MétaCan
Menu
Back to cohort
Record W4407866630 · doi:10.1158/2326-6074.io2025-b054

Abstract B054: Immune checkpoint inhibitor-induced myocarditis and myocarditis-myositis overlap syndrome: identifying distinct molecular pathways for targeted therapeutic interventions

2025· article· en· W4407866630 on OpenAlexaffabout
Steph A. Pang, Manuel Flores Molina, Paméla Thébault, Yuming Zheng, Hsiang Chou, Christophe Gonçalves, Jingtao Wang, Sabin Filimon, Paulo Nunes Filho, Mariana Pilon Capella, Khashayar Esfahani, Caroline Michel, Jun Ding, Sonia V. del Rincón, Marie Hudson, Réjean Lapointe, Wilson H. Miller

Bibliographic record

VenueCancer Immunology Research · 2025
Typearticle
Languageen
FieldMedicine
TopicCancer Immunotherapy and Biomarkers
Canadian institutionsChristie (Canada)McGill University Health CentreCentre Hospitalier de l’Université de MontréalMcGill UniversityJewish General Hospital
Fundersnot available
KeywordsMyocarditisMyositisMedicineImmunotherapyImmune checkpointImmunologyImmune systemInternal medicine

Abstract

fetched live from OpenAlex

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%. ICI-Myocarditis frequently overlaps with ICI-Myositis, and overlap is associated with increased mortality. Diagnosis and clinical management of concurrent myocarditis and myositis remains an unmet clinical challenge. We present the Montreal Immune-Related Adverse Events myocarditis project, which examines cellular and molecular drivers of ICI-myocarditis ± myositis. Methods: Our case-control study comprises 3 groups of patients on ICI: 1) ICI-myocarditis; 2) ICI-myocarditis-myositis overlap; and 3) controls without immune-related adverse events (irAEs) matched by tumor type, sex, and age. We analyzed blood prior to ICI, at time of myocarditis, and 3-6 months after ICI initiation. We developed a multiomics pipeline, spanning cytokine profiling on plasma using a 30-plex cytokine assay, multiplex proteomics analysis on plasma using the SomaScan 11K Assay, multiplexed single cell imaging technology, and spatial transcriptomics. Results: Of 560 patients treated with ICI in our biobank, 24 (4.3%) had ICI-myocarditis, of which 19 had samples that were included in this study. Nine of them had myocarditis-myositis. Five patients developed arrhythmias, but there were no major cardiac adverse events or deaths from myocarditis. Ten patients had concurrent irAE. All 19 patients were treated with steroids. Other treatments included mycophenolate (6 patients), tofacitinib (4 patients), plasmapheresis (4 patients), intravenous immunoglobulin (4 patients) and alemtuzumab (1 patient). Cytokine profiling of 13 ICI-myocarditis cases and controls demonstrated significant elevations of IP10, IL10, IL15 and IL13 at time of myocarditis. Multiplex proteomics assay demonstrated that ICI-myocarditis and ICI-myocarditis-myositis are phenotypically distinct, with higher levels of cardiomyocyte/skeletal myocyte-related proteins and IFN-gamma-induced proteins and increased interleukin-6-driven JAK-STAT3 signaling in ICI-myocarditis-myositis, compared to ICI-myocarditis. Additionally, patients who developed myocarditis with or without myositis had significantly decreased pre-ICI plasma levels of paired immunoglobin like type 2 receptor alpha (PILRA) compared to controls. Pathway analysis showed increased upregulation of genes known to contribute to hypertrophic cardiomyopathy, dilated cardiomyopathy and arrhythmogenic right ventricular cardiomyopathy, in ICI-myocarditis/myositis compared to ICI-myocarditis alone. Spatial transcriptomics and mapping of infiltrating and tissue cells on biopsies of ICI-myocarditis, ICI-myositis and controls is ongoing. Conclusions: Our multiomics analyses of ICI-myocarditis revealed that the plasma proteome is significantly altered during myocarditis/myositis overlap, exhibiting stronger IFNg signatures and IL6 JAK STAT3 signaling, compared to ICI-myocarditis alone. Advancing our understanding of ICI-myocarditis ± myositis allows to design screening strategies, facilitate diagnosis and optimize treatment. Citation Format: Steph A. Pang, Manuel Flores Molina, Paméla Thébault, Yuming Zheng, Hsiang Chou, Christophe Goncalves, Jingtao Wang, Sabin Filimon, Paulo Nunes Filho, Mariana Pilon Capella, Khashayar Esfahani, Caroline M Michel, Jun Ding, Sonia V. Del Rincón, Marie Hudson, Réjean Lapointe, Wilson H Miller, Jr. Immune checkpoint inhibitor-induced myocarditis and myocarditis-myositis overlap syndrome: identifying distinct molecular pathways for targeted therapeutic interventions [abstract]. In: Proceedings of the AACR IO Conference: Discovery and Innovation in Cancer Immunology: Revolutionizing Treatment through Immunotherapy; 2025 Feb 23-26; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Immunol Res 2025;13(2 Suppl):Abstract nr B054.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.720
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.083
GPT teacher head0.396
Teacher spread0.313 · 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 teacher head, not a consensus.

Study designBench or experimental
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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

Explore more

Same venueCancer Immunology ResearchSame topicCancer Immunotherapy and BiomarkersFrench-language works237,207