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1250 Molecular predictors and mechanisms of immune checkpoint inhibitor-induced myocarditis: a case-control study with translational correlates
  1. Steph A Pang1,
  2. Manuel Flores Molina1,
  3. Paméla Thébault2,
  4. Hsiang Chou1,
  5. Christophe Goncalves1,
  6. Paulo Nunes Filho1,
  7. Sabin Filimon3,
  8. Tingting Chen4,
  9. Lucas A Salas5,
  10. Khashayar Esfahani1,
  11. Caroline M Michel6,
  12. Jun Ding4,
  13. Sonia VDel Rincon1,
  14. Marie Hudson7,
  15. Réjean Lapointe2 and
  16. Wilson H Miller1
  1. 1Lady Davis Institute and Segal Cancer Centre, Sir Mortimer B. Davis Jewish General Hospital, Departments of Medicine and Oncology, McGill University, Montreal, QC, Canada
  2. 2Centre de Recherche du Centre Hospitalier de l’Université de Montréal, Montreal, QC, Canada
  3. 3McGill University Health Center, Division of Cardiology, Montreal, QC, Canada
  4. 4Meakins-Christie Laboratories, Department of Medicine, McGill University Health Centre, Montreal, QC, Canada
  5. 5Department of Epidemiology, Geisel School of Medicine at Dartmouth, Dartmouth Cancer Center, Lebanon, NH, USA
  6. 6Sir Mortimer B. Davis Jewish General Hospital, Division of Cardiology, McGill University, Montreal, QC, Canada
  7. 7Lady Davis Institute and Sir Mortimer B. Davis Jewish General Hospital, Division of Rheumatology, McGill University, Montreal, QC, Canada
  • Journal for ImmunoTherapy of Cancer (JITC) preprint. The copyright holder for this preprint are the authors/funders, who have granted JITC permission to display the preprint. All rights reserved. No reuse allowed without permission.

Abstract

Background Myocarditis from immune checkpoint inhibition (ICI) has been reported in 0.04–1.14% of patients on ICI, with mortality up to 50%.1 Murine models reveal cardiac-myosin-specific T cells contribute to ICI-myocarditis; genetic/phenotypic differences may predispose to their activity.2 We present the Montreal Immune-Related Adverse Events (MIRAE) ICI-myocarditis project, conducted by an interdisciplinary team of physicians and scientists to understand molecular drivers of ICI-myocarditis.

Methods This case-control study comprises three groups of patients treated with ICI: 1) ICI-myocarditis; 2) non-ICI troponemia (elevated troponins from non-immune etiology); and 3) controls matched by tumour type, with no IRAEs nor troponemia. We analyzed blood samples from prior to ICI, at time of troponemia, or at 3–6 months after ICI initiation if no troponemia. We developed a multi-omics pipeline to understand mechanisms of ICI-myocarditis (figure 1), with immune cell subpopulation profiling of peripheral blood mononuclear cells (PBMCs) using single cell RNA and T/B cell receptor sequencing. This is validated with genomic DNA methylation, cytokine analyses, and PhenoCycler spatial single-cell imaging proteomics to localize cellular sources of upregulated cytokines and to visualize cell-cell interactions underpinning cardiac pathology.

Results Of 473 patients treated with ICI in the MIRAE biobank, 3.59% had ICI-myocarditis. Of these, 19 had stored samples and were included in this study (see table 1 for baseline characteristics). 5 patients (26%) developed arrhythmias. 10 (53%) had concurrent IRAE. 1 (5%) died from concurrent IRAE. There were no deaths from myocarditis. Elevations of blood neutrophil-to-lymphocyte ratio, alanine transaminase, and aspartate aminotransferase were associated with ICI-myocarditis, compared to non-myocarditis patients at 3–6 months on ICI (figure 2). Plasma cytokine profiling of 13 ICI-myocarditis cases and matching controls demonstrated no significant differences in baseline cytokines prior to ICI. Significant elevations of chemokine IP10 and anti-inflammatory cytokine IL10 were detected at time of myocarditis, implicating various immune cells, including T lymphocytes and monocytes (figure 3). Immune cell subpopulation profiling of PBMCs is ongoing (figure 4). Spatial profiling of the first ICI-myocarditis biopsy demonstrated T cell and macrophage infiltration between myocardiocytes and granulocyte accumulation within fibrotic tissue. This suggests a role of innate immunity in myocardial damage, in addition to lymphocyte activation (figure 5).

