Systemic dysfunction and plasticity of the immune macroenvironment in cancer models

Abstract

Understanding of the factors governing immune responses in cancer remains incomplete, limiting patient benefit. In this study, we used mass cytometry to define the systemic immune landscape in response to tumor development across five tissues in eight mouse tumor models. Systemic immunity was dramatically altered across models and time, with consistent findings in the peripheral blood of patients with breast cancer. Changes in peripheral tissues differed from those in the tumor microenvironment. Mice with tumor-experienced immune systems mounted dampened responses to orthogonal challenges, including reduced T cell activation during viral or bacterial infection. Antigen-presenting cells (APCs) mounted weaker responses in this context, whereas promoting APC activation rescued T cell activity. Systemic immune changes were reversed with surgical tumor resection, and many were prevented by interleukin-1 or granulocyte colony-stimulating factor blockade, revealing remarkable plasticity in the systemic immune state. These results demonstrate that tumor development dynamically reshapes the composition and function of the immune macroenvironment.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: The systemic immune landscape is remodeled across tumor models.
Fig. 2: The systemic immune landscape is remodeled progressively with tumor development.
Fig. 3: Tumor burden progressively changes the systemic T cell composition.
Fig. 4: Tumor burden leads to impaired T cell responses to secondary infection.
Fig. 5: Tumor burden attenuates dendritic cell activation during secondary infection.
Fig. 6: Tumor resection completely resets the systemic immune landscape.

Data availability

All mass cytometry data are publicly available at https://premium.cytobank.org/cytobank/projects/2433/ or by request to the senior author without restrictions.

Code availability

The updated Statistical Scaffold package is available at https://github.com/SpitzerLab/statisticalScaffold.

References

  1. 1.

    Philip, M. et al. Chromatin states define tumour-specific T cell dysfunction and reprogramming. Nature 545, 452–456 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  2. 2.

    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 

  3. 3.

    Fransen, M. F. et al. Tumor-draining lymph nodes are pivotal in PD-1/PD-L1 checkpoint therapy. JCI Insight 3, 1–7 (2018).

    Article  Google Scholar 

  4. 4.

    Tang, H. et al. PD-L1 on host cells is essential for PD-L1 blockade-mediated tumor regression. J. Clin. Invest. 128, 580–588 (2018).

    PubMed  PubMed Central  Article  Google Scholar 

  5. 5.

    Chamoto, K. et al. Mitochondrial activation chemicals synergize with surface receptor PD-1 blockade for T cell-dependent antitumor activity. PNAS 114, E761–E770 (2017).

    CAS  PubMed  Article  Google Scholar 

  6. 6.

    Mathios, D. et al. Anti-PD-1 antitumor immunity is enhanced by local and abrogated by systemic chemotherapy in GBM. Sci. Transl. Med. 8, 370ra180 (2016).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  7. 7.

    Lin, H. et al. Host expression of PD-L1 determines efficacy of PD-L1 pathway blockade-mediated tumor regression. J. Clin. Invest. 128, 805–815 (2018).

    PubMed  PubMed Central  Article  Google Scholar 

  8. 8.

    Curiel, T. J. et al. Blockade of B7-H1 improves myeloid dendritic cell-mediated antitumor immunity. Nat. Med. 9, 562–567 (2003).

    CAS  PubMed  Article  Google Scholar 

  9. 9.

    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 

  10. 10.

    McAllister, S. S. & Weinberg, R. A. The tumour-induced systemic environment as a critical regulator of cancer progression and metastasis. Nat. Cell Biol. 16, 717–727 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  11. 11.

    Zhang, S. et al. The role of myeloid-derived suppressor cells in patients with solid tumors: a meta-analysis. PLoS ONE 11, e0164514 (2016).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  12. 12.

    Casbon, A.-J. et al. Invasive breast cancer reprograms early myeloid differentiation in the bone marrow to generate immunosuppressive neutrophils. Proc. Natl Acad. Sci. USA 112, E566–E575 (2015).

    CAS  PubMed  Article  Google Scholar 

  13. 13.

    Meyer, M. A. et al. Breast and pancreatic cancer interrupt IRF8-dependent dendritic cell development to overcome immune surveillance. Nat. Commun. 9, 1–19 (2018).

    Article  CAS  Google Scholar 

  14. 14.

    Barnstorf, I. et al. Chronic virus infection compromises memory bystander T cell function in an IL-6/ STAT1-dependent manner. J. Exp. Med. 216, 571–586 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  15. 15.

    Snell, L. M. et al. CD8+ T cell priming in established chronic viral infection preferentially directs differentiation of memory-like cells for sustained immunity. Immunity 49, 678–694 (2018).

    PubMed  Article  CAS  Google Scholar 

  16. 16.

    Osborne, L. C. et al. Virus-helminth coinfection reveals a microbiota-independent mechanism of immunomodulation. Science 345, 578–582 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  17. 17.

    Danna, E. A. et al. Surgical removal of primary tumor reverses tumor-induced immunosuppression despite the presence of metastatic disease. Cancer Res. 64, 2205–2211 (2004).

    CAS  PubMed  Article  Google Scholar 

  18. 18.

    Ghochikyan, A. et al. Primary 4T1 tumor resection provides critical ‘window of opportunity’ for immunotherapy. Clin. Exp. Metastasis 31, 185–198 (2014).

    CAS  PubMed  Article  Google Scholar 

  19. 19.

    Mosely, S. I. S. et al. Rational selection of syngeneic preclinical tumor models for immunotherapeutic drug discovery. Cancer Immunol. Res. 5, 29–41 (2017).

    CAS  PubMed  Article  Google Scholar 

  20. 20.

    Westcott, P. M. K. et al. The mutational landscapes of genetic and chemical models of Kras-driven lung cancer. Nature 517, 489–492 (2015).

    CAS  PubMed  Article  Google Scholar 

  21. 21.

    Zeitouni, B. et al. Abstract 1840: Whole-exome somatic mutation analysis of mouse cancer models and implications for preclinical immunomodulatory drug development. In Proceedings of the 107th Annual Meeting of the American Association for Cancer Research https://doi.org/10.1158/1538-7445.AM2017-1840 (AACR, 2017).

  22. 22.

    Heinzel, F. P., Sadick, M. D., Holaday, B. J., Coffman, R. L. & Locksley, R. M. Reciprocal expression of interferon γ or interleukin 4 during the resolution or progression of murine leishmaniasis. Evidence for expansion of distinct helper T cell subsets. J. Exp. Med. 169, 59–72 (1989).

    CAS  PubMed  Article  Google Scholar 

  23. 23.

    Kather, J. N. et al. Topography of cancer-associated immune cells in human solid tumors. eLife 7, e36967 (2018).

  24. 24.

    Spitzer, M. H. et al. An interactive reference framework for modeling a dynamic immune system. Science 349, 1259425 (2015).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  25. 25.

    Anz, D. et al. CD103 is a hallmark of tumor-infiltrating regulatory T cells. Int. J. Cancer 129, 2417–2426 (2011).

    CAS  PubMed  Article  Google Scholar 

  26. 26.

    Ross, E. A. et al. CD31 is required on CD4+ T cells to promote T cell survival during Salmonella infection. J. Immunol. 187, 1553–1565 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  27. 27.

    Hänninen, A., Maksimow, M., Alam, C., Morgan, D. J. & Jalkanen, S. Ly6C supports preferential homing of central memory CD8+ T cells into lymph nodes. Eur. J. Immunol. 41, 634–644 (2011).

    PubMed  Article  CAS  Google Scholar 

  28. 28.

    Fourcade, J. et al. Upregulation of Tim-3 and PD-1 expression is associated with tumor antigen-specific CD8+ T cell dysfunction in melanoma patients. J. Exp. Med. 207, 2175–2186 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  29. 29.

    Mita, Y. et al. Crucial role of CD69 in anti-tumor immunity through regulating the exhaustion of tumor-infiltrating T cells. Int. Immunol. 30, 559–567 (2018).

    CAS  PubMed  Article  Google Scholar 

  30. 30.

    Sun, C., Mezzadra, R. & Schumacher, T. N. Regulation and function of the PD-L1 checkpoint. Immunity 48, 434–452 (2018).

    CAS  PubMed  Article  Google Scholar 

  31. 31.

    Bianchini, M. et al. PD-L1 expression on nonclassical monocytes reveals their origin and immunoregulatory function. Sci. Immunol. 4, eaar3054 (2019).

    CAS  PubMed  Article  Google Scholar 

  32. 32.

    Busch, D. H., Pilip, I. M., Vijh, S. & Pamer, E. G. Coordinate regulation of complex T cell populations responding to bacterial infection. Immunity 8, 353–362 (1998).

    CAS  PubMed  Article  Google Scholar 

  33. 33.

    Kaech, S. M. & Ahmed, R. Memory CD8+ T cell differentiation: initial antigen encounter triggers a developmental program in naïve cells. Nat. Immunol. 2, 415–422 (2001).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  34. 34.

    Herndler-Brandstetter, D. et al. KLRG1+ effector CD8+ T cells lose KLRG1, differentiate into all memory T cell lineages, and convey enhanced protective immunity. Immunity 48, 716–729 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  35. 35.

    Jung, S. et al. In vivo depletion of CD11c+ dendritic cells abrogates priming of CD8+ T cells by exogenous cell-associated antigens. Immunity 17, 211–220 (2002).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  36. 36.

    Gabrilovich, D. I., Corak, J., Ciernik, I. F., Kavanaugh, D. & Carbone, D. P. Decreased antigen presentation by dendritic cells in patients with breast cancer. Clin. Cancer Res. 3, 483–490 (1997).

    CAS  PubMed  Google Scholar 

  37. 37.

    Coffelt, S. B. et al. IL-17-producing γδ T cells and neutrophils conspire to promote breast cancer metastasis. Nature 522, 345–348 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  38. 38.

    Wu, W.-C. et al. Circulating hematopoietic stem and progenitor cells are myeloid-biased in cancer patients. Proc. Natl Acad. Sci. USA 111, 4221–4226 (2014).

    CAS  PubMed  Article  Google Scholar 

  39. 39.

    Apte, R. N. et al. Effects of micro-environment- and malignant cell-derived interleukin-1 in carcinogenesis, tumour invasiveness and tumour-host interactions. Eur. J. Cancer 42, 751–759 (2006).

    CAS  PubMed  Article  Google Scholar 

  40. 40.

    Wu, T. C. et al. IL1 receptor antagonist controls transcriptional signature of inflammation in patients with metastatic breast cancer. Cancer Res. 78, 5243–5258 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  41. 41.

    Singer, C. F. et al. Interleukin-1ɑ protein secretion in breast cancer is associated with poor differentiation and estrogen receptor ɑ negativity. Int. J. Gynecol. Cancer 16, 556–559 (2006).

    PubMed  Article  Google Scholar 

  42. 42.

    Pickup, M., Novitskiy, S. & Moses, H. L. The roles of TGFβ in the tumour microenvironment. Nat. Rev. Cancer 13, 788–799 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  43. 43.

    Mariathasan, S. et al. TGFβ attenuates tumour response to PD-L1 blockade by contributing to exclusion of T cells. Nature 554, 544–548 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  44. 44.

    Suzuki, A. et al. IL-1 production as a regulator of G-CSF and IL-6 production in CSF-producing cell lines. Br. J. Cancer 65, 515–518 (1992).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  45. 45.

    Mittal, R., Wagener, M., Breed, E. R., Liang, Z. & Yoseph, B. P. Phenotypic T cell exhaustion in a murine model of bacterial infection in the setting of pre-existing malignancy. PLoS ONE 9, 93523 (2014).

    Article  CAS  Google Scholar 

  46. 46.

    Xie, J. et al. Pre-existing malignancy results in increased prevalence of distinct populations of CD4+ T cells during sepsis. PLoS ONE 13, e0191065 (2018).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  47. 47.

    Russ, A. J. et al. Melanoma-induced suppression of tumor antigen-specific T cell expansion is comparable to suppression of global T cell expansion. Cell. Immunol. 271, 104–109 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  48. 48.

    Klastersky, J. & Aoun, M. Opportunistic infections in patients with cancer. Ann. Oncol. 15, iv329–iv335 (2004).

    PubMed  Article  Google Scholar 

  49. 49.

    Baluch, A. & Pasikhova, Y. Influenza vaccination in oncology patients. Curr. Infect. Dis. Rep. 15, 486–490 (2013).

    PubMed  Article  Google Scholar 

  50. 50.

    O’Hara, M. H. et al. Abstract CT004: A Phase Ib study of CD40 agonistic monoclonal antibody APX005M together with gemcitabine (Gem) and nab-paclitaxel (NP) with or without nivolumab (Nivo) in untreated metastatic ductal pancreatic adenocarcinoma (PDAC) patients. Clin. Trials https://doi.org/10.1158/1538-7445.am2019-ct004 (2019).

  51. 51.

    Zuckerman, N. S. et al. Altered local and systemic immune profiles underlie lymph node metastasis in breast cancer patients. Int. J. Cancer 132, 2537–2547 (2012).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  52. 52.

    Wang, L. et al. Connecting blood and intratumoral Treg cell activity in predicting future relapse in breast cancer. Nat. Immunol. 20, 1220–1230 (2019).

    CAS  PubMed  Article  Google Scholar 

  53. 53.

    Kosaka, A., Ohkuri, T., Program, B. T. & Okada, H. Combination of an agonistic anti-CD40 monoclonal antibody and the COX-2 inhibitor celecoxib induces anti-glioma effects by promotion of type-1 immunity in myeloid cells and T-cells. Cancer Immunol. Immunother. 63, 847–857 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  54. 54.

    Tseng, W. W. et al. Development of an orthotopic model of invasive pancreatic cancer in an immunocompetent murine host. Clin. Cancer Res. 16, 3684–3695 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  55. 55.

    Kathryn, E. et al. Cutting edge: CD4 and CD8 T cells are intrinsically different in their proliferative responses. J. Immunol. 168, 1528–1532 (2002).

    Article  Google Scholar 

  56. 56.

    Zunder, E. R. et al. Palladium-based mass-tag cell barcoding with a doublet-filtering scheme and single cell deconvolution algorithm. Nat. Protoc. 10, 316–333 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  57. 57.

    Finck, R. et al. Normalization of mass cytometry data with bead standards. Cytometry A 83, 483–494 (2013).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  58. 58.

    Bair, E. & Tibshirani, R. Semi-supervised methods to predict patient survival from gene expression data. PLoS Biol. 2, 0511–0522 (2004).

    CAS  Article  Google Scholar 

  59. 59.

    Dumeaux, V. et al. Interactions between the tumor and the blood systemic response of breast cancer patients. PLoS Comput. Biol. 13, e1005680 (2017).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  60. 60.

