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1250 Molecular predictors and mechanisms of immune checkpoint inhibitor-induced myocarditis: a case-control study with translational correlates

2023· article· en· W4388076128 on OpenAlexafffundabout
Steph A. Pang, Manuel Flores Molina, Paméla Thébault, Hsiang Chou, Christophe Gonçalves, Paulo Nunes Filho, Sabin Filimon, Tingting Chen, Lucas A. Salas, Khashayar Esfahani, Caroline Michel, Jun Ding, Sonia VDel Rincon, Marie Hudson, Réjean Lapointe, Wilson H. Miller

Bibliographic record

VenueRegular and Young Investigator Award Abstracts · 2023
Typearticle
Languageen
FieldMedicine
TopicCancer Immunotherapy and Biomarkers
Canadian institutionsChristie (Canada)McGill University Health CentreCentre Hospitalier de l’Université de MontréalMcGill UniversityJewish General Hospital
FundersJewish General Hospital
KeywordsMyocarditisImmune systemPeripheral blood mononuclear cellMedicineImmune checkpointImmunologyInternal medicineBiologyImmunotherapyGenetics

Abstract

fetched live from OpenAlex

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 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. 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.011
GPT teacher head0.229
Teacher spread0.219 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2023
Admission routes3
Has abstractyes

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