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.

  • Article
  • Published:

Post-infusion CAR TReg cells identify patients resistant to CD19-CAR therapy

Nature Medicine volume 28, pages 1860–1871 (2022)Cite this article

Subjects

Abstract

Approximately 60% of patients with large B cell lymphoma treated with chimeric antigen receptor (CAR) T cell therapies targeting CD19 experience disease progression, and neurotoxicity remains a challenge. Biomarkers associated with resistance and toxicity are limited. In this study, single-cell proteomic profiling of circulating CAR T cells in 32 patients treated with CD19-CAR identified that CD4+Helios+ CAR T cells on day 7 after infusion are associated with progressive disease and less severe neurotoxicity. Deep profiling demonstrated that this population is non-clonal and manifests hallmark features of T regulatory (TReg) cells. Validation cohort analysis upheld the link between higher CAR TReg cells with clinical progression and less severe neurotoxicity. A model combining expansion of this subset with lactate dehydrogenase levels, as a surrogate for tumor burden, was superior for predicting durable clinical response compared to models relying on each feature alone. These data credential CAR TReg cell expansion as a novel biomarker of response and toxicity after CAR T cell therapy and raise the prospect that this subset may regulate CAR T cell responses in humans.

Access through your institution
Buy or subscribe

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

Access options

Access through your institution

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Clinical response in patients with LBCL treated with axi-cel is not associated with peripheral CAR T cell expansion.
Fig. 2: CyTOF identifies metaclusters of circulating CAR T cells associated with long-term clinical response or neurotoxicity.
Fig. 3: Helios-expressing population of circulating CD4+ CAR T cells on day 7 is associated with clinical progression and reduced neurotoxicity.
Fig. 4: CD4+CD57–Helios+ CAR T cells express FOXP3 and are not cytotoxic.
Fig. 5: Deep phenotyping with single-cell sequencing upholds TReg identity of CD4+CD57–Helios+ CAR T cells.
Fig. 6: CAR TReg cells and tumor burden surrogate identify patients with clinical progression.

Similar content being viewed by others

Data availability

All data associated with this study can be accessed through the Stanford Digital Repository at https://purl.stanford.edu/qb215vz6111. Raw singe-cell sequencing data are available through the Gene Expression Omnibus (accession number GSE168940).

References

  1. Abramson, J. S. et al. Lisocabtagene maraleucel for patients with relapsed or refractory large B-cell lymphomas (TRANSCEND NHL 001): a multicentre seamless design study. Lancet 396, 839–852 (2020).

    Article  PubMed  Google Scholar 

  2. Schuster, S. J. et al. Tisagenlecleucel in adult relapsed or refractory diffuse large B-cell lymphoma. N. Engl. J. Med. 380, 45–56 (2019).

    Article  CAS  PubMed  Google Scholar 

  3. Neelapu, S. S. et al. Axicabtagene ciloleucel CAR T-cell therapy in refractory large B-cell lymphoma. N. Engl. J. Med. 377, 2531–2544 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Maude, S. L. et al. Tisagenlecleucel in children and young adults with B-cell lymphoblastic leukemia. N. Engl. J. Med. 378, 439–448 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Nastoupil, L. J. et al. Standard-of-care axicabtagene ciloleucel for relapsed or refractory large B-cell lymphoma: results from the US Lymphoma CAR T Consortium. J. Clin. Oncol. 38, 3119–3128 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  6. Locke, F. L. et al. Long-term safety and activity of axicabtagene ciloleucel in refractory large B-cell lymphoma (ZUMA-1): a single-arm, multicentre, phase 1-2 trial. Lancet Oncol. 20, 31–42 (2019).

    Article  CAS  PubMed  Google Scholar 

  7. Jacobson, C. A. et al. Axicabtagene ciloleucel in the non-trial setting: outcomes and correlates of response, resistance, and toxicity. J. Clin. Oncol. 38, 3095–3106 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Locke, F. L. et al. Axicabtagene ciloleucel as second-line therapy for large B-cell lymphoma. N. Engl. J. Med. 386, 640–654 (2022).

    Article  CAS  PubMed  Google Scholar 

  9. Lee, D. W. et al. ASTCT consensus grading for cytokine release syndrome and neurologic toxicity associated with immune effector cells. Biol. Blood Marrow Transpl. 25, 625–638 (2019).

    Article  CAS  Google Scholar 

  10. Neelapu, S. S. et al. Chimeric antigen receptor T-cell therapy—assessment and management of toxicities. Nat. Rev. Clin. Oncol. 15, 47–62 (2018).

    Article  CAS  PubMed  Google Scholar 

  11. Brown, C. E. & Mackall, C. L. CAR T cell therapy: inroads to response and resistance. Nat. Rev. Immunol. 19, 73–74 (2019).

    Article  CAS  PubMed  Google Scholar 

  12. Deng, Q. et al. Characteristics of anti-CD19 CAR T cell infusion products associated with efficacy and toxicity in patients with large B cell lymphomas. Nat. Med. 26, 1878–1887 (2020).

