RAYNAUD PHENOMENON, SCLERODERMA, OVERLAP SYNDROMES AND OTHER FIBROSING SYNDROMES: Edited by John VargaNovel classifications for systemic sclerosis: challenging historical subsets to unlock new doorsSobanski, Vincenta,b,c,d,e; Lescoat, Alainf,g; Launay, Davida,b,cAuthor Information aUniv. Lille, U1286 – Infinite – Institute for Translational Research in Inflammation bInserm, U1286 cCHU 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) dCHU Lille, INCLUDE (INtegration Center of the Lille University Hospital for Data Exploration), Lille eInstitut Universitaire de France (IUF) fDepartment of Internal Medicine and Clinical Immunology, CHU Rennes, Univ. Rennes gUniv. Rennes, CHU Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) – UMR_S 1085, Rennes, France Correspondence to Prof. Vincent Sobanski, CHU Lille, 2 Avenue Oscar Lambret, F-59000 Lille, France. Tel: +33 3 20 44 54 79; fax: +33 3 20 44 54 59; e-mail: [email protected]univ-lille.fr Current Opinion in Rheumatology: November 2020 - Volume 32 - Issue 6 - p 463-471 doi: 10.1097/BOR.0000000000000747 Buy Metrics 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. Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved.