Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Tumors evade immune cytotoxicity by altering the surface topology of NK cells

Nature Immunology volume 24, pages 802–813 (2023)Cite this article

Subjects

Abstract

The highly variable response rates to immunotherapies underscore our limited knowledge about how tumors can manipulate immune cells. Here the membrane topology of natural killer (NK) cells from patients with liver cancer showed that intratumoral NK cells have fewer membrane protrusions compared with liver NK cells outside tumors and with peripheral NK cells. Dysregulation of these protrusions prevented intratumoral NK cells from recognizing tumor cells, from forming lytic immunological synapses and from killing tumor cells. The membranes of intratumoral NK cells have altered sphingomyelin (SM) content and dysregulated serine metabolism in tumors contributed to the decrease in SM levels of intratumoral NK cells. Inhibition of SM biosynthesis in peripheral NK cells phenocopied the disrupted membrane topology and cytotoxicity of the intratumoral NK cells. Targeting sphingomyelinase confers powerful antitumor efficacy, both as a monotherapy and as a combination therapy with checkpoint blockade.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Get just this article for as long as you need it

$39.95

Learn more

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Intratumoral NK cells lack abundant membrane protrusions.
Fig. 2: Intratumoral NK cells are impaired in recognizing tumor cells.
Fig. 3: Decreased SM level in intratumoral NK cell membranes.
Fig. 4: Inhibition of SM biosynthesis disrupts membrane topology of NK cells.
Fig. 5: Dysregulated serine metabolism contributes to SM decrease in NK cells.
Fig. 6: Inhibition of SM catabolism promotes the antitumor function of NK cells.

Data availability

The clinical characteristics of patients are shown in Supplementary Tables 1–3 and 5. All data are present in the article and Supplementary information files or are available from the corresponding authors upon reasonable request. Source data are provided with this paper.

References

  1. Baumeister, S. H., Freeman, G. J., Dranoff, G. & Sharpe, A. H. Coinhibitory pathways in immunotherapy for cancer. Annu. Rev. Immunol. 34, 539–573 (2016).

    Article  CAS  PubMed  Google Scholar 

  2. Topalian, S. L., Drake, C. G. & Pardoll, D. M. Immune checkpoint blockade: a common denominator approach to cancer therapy. Cancer Cell 27, 450–461 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Sharma, P. & Allison, J. P. The future of immune checkpoint therapy. Science 348, 56–61 (2015).

    Article  CAS  PubMed  Google Scholar 

  4. Okazaki, T., Chikuma, S., Iwai, Y., Fagarasan, S. & Honjo, T. A rheostat for immune responses: the unique properties of PD-1 and their advantages for clinical application. Nat. Immunol. 14, 1212–1218 (2013).

    Article  CAS  PubMed  Google Scholar 

  5. Rizvi, N. A. et al. Activity and safety of nivolumab, an anti-PD-1 immune checkpoint inhibitor, for patients with advanced, refractory squamous non-small-cell lung cancer (CheckMate 063): a phase 2, single-arm trial. Lancet Oncol. 16, 257–265 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Ansell, S. M. et al. PD-1 blockade with nivolumab in relapsed or refractory Hodgkin’s lymphoma. N. Engl. J. Med. 372, 311–319 (2015).

    Article  PubMed  Google Scholar 

  7. André, P. et al. Anti-NKG2A mAb is a checkpoint inhibitor that promotes anti-tumor immunity by unleashing both T and NK cells. Cell 175, 1731–1743.e1713 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  8. McWilliams, E. M. et al. Therapeutic CD94/NKG2A blockade improves natural killer cell dysfunction in chronic lymphocytic leukemia. Oncoimmunology 5, e1226720 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  9. Kamiya, T., Seow, S. V., Wong, D., Robinson, M. & Campana, D. Blocking expression of inhibitory receptor NKG2A overcomes tumor resistance to NK cells. J. Clin. Invest. 129, 2094–2106 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  10. Topalian, S. L. et al. Safety, activity, and immune correlates of anti-PD-1 antibody in cancer. N. Engl. J. Med. 366, 2443–2454 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Hamid, O. et al. Safety and tumor responses with lambrolizumab (anti-PD-1) in melanoma. N. Engl. J. Med. 369, 134–144 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Zou, W. Mechanistic insights into cancer immunity and immunotherapy. Cell. Mol. Immunol. 15, 419–420 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Barry, K. C. et al. A natural killer–dendritic cell axis defines checkpoint therapy-responsive tumor microenvironments. Nat. Med. 24, 1178–1191 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Zheng, X. et al. Mitochondrial fragmentation limits NK cell-based tumor immunosurveillance. Nat. Immunol. 20, 1656–1667 (2019).

