Yeung, Rae S. 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.

Background/Purpose:Genome‐wide association studies have identified susceptibility loci for many autoimmune diseases, including rheumatoid arthritis (RA) and juvenile idiopathic arthritis (JIA). About 5% of children with JIA have rheumatoid factor positive arthritis that is phenotypically similar to adult seropositive RA, thus representing childhood onset RA (CORA). To understand the genetic architecture of CORA risk relative to other autoimmune diseases, we genotyped CORA cases and controls on the Immunochip, a custom array designed by the Immunochip Consortium to fine map autoimmune disease‐associated loci shared across 11 autoimmune phenotypes.Methods:Genotyping was completed on 340 CORA cases (mean onset age: 10.2 ± 4.2 yrs) and 11624 controls. Standard SNP and sample QC was performed (e.g., removing samples with call rate <98%, admixture outliers). To test for SNP association with CORA a logistic regression model was computed with Caucasian admixture proportions (ADMIXTURE) as covariates (SNPLash). False discovery rate (FDR) adjusted p‐values (PFDR) are reported to account for the actual number of tests computed.Results:SNP rs3129769, near HLA DRB1 was the most significantly associated (PFDR <3×10‐25), and is in linkage disequilibrium with the HLA DRB1 SNP reported in RA (rs660895, PFDR <1×10‐24). Outside the HLA region, 28 regions had ≥1 SNP meeting genome‐wide significance (PFDR<0.05). The best signal of association was on 22q13 (rs9610687, OR = 0.57, PFDR < 0.0002) near IL2RB, followed by the PTPN22 locus (rs6679677, OR = 1.83, PFDR < 0.0005), an intergenic SNP on chromosome 4 (rs970036, OR = 1.78, PFDR < 0.002) and an intronic SNP (19p13, rs3787016 PFDR < 0.002) in the POLR2E gene, which encodes a subunit of RNA polymerase II. Some loci which have previously been implicated in RA had evidence of association including MMEL (rs751358 PFDR < 0.003), IRF5 (rs4731531 PFDR < 0.006) and CCR6 (rs11575078 PFDR < 0.03). In addition using the FDR‐based threshold we identified several potential SNPs (rs10918214, 1q23; rs28491312, 14q31) not previously implicated in RA.Conclusion:Immunochip analysis of the largest cohort of CORA investigated to date has confirmed the HLA DRB1 association and identified several other loci associated with CORA. Comparisons among loci associated with CORA and those identified in RA and other forms of JIA are underway and will help delineate the unique genetic factors for CORA susceptibility.