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Diagnostic profiles for precision medicine in systemic sclerosis; stepping forward from single biomarkers towards pathophysiological panels

Authors :
Wynand Alkema
Madelon C. Vonk
Ruben L. Smeets
Brigit E. Kersten
Hans J. P. M. Koenen
Irma Joosten
Charlotte Kaffa
Data Sciences for Life Science & Health
Source :
Autoimmunity Reviews, 19, Autoimmunity reviews, 19(5):102515. Elsevier, Autoimmunity Reviews, 19, 5
Publication Year :
2020

Abstract

Systemic sclerosis (SSc) is an autoimmune disease which is characterized by vasculopathy, tissue fibrosis and activation of the innate and adaptive immune system. Clinical features of the disease consists of skin thickening and internal organ involvement. Due to the heterogeneous nature of the disease it is difficult to predict disease progression and complications. Despite the discovery of novel autoantibodies associated with SSc, there is an unmet need for biomarkers for diagnosis, disease progression and response to treatment. To date, the use of single (surrogate) biomarkers for these purposes has been unsuccessful. Combining multiple biomarkers in to predictive panels or ultimately algorithms could be more precise. Given the limited therapeutic options and poor prognosis of many SSc patients, a better understanding of the immune-pathofysiological profiles might aid to an adjusted therapeutic approach. Therefore, we set out to explore immunological fingerprints in various clinically defined forms of SSc. We used multilayer profiling to identify unique immune profiles underlying distinct autoantibody signatures. These immune profiles could fill the unmet need for prognosis and response to therapy in SSc. Here, we present 3 pathophysiological fingerprints in SSc based on the expression of circulating antibodies, vascular markers and immunomodulatory mediators.

Details

ISSN :
15689972
Database :
OpenAIRE
Journal :
Autoimmunity Reviews, 19, Autoimmunity reviews, 19(5):102515. Elsevier, Autoimmunity Reviews, 19, 5
Accession number :
edsair.doi.dedup.....b2c277776e1e3390ae71e6d0d70caa8d