Zhenli Li, Jing He, Zipei Ma, Piaoran Liu, Zhengkun Guan, Shaoyan Du, Tiezhu Yao, Guang Liu, Jing Liu, Ling Guo, Yaozhong Zhang, Tenghui Wang, Mengjia Li, Jingtao Ma
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.
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.