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Map** variant effects on anti-tumor hallmarks of primary human T cells with base-editing screens

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Abstract

Single-nucleotide variants (SNVs) in key T cell genes can drive clinical pathologies and could be repurposed to improve cellular cancer immunotherapies. Here, we perform massively parallel base-editing screens to generate thousands of variants at gene loci annotated with known or potential clinical relevance. We discover a broad landscape of putative gain-of-function (GOF) and loss-of-function (LOF) mutations, including in PIK3CD and the gene encoding its regulatory subunit, PIK3R1, LCK, SOS1, AKT1 and RHOA. Base editing of PIK3CD and PIK3R1 variants in T cells with an engineered T cell receptor specific to a melanoma epitope or in different generations of CD19 chimeric antigen receptor (CAR) T cells demonstrates that discovered GOF variants, but not LOF or silent mutation controls, enhanced signaling, cytokine production and lysis of cognate melanoma and leukemia cell models, respectively. Additionally, we show that generations of CD19 CAR T cells engineered with PIK3CD GOF mutations demonstrate enhanced antigen-specific signaling, cytokine production and leukemia cell killing, including when benchmarked against other recent strategies.

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Fig. 1: Experimental design and phenotypic readouts for massively parallel base-editor screens in T cells.
Fig. 2: Results of BE screens across donors.
Fig. 3: Structure–function analysis of PIK3CD and other gene variants on T cell signaling and activity.
Fig. 4: Enhanced signaling, polyfunctionality and melanoma cell-lytic capacity of epitope-specific T cells engineered with gene variants identified in BE screens.
Fig. 5: Enhanced signaling, cytokine production and leukemia cell-lytic capacity of CD19 CAR T cells equipped with PIK3CD or PIK3R1 variants identified in BE screens.

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Data availability

All processed screening data are provided as Supplementary Tables. Source data are provided with this paper.

Code availability

Code for generating in silico predicted structures is deposited here: https://github.com/gnikolenyi/izar_vis (ref. 73).

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Acknowledgements

N.K. and S.B.S. are equally contributing second authors. B.I. is supported by National Institute of Health grants (R37CA258829, R01CA280414, R01CA266446, U54CA274506); and additionally by the Pershing Square Sohn Cancer Research Alliance Award; the Burroughs Wellcome Fund Career Award for Medical Scientists; a Tara Miller Melanoma Research Alliance Young Investigator Award; the Louis V. Gerstner, Jr. Scholars Program; and the V Foundation Scholars Award. This work was supported by a Herbert Irving Comprehensive Cancer Center (HICCC) Velocity Grant (to B.I.), the HICCC Human Tissue Immunology and Immunotherapy Initiative and NIH Grant P30CA013696. Medical illustrations were prepared by U. Mackensen. The illustration in Extended Data Fig. 9a was created with https://www.biorender.com.

Author information

Author notes

  1. These authors contributed equally: Zachary H. Walsh, Parin Shah.

  2. These authors jointly supervised this work: Johannes C. Melms, Benjamin Izar.

Authors and Affiliations

  1. Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA

    Zachary H. Walsh, Parin Shah, Neeharika Kothapalli, Shivem B. Shah, D. Zack Brodtman, Meri Rogava, Michael Mu, Patricia Ho, Sinan Abuzaid, Johannes C. Melms & Benjamin Izar

  2. Department of Medicine, Division of Hematology and Oncology, Columbia University Irving Medical Center, New York, NY, USA

    Zachary H. Walsh, Parin Shah, D. Zack Brodtman, Meri Rogava, Michael Mu, Patricia Ho, Sinan Abuzaid, Neil Vasan, Johannes C. Melms & Benjamin Izar

  3. Columbia Center for Translational Immunology, New York, NY, USA

    Zachary H. Walsh, Parin Shah, D. Zack Brodtman, Meri Rogava, Michael Mu, Patricia Ho, Sinan Abuzaid, Johannes C. Melms & Benjamin Izar

  4. Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY, USA

    Zachary H. Walsh, Parin Shah, D. Zack Brodtman, Giuseppe Leuzzi, Meri Rogava, Michael Mu, Patricia Ho, Sinan Abuzaid, Neil Vasan, Alberto Ciccia, Johannes C. Melms & Benjamin Izar

  5. Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA

    Gergo Nikolenyi, Mohammed AlQuraishi & Benjamin Izar

  6. Department of Genetics and Development, Columbia University Medical Center, New York, NY, USA

    Giuseppe Leuzzi & Alberto Ciccia

  7. Department of Pediatrics, Columbia University Irving Medical Center, New York, NY, USA

    Joshua D. Milner

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  1. Zachary H. Walsh
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Contributions

B.I. and Z.H.W. conceived the study. B.I. provided overall supervision with support from J.C.M. Z.H.W., P.S. and J.C.M. planned, designed and executed all key experiments. S.B.S., M.M., P.H., M.R. and S.A. performed experiments. N.K. performed computational analyses of screens with support from Z.H.W. and D.Z.B. G.N. performed structural modeling and visualizations. N.V., M.A., J.D.M., A.C. and G.L. provided additional guidance for the design, execution and interpretation of screens. Z.H.W., P.S., J.C.M. and B.I. wrote the manuscript with input and approval from all authors.

Corresponding author

Correspondence to Benjamin Izar.

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Competing interests

B.I. is a consultant for or received honoraria from Volastra Therapeutics, Johnson & Johnson (Janssen), Novartis, Eisai, AstraZeneca and Merck and has received research funding to Columbia University from Agenus, Alkermes, Arcus Biosciences, Checkmate Pharmaceuticals, Compugen, Immunocore, Regeneron and Synthekine. Z.H.W. and B.I. filed a patent application based on this work. The other authors do not have competing interests.

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Peer review information

Nature Biotechnology thanks Dimitrios Wagner and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Optimization of workflows for base editing in primary human T cells.

a, Overview of approach for targeted base editing in primary human T cells. b-d, Target sites of sgRNAs against CD2, B2M, and TRBC1/2 sites predicted to generate gene knockout through several mechanisms (SPLd = splice donor site mutation, SPLa = splice acceptor site mutation, SM = start codon mutation, ES = conversion to early stop codon). e, Representative flow cytometry histograms from one human donor showing ABE-mediated knockout of CD2 and B2M using sgRNAs indicated in (b-c), and f, CBE-mediated knockout of CD2, TRBC1/2, and B2M using sgRNAs indicated in (b-d). g, Quantification of base editing efficiency in (e) (n = 3 independent human donors). h, Quantification of base editing efficiency in (f), (n = independent human 4 donors for B2M_ES and TRBC1/2_ES; n = 2 independent human donors for B2M_SPLd and CD2_SM). i, Representative flow cytometry dotplots and histograms demonstrating CBE-mediated knockout of TCRab. For histograms, red indicates gated mTurquoise-negative cells, and blue indicates gated mTurquoise-positive cells. j, Quantification of ABE-mediated knockout of B2M with lentiviral integration of B2M_SM_1 sgRNA and electroporation of ABE mRNA in CD4 and CD8 T cell subsets (n = 2 independent human donors). k, Editing efficiency (measured by % B2M loss on flow cytometry) and viability of T cells transduced with B2M_SM_1 sgRNA and electroporated with varying doses of ABE. Vertical dotted line represents ABE dose selected (per 1e6 T cells) for screens. Error bars represent mean +/− SD (panels g, h, j).

Source data

Extended Data Fig. 2 Tiling screen targets, library transduction, and pooled base editing of T cells.

a, Classification of sgRNAs in the ClinVar library based on mutation subtype. b, Schematic of gene targets for the 12-Gene tiling screen and their function in T cells. c, Classification of sgRNAs in the 12-Gene tiling library based on mutation subtype. d, Schematic for generation of library base-edited T cells. e, Transduction efficiency of ClinVar base editor library in n = 2 independent human donors.

Extended Data Fig. 3 Metrics for rigor and reproducibility of large-scale base editing screens.

a, Density plots showing LFC values of different categories of guides from the ClinVar library at Day 35 post-electroporation of the long-term expansion screen arm. Dashed line represents the bottom 5% of the distribution of combined empty window and silent mutation controls. Indicated are the percentages of guides in each category falling below this threshold. sgRNAs generating variants in CD3D, CD3E, CD3G, or CD3Z were binned into the ‘CD3 complex’ category. The second donor from the screen is shown (in companion to Fig. 2a). b, Scatter plot showing LFC values of negative control sgRNAs (including both empty window and silent mutations) in both donors from the ClinVar Library at Day 28 post-electroporation in the long-term expansion screen arm. c-d, Distribution of robust rank aggregation (RRA) scores for gene-wise dropout analysis in the c, CD25 hi vs lo (activation) sort and d, CFSE lo vs hi (short-term proliferation) sort arms of the ClinVar library across both donors. The top 5 negatively selected genes in CD25 hi vs lo and in CFSE lo vs hi are listed. e, Shared positive control sgRNA (n = 600) were identified between the ClinVar and 12-gene tiling screens and sgRNA LFCs from matched long-term proliferation arm timepoints (Day 28 of ClinVar Screen, Day 26 of 12-gene Tiling Screen) are plotted. For each screen, the average LFC of each sgRNA across both donors is plotted. Simple linear regression with two-sided Pearson test (panel e).

Extended Data Fig. 4 Analysis of ClinVar screen across readouts.

a, Scatterplot showing LFC of selected sgRNAs generating mutations in LCK, SOS1, and PTPRC. Timepoint shown is Day 28 post-electroporation in the ClinVar long-term expansion screen arm. b, Volcano plot showing enriched and depleted guides in the CFSE lo vs hi proliferation sort. For visualization purposes, one mutation for each labeled sgRNA is shown. One representative donor is shown. False discovery rate (FDR) cutoff <0.05. c, Volcano plot showing enriched and depleted guides in the CD25 hi vs lo proliferation sort. For visualization purposes, one mutation for each labeled sgRNA is shown. FDR cutoff <0.05. One representative donor is shown.

Extended Data Fig. 5 Characterization of variant effects by ClinVar classification.

a, (Top) distribution of negative control sgRNAs in the ClinVar library at day 28 of the long-term proliferation screen arm. (Bottom) sgRNA LFC distributions for selected genes targeted in the ClinVar library. Red lines indicate sgRNAs generating amino acid mutations which are identical to ClinVar-annotated pathogenic variants. b-j, Scatterplots of sgRNAs targeting selected genes in the ClinVar library at day 28 of the long-term proliferation screen arm. Dotted lines represent top and bottom 5% cutoffs of negative control sgRNA distribution. sgRNAs are binned into four distinct categories: predicted to generate an identical mutation to a ClinVar ‘VUS’ (‘Same VUS’; dark blue), predicted to generate a different mutation at an amino acid with a ClinVar ‘VUS’ (‘Diff VUS’; light blue), predicted to generate an identical mutation to a ClinVar ‘pathogenic’, ‘pathogenic/likely pathogenic’, or ‘likely pathogenic’ variant (‘Same P’; red), and sgRNAs predicted to generate a different mutation at an amino acid with a ClinVar ‘pathogenic’, ‘pathogenic/likely pathogenic’, or ‘likely pathogenic’ variant (‘Diff P’; yellow). Selected sgRNAs are annotated. Simple linear regression (panels b-j).

