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Gene Expression Profiles of Treatment Response and Non‐Response in Children With Juvenile Dermatomyositis

Objective The study objective was to identify differences in gene expression between treatment responders (TRs) and treatment non‐responders (TNRs) diagnosed with juvenile dermatomyositis (JDM). Methods Gene 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. Results At 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. Conclusion Numerous 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.


INTRODUCTION
Juvenile dermatomyositis (JDM) is a systemic autoimmune disease that primarily affects the skin, muscles, and microvasculature and is diagnosed in fewer than 4 in 1,000,000 children annually. Weakness and rash are the primary manifestations; however, some children have a more severe course that can lead to physical disability, calcinosis, gastrointestinal perforations, interstitial lung disease, and even death.
Mortality rates have decreased from approximately 30% prior to the use of glucocorticoids to about 3% in many countries today (1). Despite these improvements in survival, many children continue to have active disease years after diagnosis, suggesting a substantial failure rate of first-line treatments (1). In the recent Pediatric Rheumatology International Trials Organization (PRINTO) clinical trial of prednisone, methotrexate, and cyclosporine, even the most effective treatment arm-prednisone plus methotrexate-had a failure rate of approximately 25% in the first 6 months (2).
Unfortunately, determining which children will have a good response to first-line treatment versus those for whom first-line treatment will fail has rarely been studied in JDM, and few predictive biomarkers of response at diagnosis have been identified to date.
Aware of the issues related to incomplete treatment response, this study sought to understand whether gene expression markers obtained at diagnosis differed between children who would versus would not respond to first-line medications in JDM. Such information could alter early clinical treatment by identifying children at diagnosis for whom first-line medications are more likely to fail and who may need closer follow-up or early escalation to second-line treatments.
Gene expression studies in adult dermatomyositis and JDM have shown dysregulation of genes in the muscles, skin, and blood of these patients (3)(4)(5). Genes and proteins related to activation of T cells, dendritic cells, and monocytes, including the Type-I interferon and Interluekin (IL)-6 pathways, are commonly overexpressed, and gene expression changes are seen with improvement after 6 or more months of treatment (3). Studies in juvenile idiopathic arthritis and rheumatoid arthritis have shown that gene expression profiles at diagnosis can distinguish patients who will from those who will not respond to treatment, including methotrexate, infliximab, tocilizumab, abatacept, and rituximab (6,7). These findings, taken together, suggest that gene expression profiling is a useful modality for evaluating treatment response, including in JDM.

MATERIALS AND METHODS
Subjects. Two JDM cohorts were used for this study. The first comprised subjects enrolled in a prior study at the Mayo Clinic (8), whereas the second was obtained from the prospective registry maintained by the Duke University Pediatric Myositis clinic, as were healthy controls (HCs; n = 10). For inclusion in the study, all subjects met criteria for probable or definite JDM from Bohen and Peter (9,10), as assessed by a JDM expert (Dr. Reed or Dr. Dvergsten), had clinical data available regarding their disease activity at baseline and outcome visit, and had provided an RNA sample at their baseline visit. These subjects were included in the preplanned analyses as appropriate to the conditions outlined later in the "Preplanned Analyses" section and presented in Table 1. The sole exclusion criterion was an RNA Integrity Number (RIN) less than 7.0. The RIN is a measure of RNA quality that ranges from 0 (lowest) to 10 (highest). A cutoff of 6.5-7.0 is typical in RNA-sequencing (RNA-seq) studies. The HCs, who were recruited from a primary care clinic, had no known chronic medical conditions or medication use and presented for a screening health visit without any concern of an acute change in health; these individuals were not being treated with immunomodulatory medications. HCs were matched by age to study participants. HC specimens were collected in PaxGene RNA tubes, stored in the same freezer as study samples, and processed using the PaxGene recommend protocol. The study protocol was approved by the Duke University Institutional Review Board (IRB) and conducted in accordance with the principles of the Declaration of Helsinki. The study was deemed exempt by the Mayo Clinic IRB because all data were deidentified and subjects consented under a prior study to ongoing use of data and specimens.