    Article  CAS  PubMed  Google Scholar 

  15. Ghesquière, B., Wong, B. W., Kuchnio, A. & Carmeliet, P. Metabolism of stromal and immune cells in health and disease. Nature 511, 167–176 (2014).

    Article  PubMed  Google Scholar 

  16. Morvan, M. G. & Lanier, L. L. NK cells and cancer: you can teach innate cells new tricks. Nat. Rev. Cancer 16, 7–19 (2016).

    Article  CAS  PubMed  Google Scholar 

  17. O’Brien, K. L. & Finlay, D. K. Immunometabolism and natural killer cell responses. Nat. Rev. Immunol. 19, 282–290 (2019).

    Article  PubMed  Google Scholar 

  18. Orange, J. S. Formation and function of the lytic NK-cell immunological synapse. Nat. Rev. Immunol. 8, 713–725 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Davis, D. M. et al. The human natural killer cell immune synapse. Proc. Natl Acad. Sci. USA 96, 15062–15067 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Williams, G. S. et al. Membranous structures transfer cell surface proteins across NK cell immune synapses. Traffic 8, 1190–1204 (2007).

    Article  CAS  PubMed  Google Scholar 

  21. McCann, F. E. et al. The size of the synaptic cleft and distinct distributions of filamentous actin, ezrin, CD43, and CD45 at activating and inhibitory human NK cell immune synapses. J. Immunol. 170, 2862–2870 (2003).

    Article  CAS  PubMed  Google Scholar 

  22. Mattaini, K. R., Sullivan, M. R. & Vander Heiden, M. G. The importance of serine metabolism in cancer. J. Cell Biol. 214, 249–257 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Herz, J. et al. Acid sphingomyelinase is a key regulator of cytotoxic granule secretion by primary T lymphocytes. Nat. Immunol. 10, 761–768 (2009).

    Article  CAS  PubMed  Google Scholar 

  24. Jung, Y. et al. Three-dimensional localization of T-cell receptors in relation to microvilli using a combination of superresolution microscopies. Proc. Natl Acad. Sci. USA 113, E5916–E5924 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Cai, E. et al. Visualizing dynamic microvillar search and stabilization during ligand detection by T cells. Science 356, eaal3118 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  26. Yi, J. C. & Samelson, L. E. Microvilli set the stage for T-cell activation. Proc. Natl Acad. Sci. USA 113, 11061–11062 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Kim, H.-R. et al. T cell microvilli constitute immunological synaptosomes that carry messages to antigen-presenting cells. Nat. Commun. 9, 3630 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  28. Majstoravich, S. et al. Lymphocyte microvilli are dynamic, actin-dependent structures that do not require Wiskott-Aldrich syndrome protein (WASp) for their morphology. Blood 104, 1396–1403 (2004).

    Article  CAS  PubMed  Google Scholar 

  29. Pettmann, J., Santos, A. M., Dushek, O. & Davis, S. J. Membrane ultrastructure and T cell activation. Front. Immunol. 9, 2152 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  30. Razvag, Y., Neve-Oz, Y., Sajman, J., Reches, M. & Sherman, E. Nanoscale kinetic segregation of TCR and CD45 in engaged microvilli facilitates early T cell activation. Nat. Commun. 9, 732 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  31. Fisher, P. J., Bulur, P. A., Vuk-Pavlovic, S., Prendergast, F. G. & Dietz, A. B. Dendritic cell microvilli: a novel membrane structure associated with the multifocal synapse and T-cell clustering. Blood 112, 5037–5045 (2008).

