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γδ T cell dichotomy with opposing cytotoxic and wound healing functions in human solid tumors

Nature Cancer volume 4, pages 1122–1137 (2023)Cite this article

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Abstract

γδ T cells are important tissue-resident, innate T cells that are critical for tissue homeostasis. γδ cells are associated with positive prognosis in most tumors; however, little is known about their heterogeneity in human cancers. Here, we phenotyped innate and adaptive cells in human colorectal (CRC) and endometrial cancer. We found striking differences in γδ subsets and function in tumors compared to normal tissue, and in the γδ subsets present in tumor types. In CRC, an amphiregulin (AREG)-producing subset emerges, while endometrial cancer is infiltrated by cytotoxic cells. In humanized CRC models, tumors induced this AREG phenotype in Vδ1 cells after adoptive transfer. To exploit the beneficial roles of γδ cells for cell therapy, we developed an expansion method that enhanced cytotoxic function and boosted metabolic flexibility, while eliminating AREG production, achieving greater tumor infiltration and tumor clearance. This method has broad applications in cellular therapy as an ‘off-the-shelf’ treatment option.

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Fig. 1: Differences in prognostic value of Vδ1 T cells is not due to exhaustion.
Fig. 2: Tumor-infiltrating γδ T cells are not exhausted and maintain their cytotoxic phenotype.
Fig. 3: γδ T cells in CRC are heterogenous compared to endometrial cancer.
Fig. 4: AREG is associated with tumors characterized by wound healing, present in healthy colon and upregulated in response to malignancy in γδ T cells alone.
Fig. 5: AREG is associated with NKp80+ yδ T cells and induces proliferation of CRC tumor cells.
Fig. 6: AREG production is enhanced by proinflammatory cytokines and can be inhibited in Gen 2 cells.
Fig. 7: Characterization of the metabolic advantages of Gen 2 cells.
Fig. 8: Gen 2 cells inhibit PDX tumor growth and do not produce AREG in the TME.

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Data availability

Sequencing data from this study have been deposited in the NCB Gene Expression Omnibus under accession no. GSE210040. Furthermore, data from this study have been provided in the supplementary information. Publicly available data that were used to generate the survival curves are available from the NCI Genetic Data Commons (https://portal.gdc.cancer.gov/). Further scRNA-seq datasets were used from published sources, including GSE178341 and GSE216534. Source data are provided with this paper. All other data supporting the findings of this study are available from the corresponding author on reasonable request.

Code availability

The code used in this paper was modified from publicly available vignettes for the Seurat package (https://satijalab.org/seurat/index.html) and Scanpy package (https://scanpy.readthedocs.io/en/stable/). Survival analysis code can be found at https://brucemoran.github.io/charmon_tcga.

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Acknowledgements

This work was supported by National Institutes of Health grant no. 5R01AI134861 and a BWH IGNITE Grant and NIH R24 OD026440 (to M.B). D.B. acknowledges funding from Science Foundation Ireland under the Strategic Research Programme Precision Oncology Ireland (18/SPP/3522), the Ireland East Hospital Group and the National Maternity Hospital Foundation. Biobanking activities at UCD-GOG are supported by the UCD Clinical Research Centre. We thank our collaborators in the Evergrande Flagship Project for early access to the scRNA-seq datasets. We thank our patients and their families for participating in this research project.

