Peripheral blood cells and immune checkpoint inhibitor-associated myocarditis: a retrospective clinical study with pharmacovigilance and single-cell analysis - Zhenli Li; Jing He; Zipei Ma; Piaoran Liu; Zhengkun Guan; Shaoyan Du; Tiezhu Yao; Guang Liu; Jing Liu; Ling Guo; Yaozhong Zhang; Tenghui Wang; et al. (Frontiers in Immunology, 2026)
Background Immune checkpoint inhibitor (ICI)-associated myocarditis (ICI-M) is a rare but potentially lethal immune-related adverse event (irAE). Early identification of fulminant (severe) cases remains challenging, and the peripheral immune derangements underlying ICI-M are incompletely characterized. Methods The disproportionality analysis was performed using the FDA Adverse Event Reporting System (FAERS) database (2015–2026) for patients treated with eight ICIs. A retrospective cohort of 100 patients who developed ICI-M was stratified into severe and non-severe groups. Peripheral blood cells and their derived ratio index were analyzed. A severity-predictive nomogram was developed using features selected by three machine learning methods, and was evaluated by calibration, receiver operating characteristic, and decision curve analyses. Single-cell RNA-seq data from peripheral blood mononuclear cells were analyzed to explore immune landscape alterations for ICI-M. Results FAERS analysis revealed strong signals for ICI-M across all eight ICIs, with >80% of cases occurring within three months of therapy. Associations between peripheral blood cells/derived ratio indexes and ICI-M were identified: the (neutrophil + monocyte) to lymphocyte ratio (NMLR), cTnI, and NT-proBNP were identified as key predictors of severe ICI-M. The 3-feature-based exploratory nomogram demonstrated a relatively good predictive performance (Area under curve = 0.849) with internal validation performed via 1000 bootstrap samples. Single-cell profiling identified monocyte expansion and lymphocyte contraction for ICI-M and its severity. The pseudotime ordering, ligand–receptor inference, subcluster differential abundance analysis, and pathway enrichment analyses associated a distinct FCGR3A + monocyte subset with a potential role in ICI-M development. Conclusion This study provides a multi-dimensional view of ICI-M, integrating clinical and single-cell immune profiling analysis of peripheral blood cells as well as pharmacovigilance data. The developed nomogram may serve as a potential tool for identifying severe ICI-M, although external validation in independent cohorts is warranted. Insight into the immune landscape may inform future therapeutic strategies targeting specific cell–cell interactions in ICI-M.