Mutual regulation of tumour vessel normalization and immunostimulatory reprogramming

Abstract

Blockade of angiogenesis can retard tumour growth, but may also paradoxically increase metastasis1,2. This paradox may be resolved by vessel normalization3, which involves increased pericyte coverage, improved tumour vessel perfusion, reduced vascular permeability, and consequently mitigated hypoxia3. Although these processes alter tumour progression, their regulation is poorly understood. Here we show that type 1 T helper (TH1) cells play a crucial role in vessel normalization. Bioinformatic analyses revealed that gene expression features related to vessel normalization correlate with immunostimulatory pathways, especially T lymphocyte infiltration or activity. To delineate the causal relationship, we used various mouse models with vessel normalization or T lymphocyte deficiencies. Although disruption of vessel normalization reduced T lymphocyte infiltration as expected4, reciprocal depletion or inactivation of CD4+ T lymphocytes decreased vessel normalization, indicating a mutually regulatory loop. In addition, activation of CD4+ T lymphocytes by immune checkpoint blockade increased vessel normalization. TH1 cells that secrete interferon-γ are a major population of cells associated with vessel normalization. Patient-derived xenograft tumours growing in immunodeficient mice exhibited enhanced hypoxia compared to the original tumours in immunocompetent humans, and hypoxia was reduced by adoptive TH1 transfer. Our findings elucidate an unexpected role of TH1 cells in vasculature and immune reprogramming. TH1 cells may be a marker and a determinant of both immune checkpoint blockade and anti-angiogenesis efficacy.

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Figure 1: The dichotomy of angiogenesis-related genes supports the ‘vessel normalization theory’ and links good prognosis angiogenesis genes to T-cell signalling.
Figure 2: Depletion of CD4+-TLs decreases tumour vessel pericyte coverage and increases metastasis.
Figure 3: ICB therapy increases vessel normalization and decreases metastasis.
Figure 4: Human tumours transplanted into immunocompromised mice (PDX) exhibit enhanced hypoxia features, which can be mitigated by TH1 adoptive transfer.

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References

  1. 1

    Ebos, J. M. L. et al. Accelerated metastasis after short-term treatment with a potent inhibitor of tumor angiogenesis. Cancer Cell 15, 232–239 (2009)

    CAS  Article  Google Scholar 

  2. 2

    Pàez-Ribes, M. et al. Antiangiogenic therapy elicits malignant progression of tumors to increased local invasion and distant metastasis. Cancer Cell 15, 220–231 (2009)

    Article  Google Scholar 

  3. 3

    Goel, S., Wong, A. H.-K. & Jain, R. K. Vascular normalization as a therapeutic strategy for malignant and nonmalignant disease. Cold Spring Harb. Perspect. Med. 2, a006486 (2012)

    Article  Google Scholar 

  4. 4

    Hamzah, J. et al. Vascular normalization in Rgs5-deficient tumours promotes immune destruction. Nature 453, 410–414 (2008)

    ADS  CAS  Article  Google Scholar 

  5. 5

    Curtis, C. et al. The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature 486, 346–352 (2012)

    CAS  Article  Google Scholar 

  6. 6

    Armulik, A., Genové, G. & Betsholtz, C. Pericytes: developmental, physiological, and pathological perspectives, problems, and promises. Dev. Cell 21, 193–215 (2011)

    CAS  Article  Google Scholar 

  7. 7

    Bergers, G. & Benjamin, L. E. Tumorigenesis and the angiogenic switch. Nat. Rev. Cancer 3, 401–410 (2003)

    CAS  Article  Google Scholar 

  8. 8

    Herschkowitz, J. I . et al. Comparative oncogenomics identifies breast tumors enriched in functional tumor-initiating cells. Proc. Natl Acad. Sci. USA 109, 2778–2783 (2012)

    ADS  CAS  Article  Google Scholar 

  9. 9

    Arwert, E. N., Hoste, E. & Watt, F. M. Epithelial stem cells, wound healing and cancer. Nat. Rev. Cancer 12, 170–180 (2012)

    CAS  Article  Google Scholar 

  10. 10

    Jain, R. K. Molecular regulation of vessel maturation. Nat. Med. 9, 685–693 (2003)

    CAS  Article  Google Scholar 

  11. 11

    Sharma, P. & Allison, J. P. Immune checkpoint targeting in cancer therapy: toward combination strategies with curative potential. Cell 161, 205–214 (2015)

    CAS  Article  Google Scholar 

  12. 12

    Pober, J. S. & Sessa, W. C. Evolving functions of endothelial cells in inflammation. Nat. Rev. Immunol. 7, 803–815 (2007)

    CAS  Article  Google Scholar 

  13. 13

    Bromley, S. K., Mempel, T. R. & Luster, A. D. Orchestrating the orchestrators: chemokines in control of T cell traffic. Nat. Immunol. 9, 970–980 (2008)

    CAS  Article  Google Scholar 

  14. 14

    Bonacchi, A. et al. Signal transduction by the chemokine receptor CXCR3: activation of Ras/ERK, Src, and phosphatidylinositol 3-kinase/Akt controls cell migration and proliferation in human vascular pericytes. J. Biol. Chem. 276, 9945–9954 (2001)

    CAS  Article  Google Scholar 

  15. 15

    Hu, Z. et al. A compact VEGF signature associated with distant metastases and poor outcomes. BMC Med. 7, 9 (2009)

    Article  Google Scholar 

  16. 16

    Newman, A. M. et al. Robust enumeration of cell subsets from tissue expression profiles. Nat. Methods 12, 453–457 (2015)

    CAS  Article  Google Scholar 

  17. 17

    Freeman, M. R. et al. Peripheral blood T lymphocytes and lymphocytes infiltrating human cancers express vascular endothelial growth factor: a potential role for T cells in angiogenesis. Cancer Res. 55, 4140–4145 (1995)

    CAS  PubMed  Google Scholar 

  18. 18

    Qin, Z. & Blankenstein, T. CD4+ T cell-mediated tumor rejection involves inhibition of angiogenesis that is dependent on IFNγ receptor expression by nonhematopoietic cells. Immunity 12, 677–686 (2000)

    CAS  Article  Google Scholar 

  19. 19

    Beatty, G. & Paterson, Y. IFN-γ-dependent inhibition of tumor angiogenesis by tumor-infiltrating CD4+ T cells requires tumor responsiveness to IFN-γ. J. Immunol. 166, 2276–2282 (2001)

    CAS  Article  Google Scholar 

  20. 20

    Gao, J. et al. Loss of IFN-γ pathway genes in tumor cells as a mechanism of resistance to anti-CTLA-4 therapy. Cell 167, 397–404 (2016)

    CAS  Article  Google Scholar 

  21. 21

    Carmeliet, P. & Jain, R. K. Molecular mechanisms and clinical applications of angiogenesis. Nature 473, 298–307 (2011)

    ADS  CAS  Article  Google Scholar 

  22. 22

    Zhang, X. et al. A renewable tissue resource of phenotypically stable, biologically and ethnically diverse, patient-derived human breast cancer xenograft models. Cancer Res. 73, 4885–4897 (2013)

    CAS  Article  Google Scholar 

  23. 23

    Xing, Y. & Hogquist, K. A. Isolation, identification, and purification of murine thymic epithelial cells. J. Vis. Exp. 51780, e51780 (2014)

    Google Scholar 

  24. 24

    Quah, B. J. C. & Parish, C. R. The use of carboxyfluorescein diacetate succinimidyl ester (CFSE) to monitor lymphocyte proliferation. J. Vis. Exp. 44, 2259 (2010)

    Google Scholar 

  25. 25

    Radu, M. & Chernoff, J. An in vivo assay to test blood vessel permeability. J. Vis. Exp. 50062, e50062 (2013)

    Google Scholar 

  26. 26

    Garcia, A. N., Vogel, S. M., Komarova, Y. A. & Malik, A. B. Permeability of endothelial barrier: cell culture and in vivo models. Methods Mol. Biol. 763, 333–354 (2011)

    CAS  Article  Google Scholar 

  27. 27

    Sheng, K., Cao, W., Niu, Y., Deng, Q. & Zong, C. Effective detection of variation in single-cell transcriptomes using MATQ-seq. Nat. Methods 14, 267–270 (2017)

    CAS  Article  Google Scholar 

  28. 28

    Dobin, A . et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013)

    CAS  Article  Google Scholar 

  29. 29

    Li, B. & Dewey, C. N. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics 12, 323 (2011)