  13. Rossi, J. et al. Preinfusion polyfunctional anti-CD19 chimeric antigen receptor T cells are associated with clinical outcomes in NHL. Blood 132, 804–814 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Fraietta, J. A. et al. Determinants of response and resistance to CD19 chimeric antigen receptor (CAR) T cell therapy of chronic lymphocytic leukemia. Nat. Med. 24, 563–571 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Locke, F. L. et al. Tumor burden, inflammation, and product attributes determine outcomes of axicabtagene ciloleucel in large B-cell lymphoma. Blood Adv. 4, 4898–4911 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Frank, M. J. et al. Monitoring of circulating tumor DNA improves early relapse detection after axicabtagene ciloleucel infusion in large B-cell lymphoma: results of a prospective multi-institutional trial. J. Clin. Oncol. 39, 3034–3043 (2021).

    Article  CAS  PubMed  Google Scholar 

  17. Majzner, R. G. & Mackall, C. L. Tumor antigen escape from CAR T-cell therapy. Cancer Discov. 8, 1219–1226 (2018).

    Article  CAS  PubMed  Google Scholar 

  18. Zhang, Z. et al. Point mutation in CD19 facilitates immune escape of B cell lymphoma from CAR-T cell therapy. J. Immunother. Cancer 8, e001150 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  19. Majzner, R. G. et al. Tuning the antigen density requirement for CAR T-cell activity. Cancer Discov. 10, 702–723 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Lynn, R. C. et al. c-Jun overexpression in CAR T cells induces exhaustion resistance. Nature 576, 293–300 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Norelli, M. et al. Monocyte-derived IL-1 and IL-6 are differentially required for cytokine-release syndrome and neurotoxicity due to CAR T cells. Nat. Med. 24, 739–748 (2018).

    Article  CAS  PubMed  Google Scholar 

  22. Gust, J. et al. Endothelial activation and blood–brain barrier disruption in neurotoxicity after adoptive immunotherapy with CD19 CAR-T cells. Cancer Discov. 7, 1404–1419 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Parker, K. R. et al. Single-cell analyses identify brain mural cells expressing CD19 as potential off-tumor targets for CAR-T immunotherapies. Cell 183, 126–142 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Taraseviciute, A. et al. Chimeric antigen receptor T cell-mediated neurotoxicity in nonhuman primates. Cancer Discov. 8, 750–763 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Chou, C. K. & Turtle, C. J. Insight into mechanisms associated with cytokine release syndrome and neurotoxicity after CD19 CAR-T cell immunotherapy. Bone Marrow Transpl. 54, 780–784 (2019).

    Article  Google Scholar 

  26. Santomasso, B. D. et al. Clinical and biological correlates of neurotoxicity associated with CAR T-cell therapy in patients with B-cell acute lymphoblastic leukemia. Cancer Discov. 8, 958–971 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Jena, B. et al. Chimeric antigen receptor (CAR)-specific monoclonal antibody to detect CD19-specific T cells in clinical trials. PLoS ONE 8, e57838 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Wei, J. et al. The model of cytokine release syndrome in CAR T-cell treatment for B-cell non-Hodgkin lymphoma. Signal Transduct. Target Ther. 5, 134 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Bendall, S. C. et al. Single-cell mass cytometry of differential immune and drug responses across a human hematopoietic continuum. Science 332, 687–696 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Tibshirani, R. Regression shrinkage and selection via the lasso. J. R. Stat. Soc. Series B 58, 267–288 (1996).

  31. Azuma, M., Phillips, J. H. & Lanier, L. L. CD28− T lymphocytes. Antigenic and functional properties. J. Immunol. 150, 1147–1159 (1993).

    CAS  PubMed  Google Scholar 

  32. Lun, A. T. L., Richard, A. C. & Marioni, J. C. Testing for differential abundance in mass cytometry data. Nat. Methods 14, 707–709 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Walker, A. J. et al. Tumor antigen and receptor densities regulate efficacy of a chimeric antigen receptor targeting anaplastic lymphoma kinase. Mol. Ther. 25, 2189–2201 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Kared, H., Martelli, S., Ng, T. P., Pender, S. L. & Larbi, A. CD57 in human natural killer cells and T-lymphocytes. Cancer Immunol. Immunother. 65, 441–452 (2016).

    Article  CAS  PubMed  Google Scholar 

  35. Spiegel, J. Y. et al. CAR T cells with dual targeting of CD19 and CD22 in adult patients with recurrent or refractory B cell malignancies: a phase 1 trial. Nat. Med. 27, 1419–1431 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Stoeckius, M. et al. Simultaneous epitope and transcriptome measurement in single cells. Nat. Methods 14, 865–868 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Kern, F. et al. Distribution of human CMV-specific memory T cells among the CD8pos. subsets defined by CD57, CD27, and CD45 isoforms. Eur. J. Immunol. 29, 2908–2915 (1999).