    Article  CAS  PubMed  Google Scholar 

  32. Sivori, S. et al. Human NK cells: surface receptors, inhibitory checkpoints, and translational applications. Cell. Mol. Immunol. 16, 430–441 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Habif, G., Crinier, A., André, P., Vivier, E. & Narni-Mancinelli, E. Targeting natural killer cells in solid tumors. Cell. Mol. Immunol. 16, 415–422 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Kumari, S. et al. Actin foci facilitate activation of the phospholipase C-γ in primary T lymphocytes via the WASP pathway. eLife 4, e04953 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  35. Zhu, H. et al. Single-neuron identification of chemical constituents, physiological changes, and metabolism using mass spectrometry. Proc. Natl Acad. Sci. USA 114, 2586–2591 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Zhu, H. et al. Moderate UV exposure enhances learning and memory by promoting a novel glutamate biosynthetic pathway in the brain. Cell 173, 1716–1727.e1717 (2018).

    Article  CAS  PubMed  Google Scholar 

  37. Zhuang, M. H., Hou, Z. H., Chen, P. Y., Liang, G. L. & Huang, G. M. Introducing charge tag via click reaction in living cells for single cell mass spectrometry. Chem. Sci. 11, 7308–7312 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Allen, F., Greiner, R. & Wishart, D. Competitive fragmentation modeling of ESI-MS/MS spectra for putative metabolite identification. Metabolomics 11, 98–110 (2015).

    Article  CAS  Google Scholar 

  39. Wishart, D. S. et al. HMDB 4.0: the human metabolome database for 2018. Nucleic Acids Res. 46, D608–D617 (2018).

    Article  CAS  PubMed  Google Scholar 

  40. Song, J. W. et al. Omics-driven systems interrogation of metabolic dysregulation in COVID-19 pathogenesis. Cell Metab. 32, 188–202.e185 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Lee, S. M. et al. FABP3-mediated membrane lipid saturation alters fluidity and induces ER stress in skeletal muscle with aging. Nat. Commun. 11, 5661 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Tschaikowsky, K., Meisner, M., Schönhuber, F. & Rügheimer, E. Induction of nitric oxide synthase activity in phagocytic cells inhibited by tricyclodecan-9-yl-xanthogenate (D609). Br. J. Pharmacol. 113, 664–668 (1994).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Taniguchi, M. & Okazaki, T. The role of sphingomyelin and sphingomyelin synthases in cell death, proliferation and migration—from cell and animal models to human disorders. Biochim. Biophys. Acta 1841, 692–703 (2014).

    Article  CAS  PubMed  Google Scholar 

  44. Huitema, K., van den Dikkenberg, J., Brouwers, J. F. & Holthuis, J. C. Identification of a family of animal sphingomyelin synthases. EMBO J. 23, 33–44 (2004).

    Article  CAS  PubMed  Google Scholar 

  45. Luberto, C. et al. Inhibition of tumor necrosis factor-induced cell death in MCF7 by a novel inhibitor of neutral sphingomyelinase. J. Biol. Chem. 277, 41128–41139 (2002).

    Article  CAS  PubMed  Google Scholar 

  46. Bai, A. et al. Targeting (cellular) lysosomal acid ceramidase by B13: design, synthesis and evaluation of novel DMG-B13 ester prodrugs. Bioorg. Med. Chem. 22, 6933–6944 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Konjević, G. M., Vuletić, A. M., Mirjačić Martinović, K. M., Larsen, A. K. & Jurišić, V. B. The role of cytokines in the regulation of NK cells in the tumor environment. Cytokine 117, 30–40 (2019).

    Article  PubMed  Google Scholar 

  48. Ndhlovu, L. C. et al. Tim-3 marks human natural killer cell maturation and suppresses cell-mediated cytotoxicity. Blood 119, 3734–3743 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. da Silva, I. P. et al. Reversal of NK-cell exhaustion in advanced melanoma by Tim-3 blockade. Cancer Immunol. Res. 2, 410–422 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  50. Stinchcombe, J. C., Majorovits, E., Bossi, G., Fuller, S. & Griffiths, G. M. Centrosome polarization delivers secretory granules to the immunological synapse. Nature 443, 462–465 (2006).

    Article  CAS  PubMed  Google Scholar 

  51. Choudhuri, K. et al. Polarized release of T-cell-receptor-enriched microvesicles at the immunological synapse. Nature 507, 118–123 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Suttmann, H. et al. Antimicrobial peptides of the Cecropin-family show potent antitumor activity against bladder cancer cells. BMC Urol. 8, 5 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  53. Gao, Q. et al. Cell culture system for analysis of genetic heterogeneity within hepatocellular carcinomas and response to pharmacologic agents. Gastroenterology 152, 232–242.e234 (2017).

    Article  CAS  PubMed  Google Scholar 

  54. Pietras, A. Cancer stem cells in tumor heterogeneity. Adv. Cancer Res. 112, 255–281 (2011).

    Article  CAS  PubMed  Google Scholar 

  55. Sage, P. T. et al. Antigen recognition is facilitated by invadosome-like protrusions formed by memory/effector T cells. J. Immunol. 188, 3686–3699 (2012).

    Article  CAS  PubMed  Google Scholar 

  56. Beyersdorf, N. & Müller, N. Sphingomyelin breakdown in T cells: role in activation, effector functions and immunoregulation. Biol. Chem. 396, 749–758 (2015).

    Article  CAS  PubMed  Google Scholar 

  57. Di Pietro, A. et al. Targeting BMI-1 in B cells restores effective humoral immune responses and controls chronic viral infection. Nat. Immunol. 23, 86–98 (2022).

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

This work was supported by the Natural Science Foundation of China (grant reference nos. 82122055, 82241216, 81872318 and 22025405), the National Key Research and Development Program of China (grant no. 2019YFA0405603), the CAMS Innovation Fund for Medical Sciences (CIFMS; grant no. 2019-I2M-5-073) and the Strategic Priority Research Program of the Chinese Academy of Sciences (grant nos. XDB04050200, XDPB1002 and XDB39020700).

Author information

Author notes

  1. These authors contributed equally: Xiaohu Zheng, Zhuanghao Hou.

Authors and Affiliations

  1. Hefei National Research Center for Physical Sciences at the Microscale, CAS Key Laboratory of Innate Immunity and Chronic Disease, School of Basic Medical Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China

    Xiaohu Zheng, Xuben Wang, Yiqing Shen, Yonggang Zhou, Binqing Fu, Rui Sun, Zhigang Tian & Haiming Wei

  2. Institute of Immunology, Biomedical Sciences and Health Laboratory of Anhui Province, University of Science and Technology of China, Hefei, China

    Xiaohu Zheng, Xuben Wang, Yiqing Shen, Yonggang Zhou, Binqing Fu, Rui Sun, Zhigang Tian & Haiming Wei

  3. The First Affiliated Hospital of USTC, University of Science and Technology of China, Hefei, China

    Xiaohu Zheng, Zhuanghao Hou, Guangming Huang & Haiming Wei

  4. School of Chemistry and Materials Science and National Synchrotron Radiation Laboratory, University of Science and Technology of China, Hefei, China

    Zhuanghao Hou & Guangming Huang

  5. Department of General Surgery, First Affiliated Hospital of Anhui Medical University, Hefei, China

    Yeben Qian, Yongwei Zhang & Quanwei Cui

  6. Core Facility Center for Life Sciences, University of Science and Technology of China, Hefei, China

    Zhenbang Liu

  7. Research Unit Of NK Cells, Chinese Academy Of Medical Sciences, Hefei, China

    Zhigang Tian & Haiming Wei

Authors
  1. Xiaohu Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhuanghao Hou
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yeben Qian
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yongwei Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Quanwei Cui
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Xuben Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Yiqing Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Zhenbang Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Yonggang Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Binqing Fu
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Rui Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Zhigang Tian
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Guangming Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Haiming Wei
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

H.W., G.H. and Z.T. conceived and conducted the project. H.W., X.Z. and G.H. supervised the project. X.Z. and Z.H. wrote the paper. X.Z. and Z.H. performed the experiments and data analysis. X.Z. contributed to mouse models and cell culture. Y.Q., Y.S., Q.C. and Y.W.Z. collected tissue samples and information from patients. R.S., Y.G.Z, Z.L., X.W. and B.F. contributed to imaging analysis and interpreted the data.

