Vincent Sobanski1,2,3,4,5, Alain Lescoat6,7, David Launay1,2,3. 1. Univ. Lille, U1286 - Infinite - Institute for Translational Research in Inflammation. 2. Inserm, U1286. 3. CHU Lille, Département de Médecine Interne et Immunologie Clinique, Centre de Référence des Maladies Auto-immunes Systémiques Rares du Nord et Nord-Ouest de France (CeRAINO). 4. CHU Lille, INCLUDE (INtegration Center of the Lille University Hospital for Data Exploration), Lille. 5. Institut Universitaire de France (IUF). 6. Department of Internal Medicine and Clinical Immunology, CHU Rennes, Univ. Rennes. 7. Univ. Rennes, CHU Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) - UMR_S 1085, Rennes, France.
Abstract
PURPOSE OF REVIEW: Systemic sclerosis (SSc) is a severe rheumatic disease characterized by a considerable heterogeneity in clinical presentations and pathophysiological mechanisms. This variability has a substantial impact on morbidity and mortality and limits the generalizability of clinical trial results. This review aims to highlight recent studies that have proposed new innovative approaches to decipher this heterogeneity, in particular, by attempting to optimize disease classification. RECENT FINDINGS: The historical dichotomy limited/diffuse subsets based on cutaneous involvement has been challenged by studies highlighting an underestimated heterogeneity between these two subtypes and showing that presence of organ damage and autoantibody profiles markedly influenced survival beyond skin extension. Advanced computational methods using unsupervised machine learning analyses of clinical variables and/or high-throughput omics technologies, clinical variables trajectories modelling overtime or radiomics have provided significant insights on key pathogenic processes that could help defining new subgroups beyond the diffuse/limited subsets. SUMMARY: We can anticipate that a future classification of SSc patients will integrate innovative approaches encompassing clinical phenotypes, variables trajectories, serological features and innovative omics molecular signatures. It nevertheless seems crucial to also pursue the implementation and standardization of readily available and easy to use tools that can be used in clinical practice.
PURPOSE OF REVIEW: Systemic sclerosis (SSc) is a severe rheumatic disease characterized by a considerable heterogeneity in clinical presentations and pathophysiological mechanisms. This variability has a substantial impact on morbidity and mortality and limits the generalizability of clinical trial results. This review aims to highlight recent studies that have proposed new innovative approaches to decipher this heterogeneity, in particular, by attempting to optimize disease classification. RECENT FINDINGS: The historical dichotomy limited/diffuse subsets based on cutaneous involvement has been challenged by studies highlighting an underestimated heterogeneity between these two subtypes and showing that presence of organ damage and autoantibody profiles markedly influenced survival beyond skin extension. Advanced computational methods using unsupervised machine learning analyses of clinical variables and/or high-throughput omics technologies, clinical variables trajectories modelling overtime or radiomics have provided significant insights on key pathogenic processes that could help defining new subgroups beyond the diffuse/limited subsets. SUMMARY: We can anticipate that a future classification of SSc patients will integrate innovative approaches encompassing clinical phenotypes, variables trajectories, serological features and innovative omics molecular signatures. It nevertheless seems crucial to also pursue the implementation and standardization of readily available and easy to use tools that can be used in clinical practice.
Authors: Alain Lescoat; Susan L Murphy; Yen T Chen; Nadia Vann; Francesco Del Galdo; David Cella; Maya H Buch; Dinesh Khanna Journal: Semin Arthritis Rheum Date: 2021-11-07 Impact factor: 5.431