    Article  CAS  PubMed  Google Scholar 

  39. Seo, H. et al. TOX and TOX2 transcription factors cooperate with NR4A transcription factors to impose CD8+ T cell exhaustion. Proc. Natl Acad. Sci. USA 116, 12410–12415 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Scott, A. C. et al. TOX is a critical regulator of tumour-specific T cell differentiation. Nature 571, 270–274 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Khan, O. et al. TOX transcriptionally and epigenetically programs CD8+ T cell exhaustion. Nature 571, 211–218 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Alfei, F. et al. TOX reinforces the phenotype and longevity of exhausted T cells in chronic viral infection. Nature 571, 265–269 (2019).

    Article  CAS  PubMed  Google Scholar 

  43. Chen, J. et al. NR4A transcription factors limit CAR T cell function in solid tumours. Nature 567, 530–534 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Bengsch, B. et al. Epigenomic-guided mass cytometry profiling reveals disease-specific features of exhausted CD8 T cells. Immunity 48, 1029–1045 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Tirosh, I. et al. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science 352, 189–196 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Good, C. R. et al. An NK-like CAR T cell transition in CAR T cell dysfunction. Cell 184, 6081–6100 (2021).

    Article  CAS  PubMed  Google Scholar 

  47. Lee, D. W. et al. T cells expressing CD19 chimeric antigen receptors for acute lymphoblastic leukaemia in children and young adults: a phase 1 dose-escalation trial. Lancet 385, 517–528 (2015).

    Article  CAS  PubMed  Google Scholar 

  48. Sauter, C. S. et al. CD19 CAR T cells following autologous transplantation in poor-risk relapsed and refractory B-cell non-Hodgkin lymphoma. Blood 134, 626–635 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Shah, N. N. et al. Bispecific anti-CD20, anti-CD19 CAR T cells for relapsed B cell malignancies: a phase 1 dose escalation and expansion trial. Nat. Med. 26, 1569–1575 (2020).

    Article  CAS  PubMed  Google Scholar 

  50. Wang, X. et al. Phase 1 studies of central memory-derived CD19 CAR T-cell therapy following autologous HSCT in patients with B-cell NHL. Blood 127, 2980–2990 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Kebriaei, P. et al. Phase I trials using Sleeping Beauty to generate CD19-specific CAR T cells. J. Clin. Invest. 126, 3363–3376 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  52. Lamure, S. et al. Clinical and product features associated with outcome of DLBCL patients to CD19-targeted CAR T-cell therapy. Cancers (Basel) 13, 4279 (2021).

    Article  CAS  Google Scholar 

  53. Kim, H. J. et al. Stable inhibitory activity of regulatory T cells requires the transcription factor Helios. Science 350, 334–339 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Huehn, J., Polansky, J. K. & Hamann, A. Epigenetic control of FOXP3 expression: the key to a stable regulatory T-cell lineage? Nat. Rev. Immunol. 9, 83–89 (2009).

    Article  CAS  PubMed  Google Scholar 

  55. Lopez-Verges, S. et al. CD57 defines a functionally distinct population of mature NK cells in the human CD56dimCD16+ NK-cell subset. Blood 116, 3865–3874 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Abo, T. & Balch, C. M. A differentiation antigen of human NK and K cells identified by a monoclonal antibody (HNK-1). J. Immunol. 127, 1024–1029 (1981).

    CAS  PubMed  Google Scholar 

  57. Dolstra, H. et al. Expansion of CD8+CD57+ T cells after allogeneic BMT is related with a low incidence of relapse and with cytomegalovirus infection. Br. J. Haematol. 90, 300–307 (1995).

    Article  CAS  PubMed  Google Scholar 

  58. Melenhorst, J. J. et al. Decade-long leukaemia remissions with persistence of CD4+ CAR T cells. Nature 602, 503–509 (2022).

  59. Sheih, A. et al. Clonal kinetics and single-cell transcriptional profiling of CAR-T cells in patients undergoing CD19 CAR-T immunotherapy. Nat. Commun. 11, 219 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Cheson, B. D. et al. Recommendations for initial evaluation, staging, and response assessment of Hodgkin and non-Hodgkin lymphoma: the Lugano classification. J. Clin. Oncol. 32, 3059–3068 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  61. Lee, D. W. et al. Current concepts in the diagnosis and management of cytokine release syndrome. Blood 124, 188–195 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Long, A. H. et al. 4-1BB costimulation ameliorates T cell exhaustion induced by tonic signaling of chimeric antigen receptors. Nat. Med. 21, 581–590 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Fienberg, H. G., Simonds, E. F., Fantl, W. J., Nolan, G. P. & Bodenmiller, B. A platinum-based covalent viability reagent for single-cell mass cytometry. Cytometry A 81, 467–475 (2012).

    Article  PubMed  Google Scholar 

  64. Zunder, E. R. et al. Palladium-based mass tag cell barcoding with a doublet-filtering scheme and single-cell deconvolution algorithm. Nat. Protoc. 10, 316–333 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Finck, R. et al. Normalization of mass cytometry data with bead standards. Cytometry A 83, 483–494 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  66. Hahne, F. et al. flowCore: a Bioconductor package for high throughput flow cytometry. BMC Bioinformatics 10, 106 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  67. Van Gassen, S. et al. FlowSOM: using self-organizing maps for visualization and interpretation of cytometry data. Cytometry A 87, 636–645 (2015).

