ObjectiveSystemic sclerosis (SSc) patients are classified according to degree of skin fibrosis (limited and diffuse cutaneous [lc and dc]) and serum autoantibodies. We undertook the present multicenter study to determine whether intrinsic subset (IS) classification based upon skin gene expression yields additional valuable clinical information.MethodsSSc patients and healthy participants (HPs) were classified into Normal‐like, Limited, Fibroproliferative, and Inflammatory ISs using a previously trained classifier. Clinical data were obtained (serum autoantibodies, pulmonary function testing, modified Rodnan skin thickness scores [mRSS], and high‐resolution chest computed tomography [HRCT]). Statistical analyses were performed to compare patients classified by IS, traditional cutaneous classification, and serum autoantibodies.ResultsA total of 223 participants (165 SSc [115 dcSSc and 50 lcSSc] and 58 HPs) were classified. Inflammatory IS patients had higher mRSS (22.1 ± 9.9; P < 0.001) than other ISs and dcSSc patients (19.4 ± 9.4; P = 0.05) despite similar disease duration (median [interquartile range] months 14.9 [19.9] vs. 18.4 [31.6]; P = 0.48). In multivariable modeling, no significant association between mRSS and RNA polymerase III (P = 0.07) or anti–topoisomerase I (Scl‐70) (P = 0.09) was found. Radiographic interstitial lung disease (ILD) was more prevalent in Fibroproliferative IS compared with other ISs (91%; P = 0.04) with similar prevalence between lcSSc and dcSSc (67% vs. 76%; P = 0.73). Positive Scl‐70 antibody was the strongest ILD predictor (P < 0.001). Interestingly, all lcSSc/Fibroproliferative patients demonstrated radiographic ILD.ConclusionsClassification by IS identifies patients with distinct clinical phenotypes versus traditional cutaneous or autoantibody classification. IS classification identifies subgroups of SSc patients with more radiographic ILD (Fibroproliferative), higher mRSS (Inflammatory), and milder phenotype (Normal‐like) and may provide additional clinically useful information to current SSc classification systems.