An Easy and Reliable Strategy for Making Type I Interferon Signature Analysis Comparable among Research Centers

• Published in: Diagnostics (Basel) Vol. 9; no. 3; p. 113
• Main Authors: Pin, Alessia, Monasta, Lorenzo, Taddio, Andrea, Piscianz, Elisa, Tommasini, Alberto, Tesser, Alessandra
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 04.09.2019; MDPI
• Subjects: Bioinformatics; biostatistics; Bowel disease; Clinical medicine; Cytokines; data sharing; Datasets; Gene expression; inter-laboratory variability; interferon signature score; interferonopathies; Laboratories; Lupus; Patients; Principal components analysis; Real time; Research centers; Sample size; Software; systemic lupus erythematosus; Values; United States > US; Switzerland; biostatistics; interferon signature score; interferonopathies; systemic lupus erythematosus; inter-laboratory variability; data sharing
• ISSN: 2075-4418; 2075-4418
• DOI: 10.3390/diagnostics9030113

Interferon-stimulated genes (ISGs) are a set of genes whose transcription is induced by interferon (IFN). The measure of the expression of ISGs enables calculating an IFN score, which gives an indirect estimate of the exposition of cells to IFN-mediated inflammation. The measure of the IFN score is proposed for the screening of monogenic interferonopathies, like the Aicardi-Goutières syndrome, or to stratify subjects with systemic lupus erythematosus to receive IFN-targeted treatments. Apart from these scenarios, there is no agreement on the diagnostic value of the score in distinguishing IFN-related disorders from diseases dominated by other types of cytokines. Since the IFN score is currently measured in several research hospitals, merging experiences could help define the potential of scoring IFN inflammation in clinical practice. However, the IFN score calculated at different laboratories may be hardly comparable due to the distinct sets of IFN-stimulated genes assessed and to different controls used for data normalization. We developed a reliable approach to minimize the inter-laboratory variability, thereby providing shared strategies for the IFN signature analysis and allowing different centers to compare data and merge their experiences.