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T cell egress via lymphatic vessels is tuned by antigen encounter and limits tumor control

Nature Immunology volume 24, pages 664–675 (2023)Cite this article

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An Author Correction to this article was published on 17 March 2023

This article has been updated

Abstract

Antigen-specific CD8+ T cell accumulation in tumors is a prerequisite for effective immunotherapy, and yet the mechanisms of lymphocyte transit are not well defined. Here we show that tumor-associated lymphatic vessels control T cell exit from tumors via the chemokine CXCL12, and intratumoral antigen encounter tunes CXCR4 expression by effector CD8+ T cells. Only high-affinity antigen downregulates CXCR4 and upregulates the CXCL12 decoy receptor, ACKR3, thereby reducing CXCL12 sensitivity and promoting T cell retention. A diverse repertoire of functional tumor-specific CD8+ T cells, therefore, exit the tumor, which limits the pool of CD8+ T cells available to exert tumor control. CXCR4 inhibition or loss of lymphatic-specific CXCL12 boosts T cell retention and enhances tumor control. These data indicate that strategies to limit T cell egress might be an approach to boost the quantity and quality of intratumoral T cells and thereby response to immunotherapy.

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Fig. 1: Dermal lymphatic vessels limit CD8+ T cell accumulation in melanoma.
Fig. 2: Functional tumor-specific effector CD8+ T cells egress from tumors via lymphatic vessels.
Fig. 3: Local antigen encounter links T cell function and migratory potential.
Fig. 4: Egressing T cell state is associated with response to ICB in humans.
Fig. 5: LEC-secreted CXCL12 sequesters CD8+ T cells in the tumor periphery.
Fig. 6: CXCL12–CXCR4 signaling blockade improves tumor control and immunotherapy.

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Data availability

Sequencing datasets have been deposited in the Gene Expression Omnibus database under accession codes GSE203307 (LEC bulk RNA-seq), GSE203557 (CD8+ T cell bulk RNA-seq) and GSE218455 (CD8+ T cell scRNA-seq) and are publicly available.

Code availability

Original code for vessel segmentation is available at https://github.com/amandalundlab/Vessel_Segmentation.

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Acknowledgements

We would like to acknowledge S. R. Schwab (New York University Grossman School of Medicine) for critical input and tools, I. Dolgalev of the Applied Bioinformatics Laboratory (New York University Grossman School of Medicine) for technical support and all members of the Lund Lab for critical feedback and technical support as well as our sources of funding: National Institutes of Health grant P30-CA069533 (L.M.C. and A.W.L.), National Institutes of Health grant U54 CA263001-01A1 (A.W.L.), National Institutes of Health grant P50 CA225450-01A1 (A.W.L.), National Institutes of Health grant R01CA238163 (A.W.L.), National Institutes of Health grant T32CA106195 (M.M.S.), National Institutes of Health grant T32GM136542 (H.d.B.), National Institutes of Health grant F31 CA268726-01A1 (H.d.B.), National Institutes of Health grant R56 AR078686 (N.A.), National Institutes of Health grant R01 AR080436 (N.A.), Cancer Research Institute, Lloyd J. Old STAR Award (A.W.L.), Melanoma Research Alliance 403191 https://doi.org/10.48050/pc.gr.44893 (A.W.L.), American Cancer Society RSG-18-169-01-LIB (A.W.L.), Brenden-Colson Center for Pancreatic Health (L.M.C.), Stand Up to Cancer—Lustgarten Foundation Pancreatic Cancer Convergence Dream Team Translational Research Grant SU2C-AACR-DT14-14 (L.M.C.), Swedish Research Council (Vetenskapsrådet), International Postdoc grant, Reg.Nr:2016-00215 (J.F.), National Institutes of Health grant P30-CA016087 (Laura and Isaac Perlmutter Cancer Center supporting the Flow Cytometry and Cell Sorting Core, the Experimental Pathology Research Laboratory and the Genome Technology Center (RRID: SCR_017929)) and National Institutes of Health grant P30-CA069533 (OHSU Knight Cancer Center supporting the Flow Cytometry and Cell Sorting Core, Massively Parallel Sequencing Shared Resource and Gene Profiling Shared Resource).

Author information

Authors and Affiliations

  1. Ronald O. Perelman Department of Dermatology, NYU Grossman School of Medicine, New York University Langone Health, New York, NY, USA

    Maria M. Steele, Ines Delclaux, Ian D. Dryg, Sunny Son, Haley du Bois, Cameron Hill & Amanda W. Lund

  2. Department of Cell, Developmental and Cancer Biology and Knight Cancer Institute, Oregon Health and Science University, Portland, OR, USA

    Maria M. Steele, Dhaarini Murugan, Julia Femel, Lisa M. Coussens & Amanda W. Lund

  3. Department of Dermatology, Microbiology and Immunology, Meyer Cancer Center, Englander Institute of Precision Medicine, Weill Cornell Medicine, New York, NY, USA

    Abhinav Jaiswal & Niroshana Anandasabapathy

  4. Applied Bioinformatics Laboratories, NYU Langone Health, New York, NY, USA

    Sunny Son

  5. Department of Dermatology, Oregon Health and Science University, Portland, OR, USA

    Sancy A. Leachman

  6. Department of Biomedical Engineering and Computational Biology Program, Oregon Health and Science University, Portland, OR, USA

    Young H. Chang

  7. OHSU Center for Spatial Systems Biomedicine, Oregon Health and Science University, Portland, OR, USA

    Young H. Chang

  8. Department of Pathology, NYU Grossman School of Medicine, New York University Langone Health, New York, NY, USA

    Amanda W. Lund

  9. Laura and Isaac Perlmutter Cancer Center, NYU Grossman School of Medicine, New York University Langone Health, New York, NY, USA

    Amanda W. Lund

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Contributions

Conceptualization: M.M.S. and A.W.L. Methodology: M.M.S., C.H., D.M., J.F. and Y.H.C. Biospecimen acquisition: S.A.L. Investigation: M.M.S., A.J., I.D., I.D.D., S.S., H.d.B., D.M. and J.F. Funding acquisition: L.M.C. and A.W.L. Supervision: L.M.C., N.A. and A.W.L. Writing original draft: M.M.S. and A.W.L. Writing, reviewing and editing: M.M.S., A.J., I.D., S.S., I.D.D., H.d.B., D.M., C.H., J.F., L.M.C., N.A. and A.W.L.

Corresponding author

Correspondence to Amanda W. Lund.

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Competing interests

A.W.L. declares having provided consulting services for AGS Therapeutics. L.M.C. declares having provided consulting services for Cell Signaling Technologies, AbbVie, the Susan G Komen Foundation and Shasqi, has received reagents and/or research support from Cell Signaling Technologies, Syndax Pharmaceuticals, ZelBio, Inc., Hibercell, Inc. and Acerta Pharma and has participated in advisory boards for Pharmacyclics, Syndax, Carisma, Verseau, CytomX, Kineta, Hibercell, Cell Signaling Technologies, Alkermes, Zymeworks, Genenta Sciences, Pio Therapeutics Pty, Ltd., PDX Pharmaceuticals, the AstraZeneca Partner of Choice Network, the Lustgarten Foundation and the NIH/NCI-Frederick National Laboratory Advisory Committee. N.A. declares having provided consulting/lecture services for Cellino Biotech, Immunitas, Shennon Biotechnologies and Janssen. The other authors declare no competing interests.

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Nature Immunology thanks Evan Newell, Steven Stacker and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: N. Bernard, in collaboration with the Nature Immunology team. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 CD8+ T cells egress from tumors.

(a) Schematic representation of strategy to label and track endogenous leukocyte egress using the Kaede-Tg mouse model. (b) Representative gating scheme to identify egressed Kaede red+CD8+ T cells in tumor draining brachial LNs 24hrs post photoconversion of the tumor. (c) Representative flow plot of non-draining inguinal LNs 24hrs post photoconversion of the tumor. (d) Frequency of CD4+, CD4+CD25+Foxp3+ regulatory T cells (Treg), and CD8+ T cells of all photoconverted CD45+ leukocytes in the draining LN 24 hrs post photoconversion (n = 6) (e–f) Representative flow plots (e) and frequencies (f) of CD44/CD62L cell populations among intratumoral (T) or egressed (e) CD8+ T cells from YUMMER1.7-bearing Kaede-Tg mice (n = 6) 24hrs post photoconversion. For all experiments, each symbol represents one mouse. One-way ANOVA adjusted for multiple comparisons (d) and two-sided, paired student’s t-test (f).

Source data

Extended Data Fig. 2 Identification of tumor-specific CD44+ CD8+ T cells egressing tumors.

(a) Representative gating scheme identifying endogenous egressed Kaede red+ H-2Kb-OVA257-264+ CD44+CD8+ T cells in tumor draining brachial LNs 24hrs post photoconversion of MCA.205WT (WT) or MCA.205OVA (OVA) tumors. (b) Representative gating scheme identifying egressed Kaede red+ Thy1.1/1.1CD44+CD8+ OT-1-TCR-Tg T cells in tumor draining brachial LNs 24hrs post photoconversion of B16.F10WT (WT) or B16.F10OVA (OVA) tumors.

Extended Data Fig. 3 TCR-β-sequencing indicates clonal overlap between intratumoral and egressed CD8+ T cells.

Kaede red+ CD3ε+ T cells were sorted from YUMM1.7 draining LNs 24hrs post photoconversion and submitted for TCRβ deep sequencing along with matched intratumoral YUMM1.7 T cells (n = 5). In total, 5 mice were submitted for sequencing (a) Representative bubble plot (mouse 003) identifying clones sequenced in the egressed compartment (red), tumor compartment (blue), or both (Shared; green). (b) The percentage of sequenced clones detected in the tumor (T) or egressed (e) compartments, or both (shared; Sh). Each symbol represents one mouse. One-way ANOVA adjusted for multiple comparisons was used to determine statistical significance. (c) Pie charts for each mouse indicating the percentage of sequenced clones detected in the tumor (blue) or egressed (red) compartments or both (Shared; green). (d) Table demonstrating the clonal size criteria for classifying clones as hyperexpanded, large, medium, small, or rare clones. (e) Pie chart demonstrating the percentage of shared clones classified as hyperexpanded, large, medium, small, or rare clones averaged across all mice. (f) Pie charts demonstrating the percentage of shared clones classified as hyperexpanded, large, medium, small, or rare clones for each individual mouse.

Source data

Extended Data Fig. 4 Functional effector CD44+ CD8+ T cells egress from melanoma microenvironments.

(a) Volcano plot depicting differentially expressed genes (DEGs) between tumor retained and egressed CD8+CD44+ T cells (logFC≥1.5, FDR ≤ 0.05). (b) Pathway analysis of DEGs in (A) using HumanBase integrative analysis. (c and d) Representative flow plots (c) and frequencies (d) of LAG3+ intratumoral (T) or egressed (E) CD44+CD8+ T cells from YUMMER1.7-bearing Kaede-Tg mice (n = 3) 24hrs post photoconversion. (e and f) Representative flow plots (e) and frequencies (f) of PD-1+TIM3+ and PD-1-TIM3− intratumoral (T) or egressed (E) Thy1.1/1.1 CD44+ OT-1-Kaede-TCR-Tg T cells from B16.F10OVA-bearing mice (n = 4) 24hrs post photoconversion. (g and h) Representative flow plots (g) and frequencies (h) of PD-1/TCF1 intratumoral (T) or egressed (E) CD44+CD8+ T cells YUMMER1.7-bearing Kaede-Tg mice (n = 6) 24hrs post photoconversion. (i) PD-1 MFI of PD-1+CD8+ T cells from (g). (J and K) Representative flow plots (j) and frequency (k) of ex vivo IFNγ and TNFα production by intratumoral (T) or egressed (E) CD44+CD8+ T cells from YUMM1.7 tumors (day 21; n = 4) following ex-vivo restimulation. For all graphs, each symbol represents one mouse. Two-sided, paired and (D, F, H, and I) and unpaired student’s t-test (K). n.s. = not significant.

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Extended Data Fig. 5 CD44+ CD8+ T cells egress from tumors is G protein-coupled receptor and CXCR4-dependent.

(a–c) YUMM1.7-bearing Kaede-Tg mice were treated with FTY720 (FTY) or pertussis toxin (PTx) the day before and day of photoconversion. (a) Frequency of CD8+ T cells (among live CD45+ cells) in the blood post treatment with pertussis toxin (PTx) or FTY720 (FTY). (b) Numbers of CD8+ T cells in YUMM1.7 tumors of Kaede-Tg mice post treatment with PTx or FTY720. (c) Numbers of Kaede red+CD8+ T cells in the dLNs of YUMM1.7 tumors of Kaede-Tg mice post treatment with PTx or FTY720. n = 7 vehicle control mice; n = 8 PTx-treated mice; n = 8 FTY-treated mice; each symbol represents one mouse; results of 2 independent experiments. (d) Representative histogram of CXCR4 surface expression on CD44+CD8+ T cells from spleen and YUMMER1.7 tumors. FMO = Fluorescence minus one (CXCR4). (e) Quantification of CXCR4 MFI in tumor and spleen. n = 3 mice; error bars = standard deviation. (f) Histograms confirming CXCR4 knockout on CD8+ T cells (left panel) or CD19+ B cells (right panel) from CXCR4WT (red) or CXCR4ΔUBC (blue) mice. (g) Numbers of CD45.2+CD8+ CXCR4WT (WT; n = 14) or CXCR4ΔUBC (KO; n = 13) T cells in dLNs of YUMMER1.7 tumors 16-20hrs following intratumoral transfer (per 104 transferred T cells). Each symbol represents one mouse. One-way ANOVA adjusted for multiple comparisons (a–c) and two-sided, paired (e) or unpaired (g) student’s t-test. n.s. = not significant.

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Extended Data Fig. 6 CD8+ T cell functional states in murine melanomas.

(a) Heatmap of Seurat cluster defining genes arranged by Monocle Branch. (b–c) Bubble plot representation of key transcripts that distinguish Seurat (b) and Monocle clusters (c). (d) UMAP depicting Seurat clusters and (e) Monocle pseudotime trajectory analysis following cell cycle regression.

Extended Data Fig. 7 RNA-Seq of LECs sorted from naïve skin and BPC melanomas.

(a) Gating scheme for FACS of LECs (CD45−CD31+gp38+) from naïve skin and BPC tumors. (b) Normalized transcript counts of genes validating LEC identity for CD45−CD31+gp38+ cells sorted from naïve skin and BPC tumors. (c) Principal component analysis comparing the transcriptomes of LECs sorted from naïve skin and BPC tumors. (d) Heatmap of top differentially expressed genes among naïve and BPC-associated LECs. (e) Gene set enrichment analysis of pathways enriched in naïve vs BPC-associated LECs. (f–h) Normalized transcript counts for Ccl21a (f), Sphk2 (g), and Sphk1 (h) in naïve and BPC-associated LECs. Each symbol represents one mouse; n = 3 naïve and 4 BPC mice. Two-sided, unpaired student’s t test (g–h); n.s. = not significant.

Extended Data Fig. 8 Expression of Cxcl12 transcripts in YUMMER1.7 and YUMM1.7 tumor microenvironments.

(a) RNAScope of Cxcl12 transcripts in YUMMER1.7 tumors (purple); hematoxylin (blue). Repeated in 3 tumors. Left image: scale bar = 400 μm; middle and right images: scale bar = 200 μm (b) Frequency of Cxcl12-dsRed+ cells in YUMMER1.7 tumors (n = 11 across 2 independent experiments). (c) Representative flow plots of CD45−CD31+gp38+dsRed+ lymphatic endothelial cells (LEC) in Cxcl12-dsRed reporter mice or littermate controls (Cxcl12+/+). (d) Frequency of Cxcl12+ in LECs, blood endothelial cells (CD45−CD31+gp38−, BEC), epithelial cells (CD45−CD31−EpCAM+, Epi), fibroblasts (CD45−CD31−PDGFRα+, Fibro) in YUMMER1.7 tumors of Cxcl12-dsRed reporter mice (n = 13; 3 independent experiments). (e) Frequency of Cxcl12+ in B cells (CD3ε-B220+), T cells (CD3ε+B220−), neutrophils (CD3ε-B220−CD11b+Ly6G+, Neutro), dendritic cells (CD3ε-B220−CD11c+MHCII+, DCs), immature monocytes (CD3ε-B220−CD11b+Ly6C+, Imm Mono), and macrophages (CD3ε−B220−CD11b+Ly6C−F4/80+MHCII+/−, TAMs) in YUMMER1.7 tumors of Cxcl12-dsRed reporter mice (n = 3). (f) RNAScope of Cxcl12 transcripts in YUMM1.7 tumors (purple); hematoxylin (blue). Repeated in 3 tumors. Left image: scale bar = 400 μm; middle and right images: scale bar = 200 μm (g) Frequency of Cxcl12+ in CD45−stromal cells in YUMM1.7 tumors of Cxcl12-dsRed reporter mice (n = 3). (h) Frequency of Cxcl12+ CD45+ cells in YUMM1.7 tumors of Cxcl12-dsRed reporter mice (n = 3). (I and J) Frequency of Cxcl12+ BECs (i) or fibroblasts (j) from normal adjacent skin or YUMMER1.7 tumors in Cxcl12-dsRed reporter mice (n = 8; 2 independent experiments). (k) qRT-PCR of CXCL12 transcript levels from hypoxic LN LECs isolated from CXCL12WT or CXCL12ΔiProx1 mice (n = 3). Transcripts normalized to β-actin and represented as fold change from WT. (l) Lymphatic vessel dilation in tumors implanted in CXCL12WT or CXCL12ΔiProx1 mice (n = 5). (M and N) Quantification of total LEC as percent of live (m) and proportion of LYVE-1+ LECs (n). (O and P) Quantification of regulatory T cells (TREG, CD3ε+CD4+CD25+Foxp3+) as a percent of live (o) or of CD4+ T cells (p). For all graphs, each symbol represents one mouse. Two-sided paired (B, I, and J) and unpaired (K-P) student’s t-test. n.s. = not significant.

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Extended Data Fig. 9 CXCR4 inhibition does not impact effector circulation or function.

(a) Frequency of CD8+ T cells in the blood (left) or lymph (right) following acute treatment with FTY720 (FTY) or AMD3100 (AMD). n = 3–5 mice per group. (b) Shannon diversity index for TCRβ sequences detected in YUMMER1.7 tumors 7 days post treatment with AMD3100 (AMD; n = 10) or vehicle control (Cont; n = 8). Error bars = standard error; center = mean. (c) Frequency of IFNγ+TNFα+ producing CD44+CD8+ T cells following ex vivo CD3/CD28 restimulation in the presence or absence of AMD3100. n = 5 (d) Frequency of IFNγ+TNFα+ producing endogenous CXCR4WT (n = 3) or CXCRΔUBC (n = 3) CD44+CD8+ T cells following ex vivo CD3/CD28 restimulation. (e) Frequency of IFNγ+TNFα+ producing CD44+CD8+ T cells following ex vivo CD3/CD28 restimulation in the presence (n = 3) or absence (n = 3) of recombinant murine 100 ng/ml CXCL12. For D-E, error bars = standard deviation; center = mean. For all graphs, each symbol represents one mouse. One-way ANOVA adjusted for multiple comparisons (A and C), two-sided Mann-Whitney test (b), two-sided, unpaired student’s t-test (D and E). (f) YUMM1.7 tumor growth during treatment with vehicle control (Cont; black; n = 5), AMD3100 (AMD; blue; n = 5), αPD-L1 (PD-L1; red; n = 5), or combination AMD3100 + αPD-L1 (A + P; green n = 5); one experiment. Fractions indicate rate of tumor control defined as daily growth rate <30mm3 post onset of therapy.

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Supplementary information

Reporting Summary

Peer Review File

Supplementary Table 1

TCRβ deep-sequencing data from intratumoral and egressed CD3ε+ T cells.

Supplementary Table 2

List of differentially expressed genes isolated from splenic, egressed and intratumoral CD44+CD8+ T cells.

Supplementary Table 3

List of differentially expressed genes by LECs isolated from naive skin and BPC melanomas.

Supplementary Table 4

TCRβ deep-sequencing data from CD3ε+ T cells collected from untreated or AMD3100-treated YUMMER1.7 tumors.

Supplementary Table 5

Antibody list.

Supplementary Table 6

Transcriptional signatures.

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Steele, M.M., Jaiswal, A., Delclaux, I. et al. T cell egress via lymphatic vessels is tuned by antigen encounter and limits tumor control. Nat Immunol 24, 664–675 (2023). https://doi.org/10.1038/s41590-023-01443-y

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