A case report of clonal EBV-like memory CD4+ T cell activation in fatal checkpoint inhibitor-induced encephalitis

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Abstract

Checkpoint inhibitors produce durable responses in numerous metastatic cancers, but immune-related adverse events (irAEs) complicate and limit their benefit. IrAEs can affect organ systems idiosyncratically; presentations range from mild and self-limited to fulminant and fatal. The molecular mechanisms underlying irAEs are poorly understood. Here, we report a fatal case of encephalitis arising during anti-programmed cell death receptor 1 therapy in a patient with metastatic melanoma. Histologic analyses revealed robust T cell infiltration and prominent programmed death ligand 1 expression. We identified 209 reported cases in global pharmacovigilance databases (across multiple cancer types) of encephalitis associated with checkpoint inhibitor regimens, with a 19% fatality rate. We performed further analyses from the index case and two additional cases to shed light on this recurrent and fulminant irAE. Spatial and multi-omic analyses pinpointed activated memory CD4+ T cells as highly enriched in the inflamed, affected region. We identified a highly oligoclonal T cell receptor repertoire, which we localized to activated memory cytotoxic (CD45RO+GZMB+Ki67+) CD4 cells. We also identified Epstein–Barr virus-specific T cell receptors and EBV+ lymphocytes in the affected region, which we speculate contributed to neural inflammation in the index case. Collectively, the three cases studied here identify CD4+ and CD8+ T cells as culprits of checkpoint inhibitor-associated immune encephalitis.

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Fig. 1: Clinical course of anti-PD-1-induced encephalitis and histologic findings at autopsy.
Fig. 2: Digital spatial profiling of immune-related protein markers across inflamed and non-inflamed neural tissue.
Fig. 3: TCR sequencing identification of oligoclonal CD4+ cytotoxic T cells in inflamed encephalitic tissue.

Data availability

Source data for RNA-seq analyses (IFN-γ genes) are included as Supplementary Data Set 1. All other data, including processed TCR sequencing data, are available on request from the corresponding authors. The Center for Technology Transfer and Commercialization at Vanderbilt University Medical Center will promptly review all data requests to ensure that intellectual property and confidentiality obligations are met; a Material Transfer Agreement will be used to transfer any and all data that can be shared, including TCR sequencing data.

RNA-seq data from this study have been deposited in the database of Genotypes and Phenotypes (https://prod.tbilab.org/balko_lab/encephalitis_NMED/).

References

  1. 1.

    Darvin, P., Toor, S. M., Sasidharan Nair, V. & Elkord, E. Immune checkpoint inhibitors: recent progress and potential biomarkers. Exp. Mol. Med. 50, 165 (2018).

  2. 2.

    Johnson, D. B., Chandra, S. & Sosman, J. A. Immune checkpoint inhibitor toxicity in 2018. JAMA 320, 1702–1703 (2018).

  3. 3.

    Postow, M. A., Sidlow, R. & Hellmann, M. D. Immune-related adverse events associated with immune checkpoint blockade. N. Engl. J. Med. 378, 158–168 (2018).

  4. 4.

    Moslehi, J. J., Salem, J.-E., Sosman, J. A., Lebrun-Vignes, B. & Johnson, D. B. Increased reporting of fatal immune checkpoint inhibitor-associated myocarditis. Lancet 391, 933 (2018).

  5. 5.

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

  6. 6.

    Naidoo, J. et al. Pneumonitis in patients treated with anti-programmed death-1/programmed death ligand 1 therapy. J. Clin. Oncol. 35, 709–717 (2017).

  7. 7.

    Gonzalez, R. S. et al. PD-1 inhibitor gastroenterocolitis: case series and appraisal of ‘immunomodulatory gastroenterocolitis’. Histopathology 70, 558–567 (2017).

  8. 8.

    Verschuren, E. C. et al. Clinical, endoscopic, and histologic characteristics of ipilimumab-associated colitis. Clin. Gastroenterol. Hepatol. 14, 836–842 (2016).

  9. 9.

    Larkin, J. et al. Neurologic serious adverse events associated with nivolumab plus ipilimumab or nivolumab alone in advanced melanoma, including a case series of encephalitis. Oncologist 22, 709–718 (2017).

  10. 10.