Author information

Author notes

  1. These authors contributed equally: Alexandra Zaborowski, Haim Moore.

Authors and Affiliations

  1. Department of Endocrinology, Brigham & Women’s Hospital, Boston, MA, USA

    Cathal Harmon, Danielle Duquette, John Scanlan, Harry Kane, Britta Kunkemoeller, Claire L. McIntyre, Aine Ni Scannail & Lydia Lynch

  2. Department of Medicine, Harvard Medical School, Boston, MA, USA

    Cathal Harmon, Haim Moore, Danielle Duquette, John Scanlan, Harry Kane, Britta Kunkemoeller, Claire L. McIntyre, Aine Ni Scannail & Lydia Lynch

  3. School of Biochemistry and Immunology, Trinity College Dublin, Dublin, Ireland

    Alexandra Zaborowski, Karen Slattery, Danielle Duquette, John Scanlan, Harry Kane & Lydia Lynch

  4. Centre for Colorectal Disease, St. Vincent’s University Hospital, Dublin, Ireland

    Alexandra Zaborowski & Des Winter

  5. Program in Molecular Medicine and the Diabetes Center of Excellence, University of Massachusetts Chan Medical School, Worcester, MA, USA

    Pamela St. Louis & Michael A. Brehm

  6. Department of Pathology, St. Vincent’s University Hospital, Dublin, Ireland

    Bruce Moran

  7. Evergrande Center for Immunologic Diseases and Ann Romney Center for Neurologic Diseases, Harvard Medical School and Brigham & Women’s Hospital, Boston, MA, USA

    Ana C. Anderson

  8. Gynecological Oncology Group, School of Medicine, University College Dublin, Dublin, Ireland

    Donal Brennan

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Contributions

C.H. and L.L. conceptualized the study. C.H., H.K., M.A.B. and L.L. devised the methodology. C.H., H.M., H.K., B.M. and J.S. were responsible for the software. H.M. and K.S. validate the data. C.H., H.M., J.S., B.M. and L.L. carried out the formal analysis. C.H., H.M., A.Z., P.S.L., K.S., D.D., B.K., C.L.M. and A.N.S. carried out the investigation. A.C.A., D.W., D.B., M.A.B. and L.L. managed the resources. C.H., H.M., A.Z., D.W. and D.B. curated the data. C.H. and L.L. wrote the original paper. C.H., H.M., D.W., D.B., M.A.B. and L.L. reviewed and edited the paper. C.H., H.M. and L.L. visualized the data. M.A.B. and L.L. supervised the study. L.L. was responsible for project administration. D.W., D.B., M.A.B. and L.L. were responsible for funding.

Corresponding author

Correspondence to Lydia Lynch.

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Competing interests

L.L. is a member of the scientific advisory board for MiNK Therapeutics and a consultant for Bayer. A.C.A. is a member of the scientific advisory board for Tizona Therapeutics, Trishula Therapeutics, Compass Therapeutics, Zumutor Biologics, ImmuneOncia and Nekonal Sarl. A.C.A. is also a paid consultant for iTeos Therapeutics, Larkspur Biosciences and ExcepGen. The interests of L.L. and A.C.A. were reviewed and managed by the Brigham and Women’s Hospital and Partners Healthcare in accordance with their conflict-of-interest policies. The other authors declare no competing interests.

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Nature Cancer thanks Florian Greten 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 Prognostic value of Vd1 T cells in solid tumors.

(a-d) Progression free survival analysis of ovarian (OV), lung (LUAD), endometrial (UCEC) and colorectal (COAD) cancer patients from the TCGA database, stratified by high and low expression of γδ TCR genes TRDV1 & TRDV2. (e) Absolute numbers of Vδ1 and Vδ2T cells in endometrial tumors and healthy tissue (f) Absolute numbers of Vδ1 and Vδ2T cells in colon tumors and healthy tissue. Data presented as mean ± SEM. Data were analyzed using Cox Proportional Hazards Model (A-D), Wilcoxon matched pairs test two tailed (E-F). A–D n = 379-575, E-F n = 5 patients, * p < 0.05.

Source data

Extended Data Fig. 2 Identification of clusters.

scRNAseq was performed 7 CRC patients using the 10x platform. Data was analyzed in R using the Seurat package. (a) Gating strategy for the isolation of NK cells, γδ T cells, MAIT cells and adaptive T cells. (b) Top 20 differentially expressed genes in clusters from CRC samples. (c) Lineage markers used to identify clusters.

