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Screening for CD19-specific chimaeric antigen receptors with enhanced signalling via a barcoded library of intracellular domains

Nature Biomedical Engineering volume 6, pages 855–866 (2022)Cite this article

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Abstract

The immunostimulatory intracellular domains (ICDs) of chimaeric antigen receptors (CARs) are essential for converting antigen recognition into antitumoural function. Although there are many possible combinations of ICDs, almost all current CARs rely on combinations of CD3𝛇, CD28 and 4-1BB. Here we show that a barcoded library of 700,000 unique CD19-specific CARs with diverse ICDs cloned into lentiviral vectors and transduced into Jurkat T cells can be screened at high throughput via cell sorting and next-generation sequencing to optimize CAR signalling for antitumoural functions. By using this screening approach, we identified CARs with new ICD combinations that, compared with clinically available CARs, endowed human primary T cells with comparable tumour control in mice and with improved proliferation, persistence, exhaustion and cytotoxicity after tumour rechallenge in vitro. The screening strategy can be adapted to other disease models, cell types and selection conditions, and could be used to improve adoptive cell therapies and to expand their utility to new disease indications.

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Fig. 1: Selecting for CD69high expression enriches CARs encompassing ITAM-signalling ICDs.
Fig. 2: CARPOOL selections reveal new functional signalling domain configurations.
Fig. 3: CAR variants show enhanced cytotoxicity and cytokine secretion in response to antigen stimulation.
Fig. 4: CAR variants exhibit distinct proliferation and exhaustion profiles upon repeated antigen exposure.
Fig. 5: CARs identified by CARPOOL differ in transcriptional profile and predicted efficacy correlates.
Fig. 6: CAR variants show tumour control similar to that of a BBζ CAR in vivo.

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Data availability

The NGS selection datasets have been deposited in the Sequence Read Archive and are available under the accession number PRJNA744269. The scRNA-seq data have been deposited in the Gene Expression Omnibus under accession number GSE179767. All data generated or analysed during the study (including the DomainSeq-processed CARPOOL selection data) are included in the paper or its supplementary information.

Code availability

The code used to analyse the domain composition of selected CARs can be accessed in the DomainSeq repository at https://github.com/birnbaumlab/Gordon-et-al-2022.

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Acknowledgements

We thank the Koch Institute’s Robert A. Swanson (1969) Biotechnology Center for their technical support, especially the Flow Cytometry Facility, Preclinical Modeling, Imaging and Testing Core, MIT BioMicro Center and High Throughput Sciences Core. We thank G. Paradis, P. Chamberlain, H. Holcombe, V. Spanoudaki and S. Levine for many helpful discussions and suggestions. We also thank Y. Chen for providing the sequence for the 19BB𝛇 CAR. M.E.B. was supported by a Packard Fellowship, a Pew-Stewart Scholarship and a grant from the Deshpande Center. D.J.I. was supported by the Mark Foundation, the National Institutes of Health (R01-CA247632) and the Bridge Project, a partnership between the Koch Institute for Integrative Cancer Research at MIT and the Dana-Farber/Harvard Cancer Center. D.J.I. is an investigator of the Howard Hughes Medical Institute. D.A.L. was supported by US Army Research Office Cooperative Agreement W911NF-19-2-0026 Institute for Collaborative Biotechnologies. This work was supported in part by the Koch Institute Frontier Research Program (to M.E.B. and M.T.H.), and the Koch Institute Support (core) Grant P30-CA14051 from the National Cancer Institute. This work was additionally supported by a fellowship from Human Frontier Science Program to T.K., National Science Foundation Graduate Research Fellowships to K.S.G., C.R.P. and P.V.H., a Paul and Daisy Soros Fellowship to A.R., and support from the National Institute of General Medical Sciences (T32-GM007753) to A.Q.Z. and C.K. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of General Medical Sciences or the National Institutes of Health. This research is supported in part by the National Research Foundation, Prime Minister’s Office, Singapore under its Campus for Research Excellence and Technological Enterprise (CREATE) programme, through Singapore MIT Alliance for Research and Technology (SMART): Critical Analytics for Manufacturing Personalised-Medicine (CAMP) Inter-Disciplinary Research Group.

