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Characteristics of anti-CD19 CAR T cell infusion products associated with efficacy and toxicity in patients with large B cell lymphomas

Abstract

Autologous chimeric antigen receptor (CAR) T cell therapies targeting CD19 have high efficacy in large B cell lymphomas (LBCLs), but long-term remissions are observed in less than half of patients, and treatment-associated adverse events, such as immune effector cell-associated neurotoxicity syndrome (ICANS), are a clinical challenge. We performed single-cell RNA sequencing with capture-based cell identification on autologous axicabtagene ciloleucel (axi-cel) anti-CD19 CAR T cell infusion products to identify transcriptomic features associated with efficacy and toxicity in 24 patients with LBCL. Patients who achieved a complete response by positron emission tomography/computed tomography at their 3-month follow-up had three-fold higher frequencies of CD8 T cells expressing memory signatures than patients with partial response or progressive disease. Molecular response measured by cell-free DNA sequencing at day 7 after infusion was significantly associated with clinical response (P = 0.008), and a signature of CD8 T cell exhaustion was associated (q = 2.8 × 10−149) with a poor molecular response. Furthermore, a rare cell population with monocyte-like transcriptional features was associated (P = 0.0002) with high-grade ICANS. Our results suggest that heterogeneity in the cellular and molecular features of CAR T cell infusion products contributes to variation in efficacy and toxicity after axi-cel therapy in LBCL, and that day 7 molecular response might serve as an early predictor of CAR T cell efficacy.

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Fig. 1: Study design.
Fig. 2: Single-cell analysis of standard-of-care CAR T cell infusion products.
Fig. 3: Molecular phenotypes of CAR T cell infusion products associated with response determined by PET/CT.
Fig. 4: Association between EMR measured by cfDNA sequencing and clinical response measured by PET/CT.
Fig. 5: Association between CD8 T cell exhaustion markers and EMR.
Fig. 6: IACs in CD19 CAR T cell infusion products.

Data availability

All requests for raw and analyzed data and materials are promptly reviewed by The University of Texas MD Anderson Cancer Center to verify if the request is subject to any intellectual property or confidentiality obligations. Patient-related data not included in the paper might be subject to patient confidentiality. Any data and materials that can be shared will be released via a Material Transfer Agreement. Transcriptome and CapID scRNA-seq data sets are available through the Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo/) under accessions GSE150992 and GSE151511, respectively. Raw data used in the generation of Figs. 16 and Extended Data Figs. 110 are available through the European Genome-phenome Archive (https://www.ebi.ac.uk/ega/home): accessions EGAD00001006327 and EGAD00001006325.

References

  1. 1.

    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).

    CAS  PubMed  Google Scholar 

  2. 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).

    CAS  PubMed  Google Scholar 

  3. 3.

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

    CAS  PubMed  Google Scholar 

  4. 4.

    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).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. 5.

    Mueller, K. T. et al. Clinical pharmacology of tisagenlecleucel in B-cell acute lymphoblastic leukemia. Clin. Cancer Res. 24, 6175–6184 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. 6.

    Green, M. R. & Neelapu, S. S. Not so FASt: tumor cells resisting death drive CAR T-cell dysfunction. Cancer Discov. 10, 492–494 (2020).

    PubMed  Google Scholar 

  7. 7.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  8. 8.

    Finney, O. C. et al. CD19 CAR T cell product and disease attributes predict leukemia remission durability. J. Clin. Invest. 130, 2123–2132 (2019).

  9. 9.

    Sommermeyer, D. et al. Chimeric antigen receptor-modified T cells derived from defined CD8+ and CD4+ subsets confer superior antitumor reactivity in vivo. Leukemia 30, 492–500 (2016).

    CAS  PubMed  Google Scholar 

  10. 10.

    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).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. 11.

    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).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. 12.

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

    CAS  PubMed  Google Scholar 

  13. 13.

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

    CAS  PubMed  Google Scholar 

  14. 14.

    Teachey, D. T. et al. Identification of predictive biomarkers for cytokine release syndrome after chimeric antigen receptor T-cell therapy for acute lymphoblastic leukemia. Cancer Discov. 6, 664–679 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Hay, K. A. et al. Kinetics and biomarkers of severe cytokine release syndrome after CD19 chimeric antigen receptor-modified T-cell therapy. Blood 130, 2295–2306 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. 16.

    Le, R. Q. et al. FDA approval summary: tocilizumab for treatment of chimeric antigen receptor T cell-induced severe or life-threatening cytokine release syndrome. Oncologist 23, 943–947 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. 17.

    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).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. 18.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  19. 19.

    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).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. 20.

    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).

    CAS  PubMed  Google Scholar 

  21. 21.

    Sade-Feldman, M. et al. Defining T cell states associated with response to checkpoint immunotherapy in melanoma. Cell 175, 998–1013 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. 22.

    Blackburn, S. D. et al. Coregulation of CD8+ T cell exhaustion by multiple inhibitory receptors during chronic viral infection. Nat. Immunol. 10, 29–37 (2009).

    CAS  PubMed  Google Scholar 

  23. 23.

    Doering, T. A. et al. Network analysis reveals centrally connected genes and pathways involved in CD8+ T cell exhaustion versus memory. Immunity 37, 1130–1144 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. 24.

    Quigley, M. et al. Transcriptional analysis of HIV-specific CD8+ T cells shows that PD-1 inhibits T cell function by upregulating BATF. Nat. Med. 16, 1147–1151 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. 25.

    Miller, B. C. et al. Subsets of exhausted CD8+ T cells differentially mediate tumor control and respond to checkpoint blockade. Nat. Immunol. 20, 326–336 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. 26.

    Man, K. et al. Transcription factor IRF4 promotes CD8+ T cell exhaustion and limits the development of memory-like T cells during chronic infection. Immunity 47, 1129–1141 (2017).

    CAS  PubMed  Google Scholar 

  27. 27.

    Li, H. et al. Dysfunctional CD8 T cells form a proliferative, dynamically regulated compartment within human melanoma. Cell 176, 775–789 (2019).

    CAS  PubMed  Google Scholar 

  28. 28.

    Goldrath, A. W., Luckey, C. J., Park, R., Benoist, C. & Mathis, D. The molecular program induced in T cells undergoing homeostatic proliferation. PNAS 101, 16885–16890 (2004).

    CAS  PubMed  Google Scholar 

  29. 29.

    Josefsson, S. E. et al. TIGIT and PD-1 mark intratumoral T cells with reduced effector function in B-cell non-Hodgkin lymphoma. Cancer Immunol. Res. 7, 355–362 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. 30.

    Scherer, F. et al. Distinct biological subtypes and patterns of genome evolution in lymphoma revealed by circulating tumor DNA. Sci. Transl. Med. 8, 364ra155 (2016).

    PubMed  PubMed Central  Google Scholar 

  31. 31.

    Lee, B., Sharron, M., Montaner, L. J., Weissman, D. & Doms, R. W. Quantification of CD4, CCR5, and CXCR4 levels on lymphocyte subsets, dendritic cells, and differentially conditioned monocyte-derived macrophages. PNAS 96, 5215–5220 (1999).

    CAS  PubMed  Google Scholar 

  32. 32.

    Kazazi, F., Mathijs, J. M., Foley, P. & Cunningham, A. L. Variations in CD4 expression by human monocytes and macrophages and their relationships to infection with the human immunodeficiency virus. J. Gen. Virol. 70, 2661–2672 (1989).

    CAS  PubMed  Google Scholar 

  33. 33.

    Uhlen, M. et al. A genome-wide transcriptomic analysis of protein-coding genes in human blood cells. Science 366, eaax9198 (2019).

  34. 34.

    Gargett, T., Truong, N., Ebert, L. M., Yu, W. & Brown, M. P. Optimization of manufacturing conditions for chimeric antigen receptor T cells to favor cells with a central memory phenotype. Cytotherapy 21, 593–602 (2019).

    CAS  PubMed  Google Scholar 

  35. 35.

    Wang, J. et al. Histone deacetylase inhibitors and IL21 cooperate to reprogram human effector CD8+ T cells to memory T cells. Cancer Immunol. Res. 8, 794–805 (2020).

  36. 36.

    Kurtz, D. M. et al. Noninvasive monitoring of diffuse large B-cell lymphoma by immunoglobulin high-throughput sequencing. Blood 125, 3679–3687 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. 37.

    Roschewski, M. et al. Circulating tumour DNA and CT monitoring in patients with untreated diffuse large B-cell lymphoma: a correlative biomarker study. Lancet Oncol. 16, 541–549 (2015).

    PubMed  PubMed Central  Google Scholar 

  38. 38.

    Kurtz, D. M. et al. Circulating tumor DNA measurements as early outcome predictors in diffuse large B-cell lymphoma. J. Clin. Oncol. 36, 2845–2853 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. 39.

    Cherkassky, L. et al. Human CAR T cells with cell-intrinsic PD-1 checkpoint blockade resist tumor-mediated inhibition. J. Clin. Invest. 126, 3130–3144 (2016).

    PubMed  PubMed Central  Google Scholar 

  40. 40.

    Gargett, T. et al. GD2-specific CAR T cells undergo potent activation and deletion following antigen encounter but can be protected from activation-induced cell death by PD-1 blockade. Mol. Ther. 24, 1135–1149 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. 41.

    Chong, E. A. et al. PD-1 blockade modulates chimeric antigen receptor (CAR)-modified T cells: refueling the CAR. Blood 129, 1039–1041 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. 42.

    Baumeister, S. H., Freeman, G. J., Dranoff, G. & Sharpe, A. H. Coinhibitory pathways in immunotherapy for cancer. Annu. Rev. Immunol. 34, 539–573 (2016).

    CAS  PubMed  Google Scholar 

  43. 43.

    Burugu, S., Dancsok, A. R. & Nielsen, T. O. Emerging targets in cancer immunotherapy. Semin. Cancer Biol. 52, 39–52 (2018).

    CAS  PubMed  Google Scholar 

  44. 44.

    Strati, P. et al. Clinical efficacy of anakinra to mitigate CAR T-cell therapy-associated toxicity in large B-cell lymphoma. Blood Adv. 4, 3123–3127 (2020).

    PubMed  PubMed Central  Google Scholar 

  45. 45.

    Fraietta, J. A. et al. Disruption of TET2 promotes the therapeutic efficacy of CD19-targeted T cells. Nature 558, 307–312 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. 46.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  47. 47.

    Savas, P. et al. Single-cell profiling of breast cancer T cells reveals a tissue-resident memory subset associated with improved prognosis. Nat. Med. 24, 986–993 (2018).

    CAS  PubMed  Google Scholar 

  48. 48.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  49. 49.

    Jerby-Arnon, L. et al. A cancer cell program promotes T cell exclusion and resistance to checkpoint blockade. Cell 175, 984–997 e924 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. 50.

    Hanzelmann, S., Castelo, R. & Guinney, J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinf. 14, 7 (2013).

    Google Scholar 

  51. 51.

    van der Leun, A. M., Thommen, D. S. & Schumacher, T. N. CD8+ T cell states in human cancer: insights from single-cell analysis. Nat. Rev. Cancer 20, 218–232 (2020).

    PubMed  PubMed Central  Google Scholar 

  52. 52.

    Nazarov, V. I. et al. tcR: an R package for T cell receptor repertoire advanced data analysis. BMC Bioinf. 16, 175 (2015).

    Google Scholar 

  53. 53.

    Reuben, A. et al. TCR repertoire intratumor heterogeneity in localized lung adenocarcinomas: an association with predicted neoantigen heterogeneity and postsurgical recurrence. Cancer Discov. 7, 1088–1097 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. 54.

    Koboldt, D. C. et al. VarScan 2: somatic mutation and copy number alteration discovery in cancer by exome sequencing. Genome Res. 22, 568–576 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. 55.

    McKenna, A. et al. The genome analysis toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. 56.