Corresponding authors

Correspondence to Xiaohu Zheng, Zhigang Tian, Guangming Huang or Haiming Wei.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Immunology thanks Thomas Braun, David Finlay, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available. N. Bernard was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Additional information

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

Extended data

Extended Data Fig. 1 Imaging of membrane protrusions of NK cells.

a, Gating strategy of NK cells for flow cytometry. b, Representative density plots show the purity of NK cells. c–d, Related to Fig. 1a–g, The mean diameter of membrane protrusions (c, TEM; d, SEM) in each sample is shown. Each dot represents one sample. n = 10 in each group. e–g, CLSM shows the membrane protrusions of purified NK cells. e, Representative 2D CLSM images (up) and 3D resultant ‘cartoon’ images (down). Scale bar, 1 μm. f–g, The mean number of membrane protrusions and mean length of membrane protrusions (2D CLSM) in each sample are shown. Each dot represents one sample. n = 8 in each group. Data are the mean ± s.d. Data were analysed by two-way ANOVA.

Source data

Extended Data Fig. 2 SEM showing the membrane protrusions of intratumoral NK cells from multiple cancers.

a–c, SEM showing the membrane protrusions of the NK cells purified from resected tumors and outside the tumor of lung-cancer, colon-cancer, and ovarian-cancer patients. The mean number of membrane protrusions and the mean length of membrane protrusions in each sample are shown. Each dot represents one sample. n = 5 in each group. Data are the mean ± s.d. Scale bar, 1 μm. Data were analysed by two-tailed, unpaired Student’s t-tests.

Source data

Extended Data Fig. 3 Analyses of binding and immunological synapses of human liver-cancer cells and NK cells.

a, SEM showing the immunological synapses of human liver-cancer cells and NK cells. Primary NK cells purified from resected tumors of liver-cancer patients, of liver NK cells from outside the tumor of these liver-cancer patients, and of NK cells isolated from peripheral blood of normal donors and were co-cultured with liver-cancer cells (HuH7 cell line) for 1, 2 and 4 h. The large cells on the left represent HuH7 cells. The small cells on the right represent NK cells. Scale bar, 0.5 μm. Data are representative of 9 samples. b, (related to Fig. 2c), TEM showing the interface of human liver-cancer cells and NK cells at low magnification. NK cells freshly purified from liver cancer patients (tumor and liver tissue outside tumor) and normal donor peripheral blood and were co-cultured with HepG2 cells for 1, 2 or 4 h. The large cells are HepG2 cells; the small cells are NK cells. The red arrows guides the junction of human liver-cancer cells and NK cells. Scale bar, 1 μm. Data are representative of 5 samples in each group. c, Super resolution microscopy (Leica, STED) show the polymerized actin filaments (microclusters) of purified NK cells. Representative 3D images were shown. Scale bar, 1 μm. d, Fluorescence photobleaching recovery (FPR) was used to assess membrane fluidity (DiI membrane stain) based on CLSM imaging. Data are representative of 2 samples in each group. e, CLSM showing the binding of human liver-cancer cells and NK cells. NK cells freshly purified from liver cancer patients (tumor and liver tissue outside tumor) and normal donor peripheral blood and were co-cultured with HepG2 (liver cancer cell line) cells. The large cells on the left are HepG2 cells; the small cells on the right are NK cells. Green, F-actin staining. Scale bar, 0.5 μm. f, K562 cells were used as target cells in cytotoxicity assays with NK cells isolated from liver cancer patients (tumor and liver tissue outside tumor) and normal donor peripheral blood. % target cell death presents the percent of dead target cells among total target cells (7AAD + CFSE + ) assessed by flow cytometry. n = 3 samples per group. g–h, CLSM showing GZMB and CD107a stain in NK cells. NK cells freshly purified from liver cancer patients (tumor and liver tissue outside tumor) and normal donor peripheral blood and were co-cultured with HepG2 (liver cancer cell line) cells. Scale bar, 0.5 μm. Data are the mean ± s.d. Data were analyzed by two-way ANOVA.

Source data

Extended Data Fig. 4 MS/MS assays sphingomyelins of NK cells.

a–f, Representative MS/MS spectra for membrane sphingomyelins (a, SM(d34:1); b, SM(d38:0); c, SM(d36:1); d, SM(d36:0); e, SM(d42:2); f, SM(d43:2)) obtained from NK-cell lysates. Fragment ions correspond to relative neutral loss from the headgroup of SMs are marked in red. g, The p values for Fig. 3F. Data are the mean ± s.d. Data were analysed by two-way ANOVA.

