Data from: Immunotherapy-related adverse events (irAEs): extraction from FDA drug labels and comparative analysis
Wang, Quanqiu; Xu, Rong (2019), Data from: Immunotherapy-related adverse events (irAEs): extraction from FDA drug labels and comparative analysis, Dryad, Dataset, https://doi.org/10.5061/dryad.fg6kt38
Objectives: Immune checkpoint inhibitors (ICIs) have dramatically improved outcomes in cancer patients. However, ICIs are associated with significant immune-related adverse events (irAEs) and the underlying biological mechanisms are not well-understood. To ensure safe cancer treatment, research efforts are needed to comprehensively detect and understand irAEs. Materials and Methods: We manually extracted and standardized irAEs from FDA drug labels for six FDA-approved ICIs. We compared irAE profile similarities among ICIs and 1,507 FDA-approved non-ICI drugs. We investigated how irAEs have differential effects on human organs by classifying irAEs based on their targeted organ systems. Finally, we identified broad-spectrum (non-target specific) and narrow-spectrum (target-specific) irAEs. Results: A total of 893 irAEs were extracted. 31.4% irAEs were shared among ICIs as compared to the 8.0% between ICIs and non-ICI drugs. irAEs were resulted from both on- and off-target effects: irAE profiles were more similar for ICIs with same target than different targets, demonstrating the on-target effects; irAE profile similarity for ICIs with the same target is less than 50%, demonstrating unknown off-target effects. ICIs significantly target many organ systems, including endocrine system (3.4-fold enrichment), metabolism (3.7-fold enrichment), immune system (3.6-fold enrichment) and autoimmune system (4.8-fold enrichment). We identified 21 broad-spectrum irAEs shared among all ICIs, 20 irAEs specific for PD-L1/PD-1 inhibition, and 28 irAEs specific for CTLA-4 inhibition. Discussion and Conclusion: Our study presents the first effort toward building a standardized database of irAEs. The extracted irAEs can serve as a goldstandard for automatic irAE extractions from other data resources and set a foundation to understand biological mechanisms of irAEs.