Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

T cells specific for α-myosin drive immunotherapy-related myocarditis

Abstract

Immune-related adverse events, particularly severe toxicities such as myocarditis, are major challenges to the utility of immune checkpoint inhibitors (ICIs) in anticancer therapy1. The pathogenesis of ICI-associated myocarditis (ICI-MC) is poorly understood. Pdcd1–/–Ctla4+/– mice recapitulate clinicopathological features of ICI-MC, including myocardial T cell infiltration2. Here, using single-cell RNA and T cell receptor (TCR) sequencing of cardiac immune infiltrates from Pdcd1–/–Ctla4+/– mice, we identify clonal effector CD8+ T cells as the dominant cell population. Treatment with anti-CD8-depleting, but not anti-CD4-depleting, antibodies improved the survival of Pdcd1–/–Ctla4+/– mice. Adoptive transfer of immune cells from mice with myocarditis induced fatal myocarditis in recipients, which required CD8+ T cells. The cardiac-specific protein α-myosin, which is absent from the thymus3,4, was identified as the cognate antigen source for three major histocompatibility complex class I-restricted TCRs derived from mice with fulminant myocarditis. Peripheral blood T cells from three patients with ICI-MC were expanded by α-myosin peptides. Moreover, these α-myosin-expanded T cells shared TCR clonotypes with diseased heart and skeletal muscle, which indicates that α-myosin may be a clinically important autoantigen in ICI-MC. These studies underscore the crucial role for cytotoxic CD8+ T cells, identify a candidate autoantigen in ICI-MC and yield new insights into the pathogenesis of ICI toxicity.

This is a preview of subscription content, access via your institution

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Fig. 1: scRNA–TCR-seq reveals abundant clonal effector CD8+ T cells in ICI-MC.
Fig. 2: CD8T cells are necessary for myocarditis.
Fig. 3: α-myosin is a MHC-I-restricted autoantigen in mouse myocarditis.
Fig. 4: α-myosin-expanded TCRs are present in cardiac and skeletal muscle of patients with ICI-MC.

Data availability

Sequencing data have been deposited in the Gene Expression Omnibus under accession number GSE213486Source data are provided with this paper.

References

  1. Wang, D. Y. et al. Fatal toxic effects associated with immune checkpoint inhibitors: a systematic review and meta-analysis. JAMA Oncol. 4, 1721–1728 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  2. Wei, S. C. et al. A genetic mouse model recapitulates immune checkpoint inhibitor-associated myocarditis and supports a mechanism-based therapeutic intervention. Cancer Discov. 11, 614–639 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  3. Lv, H. et al. Impaired thymic tolerance to α-myosin directs autoimmunity to the heart in mice and humans. J. Clin. Invest. 121, 1561–1573 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Gabrielsen, I. S. M. et al. Transcriptomes of antigen presenting cells in human thymus. PLoS ONE 14, e0218858 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Johnson, D. B. et al. Fulminant myocarditis with combination immune checkpoint blockade. N. Engl. J. Med. 375, 1749–1755 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  6. Hu, J.-R. R. et al. Cardiovascular toxicities associated with immune checkpoint inhibitors. Cardiovasc. Res. 115, 854–868 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Salem, J. E. et al. Spectrum of cardiovascular toxicities of immune checkpoint inhibitors: a pharmacovigilance study. Lancet Oncol. 19, 1579–1589 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Moslehi, J., Lichtman, A. H., Sharpe, A. H., Galluzzi, L. & Kitsis, R. N. Immune checkpoint inhibitor–associated myocarditis: manifestations and mechanisms. J. Clin. Invest. https://doi.org/10.1172/JCI145186 (2021).

  9. Zamami, Y. et al. Factors associated with immune checkpoint inhibitor-related myocarditis. JAMA Oncol. 5, 1635–1637 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  10. Salem, J.-E. et al. Abatacept for severe immune checkpoint inhibitor-associated myocarditis. N. Engl. J. Med. 380, 2377–2379 (2019).

    Article  PubMed  Google Scholar 

  11. Yang, X., Bam, M., Becker, W., Nagarkatti, P. S. & Nagarkatti, M. Long noncoding RNA AW112010 promotes the differentiation of inflammatory T cells by suppressing IL-10 expression through histone demethylation. J. Immunol. 205, 987–993 (2020).

