Reed, Ann M

ObjectiveThe study objective was to identify differences in gene expression between treatment responders (TRs) and treatment non‐responders (TNRs) diagnosed with juvenile dermatomyositis (JDM).MethodsGene expression analyses were performed using whole blood messenger RNA sequencing in patients with JDM (n = 17) and healthy controls (HCs; n = 10). Four analyses were performed (A1‐4) comparing differential gene expression and pathways analysis exploiting the timing of sample acquisition and the treatments received to perform these comparative analyses. Analyses were done at diagnosis and follow‐up, which averaged 7 months later in the cohort.ResultsAt diagnosis, the expression of 10 genes differed between TRs and TNRs. Hallmark and canonical pathway analysis revealed 11 and 60 pathways enriched in TRs and 3 and 21 pathways enriched in TNRs, respectively. Pathway enrichment at diagnosis in TRs was strongest in pathways involved in metabolism, complement activation, and cell signaling as mediated by IL‐8, p38/microtubule associated protein kinases (MAPK)/extracellular signal‐regulated kinases (ERK), Phosphatidylinositol 3 Kinase Gamma (PI3Kγ), and the B cell receptor. Follow‐up hallmark and canonical pathway analysis showed that 2 and 14 pathways were enriched in TRs, whereas 24 and 123 pathways were enriched in treatment TNRs, respectively. Prior treatment with glucocorticoids significantly altered expression of 13 genes in the analysis of subjects at diagnosis with JDM as compared with HCs.ConclusionNumerous genes and pathways differ between TRs and TNRs at diagnosis and follow‐up. Prior treatment with glucocorticoids prior to specimen acquisition had a small effect on the performed analyses.

ObjectivePublished predictive models of disease outcomes in idiopathic inflammatory myopathies (IIMs) are sparse and of limited accuracy due to disease heterogeneity. Computational methods may address this heterogeneity by partitioning patients based on clinical and biological phenotype.MethodsTo identify new patient groups, we applied similarity network fusion (SNF) to clinical and biological data from 168 patients with myositis (64 adult polymyositis [PM], 65 adult dermatomyositis [DM], and 39 juvenile DM [JDM]) in the Rituximab in Myositis trial. We generated a sparse proof‐of‐concept bedside classifier using multinomial regression and identified characteristics that distinguished these groups. We conducted χ2 tests to link new patient groups with the myositis subtypes.ResultsSNF identified five patient groups in the discovery cohort that subdivided the myositis subtypes. The sparse multinomial regressor to predict patient group assignments (areas under the receiver operating characteristic curve = [0.78, 0.97]; areas under the precision‐recall curve = [0.55, 0.96]) found that autoantibody enrichment defined four of these groups: anti–Mi‐2, anti–signal recognition peptide (SRP), anti–nuclear matrix protein 2 (NXP2), and anti‐synthetase (Syn). Depletion of immunoglobulin M (IgM) defined the fifth group. Each group was associated with one subtype, with adult DM being associated with anti–Mi‐2 and anti‐Syn autoantibodies, JDM being associated with anti‐NXP2 autoantibodies, and adult PM being associated with IgM depletion and anti‐SRP autoantibodies. These associations enabled us to further resolve the current myositis subtypes.ConclusionUsing unsupervised machine learning, we identified clinically and biologically homogeneous groups of patients with IIMs, forming the basis of an integrated disease classification based on both clinical and biological phenotype, thus validating other approaches and what has been previously described.

ObjectiveOur aim was to identify cytokines and chemokines in patients with adult dermatomyositis (DM) and juvenile dermatomyositis (JDM) that predict changes in disease activity.MethodsMultiplexed immunoassays (Meso Scale Discovery) enabled simultaneous measurement of interferon (IFN)‐regulated chemokines and other pro‐ and anti‐inflammatory cytokines specific to differentiation of specific T‐cell and innate pathways. Cytokine scores were computed for IFNCK (IP‐10, MCP‐1), Th1 (IFNɣ, TNFα, and IL2), Th2 (IL4, IL10, IL12, and IL 13), Th17 (IL6, IL17, IL1β), macrophage (MIP‐1α, MIP‐1β, IL8), and regulatory (IL10, TNFα) factors. Spearman correlation and mixed models were used to examine whether cytokines at a previous visit predict change in disease activity at the next visit.ResultsThe study included 36 patients (16 DM and 20 JDM) with at least two visits (87 patient intervals between two visits). Mean age (SD) at inclusion was 56.9 (18.4) years for DM and 10.8 (6.6) years in JDM, 67% of patients were female, 89% Caucasian. The mean (SD) physician global, muscle and extra‐muscular disease activity Visual Analog Scale scores at inclusion were 41 (26), 36 (30), and 34 (21) mm, respectively. The change in IFN score from one visit to the next was associated with the change in physician global (P = 0.010) and extramuscular (P < 0.001) disease activity scores. Preliminary results revealed significant correlations of previous IFNCK score and IL‐6 with subsequent disease activity measures, but after adjustment for multiple visits per patient, these associations did not reach statistical significance.ConclusionThere is a potential relationship between IFNCK and other cytokine scores seen in adult and juvenile DM with future disease states.