RNA-seq. Blood from subjects (n = 17) and controls (n = 10) was collected in PaxGene RNA tubes (PreAnalytix) and stored at −80 C. Extractions followed the PaxGene RNA protocol for all specimens. Samples from the Mayo Clinic cohort were provided as extracted specimens, previously processed as earlier. Duke JDM cohort and HC specimens were extracted on a QIAsymphony instrument (Qiagen) in batches of 96 samples. Extracted total RNA quality and concentration for all specimens was assessed on a 2100 Bioanalyzer (Agilent Technologies) and Qubit 2.0 (Thermo-Fisher Scientific), respectively. RNA-seq libraries were prepared using the commercially available Nugen Universal Plus messenger RNA (mRNA)-sequencing kit according to the manufacturer's protocol. Nugen's AnyDeplete-mediated transcript depletion technology was used to eliminate globin mRNA transcripts from the final libraries.
Libraries were indexed using a dual indexing approach allowing for multiple libraries to be pooled and sequenced on the same sequencing flow cell of an Illumina HiSequencing platform. Before pooling and sequencing, fragment length distribution and library quality were assessed on a Fragment Analyzer (Agilent). All libraries were eventually pooled in equimolar ratio and sequenced at 50-bp single end reads on the Illumina HiSeq 4000 instrument. Once generated, sequence data were demultiplexed and Fastq files generated using Illumina's Bcl2Fastq version 2 conversion software.
RNA-seq data were then processed using the TrimGalore toolkit (11), which employs Cutadapt (12) to trim low-quality bases and Illumina sequencing adapters from the 3' end of the reads. Only reads that were 20 nucleotides or longer after trimming were kept for further analysis. Reads were mapped to the GRCh37v75 version of the human genome and transcriptome (13) using the STAR RNA-seq alignment tool (14). Reads were kept for subsequent analysis if they mapped to a single genomic location. Gene counts were compiled using the HTSeq tool (15). Only genes that had at least 10 reads in any given library were used in subsequent analysis. Normalization of gene counts was performed using variance stabilizing transformation in the DESeq2 package in R (16).
Autoantibody determination. Sera were tested for myositis-specific autoantibodies (MSAs) and myositis-associated autoantibodies by a commercially available line immunoassay Test results for each analyte were considered positive if the densitometry scans had values of more than 10 density units, which represents a value of more than two standard deviations above the mean of control samples.
Data analysis. Differential gene expression was carried out using the DESeq2 Bioconductor package with the R statistical programming environment, version 1.3.959 (17). Gene Set Enrichment Analysis (GSEA) was conducted with GSEA software from the Broad Institute using the hallmark and canonical gene sets compiled in MSigDB (18,19). Using GSEA affords many advantages when compared with evaluating for the differential expression of individual genes. This includes simplifying analysis by reducing a large set of differentially expressed genes into groups of genes that share a function. Additionally, GSEA allows for the detection of variations in the activity of the pathway when these variations are due to small alterations in many genes, versus a large alteration in a single gene.
References for the canonical pathways include Reactome, the Pathway Interaction Database, Kyoto Encyclopedia of Genes and Genomes (KEGG), and the Biocarta databases. All analyses were corrected for multiple hypothesis testing using the false discovery rate (FDR) (20). Enrichment is defined as an increase in mRNA expression.
Response to treatment was based on the 2016 American College of Rheumatology (ACR)/European League Against Rheumatism (EULAR) Criteria for Minimal, Moderate, and Major Clinical Response in Juvenile Dermatomyositis (21). These criteria assign a weighted score to the absolute change in six variables in either the International Myositis Assessment and Clinical Studies Group core dataset or the PRINTO core dataset between two time points. The weighted score for each of the six variables is summed to get the total improvement score (TIS), which is a number between 0 and 100. The 2016 Criteria also predefined key clinical categories of improvement along the TIS range. In children, a score of 0-29 represents no improvement, 30-44 is minimal improvement, 45-69 is moderate improvement, and 70 or higher represents major improvement. TIS and clinical categories of improvement were determined using the outcome calculator available on the National Institutes of Environmental Health Sciences website (22).
