A four‐gene biomarker predicts skin disease in patients with diffuse cutaneous systemic sclerosis

G Farina, D Lafyatis, R Lemaire… - Arthritis & Rheumatism …, 2010 - Wiley Online Library
G Farina, D Lafyatis, R Lemaire, R Lafyatis
Arthritis & Rheumatism: Official Journal of the American College …, 2010Wiley Online Library
Objective Improved outcome measures in systemic sclerosis (SSc) are critical to finding
active therapeutics for this disease. The modified Rodnan skin thickness score (MRSS) is
the current standard for evaluating skin disease in SSc, but it is not commonly used in the
clinical setting, in part because it requires specialized training to perform accurately and
consistently. The purpose of this study was to investigate whether skin gene expression
might serve as a more objective surrogate outcome measure to supplement skin score …
Objective
Improved outcome measures in systemic sclerosis (SSc) are critical to finding active therapeutics for this disease. The modified Rodnan skin thickness score (MRSS) is the current standard for evaluating skin disease in SSc, but it is not commonly used in the clinical setting, in part because it requires specialized training to perform accurately and consistently. The purpose of this study was to investigate whether skin gene expression might serve as a more objective surrogate outcome measure to supplement skin score evaluations.
Methods
Skin RNAs from a group of patients with diffuse cutaneous SSc were studied for expression levels of genes known to be regulated by transforming growth factor β (TGFβ) and interferon (IFN). These levels were correlated with the MRSS, using multiple regression analyses to obtain best‐fit models.
Results
Skin expression of the TGFβ‐regulated genes cartilage oligomeric matrix protein (COMP) and thrombospondin 1 (TSP‐1) correlated moderately well with the MRSS, but the addition of other TGFβ‐regulated genes failed to significantly improve best‐fit models. IFN‐regulated genes were also found to correlate with the MRSS, and the addition of interferon‐inducible 44 (IFI44) and sialoadhesin (Siglec‐1) to COMP and TSP‐1 in multiple regression analyses significantly improved best‐fit models, achieving an R2 value of 0.89. These results were validated using an independent group of skin biopsy samples. Longitudinal scores using this 4‐gene biomarker indicated that it detects change over time that corresponds to changes in the MRSS.
Conclusion
We describe a 4‐gene predictor of the MRSS and validate its performance. This objective measure of skin disease could provide a strong surrogate outcome measure for patient care and for clinical trials.
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