Multi-omics prediction of immune-related adverse events during checkpoint immunotherapy
Menée à partir de données du projet "The Cancer Genome Atlas" portant sur 26 types de cancer et à partir des données de l'"US Food and Drug Administration Adverse Event Reporting System" portant sur des anti-PD-1 (nivolumab, pembrolizumab, cemiplimab) et des anti-PD-L1 (atézolizumab, avélumab, durvalumab), cette étude identifie des biomarqueurs permettant de prédire le risque d'événements immunitaires indésirables durant un traitement par inhibiteurs des points de contrôle PD-1 et PD-L1
Immune-related adverse events (irAEs), caused by anti-PD-1/PD-L1 antibodies, can lead to fulminant and even fatal consequences and thus require early detection and aggressive management. However, a comprehensive approach to identify biomarkers of irAE is lacking. Here, we utilize a strategy that combines pharmacovigilance data and omics data, and evaluate associations between multi-omics factors and irAE reporting odds ratio across different cancer types. We identify a bivariate regression model of LCP1 and ADPGK that can accurately predict irAE. We further validate LCP1 and ADPGK as biomarkers in an independent patient-level cohort. Our approach provides a method for identifying potential biomarkers of irAE in cancer immunotherapy using both pharmacovigilance data and multi-omics data.
Nature Communications , article en libre accès, 2020