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Distinct cellular dynamics associated with response to CAR-T therapy for refractory B cell lymphoma

Nature Medicine volume 28, pages 1848–1859 (2022)Cite this article

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Abstract

Chimeric antigen receptor (CAR)-T cell therapy has revolutionized the treatment of hematologic malignancies. Approximately half of patients with refractory large B cell lymphomas achieve durable responses from CD19-targeting CAR-T treatment; however, failure mechanisms are identified in only a fraction of cases. To gain new insights into the basis of clinical response, we performed single-cell transcriptome sequencing of 105 pretreatment and post-treatment peripheral blood mononuclear cell samples, and infusion products collected from 32 individuals with large B cell lymphoma treated with either of two CD19 CAR-T products: axicabtagene ciloleucel (axi-cel) or tisagenlecleucel (tisa-cel). Expansion of proliferative memory-like CD8 clones was a hallmark of tisa-cel response, whereas axi-cel responders displayed more heterogeneous populations. Elevations in CAR-T regulatory cells among nonresponders to axi-cel were detected, and these populations were capable of suppressing conventional CAR-T cell expansion and driving late relapses in an in vivo model. Our analyses reveal the temporal dynamics of effective responses to CAR-T therapy, the distinct molecular phenotypes of CAR-T cells with differing designs, and the capacity for even small increases in CAR-T regulatory cells to drive relapse.

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Fig. 1: The landscape of CAR-T and host immune populations in CAR-T treated patients.
Fig. 2: Pseudobulk analysis of genes related to treatment, timepoint and response.
Fig. 3: Temporal evolution of T cells in responders and nonresponders of different products.
Fig. 4: Tracking temporal evolution of CAR-T clones by TCR sequencing.
Fig. 5: Mediation of relapse by CAR-Treg cells in patients and in vivo validation experiments.
Fig. 6: Differential characteristics of tisa-cel expansion in patient with relapse and subsequent retreatment.

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Data availability

Gene expression matrices from the scRNA data have been deposited with the Gene Expression Omnibus (GEO accession GSE197268). Raw sequencing data is available on the database of Genotypes and Phenotypes (phs002922.v1.p1). Data from a previous study of axi-cel infusion products is available on GEO at GSE151511.

Code availability

Code for our method for measurement of CD45 isoforms is publicly accessible on GitHub at https://github.com/getzlab/10x-cd45-isoform-quantification. Other code supporting the analyses performed in this paper is available at https://github.com/getzlab/Haradhvala_et_al_2022.

References

  1. Maude, S. L. et al. Chimeric antigen receptor T cells for sustained remissions in leukemia. N. Engl. J. Med. 371, 1507–1517 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  2. Schuster, S. J. et al. Chimeric antigen receptor T cells in refractory B-cell lymphomas. N. Engl. J. Med. 377, 2545–2554 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Singh, N. et al. Impaired death receptor signaling in leukemia causes antigen-independent resistance by inducing CAR T-cell dysfunction. Cancer Discov. 10, 552–567 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Dufva, O. et al. Integrated drug profiling and CRISPR screening identify essential pathways for CAR T-cell cytotoxicity. Blood 135, 597–609 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  5. Orlando, E. J. et al. Genetic mechanisms of target antigen loss in CAR19 therapy of acute lymphoblastic leukemia. Nat. Med. 24, 1504–1506 (2018).

    Article  CAS  PubMed  Google Scholar 

  6. Das, R. K., Vernau, L., Grupp, S. A. & Barrett, D. M. Naïve T-cell deficits at diagnosis and after chemotherapy impair cell therapy potential in pediatric cancers. Cancer Discov. 9, 492–499 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. 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 

  8. Cappell, K. M. & Kochenderfer, J. N. A comparison of chimeric antigen receptors containing CD28 versus 4-1BB costimulatory domains. Nat. Rev. Clin. Oncol. 18, 715–727 (2021).

    Article  CAS  PubMed  Google Scholar 

  9. Plaks, V. et al. CD19 target evasion as a mechanism of relapse in large B-cell lymphoma treated with axicabtagene ciloleucel. Blood 138, 1081–1085 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Squair, J. W. et al. Confronting false discoveries in single-cell differential expression. Nat. Commun. 12, 5692 (2021).

  12. Boroughs, A. C. et al. A distinct transcriptional program in human CAR T cells bearing the 4-1BB signaling domain revealed by scRNA-Seq. Mol. Ther. 28, 2577–2592 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Zhao, Z. et al. Structural design of engineered costimulation determines tumor rejection kinetics and persistence of CAR T cells. Cancer Cell 28, 415–428 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Salter, A. I. et al. Phosphoproteomic analysis of chimeric antigen receptor signaling reveals kinetic and quantitative differences that affect cell function. Sci. Signal. 11, eaat6753 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  15. Lindner, S. E., Johnson, S. M., Brown, C. E. & Wang, L. D. Chimeric antigen receptor signaling: functional consequences and design implications. Sci. Adv. 6, eaaz3223 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Yigit, B. et al. SLAMF6 as a regulator of exhausted CD8+ T cells in cancer. Cancer Immunol. Res. 7, 1485–1496 (2019).

    Article  CAS  PubMed  Google Scholar 

  17. Wang, Y. et al. Low-dose decitabine priming endows CAR T cells with enhanced and persistent antitumour potential via epigenetic reprogramming. Nat. Commun. 12, 409 (2021).

  18. Yang, D. et al. ZBP1 mediates interferon-induced necroptosis. Cell. Mol. Immunol. 17, 356–368 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  19. Zhan, Y., Carrington, E. M., Zhang, Y., Heinzel, S. & Lew, A. M. Life and death of activated T cells: how are they different from naïve T cells? Front. Immunol. 8, 1809 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  20. Seo, H. et al. BATF and IRF4 cooperate to counter exhaustion in tumor-infiltrating CAR T cells. Nat. Immunol. 22, 983–995 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Palmer, D. C. et al. Cish actively silences TCR signaling in CD8+ T cells to maintain tumor tolerance. J. Exp. Med. 212, 2095–2113 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Büttner, M., Ostner, J., Müller, C. L., Theis, F. J. & Schubert, B. scCODA is a Bayesian model for compositional single-cell data analysis. Nat. Commun. 12, 6876 (2021).

  23. Jain, M. D. et al. Tumor interferon signaling and suppressive myeloid cells are associated with CAR T-cell failure in large B-cell lymphoma. Blood 137, 2621–2633 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. 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 

  25. Kafri, R. et al. Dynamics extracted from fixed cells reveal feedback linking cell growth to cell cycle. Nature 494, 480–483 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. 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 

  27. Talleur, A. et al. Preferential expansion of CD8+ CD19-CAR T cells postinfusion and the role of disease burden on outcome in pediatric B-ALL. Blood Adv. https://doi.org/10.1182/bloodadvances.2021006293 (2022).

  28. Monfrini, C. et al. Phenotypic composition of commercial anti-CD19 CAR-T cells affects in vivo expansion and disease response in large B-cell lymphoma patients. Clin. Cancer Res. https://doi.org/10.1158/1078-0432.CCR-22-0164 (2022).

  29. Martin, M. D. & Badovinac, V. P. Defining memory CD8 T cell. Front. Immunol. 9, 2692 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  30. Sakaguchi, S., Yamaguchi, T., Nomura, T. & Ono, M. Regulatory T cells and immune tolerance. Cell 133, 775–787 (2008).

    Article  CAS  PubMed  Google Scholar 

  31. Lee, J. C. et al. In vivo inhibition of human CD19-targeted effector T cells by natural T regulatory cells in a xenotransplant murine model of B cell malignancy. Cancer Res. 71, 2871–2881 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Todo, S. et al. A pilot study of operational tolerance with a regulatory T-cell-based cell therapy in living donor liver transplantation. Hepatology 64, 632–643 (2016).

    Article  CAS  PubMed  Google Scholar 

  33. Chandran, S. et al. Polyclonal regulatory T cell therapy for control of inflammation in kidney transplants. Am. J. Transpl. 17, 2945–2954 (2017).

    Article  CAS  Google Scholar 

  34. Boroughs, A. C. et al. Chimeric antigen receptor costimulation domains modulate human regulatory T cell function. JCI Insight 5, e126194 (2019).

    Article  Google Scholar 

  35. Togashi, Y., Shitara, K. & Nishikawa, H. Regulatory T cells in cancer immunosuppression—implications for anticancer therapy. Nat. Rev. Clin. Oncol. 16, 356–371 (2019).