    CAS  Article  Google Scholar 

  30. 30

    Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014)

    Article  Google Scholar 

  31. 31

    Subramanian, A . et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005)

    ADS  CAS  Article  Google Scholar 

  32. 32

    Xie, C. et al. KOBAS 2.0: a web server for annotation and identification of enriched pathways and diseases. Nucleic Acids Res. 39, W316–W322 (2011)

    CAS  Article  Google Scholar 

  33. 33

    Supek, F., Bošnjak, M., Škunca, N. & Šmuc, T. REVIGO summarizes and visualizes long lists of gene ontology terms. PLoS One 6, e21800 (2011)

    ADS  CAS  Article  Google Scholar 

  34. 34

    Barbie, D. A. et al. Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1. Nature 462, 108–112 (2009)

    ADS  CAS  Article  Google Scholar 

  35. 35

    Ragnum, H. B. et al. The tumour hypoxia marker pimonidazole reflects a transcriptional programme associated with aggressive prostate cancer. Br. J. Cancer 112, 382–390 (2015)

    CAS  Article  Google Scholar 

  36. 36

    Eustace, A. et al. A 26-gene hypoxia signature predicts benefit from hypoxia-modifying therapy in laryngeal cancer but not bladder cancer. Clin. Cancer Res. 19, 4879–4888 (2013)

    CAS  Article  Google Scholar 

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Acknowledgements

We thank D. Weiss for critically editing the manuscript, D. Liang for advice on TH1-skewed differentiation of CD4+-TLs; S. Donepudi and F. Jin for their technical support on metabolite profiling. X.H.-F.Z. is supported by Breast Cancer Research Foundation, NCI CA183878, DoD W81XWH-16-1-0073, SGK CCR14298445, and McNair Medical Institute. A.S. and N.P. were supported by the CPRIT Core Facility Support Award RP120092, NCI 2P30CA125123-09. Flow cytometry and cell sorting was performed at the Cytometry and Cell Sorting Core supported by NIH P30 AI036211, P30 CA125123, and S10 RR024574. Mosaic scanning was supported by a grant from the NIH 1S10OD016167.

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Contributions

L.T. and X.H.-F.Z. developed the concepts, analysed data and designed the experiments. L.T. performed the experiments. I.S.K. and T.W. sorted TECs for RNA-seq; K.S., A.G. and H.W. performed MATQ-seq; N.P. performed LC-MS/MS; L.E.D. and X.Z. prepared PDX; F.S. assisted epifluorescence imaging. T.L.P., S.A.M., A.S., M.A.M., W.K.D., C.Z. and M.T.L. developed methodology and interpreted the data. X.H.-F.Z., L.T., H.C.L. and A.G. wrote the paper. X.H.-F.Z. supervised the project.

Corresponding author

Correspondence to Xiang H.-F. Zhang.

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The authors declare no competing financial interests.

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Reviewer Information Nature thanks G. Dranoff, D. Felsher and the other anonymous reviewer(s) for their contribution to the peer review of this work.

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Extended data figures and tables

Extended Data Figure 1 Evaluation of GPAGs and PPAGs in hepatocellular carcinoma links T-cell activity with tumour vessel normalization.

a, Schematic diagram for the bioinformatic analysis. MACS, magnetic-activated cell sorting; NEC, normal endothelial cells; TEC, tumour-associated endothelial cells. The numbers of patients are denoted in parentheses. b, GPAG and PPAG signatures of NECs versus TECs. n = NEC–TEC pairs from 16 patients. c, Comparison of GPAG and PPAG signatures in tumour with vascular invasion (n = 40 patients) or without vascular invasion (n = 95 patients). d, Pathways in the non-TEC cells that positively correlate with higher (∑GPAGs − ∑PPAGs) in the paired TECs. FDR, false discovery rate; NES, normalized enrichment score. e, GSEA mountain plot showing a strong association between (∑GPAGs − ∑PPAGs) in the TEC and T-cell activation signalling in the paired non-TEC cells. Data are presented as means ± s.e.m. for dot plots. Data are obtained from GSE51401 (b, d, e), GSE20017 (c). P values were calculated using two-tailed paired Student’s t-test (b), two tailed unpaired Mann–Whitney U-test (c), or a permutation-based approach with Benjamini–Hochberg multiple testing correction (d, e).

Extended Data Figure 2 NG2+ cell-depleted mice display decreased immune infiltration in E0771 tumours.

a, Schematic of the experimental design. b, Quantification of tumour-infiltrating NG2+ cells (NG2-CreERTM;tdRed, n = 3; NG2-CreERTM; tdRed;iDTR, n = 4). c, Flow cytometry gating strategy for tumour-infiltrating leukocytes. d, Flow cytometric quantification showing the decreased infiltration of TLs (CD3+CD4+ and CD3+CD8+), B cells (B220+), and dendritic cells (CD11b+CD11c+), but the percentage of CD11b+CD11c- cells remains unchanged (WT, n = 10; PeriDel, n = 5). Data are presented as means ± s.e.m. P values are determined by two-tailed paired Student’s t-test (b, d).

Extended Data Figure 3 The effect of CD4+-TLs on promoting vessel normalization is dependent on the number of cells.

a, Flow cytometric plots of CD3+ cells of 4T1 tumour from wild-type and CD4KO mice. Five animals were examined in each group. Representative plots are shown. bg, Quantification of tumour vascular normalization markers including pericyte coverage (b), vessel density and vessel length (c), VE-cadherin expression (white arrow heads show vessels without VE-cadherin expression) (d), hypoxia measured by pimonidazole staining (e), lectin perfusion efficiency (f), and dextran leakage (g) (n = 5 per group; scale bars, 50 μm (b, d, f, g), 1 mm (e)). h, Top, schematic of the experimental design. Bottom, the two doses of antibody deplete CD4+-TLs for 2 weeks. One point represents one mouse. Whole blood was collected for the flow cytometric analysis. i, Dot plots showing that tumours were resected at similar size/weight. j, Representative whole-animal bioluminescence images showing spontaneous 4T1 metastasis in anti-IgG or anti-CD4 treated mice. Dots representing mice with detected metastases are labelled with a red boundary. k, Kaplan–Meier curves showing the metastasis-free frequency of 4T1 tumour-bearing mice treated with anti-IgG or anti-CD4. n = 10 and 9 for anti-IgG and anti-CD4 groups, respectively (ik). l, Top, flow cytometry quantification of tumour-infiltrating CD4+-TLs across three murine tumour models (4T1, n = 4; E0771, n = 5; AT3, n = 4). The tumours were resected at similar size/weight (around 1 g) from the same batch of experiments. Bottom, a table summarizing the results from vessel normalization assays. The number indicates the fold change increased (red) or decreased (green) in wild-type mice compared to CD4KO mice. In E0771 model, RNA-seq reveals an increase in expression of genes encoding extracellular matrix and adhesion molecules in wild-type mice over CD4KO mice. mp, Quantification of tumour vascular normalization markers (n = 5 per group; scale bars, 50 μm (m, o, p); n = 10 per group; scale bars,1 mm (n)). q, Scatter plot showing the Cd4 and Ifng gene expression levels of different p53−/− mouse breast tumour models. The Cd8 gene expression and ER status are denoted by the colours of the dots and dot outlines, respectively. Two models chosen for hypoxia measurement are highlighted. r, Hypoxia quantification for T1 and T11 tumours in wild-type and CD4KO background as measured by pimonidazole staining (T1: n = 5 per group; T11: WT, n = 5; CD4KO, n = 8; scale bars,1 mm). Data are presented as means ± s.e.m. Animal numbers used in ik are denoted in (k). P values are determined by two-tailed unpaired Student’s t-test (bg, i, m–p, r), Fisher’s exact test (j) and log-rank test (k). NS, not significant.

Extended Data Figure 4 Comparison of pericyte coverage of normal tissues and wound tissues in different T-cell-deficient backgrounds.

a, Quantification of pericyte coverage of blood vessels in the mammary gland. As NG2 is also expressed by adipocytes, PDGFRβ was used as the marker for pericyte (n = 4 per group; scale bars, 50 μm; inset, 20 μm). b, Representative fluorescent images and flow cytometric quantification of pericyte coverage of lung (n = 5 per group; scale bars, 50 μm). c, Quantification of pericyte coverage of normal skin tissues and skin wound tissues (WT, n = 10; CD4KO, n = 10; CD8KO, n = 9; scale bars, 50 μm). Different immunodeficient backgrounds are denoted by the colours of the dots, and the strain information is denoted by the colours of point outlines. The points with arrows are the represented images of wound tissues in the left. Data are presented as means ± s.e.m. P values were calculated using non-parametric one-way ANOVA (Kruskal–Wallis) test (b, c).

Extended Data Figure 5 Immune profiling on M-IIKO mice shows that CD4+-TL cell activation is inhibited.

a, b, Flow cytometry quantifications validate that M-IIKO mice have decreased MHC-II expression in tumour-infiltrating immune cells (CD45+), including macrophages (MΦ, CD45+CD11b+Ly6GF4/80+), dendritic cells (DC, CD45+CD11b+Ly6GF4/80CD11c+), and B cells (CD45+B220+) (control, n = 10; M-IIKO, n = 11). c, Flow cytometry gating of suspension cells dissociated from thymus, characterized as CD45+EpCAM immune cells and CD45EpCAM+ epithelial cells. d, e, Quantification of MHC-II expression of thymus showing that MHC-II expression is inhibited in immune cells but preserved in epithelial cells in M-IIKO mice (control, n = 7; M-IIKO, n = 6). f, Quantification of different types of tumour-infiltrating stroma cells (n = 10 per group). g, Quantification of MHC-II expression in different cell types (control, n = 10; Tie2Cre;H2Ab+/floxP, n = 5; M-IIKO, n = 11). h, Quantification of T cells in spleens from 5–6-week-old female mice showing that the number of T cells is independent of MHC-II expression on Tie2Cre+ cells (control, n = 7; M-IIKO, n = 6). i, Quantification of activated CD4+-TLs and effector CD4+-TLs from tumours of similar sizes. Activated CD4+-TL: CD45+CD3+CD4+CD25+FoxP3; TREG: CD45+CD3+CD4+CD25+FoxP3+; effector memory cell: CD44+CD62L; naive CD4+-TL: CD44CD62L+) (n = 11 per group). j, The percentages of CD4+-TL activation markers in spleen showing a similar pattern as in tumour (i) (control: n = 7; M-IIKO: n = 6). k, Quantification of different E0771 tumour-infiltrating T helper cells (IFNγ+ TH1, IL4+ TH2 and IL17A+ TH17) (n = 11 per group). l, Quantification of E0771 tumour-infiltrating CD4+-TLs, macrophages, dendritic cells, B cells and neutrophils (CD45+CD11b+Ly6Ghi) (n = 11 per group). Data are presented as means ± s.e.m. The genetic backgrounds of mice are denoted with different colours shown on the right of l. Wild type, Tie2Cre and H2AbfloxP/floxP were combined as a control group. P values were calculated using two-tailed unpaired Student’s t-test (a, b, df, hk) or two-tailed unpaired Mann–Whitney U-test (l). NS, not significant.

Extended Data Figure 6 Inhibition of MHC-II-mediated CD4+-TL activation phenocopies the depletion of CD4+-TL or NG2+ pericytes with regard to tumour vessel normalization and hypoxia.

a, Immunofluorescence quantification of percentage endothelial cells (CD31+/MECA-32+) attached by pericytes (NG2+) (n = 4 per group), and flow cytometry quantification of endothelial cell to pericytes ratio (control, n = 10; M-IIKO, n = 11). b, c, Quantification of tumour-vasculature leakiness as measured by dextran (WT, n = 5; M-IIKO, n = 4; CD4KO, n = 5; scale bars, 50 μm) and Evans blue (WT, n = 11; M-IIKO, n = 8), respectively. d, Quantification of perfusion efficiency with lectin (WT, n = 5; M-IIKO, n = 4; CD4KO, n = 5; scale bars, 50 μm). e, f, Quantification of tumour hypoxia with HIF-1α (WT, n = 5; M-IIKO, n = 4; CD4KO, n = 4; PeriDel, n = 3; scale bars, 50 μm) and pimonidazole (WT, n = 5; M-IIKO, n = 4; CD4KO, n = 5; PeriDel, n = 8; scale bars, 1 mm). Data are presented as means ± s.e.m. P values were calculated using two-tailed unpaired Student’s t-test (af).

