Letter | Published:

T cell receptor gene therapy targeting WT1 prevents acute myeloid leukemia relapse post-transplant

Abstract

Relapse after allogeneic hematopoietic cell transplantation (HCT) is the leading cause of death in patients with acute myeloid leukemia (AML) entering HCT with poor-risk features1,2,3. When HCT does produce prolonged relapse-free survival, it commonly reflects graft-versus-leukemia effects mediated by donor T cells reactive with antigens on leukemic cells4. As graft T cells have not been selected for leukemia specificity and frequently recognize proteins expressed by many normal host tissues, graft-versus-leukemia effects are often accompanied by morbidity and mortality from graft-versus-host disease5. Thus, AML relapse risk might be more effectively reduced with T cells expressing receptors (TCRs) that target selected AML antigens6. We therefore isolated a high-affinity Wilms’ Tumor Antigen 1-specific TCR (TCRC4) from HLA-A2+ normal donor repertoires, inserted TCRC4 into Epstein–Bar virus-specific donor CD8+ T cells (TTCR-C4) to minimize graft-versus-host disease risk and enhance transferred T cell survival7,8, and infused these cells prophylactically post-HCT into 12 patients (NCT01640301). Relapse-free survival was 100% at a median of 44 months following infusion, while a concurrent comparative group of 88 patients with similar risk AML had 54% relapse-free survival (P = 0.002). TTCR-C4 maintained TCRC4 expression, persisted long-term and were polyfunctional. This strategy appears promising for preventing AML recurrence in individuals at increased risk of post-HCT relapse.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Data availability

All requests for raw and analyzed data and materials will be promptly reviewed by the Fred Hutchinson Cancer Research Center to verify whether the request is subject to any intellectual property confidential obligations. Patient-related data not included in the paper were generated as part of a clinical trial and may be subject to patient confidentiality. Any data and materials that can be shared will be released via a Material Transfer Agreement. PBMC scRNAseq data for patients 10 and 18, as well as the R code to generate the tSNE plots using Seurat software packages, are available at the National Center for Biotechnology Information Gene Expression Omnibus, accession number GSE128933.

References

  1. 1.

    Araki, D. et al. Allogeneic hematopoietic cell transplantation for acute myeloid leukemia: time to move toward a minimal residual disease-based definition of complete remission? J. Clin. Oncol. 34, 329–336 (2016).

  2. 2.

    Armand, P. et al. Validation and refinement of the disease risk index for allogeneic stem cell transplantation. Blood 123, 3664–3671 (2014).

  3. 3.

    Dohner, H. et al. Diagnosis and management of AML in adults: 2017 ELN recommendations from an international expert panel. Blood 129, 424–447 (2017).

  4. 4.

    Parmar, S., Fernandez-Vina, M. & de Lima, M. Novel transplant strategies for generating graft-versus-leukemia effect in acute myeloid leukemia. Curr. Opin. Hematol. 18, 98–104 (2011).

  5. 5.

    Kolb, H. J. et al. Graft-versus-leukemia effect of donor lymphocyte transfusions in marrow grafted patients. Blood 86, 2041–2050 (1995).

  6. 6.

    Harris, D. T. & Kranz, D. M. Adoptive T cell therapies: a comparison of T cell receptors and chimeric antigen receptors. Trends Pharm. Sci. 37, 220–230 (2016).

  7. 7.

    Appay, V. et al. Memory CD8+ T cells vary in differentiation phenotype in different persistent virus infections. Nat. Med. 8, 379–385 (2002).

  8. 8.

    Berger, C. et al. Adoptive transfer of effector CD8+ T cells derived from central memory cells establishes persistent T cell memory in primates. J. Clin. Invest. 118, 294–305 (2008).

  9. 9.

    Ochsenreither, S. et al. Cyclin-A1 represents a new immunogenic targetable antigen expressed in acute myeloid leukemia stem cells with characteristics of a cancer-testis antigen. Blood 119, 5492–5501 (2012).

  10. 10.

    Sugiyama, H. WT1 (Wilms’ tumor gene 1): biology and cancer immunotherapy. Jpn J. Clin. Oncol. 40, 377–387 (2010).

  11. 11.

    Ariyaratana, S. & Loeb, D. M. The role of the Wilms tumour gene (WT1) in normal and malignant haematopoiesis. Expert Rev. Mol. Med. 9, 1–17 (2007).

  12. 12.

    Cheever, M. A. et al. The prioritization of cancer antigens: a national cancer institute pilot project for the acceleration of translational research. Clin. Cancer Res. 15, 5323–5337 (2009).

  13. 13.

    Stone, J. D. & Kranz, D. M. Role of T cell receptor affinity in the efficacy and specificity of adoptive T cell therapies. Front. Immunol. 4, 244 (2013).

  14. 14.

    Chapuis, A. G. et al. Transferred WT1-reactive CD8+ T cells can mediate antileukemic activity and persist in post-transplant patients. Sci. Transl. Med. 5, 174ra127 (2013).

  15. 15.

    Stromnes, I. M., Schmitt, T. M., Chapuis, A. G., Hingorani, S. R. & Greenberg, P. D. Re-adapting T cells for cancer therapy: from mouse models to clinical trials. Immunol. Rev. 257, 145–164 (2014).

  16. 16.

    Coulie, P. G., Van den Eynde, B. J., van der Bruggen, P. & Boon, T. Tumour antigens recognized by T lymphocytes: at the core of cancer immunotherapy. Nat. Rev. Cancer 14, 135–146 (2014).

  17. 17.

    Styczynski, J. et al. Impact of donor Epstein–Barr virus serostatus on the incidence of graft-versus-host disease in patients with acute leukemia after hematopoietic stem-cell transplantation: a study from the Acute Leukemia and Infectious Diseases Working Parties of the European Society for Blood and Marrow Transplantation. J. Clin. Oncol. 34, 2212–2220 (2016).

  18. 18.

    Busch, D. H., Frassle, S. P., Sommermeyer, D., Buchholz, V. R. & Riddell, S. R. Role of memory T cell subsets for adoptive immunotherapy. Semin. Immunol. 28, 28–34 (2016).

  19. 19.

    Wherry, E. J. et al. Lineage relationship and protective immunity of memory CD8 T cell subsets. Nat. Immunol. 4, 225–234 (2003).

  20. 20.

    Jones, S. et al. Lentiviral vector design for optimal T cell receptor gene expression in the transduction of peripheral blood lymphocytes and tumor-infiltrating lymphocytes. Hum. Gene Ther. 20, 630–640 (2009).

  21. 21.

    Topp, M. S. et al. Restoration of CD28 expression in CD28 CD8+ memory effector T cells reconstitutes antigen-induced IL-2 production. J. Exp. Med. 198, 947–955 (2003).

  22. 22.

    Ochsenbein, A. F. et al. CD27 expression promotes long-term survival of functional effector-memory CD8+ cytotoxic T lymphocytes in HIV-infected patients. J. Exp. Med. 200, 1407–1417 (2004).

  23. 23.

    Kimura, M. Y. et al. IL-7 signaling must be intermittent, not continuous, during CD8(+) T cell homeostasis to promote cell survival instead of cell death. Nat. Immunol. 14, 143–151 (2013).

  24. 24.

    Gattinoni, L. et al. A human memory T cell subset with stem cell-like properties. Nat. Med. 17, 1290–1297 (2011).

  25. 25.

    Legat, A., Speiser, D. E., Pircher, H., Zehn, D. & Fuertes Marraco, S. A. Inhibitory receptor expression depends more dominantly on differentiation and activation than ‘exhaustion’ of human CD8 T cells. Front. Immunol. 4, 455 (2013).

  26. 26.

    Utzschneider, D. T. et al. T cells maintain an exhausted phenotype after antigen withdrawal and population reexpansion. Nat. Immunol. 14, 603–610 (2013).

  27. 27.

    Fang, M. et al. Prognostic impact of discordant results from cytogenetics and flow cytometry in patients with acute myeloid leukemia undergoing hematopoietic cell transplantation. Cancer 118, 2411–2419 (2012).

  28. 28.

    Festuccia, M. et al. Minimal identifiable disease and the role of conditioning intensity in hematopoietic cell transplantation for myelodysplastic syndrome and acute myelogenous leukemia evolving from myelodysplastic syndrome. Biol. Blood Marrow Transpl. 22, 1227–1233 (2016).

  29. 29.

    Zhou, Y. & Wood, B. L. Methods of detection of measurable residual disease in AML. Curr. Hematol. Malig. Rep. 12, 557–567 (2017).

  30. 30.

    Storb, R. et al. Graft-versus-host disease and graft-versus-tumor effects after allogeneic hematopoietic cell transplantation. J. Clin. Oncol. 31, 1530–1538 (2013).

  31. 31.

    Chapuis, A. G. et al. Tracking the fate and origin of clinically relevant adoptively transferred CD8+ T cells in vivo. Sci. Immunol. 2, eaal2568 (2017).

  32. 32.

    Klebanoff, C. A. et al. Determinants of successful CD8+ T-cell adoptive immunotherapy for large established tumors in mice. Clin. Cancer Res. 17, 5343–5352 (2011).

  33. 33.

    Champagne, P. et al. Skewed maturation of memory HIV-specific CD8 T lymphocytes. Nature 410, 106–111 (2001).

  34. 34.

    Chapuis, A. G. et al. HIV-specific CD8+ T cells from HIV+ individuals receiving HAART can be expanded ex vivo to augment systemic and mucosal immunity in vivo. Blood 117, 5391–5402 (2011).

  35. 35.

    Chapuis, A. G. et al. T-cell therapy using interleukin-21-primed cytotoxic T-cell lymphocytes combined with cytotoxic T-cell lymphocyte antigen-4 blockade results in long-term cell persistence and durable tumor regression. J. Clin. Oncol. 34, 3787–3795 (2016).

  36. 36.

    Zheng, G. X. et al. Massively parallel digital transcriptional profiling of single cells. Nat. Commun. 8, 14049 (2017).

  37. 37.

    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).

  38. 38.

    Gill, S. Chimeric antigen receptor T cell therapy in AML: how close are we? Best Pr. Res. Clin. Haematol. 29, 329–333 (2016).

  39. 39.

    Tauro, S. et al. Allogeneic stem-cell transplantation using a reduced-intensity conditioning regimen has the capacity to produce durable remissions and long-term disease-free survival in patients with high-risk acute myeloid leukemia and myelodysplasia. J. Clin. Oncol. 23, 9387–9393 (2005).

  40. 40.

    Pule, M. A. et al. Virus-specific T cells engineered to coexpress tumor-specific receptors: persistence and antitumor activity in individuals with neuroblastoma. Nat. Med. 14, 1264–1270 (2008).

  41. 41.

    Horowitz, M. M. High-resolution typing for unrelated donor transplantation: how far do we go? Best Pr. Res. Clin. Haematol. 22, 537–541 (2009).

  42. 42.

    Deeg, H. J. et al. Transplant conditioning with treosulfan/fludarabine with or without total body irradiation: a randomized phase II trial in patients with myelodysplastic syndrome and acute myeloid leukemia. Biol. Blood Marrow Transplant 24, 956–963 (2018).

  43. 43.

    Hemmati, P. G. et al. Predictive significance of the European LeukemiaNet classification of genetic aberrations in patients with acute myeloid leukaemia undergoing allogeneic stem cell transplantation. Eur. J. Haematol. 98, 160–168 (2017).

  44. 44.

    Scholten, K. B. et al. Codon modification of T cell receptors allows enhanced functional expression in transgenic human T cells. Clin. Immunol. 119, 135–145 (2006).

  45. 45.

    Dossett, M. L. et al. Adoptive immunotherapy of disseminated leukemia with TCR-transduced, CD8+ T cells expressing a known endogenous TCR. Mol. Ther. 17, 742–749 (2009).

  46. 46.

    Kuball, J. et al. Facilitating matched pairing and expression of TCR chains introduced into human T cells. Blood 109, 2331–2338 (2007).

  47. 47.

    Riddell, S. R. et al. Restoration of viral immunity in immunodeficient humans by the adoptive transfer of T cell clones. Science 257, 238–241 (1992).

  48. 48.

    Jedema, I., van der Werff, N. M., Barge, R. M., Willemze, R. & Falkenburg, J. H. New CFSE-based assay to determine susceptibility to lysis by cytotoxic T cells of leukemic precursor cells within a heterogeneous target cell population. Blood 103, 2677–2682 (2004).

  49. 49.

    Robins, H. Immunosequencing: applications of immune repertoire deep sequencing. Curr. Opin. Immunol. 25, 646–652 (2013).

  50. 50.

    Yousfi Monod, M., Giudicelli, V., Chaume, D. & Lefranc, M. P. IMGT/JunctionAnalysis: the first tool for the analysis of the immunoglobulin and T cell receptor complex V-J and V-D-J JUNCTIONs. Bioinformatics 20, i379–i385 (2004).

  51. 51.

    Robins, H. et al. Ultra-sensitive detection of rare T cell clones. J. Immunol. Methods 375, 14–9 (2012).

  52. 52.

    Horton, H. et al. Optimization and validation of an 8-color intracellular cytokine staining (ICS) assay to quantify antigen-specific T cells induced by vaccination. J. Immunol. Methods 323, 39–54 (2007).

  53. 53.

    Limaye, A. P., Huang, M. L., Atienza, E. E., Ferrenberg, J. M. & Corey, L. Detection of Epstein–Barr virus DNA in sera from transplant recipients with lymphoproliferative disorders. J. Clin. Microbiol. 37, 1113–1116 (1999).

  54. 54.

    Boeckh, M. et al. Optimization of quantitative detection of cytomegalovirus DNA in plasma by real-time PCR. J. Clin. Microbiol. 42, 1142–1148 (2004).

  55. 55.

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

  56. 56.

    Buettner, F. et al. Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells. Nat. Biotechnol. 33, 155–160 (2015).

  57. 57.

    Ilicic, T. et al. Classification of low quality cells from single-cell RNA-seq data. Genome Biol. 17, 29 (2016).

  58. 58.

    Finak, G. et al. MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data. Genome Biol. 16, 278 (2015).

  59. 59.

    Kalbfleisch, J. D. & Prentice, R. L. The Statistical Analysis of Failure Time Data (Wiley, 2002).

  60. 60.

    Freeman, S. D. et al. Measurable residual disease at induction redefines partial response in acute myeloid leukemia and stratifies outcomes in patients at standard risk without NPM1 mutations. J. Clin. Oncol. 36, 1486–1497 (2018).

Download references

Acknowledgements

We thank E. Estey and F. Milano for their contribution to the manuscript; the Fred Hutchinson Cancer Research Center Good Manufacturing Practice Cell Processing Facility for generating TTCR-C4; the Immune Monitoring Laboratory for generating tetramers; the Flow Cytometry Facility for providing instruments and assistance in flow cytometry assays; and the Program in Immunology, Immunotherapy Integrated Research Center, Seattle Cancer Care Alliance Immunotherapy Clinic staff and K.-T. Bui for supporting the clinical protocol implementation. We received funding from grant no. P01CA18029-41 (P.G.), grant no. NIH-5K08CA169485 (A.G.C.), the Immunotherapy Integrated Research Center at the Fred Hutchinson Cancer Research Center (A.G.C.), Damon Runyon (A.G.C.), the Guillot Family ZachAttacksLeukemia Foundation and Juno Therapeutics.

Author information

Conception and design were performed by P.D.G., A.G.C., M.B., G.B.R. and T.M.S. Collection and assembly of data were carried out by A.G.C., D.N.E., M.B., M.S.M., K.G.P., V.V., R.G., M.B., C.C.Y., P.M., H.N.N., L.A.K., L.C., F.W., D.H., M.L., K.C., A.S., M.J.M. and N.D. Data analysis and interpretation were performed by A.G.C., D.N.E., T.M.S., K.G.P., V.V., R.G., M.S.M., T.A.G. and P.D.G. All of the authors contributed to the writing of the manuscript. Final approval of the manuscript was given by all of the authors.

Correspondence to Philip D. Greenberg.

Ethics declarations

Competing interests

A.G.C. has received support from Juno Therapeutics. P.D.G. is a consultant, has received support from and had ownership interest in Juno Therapeutics. He consults and has ownership interest in Immune Design Corp, Innate Pharma and FLX Bio. P.D.G., T.M.S., H.N.N. and the Fred Hutchinson Cancer Research Center have intellectual property related to TCRC4. A.G.C. and K.G.P. have received reagents from 10X Genomics. R.G. has received consulting fees from Juno Therapeutics, Takeda Pharmaceuticals and Infotech Soft, and has received support from Johnson and Johnson and Juno Therapeutics. The authors declare no other competing interests.

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 Flow diagram of patients enrolled on the clinical study.

Follow-up of the 64 patients screened for participation in the study. Patients could either enter the study by being screened/enrolled pre-HCT (49 patients) or after post-HCT relapse (15 patients). Patients with no detectable disease at day ~28 post-HCT were eligible to receive treatment on the prophylactic arm described here, and patients with evidence of relapse post-HCT alternatively received therapy on the treatment arm (not described here). Between 29 April 2013 and 19 October 2016, 29 patient/donor pairs were enrolled pre-HCT. Of these, TCRC4-transduced cells were initiated for 23 patients, and 12 patients who had not relapsed before day 28 received TCRC4-transduced cells on the prophylactic arm. One additional patient was considered not evaluable for response outcomes as this patient received <12% the intended dose and was unable to receive further infusions due an inability to generate additional T cells from the donor.

Extended Data Fig. 2 Treatment plan.

The first patient received 4 escalating doses of TTCR-C4 starting at 109 cells per m2 on day 0, then 3.3 × 109 cells per m2 on day 28 and 1010 cells per m2 on days 56 and 84, followed by low-dose subcutaneous IL-2 (2.5 × 105 IU twice daily) for 14 d (stage 1) administered to enhance the survival of transferred T cells. After safety was established, to increase the likelihood of delivering therapeutic cell doses to patients before disease progression, all remaining patients were planned to receive 2 doses of 1010 cells per m2. The next 11 patients received 1 infusion at doses of 1010 cells per m2 on day 0 and 1010 cells per m2 on day 28 with the latter infusion followed by 14 d of low-dose subcutaneous IL-2. For safety, no subsequent infusion could be administered if persistence of the transfused cells was >3% of total CD8+ cells.

Extended Data Fig. 3 Characteristics of treated and comparative group patients.

Characteristics of treated and comparative group patients. AML risk stratification by genetics was assessed using the European Leukemia Net criteria3. Persistent AML after one induction is associated with unfavorable outcomes60. The post-HCT relapse risk was assessed using published criteria2. NED, no evaluable disease.

Extended Data Fig. 4 Normal count recovery after HCT and TTCR-C4 infusions.

ad, Percentages of hematocrit (a), platelet (b), neutrophil (c) and lymphocyte (d) counts for TTCR-C4-treated (n = 12) and comparative group (n = 70) patients alive at day 80 (2.5 months (mo)). Baseline for TTCR-C4-treated and comparative group patients was their pre-infusion value and their day 80 value, respectively. Box and whiskers plots including range (whiskers), interquartile range (box), median (horizontal line) and all values are shown. Two-sided paired t-tests were used for statistical analysis. Source Data

Extended Data Fig. 5 Adverse events.

One patient experienced NCI-CTCAE v.4.0 grade 3 symptoms (fever, chills, rigors, nausea) commonly associated with cytokine release syndrome after each of 2 infusions, which were managed on the general hospital ward and resolved within 24 h with supportive treatment (fluids, acetaminophen, antihistamines). Five patients experienced a temporary (1–13 d) decrease in lymphocytes, which is a routinely observed, transient side effect of T cell infusions, presumably reflecting T cell margination14. Three patients experienced a hemoglobin drop that returned to pre-infusion levels within 1–4 d, 2 patients experienced a drop in platelets that returned to pre-infusion levels within 1–7 d and 2 patients experienced a drop in absolute neutrophil counts that recovered within 11–23 d; the transient nature of these hematologic events is consistent with cytokine-mediated effects14.

Extended Data Fig. 6 GVHD observed after TTCR-C4 infusions.

aGVHD, acute GVHD; cCVHD, chronic GVHD.

Extended Data Fig. 7 In vivo persistence kinetics and clonotype evolution of transferred TTCR-C4.

a,b, Tetramer+CD8+ T cells per microliter (left y axis) in PBMCs collected after the first infusion for all patients (n = 12) (a), and for selected patients (n = 6) who received a second infusion within 365 d of their first (note: patient 11 received a second infusion 795 d after the first and is not depicted here) (b). Black asterisks indicate timing of TTCR-C4 infusion and blue asterisks indicate that the infusion was followed by low-dose subcutaneous IL-2. c, Graphs to the left, frequency of the first 25 most frequent clonotypes (shaded in color) and remainder of the clonotypes (shaded in gray) expanded and detected at any time-point after infusions (Supplementary Table 1) present in infusion products in selected patients (n = 11). The proportions of the sum of clonotype frequencies present in the infusion product but not detected after infusion are indicated in white (see Supplementary Table 1 for exact numbers relative to each patient). c, Graphs to the right, sum of the percentage of clonotypes detected at designated time-points after infusions (black lines), individual percentage contributions of the most prevalent 25 clonotypes in the infused products (colored lines), sum of the remaining sequences (gray lines), sum of all product clonotypes (black lines) and percentage of multimer+ T cells (open blue circles) for each corresponding patient. Regression was performed on the log of the sum of clonotype frequencies obtained by HTTCS and log percentage tet+ cells (n = 5 data points for patients 3, 10, 11, 12, 13, 18, 24 and 25; n = 4 data points for patients 17, 20 and 21). Goodness of fit was evaluated with R2. Significance of the correlation (R2 and P values) between the sum of the percentage clonotypes and multimer are depicted on the right side of each graph. Source Data

Extended Data Fig. 8 TEM Clonotypes composing TTCR-C4 differentiate into TCM and TTD in vivo.

a, Schematic of differentiation grouping used for assigning subset identity: CD45RA, CCR7+ (TCM); CD45RA, CCR7 (TEM); and CD45RA+, CCR7 (TTD). b, Grouping of TTCR-C4 WT1 tetramer-binding cells into differentiation subsets immediately before infusion (n = 12) and after 7 d (n = 10), ~30 d (n = 9), ~100 d (n = 9) and 200–300 d (n = 9) in vivo. Box and whisker plots include range (whiskers), interquartile range (box), median (horizontal line) and all individual values. Two-sided paired t-tests were used to compare product with in vivo respective subsets and are indicated above each one. c, Venn diagram indicating the number of clonotypes (total clonotypes from patients 10, 11,12 and 24) in each subset. d, Percentage of total WT1-binding clonotypes detected ~100 d after transfer identified in each subset (4 patients combined). Source Data

Extended Data Fig. 9 Identification of PBMC subpopulations.

a, Clusters identified by principal component analysis and visualized with tSNE for both patients combined (n = 18,372, each represented with a dot). bl, Expression patterns of transcripts encoding key genes (http://www.ncbi.nlm.nih.gov/gene), shown above each plot, that assisted in confirmation of cluster identification. Blue color indicates expressed, with darker blue indicating greater expression.

Extended Data Fig. 10 Significantly expressed genes in TTCR-C4.

Expression of transcripts encoding granulysin (GNLY), granzyme K (GZMK) (patients 10 and 18), granzyme H (GZMH), homeodomain protein homeobox (HOPX), inhibitor of DNA binding 2 (ID2) and lymphotoxin B (LTB) (patient 18 only) are compared in endogenous CD8+ effector (CCR7) T cells (red, n = 1,298 (patient 10) and 1,282 (patient 18)) and TTCR-C4 (blue, n = 695 (patient 10) and 327 (patient 18)), shown as violin plots. The shape of the violin displays frequencies of values. All individual values are shown. MAST (see Methods) was used to determine the significance shown below each plot. Significance thresholds were set a priori at a threshold of false discovery rate of 5% and positive or negative fold change > log2(1.3). All significantly differentially expressed genes are shown in this figure.

Supplementary information

Supplementary Information

Supplementary Figs. 1–5 and Supplementary Tables 1 and 2

Reporting Summary

Source Data

Source Data Fig. 3

Statistical source data

Source Data Extended Data Fig. 4

Statistical source data

Source Data Extended Data Fig. 7

Statistical source data

Source Data Extended Data Fig. 8

Statistical source data

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Further reading

Fig. 1: Phenotypic and functional characteristics of EBV-specific donor-derived TTCR-C4.
Fig. 2: Prophylactic effect, kinetics of in vivo persistence and clonotype evolution of TTCR-C4 in vivo.
Fig. 3: Phenotypic, functional characteristics and transcriptome of TTCR-C4 in vivo.
Extended Data Fig. 1: Flow diagram of patients enrolled on the clinical study.
Extended Data Fig. 2: Treatment plan.
Extended Data Fig. 3: Characteristics of treated and comparative group patients.
Extended Data Fig. 4: Normal count recovery after HCT and TTCR-C4 infusions.
Extended Data Fig. 5: Adverse events.
Extended Data Fig. 6: GVHD observed after TTCR-C4 infusions.
Extended Data Fig. 7: In vivo persistence kinetics and clonotype evolution of transferred TTCR-C4.
Extended Data Fig. 8: TEM Clonotypes composing TTCR-C4 differentiate into TCM and TTD in vivo.
Extended Data Fig. 9: Identification of PBMC subpopulations.
Extended Data Fig. 10: Significantly expressed genes in TTCR-C4.