Peripheral blood cells and immune checkpoint inhibitor-associated myocarditis: a retrospective clinical study with pharmacovigilance and single-cell analysis.

Strong pharmacovigilance signals for ICI-associated myocarditis were identified across all eight ICIs analyzed in the FAERS database, with more than 80% of cases occurring within three months of therapy initiation.

• Disproportionality analysis was performed using the FDA Adverse Event Reporting System (FAERS) database covering 2015–2026.
• Eight immune checkpoint inhibitors were included in the analysis.
• More than 80% of ICI-M cases occurred within three months of therapy initiation.
• The analysis confirmed ICI-M as a rare but consistently reported adverse event signal across all agents examined.

The (neutrophil + monocyte) to lymphocyte ratio (NMLR), cTnI, and NT-proBNP were identified as key predictors of severe ICI-associated myocarditis.

• 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 indices were analyzed across the cohort.
• Three machine learning methods were used for feature selection, converging on NMLR, cTnI, and NT-proBNP as the top predictors.
• These three features were incorporated into an exploratory nomogram for severity prediction.

A 3-feature nomogram based on NMLR, cTnI, and NT-proBNP demonstrated relatively good predictive performance for identifying severe ICI-associated myocarditis.

• The nomogram achieved an Area Under the Curve (AUC) of 0.849.
• Internal validation was performed via 1000 bootstrap samples.
• Model performance was evaluated by calibration, receiver operating characteristic, and decision curve analyses.
• The authors note that external validation in independent cohorts is warranted before clinical application.

Single-cell RNA sequencing of peripheral blood mononuclear cells revealed monocyte expansion and lymphocyte contraction associated with ICI-M and its severity.

• Single-cell RNA-seq data from peripheral blood mononuclear cells were analyzed to explore immune landscape alterations in ICI-M.
• Both monocyte expansion and lymphocyte contraction were identified as features of ICI-M and correlated with disease severity.
• Pseudotime ordering, ligand-receptor inference, subcluster differential abundance analysis, and pathway enrichment analyses were employed.
• These findings at the single-cell level were consistent with the peripheral blood cell ratio findings from the clinical cohort.

A distinct FCGR3A+ monocyte subset was associated with a potential role in ICI-M development through cell-cell interaction analyses.

• Subcluster differential abundance analysis identified FCGR3A+ monocytes as disproportionately expanded in ICI-M.
• Ligand-receptor inference analyses implicated this subset in specific intercellular communication networks relevant to ICI-M pathogenesis.
• Pathway enrichment analyses further characterized the functional profile of this monocyte subset.
• The authors suggest this subset may inform future therapeutic strategies targeting specific cell-cell interactions in ICI-M.

Associations between peripheral blood cell counts and derived ratio indices and ICI-M severity were identified in the retrospective clinical cohort.

• The study enrolled 100 patients who developed ICI-M, stratified into severe and non-severe groups.
• Multiple peripheral blood cell-derived ratio indices were evaluated as candidate biomarkers.
• The NMLR emerged as the strongest peripheral blood cell-derived ratio predictor of severity among those tested.
• This is described as providing 'a multi-dimensional view of ICI-M, integrating clinical and single-cell immune profiling analysis of peripheral blood cells as well as pharmacovigilance data.'