Identification of distinct cytokine/chemokine profiles in dermatomyositis with anti-transcriptional intermediary factor 1-γ antibody.

OBJECTIVES Dermatomyositis (DM) and clinically amyopathic dermatomyositis (CADM) patients with positive expression of anti-transcription intermediary factor 1-γ (anti-TIF1-γ) antibody (Ab) are characterized by distinct clinicopathological features. We aimed to determine the role of cytokine/chemokine profiles in the classification of anti-TIF1-γ positive DM/CADM patients. METHODS Serum levels of 24 cytokines/chemokines were measured in 27 anti-TIF1-γ positive DM/CADM patients by a Luminex 200 system. Principal components analysis (PCA) and unsupervised hierarchical clustering were used to reduce variables and establish patient subgroups. Spearman's correlation coefficient was calculated between cytokine/chemokine levels and disease activity markers. RESULTS Among anti-TIF1-γ positive DM/CADM patients, two distinct patient clusters were identified. The diagnosis of CADM was more common in Cluster 1 than in Cluster 2 (58.3% vs 6.7%, p = 0.008). Skin disease activity was higher in Cluster 2 than in Cluster 1 as measured by CDASI-A (38.6 ± 10.4 vs 25.3 ± 10.0, p = 0.003). Patients within Cluster 2 exhibited significant muscle weakness (MRC ≤ 3, 33.3% vs 0.0%, p = 0.047), higher levels of anti-TIF1-γ Ab (92.4 ± 20.6 vs 66.9 ± 13.9, p = 0.001), and an increased malignancy rate (73.3% vs 25.0%, p = 0.021). Cluster 2 exhibited higher serum levels of CXCL10 (564.2 ± 258.8 vs 122.0 ± 97.8, p < 0.001), CCL2 (1136.6 ± 545.4 vs 441.6 ± 163.3, p < 0.001), Galectin-9 (38879.6 ± 20009.3 vs 12612.4 ± 6640.0, p < 0.001), IL-18 (436.1 ± 188.9 vs 243.0 ± 114.5, p = 0.003), TNF-α (9.3 ± 3.8 vs 5.6 ± 2.4, p = 0.007), and TNFRI (1385.1 ± 338.2 vs 2605.6 ± 928.5, p < 0.001) than Cluster 1. CONCLUSION In anti-TIF1-γ positive DM/CADM, we identified a "skin-predominant" cluster and a "hyperinflammation" cluster based on the cytokine/chemokine profiles. Cytokine/chemokine profiles in anti-TIF1-γ positive DM/CADM can identify discrete clusters of patients with different disease patterns, organ involvements, and clinical outcomes.