    Article  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Hafemeister, C. & Satija, R. Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. Genome Biol. 20, 296 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Gillespie, M. et al. The reactome pathway knowledgebase 2022. Nucleic Acids Res. 50, D687–D692 (2022).

    Article  CAS  PubMed  Google Scholar 

  71. Gu, Z., Eils, R. & Schlesner, M. Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics 32, 2847–2849 (2016).

    Article  CAS  PubMed  Google Scholar 

  72. Rowland, M. & Tozer, T. N. Clinical Pharmacokinetics and Pharmacodynamics—Concepts and Applications (Wolters Kluwer Health/Lippincott William & Wilkins, 2011).

  73. Friedman, J., Hastie, T. & Tibshirani, R. Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw. 33, 1–22 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  74. Kuhn, M. Building predictive models in R using the caret package. J. Stat. Softw. 28, 1–26 (2008).

    Article  Google Scholar 

Download references

Acknowledgements

We thank T. Murty and C. Ramello for critical review of the manuscript. R. Majzner, A. M. Tousley and S. Heitzeneder provided healthy T cells expressing the CD19-CD28ζ CAR construct. Monoclonal anti-FMC63 idiotype antibody was kindly provided by B. Jena and L. J. N. Cooper. This work was supported by the California Institute for Regenerative Medicine (award CLIN2-10846; principal investigator: C.L.M.); the National Cancer Institute (NCI) (5P30CA124435, C.L.M.; 2P01CA049605-29A1, C.L.M. and D.B.M.; and U54-CA209971, S.K.P.); a sponsored research agreement with Kite Pharma, a subsidiary of Gilead Sciences (D.B.M.); and a St. Baldrick’s/Stand Up 2 Cancer Pediatric Dream Team Translational Cancer Research Grant (C.L.M.). Stand Up 2 Cancer is a program of the Entertainment Industry Foundation administered by the American Association for Cancer Research. This study was also supported by the Virginia and D. K. Ludwig Fund for Cancer Research (C.L.M.). Z.G. was supported by fellowships from the Parker Institute for Cancer Immunotherapy and the Stanford Cancer Institute, an NCI-designated Comprehensive Cancer Center. Z.G., S.P., S.C.B., D.B.M. and C.L.M. are members of the Parker Institute for Cancer Immunotherapy, which supports the Stanford University Cancer Immunotherapy Program. The Illumina HiSeq 4000 used here was purchased with National Institutes of Health funds (award S10OD018220).

Author information

Author notes

  1. Jay Y. Spiegel

    Present address: Sylvester Comprehensive Cancer Center, University of Miami, Miami, FL, USA

  2. Sreevidya Kurra

    Present address: Homer Stryker M.D. School of Medicine, Western Michigan University, Kalamazoo, MI, USA

  3. Anita Wong Lin

    Present address: Cancer Research Lab, Flow Cytometry Core Facility, University of California, Berkeley, Berkeley, CA, USA

  4. John H. Baird

    Present address: Department of Hematology and Hematopoietic Cell Transplantation, Division of Lymphoma, City of Hope National Medical Center, Duarte, CA, USA

  5. Juliana Craig

    Present address: School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA

  6. These authors contributed equally: Zinaida Good, Jay Y. Spiegel.

Authors and Affiliations

  1. Center for Cancer Cell Therapy, Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA

    Zinaida Good, Jay Y. Spiegel, Bita Sahaf, Meena B. Malipatlolla, Zach J. Ehlinger, Sreevidya Kurra, Moksha H. Desai, Warren D. Reynolds, Anita Wong Lin, Panayiotis Vandris, Fang Wu, Snehit Prabhu, Mark P. Hamilton, Paul J. Hanson, Shabnum Patel, Steven A. Feldman, Matthew J. Frank, John H. Baird, Lori Muffly, Gursharan K. Claire, Juliana Craig, Katherine A. Kong, Sean C. Bendall, David B. Miklos & Crystal L. Mackall

  2. Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA

    Zinaida Good, John S. Tamaresis, Robert J. Tibshirani & Sylvia K. Plevritis

  3. Parker Institute for Cancer Immunotherapy, Stanford University School of Medicine, Stanford, CA, USA

    Zinaida Good, Sean C. Bendall & Crystal L. Mackall

  4. Department of Medicine, Division of Blood and Marrow Transplantation and Cellular Therapy, Stanford University School of Medicine, Stanford, CA, USA

    Jay Y. Spiegel, Mark P. Hamilton, Paul J. Hanson, Matthew J. Frank, John H. Baird, Lori Muffly, Gursharan K. Claire, Juliana Craig, David B. Miklos & Crystal L. Mackall

