Potential biomarker identification by RNA-seq analysis in Antibiotic-related Drug Reaction with Sosinophilia and Systemic Symptoms (DRESS): a Pilot Study
Potential biomarker identification by RNA-seq analysis in Antibiotic-related Drug Reaction with Sosinophilia and Systemic Symptoms (DRESS): a Pilot Study
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 to 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 (PBMCs) from a ‘discovery’ cohort (n = 5) challenged to causative antibiotic or control were analysed for transcriptomic profile. A panel of genes was then tested in a validation cohort (n = 6) and compared to 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 six 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.
Teo, Ying Xin
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Haw, Wei Yann
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Vallejo, Andreas
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Mcguire, Carolann
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Woo, Jeongmin
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Friedman, Peter Simon
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Ardern-Jones, Michael
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Polak, Marta Ewa
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15 June 2022
Teo, Ying Xin
a9478dfb-fcbd-4e68-b5ee-00a5bfd564b4
Haw, Wei Yann
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Vallejo, Andreas
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Mcguire, Carolann
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Woo, Jeongmin
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Friedman, Peter Simon
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Ardern-Jones, Michael
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Polak, Marta Ewa
bf81bccc-6e22-4182-beed-ccf2fdbc9f8c
Teo, Ying Xin, Haw, Wei Yann, Vallejo, Andreas, Mcguire, Carolann, Woo, Jeongmin, Friedman, Peter Simon, Ardern-Jones, Michael and Polak, Marta Ewa
(2022)
Potential biomarker identification by RNA-seq analysis in Antibiotic-related Drug Reaction with Sosinophilia and Systemic Symptoms (DRESS): a Pilot Study.
Toxicological Sciences.
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 to 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 (PBMCs) from a ‘discovery’ cohort (n = 5) challenged to causative antibiotic or control were analysed for transcriptomic profile. A panel of genes was then tested in a validation cohort (n = 6) and compared to 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 six 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.
Text
Tox Sci DRESS biomarker_corrections_clean
- Accepted Manuscript
Text
kfac062 (2)
- Accepted Manuscript
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Published date: 15 June 2022
Identifiers
Local EPrints ID: 467552
URI: http://eprints.soton.ac.uk/id/eprint/467552
ISSN: 1096-6080
PURE UUID: fe5306d6-2563-4739-ac88-6c5916f855b4
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Date deposited: 13 Jul 2022 16:52
Last modified: 17 Mar 2024 03:10
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Contributors
Author:
Ying Xin Teo
Author:
Wei Yann Haw
Author:
Andreas Vallejo
Author:
Carolann Mcguire
Author:
Jeongmin Woo
Author:
Peter Simon Friedman
Author:
Marta Ewa Polak
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