    Aran, D., Hu, Z. & Butte, A. J. xCell: digitally portraying the tissue cellular heterogeneity landscape. Genome Biol. 18, 1–14 (2017).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We thank the UCSF Flow Cytometry Core and S. Tamaki for CyTOF maintenance and M.H. Barcellos-Hoff, R. Levine, H. Okada, E. Engleman and J. Bluestone for cell lines, transgenic mice and reagents. We thank L. Lanier, Z. Werb, M.H. Barcellos-Hoff and L. Fong for insightful feedback. This work was supported by National Institutes of Health (NIH) grants DP5OD023056 and P50CA097257 (UCSF Brain Tumor SPORE Developmental Research Program), funds from the UCSF Program for Breakthrough Biomedical Research and investigator funding from the Parker Institute for Cancer Immunotherapy to M.H.S. and by NIH grant S10OD018040, which enabled procurement of the mass cytometer used in this study. This study makes use of data generated by the Norwegian Women and Cancer Study. A full list of investigators who contributed to the generation of the data is available at http://site.uit.no/nowac/. Funding for the project was provided by European Research Council grant ERC-2008-AdG 232997. The Norwegian Women and Cancer Study group is not responsible for the analysis or interpretation of the data presented.

Author information

Affiliations

Authors

Contributions

Conceptualization: B.M.A, K.J.H., Y.C. and M.H.S.; experimental methodology: B.M.A., K.J.H., C.E.B., A.V., R.D., I.T., D.M.M., N.W.C., Y.C. and M.H.S.; computational methodology: B.M.A. and M.H.S.; investigation: all authors; writing, original draft: B.M.A.; writing, review and editing: all authors; funding acquisition: M.H.S.; supervision: M.H.S.

Corresponding author

Correspondence to Matthew H. Spitzer.

Ethics declarations

Competing interests

M.H.S. receives research funding from Roche/Genentech, Bristol-Myers Squibb and Valitor and has been a paid consultant for Five Prime Therapeutics, Ono Pharmaceutical and January Inc.

Additional information

Peer review information Saheli Sadanand was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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

Extended data

Extended Data Fig. 1 Main Mass Cytometry Gating Scheme.

a, Main gating strategy for identifying major immune cell populations from mass cytometry datasets.

Extended Data Fig. 2 Systemic immunity is distinctly remodeled across tumor models.

a, Relative abundance of total leukocytes infiltrating the TME across eight tumor models. b-f, Scaffold maps of spleen cell frequencies across five distinct tumor models, SB28 glioblastoma (b), MC38 colorectal (c), LMP pancreatic (d), B16 melanoma (e), and Braf/PTEN melanoma (f), comparing late stage tumor burden to their respective health littermate controls. g, Heatmaps of the log2 adjusted fold change in bulk immune cell frequencies across all five tissues, where relevant, across all models. h, Heatmaps of the log2 adjusted fold change in bulk immune cell frequencies comparing the parental AT3 and engineered AT3 expressing reporters GFP and Luciferase, with cell labels in g. Lower inset shows Pearson’s correlation between these systemic immune features.

Extended Data Fig. 3 Systemic immunity is distinctly remodeled over tumor development.

a, Pearson’s correlation between MMTV-PyMT primary tumor size and change in systemic immune composition, measured as Aitchison distance. b, Degree of systemic immune change by Aitchison distance over tumor growth (left) and after removing the contribution of primary tumor size by linear regression (right). c, Percent of PyMT expressing metastatic cancer cells in the lung (green) and primary draining lymph node (blue). d, Pearson’s correlation between lung or lymph node metastasis and the residual changes in systemic immune composition after regressing out primary tumor burden. e, Heatmap of the log2 adjusted fold change in bulk spleen immune cell frequencies for each 400mm2 tumor-bearing mouse, ranging from 0 to high metastatic disease. f, Pearson’s correlation between tumor mass and absolute number of infiltrating leukocytes in 4T1 breast tumors. g, Spleen immune absolute cell counts, adjusted absolute cell counts per mg of tissue, and unadjusted immune frequencies at each time point for neutrophils, B cells and T cells of the 4T1 breast tumor model. h, PCA of relative immune cell frequencies from each major immune tissue over time in the MMTV-PyMT breast tumor model. Vectors designate progression from control (first point) to 25 mm2, 50mm2, 125mm2, and 400mm2 (last point, arrowhead). i, Scaffold maps of immune cell frequencies in the spleen at each time point of 4T1 tumor burden, colored by log2 fold change in frequency compared to the previous time point.