    Article  CAS  PubMed  Google Scholar 

  12. Jackson, R. et al. The translation of non-canonical open reading frames controls mucosal immunity. Nature. 564, 434–438 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Adamo, L. et al. Myocardial B cells are a subset of circulating lymphocytes with delayed transit through the heart. JCI Insight https://doi.org/10.1172/jci.insight.134700 (2020).

  14. Bönner, F., Borg, N., Burghoff, S. & Schrader, J. Resident cardiac immune cells and expression of the ectonucleotidase enzymes CD39 and CD73 after ischemic injury. PLoS ONE https://doi.org/10.1371/journal.pone.0034730 (2012).

  15. Martini, E. et al. Single-cell sequencing of mouse heart immune infiltrate in pressure overload-driven heart failure reveals extent of immune activation. Circulation. 140, 2089–2107 (2019).

    Article  CAS  PubMed  Google Scholar 

  16. Li, O., Zheng, P. & Liu, Y. CD24 expression on T cells is required for optimal T cell proliferation in lymphopenic host. J. Exp. Med. 200, 1083–1089 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Hubbe, M. & Altevogt, P. Heat-stable antigen/CD24 on mouse T lymphocytes: evidence for a costimulatory function. Eur. J. Immunol. 24, 731–737 (1994).

    Article  CAS  PubMed  Google Scholar 

  18. Szabo P. A., Miron M. & Farber D. L. Location, location, location: tissue resident memory T cells in mice and humans. Sci. Immunol. https://doi.org/10.1126/sciimmunol.aas9673 (2019).

  19. Fonseca, R. et al. Runx3 drives a CD8+ T cell tissue residency program that is absent in CD4+ T cells. Nat. Immunol. 23, 1236–1245 (2022).

    Article  CAS  PubMed  Google Scholar 

  20. Zhang, L. et al. Major adverse cardiovascular events and the timing and dose of corticosteroids in immune checkpoint inhibitor-associated myocarditis. Circulation 141, 2031–2034 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Coutinho, A. E. & Chapman, K. E. The anti-inflammatory and immunosuppressive effects of glucocorticoids, recent developments and mechanistic insights. Mol. Cell. Endocrinol. 335, 2–13 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Heather, J. M. et al. Stitchr: stitching coding TCR nucleotide sequences from V/J/CDR3 information. Nucleic Acids Res. 1, e68 (2022).

    Article  Google Scholar 

  23. Rosskopf, S. et al. A Jurkat 76 based triple parameter reporter system to evaluate TCR functions and adoptive T cell strategies. Oncotarget 9, 17608–17619 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  24. Jutz, S. et al. Assessment of costimulation and coinhibition in a triple parameter T cell reporter line: simultaneous measurement of NF-κB, NFAT and AP-1. J. Immunol. Methods. 430, 10–20 (2016).

    Article  CAS  PubMed  Google Scholar 

  25. Gil-Cruz, C. et al. Microbiota-derived peptide mimics drive lethal inflammatory cardiomyopathy. Science 366, 881–886 (2019).

    Article  CAS  PubMed  Google Scholar 

  26. Massilamany, C., Gangaplara, A., Steffen, D. & Reddy, J. Identification of novel mimicry epitopes for cardiac myosin heavy chain-α that induce autoimmune myocarditis in A/J mice. Cell Immunol. 271, 438–449 (2011).

    Article  CAS  PubMed  Google Scholar 

  27. Meier, S. L., Satpathy, A. T. & Wells, D. K. Bystander T cells in cancer immunology and therapy. Nat. Cancer 3, 143–155 (2022).

    Article  PubMed  Google Scholar 

  28. Maurice, N. J., McElrath, M. J., Andersen-Nissen, E., Frahm, N. & Prlic, M. CXCR3 enables recruitment and site-specific bystander activation of memory CD8+ T cells. Nat. Commun. 10, 1–15 (2019).

    Article  CAS  Google Scholar 

  29. Simoni, Y. et al. Bystander CD8+ T cells are abundant and phenotypically distinct in human tumour infiltrates. Nature 557, 575–579 (2018).

    Article  CAS  PubMed  Google Scholar 

  30. Maurice, N. J., Taber, A. K. & Prlic, M. The ugly duckling turned to swan: a change in perception of bystander-activated memory CD8 T Cells. J. Immunol. 206, 455–462 (2021).

    Article  CAS  PubMed  Google Scholar 

  31. Scheper, W. et al. Low and variable tumor reactivity of the intratumoral TCR repertoire in human cancers. Nat. Med. 25, 89–94 (2019).

    Article  CAS  PubMed  Google Scholar 

  32. Paul, S., Sidney, J., Sette, A. & Peters, B. TepiTool: a pipeline for computational prediction of T cell epitope candidates. Curr. Protoc. Immunol. 2016, 18.19.1–18.19.24 (2016).

    Google Scholar 

  33. Falk, K., Rötzschke, O., Stevanović, S., Jung, G. & Rammensee, H. G. Allele-specific motifs revealed by sequencing of self-peptides eluted from MHC molecules. Nature 351, 290–296 (1991).

    Article  CAS  PubMed  Google Scholar 

  34. Luoma, A. M. et al. Molecular pathways of colon inflammation induced by cancer immunotherapy. Cell 182, 655–671.e22 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Johnson, D. B. et al. Tumor-specific MHC-II expression drives a unique pattern of resistance to immunotherapy via LAG-3/FCRL6 engagement. JCI Insight 3, e120360 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  36. Ji, C. et al. Myocarditis in cynomolgus monkeys following treatment with immune checkpoint inhibitors. Clin. Cancer Res. 25, 4735–4748 (2019).

    Article  CAS  PubMed  Google Scholar 

  37. Woo, S. R. et al. Immune inhibitory molecules LAG-3 and PD-1 synergistically regulate T-cell function to promote tumoral immune escape. Cancer Res. 72, 917–927 (2012).

    Article  CAS  PubMed  Google Scholar 

  38. Okazaki, T. et al. PD-1 and LAG-3 inhibitory co-receptors act synergistically to prevent autoimmunity in mice. J. Exp. Med. 208, 395–407 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Chowell, D. et al. Patient HLA class I genotype influences cancer response to checkpoint blockade immunotherapy. Science 359, 582–587 (2018).

    Article  CAS  PubMed  Google Scholar 

  40. Naranbhai, V. et al. HLA-A*03 and response to immune checkpoint blockade in cancer: an epidemiological biomarker study. Lancet Oncol. https://doi.org/10.1016/S1470-2045(21)00582-9 (2022).

  41. Correale, P. et al. HLA expression correlates to the risk of immune checkpoint inhibitor-induced pneumonitis. Cells https://doi.org/10.3390/cells9091964 (2020).

  42. Hasan Ali, O. et al. Human leukocyte antigen variation is associated with adverse events of checkpoint inhibitors. Eur. J. Cancer 107, 8–14 (2019).

    Article  CAS  PubMed  Google Scholar 

  43. McCulloch, J. A. et al. Intestinal microbiota signatures of clinical response and immune-related adverse events in melanoma patients treated with anti-PD-1. Nat. Med. https://doi.org/10.1038/s41591-022-01698-2 (2022).

  44. Andrews, M. C. et al. Gut microbiota signatures are associated with toxicity to combined CTLA-4 and PD-1 blockade. Nat. Med. https://doi.org/10.1038/s41591-021-01406-6 (2021).

  45. Van der Borght, K. et al. Myocarditis elicits dendritic cell and monocyte infiltration in the heart and self-antigen presentation by conventional type 2 dendritic cells. Front. Immunol. 9, 2714 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  46. Rieckmann, M. et al. Myocardial infarction triggers cardioprotective antigen-specific T helper cell responses. J. Clin. Invest. https://doi.org/10.1172/jci123859 (2019).

  47. Lee, J. H. et al. Myosin-primed tolerogenic dendritic cells ameliorate experimental autoimmune myocarditis. Cardiovasc. Res. 101, 203–210 (2014).

    Article  CAS  PubMed  Google Scholar 

  48. Tajiri, K. et al. A new mouse model of chronic myocarditis induced by recombinant Bacille Calmette–Guèrin expressing a T-cell epitope of cardiac myosin heavy chain-α. Int. J. Mol. Sci. 22, 794 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Hua, X. et al. Single-cell RNA sequencing to dissect the immunological network of autoimmune myocarditis. Circulation https://doi.org/10.1161/circulationaha.119.043545 (2020).

  50. Taylor, J. A. et al. A spontaneous model for autoimmune myocarditis using the human MHC molecule HLA-DQ8. J. Immunol. 172, 2651–2658 (2004).

  51. Mombaerts, P. et al. RAG-1-deficient mice have no mature B and T lymphocytes. Cell 68, 869–877 (1992).

    Article  CAS  PubMed  Google Scholar 

  52. Satija, R., Farrell, J. A., Gennert, D., Schier, A. F. & Regev, A. Spatial reconstruction of single-cell gene expression data. Nat. Biotechnol. 33, 495–502 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411–420 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Stuart, T. et al. Comprehensive integration of single-cell data. Cell. 177, 1888–1902.e21 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Wu, Y. et al. FOXP3 controls regulatory T cell function through cooperation with NFAT. Cell. 126, 375–387 (2006).

    Article  CAS  PubMed  Google Scholar 

  56. Gao, J. et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci. Signal. https://doi.org/10.1126/scisignal.2004088 (2013).

  57. Cerami, E. et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2, 401–404 (2012).

    Article  PubMed  Google Scholar 

  58. Oh, H. M. et al. An efficient method for the rapid establishment of Epstein-Barr virus immortalization of human B lymphocytes. Cell Prolif. 36, 191–197 (2003).

    Article  MathSciNet  PubMed  Google Scholar 

  59. Granato, M. et al. Epstein–Barr virus blocks the autophagic flux and appropriates the autophagic machinery to enhance viral replication. J. Virol. 88, 12715 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  60. Wölfl, M. & Greenberg, P. D. Antigen-specific activation and cytokine-facilitated expansion of naive, human CD8+ T cells. Nat. Protoc. 9, 950–966 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  61. Eberhardt, C. S. et al. Functional HPV-specific PD-1+ stem-like CD8 T cells in head and neck cancer. Nature 597, 279–284 (2021).

    Article  CAS  PubMed  Google Scholar 

  62. Oksanen, J. et al. vegan: Community Ecology Package. R package version 2.5-7 (2020).

  63. Nazarov, V., immunarch.bot, Rumynskiy, E. immunomind/immunarch: 0.6.5: Basic single-cell support. Zenodo https://doi.org/10.5281/zenodo.3893991 (2020).

Download references

Acknowledgements

We thank the patients and families who donated tissue to this study; M. Madden and M. Korrer for assistance with sample acquisition; members of the J.M.B. laboratory and B. I. Reinfeld for their constructive input; and E. J. Philips, S. Mallal, P. Steinberger and J. Sidney for cell lines and reagents. This work was supported by F30CA236157, T32GM007347 (to M.L.A.), T32DK007061 (to J.J.W.), R01HL141466 (to J.J.M.), R01HL155990 (to J.J.M.), R01CA227481 (to D.B.J. and J.M.B.), R01HL156021 (to J.M.B. and J.J.M.), P30 AI110527 (to S.M.), Susan and Luke Simons Directorship in Melanoma, Van Stephenson Melanoma Research Fund, and James C. Bradford Melanoma Fund (to D.B.J). K.A. is supported by an American Heart Association Career Development Award (929347). W.C.M. is supported by the Mandema-Stipendium of the Junior Scientific Masterclass 2020-10 of the University Medical Center Groningen and by the Dutch Heart Foundation (Dekker grant 03-005-2021-T005). S.Z. is supported by National Natural Science Foundation of China (82100677). E.J.P. receives funding from the National Institutes of Health R01HG010863, R01AI152183 U01AI154659) and from the National Health and Medical Research Council of Australia. J.P.A. is a CPRIT Distinguished Scholar in Cancer Research. We acknowledge the Translational Pathology Shared Resource supported by NCI/NIH Cancer Center Support grant P30CA068485 and the Shared Instrumentation grant S10 OD023475-01A1 for the Leica Bond RX. Staff at The Vanderbilt VANTAGE Core, including A. Jones and L. Raju, provided technical assistance for this work. VANTAGE is supported in part by a CTSA grant (UL1TR002243), the Vanderbilt Ingram Cancer Center (P30 CA68485), the Vanderbilt Vision Center (P30 EY08126), the NIH/NCRR (G20 RR030956), and an NIH High-End Equipment Grant (S10OD025092). Figs. 1a and 4b were created with BioRender.com.