Conclusions This is one of the largest translational studies of ICI-myocarditis patients and matched controls. The preliminary data highlight the role of innate immunity, in addition to the previously known role of T lymphocytes. Advancing molecular understandings of ICI-myocarditis will allow us to design more targeted, effective immunosuppressive treatments for ICI-myocarditis.

Acknowledgements Laboratories of Dr Wilson Miller, Dr Sonia Del Rincón, Dr Réjean Lapointe, Dr Jun Ding, and Dr Lucas Salas.

Funding from Canadian Institutes of Health Research and a generous donation to the Jewish General Hospital Clinical Research Unit by Cathy Monticciolo-Cianci in memory of her mother Maria Monticciolo.

References

  1. Mahmood SS, Fradley MG, Cohen JV, Nohria A, Reynolds KL, Heinzerling LM, et al. Myocarditis in Patients Treated With Immune Checkpoint Inhibitors. J Am Coll Cardiol. 2018;71(16):1755–64.

  2. Axelrod ML, Meijers WC, Screever EM, Qin J, Carroll MG, Sun X, et al. T cells specific for α-myosin drive immunotherapy-related myocarditis. Nature. 2022;611(7937):818–26.

Ethics Approval This study was approved by the CIUSS West-Central Montreal Ethics Board; approval number 2022–3081. All patient participants gave informed consent to be enrolled in the Montreal Immune-Related Adverse Events project.

Abstract 1250 Figure 1

Montreal Immune-Related Adverse Events ICI-myocarditis research pipeline

Abstract 1250 Figure 2

ICI-Myocarditis is associated with elevation of NLR, ALT and AST in the blood. (A-D) NLR, ALT, AST, and CK in the blood of ICI-Myocarditis cases and controls at baseline and during ICI (follow up vs. irAE). Data are shown as mean +/- standard error of the mean. Statistical analysis was performed using the Mann-Whitney U test. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001

Abstract 1250 Figure 3

Distinctive cytokine profile in plasma during ICI-Myocarditis. A panel of 30 cytokines, including all signature type 1, type 2 and type 3 cytokines, was used. IP-10 and IL-10 were found to be significantly elevated at time of myocarditis event, compared to controls at 3–6 month follow up on ICI. (A-E) Plasma levels of IP-10, IL-10, GMCSF, IL-15, and IL-13 in ICI-myocarditis cases versus ICI-controls. (F) Heatmap of data represented in A-E with cases organized according to grade of myocarditis. Data are shown as mean +/- SEM. Statistical analysis was performed using the Mann-Whitney U test. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001

Abstract 1250 Figure 4

Analysis of single-cell RNA-sequencing data from 6 patient samples. Single cell sequencing is ongoing for our 17 patients with ICI myocarditis, as well as 5 patients with troponemia without myocarditis, and 22 matched controls. The goal is to determine the composition of the PBMCs at baseline and during ICI-myocarditis, to identify potentially pathogenic cell subsets and their associated transcriptional signatures. (A) UMAP plots depicting cells from myocarditis patients, distinguished by Leiden cluster labels indicating cell subpopulations. Cells sourced from different patients were amalgamated using Harmony to mitigate potential batch effects. (B) UMAP plots demonstrating cells from myocarditis patients, categorized by the defined cell type. (C) Illustration of the biomarkers utilized for cell type annotation. (D) Representation of the cell type composition within each condition, namely, baseline, follow, and irAE

Abstract 1250 Figure 5

Optimized 54-marker myocarditis antibody panel for use in Highly Multiplex Phenotyping with the PhenoCycler Imaging platform. (A) List of markers. (B) Low magnification view of whole FFPE myocardial tissue section. Inset shows high magnification immunofluorescence images of MYH6 (cardiomyocytes), CD45 (immune cells), and CD68 (macrophages). (C) Fibrotic region. Inset (below) shows single channels for CD45, CD11b, CD45RO, CD15, VISTA, DAPI, and Merge

Abstract 1250 Table 1

Baseline characteristics

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