Juvenile idiopathic arthritis (JIA) is a term used to designate a family of childhood-onset diseases that are characterized by chronic inflammation of synovial membranes. Because the etiology of JIA is unknown, therapy remains entirely empirical, is sometimes only marginally effective, and frequently is associated with unwanted side effects.
The empirical nature of therapy for JIA is one of the most vexing problems in the field of pediatric rheumatology. A critical question, which is often asked by parents as well as physicians, is when and whether children who are doing well on medication can have treatment with those medications reduced or discontinued. Answering this question relies on 2 suppositions: 1) there is something that can be called “remission” in JIA, and 2) remission can be identified on the basis of specific clinical or laboratory features of the disease. Unfortunately, neither of these suppositions is necessarily valid. Studies in the past 10 years have shown that a significant percentage of children with polyarticular JIA experience disease flares when methotrexate is discontinued, even when disease has been stable during treatment with that drug for years (1, 2). No reliable biomarker or set of biomarkers accurately separates those children fated to experience disease recurrence as methotrexate is discontinued from those children in whom treatment with the medication can safely be discontinued.
Only recently have investigators arrived at a consensus regarding the meaning of terms such as “active disease,” “inactive disease,” and “clinical remission” (3). Although the definitions of these terms have been validated clinically, it is currently unknown whether they actually represent distinct biologic states. The development of predictive biomarkers would certainly be facilitated if these distinct disease states could be identified biologically in children with treated disease.
Because conventional biomarkers have, to date, shown limited capacity to identify remission, we elected to use genome-wide transcription profiling to determine whether the clinically derived criteria for disease states represent underlying immunobiology in children with polyarticular, IgM–rheumatoid factor (RF)–negative JIA.
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- PATIENTS AND METHODS
- AUTHOR CONTRIBUTIONS
The development of clinically useful prognostic genomic biomarkers in JIA requires the ability to discern specific disease states (e.g., active disease, remission) as a first step. If clinical remission while receiving medication is indistinguishable from active disease, for example, it is unlikely that this technology will provide much assistance. That is, it is possible that the clinically derived consensus criteria for disease status do not reflect underlying disease biology. This study tested and confirmed the hypothesis that these disease states are distinguishable at the molecular level using gene expression profiling and thus provides an important first step in biomarker development.
We report here that gene profiling and hierarchical cluster analysis can distinguish different disease states, although there is more “blurring” of the groups at the biologic level than at the clinical level (Figure 1). This suggests that underlying cellular abnormalities persist in PBMCs from patients with JIA, even when treatment is successful in controlling symptoms. A plausible (but not currently provable) explanation for this observation is that the synovium is a more critical target of drug action than has previously been supposed. This hypothesis is supported by what we observed in children with inactive disease who had not received medication for at least 6 months (that is, children who had achieved CR status). Remission, as reflected at the molecular level, is clearly not a return to a normal immune/inflammatory state. Rather, the gene expression profiles suggest that remission is a state of homeostasis in which antiinflammatory (e.g., TGFβ-driven) mechanisms balance the dysregulatory elements that lead to chronic inflammation.
It is difficult to determine how accurate or predictive our models of the specific disease states in polyarticular JIA are, until we test them in an independent cohort. However, the use of the 5-fold cross-validation does provide validation that the model is not overfit. For example, the major misclassification occurred in the CRM group, in which 3 patients classified as CRM were predicted to have active disease. It is possible that our model is correct and that disease in these children is not in remission on a molecular level. This hypothesis is supported by Ingenuity modeling, which suggests that networks of proinflammatory genes are still active, even in these children who have achieved a state of remission. Under any circumstances, we will need to follow up these children over time to determine whether our cross-sectional model has prognostic capabilities.
Taken together, these findings explain 2 observations that have puzzled physicians caring for children with polyarticular JIA for many years. First, our data explain at least conceptually why recurrences or flares are so common when medications are tapered or discontinued in children who seem to be doing well: underlying abnormalities at the gene expression level are still present, even if such abnormalities are not reflected in standard clinical measures such as the ESR, the serum CRP level, the hemoglobin concentration, or the white blood cell count. These findings also explain why disease recurrences are common: remission is still a biologically abnormal state. Although it is impossible to speculate on what extrinsic factors might disrupt the complex homeostatic mechanisms that are reflected during disease remission, it is reasonable to hope that a longitudinal analysis of a large cohort of children will be highly informative.
It is important to point out that many of the pathologic networks visualized in these studies demonstrate the structure of scale-free systems (26), as we have previously seen in neutrophils from patients with JIA (27). That is, the network structures demonstrate areas of high connectivity between some genes (designated “hubs” in systems biology) and other genes showing only limited connectivity to the system (“nodes”). Furthermore, the meta-structure of the collected profiles, especially in neutrophils, demonstrated modularity, which is another feature of cellular–physiologic systems (28).
These findings have interesting implications both for our understanding of pathogenesis and for elucidating new targets of therapy. From the standpoint of pathogenesis, we note that the pathologic structures revealed on Ingenuity are organized and are therefore as likely to represent physiologic adaptation to an externally applied force as they are an unraveling of basic biologic processes (e.g., the distinction between self and non-self). From the standpoint of therapy, it is useful to mention one of the primary characteristics of scale-free systems: they are highly resistant to perturbation at their peripheral nodes but vulnerable to attack at their hubs (29) (think of what happens to air traffic when inclement weather disrupts flights into and out of Atlanta or Chicago). This means that successful new treatments for JIA will have to focus on pathophysiologic structures, not specific genes. A gene that is expressed “20-fold above controls” is not necessarily a promising target if it represents a peripheral node. A gene that shows no differential expression at all might be a promising target if it represents a system hub.
We are still a long way from the ultimate goal of developing gene expression–based disease biomarkers that will direct therapy in polyarticular JIA. What this study has done is confirm that clinical remission (both during treatment with medication and without medical treatment) is a biologically distinct state. Furthermore, we have demonstrated that clinical remission in the absence of treatment with medication is not a normal state but represents a homeostatic condition in which proinflammatory and antiinflammatory mechanisms appear to be in balance. Answering the critical questions about biomarkers will require the study of large groups of children prospectively, a task that we have already undertaken.
- Top of page
- PATIENTS AND METHODS
- AUTHOR CONTRIBUTIONS
Dr. Jarvis had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
Study design. Knowlton, Frank, Jarvis.
Acquisition of data. Jiang, Frank, Aggarwal, Wallace, McKee, Chaser, Tung, Smith, Chen, Osban, O'Neil, Centola, McGhee, Jarvis.
Analysis and interpretation of data. Knowlton, Jiang, Frank, O'Neil, Centola, Jarvis.
Manuscript preparation. Knowlton, Jiang, Frank, Aggarwal, Wallace, O'Neil, Jarvis.
Statistical analysis. Knowlton.