Extended Data Figure 7 RNA-seq further supports that CD4+-TLs promote tumour vessel normalization.

a, RNA-seq experiment design. FACS, fluorescence-activated cell sorting; MATQ-seq, multiple annealing and tailing-based quantitative sequencing. b, t-distributed stochastic neighbour embedding (t-SNE) analysis of tumour-associated endothelial cells based on RNA-seq profiles of different transgenic mice. Different genetic backgrounds are denoted with different colours. c, ssGSEA projection of RNA-seq data validated the downregulation of ‘Immune Effector Process’ pathway (GO:0002697) in CD4+-TL-deficient group. d, Analyses on RNA-seq data validated the downregulation of GPAGs and upregulation of PPAGs in CD4+-TL-deficient group. e, f, Gene expression analysis of Vegfa and Angpt1/Angpt2 in tumour-associated CD31+ cells from different genetic backgrounds of mice. g, GSEA mountain plots showing increased biological activities in the tumour-associated vessel isolated from CD4+-TL-competent backgrounds. h, Heat map summarizing the top 20 genes upregulated in tumour-associated CD31+ cells isolated from CD4+-TL competent genetic background, compared to that from CD4+-TL-deficient background. i, Analysis of sphingolipid metabolic process signature (GO:0006665) for tumour-associated CD31+ cells from different genetic background. j, Sphingolipid metabolite profiling of sphingolipid associated metabolites on whole E0771 tumour lysates from mice of different T-cell-deficient backgrounds (WT, n = 5; CD4KO, n = 5; CD8KO, n = 6; TCRKO, n = 5). AC, acid ceramidase; ASMase, acid sphingomyelinase; FA, fatty acid; P-choline, phosphatidylcholine; SPHK, sphingosine kinase. All genotypes are divided into two groups based on CD4 status. CD4KO, TCRKO and conditional knockout of H2Ab are deficient of CD4+-TL, and the others are not. The two groups have n = 9 and 10 animals, respectively. Data are presented as means ± s.e.m. Animal numbers used (bi) are denoted in (b). P values were calculated using two-tailed unpaired Mann–Whitney U-test (cf, i), two-tailed one-way analysis of variance (ANOVA) (j) or permutation (g). NS, not significant.

Extended Data Figure 8 Spatial relationships between activated CD4+-TLs and lectin+ tumour-associated endothelial cells.

a, Schematic of the experimental design. b, A table showing the counts of naive CD4+-TLs (tdRed+CFSE+) and activated CD4+-TLs (tdRed+) in whole cross sectional area of five animals (n = 5). c, The violin plots showing the kernel probability density of the distances of naive and activated CD4+-TLs to the nearest lectin+ endothelial cells. Smaller dots without an outline are distances of individual CD4+-TL, and larger circles that are outlined represent mean distances taken over all CD4+-TLs in the section from the same mouse. CD4+-TLs from the same mouse are denoted with the same colour. The P value was calculated using one sample Student’s t-test by comparing the mean distances of activated CD4+-TLs from individual mouse with the mean distance of all naive CD4+-TLs (dashed horizontal line) (n = 5 mice). d, Top, mosaic scanning images of whole tumour sections. Representative areas are magnified and naive CD4+-TLs (yellow) are pinpointed with arrowhead. Bottom, solid lines show the distribution of distances between CD4+-TLs and lectin+ endothelial cells in whole tumour sections. The mean distances observed are shown as a vertical straight line. For comparison, dashed lines show the probability distribution of mean distances between endothelial cells and computer-simulated random dots. P values were calculated using a permutation-based approach. More detailed information about image simulation is described in the Methods.

Extended Data Figure 9 ICB therapy promotes TH1 differentiation of CD4+-TLs and induces further immune reprogramming.