Author information

Authors and Affiliations

Authors

Contributions

M.L.A., J.J.M. and J.M.B. conceived and designed the study and drafted the manuscript. W.C.M., E.M.S., E.T., J.Q., S.Z. and Y.Z. managed the mouse colony and identified appropriate mice for each experiment. M.L.A., W.C.M., E.M.S., M.G.C., X.S., J.J.W., E.T., J.Q., A.H. and A.L.T. performed mouse necropsies, tissue dissociation and cryopreservation. M.L.A. performed scRNA–TCR-seq and bulk TCR-seq data analyses and figure generation. M.L.A., A.L.T. and X.S. prepared samples for single-cell sequencing. W.C.M. and E.M.S. performed antibody-mediated depletion studies. A.S. provided expertise and performed tail vein injections for adoptive transfer studies. M.L.A. and M.G.C. performed antigen discovery, epitope identification and MHC blocking experiments. B.C.T. performed quantitative PCR. W.T. performed the dexamethasone treatment study. S.C.W. and J.P.A. provided expertise regarding the mouse model and myocarditis phenotype. S.R.O. provided technical expertise in experimental design and manuscript editing. J.C.R. provided immunology expertise. P.B.F. provided expertise regarding single-cell studies. E.J.P. and S.M. provided expertise in experimental design regarding LCLs, antigen discovery and MHC blockade. K.A. provided expertise in cardiac pathology from non-myocarditis phenotypes. D.B.J. and J.J.M. provided clinical expertise, assistance in acquiring human samples for this study and assistance with manuscript writing. M.L.A. analysed all data generated in this study. J.J.M. and J.M.B. obtained funding for this study.

Corresponding authors

Correspondence to Javid J. Moslehi or Justin M. Balko.

Ethics declarations

Competing interests

M.L.A. is listed as a co-inventor on a provisional patent application for methods to predict therapeutic outcomes using blood-based gene expression patterns, which is owned by Vanderbilt University Medical Center, and is currently unlicensed. S.C.W. is currently an employee of, and has ownership interest in, Spotlight Therapeutics. S.C.W. has received personal fees from BioEntre and AlphaSights. S.C.W. is an inventor on a patent related to a genetic mouse model of immune checkpoint blockade induced immune-related adverse events (PCT/US2019/050551) pending to the Board of Regents, The University of Texas System and patent applications submitted by Spotlight Therapeutics related to in vivo gene editing. K.A. serves on the Data Safety Monitoring Board for ACI Clinical. J.C.R. is a founder, scientific advisory board member and stockholder of Sitryx Therapeutics, a scientific advisory board member and stockholder of Caribou Biosciences, a member of the scientific advisory board of Nirogy Therapeutics, has consulted for Merck, Pfizer and Mitobridge within the past 3 years, and has received research support from Incyte, Calithera Biosciences and Tempest Therapeutics. P.B.F. receives research support from Incyte. D.B.J. has served on advisory boards or as a consultant for BMS, Catalyst Biopharma, Iovance, Jansen, Mallinckrodt, Merck, Mosaic ImmunoEngineering, Novartis, Oncosec, Pfizer and Targovax, has received research funding from BMS and Incyte, and has patents pending for use of MHC-II as a biomarker for ICI response, and abatacept as treatment for irAEs. J.P.A. reports personal fees from Achelois, Adaptive Biotechnologies, Apricity Health, BioAtla, Candel Therapeutics, Codiak BioSciences, Dragonfly Therapeutics, Earli, Enable Medicine, Hummingbird, ImaginAb, Jounce Therapeutics, Lava Therapeutics, Lytix Biopharma, Marker Therapeutics, PBM Capital, Phenomic AI, BioNTech, Polaris Pharma, Time Bioventures, Trained Therapeutics, Two Bear Capital, and Venn Biosciences outside the submitted work. In addition, J.P.A. has a patent for a genetic mouse model of immune-checkpoint-blockade-induced irAEs pending to The University of Texas MD Anderson Cancer Center and has received royalties from intellectual property licensed to BMS and Merck. J.J.M. has served on advisory boards for Bristol Myers Squibb, Takeda, Audentes, Deciphera, Janssen, Immuno-Core, Boston Biomedical, Amgen, Myovant, Kurome Therapeutics, Star Therapeutics, ProtinQure, Pharmacyclics, Pfizer, Mallinckrodt Pharmaceuticals, Silverback Therapeutics, Cytokinetics, and AstraZeneca. J.M.B. receives research support from Genentech/Roche, and Incyte, and is an inventor on provisional patents regarding immunotherapy targets and biomarkers in cancer. No disclosures are reported by the other authors.

Peer review

Peer review information

Nature thanks the anonymous reviewers for their contribution to the peer review of this work. Peer reviewer reports are available.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended Data Fig. 1 Myocardial immune infiltrate does not differ by sex.

a) Quantification of immunohistochemistry (IHC) for CD8 and CD4 in male and female Pdcd1−/−Ctla4+/− mice with MC. Cells are counted as number of positive cells per high power (40x) field (HPF). Each data point represents an average of three high power fields per mouse. n = 4 female mice, n = 4 male mice. Box plots show the median, first and third quartiles. The whiskers extend to the maxima and minima but no further than 1.5 times the inter-quartile range. b) Representative IHC for IgG and B220 (CD45R) in hearts of mice with MC and positive control staining in spleen. Images are representative of n = 8 independent Pdcd1−/−Ctla4+/− mice with MC (n = 4 male; n = 4 female). Scale bars represent 50 µm

Source data

Extended Data Fig. 2 MC is characterized by activated immune cells and clonal T cells.

a) Gene expression of key identity genes, showing cell types of clusters. b) Differential gene expression for T, c) myeloid, B and NK cells in MC compared to control cardiac CD45+ cells. Red indicates FDR-corrected p-value (q-value) < 0.05. Black indicates not significant.

Extended Data Fig. 3 T cells in MC are effector or proliferating, tissue-resident and clonal.

a) Expression of key T cell genes by cluster in single cell data. b) Differential gene expression for Cluster 0 vs. Cluster 3 T cells. Red indicates FDR-corrected p-value (q-value) < 0.05. Black indicates not significant. c) Violin plots show expression of key tissue residency associated genes by cluster and MC vs. control. d) Shannon diversity on bulk TCR sequencing beta chain repertoires. Color indicates sex. Shape indicates whether the tissue was derived from a control wild type mouse (open circle) or a Pdcd1−/−Ctla4+/− mouse with MC (filled circle). P = 0.0002, two-sided Wilcoxon test. Box plots show the median, first and third quartiles. The whiskers extend to the maxima and minima but no further than 1.5 times the inter-quartile range. e) TCR counts in single cell data. MC sample is divided by mouse of origin. Clonal TCRs are found in all 4 sequenced hearts

Source data

Extended Data Fig. 4 Confirmation of cell type depletion.

a) Female Pdcd1−/−Ctla4+/− mice were treated with dexamethasone (1mg/kg/day; n = 18) or vehicle (n = 17) starting at 21 days of life. Time is measured since birth. P = 0.49, two-sided cox proportional hazard test. b) Representative flow cytometry gated on live CD45+ cells isolated from blood of different treatment groups, at week 3 of treatment. c) Representative flow cytometry on CD8 depleted (via magnetic beads) compared to whole splenocytes used for adoptive transfer. d) Representative immunohistochemistry on hearts of a CD8 depleted splenocyte recipient compared to a whole splenocyte recipient. Only cardiac sections are shown. Scale bars show 50 μm. Representative of n = 10 animals per group. e) Total TCR reads for cardiac TCR beta chain sequencing on donor and recipient hearts

Source data

Extended Data Fig. 5 Thymic expression of Myh6 and flow cytometry gating for murine α-myosin tetramers.

a) Gene expression for Myh6, Nppa, Nppb, and Sbk2 in the heart and thymus of n = 3 each male and female Pdcd1−/−Ctla4+/− mice. Gene expression is normalized to beta-actin. Gene expression is plotted as 2^-(Ct gene of interest minus Ct of beta-actin). Box plots show the median, first and third quartiles. The whiskers extend to the maxima and minima but no further than 1.5 times the inter-quartile range. b) Gating strategy for H2-Kb tetramers on murine heart samples. Debris, doublets and dead cells (Zombie Violet positive) are excluded. CD3+CD8+ cells are used for tetramer analysis. Staining for Control (SIINFEKL) H2-Kb, and VQQVYYSI H2-Kb tetramers are shown. c) Quantification of spleen tetramer positive CD3+CD8+ cells, by sex of the mouse. The spleens used in this experiment correspond to the mice show in Fig. 3f, which all have α-myosin tetramer positive MC