Extended Data Fig. 5 Detection of the components of sphingolipin in NK cells by sIC-MS and bulk LC-MS.

a, sIC-MS and bulk LC-MS in detecting standard of sphingomyelin (SM(d34:1) and SM(d36:1)). The range of concentration from 0.2 to 50 mg/L. With sIC-MS, for the standard curve of SM(d34:1), the slope was 2.021 × 10−2, R2 = 0.9925 and the standard curve of SM(d36:1)), the slope was 2.012 × 10−2, R2 = 0.9977. With bulk LC-MS, for the standard curve of SM(d34:1), the slope was 1.977 × 10−2, R2 = 0.9981. And the standard curve of SM(d36:1)), the slope was 1.979 × 10−2, R2 = 0.9923. b, LC-MS was used to analyse the SM content of purified peripheral NK cells from healthy donors. Freshly purified NK cells were treated with D609 (25, 100, 400 μM) or PBS (control) for 24 h. n = 5 samples per group. Data are the mean ± s.d. Data were analysed by two-way ANOVA.

Source data

Extended Data Fig. 6 The phenotype of NK cells after inhibition of sphingomyelin synthesis.

a, CLSM showing the binding of human liver-cancer cells and NK cells. NK cells freshly purified from normal donor peripheral blood and were co-cultured with HepG2 (liver cancer cell line) cells. Peripheral NK cells were treated with or without the small molecule sphingomyelin synthase inhibitor D609 (25, 100, 400 μM) or PBS (control) for 24 h. The large cells on the left are HepG2 cells; the small cells on the right are NK cells. Green, F-actin staining. Scale bar, 0.5 μm. b–c, Flow cytometry showing apoptosis (annexin-V+) in NK cells after treatment with D609 (25, 100, 400 μM) or PBS (control) for 24 h or knockdown of sphingomyelin synthase 1 (si-SGMS1). n = 5 samples per group. c (left), Western blotting analysis of SGMS1 expression in primary NK cells isolated from normal donor peripheral blood. The siRNA-SGMS1 and siRNA-mock were transfected into peripheral NK cells for 24 h until analysis. d, Flow cytometry assaying proliferation (Ki67+) in NK cells after treatment with D609 (25, 100, 400 μM) or PBS (control) for 24 h or knockdown of sphingomyelin synthase 1 (si-SGMS1). n = 5 samples per group. e–f, Flow cytometry assay of the percentage of IFN-γ, perforin, and granzyme-B (GZMB) producing cells among IL-12+IL-18 or PBS (Control) stimulated NK cells from normal donor peripheral blood. The MFI of IFN-γ in IL-12+IL-18-stimulated IFN-γ+ NK cells, perforin in IL-12+IL-18-stimulated perforin+ NK cells, and GZMB in IL-12+IL-18-stimulated GZMB+ NK cells are presented as the mean ± s.d.. Peripheral NK cells pre-treated with D609 (25, 100, 400 μM) or PBS (control) for 24 h before IL-12 and IL-18 stimulation. n = 5 samples per group. N.D., not detected. g, FACS assay of CD107a levels in peripheral NK cells. Peripheral NK cells were treated with D609 or PBS for 24 h. Then, NK cells were co-cultured (Co-cultured group) with or without (Control, single-cultured group) HepG cells before FACS assay. n = 5 donor samples per group. h, i (related to Fig. 5m and n) HepG2 cells and (J) K562 cells were used as target cells in cytotoxicity assays with indicated NK cells. % cytolysis is shown (n = 5 per group). h, j, Purified NK cells were co-cultured with HepG2 cells or K562 cells for cytotoxicity assays after treatment with the small molecule sphingomyelin synthase inhibitor D609 (25, 100, 400 μM) or PBS (control) for 24 h. i, Purified NK cells were co-cultured with HepG2 cells for cytotoxicity assays after knockdown of sphingomyelin synthase 1 (si-SGMS1) or control (si-mock) for 24 h. j, n = 3 per group. k, The ADCC (antibody-dependent cell-mediated cytotoxicity) activity assays of NK cells. Target cells, HepG2 cells (An EpCAM+ liver cancer cell line); Ab, humanized antibodies against EpCAM; peripheral NK cells pre-treated with D609 (25, 100, 400 μM) or PBS (control) for 24 h. n = 3 samples. l, SEM images showing the membrane protrusions of purified NK cells. Representative SEM imaging (left) and the mean number of membrane protrusions (right) in each sample are shown. NK cells were treated with IL-2, IL-2 + IL-15, or IL-2 + IL-15 + IL-12. Each dot represents one sample. n = 6 in each group. Scale bar, 1 μm. m, Fresh NK cells isolated from liver cancer patients (tumor and liver tissue outside tumor) and normal donor peripheral blood (n = 8 in each group). Peripheral NK cells were treated with or without the small molecule sphingomyelin synthase inhibitor D609 (25, 100, 400 μM) or PBS (control) for 24 h (n = 5 in each group). Using a Transwell™ apparatus, we tested the migration of the NK cells from the upper compartment to the lower compartment. Counting the numbers of NK cell migrating to the lower layer. n, Flow cytometry assay of CD29 and CD18 levels in intratumoral NK cells, liver NK cells and peripheral NK cells. n = 5 samples per group. Data are the mean ± s.d. Data were analysed by two-way ANOVA.

Source data

Extended Data Fig. 7 The potential relationships between cholesterol and sphingomyelin levels in NK cells.

a, CLSM showing the cholesterol signals for purified intratumoral NK cells, liver NK cells and peripheral NK cells by filipin III staining. Scale bar, 1 μm. b–c, CLSM of filipin III stained purified peripheral NK cells staining (cyclodexrin treated or untreated (vehicle, PBS); D609 treated or untreated (vehicle, PBS) to assess cholesterol levels. sIC-MS was used to analyse the membrane SM content of purified peripheral NK cells (cyclodexrin treated or PBS). Scale bar, 1 μm. n = 9 in each group. d, SEM image showing the membrane protrusions of purified peripheral NK cells. Peripheral NK cells were treated with cyclodextrin. Each dot represents one sample. n = 5 samples per group. Scale bar, 1 μm. Data are the mean ± s.d. Data were analyzed by two-tailed unpaired Student’s t-test. e, Purified peripheral NK cells were co-cultured with HepG2 cells for cytotoxicity assays after treatment of cyclodextrin. n = 4 samples per group. f, Single-cell MS was employed to analyze the cholesterol in intratumoral NK cells, peripheral NK cells, and liver NK cells. n = 5 samples per group. Data are the mean ± s.d. Data were analyzed by two-tailed unpaired Student’s t-test.

Source data

Extended Data Fig. 8 The phenotype of intratumoral NK cells after blockade of SM catabolism.

a, SEM image showing the membrane protrusions of purified intratumoral NK cells from liver cancer patients. Intratumoral NK cells were subjected to knockdown of an acidic SMase (si-ASM), of neutral SMases (si-NSMASE1/2/3), or control (si-mock). Each dot represents one patient sample. n = 5 samples. Scale bar, 1 μm. b–e, SEM image showing the membrane protrusions of purified peripheral and liver NK cells. Representative SEM imaging is presented. The mean number of membrane protrusions and the mean length of membrane protrusions in each sample are shown. Each dot represents one sample. n = 5 samples. Scale bar, 1 μm. f, Flow cytometry assaying proliferation (Ki67+) and apoptosis (annexin-V+) of intratumoral NK cells. g, Flow cytometry assaying the expression of IFN-γ, granzyme-B (GZMB), CD107a and perforin in intratumoral NK cells. n = 5 per group. f–h, Intratumoral NK cells were treated with GW4869 (1 μM), LCL521 (1 μM), or DMSO (control) for 24 h. h–i, (Related to Fig. 6g and i) human liver-cancer cells (HepG2) were employed as targets in cytotoxicity assays with indicated intratumoral cells by real-time Cell Index measurements (xCELLigence). % cytolysis is shown (n = 6 per group). Intratumoral NK cells cells were co-cultured with liver-cancer cells for cytotoxicity assays after treatment with GW4869 (1 μM) + anti-Tim3-blocking antibody (10 μg/mL), LCL521 (1 μM)+anti-Tim3-blocking antibody (10 μg/mL), GW4869 (1 μM), LCL521 (1 μM), anti-Tim3-blocking antibody (10 μg/mL), or DMSO (control) for 24 h. Data are the mean ± s.d. Data were analysed by two-way ANOVA.

