ORCID Profile
0000-0002-1933-6415
Current Organisations
Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico
,
Università degli Studi di Milano
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Publisher: Springer Science and Business Media LLC
Date: 14-07-2020
DOI: 10.1038/S41598-020-68468-3
Abstract: One of the most challenging objective for clinical cytometry in prospective multicenter immunomonitoring trials is to compare frequencies, absolute numbers of leukocyte populations and further the mean fluorescence intensities of cell markers, especially when the data are generated from different instruments. Here, we describe an innovative standardization workflow to compare all data to carry out any large-scale, prospective multicentric flow cytometry analysis whatever the duration, the number or type of instruments required for the realization of such projects.
Publisher: BMJ
Date: 10-2020
DOI: 10.1136/ANNRHEUMDIS-2020-218558
Abstract: Genomic Risk Scores (GRS) successfully demonstrated the ability of genetics to identify those in iduals at high risk for complex traits including immune-mediated inflammatory diseases (IMIDs). We aimed to test the performance of GRS in the prediction of risk for systemic sclerosis (SSc) for the first time. Allelic effects were obtained from the largest SSc Genome-Wide Association Study (GWAS) to date (9 095 SSc and 17 584 healthy controls with European ancestry). The best-fitting GRS was identified under the additive model in an independent cohort that comprised 400 patients with SSc and 571 controls. Additionally, GRS for clinical subtypes (limited cutaneous SSc and diffuse cutaneous SSc) and serological subtypes (anti-topoisomerase positive (ATA+) and anti-centromere positive (ACA+)) were generated. We combined the estimated GRS with demographic and immunological parameters in a multivariate generalised linear model. The best-fitting SSc GRS included 33 single nucleotide polymorphisms (SNPs) and discriminated between patients with SSc and controls (area under the receiver operating characteristic (ROC) curve (AUC)=0.673). Moreover, the GRS differentiated between SSc and other IMIDs, such as rheumatoid arthritis and Sjögren’s syndrome. Finally, the combination of GRS with age and immune cell counts significantly increased the performance of the model (AUC=0.787). While the SSc GRS was not able to discriminate between ATA+ and ACA+ patients (AUC .5), the serological subtype GRS, which was based on the allelic effects observed for the comparison between ACA+ and ATA+ patients, reached an AUC=0.693. GRS was successfully implemented in SSc. The model discriminated between patients with SSc and controls or other IMIDs, confirming the potential of GRS to support early and differential diagnosis for SSc.
Publisher: BMJ
Date: 19-06-2020
DOI: 10.1136/ANNRHEUMDIS-2020-217116
Abstract: The analysis of annotated transcripts from genome-wide expression studies may help to understand the pathogenesis of complex diseases, such as systemic sclerosis (SSc). We performed a whole blood (WB) transcriptome analysis on RNA collected in the context of the European PRECISESADS project, aiming at characterising the pathways that differentiate SSc from controls and that are reproducible in geographically erse populations. S les from 162 patients and 252 controls were collected in RNA stabilisers. Cases and controls were ided into a discovery (n=79+163 Southern Europe) and validation cohort (n=83+89 Central-Western Europe). RNA sequencing was performed by an Illumina assay. Functional annotations of Reactome pathways were performed with the Functional Analysis of In idual Microarray Expression (FAIME) algorithm. In parallel, immunophenotyping of 28 circulating cell populations was performed. We tested the presence of differentially expressed genes athways and the correlation between absolute cell counts and RNA transcripts/FAIME scores in regression models. Results significant in both populations were considered as replicated. Overall, 15 224 genes and 1277 functional pathways were available of these, 99 and 225 were significant in both sets. Among replicated pathways, we found a deregulation in type-I interferon, Toll-like receptor cascade, tumour suppressor p53 protein function, platelet degranulation and activation. RNA transcripts or FAIME scores were jointly correlated with cell subtypes with strong geographical differences neutrophils were the major determinant of gene expression in SSc-WB s les. We discovered a set of differentially expressed genes athways validated in two independent sets of patients with SSc, highlighting a number of deregulated processes that have relevance for the pathogenesis of autoimmunity and SSc.
Publisher: Wiley
Date: 26-04-2021
DOI: 10.1002/ART.41610
Abstract: 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. 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. 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 in idual patient. 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.
Location: Italy
No related grants have been discovered for Elena Trombetta.