    Lek, M. et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature 536, 285–291 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

This work was supported by the Schweitzer Family Fund (J.R.W., R.E.D. and M.R.G.), The University of Texas MD Anderson Cancer Center B-Cell Lymphoma Moonshot (S.S.N. and M.R.G.), a National Cancer Institute Cancer Center Support Grant to The University of Texas MD Anderson Cancer Center (P30 CA016672) and Institutional Research Grant start-up research funds provided to L.W. and M.R.G. by The University of Texas MD Anderson Cancer Center. H.Y. is supported by a Fellow Award from the Leukemia and Lymphoma Society. M.R.G. is supported by a Scholar Award from the Leukemia and Lymphoma Society.

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Contributions

Q.D., G.H., N.P.-O., M.C.J.M., N.J., H.Y., J.S., S.G., Q.Z. and S.P. performed experiments. L.W. and M.R.G. supervised the bioinformatics analysis. G.H., M.C., J.M., B.C., P.S., R.S., E.D., M.D., Y.W., S.Z., R.W., R.C., R.E.D., S.S.N., L.W. and M.R.G. analyzed data. F.H., L.F., F.S., H.C.L., L.J.N., N.F., J.W. and S.S.N. provided patient care. M.R.G. and S.S.N. conceived the study. M.R.G., L.W. and S.S.N. supervised the study and wrote the manuscript. All authors read and approved the manuscript.

Corresponding authors

Correspondence to Sattva S. Neelapu or Linghua Wang or Michael R. Green.

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Competing interests

B.C. reports advisory board membership for Advanced Accelerator Application and Clovis Oncology. N.F. reports advisory board membership for Roche, BMS, Gilead and Novartis; research funding from Roche, BMS, Novartis and BostonGene; and employment from BostonGene. H.C.L. reports consulting fees from Adaptive Biotechnologies, Amgen, Celgene, GlaxoSmithKline, Janssen, Sanofi and Takeda Pharmaceuticals and research funding from Amgen, Celgene, Daiichi Sankyo, GlaxoSmithKline, Janssen, Regeneron and Takeda Pharmaceuticals. L.J.N. reports personal fees and research fees from Celgene, Genentech, Juno, Merck and TG Therapeutics and personal fees from Bayer, Novartis and Spectrum Pharmaceuticals. J.W. reports advisory board membership for Kite/Gilead, Juno/Celgene/BMS, Novartis, Genentech, Janssen, Amgen, AstraZeneca, Curis and Morphosys and research funding from Kite/Gilead, June/Celgene/BMS, Novartis, Genentech, Janssen, AstraZeneca, 47, Unum Therapeutics, Curis and Morphosys. S.S.N. has received research support from Kite/Gilead, Cellectis, Poseida, Merck, Acerta, Karus, BMS, Unum Therapeutics, Allogene and Precision Biosciences; has served as consultant and advisory board member for Kite/Gilead, Celgene, Novartis, Unum Therapeutics, Pfizer, Merck, Precision Biosciences, Cell Medica, Incyte, Allogene, Calibr and Legend Biotech; has received royalty income from Takeda Pharmaceuticals; and has patents related to cell therapy. M.R.G. reports consulting for VeraStem Oncology and stock ownership interest in KDAc Therapeutics.

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

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Extended data

Extended Data Fig. 1

Heatmap showing the top 50 signature genes of each cluster and putative assignments to cell types according to canonical marker genes.

Extended Data Fig. 2 Increased sequencing saturation and marker gene detection rate by CapID.

a, Sub-sampling of reads from a single CAR T infusion product with 600 million reads for whole transcriptome and 20 million reads for CapID, showing the saturation (flattening of curve) for CapID (orange) at ~10 million reads, for 10X whole transcriptome sequencing (blue) at ~400 M reads, and the effect of supplementing whole transcriptome data with 10 million reads of CapID data (green). b, Density plots from the entire dataset show the reduced number of cells with UMI counts of zero and increased signal-to-noise ratio for CapID sequencing compared to 10X whole transcriptome sequencing. The 10X whole transcriptome sequencing in this study were performed to an average of 73,521 reads per cell, vastly exceeding the minimum of 20,000 reads per cell recommended by 10X.

Extended Data Fig. 3 Correlation of cell frequencies measured by scRNA-seq and flow cytometry.

Correlations are shown for 16 patients that had sufficient cells for flow cytometry (a), compared to the fractions measured using traditional 10X data (b) or CapID+ (c). All comparisons showed a significant correlation with Pearson’s correlation 2-tailed P-value < 0.001.

Extended Data Fig. 4 Volcano plots of differentially expressed genes between CAR-positive and CAR-negative CD8 and CD4 T-cells.

Q-values were calculated with a two-sided Wilcoxon rank sum test with Bonferonni correction.

Extended Data Fig. 5 Variant allele fractions of somatic variants detected by cfDNA sequencing.

a, b, Comparison of the average VAF of mutations at day 0 (a) and the number of calibrated mutations (b) between clinical response groups. P values were calculated by a two-sided Student’s t-test. c, Raw variant allele frequencies for each patient are shown for >5FMR (above) and <5FMR (below) groups. Lines are colored by clinical response as in Fig. 4a. The grey dashed line represents the 5-fold reduction threshold for each patient.

Extended Data Fig. 6 T-cell clonotypic diversity in patients grouped by clinical and molecular response.

a, The frequency of the top 10 clonotypes for each patient among all cells (above), CD4 T-cells (middle) and CD8 T-cells (below). Box, median ± interquartile range. Whiskers, minimum and maximum. P-values calculated by a two-sided Wilcoxon rank sum test with Benjamini-Hochberg correction. b, Shannon’s clonality score for patients grouped by clinical or molecular response, shown for all cells (above), CD4 T-cells (middle) and CD8 T-cells (below). CR, n = 9. PR/PD, n = 14. >5FMR, n = 8. <5FMR, n = 9. Box, median ± interquartile range. Whiskers, minimum and maximum. P-values calculated by a two-sided Wilcoxon rank sum test with Benjamini-Hochberg correction.

Extended Data Fig. 7 Analysis of association between molecular features of CAR T-cell infusion products and the development of high-grade cytokine release syndrome (CRS).

a, Comparison of functional states between patients with grade 0-2 and grade 3-4 identified reduced frequencies of exhausted CD8 T-cells and increased frequencies of exhausted CD4 T-cells to be associated with the development of high-grade CRS. Q-values were calculated by a two-sided Fisher exact test with a Benjamini-Hochberg correction. b, c, Heatmaps show differentially expressed genes between CD4 T-cells (b) and CD8 T-cells. (c) from the infusion products of patients with grade 0-2 CRS versus those that developed grade 3-4 CRS. The CD69 gene shows higher expression and the CCL3 and CLL4 genes show lower expression on both CD4 and CD8 T-cells from the CAR T-cell infusion products of patients that developed high grade CRS. All differentially expressed genes are shown in Supplementary Tables 10, 11.

Extended Data Fig. 8 Percentage of CAR-positive cells in patients with grade 0-2 vs grade 3-4 ICANS.

Grade 0-2 ICANS, n = 22. Grade 3-4 ICANS, n = 18. Box, median ± interquartile range. Whiskers, minimum and maximum. P-values calculated by a two-sided Wilcoxon rank-sum test.

Extended Data Fig. 9 Cytokine levels in serum between patients with IACs and those without IACs.

Significance level was tested with Mann-Whitney U test. FDR q-value was calculated for multiple testing correction.

Extended Data Fig. 10 Quantification of the ICANS-associated cells (IACs) signature by scGSVA in CAR T-cell infusion products.

a, A stringent threshold was set to ensure high confidence classification of IACs by scGSVA analysis of the 109 signature genes measured by CapID, as shown for the cells that were originally identified as IACs by unsupervised clustering of 10X whole-transcriptome data in Fig. 4a. b, The distribution of scGSVA scores shows a clear difference between infusion products from patients with grade 0-2 ICANS vs patients with grade 3-4 ICANS. The threshold for classification of cells as IACs (scGSVA score >1.5) is shown.

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Deng, Q., Han, G., Puebla-Osorio, N. 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). https://doi.org/10.1038/s41591-020-1061-7

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