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

DM and clinically amyopathic DM (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.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 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.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 Cutaneous DM Disease Area and Severity Index-Activity [38.6 (10.4) vs 25.3 (10.0), P = 0.003]. Patients within cluster 2 exhibited significant muscle weakness (Medical Research Council scale ≤ 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.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.