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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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. 1–6 and Extended Data Figs. 1–10 are available through the European Genome-phenome Archive (https://www.ebi.ac.uk/ega/home): accessions EGAD00001006327 and EGAD00001006325.
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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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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.
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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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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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Supplementary Figs. 1–10.
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Supplementary Tables 1–12.
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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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DOI: https://doi.org/10.1038/s41591-020-1061-7
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