    Article  CAS  PubMed  Google Scholar 

  36. 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 

  37. 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 

  38. Shah, N. N. & Fry, T. J. Mechanisms of resistance to CAR T cell therapy. Nat. Rev. Clin. Oncol. 16, 372–385 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. 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 

  40. Larson, R. C. & Maus, M. V. Recent advances and discoveries in the mechanisms and functions of CAR T cells. Nat. Rev. Cancer 21, 145–161 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Zhang, J. et al. A review of two regulatory approved anti-CD19 CAR T-cell therapies in diffuse large B-cell lymphoma: why are indirect treatment comparisons not feasible? Adv. Ther. 37, 3040–3058 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Gauthier, J. et al. Factors associated with outcomes after a second CD19-targeted CAR T-cell infusion for refractory B-cell malignancies. Blood 137, 323–335 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Derman, B. A. et al. Regulatory T-cell depletion in the setting of autologous stem cell transplantation for multiple myeloma: pilot study. J. Immunother. Cancer 8, e000286 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  44. Golab, K. et al. Challenges in cryopreservation of regulatory T cells (Tregs) for clinical therapeutic applications. Int. Immunopharmacol. 16, 371–375 (2013).

    Article  CAS  PubMed  Google Scholar 

  45. Florek, M. et al. Freeze and thaw of CD4+CD25+Foxp3+ regulatory T cells results in loss of CD62L expression and a reduced capacity to protect against graft-versus-host disease. PLoS ONE 10, e0145763 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  46. Gołąb, K. et al. Cell banking for regulatory T cell-based therapy: strategies to overcome the impact of cryopreservation on the Treg viability and phenotype. Oncotarget 9, 9728–9740 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  49. Cheson, B. D. et al. Revised response criteria for malignant lymphoma. J. Clin. Oncol. 25, 579–586 (2007).

    Article  PubMed  Google Scholar 

  50. Tirkes, T. et al. Response criteria in oncologic imaging: review of traditional and new criteria. Radiographics 33, 1323–1341 (2013).

    Article  PubMed  Google Scholar 

  51. 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 

  52. June, C. H., Levine, B. L., Porter, D. L., Kalos, M. D. & Milone, M. C. Use of chimeric antigen receptor-modified t cells to treat cancer. European Patent EP3214091B1 (2017).

  53. Kochenderfer, J. N. et al. Construction and preclinical evaluation of an anti-CD19 chimeric antigen receptor. J. Immunother. 32, 689–702 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  55. Wolock, S. L., Lopez, R. & Klein, A. M. Scrublet: computational identification of cell doublets in single-cell transcriptomic data. Cell Syst. 8, 281–291.e9 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Ilya, K. N. et al. Fast sensitive and accurate integration of single-cell data with Harmony. Nat. Methods. 16, 1289–1296 (2019).

  57. Pachter, L. Models for transcript quantification from RNA-Seq. Preprint at https://arxiv.org/abs/1104.3889 (2011).

  58. Traag, V. A., Waltman, L. & van Eck, N. J. From Louvain to Leiden: guaranteeing well-connected communities. Sci. Rep. 9, 5233 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank E. Berg, M. O’Reilly and the rest of the Broad Institute Patterns team for their contributions to the figure design. This work was partially supported by the Broad/IBM Cancer Resistance Research Project (principal investigators (PIs): G.G., L.P.) and by National Institutes of Health (NIH) R01CA252940 (PIs: M.V.M. and G.G.). A special thank you to S. Arrom and D. Oran for their thoughtful donation that helped to support a portion of this work. N.J.H. was partially funded by the Landry Cancer Biology Consortium fellowship. S.L. is supported by the National Cancer Institute Research Specialist Award (R50CA251956). R.C.L. is supported by NIH T32 AI007529. BioRender.com was utilized for Extended Data Fig. 6c.

Author information

Author notes

  1. These authors contributed equally: Nicholas J. Haradhvala, Mark B. Leick, Katie Maurer, Satyen H. Gohil.

  2. These authors jointly supervised this work: Catherine J. Wu, Gad Getz, Marcela V. Maus.

Authors and Affiliations

  1. Harvard Graduate Program in Biophysics, Harvard University, Cambridge, MA, USA

    Nicholas J. Haradhvala

  2. Cancer Program, The Broad Institute, Cambridge, MA, USA

    Nicholas J. Haradhvala, Katie Maurer, Satyen H. Gohil, Ning Yao, Shuqiang Li, Raquel A. Jacobs, Kara Slowik, Brian P. Danysh, Catherine J. Wu, Gad Getz & Marcela V. Maus

  3. Cancer Center, Massachusetts General Hospital, Boston, MA, USA

    Mark B. Leick, Rebecca C. Larson, Kathleen M. E. Gallagher, Katelin Katsis, Matthew J. Frigault, Michael C. Kann, Harrison Silva, Max Jan, Judith Ferry, Gad Getz & Marcela V. Maus

  4. Harvard Medical School, Boston, MA, USA

    Mark B. Leick, Rebecca C. Larson, Kathleen M. E. Gallagher, Matthew J. Frigault, Max Jan, Gad Getz & Marcela V. Maus

  5. Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA

    Katie Maurer, Satyen H. Gohil, Jackson Southard, Shuqiang Li, Kenneth J. Livak, Caron Jacobson & Catherine J. Wu

  6. Department of Haematology, University College London Hospitals, London, UK

    Satyen H. Gohil

  7. Program in Computational Biology and Quantitative Genetics, Harvard T.H. Chan School of Public Health, Boston, MA, USA

    Ning Yao

  8. IBM Research, New York, NY, USA

    Kahn Rhrissorrakrai, Filippo Utro, Chaya Levovitz & Laxmi Parida

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  1. Nicholas J. Haradhvala
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Contributions

N.J.H., M.B.L., K.M., S.H.G., R.C.L., C.J.W., G.G. and M.V.M. conceived and designed the study. M.B.L., K.M., S.H.G., C.J. and J.F. collected patient samples and clinical data. M.B.L., K.M., K.M.E.G., K.K., S.H.G. and R.C.L. processed patient samples. S.L., J.S. and K.J.L. performed the sequencing. N.J.H. analyzed the data. N.J.H., M.B.L., K.M., S.H.G., R.C.L., N.Y., K.R., F.U., C.L., B.P.D., L.P., C.J.W., G.G. and M.V.M. interpreted the data. M.B.L., H.S., M.C.K. and M.J. performed in vitro and in vivo experiments. R.A.J., K.S., M.J.F. and B.P.D. participated in project administration. C.J.W., G.G. and M.V.M. supervised the work. N.J.H., M.B.L., K.M, S.H.G., C.J.W., G.G. and M.V.M. wrote the manuscript. N.J.H., M.B.L., K.M., S.H.G., R.C.L., N.Y., K.M.E.G., S.L., K.R., F.U., C.J.W., G.G. and M.V.M. revised the manuscript.

Corresponding authors

Correspondence to Catherine J. Wu, Gad Getz or Marcela V. Maus.

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Competing interests

N.J.H. is a consultant for MorphoSys. C.J.W. holds equity in BioNTech Inc. and receives research funding from Pharmacyclics. S.H.G. holds patents related to adoptive cell therapies, held by University College London and Novalgen Limited. S.H.G. provides consultancy to Novalgen Ltd. G.G. receives research funds from IBM and Pharmacyclics, and is an inventor on patent applications related to MSMuTect, MSMutSig, MSIDetect, POLYSOLVER and SignatureAnalyzer-GPU. G.G. is a founder, consultant and holds privately held equity in Scorpion Therapeutics. M.V.M., M.B.L. and R.C.L. are inventors on patents related to adoptive cell therapies, held by MGH. M.V.M. is also an inventor on patents related to CAR-T cell therapies held by the University of Pennsylvania (some licensed to Novartis). M.V.M. is on the Board of Directors of 2SeventyBio, and holds equity in TCR2, Century Therapeutics, Oncternal and Neximmune, and has served as a consultant for multiple companies involved in cell therapies. The other authors declare no competing interests.

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

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Extended data

Extended Data Fig. 1 Single-cell isolation and classification.

a, Live CD14-CD3+CAR+ cells were isolated as CAR-T cells while CD3+CAR- cells were combined with CD14+CD3- cells as non-CAR-T cells for subsequent scRNA-seq workflows. b, Selected marker genes for PBMC cell population identification in scRNA-seq data. c, Kernel density estimate plots of knn-smoothed (k = 100) CD4 and CD8A expression across cells at day 7 and in the IP. Lines are drawn for thresholds used for classification. d, Fractions of cells of each coarse cell type for every baseline sample sorted by timepoint of sampling, as well as e, baseline tumor volume, measured by SPD (cm2). The same shown for f,g fine-grained T cell subsets.

Extended Data Fig. 2 PBMC cell composition differences between responders and non-responders pre- and post-infusion.

a, Boxplots of the cell type frequencies for each cell type stratified by product and response. The final cell type coefficient (with its posterior 95% high density interval) and FDR value (one minus the inclusion probability) estimated by scCODA are shown. Boxes show the median, interquartile range, and maximum/minimum values. n = 20 and n = 22 biologically independent samples are shown for baseline and D7-CAR-negative samples, respectively. b, Fraction of cells which were CD8+CD4- at baseline, in IPs, and at day 7 in CAR+ and CAR- populations. Boxes show the median, interquartile range, and maximum/minimum values. n = 20 baseline, n = 30 infusion, n = 29 day 7 CAR-negative, and n = 29 day 7 CAR-positive independent samples are shown from 31 patients. c, Flow-cytometric measurements for the mean fraction of CAR positive cells which were CD8+CD4- at day 7 for n = 27 biologically independent samples. Error bars represent standard deviation. P-values represent two-tailed Mann -Whitney U tests. d, Changes in CD8+ frequencies between IP and day 7 CAR-T cells as shown in Fig. 3b, colored by CD19 status of relapse. Samples with no relapse, or relapse without a biopsy, are grayed out.

Extended Data Fig. 3 Expression of previously proposed axi-cel response genes.

Pseudobulk expression (z-scored log transcripts per million) of CD8+CAR-T axi-cel IP cells for genes proposed to be response-associated by Deng et al. Fisher Exact test for association between response and denoted two clusters driven by putative memory- and dysfunction-associated genes is p = 1.

Extended Data Fig. 4 Detection of CD45 isoforms and T cell subset classification in 5’ RNA-sequencing data.

a, Illustration of signal used by CD45 isoform detection model. For each read in an illustrative sample, a histogram of the fragment length assuming no splicing (RABC isoform) is shown. The distribution of reads beyond exon 3 become shifted if they come from an isoform lacking an upstream exon. b, A histogram of the expected fragment length after inference of each read is shown for the same sample in blue. The gaussian distribution modeling fragment lengths inferred by EM is plotted in orange. c, U-MAP representation of T cells in 10x healthy PBMC demonstration dataset with both RNA-sequencing and feature-barcoding measurements. Dataset is colored by CD45RA and CD45RO expression measured by RNA (top, with k = 20 knn smoothing as applied in the paper) and feature barcoding (bottom). d, Kernel density estimate plots sorting plots of cells into different memory subsets. Black lines represent cutoff used for gating. e, Confusion matrix showing concordance of cell classification by protein-based and RNA-based approaches. f, Scatterplot showing similarity of cell fraction measurements using either the RNA-based (x-axis) or protein-based (y-axis) measurements. g, Kernel density estimate distributions of knn-smoothed (k = 20) CD45RA (x-axis) and CD45RO (y-axis) expression measurements in our dataset for Baseline T cells, Infusion products, day 7 CAR- cells and day 7 CAR+ cells. A plot is shown for each sample, and the CD45RA cutoff used for classification is drawn with a blue line. All plots share x and y axis scales.

Extended Data Fig. 5 Classification of T cell subtypes.

UMAP representations of all a, CD8 and b, CD4 T cells colored by their subtype assignment. Expression of marker genes used for subtype assignment for c, CD8 and d, CD4 cells. For each a knn-smoothed (k = 25) estimate is shown (right) alongside the raw measurements (left). e, Estimated fractions of CD45 isoforms that are RO (x-axis) or RA (y-axis) in CAR-T cells from infusion products and at day 7. f, Mean expression of genes related to differentiation state averaged across Naive, EM and TEMRA cells for Baseline T cells, day 7 CAR-T cells. Naive cells for day 7 CAR+ were omitted as they were not seen in quantities that could be analyzed. Error bars represent 95% confidence intervals derived from bootstrapping cells for 1000 iterations. Shown are n = 20,361 baseline (12,799 EM, 7,108 TEMRA, and 454 Naive) cells from 20 individuals, as well as n = 46,750 day 7 CAR-negative (24,436 EM, 22,064 TEMRA, and 250 Naive), and n = 10,010 day 7 CAR-positive (8,712 EM and 1,298 TEMRA) T cells from 29 individuals.

Extended Data Fig. 6 CAR-T subclusters and clonal dynamics.

a, Top differentially expressed genes in each CAR-T subcluster, as determined by a t-test. The expression is shown for the top 10 marker genes of each cluster displayed in Fig. 4a. b, Demonstration of unique tisa-cel responder with CD8+ cells in cluster EM 2. Shown is a scatterplot of the fraction of CD8+ cells in cluster EM 2 (y-axis) vs the fraction of all CAR-T cells that are CD8+ (x-axis). c, For each patient with at least 25T cells in both the IP and at day 7, individual TCR clones are plotted by their frequency in the IP (x-axis) and at day 7 (y-axis). Clones are colored by whether they are CD8+ or CD4+, and denoted with an ‘x’ if the two timepoint frequencies are significantly different by a two-tailed fisher exact test p < 0.05.

Extended Data Fig. 7 Regulatory T cells characteristics and in vitro suppression.

a, The fraction of T cells that are T-regs in samples of each timepoint, separated by CAR+ and CAR- cells. Only samples with at least 100 T cells of the relevant type are included in the analysis. A total of n = 20 baseline, n = 27 Infusion CAR-negative, n = 27 Infusion CAR-positive, n = 28 day 7 CAR-negative and n = 22 day 7 CAR-positive biologically independent samples are shown. P-values denote a two-tailed t-test without correction for multiple hypotheses. Boxes show the median, interquartile range, and maximum/minimum values. b, Average expression in IP T-regs (orange) and all other T cells (T-conv, blue) of the top 10 differentially expressed genes comparing T-regs and T-convs in IP cells. Genes classically associated with T-reg function are highlighted with arrows. c. Schematic for T-reg and CD4 control population isolation from healthy donor PBMC. d, CAR constructs used to identify CAR-Treg/CD4-CAR cells from CAR-Tconv. e,f, CFSE staining of CAR-Tconv cells co-cultured with either 25% CAR-Tregs or CD4-CAR control cells and stimulated at a 1:1 ratio with Jeko tumor targets at 72 hours. Dividing cells (red) are identified relative to unstimulated condition (blue). Each histogram represents an individual replicate, summarized in the plot on the right for all n = 3 technical replicates per construct over 1 independent experiment. P-value represents two-tailed unpaired t-test.

Extended Data Fig. 8 Mediation of relapse by CAR-Tregs at 25%via in vivo validation experiments.

a, NSG mice were injected with 1 ×106 Jeko-CBG lymphoma cells on day −7. On day 0 mice were injected with 1 ×106 CAR-T cells representing 100% CAR-T convs or 75% CAR-Tconvs with either 25% CAR-Tregs or 25% CD4-CAR-T control cells. Experiment performed with CD19-CD28 (left) or CD19-4-1BB (right) constructs. b, c Time course tumor radiance (photons/sec/cm2/sr). d, e Flow cytometric quantification of CAR-Tconv day 14 after CAR injection for n = 15 biologically independent animals per construct examined over 1 independent experiment. P-value represents two-tailed unpaired t-test. f, g Time course flux (photons/s). Mean ± SEM overlaid on individual subject curves for n = 14 biologically independent animals per construct examined over 1 independent experiment. P-value represents the result of two-way ANOVA. h, i Representative immunohistochemical staining for human CD3 in the spleen. j, k Flow cytometric quantification of CD3 cells from the spleens of the indicated conditions for n = 15 (CD28) and n = 14 (4-1BB) biologically independent animals examined over 1 independent experiment. P-value represents two-tailed unpaired t-test.

Extended Data Fig. 9 Changes in PBMC populations in patient treated with second infusion of tisa-cel.

a, Expression of marker genes for PBMC cell type classification. b, Fractions of day 7 CAR-negative cells falling into each cell type cluster, stratified by first and second infusion. c, Top 20 genes differentially increased and decreased (by Mann-Whitney U test) comparing CD8+CAR-negative T cells between the first and second treatments at day 7. Plotted expression values for each gene are log transcripts-per-10,000 translated and scaled to a range of [0,1] based on the minimum and maximum observed values.

Extended Data Fig. 10 Summary of cellular and transcriptomic changes associated with clinical outcome and timepoint.

Graphic depicting the cellular associations of response and temporal changes identified in this study.

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Haradhvala, N.J., Leick, M.B., Maurer, K. et al. Distinct cellular dynamics associated with response to CAR-T therapy for refractory B cell lymphoma. Nat Med 28, 1848–1859 (2022). https://doi.org/10.1038/s41591-022-01959-0

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