Source data

Extended Data Fig. 6 TCR sequencing on exPBMC shows expansion of α-myosin and CEF specific TCRs.

a) Comparison of TCR beta chain abundance in α-myosin exPBMC and pre-expansion PBMC for all patients. Each plot is within the same patient only. Color represents change from PBMC to exPBMC. Minimal change is less than a 50 read count change. b) Comparison of TCR beta chain abundance in CEF exPBMC and pre-expansion PBMC for all healthy donors. Color represents change from PBMC to CEF exPBMC. Minimal change is less than a 50 read count change. c) Total TCR reads for biopsy (heart), autopsy, and PBMC samples from patients 1, 2 and 3.

Extended Data Fig. 7 α-myosin expanded TCRs are found in the hearts and skeletal muscles of patients with ICI-MC.

a) Change in TCR counts from PBMC to α-myosin exPBMC plotted by abundance of the same TCR beta chain in the autologous inflamed cardiac or skeletal muscle tissue of each patient. Minimal change is less than a 50 read count change. Not present means not found in either PBMC or exPBMC, but present in indicated tissue. b) Comparison of TCR beta chain abundance in α-myosin exPBMC and heart (biopsy for patient 1 and 3 or right ventricle for patient 2). Color represents change from PBMC to exPBMC. Minimal change is less than a 50 read count change. Not present means not found in either PBMC or exPBMC, but present in heart.

Extended Data Fig. 8 Purity analysis for single cell sequencing on exPBMCs from patient 1.

a) Gene expression is shown on single cell sequencing of CD3 sorted exPBMCs from patient 1. b) Violin plots of key gene expression by presence or absence in cardiac TCR repertoire and clonality in exPBMC. Identity genes are shown in light blue. Genes associated with naïve T cells are shown in dark blue. Genes associated with T cell activation are shown in red.

Extended Data Fig. 9 TCR from Pt 1 exPBMC recognizes α-myosin epitope.

a) TCR-Pt1, which was cloned and transduced into Jurkat NFAT-GFP reporter cells, is shown in red on the same plot show in Fig. 4c. This shows the expansion of this TCR in the exPBMC and abundance in the heart. b) Representative flow cytometry scatter plots are shown for the TCR-pt1 Jurkat cell line is stained with A*24:02 tetramer with RINATLETK or A*03:01 tetramer with RINATLETK. c) Full flow cytometry gating strategy for human PBMC and exPBMC tetramer staining. Debris, doublets and dead cells (Zombie Violet positive) are excluded. CD3+CD8+ cells are used for tetramer analysis. Tetramer staining for all samples is shown.

Extended Data Fig. 10 Tumor-specific MYH6 expression.

a) MYH6 transcripts per million are shown for n = 91 pre-treatment RNA-sequencing melanoma samples. Bars are colored by what ICI treatment the patient received. b) MYH6 expression is shown for n = 363 melanoma samples accessed from TCGA. Samples to the right of the dotted lines have detectable MYH6 expression.

Extended Data Table 1 Amino acid sequences for α-myosin, ANP, BNP and SBK2 peptides included in the peptide library
Extended Data Table 2 Prediction scores for binding of α-myosin peptides to MHC-I molecules in C57BL/6 mice generated by TepiTool
Extended Data Table 3 HLA types of healthy donors and patients with ICI-MC

Supplementary information

Supplementary Information

Full nucleotide sequences for reconstructed TCRs.

Reporting Summary

Peer Review File

Source data

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Axelrod, M.L., Meijers, W.C., Screever, E.M. et al. T cells specific for α-myosin drive immunotherapy-related myocarditis. Nature 611, 818–826 (2022). https://doi.org/10.1038/s41586-022-05432-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41586-022-05432-3

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Quick links

Nature Briefing: Cancer

Sign up for the Nature Briefing: Cancer newsletter — what matters in cancer research, free to your inbox weekly.

Get what matters in cancer research, free to your inbox weekly. Sign up for Nature Briefing: Cancer