Clinical characteristics and influencing factors of anti-PD-1/PD-L1-related severe cardiac adverse event: based on FAERS and TCGA databases

Abstract

Combining the FDA Adverse Event Reporting System (FAERS) and the Cancer Genome Atlas (TCGA) databases, we aim to explore the factors that influence anti-programmed cell death protein-1 inhibitors/programmed death-ligand-1 (PD-1/PD-L1) related severe cardiac adverse events (cAEs). We obtained anti-PD-1/PD-L1 adverse event reports from January 2014 to December 2022 from the FAERS database. Disproportionality analysis was performed to find anti-PD-1/PD-L1-related cAEs using the proportional reporting ratio (PRR). We were exploring influencing factors based on multivariate logistic regression analysis. Finally, we utilized a strategy that combines FAERS and TCGA databases to explore the potential immune and genetic influencing factors associated with anti-PD-1/PD-L1-related severe cAEs. Reports of severe cAEs accounted for 7.10% of the overall anti-PD-1/PD-L1 adverse event reports in the FAERS database. Immune-mediated myocarditis (PRR = 77.01[59.77-99.23]) shows the strongest toxic signal. The elderly group (65-74: OR = 1.34[1.23-1.47], ≥ 75: OR = 1.64[1.49-1.81]), male (OR = 1.14[1.05-1.24]), anti-PD-L1 agents (OR = 1.17[1.03-1.33]), patients with other adverse events (OR = 2.38[2.17-2.60]), and the concomitant use of proton pump inhibitor (OR = 1.29[1.17-1.43]), nonsteroidal anti-inflammatory drugs (OR = 1.17[1.04-1.31]), or antibiotics (OR = 1.24[1.08-1.43]) may increase the risk of severe cAEs. In addition, PD-L1 mRNA (Rs = 0.71, FDR = 2.30 × 10^{- 3}) and low-density lipoprotein receptor-related protein 3 (LRP3) (Rs = 0.82, FDR = 2.17 × 10^{- 2}) may be immune and genetic influencing factors for severe cAEs. Severe cAEs may be related to antigen receptor-mediated signalling pathways. In this study, we found that age, gender, anti-PD-1/PD-L1 agents, concomitant other adverse events, concomitant medication, PD-L1 mRNA, and LRP3 may be influencing factors for anti-PD-1/PD-L1-related severe cAEs. However, our findings still require a large-scale prospective cohort validation.

Keywords: Anti-PD-1/PD-L1; FAERS; Influence factors; Severe cardiac adverse events; TCGA.

Fig. 1

The flow chart shows the study’s analysis process. A detailed description of the selection process of anti-PD-1/PD-L1-related severe cAEs in the FAERS database.

Fig. 2

Statistics on the occurrence of severe cardiac adverse events in anti-PD-1/PD-L1 reports from the FAERS database during 2014–2022. (A) The upper bar plot shows the number of anti-PD-1/PD-L1 reports with cardiac adverse events versus anti-PD-1/PD-L1 reports without cardiac adverse events in the FAERS database during 2014 and 2022, as well as the overall situation. The proportional bar plot below shows the number of anti-PD-1/PD-L1 reports with cardiac adverse events versus anti-PD-1/PD-L1 reports without cardiac adverse events in the FAERS database during 2014 and 2022, as well as the overall situation. (B) The upper bar plot shows the number of anti-PD-1/PD-L1 reports with cardiac adverse events versus anti-PD-1/PD-L1 reports without cardiac adverse events for different anti-PD-1/PD-L1 treatment strategies in the FAERS database from 2014 to 2022 and the overall situation. The proportional bar plot below shows the amount of anti-PD-1/PD-L1 reports with cardiac adverse events versus anti-PD-1/PD-L1 reports without cardiac adverse events for different anti-PD-1/PD-L1 treatment strategies in the FAERS database from 2014 to 2022 and overall situation. AEs: adverse events.

Fig. 3

Scanning for anti-PD-/PD-L1-related cardiac adverse events based on the FAERS database. (A) The heatmap shows the PRR for 99 severe cardiac adverse events (with cases no less than 3) in the FAERS database under different anti-PD-1/PD-L1 treatment strategies (including overall, anti-PD-1, and anti-PD-L1). The heatmap was generated by Microsoft Office Excel 365 (https://www.microsoft.com/). Severe cardiac adverse events labelled with dark blue colour meet the criteria that the lower limit of the 95% confidence interval for the PRR is greater than one, and the number of cases occurring is no less than 3. The numbers in the figure represent the number of severe cardiac adverse events. (B) The forest plot shows the PRR of anti-PD-1/PD-L1-related severe cAEs. The forest plot was generated by Microsoft Office Excel 365. (C) The time interval between drug initiation and anti-PD-1/PD-L1-related severe cardiac adverse events. The bar chart was generated by Microsoft Office Excel 365. IQR: interquartile rang. (D) The cumulative distribution curves demonstrate the onset time of anti-PD-1/PD-L1-related severe cardiac adverse events after treatment with anti-PD-1/PD-L1 in different subgroups (the order is whether other adverse reactions accompany, whether it is fatal, and whether it is combined with metformin or targeted treatment drugs). The cumulative distribution curves was generated by R software (https://www.r-project.org/; version 4.3.0).

Fig. 4

Anti-PD-1/PD-L1-related severe cardiac adverse events. ALL figures was generated by Microsoft Office Excel 365. (A) The bar plot shows the statistics of the top 28 PTs of co-reported adverse events. The colour indicates the SOC of the corresponding PT. The percentage values in the figure represent the proportion of cases with such adverse events out of the total anti-PD-1/PD-L1-related severe cardiac adverse event cases with co-reported adverse events. (B) The bar plot shows the SOC statistics regarding PTs of co-reported adverse events. The percentage values in the figure represent the proportion of cases with such adverse events out of the total anti-PD-1/PD-L1-related severe cardiac adverse event cases with co-reported adverse events. (C) The forest plot shows the multifactor logistic regression analysis results regarding the factors influencing anti-PD-1/PD-L1-related severe cardiac adverse events. The variables represented in red and bold have statistical significance. ACEI/ARB: angiotensin-converting enzyme inhibitor/angiotensin receptor blocker, AEs: adverse events, Chemo: chemotherapy, NSAIDs: non-steroidal anti-inflammatory drugs, OR: odds ratio, PPI: proton pump inhibitor, PT: preferred term, SOC: systemic organ class, TTD: targeted therapeutic drugs.

Fig. 5

(A) Anatomic sites of cancer types (left panel) and anti-PD-1/PD-L1-related severe cAEs PRR across 27 cancer types (right panel). The anatomic illustration was generated by R package gganatogram v1.1³⁰. (B) Spearman correlation between anti-PD-1/PD-L1-related severe cAEs PRR and 39 factors for positive correlation (right) and negative correlation (left). The correlation bar chart was generated by Microsoft Office Excel 365. * indicates significant correlation (FDR < 0.05); PD-L1 mRNA FDR = 2.30 × 10^− 3, TCR Diversity FDR = 4.16 × 10^− 3, PD-1 mRNA FDR = 7.03 × 10^− 3, Lymphocytes FDR = 3.18 × 10^− 2, BCR Diversity FDR = 3.18 × 10^− 2, Stromal Fraction FDR = 3.18 × 10^− 2, Leukocyte Fraction FDR = 3.18 × 10^− 2, CD8 + T Cells FDR = 3.44 × 10^− 2, Dendritic Cells Resting FDR = 3.44 × 10^− 2, T Cells Follicular Helper FDR = 3.44 × 10^− 2, Macrophages M1 FDR = 4.02 × 10^− 2; (C) Comparison of performance of bivariate models in predicting anti-PD-1/PD-L1-related severe cAEs for all combinations of eleven significantly correlated variables. The heat map of bivariate combination model was generated by Microsoft Office Excel 365. Spearman R (Rs) was calculated between predicted and observed anti-PD-1/PD-L1-related severe cAEs PRR. The shade of the square indicates the Rs, and the size indicates the significance of the log-likelihood ratio test. (D) The combined effect of PD-L1 mRNA and CD8 + T Cells bivariate model (Spearman correlation, Rs = 0.81, FDR = 9.46 × 10^− 8). The scatter plot was generated by Microsoft Office Excel 365. The equation of the bivariate model is -0.3164 + 0.6891 × PD-L1 mRNA + 7.5227 × CD8 + T Cells. ACC: adrenocortical carcinoma, AEs: adverse events, BCR Diversity: B cell receptor diversity, BLCA: bladder urothelial carcinoma, BRCA: breast invasive carcinoma, CESC: cervical squamous cell carcinoma and endocervical adenocarcinoma, CHOL: cholangiocarcinoma, COAD: colon adenocarcinoma, ESCA: esophageal carcinoma, FDR: false discovery rate, GBM: glioblastoma multiforme, HNSC: head and neck squamous cell carcinoma, IFN-γ response: interferon-γ response, KICH: Kidney Chromophobe, KIRC: kidney renal clear cell carcinoma, KIRP: Kidney renal papillary cell carcinoma, LGG: brain lower-grade glioma, LIHC: liver hepatocellular carcinoma, LUAD: lung adenocarcinoma, LUSC: lung squamous cell carcinoma, MESO: mesothelioma, OV: ovarian serous cystadenocarcinoma, PAAD: pancreatic adenocarcinoma, PRAD: prostate adenocarcinoma, PRR: proportional reporting ratio, READ: rectum adenocarcinoma, SARC: sarcoma, SKCM: skin cutaneous melanoma, SNV neoantigens: single-nucleotide variant neoantigens, STAD: stomach adenocarcinoma, TCR Diversity: T cell receptor diversity, TGF-β response: transforming growth factor-β response, THCA: thyroid carcinoma, Th1 Cells: T helper 1 cells, Th2 Cells: T helper 2 cells, Th17 Cells: T helper 17 cells, THYM: Thymoma, TMB: tumour mutation burden, UVM: uveal melanoma.

