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Solid stress impairs lymphocyte infiltration into lymph-node metastases

Abstract

Strong and durable anticancer immune responses are associated with the generation of activated cancer-specific T cells in the draining lymph nodes. However, cancer cells can colonize lymph nodes and drive tumour progression. Here, we show that lymphocytes fail to penetrate metastatic lesions in lymph nodes. In tissue from patients with breast, colon, and head and neck cancers, as well as in mice with spontaneously developing breast-cancer lymph-node metastases, we found that lymphocyte exclusion from nodal lesions is associated with the presence of solid stress caused by lesion growth, that solid stress induces reductions in the number of functional high endothelial venules in the nodes, and that relieving solid stress in the mice increased the presence of lymphocytes in lymph-node lesions by about 15-fold. Solid-stress-mediated impairment of lymphocyte infiltration into lymph-node metastases suggests a therapeutic route for overcoming T-cell exclusion during immunotherapy.

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Fig. 1: Immune evasion by human cancer cells within metastatic LNs.
Fig. 2: Selective exclusion of T cells from LN metastases.
Fig. 3: Effect of sphingosine-1-phosphate receptor inhibition on metastatic burden.
Fig. 4: Reduced HEVs in metastatic lesions.
Fig. 5: Impaired blood vessel function in metastatic lesions.
Fig. 6: Solid stress impairs lymphocyte trafficking into LNs.
Fig. 7: Losartan treatment increases T cells within LN lesions.

Data availability

The main data supporting the results in this study are available within the paper and its Supplementary Information. The raw and analysed datasets generated during the study are too large to be publicly shared, yet they are available for research purposes from the corresponding authors on reasonable request.

Code availability

Custom MATLAB codes for the signal processing and volume projection of angiography are available online (https://octresearch.org/resources). Custom MATLAB code for the analysis of IF stainings is available at Figshare (https://doi.org/10.6084/m9.figshare.14794827).

References

  1. 1.

    Jatoi, I., Hilsenbeck, S. G., Clark, G. M. & Osborne, C. K. Significance of axillary lymph node metastasis in primary breast cancer. J. Clin. Oncol. 17, 2334–2340 (1999).

    CAS  PubMed  Article  Google Scholar 

  2. 2.

    Ferris, R. L., Lotze, M. T., Leong, S. P., Hoon, D. S. & Morton, D. L. Lymphatics, lymph nodes and the immune system: barriers and gateways for cancer spread. Clin. Exp. Metastasis 29, 729–736 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  3. 3.

    Nathanson, S. D., Kwon, D., Kapke, A., Alford, S. H. & Chitale, D. The role of lymph node metastasis in the systemic dissemination of breast cancer. Ann. Surg. Oncol. 16, 3396–3405 (2009).

    PubMed  Article  Google Scholar 

  4. 4.

    Pereira, E. R. et al. Lymph node metastases can invade local blood vessels, exit the node, and colonize distant organs in mice. Science 359, 1403–1407 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  5. 5.

    Brown, M. et al. Lymph node blood vessels provide exit routes for metastatic tumor cell dissemination in mice. Science 359, 1408–1411 (2018).

    CAS  PubMed  Article  Google Scholar 

  6. 6.

    Ubellacker, J. M. et al. Lymph protects metastasizing melanoma cells from ferroptosis. Nature 585, 113–118 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  7. 7.

    Fransen, M. F. et al. Tumor-draining lymph nodes are pivotal in PD-1/PD-L1 checkpoint therapy. JCI Insight 3, e124507 (2018).

    PubMed Central  Article  PubMed  Google Scholar 

  8. 8.

    Osorio, J. C. et al. Lesion-level response dynamics to programmed cell death protein (PD-1) blockade. J. Clin. Oncol. 37, 3546–3555 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  9. 9.

    Spitzer, M. H. et al. Systemic immunity is required for effective cancer immunotherapy. Cell 168, 487–502 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  10. 10.

    Allen, B. M. et al. Systemic dysfunction and plasticity of the immune macroenvironment in cancer models. Nat. Med. 26, 1125–1134 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  11. 11.

    Oh, S. et al. PD-L1 expression by dendritic cells is a key regulator of T-cell immunity in cancer. Nat. Cancer 1, 681–691 (2020).

    Article  Google Scholar 

  12. 12.

