The idiopathic inflammatory myopathies (IIMs) are a heterogeneous group of chronic acquired disorders characterized by proximal muscle weakness and muscle inflammation [1] [2] . In adults, the most common IIMs are dermatomyositis (DM) and polymyositis (PM) while in children, the most common IIM is juvenile DM [3] . Corticosteroids are the standard first-line therapy alone or in combination with immunosuppressive agents [4] . Unfortunately, many patients are refractory to corticosteroids and immunosuppressive agents, and therefore newer modes of therapy are currently being studied.

B cell depletion therapy with rituximab, which has been used for many years to treat B-cell lymphomas, has recently gained popularity in the treatment of autoimmune diseases. The prospective, randomized, double-blind, clinical trial (the Rituximab in Myositis, (RIM) trial) assesses the effectiveness of rituximab in refractory adult PM and adult and juvenile DM [5] . Although the RIM trial does not meet its primary or secondary end-points, 83% of study patients achieve the definition of improvement, and individual core set measures are improved in both rituximab-treated groups throughout the 44-week trial. Furthermore, the addition of rituximab provides a significant steroid-sparing effect between the start and conclusion of the trial [5] . However, there is a variation in the clinical response to rituximab among the patients that participate in the trial. Prediction of response to rituximab can prevent further exposing patients to side effects of rituximab. While rituximab is generally well tolerated and serious adverse reactions are uncommon, approximately 32% of patients in the RIM trial and about 22% of patients in a trial of rituximab in vasculitis experience one or more adverse events [5] [6] . Identification of predictors of response to rituximab may also be helpful to develop recommendations for rituximab use, especially starting of rituximab as well as further continuation or early discontinuation.

Data regarding predictors of response to rituximab in refractory myositis is limited. Since particular clinical syndromes observed in myositis are closely linked to detection of myositis antibodies, Aggarwal et al. studies if autoantibodies can predict clinical improvement in rituximab-treated refractory myositis patients [7] . Their results indicate that anti-synthetase (mainly anti-Jo-1) and anti-Mi-2 autoantibodies are the strongest predictors of clinical improvement in rituximab-treated myositis patients [7] . Similarly, since inflammatory cytokines and chemokines have been shown to contribute to disease pathogenesis in myositis [8] - [11] , Reed et al. explores whether a pre-treatment cytokine and chemokine profile help to identify IIM patients who respond favorably to rituximab therapy [12] . Their results indicate that the composite interferon (IFN) chemokine score before treatment can potentially predict response to rituximab in refractory myositis [12] . In light of these recent findings, we seek to determine clinical, laboratory, and gene expression/cytokine/chemokine predictors of clinical improvement in rituximab-treated refractory myositis patients, and explore whether the inclusion of gene expression, cytokine and chemokine data into the regression models offers a predictive advantage compared to models with clinical and laboratory data only.

The recent RIM trial demonstrates that rituximab is an effective therapy for patients with treatment-resistant myositis [5] . However, rituximab does not benefit all patients in the RIM trial. To support an “individualized-medicine” approach to the treatment of myositis, the identification of clinical features and biomarkers that predict a beneficial response to rituximab is necessary. Herein, we seek to identify clinical, laboratory, and gene expression/cytokine/chemokine predictors of clinical improvement in a cohort of rituximab-treated refractory myositis. Interestingly, the “final” models that include gene expression/cytokine/chemokinedata are superior in terms of predictive ability when compared to the “base” models with only clinical and laboratory data. Our “final” models demonstrate that Physician Global VAS, IFN chemokine score, and IL-2 levels at baseline help predict to some extent Physician Global VAS at 24 weeks. Similarly, they demonstrate that race (Caucasian), Muscle Disease Activity VAS, IFN chemokine score and IL-2 levels at baseline help predict somewhat Muscle Disease Activity VAS at 24 weeks; and Extramuscular Global VAS, Pulmonary VAS, presence of anti-Mi-2, and MIP-1β levels at baseline help predict to a certain degree Extramuscular Global VAS at 24 weeks.

Cytokines and chemokines associated with innate and adaptive immune-pathways have emerged as important factors in the pathogenesis of inflammatory myopathies. Previous studies have reported up-regulation of numerous Type I IFN genes, cytokines, and chemokines in both peripheral blood and muscle tissue of PM and DM patients with active disease [13] . Nevertheless, in order to reduce comparisons of individual measurements as well as simplify comparisons across studies, composite scores using subsets of Type I IFN up-regulate genes, cytokines and chemokines have been developed. For example, the whole blood type I IFN gene expression signature is defined by expression levels of 3 IFN-regulated genes: IFIT1, G1P2, and IRF7 [10] [13] . In a manner similar to the calculation of the Type I IFN gene score, a summary chemokine score based on serum levels of IP-10, ITAC and MCP-1 has also been defined [13] . Interestingly, the composite Type I IFN gene and chemokine scores have been shown to be sensitive to change in myositis disease activity (especially the Physician Global VAS and Muscle Disease Activity VAS scores) [13] . Therefore, these composite scores have the potential to serve as sensitive and responsive longitudinal biomarkers of change in myositis disease activity.

Evidence suggests that an altered expression of cytokines and chemokines in both peripheral blood and muscle tissue of myositis patients may be particularly relevant in refractory disease. For example, expression of IL- 18 at in muscle from patients with myositis has been associated with treatment-resistant disease [20] . Similarly, expression of Th1-associated cytokines has been associated with corticosteroid-resistant interstitial lung disease in both PM and DM [21] . Therefore, in our previous study we sought to examine the IFN regulated chemokine and cytokine profiles in the serum of rituximab-treated refractory myositis patients, and explore the impact of rituximab on these cytokine/chemokine profiles at 8 and 16 weeks after rituximab treatment [12] . We find that the scores of Th2- and Th17-derived cytokines significantly increase at 16 weeks after rituximab, while the scores of regulatory cytokines decrease when compared with baseline level [12] . Interestingly, the IFN chemokine score does not differ significantly from baseline at either 8 or 16 weeks after initiation of rituximab [12] . In this study we demonstrate that patients with a high IFN chemokine score and high IL-2 levels at baseline experience a clinical improvement at 24 weeks post-rituximab in terms of Physician Global VAS and Muscle Disease Activity VAS.

Earlier studies have analyzed myositis autoantibodies as potential predictors of clinical improvement after initiation of rituximab in refractory myositis. Presence of anti-synthetases (mainly anti-Jo-1) and anti-Mi-2 were the strongest predictors of clinical improvement in a cohort of rituximab-treated myositis patients [7] . In our current study, presence or absence of autoantibodies was not predictive of Physician Global VAS or Muscle Disease Activity VAS at 24 weeks. However, in both “final” models of Physician Global VAS and Muscle Disease Activity VAS, the IFN chemokine score and IL-2 levels were predictors to some extent. Since the predic tive ability of the models including gene expression/cytokine/chemokine data was superior to that of the models without gene expression/cytokine/chemokine data, our findings suggest that gene expression/cytokine/hemone data, such as the IFN chemokine score and IL-2 levels, have greater predictive ability than autoantibody status for clinical improvement in terms of Physician Global and Muscle Disease Activity VAS. Nevertheless, our study also showed that presence of anti-Mi-2 and MIP-1β levels at baseline predict somewhat Extramuscular Global VAS at 24 weeks. These findings suggest that autoantibody status in conjunction with biomarkers such as MIP-1β could be beneficial for assessing clinical improvement in terms of Extramuscular Global VAS after rituximab therapy in patients with myositis.

Treatment strategies for DM and PM commonly include trials of medication based on provider preference and experience with disease features leading to multiple medication exposures and expense. Therapies such as rituximab have brought improved efficacy in the realm of autoimmune diseases, but their use in clinical practice may be limited by concerns over side effects, ineffectiveness and cost. Predictive models are needed to help identify patients with autoimmune diseases that would most likely benefit from rituximab therapy. Therefore, in this study, we sought to determine clinical, laboratory, and gene expression/cytokine/chemokine predictors of clinical improvement in rituximab-treated refractory myositis patients, and explore whether inclusion of gene expression/cytokine/chemokine data into the regression models offers a predictive advantage compared to models with clinical and laboratory data only. Interestingly, we found that “final” models that included gene expression/cytokine/chemokine data were superior in terms of predictive ability when compared to the “base” models with only clinical and laboratory data. Furthermore, our study indicates that IFN chemokine score and IL-2 levels predict to some extent clinical improvement in terms of both Physician Global VAS and Muscle Disease Activity VAS in rituximab-treated refractory myositis patients. Nevertheless, the penalized regression methods resulted in multivariable models with relatively low cross-validated R² values. This indicates that any predictive model built from the baseline variables available in our study is unlikely to demonstrate good performance when applied to new patients. Therefore, future studies should explore further the utility of gene expression, cytokine and chemokine data for predicting improvement in refractory myositis patients after initiating therapy with rituximab.

The authors declare no conflicts of interest.

We would like to thank Peter Wettstein, PhD and Michael Strausbauch from the Immune Monitoring Laboratory, Mayo Clinic, Rochester, MN, for their roles in generating the cytokine/chemokine data. Also, we would like to thank Diane Koontz, University of Pittsburgh Medical Center, Pittsburgh, PA for the coordination of sample retrieval and shipment. Lastly, we would like to thank the RIM investigators and subjects. This work was also supported by Genentech.

This work was supported by the National Institute of Health: Mechanisms of Response and Relapse in Rituximab-treated Refractory Idiopathic Inflammatory Myopathies [AR61298-03]; the National Institute of Arthritis and Musculoskeletal and Skin Diseases [N01-AR-4-2273, R01 AR061298, R01 AR57781], and the National Center for Advancing Translational Sciences [UL1 TR000135]. This work was also supported by The Myositis Association (TMA) and Genentech.

Jeannette M.Olazagasti,Cynthia S.Crowson,Molly S.Hein,Consuelo Lopezde Padilla,RohitAggarwal,Chester V.Oddis,Ann M.Reed, (2015) Predictive Models of Clinical Improvement in Rituximab-Treated Myositis Patients Using Clinical Features, Autoantibodies, and Biomarkers. Open Journal of Rheumatology and Autoimmune Diseases,05,68-80. doi: 10.4236/ojra.2015.53012

The following examples illustrate the direct application of our multivariable linear regression models in the clinical setting such as when a clinician is considering rituximab for a refractory myositis patient.

General formula for predicting absolute change in Physician Global VAS 24 weeks after rituximab treatment:

where y is the estimated absolute change in Physician Global VAS 24 weeks after rituximab treatment, x₁ is the estimated regression coefficient of baseline Physician Global VAS, x₂ is the estimated regression coefficient of baseline IFN chemokine score, and x₃ is the estimated regression coefficient of baseline IL-2.

Case 1―A 40-year-old woman with newly diagnosed dermatomyositis presents with baseline Physician Global VAS of 40 mm, IFN chemokine score of 55.7, and IL-2 of 3.5 pg/mL. The estimated absolute change in Physician Global VAS 24 weeks after rituximab based on our multivariable linear regression model is computed as follows:

In this example, the estimated absolute change in Physician Global VAS 24 weeks after rituximab treatment is −22.9 mm. So by 24 weeks this patient’s Physician Global VAS is estimated to decrease to 40.0 - 22.9 = 17.1 mm. Since her baseline Physician Global VAS was 40 mm, we can say that she could experience around a 50% reduction in her Physician Global VAS by 24 weeks if she were treated with rituximab. Therefore, this hypothetical patient could be a potential candidate for rituximab.

Case 2―A 61-year-old woman with newly diagnosed dermatomyositis presents with baseline Physician Global VAS of 12 mm, IFN chemokine score of 1.9, and IL-2 of 2.2 pg/mL. The estimated absolute change in Physician Global VAS 24 weeks after rituximab based on our multivariable linear regression model is computed as follows:

In this example, the estimated absolute change in Physician Global VAS 24 weeks after rituximab treatment is −0.8 mm. So by 24 weeks this patient’s Physician Global VAS is estimated to decrease to 12.0 - 0.8 = 11.2, which is very little change from baseline. Since the benefit from rituximab is not significant in this case, this hypothetical patient would not be an ideal candidate for rituximab.