Source data

Extended Data Fig. 9 Analyses of the membrane protrusions of mouse liver cancer NK cells.

a–f, A genetically engineered mouse model of liver cancer developed with weakened PTEN signaling and with an activated K-RAS signal. a, Images of the liver from liver cancer model and normal mice. b, Representative density plots showing the purity of sorted mouse NK cells. c, SEM showing the membrane protrusions of the mouse NK cells from normal liver, spleen, and liver cancer. Representative SEM imaging (left). The mean number of membrane protrusions and the mean length of membrane protrusions (SEM) in each sample are shown (middle and right). n = 5 per group. d, SEM showing the membrane protrusions of the mouse NK cells from liver cancer. Freshly purified NK cells were treated with sphingomyelinase inhibitors (GW4869; 1 μM) or DMSO (control) for 24 h. n = 5 per group. e, Mouse liver-cancer cells (K-RASG12DPTEN−/− primary liver caner cell and Hep1-6 cell line) were employed as targets in cytotoxicity assays with the indicated NK cells, assessed with real-time Cell Index measurements (xCELLigence). % cytolysis is shown. After treatment with GW4869 (1 μM), or DMSO (control) for 24 h, liver cancer NK cells were co-cultured with target cells for cytotoxicity assays. n = 4 per group. f, Two mouse liver cancer models were established by subcutaneous injection of liver cancer cells (K-RASG12DPTEN−/− primary liver caner cell and Hep1-6 cell line). Freshly purified normal spleen NK cells were treated with D609 or PBS (control) for 24 h, followed by treated with GW4869 (1 μM), or DMSO (control) for 24 h. n = 6 per group. g, Single-cell MS was employed to analyse membrane sphingomyelins (SMs) of purified mouse NK cells from normal liver, spleen, and liver cancer. Normalized intensities of membrane SMs are shown. n = 5 in each group. h, SEM showing the membrane protrusions of the mouse NK cells from normal spleen. Freshly purified NK cells were treated with D609 (25, 100, 400 μM) or PBS (control) for 24 h. Each dot represents one mouse. n = 5 in each group. Data are the mean ± s.d. Data were analysed by two-way ANOVA. Scale bar, 1 μm.

Source data

Supplementary information

Supplementary Information

Captions for supplementary videos 1–7 and tables 1–5.

Reporting Summary

Peer Review File

Dynamical imaging for coincubation with intratumoral NK cells and HepG2 cells.

Dynamical imaging for coincubation with liver NK cells and HepG2 cells.

Dynamical imaging for coincubation with peripheral NK cells and HepG2 cells.

Dynamical imaging for coincubation with NK (PBS) cells and HepG2 cells.

Dynamical imaging for coincubation with NK (25 μM D609) cells and HepG2 cells.

Dynamical imaging for coincubation with NK (100 μM D609) cells and HepG2 cells.

Dynamical imaging for coincubation with NK (400 μM D609) cells and HepG2 cells.

Source data

Source Data Fig. 1

Statistical source data.

Source Data Fig. 2

Statistical source data.

Source Data Fig. 3

Statistical source data.

Source Data Fig. 4

Statistical source data.

Source Data Fig. 5

Statistical source data.

Source Data Fig. 6

Statistical source data.

Source Data Extended Data Fig. 1

Statistical source data.

Source Data Extended Data Fig. 2

Statistical source data.

Source Data Extended Data Fig. 3

Statistical source data.

Source Data Extended Data Fig. 5

Statistical source data.

Source Data Extended Data Fig. 6

Statistical source data.

Source Data Extended Data Fig. 6

Unprocessed western blots.

Source Data Extended Data Fig. 7

Statistical source data.

Source Data Extended Data Fig. 8

Statistical source data.

Source Data Extended Data Fig. 9

Statistical source data.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zheng, X., Hou, Z., Qian, Y. et al. Tumors evade immune cytotoxicity by altering the surface topology of NK cells. Nat Immunol 24, 802–813 (2023). https://doi.org/10.1038/s41590-023-01462-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41590-023-01462-9

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing