Machine-learning–driven biomarker discovery for the discrimination between allergic and irritant contact dermatitis

• Published in: Proceedings of the National Academy of Sciences - PNAS Vol. 117; no. 52; pp. 33474 - 33485
• Main Authors: Fortino, Vittorio, Wisgrill, Lukas, Werner, Paulina, Suomela, Sari, Linder, Nina, Jalonen, Erja, Suomalainen, Alina, Marwah, Veer, Kero, Mia, Pesonen, Maria, Lundin, Johan, Lauerma, Antti, Aalto-Korte, Kristiina, Greco, Dario, Alenius, Harri, Fyhrquist, Nanna
• Format: Journal Article
• Language: English
• Published: United States National Academy of Sciences 29-12-2020
• Subjects: Adult; Algorithms; Allergens; allergic contact dermatitis; artificial intelligence; Biological Sciences; biomarker; Biomarkers - metabolism; Databases, Genetic; Dermatitis, Allergic Contact - diagnosis; Dermatitis, Allergic Contact - genetics; Dermatitis, Irritant - diagnosis; Dermatitis, Irritant - genetics; Diagnosis, Differential; Female; Gene Expression Regulation; Gene Regulatory Networks; Humans; irritant contact dermatitis; Irritants; Leukocytes - metabolism; Machine Learning; Male; Medicin och hälsovetenskap; Patch Tests; Reproducibility of Results; Severity of Illness Index; Skin - pathology; Transcriptome - genetics; irritant contact dermatitis; biomarker; machine learning; allergic contact dermatitis; artificial intelligence
• ISSN: 0027-8424; 1091-6490; 1091-6490
• DOI: 10.1073/pnas.2009192117

Contact dermatitis tremendously impacts the quality of life of suffering patients. Currently, diagnostic regimes rely on allergy testing, exposure specification, and follow-up visits; however, distinguishing the clinical phenotype of irritant and allergic contact dermatitis remains challenging. Employing integrative transcriptomic analysis and machine-learning approaches, we aimed to decipher disease-related signature genes to find suitable sets of biomarkers. A total of 89 positive patch-test reaction biopsies against four contact allergens and two irritants were analyzed via microarray. Coexpression network analysis and Random Forest classification were used to discover potential biomarkers and selected biomarker models were validated in an independent patient group. Differential gene-expression analysis identified major gene-expression changes depending on the stimulus. Random Forest classification identified CD47, BATF, FASLG, RGS16, SYNPO, SELE, PTPN7, WARS, PRC1, EXO1, RRM2, PBK, RAD54L, KIFC1, SPC25, PKMYT, HISTH1A, TPX2, DLGAP5, TPX2, CH25H, and IL37 as potential biomarkers to distinguish allergic and irritant contact dermatitis in human skin. Validation experiments and prediction performances on external testing datasets demonstrated potential applicability of the identified biomarker models in the clinic. Capitalizing on this knowledge, novel diagnostic tools can be developed to guide clinical diagnosis of contact allergies.