Preplanned analyses. Several analyses were planned prior to review of the data for this study, and samples were obtained at defined time points: baseline, outcome visit, and visit three. Baseline samples were the first obtained from the JDM cohort (n = 17) acquired either before treatment began (treatment naïve, n = 11) or soon after onset of symptoms but following short-term treatment (prior treatment, n = 6). The outcome visit was the earlier of either 1) the visit therapy was escalated because of clinical worsening/lack of response (n = 2) or 2) the visit closest to 6 months after diagnosis (mean 208 days). Baseline and outcome visits were used to determine clinical response, TIS, as defined earlier with a 6-month interval considered a sufficient period to determine response to treatment. Visit three samples were obtained at a mean of 79 days from the outcome samples but less than 12 months from baseline measurement. Table 1 summarizes the analyses and the requirements for inclusion in each. Subjects were included in every analysis for which the inclusion criteria were met.
In analysis 1 (A1), baseline data were compared with HCs (n = 10). Analysis 2 (A2) compared gene expression at baseline and outcome visits between JDM treatment responders (TRs; n = 7) and treatment non-responders (TNRs; n = 5). Analysis 3 (A3) was a longitudinal comparison of gene expression using data from baseline visit, outcome visit, and visit 3 in treated JDM subjects (n = 11). Analysis 4 (A4) assessed whether short-term treatment with glucocorticoids and/or methotrexate prior to RNA specimen collection significantly altered gene expression.
Because this work is not based on a longitudinal cohort and analysis is not related to length of treatment, we segregated the patients based on response; if the patient changed therapy during our study period, they were considered an NR. We found an equal number of TRs and TNRs (1/1) treated with methotrexate, prednisone, intravenous immunoglobulin (IVIG), and methylprednisolone; one more patient in the TR group treated with methotrexate, prednisone, and IVIG (2/1); and two more patients in the TR group treated with methotrexate and prednisone (3/1).

RESULTS
Subject characteristics. Characteristics for all 17 subjects with JDM are shown in Table 2 and summarized in column "A1." None of the subjects met exclusion criteria (RIN < 7). The remaining columns describe the JDM population in each analysis. As a group, subjects with JDM had a mean age of diagnosis of 10.8 years (median 12.0 years), were mostly White race, and received several combinations of treatment, and 6 of 17 subjects (35%) received some treatment prior to collection of the initial sample (A4). An MSA was present in 47% of subjects, which is consistent with other reported studies, and the mean TIS was 56, corresponding to moderate improvement (23,24). A1: JDM baseline versus HCs, gene expression. Seventeen subjects with JDM were compared with 10 agematched HCs. A total of 1830 genes were differentially expressed between subjects with JDM and HCs using an FDR of less than 0.05. In the JDM cohort, 1199 genes were overexpressed, whereas 631 were underexpressed. Of the 15 most overexpressed genes in JDM (Table 3), 12 are cytokine response genes (tumor necrosis factor [TNF]-α, Type-I interferons-α and -ɣ), Abbreviations: A, analysis; HC, healthy control; IVIG, intravenous immunoglobulin; MAA, myositis-associated autoantibody; MP, methylprednisolone; MSA, myositis-specific autoantibody; MTX, methotrexate; NA, not applicable; P, prednisone; R, rituximab; TIS, total improvement score; -, no data. a One subject did not have an available specimen for antibody testing. including OAS1-3, OASL, ISG15, and IFI27; two are involved in either membrane vesicle fusion/calcium sensing (OTOF) or DNA/ phosphatidyl inositol/zinc binding (ZCCHC2) using UniProt (19). The gene with the greatest expression was PCSK9 (log-fold change 23.62; P = 5.17 × 10 −24 ), a protein that is primarily known for its importance in low-density lipid metabolism but is also known to have increased expression in response to TNF-α (25). As shown in Table 4, many underexpressed genes are related to T cells, natural killer (NK) cells, myocytes, and dendritic cells (KLRF1, HOPX, GZMK, TPM2, and NPR2) (26). The most underexpressed protein-coding gene in subjects with JDM was KLRB1 (log-fold change −2.03; P = 5.86 × 10 −9 ), which codes for CD161, an inhibitor of NK cell cytotoxicity; conversely, KLRB1 is an activator of memory T cell TNF-α and interferon-ɣ (33).