Fig. 6

Evaluation of the association between anti-PD-1/PD-L1-related severe cAEs and gene-related factors. ALL figures was generated by Microsoft Office Excel 365. (A) Pathway enrichment of the top thirty genes significantly correlated with anti-PD-1/PD-L1-related severe cAEs across multiple cancer types. (B) Linear correlation model between LRP3 expression and anti-PD-1/PD-L1-related severe cAEs. (C) Comparison of performance of bivariate models in predicting anti-PD-1/PD-L1-related severe cAEs for all combinations of the top ten anti-PD-1/PD-L1-related severe cAEs PRR significantly correlated genes. Spearman correlation (Rs) was calculated between the predicted and observed anti-PD-1/PD-L1-related severe cAEs PRR. The shade of the square indicates the Rs, and the size indicates the significance of the log-likelihood ratio test. (D) The combined effect of LRP3 and TRAV27 bivariate model (Spearman correlation, Rs = 0.88, FDR = 4.05 × 10^− 10). The equation of the bivariate regression model is 0.727 + 0.538 × LRP3 + 1.399 × TRAV27. PRR: proportional reporting ratio, FDR: false discovery rate, ACC: adrenocortical carcinoma, BLCA: bladder urothelial carcinoma, BRCA: breast invasive carcinoma, CESC: cervical squamous cell carcinoma and endocervical adenocarcinoma, CHOL: cholangiocarcinoma, COAD: colon adenocarcinoma, ESCA: esophageal carcinoma, FDR: false discovery rate, GBM: glioblastoma multiforme, HNSC: head and neck squamous cell carcinoma, KICH: Kidney Chromophobe, KIRC: kidney renal clear cell carcinoma, KIRP: kidney renal papillary cell carcinoma, LGG: brain lower-grade glioma, LIHC: liver hepatocellular carcinoma, LRP3: low-density lipoprotein receptor-related protein 3, LUAD: lung adenocarcinoma, LUSC: lung squamous cell carcinoma, MESO: mesothelioma, OV: ovarian serous cystadenocarcinoma, PAAD: pancreatic adenocarcinoma, PRAD: prostate adenocarcinoma, PRR: proportional reporting ratio, READ: rectum adenocarcinoma, SARC: sarcoma, SKCM: skin cutaneous melanoma, STAD: stomach adenocarcinoma, THCA: thyroid carcinoma, THYM: Thymoma, T TRAV27: Cell Receptor Alpha Variable 27, UVM: uveal melanoma..