Source data

Extended Data Fig. 3 Expression of effector molecules, immune checkpoints, & cytotoxicity receptors.

(a) Heatmap of effector gene expression in cell clusters from CRC samples. (b) Heatmap of immune checkpoint expression in cell clusters from CRC samples. (c) Heatmap of cytotoxicity receptor expression in cell clusters from CRC samples. (d) Production of IL17A in healthy colon and tumor γδ T cell subsets after PMA stimulation. Data presented as mean ± SEM. Data were analyzed using Friedman test, with Dunn’s multiple comparison test two tailed (n = 6 (Vδ3), 12 (Vδ2), 13 (Vδ1) patients, * p < 0.05, ** p < 0.01, ** p < 0.001).

Source data

Extended Data Fig. 4 γδ T cells are not the major source of AREG in healthy colon, healthy endometrium, or endometrial tumors.

scRNAseq was performed on 2 endometrial tumors, 2 healthy endometrial samples and 5 healthy colon samples using the 10x platform. Data was analyzed in R using the Seurat package. (a) UMAP representation of cell clusters from endometrial tumors. (b) Violin plots of AREG expression in cell clusters from endometrial tumors. (c) UMAP representation of cell clusters from healthy endometrium. (d) Violin plots of AREG expression in cell clusters from healthy endometrium. (e) UMAP representation of cell clusters from healthy colon. (f) Violin plots of AREG expression in cell clusters from healthy colon.

Source data

Extended Data Fig. 5 Cell types expressing AREG in colorectal cancer patients.

(a) Proportion of cells in each cluster which express AREG in colorectal cancer. Data presented as mean ± SEM.

Source data

Extended Data Fig. 6 γδ T cells in individual patients from CRC cohort.

(a) Absolute number of γδ T cells from each patient in the Flagship CRC cohort (MMRp-red, MMRd-black). (b) Proportion of γδ T cells in lymphocyte clusters in each patient in the Flagship CRC cohort (MMRp-red, MMRd-black). (c) Proportion of γδ T cells expressing AREG in each patient in the Flagship CRC cohort (MMRp-orange, MMRd-blue) (n = 62).

Source data

Extended Data Fig. 7 Analysis of γδ subsets in CRC.

scRNAseq Data was analyzed from deVries et al. 2023. (a, b) γδ T cells were analyzed and subsetted on Vδ1T cells by expression of the TRDV1 gene. (c) AREG and IFNG gene expression in TRDV1+ cells. (d) Correlation between the expression of AREG and IFNG in TRDV1+ cells. scRNAseq was performed 7 CRC patients using the 10x platform. Data was analyzed in R using the Seurat package. (e) Expression of immune checkpoint molecules in γδ T cell subsets from CRC samples. (f) Expression of activatory receptors in γδ T cell subsets from CRC samples. (g) Correlation plots of KRLF1, TIGIT & AREG in γδ T cells in Flagship CRC dataset.

Source data

Extended Data Fig. 8 Expanded Vδ1T cells promote growth of HCT116 colon cells through AREG production.

Vδ1T cells were expanded using a published protocol. (a) Expression of NKp80 & TIGIT pre- and post-expansion. A scratch assay was performed using HCT116 cells with or without 1 × 105 Vδ1T cells for 24hrs. (b) Percentage of scratch area at 0, 6 & 24 hours, untreated or treated with Vδ1T cells. (c) Percentage of Vδ1T cells expressing AREG after 24 hours co-incubation with scratched and unscratched HCT116 cells. (d) Concentration of AREG in supernatants of HCT116 cells without Vδ1 treatment (Control) or with Vδ1T cells with or without cell scratching. (e) Representative images of scratch assay using SW480 treated with Vδ1T cells ± cetuximab (αEGFR), aIL1β, or αTCRγδ (3 independent experiments). Data presented as mean ± SEM. Data were analyzed using Wilcoxon matched pairs test two tailed or Friedman test, with Dunn’s multiple comparison test two tailed (A n = 6 donors, B n = 8 donors, C n = 5 donors, D n = 3 donors, * p < 0.05, **, p < 0.01).

