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A natural killer–dendritic cell axis defines checkpoint therapy–responsive tumor microenvironments


Intratumoral stimulatory dendritic cells (SDCs) play an important role in stimulating cytotoxic T cells and driving immune responses against cancer. Understanding the mechanisms that regulate their abundance in the tumor microenvironment (TME) could unveil new therapeutic opportunities. We find that in human melanoma, SDC abundance is associated with intratumoral expression of the gene encoding the cytokine FLT3LG. FLT3LG is predominantly produced by lymphocytes, notably natural killer (NK) cells in mouse and human tumors. NK cells stably form conjugates with SDCs in the mouse TME, and genetic and cellular ablation of NK cells in mice demonstrates their importance in positively regulating SDC abundance in tumor through production of FLT3L. Although anti-PD-1 ‘checkpoint’ immunotherapy for cancer largely targets T cells, we find that NK cell frequency correlates with protective SDCs in human cancers, with patient responsiveness to anti-PD-1 immunotherapy, and with increased overall survival. Our studies reveal that innate immune SDCs and NK cells cluster together as an excellent prognostic tool for T cell–directed immunotherapy and that these innate cells are necessary for enhanced T cell tumor responses, suggesting this axis as a target for new therapies.

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Fig. 1: BDCA-3+ DCs define overall outcome in melanoma patients and predict responsiveness to anti-PD-1 immunotherapy.
Fig. 2: Tumor-resident lymphocytes produce FLT3L.
Fig. 3: FLT3L production by NK cells controls the levels of CD103+ SDCs in tumor.
Fig. 4: NK cells make frequent, stable interactions with XCR1+ DCs and provide prosurvival signals.
Fig. 5: BDCA-3+ DC levels correlate with levels of NK cells in the human melanoma TME.
Fig. 6: NK cell and BDCA-3+ DC levels uniquely correlate with anti-PD-1 responsiveness in patients with melanoma.


  1. 1.

    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).

    CAS  PubMed  PubMed Central  Google Scholar 

  2. 2.

    Rizvi, N. A. et al. Mutational landscape determines sensitivity to PD-1 blockade in non–small cell lung cancer. Science 348, 124–128 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. 3.

    Broz, M. L. et al. Dissecting the tumor myeloid compartment reveals rare activating antigen-presenting cells critical for T cell immunity. Cancer Cell. 26, 638–652 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. 4.

    Salmon, H. et al. Expansion and activation of CD103+ dendritic cell progenitors at the tumor site enhances tumor responses to therapeutic PD-L1 and BRAF inhibition. Immunity 44, 924–938 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. 5.

    Sánchez-Paulete, A. R. et al. Cancer immunotherapy with immunomodulatory anti-CD137 and ant-PD-1 monoclonal antibodies requires BATF3-dependent dendritic cells. Cancer Discov. 6, 71–79 (2016).

    PubMed  Google Scholar 

  6. 6.

    Hildner, K. et al. Batf3 deficiency reveals a critical role for CD8α+ dendritic cells in cytotoxic T cell immunity. Science 322, 1097–1100 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. 7.

    Spranger, S., Dai, D., Horton, B. & Gajewski, T. F. Tumor-residing Batf3 dendritic cells are required for effector t cell trafficking and adoptive T cell therapy. Cancer Cell. 31, 711–723.e4 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. 8.

    Lavin, Y. et al. Innate immune landscape in early lung adenocarcinoma by paired single-cell analyses. Cell 169, 750–765.e17 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. 9.

    Spranger, S., Bao, R. & Gajewski, T. F. Melanoma-intrinsic β-catenin signalling prevents anti-tumor immunity. Nature 523, 231–235 (2015).

    CAS  PubMed  Google Scholar 

  10. 10.

    Bogunovic, D. et al. Immune profile and mitotic index of metastatic melanoma lesions enhance clinical staging in predicting patient survival. Proc. Natl. Acad. Sci. USA 106, 20429–20434 (2009).

    CAS  PubMed  Google Scholar 

  11. 11.

    Cancer Genome Atlas Network. Genomic classification of cutaneous melanoma. Cell 161, 1681–1696 (2015).

    Google Scholar 

  12. 12.

    Wei, S. C. et al. Distinct cellular mechanisms underlie anti-CTLA-4 and anti-PD-1 checkpoint blockade. Cell 170, 1120–1133.e17 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. 13.

    Liu, K. & Nussenzweig, M. C. