Potential Biomarker Identification by RNA-Seq Analysis in Antibiotic-Related Drug Reaction with Eosinophilia and Systemic Symptoms (DRESS): A Pilot Study. (15th June 2022)

• Record Type: Journal Article
• Authors: Teo, Ying Xin; Haw, Wei Yann; Vallejo, Andreas; McGuire, Carolann; Woo, Jeongmin; Friedmann, Peter Simon; Polak, Marta Ewa; Ardern-Jones, Michael Roger
• Is Part Of: Toxicological sciences. Volume 189:Number 1(2022)
• Page Start: 20
• Page End: 31
• Publication Date: 2022-06-15
• Subjects: biomarker -- diagnostics -- drug allergy -- drug reaction with eosinophilia and systemic symptoms (DRESS) -- immunology; Toxicology -- Periodicals; Toxicology -- Periodicals; Toxicology; Periodicals; 615.9
• DOI: 10.1093/toxsci/kfac062
• Languages: English
• ISSNs: 1096-6080

Abstract

Abstract: One of the most severe forms of cutaneous adverse drug reactions is "drug reaction with eosinophilia and systemic symptoms" (DRESS), hence subsequent avoidance of the causal drug is imperative. However, attribution of drug culpability in DRESS is challenging and standard skin allergy tests are not recommended due to patient safety reasons. Whilst incidence of DRESS is relatively low, between 1:1000 and 1:10 000 drug exposures, antibiotics are a commoner cause of DRESS and absence of confirmatory diagnostic test can result in unnecessary avoidance of efficacious treatment. We therefore sought to identify potential biomarkers for development of a diagnostic test in antibiotic-associated DRESS. Peripheral blood mononuclear cells from a "discovery" cohort ( n = 5) challenged to causative antibiotic or control were analyzed for transcriptomic profile. A panel of genes was then tested in a validation cohort ( n = 6) and compared with tolerant controls and other inflammatory conditions which can clinically mimic DRESS. A scoring system to identify presence of drug hypersensitivity was developed based on gene expression alterations of this panel. The DRESS transcriptomic panel identified antibiotic-DRESS cases in a validation cohort but was not altered in other inflammatory conditions. Machine learning or differential expression selection of a biomarker panel consisting of 6 genes (STAC, GPR183, CD40, CISH, CD4, and CCL8) showed high sensitivity and specificity (100% and 85.7%–100%, respectively) for identification of the culprit drug in these cohorts of antibiotic-associated DRESS. Further work is required to determine whether the same panel can be repeated for larger cohorts, different medications, and other T-cell-mediated drug hypersensitivity reactions.