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VBCR - June 2014, Volume 3, No 3 - Personalized Medicine in Rheumatology
E. K. Charles

A recent review evaluating currently available data on rheumatoid arthritis (RA) diagnosis, prognosis, and prediction of response to therapy, highlights the potential of gene expression profiling in understanding RA biology, patient management, and personalized care (Burska AN, et al. Pharmacogenomics J. 2014;14:93-106).

Several genes were consistently associated with all diagnostics, indicating inflammation or autoimmunity rather than disease specificity, according to the authors. Other genes were associated with specific events in RA. The research reinforced the need for harmonization if gene expression is to become a useful clinical tool in personalized medicine for the patient’s benefit. However, the study authors acknowledged that adoption of gene expression tests into daily clinical practice will take time.

“The rheumatology community is therefore facing a challenge to pool together the necessary resources and to use the available information already collected to reach appropriate conclusions and proceed with further validation, which is necessary to reach the next level,” according to Agata N. Burska, PhD, Leeds Institute of Rheumatic and Musculoskeletal Medicine, and Leeds Musculoskeletal Biomedical Research Unit, the University of Leeds, United Kingdom, and colleagues.

Indicators for Diagnosis and Prognosis
Burska and colleagues began their review focusing on the use of gene expression as a tool to investigate pathogenesis in RA. In particular, they explain that genome-wide expression analysis has shown gene signatures that differentiate RA from osteoarthritis.

The heterogeneity of RA has been associated with disease progression; evident differences have been observed in gene expression between early and late RA synovial tissue samples, indicating different pathophysiological mechanisms during the course of the disease. Furthermore, research has identified 3 different types of synovial tissue based on gene expression profiling: T- and B-cells, antigen-presenting cells, and major histocompatibility complex gene signature; stromal genes; and tissue with mixed information.

Burska and colleagues then discussed diagnostic, prognostic, and preclinical signatures of RA. Early diagnosis and treatment of the disease is paramount, they emphasized, and recent changes to the EULAR (European League Against Rheumatism) diagnostic criteria stress the need for other diagnostic biomarkers for anti­citrullinated protein antibody-negative disease. Genes—primarily those differentially expressed in T-cell synapse—have been identified whose expression is different in early inflammatory arthritis biopsies versus reactive arthritis. Other genes identified were apoptosis-related genes and calcium-signaling genes.

“Recognition of the preclinical phase of RA has initiated a whole new field of research aimed at the discovery of predictive biomarkers for the development of arthritis,” the study authors explained. In particular, data suggest that systemic autoimmunity, such as the presence of anticitrullinated protein antibodies, occurs before disease onset and synovitis abnormalities, which are seen at the time of symptom onset. Other mechanisms that may suppress disease development and counter autoimmunity may exist, they noted.

Researchers have also focused on identifying prognostic markers associated with disease progression. In one study, gene expression signatures were associated with severe disease at baseline and after 36 months of follow-up in the peripheral blood cells of a population of patients with early RA; however, the study authors were not able to identify a clear expression profile (Reynolds RJ, et al. Rheumatol Int. 2012;33:129-137).

The Value of Gene Expression Signatures
The review authors went on to discuss gene expression signatures as predictors of treatment response to various therapies, including methotrexate, infliximab, rituximab, anakinra, and tocilizumab. “Not surprisingly, maybe gene expression signatures for a given clinical end point published to date do not always overlap and have not always been re-validated,” according to Burska and colleagues. Gene expression signatures for progression from the preclinical phase of RA and the prediction of response to rituximab were the only exceptions, they noted.

Measuring microRNA in serum samples and cells is another area of research, although no study to date has investigated whether altered microRNA expression is associated with response to therapy in RA patients.

Although there are ethical issues associated with the development of gene expression signature in RA, the identifiable data may be less sensitive because mRNA gene expression analysis is functionally close to any other biochemical biomarker or dynamic phenotype and is not a permanently affixed label to its carrier, according to the study authors.

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Last modified: May 21, 2015
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