A1 pathway analysis. Using GSEA, hallmark and canonical pathways analysis of subjects with JDM versus HCs was performed ( Figure 1). Referencing the hallmark dataset ( Figure 1A), 38/50 pathways (76%) were enriched in subjects with JDM, whereas 1 was enriched in HCs (MYC targets). Key immunologic pathways enriched in JDM included interferon-α and -ɣ response genes, TNF-α signaling via NFκB, IL-6 JAK-STAT3 signaling, and the complement pathway. Several metabolic pathways, including cholesterol homeostasis, adipogenesis, and fatty acid metabolism, were enriched in JDM, as were genes involved in ultraviolet (UV) response. Canonical pathways analysis shows that interferon-α, -β, and -ɣ remain markedly enriched in JDM; however, a more diverse set of immunologic pathways also emerge, including signaling through the B cell and TLR receptors, TNF-α, IL-6, IL-1, NFκB signaling, and activation of neutrophil and plateletrelated pathways ( Figure 1B).

A2 gene expression: JDM TRs versus TNRs, baseline.
Baseline specimens were compared with samples obtained at a mean of 208 days from diagnosis using 2016 ACR/EULAR response criteria. Groups compared consisted of five JDM subjects with no or minimal response (TNRs) and seven subjects with moderate or major treatment response (TRs). Table 5 shows the 10 genes differentially expressed in TRs. The most overexpressed protein-coding gene was MDGA1 (log-fold change 4.28; Padjusted = 2.12 × 10 −12 ); the function of the MDGA1 protein is poorly understood outside of the central nervous system. The most underexpressed gene was STAC2 (log-fold change −5.56; P = 0.08), which regulates calcium channel inactivation and is expressed in muscle. We did not see differences in MSAs by treatment response status as we had an equal number of MI2 TRs/TNRs (1/1), one more responder in the no-antibody group (3/2), one more responder with NXP2 (2/1), and one with MDA5 and hydroxymethylglutaryl-coenzyme A (HMGCoA) reductase.
A2 pathway analysis, baseline. GSEA of the hallmark pathways showed enrichment in 14 of 50 pathways, with 11 of the 14 (79%) in TRs and 3 in TNRs (Figure 2A). Pathways enriched in TRs included those related to hypoxia, reactive oxygen species, adipogenesis, cholesterol, and lipid metabolism as well as TNF-α signaling, complement, and UV response. GSEA of the canonical pathways showed enrichment in 81 total pathways, with 60 of the 81 (74%) enriched in TRs ( Figure 2B). Enrichment in TRs was  A2 pathway analysis, outcome. GSEA of the hallmark pathways at the outcome visit showed enrichment in 26 of 50 pathways ( Figure 2C). Twenty-four of the 26 pathways were enriched in TNRs and included numerous inflammatory pathways, including interferon-α and -ɣ response, TNF-α signaling, IL-6/JAK/STAT3 signaling, complement, transforming growth factor (TGF)-β, and others. Key pathways in cell growth and proliferation were also enriched, such as Gap 2-phase mitosis (G2M) checkpoint, E2 transcription factor (E2F), and mechanistic target of rapamycin complex (MTORC) and Kirsten rat sarcoma viral oncogene homolog (KRAS) signaling. GSEA of canonical pathways in TRs versus TNRs at outcome visit demonstrated enrichment in 137 total pathways ( Figure 2D). Responders had enrichment in 14 pathways, primarily in protein synthesis. TNRs showed enrichment in 123 canonical pathways, including interferon-α, -β, and-ɣ signaling; IL-1 and IL-6/7 (FDR 0.018, not shown in figure); and numerous cell cycle pathways.