    Rustenhoven, J., Jansson, D., Smyth, L. C. & Dragunow, M. Brain pericytes as mediators of neuroinflammation. Trends Pharmacol. Sci. 38, 291–304 (2017).

  11. 11.

    Annels, N. E., Callan, M. F., Tan, L. & Rickinson, A. B. Changing patterns of dominant TCR usage with maturation of an EBV-specific cytotoxic T cell response. J. Immunol. 165, 4831–4841 (2000).

  12. 12.

    Lim, A. et al. Frequent contribution of T cell clonotypes with public TCR features to the chronic response against a dominant EBV-derived epitope: application to direct detection of their molecular imprint on the human peripheral T cell repertoire. J. Immunol. 165, 2001–2011 (2000).

  13. 13.

    Cohen, G. B. et al. Clonotype tracking of TCR repertoires during chronic virus infections. Virology 304, 474–484 (2002).

  14. 14.

    Koning, D. et al. In vitro expansion of antigen-specific CD8(+) T cells distorts the T-cell repertoire. J. Immunol. Methods 405, 199–203 (2014).

  15. 15.

    Grant, E. J. et al. Lack of heterologous cross-reactivity toward HLA-A*02:01 restricted viral epitopes is underpinned by distinct αβT cell receptor signatures. J. Biol. Chem. 291, 24335–24351 (2016).

  16. 16.

    Dash, P. et al. Quantifiable predictive features define epitope-specific T cell receptor repertoires. Nature 547, 89–93 (2017).

  17. 17.

    Glanville, J. et al. Identifying specificity groups in the T cell receptor repertoire. Nature 547, 94–98 (2017).

  18. 18.

    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).

  19. 19.

    Iwama, S. et al. Pituitary expression of CTLA-4 mediates hypophysitis secondary to administration of CTLA-4 blocking antibody. Sci. Transl. Med 6, 230ra45 (2014).

  20. 20.

    Osorio, J. C. et al. Antibody-mediated thyroid dysfunction during T-cell checkpoint blockade in patients with nonsmall cell lung cancer. Ann. Oncol. 28, 583–589 (2017).

  21. 21.

    Das, R. et al. Early B cell changes predict autoimmunity following combination immune checkpoint blockade. J. Clin. Invest. 128, 715–720 (2018).

  22. 22.

    Dubin, K. et al. Intestinal microbiome analyses identify melanoma patients at risk for checkpoint-blockade-induced colitis. Nat. Commun. 7, 10391 (2016).

  23. 23.

    Peeters, L. M. et al. Cytotoxic CD4+ T cells drive multiple sclerosis progression. Front Immunol. 8, 1160 (2017).

  24. 24.

    Curran, M. A. et al. Systemic 4-1BB activation induces a novel T cell phenotype driven by high expression of eomesodermin. J. Exp. Med. 210, 743–755 (2013).

  25. 25.

    Hirschhorn-Cymerman, D. et al. Induction of tumoricidal function in CD4+ T cells is associated with concomitant memory and terminally differentiated phenotype. J. Exp. Med. 209, 2113–2126 (2012).

  26. 26.

    Penaloza-MacMaster, P. et al. Vaccine-elicited CD4 T cells induce immunopathology after chronic LCMV infection. Science 347, 278–282 (2015).

  27. 27.

    Takeuchi, A. & Saito, T. CD4 C. T. L., a cytotoxic subset of CD4+ T Cells, their differentiation and function. Front Immunol. 8, 194 (2017).

  28. 28.

    Ranasinghe, S. et al. Antiviral CD8+ T cells restricted by human leukocyte antigen class II exist during natural HIV infection and exhibit clonal expansion. Immunity 45, 917–930 (2016).

  29. 29.

    Boyle, L. H., Goodall, J. C. & Gaston, J. S. H. Major histocompatibility complex class I-restricted alloreactive CD4+ T cells. Immunology 112, 54–63 (2004).

  30. 30.

    Wang, M. et al. High-affinity human leucocyte antigen class I binding variola-derived peptides induce CD4+ T cell responses more than 30 years post-vaccinia virus vaccination. Clin. Exp. Immunol. 155, 441–446 (2009).

  31. 31.

    Legoux, F. et al. Characterization of the human CD4(+) T-cell repertoire specific for major histocompatibility class I-restricted antigens. Eur. J. Immunol. 43, 3244–3253 (2013).

  32. 32.

    Heemskerk, M. H. et al. Dual HLA class I and class II restricted recognition of alloreactive T lymphocytes mediated by a single T cell receptor complex. Proc. Natl Acad. Sci. USA 98, 6806–6811 (2001).

  33. 33.

    Bossart, S. et al. Case report: encephalitis, with brainstem involvement, following checkpoint inhibitor therapy in metastatic melanoma. Oncologist 22, 749–753 (2017).

  34. 34.

    Andrews, S. FastQC: a quality control tool for high throughput sequence data. http://www.bioinformatics.babraham.ac.uk/projects/fastqc (2010).

  35. 35.

    Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

  36. 36.

    Wang, L., Wang, S. & Li, W. RSeQC: quality control of RNA-seq experiments. Bioinformatics 28, 2184–2185 (2012).

  37. 37.

    Li, B. & Dewey, C. N. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinforma. 12, 323 (2011).

  38. 38.

    Newman, A. M. et al. Robust enumeration of cell subsets from tissue expression profiles. Nat. Methods 12, 453–457 (2015).

  39. 39.

    Tumeh, P. C. et al. PD-1 blockade induces responses by inhibiting adaptive immune resistance. Nature 515, 568–571 (2014).

  40. 40.

    Mathew, J. M. et al. Generation and characterization of alloantigen-specific regulatory T cells for clinical transplant tolerance. Sci. Rep. 8, 1136 (2018).

  41. 41.

    Shugay, M. et al. VDJtools: unifying post-analysis of T cell receptor repertoires. PLoS Comput. Biol. 11, e1004503 (2015).

  42. 42.

    Shugay, M. et al. VDJdb: a curated database of T-cell receptor sequences with known antigen specificity. Nucleic Acids Res. 46, D419–D427 (2018).

  43. 43.

    Tickotsky, N., Sagiv, T., Prilusky, J., Shifrut, E. & Friedman, N. McPAS-TCR: a manually curated catalogue of pathology-associated T cell receptor sequences. Bioinformatics 33, 2924–2929 (2017).

  44. 44.

    Chen, G. et al. Sequence and structural analyses reveal distinct and highly diverse human CD8(+) TCR repertoires to immunodominant viral antigens. Cell Rep. 19, 569–583 (2017).

  45. 45.

    Gielis, S. et al. TCRex: a webtool for the prediction of T-cell receptor sequence epitope specificity. Preprint at bioRxiv https://doi.org/10.1101/373472 (2018).

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Acknowledgments

We thank the patients and their families for participating in this study. W.J.M. was supported by NHBLI grant no. T32HL069765, NIDDK grant nos. R01DK112262 and R56DK108352 and NHLBI grant no. K12HL143956. S.A.M. was supported by NIAID grant no. P30AI110527. D.B.J. is supported by NIH/NCI grant no. K23 CA204726 and the James C. Bradford Jr. Melanoma Fund. J.E.S. was supported by the Cancer ITMO of the French National Alliance for Life and Health Sciences (AVIESAN): ‘Plan Cancer 2014–2019’. J.M.B. was supported by the Department of Defense Era of Hope Award no. BC170037 and NIH/NCI grant no. R00CA181491. In addition, we acknowledge the Translational Pathology Shared Resource supported by NCI/NIH Cancer Center Support Grant no. 5P30 CA68485-19 and the Vanderbilt Mouse Metabolic Phenotyping Center Grant no. 2 U24 DK059637-16. The supplied data from VigiBase come from a variety of sources. The probability that a reported adverse event is related to a drug response is not equal in all cases; the information in VigiBase and these analyses does not represent the opinion of the World Health Organization or the Uppsala Monitoring Centre.

Author information

D.B.J., W.J.M. and J.M. Balko conceptualized the study. W.J.M., P.I.E.-G., J.S., K.B., Y.L., S.W. and J.M. Beecham developed the methodology. Software was designed by W.J.M. and J.M. Balko. D.B.J., W.J.M., P.I.E.-G., K.B., Y.L., S.W., J.B. and J.M. Beecham were in charge of validation. D.B.J., W.J.M., Y.W., Y.X. and J.M. Balko performed the formal analysis. C.A.C., D.Y.W., D.B.J., W.J.M., P.I.E.-G. and J.M. Balko led the investigation. Resources were sourced by R.A.-R., B.C.M., C.S., J.-E.S., A.M.M., M.T., G.V.L., J.V.C., A.C.G., M.O., S.C., A.C., B.L.-V., S.M.G., E.J.R., E.I.B., S.A.M., M.E.S., J.J.M., J.A.S. and J.M. Balko. D.B.J., D.Y.W., W.J.M., J.-E.S., Y.W., Y.X. and J.M. Balko curated the data. D.B.J., W.J.M. and J.M. Balko wrote the original draft. All authors reviewed and edited the paper. D.B.J., W.J.M. and J.M. Balko visualized the study. D.B.J., W.J.M. and J.M. Balko supervised the project and were in charge of its administration. Funding acquisition was done by D.B.J. and J.M. Balko.

Correspondence to Douglas B. Johnson or Justin M. Balko.

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Competing interests

K.B., J.B., Y.L. and S.W. are employees of NanoString and receive compensation as such. D.J. serves on advisory boards for Array, Bristol Myers Squibb, Genoptix, Incyte and Merck, and has received research funding from Bristol Myers Squibb and Incyte. J.M.B. receives consulting fees from Novartis and research support from Genentech and Incyte. J.J.M. serves as a consultant or in an advisory role for BMS, Daiichi Sankyo, Novartis, Pfizer, Regeneron, Takeda, Myokardia, Deciphera and Ipsen, and has received research funding from BMS and Pfizer. C.A.C. receives grant/research funding from Gilead Sciences, Inc. (related to hepatitis C virus). A.M.M. serves on advisory boards for Bristol Myers Squibb, Merck Sharpe and Dohme, Novartis, Roche, and Pierre-Fabre. E.I.B. serves on advisory boards for Bristol Myers Squibb and Novartis. G.V.L. reports receiving fees for serving on advisory boards of Aduro, Amgen, Array Biopharma, Bristol-Myers Squibb, Merck Sharp and Dohme (a subsidiary of Merck & Co., Inc., Kenilworth, NJ, USA), Novartis, Oncosec, Pierre Fabre and Roche.

Additional information

Peer review information: Saheli Sadanand was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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

Extended data

Extended Data Fig. 1 Inflammatory and myeloid/microglial infiltrate in infarct regions of brain.

a, H&E stain shows a dense chronic inflammatory infiltrate in the meninges (upper left; black arrow), focal hemorrhage in the underlining brain parenchyma (lower left; red arrow) and gliotic gray matter; ×20 magnification. b, CD68 positive cells at the interface of the necrotic area (black arrow) and the adjacent brain parenchyma (red arrow); ×20 magnification. c, Limited expression of CD20 staining in perivascular and parenchymal regions of brain; ×20 magnification. Stains were performed once on a single tissue section with a batch-controlled positive and negative control.

Extended Data Fig. 2 Lymphocytic and myeloid infiltrate in meninges, perivascular and parenchymal regions.

a, The lymphocytic infiltrate involving the meninges (upper image; upper left of image, black arrow), parenchyma and brain perivascular regions (upper image; lower right of image, red arrow and lower image) includes CD4. ×20 magnification. b, CD8+ positive T cells; ×20 magnification. c, CD68 positive cells accompanying lymphocytic infiltrate in meninges (upper left of image, black arrow) and brain parenchyma (lower right, red arrow); ×20 magnification. Stains were performed once on a single tissue section with a batch-controlled positive and negative control.

Extended Data Fig. 3 Absence of substantial inflammatory infiltrate in radiologically and macroscopically non-affected area.

a, H&E corresponding to radiologically and macroscopically non-affected area with preserved architecture and no apparent inflammatory infiltrate; ×20 magnification. b, CD4. c, CD8 stains demonstrating sparse presence or absence of T cell infiltrates in radiologically and macroscopically non-affected area; ×20 magnification. Stains were performed on a single tissue section with a batch-controlled positive and negative control.

Extended Data Fig. 4 PD-1 expression in inflamed region of brain.

PD-1 positive perivascular lymphocytes and pericytes; ×20 magnification. Stains were performed on a single tissue section with a batch-controlled positive and negative control.

Extended Data Fig. 5 Expression of T cell and NK cell markers of immune cell exhaustion.

Inflamed and non-inflamed adjacent regions of neural tissue were immunostained for CD244, CD160 and LAG-3; ×20 magnification. Stains were performed on a single tissue section with a batch-controlled positive and negative control.

Extended Data Fig. 6 RNA-seq analysis of encephalitic and unaffected tissue.

a, Absolute and relative quantification of immune subsets by CIBERSORT. b, Interferon-γ-inducible genes (HALLMARK_INTERFERON_GAMMA_RESPONSE; M5913; n = counts of 177 overlapping genes per sample) quantified in inflamed and unaffected regions by RNA-seq, as well as additional cases identified in Supplementary Table 3. * adjusted P < 0.0001 versus all other samples via analysis of variance (F-statistic = 59.47; DFn = 5; DFd = 1,056) with Tukey’s post hoc test to adjust for multiple comparisons; data shown are mean ± SEM. Source data

Extended Data Fig. 7 Analysis of repertoire overlap between inflamed brain and other resected and biopsied tissues.

In all panels, we display the frequency of TCRs detected in both the inflamed brain and the listed tissue using the ArcherDX Immunoverse platform, as well as the linear regression and its 95% CIs on a log10 scale. a, Lymph node biopsy (r2 = 3.19 × 10–8; 95% CI of slope, −0.03 to 0.03; F = 8.93 × 10−7; P = 0.99, df = 28). b, Prior mesentery resection (r2 = 0.04; 95% CI of slope, −0.09 to 0.02; F = 1.305; P = 0.26, df = 34). c, Brain recurrence scar—the TCR repertoire of the brain recurrence scar. d, Uninflamed brain overlapped significantly with that of the inflamed brain (r2 = 0.46; 95% CI of slope, 0.21 to 0.48; F = 28.19; P < 0.0001, df = 30); and (r2 = 0.17; 95% CI of slope, 0.07 to 0.70; F = 6.32; P = 0.02, df = 31)). EBV-specific clones in both the inflamed brain and the spleen. e,f, were observed at high frequency, though the shared TCR repertoires detected in the brain and spleen were poorly correlated ((r2 = 0.06; 95% CI of slope, −0.04 to 0.25; F = 2.03; P = 0.16, df = 32) and (r2 = 5.67 × 103; 95% CI of slope, −0.20 to 0.32; F = 0.23; P = 0.63, df = 41)). g, Hierarchical clustering on the F2 distance metric was used to evaluate the similarity of the TCR repertoires from each of the tissue sites sampled from the patient. As also observed above, the brain and spleen samples from 2016 and the time of death were most similar to each other and that the brain-resident TCR repertoire appeared to be somewhat stable over time. The TCR repertoire of the lymph node nearest to the original tumor was highly distinct in comparison with the other samples.

Extended Data Fig. 8 Overlap of HLA-A*02:01-restricted known EBV-specific TCR with CD8+ Ki67+ and GZMB+ phenotypes.

Representative images of the TCRβ RNA-ISH probe overlaid with IHC markers. Stains were performed on a single tissue section with a batch-controlled positive and negative control.

Extended Data Fig. 9 Evidence of latent EBV infection at the site of encephalitic inflammation.

a,b, EBER(1/2) staining of lymphocytes by RNA in situ hybridization in the cortex and meninges (a) and of the encephalitic infarct (b). c,d, EBNA1 staining of lymphocytes by RNA in situ hybridization in the cortex and meninges (c) and of the encephalitic infarct (d). e, Positive EBER staining of rare lymphocytes in lymph node resection pre-dating anti-PD-1 therapy, suggesting historic EBV infection. f, Positive EBER staining of rare tumor cells in brain metastasis resection predating anti-PD-1 therapy, also suggesting historic EBV infection. Stains were performed on a single tissue section with a batch-controlled positive and negative control.

Extended Data Fig. 10 Lack of detection of EBER+ cells in two additional cases of checkpoint inhibitor encephalitis.

EBER stain of neural tissue from additional case 1 and additional case 2 (details of case in methods). Stains were performed on a single tissue section with a batch-controlled positive and negative control.

Supplementary information

Supplementary Information

Supplementary Tables 1–5

Reporting Summary

Source data

Source Data Extended Data Fig. 6

Statistical source data for Extended Data 6; TPM counts from processed RNA-seq data

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