Integrative Analysis Reveals a Molecular Stratification of Systemic Autoimmune Diseases

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

O31 Integrative analysis reveals a molecular stratification of systemic autoimmune diseases

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KOMET: an unblinded, randomised, two parallel-group, stratified trial comparing the effectiveness of levetiracetam with controlled-release carbamazepine and extended-release sodium valproate as monotherapy in patients with newly diagnosed epilepsy

OBJECTIVE: To compare the effectiveness of levetiracetam (LEV) with extended-release sodium valproate (VPA-ER) and controlled-release carbamazepine (CBZ-CR) as monotherapy in patients with newly diagnosed epilepsy. METHODS: This unblinded, randomised, 52-week superiority trial (NCT00175903) recruited patients (≥16 years of age) with ≥2 unprovoked seizures in the previous 2 years and ≥1 in the previous 6 months. The physician chose VPA or CBZ as preferred standard treatment; each patient was randomised to standard treatment or LEV. The primary outcome was time to treatment withdrawal (LEV vs standard antiepileptic drugs (AEDs)). Analyses also compared LEV with VPA-ER, and LEV with CBZ-CR. FINDINGS: 1688 patients (mean age 41 years; 44% female) were randomised to LEV (n=841) or standard AEDs (n=847). Time to treatment withdrawal was not significantly different between LEV and standard AEDs: HR (95% CI) 0.90 (0.74 to 1.08). Time to treatment withdrawal (HR (95% CI)) was 1.02 (0.74 to 1.41) for LEV/VPA-ER and 0.84 (0.66 to 1.07) for LEV/CBZ-CR. Time to first seizure (HR, 95% CI) was significantly longer for standard AEDs, 1.20 (1.03 to 1.39), being 1.19 (0.93 to 1.54) for LEV/VPA-ER and 1.20 (0.99 to 1.46) for LEV/CBZ-CR. Estimated 12-month seizure freedom rates from randomisation: 58.7% LEV versus 64.5% VPA-ER; 50.5% LEV versus 56.7% CBZ-CR. Similar proportions of patients within each stratum reported at least one adverse event: 66.1% LEV versus 62.0% VPA-ER; 73.4% LEV versus 72.5% CBZ-CR. CONCLUSIONS: LEV monotherapy was not superior to standard AEDs for the global outcome, namely time to treatment withdrawal, in patients with newly diagnosed focal or generalised seizures.status: publishe

KOMET: an unblinded, randomised, two parallel-group, stratified trial comparing the effectiveness of levetiracetam with controlled-release carbamazepine and extended-release sodium valproate as monotherapy in patients with newly diagnosed epilepsy

Objective To compare the effectiveness of levetiracetam (LEV) with extended-release sodium valproate (VPA-ER) and controlled-release carbamazepine (CBZ-CR) as monotherapy in patients with newly diagnosed epilepsy. Methods This unblinded, randomised, 52-week superiority trial (NCT00175903) recruited patients (≥16 years of age) with ≥2 unprovoked seizures in the previous 2 years and ≥1 in the previous 6 months. The physician chose VPA or CBZ as preferred standard treatment; each patient was randomised to standard treatment or LEV. The primary outcome was time to treatment withdrawal (LEV vs standard antiepileptic drugs (AEDs)). Analyses also compared LEV with VPA-ER, and LEV with CBZ-CR. Findings 1688 patients (mean age 41 years; 44% female) were randomised to LEV (n=841) or standard AEDs (n=847). Time to treatment withdrawal was not significantly different between LEV and standard AEDs: HR (95% CI) 0.90 (0.74 to 1.08). Time to treatment withdrawal (HR (95% CI)) was 1.02 (0.74 to 1.41) for LEV/VPA-ER and 0.84 (0.66 to 1.07) for LEV/CBZ-CR. Time to first seizure (HR, 95% CI) was significantly longer for standard AEDs, 1.20 (1.03 to 1.39), being 1.19 (0.93 to 1.54) for LEV/VPA-ER and 1.20 (0.99 to 1.46) for LEV/CBZ-CR. Estimated 12-month seizure freedom rates from randomisation: 58.7% LEV versus 64.5% VPA-ER; 50.5% LEV versus 56.7% CBZ-CR. Similar proportions of patients within each stratum reported at least one adverse event: 66.1% LEV versus 62.0% VPA-ER; 73.4% LEV versus 72.5% CBZ-CR. Conclusions LEV monotherapy was not superior to standard AEDs for the global outcome, namely time to treatment withdrawal, in patients with newly diagnosed focal or generalised seizures

Transcriptome and Methylome Integrative Molecular Analysis Uncovers a New Systemic Autoimmune Disease Classification

International audienc

Transcriptome and Methylome Integrative Molecular Analysis Uncovers a New Systemic Autoimmune Disease Classification

International audienc

Machine learning identifies a common signature for anti-SSA/Ro60 antibody expression across autoimmune diseases.

International audienceAnti-Ro autoantibodies are among the most frequently detected extractable nuclear antigen autoantibodies, mainly associated with primary Sjögren's syndrome (pSS), systemic lupus erythematosus (SLE) and undifferentiated connective tissue disease (UCTD). Is there a common signature to all patients expressing anti-Ro60 autoantibodies regardless of their disease phenotype

A new molecular classification to drive precision treatment strategies in primary Sjogren's syndrome

There is currently no approved treatment for primary Sjogren's syndrome, a disease that primarily affects adult women. The difficulty in developing effective therapies is -in part- because of the heterogeneity in the clinical manifestation and pathophysiology of the disease. Finding common molecular signatures among patient subgroups could improve our understanding of disease etiology, and facilitate the development of targeted therapeutics. Here, we report, in a cross-sectional cohort, a molecular classification scheme for Sjogren's syndrome patients based on the multi-omic profiling of whole blood samples from a European cohort of over 300 patients, and a similar number of age and gender-matched healthy volunteers. Using transcriptomic, genomic, epigenetic, cytokine expression and flow cytometry data, combined with clinical parameters, we identify four groups of patients with distinct patterns of immune dysregulation. The biomarkers we identify can be used by machine learning classifiers to sort future patients into subgroups, allowing the re-evaluation of response to treatments in clinical trials. Sjogren's syndrome, a disease that primarily affects women, is poorly understood. Here, the authors combine data from a large cohort of patients and healthy controls to identify biomarkers that distinguish patient subgroups to improve our understanding of the disease and facilitate drug development