  5. Laboratory for Cell and Gene Medicine, Stanford University School of Medicine, Stanford, CA, USA

    Shabnum Patel & Steven A. Feldman

  6. Syncopation Life Sciences, San Mateo, CA, USA

    Shabnum Patel

  7. Stanford Genomics Facility, Stanford University School of Medicine, Stanford, CA, USA

    Dhananjay Wagh & John Coller

  8. Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA

    Sean C. Bendall

  9. Department of Statistics, Stanford University, Stanford, CA, USA

    Robert J. Tibshirani

  10. Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA

    Sylvia K. Plevritis

  11. Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA

    Crystal L. Mackall

Authors
  1. Zinaida Good
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Jay Y. Spiegel
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Bita Sahaf
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Meena B. Malipatlolla
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Zach J. Ehlinger
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Sreevidya Kurra
    View author publications

    You can also search for this author inPubMed Google Scholar

  7. Moksha H. Desai
    View author publications

    You can also search for this author inPubMed Google Scholar

  8. Warren D. Reynolds
    View author publications

    You can also search for this author inPubMed Google Scholar

  9. Anita Wong Lin
    View author publications

    You can also search for this author inPubMed Google Scholar

  10. Panayiotis Vandris
    View author publications

    You can also search for this author inPubMed Google Scholar

  11. Fang Wu
    View author publications

    You can also search for this author inPubMed Google Scholar

  12. Snehit Prabhu
    View author publications

    You can also search for this author inPubMed Google Scholar

  13. Mark P. Hamilton
    View author publications

    You can also search for this author inPubMed Google Scholar

  14. John S. Tamaresis
    View author publications

    You can also search for this author inPubMed Google Scholar

  15. Paul J. Hanson
    View author publications

    You can also search for this author inPubMed Google Scholar

  16. Shabnum Patel
    View author publications

    You can also search for this author inPubMed Google Scholar

  17. Steven A. Feldman
    View author publications

    You can also search for this author inPubMed Google Scholar

  18. Matthew J. Frank
    View author publications

    You can also search for this author inPubMed Google Scholar

  19. John H. Baird
    View author publications

    You can also search for this author inPubMed Google Scholar

  20. Lori Muffly
    View author publications

    You can also search for this author inPubMed Google Scholar

  21. Gursharan K. Claire
    View author publications

    You can also search for this author inPubMed Google Scholar

  22. Juliana Craig
    View author publications

    You can also search for this author inPubMed Google Scholar

  23. Katherine A. Kong
    View author publications

    You can also search for this author inPubMed Google Scholar

  24. Dhananjay Wagh
    View author publications

    You can also search for this author inPubMed Google Scholar

  25. John Coller
    View author publications

    You can also search for this author inPubMed Google Scholar

  26. Sean C. Bendall
    View author publications

    You can also search for this author inPubMed Google Scholar

  27. Robert J. Tibshirani
    View author publications

    You can also search for this author inPubMed Google Scholar

  28. Sylvia K. Plevritis
    View author publications

    You can also search for this author inPubMed Google Scholar

  29. David B. Miklos
    View author publications

    You can also search for this author inPubMed Google Scholar

  30. Crystal L. Mackall
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

C.L.M. and D.B.M. conceived the study, secured funding and supervised the project. J.Y.S., M.J.F., J.H.B., L.M., G.K.C., J.C., K.A.K, M.P.H., P.J.H. and D.B.M. treated patients and/or acquired clinical samples and data. Z.G., M.B.M., P.V., B.S, A.W. and Z.E. designed and performed flow cytometry assays. S.P. and S.A.F. developed the qPCR assay. W.R. and B.S. performed the qPCR assay. M.B.M. and B.S. developed the panel and performed CyTOF, with guidance from S.C.B. and Z.G. Z.G., S.K., M.H.D., B.S., D.W. and J.C. developed and performed single-cell sequencing assays. Z.G., S.P. and M.P.H. analyzed CyTOF and single-cell sequencing data, with advice from S.K.P., R.J.T. and J.C. Z.G., J.Y.S. and J.S.T. analyzed the clinical, flow cytometry and qPCR data and performed statistical analyses, with advice from R.J.T. Z.G., J.Y.S., D.B.M. and C.L.M. interpreted the results and wrote the first draft of the paper. All authors critically reviewed the manuscript.

Corresponding authors

Correspondence to David B. Miklos or Crystal L. Mackall.

Ethics declarations

Competing interests

Z.G. is an inventor on two patent applications, holds equity in Boom Capital Ventures and is a consultant for Mubadala Ventures, GLG, AlphaSights and Atheneum Partners, all of which are related to the cancer immunotherapy space. S.A.F. holds patents in the field of cell and gene therapy and serves on the scientific advisory boards for Alaunos Therapeutics and FreshWind Biotechnologies. D.B.M. holds a patent with Pharmacyclics supporting ibrutinib for chronic graft-versus-host disease and receives consulting or research fees or serves as an advisor for Pharmacyclics, Kite Pharma, Adaptive Biotechnologies, Novartis, Juno Therapeutics, Celgene, Janssen Pharmaceuticals, Roche, Genentech, Precision Bioscience, Allogene and Miltenyi Biotec. C.L.M. is an inventor on numerous CAR T cell immunotherapy patent applications and received royalties for the CD22-CAR from the National Institutes of Health after licensure to Opus Bio and Juno Therapeutics. C.L.M. is a cofounder of Lyell Immunopharma, Syncopation Life Sciences and Link Cell Therapies, which are developing CAR-based therapies, and consults for Lyell, Syncopation, Link, Mammoth, Ensoma, NeoImmune Tech, Apricity, Nektar, Immatics, GlaxoSmithKline and Bristol Myers Squibb. None of the above interests is related to the research described in this manuscript. All other authors declare no competing interests.

Peer review

Peer review information

Nature Medicine thanks Sattva Neelapu and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary handling editor: Joao Monteiro, in collaboration with the Nature Medicine team

Additional information

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

Extended data

Extended Data Fig. 1 Peripheral CAR T cell expansion is associated with toxicity and not with clinical response at 6 months.

a, Absolute counts of CD4+ (left) and CD8+ (right) CAR T cells in blood on days 7, 14, 21, and 28 following axi-cel infusion (n = 32 patients, 128 observations). LOD, limit of detection. b, CAR T cell AUMC0-28 for patients in CR or PD at 6 months (n = 29; patients 042 and 058 had PR and SD at 6 months, respectively; patient 032 died from a non-progression related cause prior to 6 months). c, CAR T cell AUC0-28 (left) and AUMC0-28 (right) stratified by the best response as CR or other (PR, n = 9; SD, n = 1; PD, n = 2; no data for patient 058) (n = 31). d, Absolute CAR T cell counts in blood on days 7, 14, 21, and 28 for patients in CR or PD at 6 months (n = 28 on day 7, n = 28 on day 14, n = 26 on day 21, n = 26 on day 28). e, Absolute counts of circulating CAR T cells at peak expansion for patients in CR or PD at 6 months (n = 29). f, Quantitative PCR (qPCR) measuring CAR copies per 50 ng DNA in blood over 28 days as CAR T AUC0-28 (left) and AUMC0-28 (right) stratified for patients in CR or PD at 6 months (n = 28). g, qPCR CAR T AUC0-28 (left) and AUMC0-28 (right) stratified by the best response at 6 months as CR or other (PR, SD, PD) (n = 30). h, qPCR CAR copies per 50 ng DNA in blood at peak expansion for patients in CR or PD at 6 months (n = 28). i, CAR T cell AUC0-28 (left) and AUMC0-28 (right) stratified by maximum CRS grade (n = 31). j, Absolute CAR T cell counts in blood at peak expansion (n = 32) and on days 7 (n = 31), 14 (n = 30), 21 (n = 28), and 28 (n = 28) stratified by maximum CRS grade. k, CAR T cell AUMC0-28 stratified by maximum ICANS grade (n = 31). l, Absolute CAR T cell counts in blood at peak expansion (n = 32) and on days 7 (n = 31), 14 (n = 30), 21 (n = 28), and 28 (n = 28) stratified by maximum ICANS grade. Boxplots in (b-l) show quartiles with a band at median, whiskers indicating 1.5x IQR, and all observations overlaid as dots. P values are from two-sided Mann-Whitney U test.

Extended Data Fig. 2 Protein expression in CyTOF metaclusters of circulating CAR T cells on day 7.

a, Expression of 24 proteins overlaid onto the minimum spanning tree from Fig. 2c, which shows hierarchical consensus clustering of circulating CAR+ T cells on day 7 following axi-cel infusion (n = 31 patients), with 25 clusters grouped into 10 metaclusters. No CyTOF data were obtained for patient 038. Expressions of CD45 and CAR are not shown, as these proteins were used for gating and were not used for clustering. See Fig. 2g for the expression of the remaining proteins. b, Contour plots showing expression of exhaustion markers CD39 and CD279 (PD1) against senescence marker CD57 in CAR– and CAR+ T cells, as well as in CAR T cell metaclusters 3, 4, and 6, for patient 004. Geometric mean for each marker on the X-axis is indicated in the top right corner of each plot.

Extended Data Fig. 3 CAR T cell hyperspheres associated with clinical response at 6 months.

a, Schematic for differential abundance analysis comparing circulating CAR T cells on day 7 between patients in CR or PD at 6 months following axi-cel infusion (n = 28). CAR T cells falling into the same region of space in all dimensions (hypersphere) were quantified to generate comparison metrics for patients in CR vs. PD at 6 months. b, Volcano plot showing hyperspheres generated as described in (a) that are significantly differentially abundant between patients in CR or PD at 6 months. c, Log2 fold change overlaid onto hyperspheres from (a) that were embedded into UMAP coordinates. Groups of hyperspheres that correspond to the 3 CAR T cell populations identified by the lasso model in Fig. 2d are highlighted. d, FDR-corrected P values overlaid onto hyperspheres from (c) and stratified by the statistical significance threshold of P < 0.05. e, Individual marker expression overlaid onto hyperspheres from (c).