A3: JDM longitudinal gene expression with treatment. Eleven subjects with JDM were evaluated for changes in gene expression comparing baseline with outcome visit, with a mean interval of 144 days. In this comparison, 876 genes were differentially expressed with 526 and 350 genes overexpressed or underexpressed at outcome visit compared with baseline, respectively. The 15 most overexpressed and underexpressed genes are shown in Supplementary Tables 3 and 4. The DAAM2 gene, which encodes a protein considered to be involved in lung function and expressed most prominently in neutrophils, had the greatest increase with a log-fold change of 5.02 (P = 7.92 × 10 −9 ). The gene with the greatest reduction was IGFBP4 (log-fold change −1.2, P = 1.18 × 10 −5 ), an inhibitor of insulin growth factor. The remaining 15 most upregulated genes at outcome visit trended toward involvement in cellular homeostasis with low cell type specificity. The remaining 15 most downregulated genes trended toward involvement in immunoglobulin structure formation and B cell function.
A3 longitudinal pathway analysis, baseline to outcome. Supplementary Figure 1 shows the results of pathway analysis between baseline and outcome for the hallmark (panels A and C) and canonical (panels B and D) pathways. At baseline, hallmark pathways involved in MYC targeting, oxidative phosphorylation, interferon-α response, the unfolded protein response, DNA repair, fatty acid metabolism, and interferon-ɣ response were overrepresented (panel A, normalized enrichment score [NES] < 0). At the outcome visit, numerous hallmark pathways involved in inflammation, including TNF-α signaling via IL-6 and JAK-STAT3 signaling, TGF-β signaling, and complement, were overexpressed (panel C, NES > 0). Pathways involved in cell replication, UV light response, and hypoxia were also more greatly overrepresented at the outcome visit. Canonical analysis showed pathways enriched at outcome, including interferon-ɣ, platelet derived growth factor receptor β, NK cell cytotoxicity, IL-6, IL-2, JAK-STAT, and vascular endothelial growth factor receptor 1 and 2 (panel B, NES > 0).   HES4, and LY6E-DT) were differentially expressed in subjects with JDM at diagnosis compared with HCs. Most of the 78 genes code for components of immunoglobulin or B cell receptors (data not shown). Of the 526 genes with increased expression at the outcome visit as compared with the baseline visit, only ITGADan integrin primarily expressed in myeloid leukocytes, NK cells, and ɣ/δ T cells-continued to be significantly differentially expressed at visit 3. In A1, ITGAD was not significantly overexpressed.
When the interval between baseline visit to outcome visit was compared with that of baseline visit to visit 3, inflammatory pathways such as IL-6 JAK-STAT3, TNF-α and IL-1 signaling, and NK-cell-mediated cytotoxicity were no longer enriched (NES > 0). In fact, the only pathway related to an inflammatory response that was enriched at visit 3 was canonical for influenza viral response (Supplementary Figure 1).
A4: Effects of prior treatment on baseline gene expression. Six of the subjects included in this study received treatment prior to initial sample collection, as outlined in Table 2. We performed several analyses to determine whether prior treatment with combinations of immunosuppressive therapy influenced gene expression in specimens collected near baseline (Supplementary Table 5A-C). Although prior treatment with glucocorticoids was associated with alterations in 41 genes, only 13 of these were differentially expressed between subjects with JDM and HCs (A1) and only 1 of the 41 significant genes between differentially expressed TRs and TNRs (A2). The length of prior therapy was 1-14 days.

DISCUSSION
Little is known about the biochemical changes in response to treatment of JDM. This is the first study, to our knowledge, evaluating mRNA sequencing in whole blood of children with JDM who responded to treatment with traditional therapies for JDM, including glucocorticoids, methotrexate, and IVIG (34). Using the 2016 criteria, our study revealed differences between children with moderate or major improvement in response to standard JDM treatments compared with those with minimal or no response. At the individual gene level, few genes are differentially expressed between TRs and TNRs at diagnosis. At the pathway level, TRs showed enrichment in diverse biological processes, including inflammation (complement, TNF-α signaling, B cell receptor signaling, and Fc-gamma receptors [FCɣR]-mediated phagocytosis), platelet activation, energy metabolism (adipogenesis and glycolysis), and UV response. TNRs showed enrichment in transcription factors and pathways involved in cell cycle regulation.
