Pan-Cancer Analysis Reveals Alternative Splicing Characteristics Associated With Immune-Related Adverse Events Elicited by Checkpoint Immunotherapy

Immune-related adverse events (irAEs) can impair the effectiveness and safety of immune checkpoint inhibitors (ICIs) and restrict the clinical applications of ICIs in oncology. The predictive biomarkers of irAE are urgently required for early diagnosis and subsequent management. The exact mechanism underlying irAEs remains to be fully elucidated, and the availability of predictive biomarkers is limited. Herein, we performed data mining by combining pharmacovigilance data and pan-cancer transcriptomic information to illustrate the relationships between alternative splicing characteristics and irAE risk of ICIs. Four distinct classes of splicing characteristics considered were associated with splicing factors, neoantigens, splicing isoforms, and splicing levels. Correlation analysis confirmed that expression levels of splicing factors were predictive of irAE risk. Adding DHX16 expression to the bivariate PD-L1 protein expression-fPD1 model markedly enhanced the prediction for irAE. Furthermore, we identified 668 and 1,131 potential predictors based on the correlation of the incidence of irAEs with splicing frequency and isoform expression, respectively. The functional analysis revealed that alternative splicing might contribute to irAE pathogenesis via coordinating innate and adaptive immunity. Remarkably, autoimmune-related genes and autoantigens were preferentially over-represented in these predictors for irAE, suggesting a close link between autoimmunity and irAE occurrence. In addition, we established a trivariate model composed of CDC42EP3-206, TMEM138-211, and IRX3-202, that could better predict the risk of irAE across various cancer types, indicating a potential application as promising biomarkers for irAE. Our study not only highlights the clinical relevance of alternative splicing for irAE development during checkpoint immunotherapy but also sheds new light on the mechanisms underlying irAEs.