Extended Data Fig. 4 CAR T cell hyperspheres associated with severe neurotoxicity.

a, Schematic for differential abundance analysis comparing circulating CAR T cells on day 7 between patients with low (max ICANS grade 0-1) or severe (max ICANS grade 2-4) neurotoxicity (n = 31). CAR T cells falling into the same region of space in all dimensions (hypersphere) were quantified to generate comparison metrics for patients with low or severe neurotoxicity. b, Volcano plot showing hyperspheres generated as described in (a) that are significantly differentially abundant between patients with low (max ICANS grade 0-1) or severe (max ICANS grade 2-4) neurotoxicity. c, Log2 fold change overlaid onto hyperspheres from (b) that were embedded into UMAP coordinates. Groups of hyperspheres that correspond to the two CAR T cell populations identified by the lasso model in Fig. 2h are highlighted. d, FDR-corrected P values overlaid onto hyperspheres from (c) and stratified by the statistical significance threshold of P < 0.05. e, Individual marker expression overlaid onto hyperspheres from (c).

Extended Data Fig. 5 Dynamics of CAR T cell populations associated with clinical response or neurotoxicity.

a, Percentage of circulating T cells in 3 gates based on CyTOF data, as defined in Fig. 3a, prior to axi-cel infusion for patients in CR or PD at 6 months (n = 27; no pre-infusion sample for patient 005). b, Percentage of circulating CAR T cells on day 21 in 3 gates for patients in CR or PD at 6 months (n = 28; n = 27 for CD4+ populations: n.d. for patient 040). c,d, Percentage of circulating CAR-negative T cells in 3 gates on day 7 (c) or day 21 (d) for patients in CR or PD at 6 months (n = 28). e, Gate defined based on metacluster 8 from the lasso model for predicting maximum ICANS grade as 0-1 vs. 2-4 based on metacluster abundance of circulating CAR T cells on day 7 (Fig. 2j). Contour plots show CyTOF data for CAR+ T cells on day 7 from patients 042 (max ICANS grade 0) and 050 (max ICANS grade 3). f, Percentage of circulating CD57+CD101+ cells among CD8+ CAR T cells on day 7 in gate from (e) for patients with maximum ICANS grade 0-1 or 2-4 (n = 31). g, Percentage of CD57–Helios+ cells among CD4+ T cells in blood prior to axi-cel infusion (left; n = 30) or among CD4+ CAR T cells on day 21 post-infusion (right; n = 30) for patients with maximum ICANS grade 0-1 or 2-4. Boxplots in (a-d,f,g) show quartiles with a band at median, whiskers indicating 1.5x IQR, and all observations overlaid as dots. P values are from two-sided Mann-Whitney U test.

Extended Data Fig. 6 CMV status is not associated with prevalence of CD57-expressing CAR T cell populations or patient outcome.

a, Percentage of circulating CD57+, CD4+CD57+, CD4+CD57+T-bet+, and CD8+CD57+T-bet+ T cells based on CyTOF data prior to axi-cel infusion stratified by patient cytomegalovirus (CMV) infection status (n = 30). b-e, Percentage of circulating CD57+ populations of CAR-negative (b-c) or CAR-positive (d-e) T cells on day 7 (b,d) or day 21 (c,e) stratified by patient CMV status (n = 31; n = 30 for CD4+ CAR+ T cells on day 21). f, Kaplan-Meier analysis of time to progression (TTP; left) and overall survival (OS; right) stratified by patient CMV status. Boxplots in (a-e) show quartiles with a band at median, whiskers indicating 1.5x IQR, and all observations overlaid as dots. P values are from two-sided Mann-Whitney U test.

Extended Data Fig. 7 Identified populations in healthy donors and CD19-CD28ζ CAR-transduced T cells.

a,b, Contour plots show three identified populations among T cells from a healthy donor (a) and among CD19-CD28ζ CAR-transduced T cells generated in the lab (b). Population statistics for two donors are shown as mean ± SEM on each plot. c, Percentage of FOXP3+CD25High and FOXP3+Helios+ cells among CD4+ T cells are shown for a healthy donor (top) and for CD19-CD28ζ CAR-transduced T cells generated in the lab (bottom). Population statistics for two donors are shown as mean ± SEM on each plot. d, Cryopreserved T cells from a healthy donor were incubated with PMA and ionomycin for 6 hours and analyzed by flow cytometry. Contour plots show gating strategy that was applied to patient samples in Fig. 4.