Extended Data Fig. 6 Independent analysis of 12-gene tiling screen and integration of results with ClinVar screen.

a, sgRNA LFCs across both donors at Day 26 of the long-term proliferation arm of the 12-gene tiling screen are plotted. Dotted lines represent top and bottom 10% cutoffs of the distribution of negative control sgRNAs (empty window and silent only sgRNAs) for each donor. Selected sgRNAs, with predicted target gene and mutation, are shown. b, sgRNA LFCs as in a, with blue overlay filtered by target gene. c, Shared sgRNAs (n = 325) were identified between the ClinVar and 12-gene tiling screens and sgRNA LFCs from matched long-term proliferation arm timepoints (Day 28 of ClinVar Screen, Day 26 of 12-gene Tiling Screen) are plotted. For each screen, the average LFC of each sgRNA across both donors is plotted. Selected sgRNAs with shared enrichment/depletion patterns across donors and screens are annotated. Simple linear regression (panel c).

Extended Data Fig. 7 Enrichment and structure-function relationship of variants promoting T cell proliferation.

a, Lollipop plot showing LFC of sgRNAs targeting PIK3CD at Day 15 post-electroporation in the long-term proliferation arm of the ClinVar screen. sgRNAs are mapped to the targeted region of the canonical isoform of PIK3CD (p110δ) and functional domains of the protein are annotated. Selected variants and their predicted mutational consequences are annotated. b,c, Timecourse line graphs of LFC of sgRNAs targeting PIK3CD in both donors in the long-term proliferation arm of the ClinVar screen. d, Lollipop plot for sgRNAs targeting AKT1 at Day 35 post-electroporation in the long-term proliferation arm of the ClinVar screen, mapped to the canonical AKT1 isoform. e, Timecourse line graphs of LFC of sgRNAs targeting AKT1 in the long-term expansion arm of the ClinVar screen. f, Structure and position of mutations in AKT1. (right) Overall predicted structure of AKT1 (blue) and mutated residues (red). (left) Wild-type (WT) and mutated (mut) residues (red). D323G is predicted to localize next to L14 (dark blue). g, Lollipop plots for sgRNAs targeting LCK at day 26 post-electroporation in the long-term proliferation arm of the 12-gene tiling screen, mapped to the canonical LCK isoform. h, Structure and position of mutations in LCK. (top) Overall predicted structure of LCK (blue) and mutated residues (red). (bottom) Wild-type (WT) and mutated (mut) residues (red).

Extended Data Fig. 8 Signaling and impact of subtle differences in editing efficiency and phenotypic readouts.

a, Quantification of S6 phosphorylation (pS235/S236) and b, AKT phosphorylation (pS473) measured by flow cytometry in T cells with indicated genotypes (x axis) after 10 minutes of stimulation with anti-CD3/CD28 antibodies. c, For all validated sgRNAs targeting PIK3CD (that is, Cys416Arg, Tyr524Cys, Glu525Gly_His526Arg, and Glu527Gly_Lys528Glu), sgRNA editing efficiency and effect size on AKT phosphorylation (pS473), d, TNFα MFI, and e, IL2 expression are plotted for each of the 3 donors used in initial validation experiments in Fig. 3. In cases where sgRNAs generated multiple edits within the editing window (for example, PIK3CD Glu525Gly_His526Arg), the average editing efficiency across all targeted bases in the editing window was used. Data in (a-b) was generated from n = 3 independent human donors. Within each donor this data was normalized to the silent control condition. One-way ANOVA with Dunnett’s test for multiple comparisons (panels a,b). Simple linear regression (panels c-e). Error bars represent mean +/− SD (panels a, b).

Source data

Extended Data Fig. 9 Experimental design, functional assays, and melanoma co-culture experiments with NY-ESO-1 TCR T cells engineered with variants identified in base editing screens.

