ABSTRACT
OBJECTIVE: Clinical heterogeneity, a hallmark of systemic autoimmune diseases, impedes early diagnosis and effective treatment, issues that may be addressed if patients could be classified into groups defined by molecular pattern. This study was undertaken to identify molecular clusters for reclassifying systemic autoimmune diseases independently of clinical diagnosis. METHODS: Unsupervised clustering of integrated whole blood transcriptome and methylome cross-sectional data on 955 patients with 7 systemic autoimmune diseases and 267 healthy controls was undertaken. In addition, an inception cohort was prospectively followed up for 6 or 14 months to validate the results and analyze whether or not cluster assignment changed over time. RESULTS: Four clusters were identified and validated. Three were pathologic, representing "inflammatory," "lymphoid," and "interferon" patterns. Each included all diagnoses and was defined by genetic, clinical, serologic, and cellular features. A fourth cluster with no specific molecular pattern was associated with low disease activity and included healthy controls. A longitudinal and independent inception cohort showed a relapse-remission pattern, where patients remained in their pathologic cluster, moving only to the healthy one, thus showing that the molecular clusters remained stable over time and that single pathogenic molecular signatures characterized each individual patient. CONCLUSION: Patients with systemic autoimmune diseases can be jointly stratified into 3 stable disease clusters with specific molecular patterns differentiating different molecular disease mechanisms. These results have important implications for future clinical trials and the study of nonresponse to therapy, marking a paradigm shift in our view of systemic autoimmune diseases.
Subject(s)
Autoimmune Diseases/classification , Autoimmune Diseases/genetics , Epigenome , Gene Expression Profiling , Adult , Aged , Antiphospholipid Syndrome/genetics , Antiphospholipid Syndrome/immunology , Arthritis, Rheumatoid/genetics , Arthritis, Rheumatoid/immunology , Autoimmune Diseases/immunology , Case-Control Studies , Cluster Analysis , Cross-Sectional Studies , Epigenomics , Female , Humans , Inflammation/immunology , Interferons/immunology , Lupus Erythematosus, Systemic/genetics , Lupus Erythematosus, Systemic/immunology , Male , Middle Aged , Mixed Connective Tissue Disease/genetics , Mixed Connective Tissue Disease/immunology , Scleroderma, Systemic/genetics , Scleroderma, Systemic/immunology , Sjogren's Syndrome/genetics , Sjogren's Syndrome/immunology , Undifferentiated Connective Tissue Diseases/genetics , Undifferentiated Connective Tissue Diseases/immunologyABSTRACT
BACKGROUND: Longer pre-centrifugation times alter the quality of serum and plasma samples. Markers for such delays in sample processing and hence for the sample quality, have been identified. METHODS: Twenty cytokines in serum, EDTA plasma and citrate plasma samples were screened for changes in concentration induced by extended blood pre-centrifugation delays at room temperature. The two cytokines that showed the largest changes were further validated for their "diagnostic performance" in identifying serum or plasma samples with extended pre-centrifugation times. RESULTS: In this study, using R&D Systems ELISA kits, EDTA plasma samples and serum samples with a pre-centrifugation delay longer than 24 h had an IL16 concentration higher than 313 pg/mL, and an IL8 concentration higher than 125 pg/mL, respectively. EDTA plasma samples with a pre-centrifugation delay longer than 48 h had an IL16 concentration higher than 897 pg/mL, citrate plasma samples had an IL8 concentration higher than 21.5 pg/mL and serum samples had an IL8 concentration higher than 528 pg/mL. CONCLUSIONS: These robust and accurate tools, based on simple and commercially available ELISA assays can greatly facilitate qualification of serum and plasma legacy collections with undocumented pre-analytics.