Extended Data Fig. 8 Selected gene and surface protein expression in three identified CAR T cell populations.

a, CAR+ T cells were sorted from 9 LBCL patients on day 7 following axi-cel infusion and analyzed by scRNA-seq, scTCR-seq, and CITE-seq on the 10X Genomics platform. Patient IDs overlaid onto the wnnUMAP coordinates that integrate scRNA-seq and CITE-seq data (n = 6,316 cells). Numbers of filtered cells analyzed for each patient are indicated in parentheses. b, Cell subsets, which were defined by projecting data onto the public reference dataset containing leukocytes from healthy donors using Azimuth, overlaid onto the wnnUMAP coordinates from (a). c, Heatmap showing distribution of selected mRNA and surface epitope markers of TReg and TEFF subsets across patients in 120 cells sampled from each CAR T-cell populations defined in Fig. 5b (n = 480 cells). d, Expression of selected genes overlaid onto the wnnUMAP coordinates from (a). Protein encoded by each gene is shown in parentheses. e, Surface expression of selected proteins overlaid onto the wnnUMAP coordinates from (a). f, Violin plots showing selected gene and surface protein expression across CAR T cell populations defined in Fig. 5b (n = 6,316 cells). Stars denote significant (P < 0.05) upregulation in the indicated population relative to all populations without stars. Other significant relationships are not denoted. P values were calculated using Kruskal-Wallis H test, followed by unpaired two-sided Wilcoxon-Mann Whitney U test applied to each treatment pair, with Bonferroni correction for multiple hypothesis testing. #CD152 surface expression was predicted using Azimuth. g, Cell cycle phases overlaid onto the wnnUMAP coordinates from (a) (top) and shown as a bar plot in each CAR T cell population (bottom).

Extended Data Fig. 9 Helios-expressing CD57+T-bet+ CAR T cells display an NK-like transition program.

a, Pearson correlation between percentage of CD57–Helios+ cells among CD4+ CAR T cells and expansion of CAR T cells, quantified as log10 AUC0-28 for all study patients with available data (n = 49). P value is from the correlation test. b, Percentage of CD57–Helios+ cells among CD4+ CAR T cells separated by maximum ICANS grade and day of maximum ICANS (n = 54). c, Percentage of CD57+T-bet+ cells among CD4+ CAR T cells (left, n = 23) or CD8+ CAR T cells (right, n = 27) in blood on day 7 post-infusion separated by response at 6 months in patients from the validation cohort with ≥100 CD4+ or CD8+ CAR T cells detected, respectively. d, Scatter plot showing percentage of CD57–Helios+ vs. CD57+T-bet+ cells among CD4+ CAR T cells in both discovery and validation cohorts (n = 54). e, Percentage of Helios+ cells among CD4+CD57+T-bet+ cells separated by response at 6 months in patients from the discovery (left, n = 21) or validation (right, n = 19) cohorts with ≥10 CD4+CD57+T-bet+ CAR T cells detected. f, Volcano plot showing differentially expressed genes comparing Helios+ to Helios– cells within CD57+T-bet+ CAR T cells (n = 774 cells) using scRNA-seq data from 9 LBCL patients on day 7 following axi-cel infusion. Differentially upregulated genes are in red; genes used to define each population are in black. g,h, Violin plots showing selected T cell (b) or NK-related (h) gene and surface protein expression in Helios– and Helios+ cells within CD57+T-bet+ CAR T cells (n = 774 cells). Stars denote significant (P < 0.05) upregulation in the indicated population relative to the population without a star. Boxplots in (b,c,e) show quartiles with a band at median, whiskers indicating 1.5x IQR, and all observations overlaid as dots. P values were calculated using two-sided Wilcoxon-Mann Whitney U test.

Extended Data Fig. 10 Risk of clinical progression based on either high LDH or CAR TReg fraction alone.

a, Pearson correlation between pre-LD LDH levels and percentage of CD57–Helios+ (TReg-like) cells among CD4+ CAR T cells in blood on day 7 post-infusion colored by response at 6 months (n = 53). P value is from the correlation test. b, Percentage of CAR TReg cells separated by normal or high pre-LD LDH levels (n = 53). c, Percentage of CAR TReg cells in patients with CR or PD at 6 months with normal pre-LD LDH (left, n = 25) or high pre-LD LDH (right, n = 26). Boxplots in (b,c) show quartiles with a band at median, whiskers indicating 1.5x IQR, and all observations overlaid as dots. P values are from two-sided Mann-Whitney U test. d, Logistic regression models for predicting response at 6 months based on either percent of CAR TReg cells (top, n = 28), or whether pre-LD LDH levels were above normal (bottom, n = 29). Models were fit using all available data from the discovery cohort, with parameters shown below the formula. e, Performance of each model from (d) on discovery (top, n = 28; bottom, n = 29) and validation (top, n = 23; bottom, n = 33) cohorts. All available data not in the discovery cohort were used to test each model (Supplementary Table 5). AUROC, area under the receiver operating characteristic. f,g, Kaplan-Meier analysis of TTP (f) and OS (g) stratified by high vs. low risk using the models from (d) on cohorts from (e).

Supplementary information

Supplementary Information

Supplementary Tables S1–S5

Reporting Summary

Rights and permissions

Springer Nature or its licensor 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

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Good, Z., Spiegel, J.Y., Sahaf, B. et al. Post-infusion CAR TReg cells identify patients resistant to CD19-CAR therapy. Nat Med 28, 1860–1871 (2022). https://doi.org/10.1038/s41591-022-01960-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41591-022-01960-7

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing