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A paradigm shift in the management of rheumatoid arthritis (RA) occurred with the introduction of biologic therapy, together with the concept of a window of opportunity, advocating for an early and aggressive treat to target approach as the optimal strategy for sustained disease remission.1,2 Guidelines published by the American College of Rheumatology and the European League Against Rheumatism recommend tight disease control, achieved by strict clinical monitoring, and adjusting therapy according to disease activity.3,4 With an expanding armamentarium of therapeutic options, together with the variety of diagnostic and disease monitoring tools now available, in theory, tight disease control and sustained disease remission should be achievable.
The clinical reality, however, is that this concept is challenging to execute, and the goals are difficult to achieve. A combination of patient, provider, treatment, and system factors contribute to diagnostic delays and a treat to target strategy that is complex to implement.5 Furthermore, despite the several classes of biologic and nonbiologic disease-modifying agents approved for the treatment of RA, no single agent or combination of agents can achieve sustained treatment response. For example, although tumor necrosis factor-alpha (TNF-α) inhibitors are effective treatments, many patients fail to respond. In fact, between 20% and 40% of patients treated with a TNF-α inhibitor are primary nonresponders, failing to achieve a 20% improvement in the American College of Rheumatology criteria; among responders, the response diminishes over time.6,7 Other studies have shown that prior primary nonresponders are 24% less likely to achieve remission with another biologic in the second-line setting.8
Given the demonstrated benefits of an early and aggressive treat to target strategy in achieving optimal RA management and disease remission, the challenges associated with its implementation point to persistent unmet needs. An alternative strategy that may provide tools to identify patients who are predisposed to developing RA, or whose RA has a higher risk for progression, may allow patient stratification for a personalized approach to early and aggressive treatment. Furthermore, such a tool may allow the identification of patients who are likely to respond to therapy, thus minimizing the current approach of treatment by trial and error, which is often associated with the inherent risk of exposing those who are nonresponders to treatment to adverse effects. Such a tool that can predict patient treatment response and outcomes is currently limited. Advances with biomarkers are attempting to address this gap. Indeed, a clinical study currently in progress is attempting to identify new biomarkers that can be used to personalize the treatment of patients with inflammatory rheumatic diseases.9 A recent novel approach is the application of the EgoNet algorithm to identify RA ego networks and pathways that may act as therapeutic markers to predict response to therapy.10 This novel concept is still very much in its infancy, and further studies are needed.
Predictive biomarkers are not new tools in the management of RA. Rheumatoid factor is perhaps the most well-known diagnostic biomarker included in the 1987 American College of Rheumatology criteria for the diagnosis of RA.11 Since then, several other biomarkers have been identified that offer potential for early diagnosis of RA as prognostic indicators to identify patients with high risk or an aggressive form of RA, to select treatment, and to monitor disease activity.12,13 These biomarkers have traditionally been used in research and clinical settings. More recently, however, biomarkers are being developed as commercial predictive tests targeted at patients to help inform their clinical decision about their RA disease management. The potential of predictive tests and their application to clinical decision-making is gaining momentum. The latter is supported by data presented at the 2018 European League Against Rheumatism meeting, suggesting that molecular changes in the synovial tissue that occur before the onset of arthritis, as revealed by biomarkers, may benefit from early interventions that can prevent progression to arthritis.14
As predictive biomarker testing enters the realm of patient accessibility, it exposes new challenges and raises important questions. Two separate qualitative studies explored patients’ understanding of predictive testing for RA, and gained insights from individuals without RA, as well as those with symptomatic and asymptomatic RA into factors that may influence their decision to undergo predictive testing.15,16 Overall, patients were supportive of predictive testing; however, individuals with symptomatic RA or at risk for RA were more willing than those with asymptomatic presentation or without RA to undergo repeat testing. Individuals with symptomatic RA or at risk for RA were also more willing to accommodate psychological distress based on the result of the test.16
The need for information about predictive testing, tailored to be understood by the lay public, was one of the important insights from the studies. These studies highlight the need for communication and appropriate patient education so that they are well informed about predictive testing and the interpretation of the results. There is also a need to develop a strategy for managing patient expectations, particularly if there is discordance between test results and treatment outcomes. As new biomarkers are developed and the options for predictive testing expand, it also raises new challenges. For example, should rheumatologists collaborate closely with patients during the selection of a predictive test, interpretation of the results, and by extension, selection of appropriate treatment based on the test results? The development of predictive tests targeted at the patient population may indeed contribute to improving RA management; however, further research is needed to identify strategies for educating patients and communicating risks and expectations. Managing the psychological burden associated with test results, or treatment decisions and outcomes that may not correlate with patient expectations, is also a challenge that needs to be carefully evaluated and addressed.
References
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11. 1987 rheumatoid arthritis classification. https://www.rheumatology.org/Portals/0/Files/1987%20Rheumatoid%20Arthritis%20Classification_Excerpt%201987.pdf. Accessed April 18, 2019.
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14. European League Against Rheumatism. Gene signatures and biomarkers predict onset of RA in at-risk individuals. https://www.eurekalert.org/pub_releases/2018-06/elar-gsa061518.php. June 15, 2018. Accessed April 18, 2019.
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