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Extended Data Fig. 9 Optimization of Vδ1T cell expansion.

Isolated Vd1 T cells were cultured for 7days with aCD3 ± Il1b, IL21, IFNγ, IL4 or IL15. Cells were then stimulated for 4 hours with PMA (a) Percentage of Vd1 T cells producing IFNγ. γδ T cells were isolated by positive selection MACS and cultured for 21 days in varying conditions using a combination of cytokines, including IL-2, IL-12, IL-15, IL-18, αCD3 and αCD2. (b) Growth curves of Vδ1T cells using cytokine cocktail based on IL-12/IL-15 combination. (c) Growth curves of Vδ1T cells using cytokine cocktail based on IL-12/IL-18 combination. (d) Growth curves of Vδ1T cells using cytokine cocktail based on IL-15/IL-18 combination. (e) Growth curves of Vδ1T cells using cytokine cocktail based on IL-12/IL-15/IL-18 combination. Conditions which resulted in cell expansion were assessed for cell phenotype. (f) Percentage of live cells. (g) Percentage of Vδ1T cells. (h-k) Percentage of TIGIT, LAG3, CTLA4, TIM3 positive cells. Data presented as mean ± SEM. Data were analyzed using Friedman test, with Dunn’s multiple comparison test two tailed. (A n = 7 donors, B-K n = 2 donors, * p < 0.05, **, p < 0.01,).

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Extended Data Fig. 10 Gen 1 cells are functionally distinct from Gen 2 cells, and Gen 2 cells prevent tumor growth in vitro.

RNAseq was performed on unexpanded Vδ1T cells, Gen 1, and Gen 2 cells, with and without PMA stimulation for 4hrs. (a) Heatmap of top 100 genes expressed in Vδ1 T cells, Gen 1 & Gen 2 cells with and without stimulation. (b) KEGG pathway analysis of differentially expressed gene between Gen 1 and Gen 2 cells. (c) Expression of genes associated with tissue residency and cell trafficking (n = 2). Data presented as mean ± SEM. (d) Percentage of scratch area of HCT116 cells imaged after 6 or 24 hours coincubation with Gen 1 or Gen 2 cells. (e) Percentage of Vδ1T cells expressing degranulation marker CD107a after 24-hour co-incubation with HCT116 cells. (f) Percentage of Vδ1T cells producing IFNγ after 24-hour co-incubation with HCT116 cells. (g) Percentage of Vδ1T cells producing AREG after 24-hour co-incubation with HCT116 cells. Data presented as mean ± SEM. Data were analyzed using Wilcoxon matched pairs test two tailed or Friedman test, with Dunn’s multiple comparison test two tailed (A n = 8 donors, B n = 5 donors, C n = 6 donors, D n = 5 donors, * p < 0.05, **, p < 0.01).

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Supplementary information

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FACS data.

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FACS data.

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FACS data.

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Gene expression and FACS data.

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FACS data and scratch assay image analysis data.

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Cell counts, FACS data and scratch assay image analysis data.

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FACS data, Seahorse data and gene expression.

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Tumor size and FACS data.

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Gene expression and FACS data.

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Gene expression data.

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Gene expression and FACS data.

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Gene expression data.

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Gene expression data.

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Gene expression data.

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Gene expression data.

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FACS, scratch assay image analysis and ELISA data.

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FACS and cell count data.

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Gene expression, FACS and scratch assay image analysis data.

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Harmon, C., Zaborowski, A., Moore, H. et al. γδ T cell dichotomy with opposing cytotoxic and wound healing functions in human solid tumors. Nat Cancer 4, 1122–1137 (2023). https://doi.org/10.1038/s43018-023-00589-w

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