    Wu, T. D. et al. Peripheral T cell expansion predicts tumour infiltration and clinical response. Nature 579, 274–278 (2020).

    CAS  Article  Google Scholar 

  13. 13.

    Yost, K. E. et al. Clonal replacement of tumor-specific T cells following PD-1 blockade. Nat. Med. 25, 1251–1259 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  14. 14.

    Mani, V. et al. Migratory DCs activate TGF-β to precondition naive CD8+ T cells for tissue-resident memory fate. Science 366, eaav5728 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  15. 15.

    Broggi, M. A. S. et al. Tumor-associated factors are enriched in lymphatic exudate compared to plasma in metastatic melanoma patients. J. Exp. Med. 216, 1091–1107 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  16. 16.

    Hirakawa, S. et al. VEGF-C-induced lymphangiogenesis in sentinel lymph nodes promotes tumor metastasis to distant sites. Blood 109, 1010–1017 (2007).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  17. 17.

    Mellor, A. L. & Munn, D. H. Creating immune privilege: active local suppression that benefits friends, but protects foes. Nat. Rev. Immunol. 8, 74–80 (2008).

    CAS  PubMed  Article  Google Scholar 

  18. 18.

    Jeanbart, L. et al. Enhancing efficacy of anticancer vaccines by targeted delivery to tumor-draining lymph nodes. Cancer Immunol. Res. 2, 436–447 (2014).

    CAS  PubMed  Article  Google Scholar 

  19. 19.

    Ma, L. et al. Enhanced CAR-T cell activity against solid tumors by vaccine boosting through the chimeric receptor. Science 365, 162–168 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. 20.

    Chang, A. Y. et al. Spatial organization of dendritic cells within tumor draining lymph nodes impacts clinical outcome in breast cancer patients. J. Transl. Med. 11, 242 (2013).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  21. 21.

    Nunez, N. G. et al. Tumor invasion in draining lymph nodes is associated with Treg accumulation in breast cancer patients. Nat. Commun. 11, 3272 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  22. 22.

    Polak, M. E. et al. Mechanisms of local immunosuppression in cutaneous melanoma. Br. J. Cancer 96, 1879–1887 (2007).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  23. 23.

    Nia, H. T., Munn, L. L. & Jain, R. K. Physical traits of cancer. Science 370, eaaz0868 (2020).

    CAS  PubMed  Article  Google Scholar 

  24. 24.

    Padera, T. P. et al. Pathology: cancer cells compress intratumour vessels. Nature 427, 695 (2004).

    CAS  PubMed  Article  Google Scholar 

  25. 25.

    Jain, R. K., Martin, J. D. & Stylianopoulos, T. The role of mechanical forces in tumor growth and therapy. Annu. Rev. Biomed. Eng. 16, 321–346 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  26. 26.

    Nia, H. T. et al. Solid stress and elastic energy as measures of tumour mechanopathology. Nat. Biomed. Eng. 1, 0004 (2016).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  27. 27.

    Harrell, M. I., Iritani, B. M. & Ruddell, A. Tumor-induced sentinel lymph node lymphangiogenesis and increased lymph flow precede melanoma metastasis. Am. J. Pathol. 170, 774–786 (2007).

    PubMed  PubMed Central  Article  Google Scholar 

  28. 28.

    Gu, Y. et al. Tumor-educated B cells selectively promote breast cancer lymph node metastasis by HSPA4-targeting IgG. Nat. Med. 25, 312–322 (2019).

    CAS  PubMed  Article  Google Scholar 

  29. 29.

    Diem, S. et al. Tumor infiltrating lymphocytes in lymph node metastases of stage III melanoma correspond to response and survival in nine patients treated with ipilimumab at the time of stage IV disease. Cancer Immunol. Immunother. 67, 39–45 (2018).

    CAS  PubMed  Article  Google Scholar 

  30. 30.

    Uyttenhove, C. et al. Evidence for a tumoral immune resistance mechanism based on tryptophan degradation by indoleamine 2,3-dioxygenase. Nat. Med. 9, 1269–1274 (2003).

    CAS  PubMed  Article  Google Scholar 

  31. 31.

    Rooney, M. S., Shukla, S. A., Wu, C. J., Getz, G. & Hacohen, N. Molecular and genetic properties of tumors associated with local immune cytolytic activity. Cell 160, 48–61 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  32. 32.

    Spranger, S. et al. Up-regulation of PD-L1, IDO, and Tregs in the melanoma tumor microenvironment is driven by CD8+ T cells. Sci. Transl. Med. 5, 200ra116 (2013).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  33. 33.

    Ruddle, N. H. High endothelial venules and lymphatic vessels in tertiary lymphoid organs: characteristics, functions, and regulation. Front. Immunol. 7, 491 (2016).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  34. 34.

    Jeong, H. S. et al. Investigation of the lack of angiogenesis in the formation of lymph node metastases. J. Natl Cancer Inst. 107, djv155 (2015).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  35. 35.

    Vakoc, B. J. et al. Three-dimensional microscopy of the tumor microenvironment in vivo using optical frequency domain imaging. Nat. Med. 15, 1219–1223 (2009).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  36. 36.

    Pablos, J. L. et al. A HEV-restricted sulfotransferase is expressed in rheumatoid arthritis synovium and is induced by lymphotoxin-α/β and TNF-α in cultured endothelial cells. BMC Immunol. 6, 6 (2005).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  37. 37.

    Maly, P. et al. The alpha(1,3)fucosyltransferase Fuc-TVII controls leukocyte trafficking through an essential role in L-, E-, and P-selectin ligand biosynthesis. Cell 86, 643–653 (1996).

    CAS  PubMed  Article  Google Scholar 

  38. 38.

    Homeister, J. W. et al. The α(1,3)fucosyltransferases FucT-IV and FucT-VII exert collaborative control over selectin-dependent leukocyte recruitment and lymphocyte homing. Immunity 15, 115–126 (2001).

    CAS  PubMed  Article  Google Scholar 

  39. 39.

    Tangemann, K., Bistrup, A., Hemmerich, S. & Rosen, S. D. Sulfation of a high endothelial venule-expressed ligand for L-selectin. Effects on tethering and rolling of lymphocytes. J. Exp. Med. 190, 935–942 (1999).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  40. 40.

    Gunn, M. D. et al. A chemokine expressed in lymphoid high endothelial venules promotes the adhesion and chemotaxis of naive T lymphocytes. Proc. Natl Acad. Sci. USA 95, 258–263 (1998).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  41. 41.

    Baekkevold, E. S. et al. The CCR7 ligand elc (CCL19) is transcytosed in high endothelial venules and mediates T cell recruitment. J. Exp. Med. 193, 1105–1112 (2001).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  42. 42.

    Drayton, D. L., Liao, S., Mounzer, R. H. & Ruddle, N. H. Lymphoid organ development: from ontogeny to neogenesis. Nat. Immunol. 7, 344–353 (2006).

    CAS  PubMed  Article  Google Scholar 

  43. 43.

    Seano, G. et al. Solid stress in brain tumours causes neuronal loss and neurological dysfunction and can be reversed by lithium. Nat. Biomed. Eng. 3, 230–245 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  44. 44.

    Cohn, R. D. et al. Angiotensin II type 1 receptor blockade attenuates TGF-β-induced failure of muscle regeneration in multiple myopathic states. Nat. Med. 13, 204–210 (2007).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  45. 45.

    Diop-Frimpong, B., Chauhan, V. P., Krane, S., Boucher, Y. & Jain, R. K. Losartan inhibits collagen I synthesis and improves the distribution and efficacy of nanotherapeutics in tumors. Proc. Natl Acad. Sci. USA 108, 2909–2914 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  46. 46.

    Chauhan, V. P. et al. Angiotensin inhibition enhances drug delivery and potentiates chemotherapy by decompressing tumour blood vessels. Nat. Commun. 4, 2516 (2013).

    PubMed  Article  CAS  Google Scholar 

  47. 47.

    Martinet, L. et al. High endothelial venules (HEVs) in human melanoma lesions: major gateways for tumor-infiltrating lymphocytes. Oncoimmunology 1, 829–839 (2012).

    PubMed  PubMed Central  Article  Google Scholar 

  48. 48.

    Allen, E. et al. Combined antiangiogenic and anti-PD-L1 therapy stimulates tumor immunity through HEV formation. Sci. Transl. Med. 9, eaak9679 (2017).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  49. 49.

    Qian, C. N. et al. Preparing the “soil”: the primary tumor induces vasculature reorganization in the sentinel lymph node before the arrival of metastatic cancer cells. Cancer Res. 66, 10365–10376 (2006).

    CAS  PubMed  Article  Google Scholar 

  50. 50.

    Zhao, Y. et al. Losartan treatment enhances chemotherapy efficacy and reduces ascites in ovarian cancer models by normalizing the tumor stroma. Proc. Natl Acad. Sci. USA 116, 2210–2219 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  51. 51.

    Chen, I. X. et al. Blocking CXCR4 alleviates desmoplasia, increases T-lymphocyte infiltration, and improves immunotherapy in metastatic breast cancer. Proc. Natl Acad. Sci. USA 116, 4558–4566 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  52. 52.

    Chauhan, V. P. et al. Reprogramming the microenvironment with tumor-selective angiotensin blockers enhances cancer immunotherapy. Proc. Natl Acad. Sci. USA 116, 10674–10680 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  53. 53.

    Meijer, E. F. J. et al. Murine chronic lymph node window for longitudinal intravital lymph node imaging. Nat. Protoc. 12, 1513–1520 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  54. 54.

    Nia, H. T. et al. In vivo compression and imaging in mouse brain to measure the effects of solid stress. Nat. Protoc. 15, 2321–2340 (2020).

    CAS  PubMed  Article  Google Scholar 

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Acknowledgements

We thank G. Lauwers, D. Wattson, E. Brachtel and H. Jeong for providing patient samples; M. Erfanzadeh for assistance with MATLAB code optimization for OCT image analysis; and R. K. Jain and J. L. Browning for discussions. This work was supported by the NIH under award numbers R21AI097745, DP2OD008780, R01CA214913 and R01HL128168 (to T.P.P.). Research reported in this publication was supported in part by the Center for Biomedical OCT Research and Translation through grant number P41EB015903, awarded by the National Institute of Biomedical Imaging and Bioengineering of the NIH. This work was supported in part by the National Cancer Institute Federal Share of Proton Income (CA059267, to T.P.P. and B.J.V.), the National Cancer Institute (R01CA163528, to B.J.V.), Massachusetts General Hospital Executive Committee on Research ISF and Research Scholars Program (to T.P.P.), the Johnson and Johnson Cancer Initiative, Boston University Dean’s Catalyst Award, and American Cancer Society and Center for Multiscale and Translational Mechanobiology Pilot Grants (H.T.N.). This work was also supported in part by the United Negro College Fund–Merck Science Initiative Postdoctoral Fellowship (to D.J.), the Burroughs Wellcome Fund Postdoctoral Enrichment Program Award (to D.J.), the NIH National Cancer Institute (F32CA183465 and K22CA230315, to D.J.), the American Cancer Society Institutional Research Grant (to D.J.), the METAvivor Early Career Investigator Grant, Shamim and Ashraf Dahod Breast Cancer Research Center (to D.J.), the Boston University Undergraduate Research Opportunities Program (to Z.W.), the NIH National Cancer Institute (K00CA234940, to H.Z.) and the MGH ECOR Fund for Medical Discovery Postdoctoral Fellowship Award (to E.R.P.). This work was conducted with support from H. Lee of MGH and Harvard Catalyst; the Harvard Clinical and Translational Science Center (National Center for Advancing Translational Sciences, National Institutes of Health Award UL 1TR002541); and financial contributions from Harvard University and its affiliated academic healthcare centres. The content is solely the responsibility of the authors and does not necessarily represent the official views of Harvard Catalyst, Harvard University and its affiliated academic healthcare centres, or the National Institutes of Health.

Author information

Affiliations

Authors

Contributions

D.J. and T.P.P. conceived and designed the study, analysed data and wrote the manuscript, which all of the co-authors commented on. D.J., Z.W., I.X.C., S.Z., R.B., P.-J.L., V.X., C.K., J.W.M.v.W. and E.R.P. designed and performed experiments and analysed data. H.Z. performed adoptive transfer and losartan experiments. H.T.N. designed the LN compression device and mathematical modelling. P.H. isolated and characterized the MCa-P1362 cell line. B.J.V. contributed to imaging analyses and imaging tools.

Corresponding authors

Correspondence to Dennis Jones or Timothy P. Padera.

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Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Biomedical Engineering thanks Raffaella Righetti and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Lack of CD8 T-cell infiltration into the metastatic lesion.

a, Representative (out of about 13 images) image of immunofluorescent staining of CD8 T cells (red) in a metastatic lymph node of a patient with head and neck squamous cell carcinoma (HNSCC). Cancer cells were stained with anti-cytokeratin (green). The dashed line indicates the margin of the lymph-node lesion. Scale bar, 636 µm. b, Quantification of the area of CD8 T cells within the metastatic tumour (T) and the adjacent non-tumour (NT) lymph-node area in LN-bearing HNSCC (n = 13). c, Representative (out of about 5 images) images of immunofluorescent staining of CD8 T cells (red) in metastatic lymph nodes of mice bearing mouse 4T1 cancer cells. Cancer cells were stained with anti-cytokeratin (green). The dashed line indicates the margin of the lymph-node lesion. Scale bar, 636 µm. d, Quantification of the area of CD8 T cells in LNs bearing 4T1 cells within the non-tumour area (NT) and tumour area (T) (n = 5). Data plotted as mean±s.e.m. Statistical significance was tested via a 2-tailed paired Students’ t-test. DAPI (blue) stains all nucleated cells.

Extended Data Fig. 2 Quantification of immune populations in tumour draining lymph nodes.

a, Histology of naïve, metastatic 4T1, and metastatic MCa lymph nodes. The dashed line indicates the margin of the lymph-node lesion. The tumour is left of the dashed lines. Scale bar= 50 µm. n = 3. b, Single-cell suspensions were generated from naïve lymph nodes or from lymph nodes of tumour-bearing mice on day 28 post-4T1 cancer cell implantation. Gating strategy of flow cytometry in d–j. c, Flow-cytometry plot showing the presence of EpCAM+ tumour cells in metastatic tumour draining lymph node (MET), right plot, compared to their absence in non-metastatic tumour draining lymph nodes (TDLN), left plot. The presence or absence of EpCAM+ tumour cells (as depicted in c) was used to define TDLNs and MET LNs in d-j. d, Flow-cytometry quantification of B cells (CD45+CD19+); e, T cells (CD45+ CD3+) from naïve lymph nodes (N) (n = 8), non-metastatic TDLNs (TDLN) (n = 15), and metastatic TDLNs (MET) (n = 7) of mice harbouring 4T1 tumours. Flow-cytometry quantification of total number of (f) CD45+CD19+ and (g) CD45+ CD3+ cells from N, TDLN and MET lymph nodes. For e and f, the total number of B and T cells, respectively, was calculated by multiplying the % population by the total lymph node cell count. Flow cytometry quantification of h, CD45 + CD3 + CD4 + Foxp3 + , i, CD45 + CD3 + CD8 + and j, CD45 + CD3 + CD4 + subpopulations from the, N, TDLN and MET LNs. Statistical analysis: Data plotted as the mean±s.e.m. Statistical significance was tested by one-way ANOVA with Tukey’s Honestly Significant Difference post-hoc test.

Extended Data Fig. 3 Analysis of lymphocytes in primary tumours.

a, Representative immunofluorescent staining of naïve lymph node (positive control, n = 9) and 4T1 primary tumour (14 days post-implantation, n = 4) stained concomitantly with anti-cytokeratin (green), anti-B220 (red), and anti-CD3 (white). b, Quantification of B220 + B cells and CD3 + T cells in 4T1 primary tumours (n = 4). c, Quantification of proliferating T cells based on CD3/Ki67 double-positive cells per 20x field of view in FTY720 or control treated mice: TDLN = non-metastatic tumour draining lymph node; MET = LN tissue remaining of a metastatic tumour draining lymph node (outside lesion) and metastatic tissue (inside lesion); L = metastatic lesion only in a metastatic tumour draining lymph node (inside lesion) Statistical analysis: Data plotted as the mean±s.e.m. Statistical significance was tested by 2-tailed unpaired Students’ t test (b) and by one-way ANOVA with Tukey’s Honestly Significant Difference post-hoc test (c).

Extended Data Fig. 4 Effect of anti-PD-1 therapy on lymph node metastasis.

Expression of Cd274 (PD-L1) (a) and Pdcd1 (PD-1) transcript (b) measured by qRT-PCR, in naïve lymph node, primary tumour (PT), contralateral (CLN) and tumour draining lymph nodes (TDLN) from mice bearing 4T1 breast cancer. Relative gene expression calculated using 2ΔCT method, as normalized against Gapdh (n = 3 biological replicates for a and b). c, Representative immunofluorescent staining (4 images taken from each group) of PD-1+ cells (red) in naïve lymph nodes of Balb/c mice and metastatic lymph nodes from Balb/c mice bearing mouse 4T1 cells. Scale bars = 500 µm. Cancer cells are stained green (cytokeratin + ) and DAPI (blue) stains all nucleated cells. d-e, 4T1 tumour-bearing Balb/c mice were treated with 200 μg of anti-PD-1 antibody (BioXcell clone RMP1-14, cat# BE0146) or isotype (rat IgG, Jackson Immunoresearch Laboratories, cat. # 012-000-003) control every 3 days following primary tumour resection 14 days post-implantation. d, Survival of animals treated with anti-PD-1 or isotype antibody. Anti-PD-1 n = 15, isotype; n = 16. e, Incidence of metastatic lymph nodes after anti-PD1 or isotype antibody treatment, as determined by cytokeratin staining of serial lymph node sections. Anti-PD-1 n = 32, isotype; n = 30. f, Tumour area of cytokeratin-positive metastatic lesions after treatment with indicated antibodies. Tumour area was measured using Image J analysis. g, Quantification of CD8 + cells within tumour and non-tumour areas of metastatic lymph nodes from 4T1 tumours in Balb/c mice treated with anti-PD-1 antibody or isotype control. Data plotted as the mean±s.e.m. Statistical significance was tested by one-way ANOVA with Tukey’s Honestly Significant Difference post-hoc test (a-b, f-g). For d, statistical analysis was calculated using Kaplan Meier analysis with non-parametric log-rank test. For e, statistical analysis was performed using a 2×2 chi-square test with Yates correction.

Extended Data Fig. 5 Effect of IDO inhibition and chemotherapy on lymph-node metastasis.

a, Expression of Ido1 measured by qRT-PCR from tumour (T) and non-tumour (NT) regions of metastatic LNs and from primary tumour of mice bearing 4T1 cells (n = 6 biological replicates for tumour and non-tumour, 4 biological replicates for tumour). Relative gene expression normalized to Gapdh and calculated using the 2ΔCT method. b, Schematic of treatment regimen. c, Measurement of primary tumour volume at time of resection for each treatment group. d, Weight of tumour draining axillary lymph node after treatment. Untreated (n = 16); 1M-DL-Try (n = 16); CYC (cyclophosphamide) (n = 15); 1M-DL-Try + CYC (n = 15). e, Incidence of metastatic lymph nodes, as determined by cytokeratin staining of serial lymph node sections. f, Quantification of pulmonary macrometastatic nodules after treatment with indicated therapies. Untreated (n = 16); 1M-DL-Try (n = 16); CYC (n = 15); 1M-DL-Try + CYC (n = 16). g, Representative immunofluorescent staining of T cells (CD3, red) and cancer cells (cytokeratin, green) in lymph nodes from control (untreated), or treated (as indicated) animals. The inset shows single tumour cells within lymph nodes of cyclophosphamide-treated animals. DAPI (blue) stains all nucleated cells. Scale bars, 500 µm. Inset scale bar = 50 µm Four images were taken per group. Data plotted as the mean±s.e.m. Statistical significance was tested by one-way ANOVA with Tukey’s Honestly Significant Difference post-hoc test (c,d,f) and between categorical variables in e using a chi-square test.

Extended Data Fig. 6 High-endothelial-venule wall-thickness in non-metastatic and metastatic lymph nodes.

a, Representative immunofluorescent staining of naïve lymph node, non-metastatic tumour draining lymph node and metastatic tumour draining lymph node with anti-cytokeratin (green) and anti-PNAd (red). DAPI (blue). Scale bars = 1272 µm. Inset below shows magnified region of interest from respective tiled lymph nodes. Scale bar = 50 µm. Four images were taken per group. b, Quantification of HEV wall thickness. Each point represents the average of 8 measurements per vessel. n = 4 animals for naïve and non-metastatic; n = 3 animals for metastatic. Data plotted as mean with 95% CI. Statistical significance was tested by one-way ANOVA with Tukey’s Honestly Significant Difference post-hoc test.

Extended Data Fig. 7 Vessel perfusion in tumour- draining lymph nodes.

a, Representative false color OCT non-metastatic TDLN on days 1 and 7 post-primary tumour (4T1) resection. Depth is denoted by color, yellow/green = superficial, red=deep. Dashed line indicates the margin of the lymph node. Scale bar = 500 µm. Experiment is representative of 5 biological replicates. b, Representative image of lectin-perfused vessels (green) in contralateral (CLN, left) and metastatic lymph node (MET, right) from animal bearing 4T1 tumour. CD31 + vessels are stained red. Adjacent hematoxylin and eosin-stained sections were used to identify the cancer cell lesion. Dashed line indicates the margin of the lymph node lesion. Scale bar = 500 µm. The number of images taken is reflected in the n values for c. c, From the 4T1 model, quantification of CD31 + Lectin+ vessels in contralateral lymph nodes (CLNs, n = 8), metastatic lymph nodes (MET), n = 6), and only within the lesion (L) of a metastatic lymph node (n = 6). Data plotted as the mean±s.e.m. Statistical significance was tested by one-way ANOVA with Tukey’s Honestly Significant Difference post-hoc test.

Extended Data Fig. 8 Trafficking of adoptively transferred T cells to naïve and tumour draining lymph nodes.

Adoptive transfer of CFSE (green)-labeled naïve T cells into naïve or 4T1-tumour bearing mice 5 or 9 days post-primary tumour resection. a, Quantification of adoptively transferred T cells (10 million) entering contralateral lymph nodes (CLN) (n = 8) and tumour draining lymph nodes (TDLN) (n = 6) within 4 hours of cell transfer at Day 5 post-tumour resection. b, Quantification of adoptively transferred T cells (14 million) entering contralateral lymph nodes (CLN) (n = 4) and tumour draining lymph nodes (TDLN) (n = 3) within 4 hours of cell transfer at Day 9 post-tumour resection. c, Quantification of the non-tumour area (NT) and tumour area (T) of D9 metastatic LNs (n = 3) within 4 hours of cell transfer. Data plotted as the mean±s.e.m. Significance tested by one-way ANOVA with Tukey’s Honestly Significant Difference post-hoc test (a,b) and 2-tailed paired Students’ t-test (c).

Extended Data Fig. 9 Gene expression in metastatic lymph nodes.

a, qRT-PCR analysis from tumour (T) and non-tumour (NT) regions of metastatic LNs a, Krt18 was used to confirm the presence of cancer cells in each individual metastatic lymph node. Transcript expression normalized to Gapdh. b–h, Analysis of genes related to T cells trafficking. Expression normalized to Pecam1. (n = 3 biological replicates) i–j, Ccl19 (i) and Ccl21 (j) expression normalized to Gapdh. (n = 3 biological replicates for both tumour and non-tumour groups in a-j). Relative gene expression calculated using 2ΔCT method. k, Pearson correlation between the number of intralesional CD3 + T cells and lesion area of 4T1 cancer cells in lymph nodes (r = − 0.37, n = 19 biological replicates). Data plotted as the mean±s.e.m. Statistical significance was tested by 2-tailed paired Students’ t-test (a-j).

Extended Data Fig. 10 ICAM-1 in lymph nodes experiencing solid stress.

a, Representative immunofluorescence staining of ICAM-1(red) and cytokeratin (green) in non-metastatic and metastatic LNs from mouse 4T1 model. The number of images taken is reflected in the n values for b. b, Quantification of ICAM-1 in non-metastatic (n = 3) and metastatic (n = 5) lymph nodes. DAPI, blue. c, Quantitation of non-tumour area (NT) and tumour area (T) of metastatic LNs (n = 5). Scale bars = 636 µm. d, Representative immunofluorescence staining of ICAM-1 (red) in an uncompressed and compressed inguinal lymph node DAPI, blue. Scale bar = 1272 µm. 3 images were taken per group and are quantified in e. f, Percentage (of total CFSE + cells) of CD3 + CFSE + cells in animals receiving adoptively transferred splenocytes. n = 7 biological replicates. Statistical analysis: Data plotted as the mean±s.e.m. Significance tested by 2-tailed unpaired (b,e) and paired Students’ t-test (c).

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Jones, D., Wang, Z., Chen, I.X. et al. Solid stress impairs lymphocyte infiltration into lymph-node metastases. Nat Biomed Eng (2021). https://doi.org/10.1038/s41551-021-00766-1

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