a, Schematic of the experimental design. b, ICB leads to CD4+-TL dependent tumour growth inhibition, measured by tumour weight at Day 15 after E0771 injection. c, d, Total number of immune cells (c) and T cells (d) in tumours from different groups. Although the number of pan tumour-infiltrating immune cells (CD45+) is not changed, the number of CD4+-TLs increased after immune checkpoint blockade therapy. e, A heat map summarizing changes to tumour-infiltrating immune components after ICB therapy. The number of different immune cells (rows) is shown for each tumour (columns) after control or checkpoint blockade treatment. The weight of each tumour is shown (top panel). Row-side annotations show P values comparing between CD8KO (anti-IgG) and CD8KO (anti-PD1 and anti-CTLA4) groups (far left column), and between CD8KO (anti-PD1 and anti-CTLA4) and TCRKO (anti-PD1 and anti-CTLA4) (far right column) (EM T, effector memory T cells). f, Quantification of different subsets among CD45+CD11b+ cells showing the effect of ICB on innate immune microenvironment (eosinophil: CD45+CD11b+SiglecF+). g, h, Quantification of the percentage of TREG cells among total CD4+-TLs, and the ratio of effector memory CD4+-TLs to naive CD4+-TLs after ICB in CD8 knockout mice. i, Quantification of the percentage of different CD4+ T helper cells. j, Percentage of IFNγ+ cells in CD4+ or CD4 cells among all the CD45+ tumour-associated immune cells, indicating CD4+-TLs make up the majority of IFNγ+ cells. Data are presented as means ± s.e.m. Animal numbers used in (bj) are denoted in a. P values were calculated using two-tailed unpaired (bi) or paired (j) Student’s t-test. NS, not significant.

Extended Data Figure 10 Molecular and cellular mechanisms that contribute to tumour immunostimulatory reprogramming positive feedback loop.

a, b, Quantitative RT–PCR analysis showing the effect of IFNγ and sCD40L on the mRNA levels of adhesion molecules, VEGFA (a), and T cell attractant chemokines (b). The experiments were repeated independently for three times (batches) with technical duplicates each time. c, Schematic of the experimental design and hypothetical model. d, Tumours resected on days 12–13 after injection of E0771 have similar size/weight, and the effect of TH1 adoptive transfer on vessel normalization as measured by the CD31+ endothelial cells to NG2+ pericytes ratio. e, Flow cytometry quantification CD45.1+ adoptive transferred TH1 cells, and CD45.2+ host immune cells. f, Characterization and quantification of CD45.2+ host immune cells showing that TH1-mediated immune infiltration is partially dependent on pericyte coverage. g, Effect of TH1 adoptive transfer and pericyte depletion on CD11b+Ly6G+ immune cells demonstrating different pattern with other tumour-infiltrating immune cells as from f. h, Schematic summary of CD4+-TL-mediated vessel normalization, and subsequent formation of positive feedback loop through cell–cell interaction, cytokine production and increased pericyte coverage. Checkpoint blockade therapy and antigen presentation enhance TH1-skewed CD4+-TL activation and promote the vessel normalization/ immunostimulatory reprogramming positive feedback loop. Data are presented as means ± s.e.m. Animal numbers used in (dg) are denoted in (c). P values were calculated using two-tailed unpaired Student’s t-test based on biological replicates (a, b, dg). Technical replicates are averaged within each biological replicate (a, b).

Supplementary information

Supplementary Table 1

Detailed information about 57 prognosis associated angiogenesis genes (XLSX 32 kb)

Supplementary Table 2

Over representation analysis of pathways in Gene Ontology database associated with GPAGs and PPAGs. (XLSX 44 kb)

Supplementary Table 3

GSEA of GPAG signature in METABRIC Discovery Dataset (XLSX 27 kb)

Supplementary Table 4

Detailed information of xenograft models and Th1 adoptive transfer strategy. (XLSX 36 kb)

Supplementary Table 5

Circulating tumour cell calibration for Clonogenic Assay. (XLSX 35 kb)

Supplementary Table 6

Primer sequences for qRT-PCR. (XLSX 39 kb)

Col-IV/CD31 staining of E0771 tumour from WT background

The extracellular matrix is continuous across the vessels. (MOV 2499 kb)

Col-IV/CD31 staining of E0771 tumour from CD4KO background

The extracellular matrix is thinner and discontinuous. (MOV 2259 kb)

Col-IV/CD31 staining of 4T1 tumour from WT background

The extracellular matrix is continuous across the vessels. (MOV 393 kb)

Col-IV/CD31 staining of 4T1 tumour from CD4KO background

The extracellular matrix is discontinuous and loses the defined structure. (MOV 560 kb)

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Tian, L., Goldstein, A., Wang, H. et al. Mutual regulation of tumour vessel normalization and immunostimulatory reprogramming. Nature 544, 250–254 (2017). https://doi.org/10.1038/nature21724

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