At an average of 208 days after treatment, pathway analyses of TRs revealed notably enriched indicators of myogenesis, protein production, and translation regulation, whereas TNRs were enriched in numerous inflammation-related pathways, including interferon-α and -ɣ, TNF-α, IL-6-JAK-STAT, TGF-β, and complement. Therefore, TRs at diagnosis have higher expression of inflammatory pathways initially but have greater resolution of their inflammation with standard JDM therapies. Responders did not, however, have complete resolution of their inflammation as they continued with enrichment of inflammatory pathways at outcome. At the individual gene level, 21.3% (390 of 1830) of genes differentially expressed in A1 remained differentially expressed at the outcome visit. These findings support the improvement, but not resolution, of disease activity in this cohort with a moderate response (TIS of 56/100) at the outcome visit.
Longitudinal gene expression while on treatment (A3), regardless of response status, revealed a decrease in the total number of differentially expressed genes from more than 1800 at the baseline visit, to 876 at the outcome visit, and 124 at visit 3. When cross-referenced in A1, only 73 (8%) genes at the outcome visit and 4 (5%) genes at visit 3 were downregulated by treatment, which suggests that most of the treatment effects on gene expression do not affect disease-related genes and may have no relevance to the underlying disease process. Pathway analysis showed ongoing enrichment in inflammatory pathways at the outcome visit but not at visit 3, suggesting that suppression of several inflammatory pathways-including interferon-α and -ɣ, TNFα, IL-6-JAK-STAT, and TGF-β-occurs somewhere between 4 and 8 months with current standard therapy. Visit 3 was notable for enrichment in several mitochondrial pathways not seen at the outcome visit (KEGG_CITRATE_CYCLE_TCA_CYCLE), but it is unclear whether these changes are due to a slightly larger ratio of TNRs at visit 3 or due to medication effects or tissue healing.
Treatment prior to specimen acquisition had a small effect on the analysis. Glucocorticoids led to significant alterations in 41 genes, but only 13 were differentially expressed between subjects with JDM and HCs and only one of the 41 significant genes between TRs and TNRs.
The role of currently proposed prognostic biomarkers of treatment response in JDM is not well understood, in part because there are few that are clinically available, and these may only apply to a subset of patients. Ferritin is one such example as it has been associated with rapidly progressive interstitial lung disease (35). Additionally, myositis-specific antibodies are associated with specific disease phenotypes; however, only 50%-60% of children with JDM have an identifiable MSA, and most are either anti-TIF or NXP2, so there is insufficient granularity to capture variability in treatment response or failure at the patient level (23,36). More recently, Wienke et al evaluated protein expression and demonstrated that subjects with high galectin-9, C-X-C motif chemokine ligand 10 (CXCL10), tumor necrosis factor binding protein 2 (TNFRII), and galectin-1 were at greater risk of requiring early intensification of treatment (37). The fact that our study detects a difference with treatment may be related to our use of gene expression versus protein expression or be secondary to cohort size.
The primary limitation of this study is the sample size. To increase confidence in the findings, subjects were also tested against HCs to verify that known differences between these groups were identified in this study. In prior gene expression studies of new-onset adult dermatomyositis or in combined studies with JDM, Type-I and -II interferons were consistently elevated in blood, skin, and muscle tissue. Our findings are consistent because genes related to these proteins were the primary genes upregulated in this study. Our results also demonstrated elevated TNF-α pathways, as has been found in gene expression studies of JDM muscle (29).
This study is relevant because the findings increase our knowledge of whole-blood gene expression in JDM at various time points with treatment. Additionally, it demonstrates how gene expression in children with a notable treatment response differs from children with minimal to no response. Furthermore, our results show that, with standard therapies, gene transcription can be affected early in treatment, and that treatment prior to acquiring the first RNA sample may have a small effect on genes that are abnormally expressed in JDM (Supplementary Table 5).