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Author notes

  1. These authors contributed equally: Khloe S. Gordon, Taeyoon Kyung.

Authors and Affiliations

  1. Koch Institute for Integrative Cancer Research, Cambridge, MA, USA

    Khloe S. Gordon, Taeyoon Kyung, Caleb R. Perez, Patrick V. Holec, Azucena Ramos, Angela Q. Zhang, Yash Agarwal, Yunpeng Liu, Catherine E. Koch, Brian A. Joughin, Darrell J. Irvine, Michael T. Hemann & Michael E. Birnbaum

  2. Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA

    Khloe S. Gordon, Taeyoon Kyung, Caleb R. Perez, Patrick V. Holec, Yash Agarwal, Alina Starchenko, Brian A. Joughin, Douglas A. Lauffenburger, Darrell J. Irvine & Michael E. Birnbaum

  3. Singapore-MIT Alliance for Research and Technology Centre, Singapore, Singapore

    Khloe S. Gordon & Michael E. Birnbaum

  4. Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA

    Azucena Ramos, Yunpeng Liu, Catherine E. Koch & Michael T. Hemann

  5. Department of Health, Science, and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA

    Angela Q. Zhang

  6. Ragon Institute of MIT, MGH and Harvard, Cambridge, MA, USA

    Douglas A. Lauffenburger, Darrell J. Irvine & Michael E. Birnbaum

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Contributions

T.K. and M.E.B. conceived the project. K.S.G., T.K., C.R.P., A.R., A.Q.Z., Y.A., Y.L., C.K. and A.S. conducted experiments. K.S.G., T.K., C.R.P, P.V.H, A.R. and B.J. performed data analyses. D.A.L., M.T.H., D.J.I. and M.E.B. supervised the work. K.S.G., T.K., C.R.P. and M.E.B. wrote the manuscript. All authors contributed to the editing of the manuscript.

Corresponding author

Correspondence to Michael E. Birnbaum.

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Competing interests

The library approach described in this paper is the subject of a US patent application (PCT/US2020/017794) with T.K. and M.E.B. as inventors. M.E.B. is a founder, consultant and equity holder of Kelonia Therapeutics and Abata Therapeutics. T.K. is presently an employee of Gingko Bioworks. The other authors declare no competing interests.

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Extended data

Extended Data Fig. 1 ICD frequency post-selection for 1st and 2nd generation CARs.

Related to Fig. 2b. Heatmap representing log2(fold-change) of individual ICDs, oriented by ICD position within the CAR relative to the plasma membrane. Fold-changes were normalized to frequencies in the EGFP+ sorted population prior to selection.

Extended Data Fig. 2 Characterization of CAR variant expression and function in Jurkat T cells.

a, Design of CD19 targeted CAR candidates. b, CAR surface expression (n = 1), (c) internalization (n = 1), and (d) CD69 upregulation upon antigen stimulation with recombinant human CD19 (n = 6 technical replicates representative of 2 biological replicates). e, Basal CD69 (activation) and (f) PD-1 expression of unstimulated CAR-T cells (n = 7 technical replicates representative of 2 biological replicates). P values shown in panel (e) are 0.002 for Var1 vs. BB𝛇 and 0.0149 for Var2 vs. BB𝛇. P values shown in panel (f) are 0.0117 for Var1 vs. BB𝛇, 0.0110 for Var2 vs. BB𝛇, and 0.0192 for Var3 vs. BB𝛇, as determined by two-tailed unpaired student’s t tests (df = 12). Data shown are individual values in panel (c), while panel (d) shows means ± s.e.m. Panels (e,f) show individual values along with means ± s.e.m.

Source data

Extended Data Fig. 3 Memory and exhaustion phenotypes following tumour rechallenge.

Related to Fig. 4d, e. a, FACS plots displaying changes in CAR-T cell memory differentiation following repeated tumour challenge (days 4 and 18 shown). b, LAG-3 expression following repeated tumour challenge on day 22 (n = 3 technical replicates representative of 2 biological replicates). P values are 0.0465 for CD4+ Var3 vs. BB𝛇, and 0.0358 for CD8+ Var1 vs. BB𝛇, and 0.0112 for CD8+ Var3 vs. BB𝛇 (df = 4 for all). P values in panel (b) were determined using an unpaired student’s t test. Data shown are means ± s.e.m.

Source data

Extended Data Fig. 4 Expression of CD4 and CD8A across different phenotypic clusters.

Differential of CD4 (left) and CD8A (right) expression as determined by single cell sequencing as shown in Fig. 5. Data points shown represent individual cells.

Extended Data Fig. 5 Differential gene expression between Var1 and 19BBζ-expressing CAR-T cells.

Volcano plot showing differentially expressed genes between Var1 (n = 863 cells) and 19BB𝛇-expressing (n = 637 cells) CAR-T cells 48 hours after a third NALM6 rechallenge. P values for each gene were determined using a two-sided Wilcoxon Rank-Sum test. Genes with both a Bonferroni-corrected P value less than 0.05 and an average log2FC of > 1 are highlighted.

Extended Data Fig. 6 High dose Var1, Var3, and BBζ achieve sustained remission.

a, Experimental design. NSG mice were infused intravenously with 5 × 105 FLuc+ CD19+ NALM6 cells, then treated i.v. with 1 × 106 of a 1:1 mixture of human CD8+ and CD4+ CAR-T cells or untransduced control T cells (n = 5 mice for all groups). b, Kaplan-Meier curve for overall survival. Tumour burden was assessed by measuring luminescent activity. c, Quantification of total photon counts are shown. d, Weight loss following ACT was monitored routinely. Data shown in panels (c,d) are individual values.

Source data

Extended Data Fig. 7 High dose BBζ and Var1 confer partial protection against B-ALL recurrence.

a, Experimental design. NSG mice were infused intravenously with 5 × 105 FLuc+ CD19+ NALM6 cells, then treated i.v. with 1 × 106 of a 1:1 mixture of human CD8+ and CD4+ CAR-T cells or untransduced control T cells (n = 5 mice for all groups). Mice were then rechallenged with 5 × 105 FLuc+ CD19+ NALM6 cells on days 44 and 58 post ACT (indicated with arrows). b, Kaplan-Meier curve for overall survival. Tumour burden was assessed by measuring luminescent activity. c, Quantification of total photon counts is shown. d, Weight loss following ACT was monitored routinely. Data shown in panels (c,d) are individual values. Arrows in panels (b–d) indicate dates of NALM6 rechallenge. e, CD19+ NALM6 cells and (f) EGFP+ CAR-T cells were harvested from the peripheral blood (PB), spleen (SP), and bone marrow (BM) of a separate cohort of mice that received the same treatment regimen at day 14 post ACT and quantified via FACS analysis (n = 3). Data shown are individuals with means ± s.e.m.

Source data

Supplementary information

Supplementary Information

Supplementary figures.

Reporting Summary

Peer Review File

Supplementary Table 1

Amino acid compositions of intracellular signalling domains incorporated into the CAR library.

Supplementary Table 2

Barcode frequencies and metadata from NGS of selected CAR-library-expressing Jurkats.

Supplementary Table 3

List of significantly differentially overexpressed genes in each cluster following scRNA-seq.

Supplementary Table 4

Differentially expressed genes between all Var1-expressing cells and all 19BBz-expressing cells.

Supplementary Table 5

List of T-cell-specific curated gene sets used for scGSVA analysis of transcriptional clusters.

Source data

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Gordon, K.S., Kyung, T., Perez, C.R. et al. Screening for CD19-specific chimaeric antigen receptors with enhanced signalling via a barcoded library of intracellular domains. Nat. Biomed. Eng 6, 855–866 (2022). https://doi.org/10.1038/s41551-022-00896-0

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