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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., (© 2020, American College of Rheumatology.)

Background: Patients infected with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), responsible for the coronavirus disease 2019 (COVID-19), exhibit a wide spectrum of disease behaviour. Since DNA methylation has been implicated in the regulation of viral infections and the immune system, we performed an epigenome-wide association study (EWAS) to identify candidate loci regulated by this epigenetic mark that could be involved in the onset of COVID-19 in patients without comorbidities., Methods: Peripheral blood samples were obtained from 407 confirmed COVID-19 patients ≤ 61 years of age and without comorbidities, 194 (47.7%) of whom had mild symptomatology that did not involve hospitalization and 213 (52.3%) had a severe clinical course that required respiratory support. The set of cases was divided into discovery (n = 207) and validation (n = 200) cohorts, balanced for age and sex of individuals. We analysed the DNA methylation status of 850,000 CpG sites in these patients., Findings: The DNA methylation status of 44 CpG sites was associated with the clinical severity of COVID-19. Of these loci, 23 (52.3%) were located in 20 annotated coding genes. These genes, such as the inflammasome component Absent in Melanoma 2 (AIM2) and the Major Histocompatibility Complex, class I C (HLA-C) candidates, were mainly involved in the response of interferon to viral infection. We used the EWAS-identified sites to establish a DNA methylation signature (EPICOVID) that is associated with the severity of the disease., Interpretation: We identified DNA methylation sites as epigenetic susceptibility loci for respiratory failure in COVID-19 patients. These candidate biomarkers, combined with other clinical, cellular and genetic factors, could be useful in the clinical stratification and management of patients infected with the SARS-CoV-2., Funding: The Unstoppable campaign of the Josep Carreras Leukaemia Foundation, the Cellex Foundation and the CERCA Programme/Generalitat de Catalunya., Competing Interests: Declaration of Competing Interest Dr. Esteller reports grants from Ferrer International, personal fees from Quimatryx, outside the submitted work. The other authors declare no conflicts of interest., (Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.)

One caveat in cancer research is the dependence of certain experimental systems that might not really reflect the properties of the primary tumours. The recent irruption of 3D cultured cells termed organoids could render a better representation of the original tumour sample. However, every laboratory has its own protocol and tissue-provider to establish these cancer models, preventing further dissemination and validation of the obtained data. To address this problem, the Human Cancer Models Initiative (HCMI) has selected the American Type Culture Collection (ATCC) to make available organoid models to the scientific community. In this regard, no epigenetic information is available for these samples and, overall, the DNA methylation profiles of human cancer organoids are largely unknown. Herein, we provide the DNA methylation landscape of 25 human cancer organoids available at the ATCC using a microarray that interrogates more than 850,000 CpG sites. We observed that the studied organoids retain the epigenetic setting of their original primary cancer type; that exhibit a DNA methylation landscape characteristic of transformed tissues excluding an overgrowth of normal-matched cells; and that are closer to the DNA methylation profiles of the corresponding primary tumours than to established 2D cell lines. Most importantly, the obtained DNA methylation results are freely available to everyone for further data mining. Thus, our findings support from the epigenetic standpoint that the ATCC human cancer organoids recapitulate many of the features of the disorder in the patient and are excellent tools to be shared among investigators for further tumour biology research.