Extended Data Fig. 4 Immunity is distinctly remodeled by compartment over tumor development.

a-d, Scaffold maps of immune cell frequencies over 4T1 tumor progression in the tumor draining lymph node (a) blood (b), bone marrow (c), and tumor (d), colored by fold change from the previous time point.

Extended Data Fig. 5 Tumor growth shifts the systemic T cell composition across models.

a-b, PCA of T cell cluster frequencies across lymphoid tissues over tumor development for the 4T1 (a) and MMTV-PyMT (b) breast tumor models. Vectors designate directional progression from control (first point) to late stage disease (last point, arrowhead). In a, tumor time points include day 7, 14, 21, and 35 after 4T1 cancer cell transplant. In b, tumor time points include tumor sizes of 25 mm2, 50 mm2, 125 mm2, and 400 mm2. c-e, CD3 + CD11b- leukocytes from all tissues clustered together from healthy and MMTV-PyMT tumor-burdened animals at progressive tumor sizes. c, Heatmap of each T cell cluster frequency, by row, in each site and across the individual 2-3 animals per time point. d, Stacked bar plot of the log2 fold change in cluster frequency between early (25 mm2) and late (400 mm2) disease time points, colored by tissue. e, Heatmap of the protein expression defining each T cell cluster, column normalized to each protein’s maximum positive expression. f-h, Representative scatter plots of key proteins that define T cell clusters changing in frequency in the designated site between early and late disease stage for CD8 T cells (f), Tregs (g), and CD4 T Cells (h).

Extended Data Fig. 6 Tumor growth shifts the systemic mononuclear phagocyte composition.

a, CD3- CD19- leukocytes from all tissues clustered together from healthy and 4T1 tumor-burdened animals at progressive time points. Left, stacked bar plot of the log2 fold change in cluster frequency between early (day 7) and late (day 35) times points, colored by tissue. Right, heatmap of the protein expression defining each cluster, column normalized to each protein’s maximum positive expression. b, Curves of the mean cell frequencies over time in the 4T1 breast tumor model from designated mononuclear phagocyte cell types, colored by tissue. c, PCA of the mononuclear phagocyte cell frequencies from each tissue over time in the 4T1 breast tumor model. Vectors designate progression from control (first point) to day 7, 14, 21, and 35 (last point, arrowhead). Coloring of tissues for a-c corresponds to labels in c.

Extended Data Fig. 7 PD-1 and PD-L1 expression is dynamic over tumor growth.

a, Distribution of PD-1 and PD-L1 signal intensities on tumor infiltrating leukocytes over time in the 4T1 or AT3 breast tumor models. Coloring of time points for a-d corresponds to legend in a. b, Percent of total infiltrating leukocytes (left of dashed line) or CD45, non-endothelial cells (right of dashed line) with high PD-1 or PD-L1 expression in the 4T1 or AT3 tumor models. c, Percent of leukocytes with high PD-1 or PD-L1 expression over time and across tissues, 4T1 model. d, Pearson’s correlation between median PD-L1 signal intensity on blood versus tumor infiltrating leukocytes, 4T1 model. e, Percent of each major immune cell subset expressing high PD-1 or PD-L1 in the tumor, blood, and spleen, identified manually. Cell subsets below 0.2% of total leukocytes were not included, X. Bars ordered by time point, beginning at healthy control. Double positive PD-1/PD-L1 expression was rare and not illustrated. p*< 0.05, One-Way ANOVA, with Tukey correction versus control tissue or healthy mammary fat pad (blue in b-c, fill corresponding to bar color in e), or versus day 7 (green in b-c).

Extended Data Fig. 8 Tumor burden induces tissue-specific changes in immune cell cycling.

a-b, Log2 fold change in bulk Ki67 expressing leukocytes in each tissue tissues for 4T1, AT3 and MMTV breast tumors (a), and over 4T1 tumor progression (b). p*< 0.05, One-Way ANOVA, with Tukey correction versus control. c-d, Statistical Scaffold maps of Ki67 expression in immune cells of the tumor draining lymph node comparing control to day 21 (c) and the Spleen over time (d) in 4T1 tumor burdened animals. e, Percent of increasing clusters (red, total of 56), decreasing clusters (blue, total of 90), or unchanged cluster that have corresponding changes in cell cycle markers Ki67 and cleaved Caspase-3.

Extended Data Fig. 9 Tumor driven deficits in T cell responses are cell-extrinsic.

a, Quantification of bulk CD8+ T cell populations in the spleen of healthy or AT3 tumor-burdened mice after 7 days of Lm infection, Two-Way ANOVA with Bonferroni correction. b, Expression of inflammatory cytokines, INFy, IL-2, and TNFa in splenic CD8 T Cells isolated from control or AT3 tumor-burdened mice after in vitro differentiation with CD3, CD28 and IL-2, and re-stimulation with BrefeldinA and PMA Ionomycin. c, Scatter plots of CD11b and Ly6G showing expected neutrophilia in OT-I TCR transgenic mice with AT3 tumor burden. d, Histograms of CD80, CD86, and CD83 signal intensity on cDCs from healthy or AT3 tumor-burdened mice at day 2 of Lm-OVA infection. e, Median signal intensity of CD80, PD-L1 and CD54 activation markers on splenic cDCs from healthy or AT3 tumor-burdened mice compared to IL-12p70 or CD40 treatment at day 7 of Lm-OVA infection. F, Median signal intensity of PD-L1 on splenic cDCs from untreated or CD40 treated AT3 tumor-burdened (day 21) mice. G, Quantification of splenic CD8 + T cell proliferation in healthy, untreated or CTLA-4 treated AT3 tumor-burdened animals in response to 7 days of Lm-OVA infection. p*<0.05, two-tailed t-test.

Extended Data Fig. 10 Tumor resection resets systemic immune organization and function.

a-c, Statistical scaffold maps of spleen immune cell frequencies (a) and proliferation by Ki67 expression (b) in 4T1 resected mice, and of spleen immune cell frequencies in MC38 resected mice (c) compared to healthy control. Insets show resected mice compared to tumor-burdened mice. d-e, Heatmap of the log2 fold changes in splenic immune cell frequencies for local or lung recurrences from control mice (d), and for IL-1, G-CSF, or TGFβ blockade from untreated AT3 tumor-burdened mice (e). f-g, Heatmaps of T cell cluster expression profiles and log2 fold change from control for AT3 (f) and 4T1 (g) for the spleen and draining lymph node. h, Median signal intensity of CD86 and PD-L1 on splenic cDCs from healthy, AT3 tumor-burdened, resected, or resected mice with local recurrence at day 7 of Lm-OVA infection. i, Concentration of circulating cytokines, IL-1α and G-CSF from healthy, AT3 tumor-burdened, resected, or resected mice with local recurrence. j, Concentration of cytokines, IL-1α, IL-1β and G-CSF from in vitro cell culture media conditioned with AT3 cancer cells. k, Concentration of circulating G-CSF from control or AT3 tumor-bearing mice, or AT3 tumor-bearing mice treated with either IL-1 or G-CSF blocking antibodies. p*<0.05, two-tailed t-test.

Supplementary information

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Allen, B.M., Hiam, K.J., Burnett, C.E. et al. Systemic dysfunction and plasticity of the immune macroenvironment in cancer models. Nat Med (2020). https://doi.org/10.1038/s41591-020-0892-6

Download citation

Further reading