Enhancer RNA-based modeling of adverse events and objective responses of immunotherapy

Abstract

Immune checkpoint inhibitors (ICI) targeting PD-1/PD-L1 or CTLA-4 are emerging and effective immunotherapy strategies. However, ICI treated patients present heterogeneous responses and adverse events, thus demanding effective ways to assess benefit over risk before treatment. Here, by integrating pan-cancer clinical and molecular data, we tried to predict immune-related adverse events (irAEs, risk) and objective response rates (ORRs, benefit) based on enhancer RNAs (eRNAs) expression among patients receiving anti-PD-1/PD-L1 therapy. We built two effective regression models, explaining 71% variance (R=0.84) of irAEs with three eRNAs and 79% (R=0.89) of ORRs with five eRNAs. Interestingly, target genes of irAE-related enhancers, including upstream regulators of MYC, were involved in metabolism, inflammation, and immune activation, while ORR-related enhancers target PAK2 and DLG1 which directly participate in T cell activation. Our study provides references for the identification of immunotherapy-related biomarkers and potential therapeutic targets during immunotherapy.