried to identify biomarkers that would predict a patient’s sensitivity to a particular therapy but have had no success. With the advent of current state-of-the-art genomic machines, we can now examine the effect of a therapy on the transcriptional landscape of patients. This could help us to develop a precision medicine approach to these studies.
Dr Katsumoto: Currently, clinicians are cycling through RA therapies, often in a somewhat haphazard fashion, without any clear guidance on which therapy is likely to provide the greatest benefit for a particular patient. The use of pharmacogenomics could help clinicians predict the likelihood of response to a specific therapy, hence minimizing treatment failures and unwanted toxicities. This approach is akin to how we are currently using pharmacogenomics to predict toxicities, such as [iopurine S-methyltransferase gene] TPMT testing for azathioprine and [human leucocyte antigen gene] HLA-B*58:01 testing for allopurinol.
In addition, pharmacogenomics could impact the way we perform trials to get drugs approved in RA. Biomarker-driven trials could enrich patients more likely to respond to a certain therapy, and thus fewer patients would be needed to see a specific effect size. This approach would certainly require discussions with regulatory agencies, regarding the requisite size of safety databases. But overall, a biomarker-driven approach could lead to more successful therapies coming to market and potentially in a shorter time frame.
Rheumatology Advisor: What are some of the most promising findings to date regarding this topic?
Dr Bluett: To date, a number of genetic biomarkers have been associated with response. PTPRC is associated with response to infliximab and etanercept, but not adalimumab in seropositive patients. This highlights that not all RA is the same, and there may be RA endotypes that determine which patients are more likely to respond to which drugs. The research also suggests that not all TNFis are the same and that we may be able to use pharmacogenomics to determine which drugs someone may respond to: The PTPRC variant, however, accounts for only 0.5% of the variance in response to TNFis and is therefore unlikely to be clinically predictive alone — it is likely that a cluster of genetic markers is required to more accurately predict response.
The PDE3A-SLC01C1 locus is associated with response to infliximab and etanercept, but again not adalimumab. In a study by Actosa-Colman, et al, the association reached genome wide significance ([odds ratio] OR, 2.91; P =3.34*10-10).3 The PDE3A gene encodes a phosphodiesterase; inhabitation of this protein suppresses TNF production in stimulated monocytes.
Dr Perlman: To date there have been 2 major studies that have shown the potential for the pharmacogenomics for precision medicine in RA.
Six leading medical centers formed RhEumatoid Arthritis SynOvial tissue Network (REASON), a consortium with an established framework for patient recruitment, curation of clinical data, ultrasound-guided synovial biopsies, cell sorting, RNA sequencing (RNA-seq), and computational analyses. Macrophages isolated from ultrasound-guided synovial tissue biopsies obtained from patients with RA are sufficient for RNA-seq, which identified transcriptional differences across patients.4 Importantly, this set the framework for biopsy-guided stratification of RA patients according to the most prominent disease pathway.
Another consortium of medical centers from the United States and from the United Kingdom, which were funded by an [National Institutes of Health] NIH-program named Accelerating Medicines Partnership (AMP), also carried out single-cell transcriptional analysis on cells from the synovium of RA or osteoarthritis patients. AMP identified 4 populations of monocytes/macrophages using single-cell RNA-seq and confirmed by Cy-ToF (based on CCR2, CD11c, and CD38).5
Dr Katsumoto: Many studies looking for polymorphisms underlying the response to RA treatment have suffered from lack of reproducibility when tested in different cohorts. Polymorphisms in the SLC19A1 gene, a transporter for methotrexate, have been reproducibly associated with methotrexate treatment response. The response to TNFis has been extensively investigated, and associations with PDE3A-SLCO1C1 and PTPRC have been consistently replicated in independent study samples. Unfortunately, the majority of studies focusing on the pharmacogenetics of anti-TNF therapies have yielded inconsistent results.
In the largest genetic study of RA conducted to date, the authors evaluated ~10 million single-nucleotide polymorphisms in >100,000 patients and identified 98 biological candidate genes at 101 risk loci.6 Several of the identified genes were the targets of approved therapies for RA, supporting the concept that pharmacogenomics could be used to enable a personalized treatment approach. Interestingly, their research also identified unexpected pathways, such as [cyclin-dependent kinase gene] CDK4 and CDK6 — known to be relevant in cancer treatment — and notably, the inhibition of cyclin-dependent kinases in an RA animal model showed a favorable response.7 This study supports the use of pharmacogenomics in drug discovery and drug repurposing efforts.
Rheumatology Advisor: What should be the focus of further research on this topic?
Dr Bluett: Studies to date have shown that it is unlikely that a single genetic variant will be clinically useful to determine a patient’s response to drug treatment. Progress in this area requires improved study design with not only larger sample sizes but also the adjustment of confounding factors, such as nonadherence, antidrug antibodies, and smoking status. With well-designed studies, we may be able to investigate gene-gene interactions and develop a pharmacogenomics risk prediction model.
Dr Perlman: Future studies should include the examination of patients’ synovial tissue prior to and following therapy using a transcriptional approach. The ultrasound-guided synovial biopsy has provided rheumatologists with a tool to recover the affected tissue and then unlock the gene expression profiles that would be able to predict therapeutic efficacy.
Dr Katsumoto: Many of the genetic studies to date have utilized [genome-wide association studies] GWAS-based approaches, which focus on common variants. The availability of whole-exome and whole-genome sequencing will enable identification of rare variants that could have larger effect sizes, and many of these variants may not have been captured in prior GWAS. In addition, the heterogeneity of RA has been well described, and the response to RA treatment is likely influenced by many genetic variants, each of which may contribute small-to-moderate effect sizes — RA is not a monogenic disorder.
As genetics do not fully explain the heritability of RA, a complementary strategy utilizing epigenomic, transcriptomic, and proteomic approaches will be critical. Recent studies have highlighted the heterogeneity of the RA synovium.8,9 Humby, et al, elegantly describe the existence of distinct RA synovial subtypes (lympho-myeloid, diffuse-myeloid, and pauci-immune-fibroid) that are linked to disease activity/severity and treatment response.9
Further, they show a strong correlation of the synovial phenotypes with ultrasonographic measures of synovial thickness and degree of erosive changes on x-rays. Whether these synovial phenotypes predict treatment responses to specific agents will be a key next question for the field. In addition, instead of synovial biopsies, which are cumbersome to obtain, the identification of blood biomarkers that correlate with these synovial phenotypes will be critical to this effort.
In summary, a combinatorial strategy using pharmacogenomic, transcriptomic, and epigenomic approaches may be the most likely to yield insights into the prediction of RA treatment response and hence our ability to realize the vision of precision medicine in RA.
1. Acosta-Herrera M, González-Serna D, Martín J. The potential role of genomic medicine in the therapeutic management of rheumatoid arthritis. J Clin Med. 2019;8(6):826.
2. Wessels JA, van der Kooij SM, le Cessie S, et al. A clinical pharmacogenetic model to predict the efficacy of methotrexate monotherapy in recent‐onset rheumatoid arthritis. Arthritis Rheum. 2007;56:1765-1775.
3. Acosta-Colman I, Palau N, Tornero J, et al. GWAS replication study confirms the association of PDE3A-SLCO1C1 with anti-TNF therapy response in rheumatoid arthritis. Pharmacogenomics. 2013;14(7):727-734.
4. Mandelin AM II, Homan PJ, Shaffer AM, et al. Transcriptional profiling of synovial macrophages using minimally invasive ultrasound‐guided synovial biopsies in rheumatoid arthritis. Arthritis Rheumatol. 2018;70:841-854.
5. Orange DE, Agius P, DiCarlo EF, et al. Identification of three rheumatoid arthritis disease subtypes by machine learning integration of synovial histologic features and RNA sequencing data. Arthritis Rheumatol. 2018;70(5):690-701.
6. Okada Y, Wu D, Trynka G, et al. Genetics of rheumatoid arthritis contributes to biology and drug discovery. Nature. 2014;506(7488):376-381.
7. Sekine C, Sugihara T, Miyake S, et al. Successful treatment of animal models of rheumatoid arthritis with small-molecule cyclin-dependent kinase inhibitors. J Immunol. 2008;180(3):1954-1961.
8. Zhang F, Wei K, Slowikowski K, et al. Defining inflammatory cell states in rheumatoid arthritis joint synovial tissues by integrating single-cell transcriptomics and mass cytometry. Nature Immunol. 2019;20(7):928-942.
9. Humby F, Lewis M, Ramamoorthi N, et al. Synovial cellular and molecular signatures stratify clinical response to csDMARD therapy and predict radiographic progression in early rheumatoid arthritis patients. Ann Rheum Dis. 2019;78(6):761-772.