a, Schematic for engineering and expanding NY-ESO-1 specific T cells. b, Representative flow cytometry dotplot of NY-ESO-1 specific T cells prior to sorting. c, Viable A375-dsRed cells relative to time t0 after culture with NY-ESO-1 specific T cells for 48 hours at varying effector to target ratios. aMHCI = MHC class I-blocking antibody (n = 3 independent biological replicates). d, Representative flow cytometry histograms of ABE-mediated knockout of B2M or CD2 in NY-ESO-1 specific T cells. e, Representative contour plots of single or multiplexed base editing of B2M and CD2. f, AKT phosphorylation (pS473), in NY-ESO-1 specific T cells after either 15 minutes of co-culture with A375 cells (+) or media alone (-) (n = 3 independent biological replicates). g, MFI of TNFα and h, GrzB in NY-ESO-1 specific T cells with indicated base edits after 8-hour co-culture with A375-dsRed cells at a 1:1 effector to target ratio (n = 3 independent biological replicates). i, Frequency of NY-ESO-1 specific T cells with indicated genotypes co-expressing TNFα, IL2, and GrzB after 8 hours of co-culture with A375 cells at a 1:1 effector to target ratio (n = 3 independent biological replicates.). NT = non-targeting control sgRNA j, Viable wild-type (WT) or CD58-KO A375 cells relative to time t0 after 48 hours of co-culture with NY-ESO-1 specific T cells at a 1:1 effector to target ratio (n = 3 independent biological replicates.) k, Viable B2M-KO A375 cells relative to time t0 after 48 hours of co-culture with NY-ESO-1 specific T cells with indicated genotypes. Dotted lines in (f-i) represent mean of the control. Dotted lines in (j-k) represent relative viable cell count at time t0. One-way ANOVA with Tukey’s test for multiple comparisons (panel c). One-way ANOVA with Dunnett’s test for multiple comparisons (panels f-i, k). Student’s t test (panel j). Error bars represent mean +/− SD (panels c, f-k).

Source data

Extended Data Fig. 10 Design and results of leukemia co-culture with CD19 CAR-T cells equipped with variants identified in base editing screens.

a, Representative histograms of GFP expression, indicating transduction efficiency of first- and second-generation CD19-CAR constructs (CD19-CD3z or CD19-BBz, respectively) in primary human T cells. Blue histograms represent untransduced control T cells. b, Expression of CTLA4 on CD19-CAR T cells, edited with a control non-targeting sgRNA (NT) or CTLA4-KO sgRNA, following 48-hour co-culture with Nalm6 leukemia cells at an 0.5:1 effector to target ratio. c, Relative cell numbers of Nalm6 cells 48 hours after co-culture with CD19-BBz CAR T variants at several E:T ratios, compared to time t0. d, Relative number of CD19-KO Nalm6 cells 48 hours after co-culture with CD19-BBz CAR T cells at an 0.25:1 effector to target ratio, compared to time t0. e, Quantification of CD19-CD3z CAR T cell AKT phosphorylation (pS473) by flow cytometry after 15 minutes of culture with either Nalm6 leukemia (+) or media alone (−) with representative flow histograms. f, Quantification of CD19-CD3z CAR T cell intracellular expression of TNFα and g, IL2 after 8-hour culture with Nalm6 leukemia (+) or in media only (-). h, Relative cell numbers of wild-type and i, CD19-KO Nalm6 cells 48 hours after co-culture with CD19-CD3z CAR T cells at an 0.5:1 effector to target ratio, compared to time t0. Dotted lines in (e-g) represent mean of the control population. Dotted lines in (panel c, d, h, i) represent relative viable cell count at time t0. One-way ANOVA with Tukey’s test for multiple comparisons (panel b), one-way ANOVA with Dunnett’s test for multiple comparisons (panels d-i). Error bars represent mean +/- SD (panels b-i).

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Supplementary information

Supplementary Information

Supplementary Figs. 1–3.

Reporting Summary

Supplementary Tables

Supplementary Tables 1–8. Each table is in a separate, labeled tab.

Supplementary Data

Raw sequencing reads and library map** percentages for all sequencing runs for screens in this study.

Source data

Source Data Figs. 1 and 3–5 and Extended Data Figs. 1 and 8–10

Statistical source data (contains labeled tabs for all relevant main and Extended Data figures).

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Walsh, Z.H., Shah, P., Kothapalli, N. et al. Map** variant effects on anti-tumor hallmarks of primary human T cells with base-editing screens. Nat Biotechnol (2024). https://doi.org/10.1038/s41587-024-02235-x

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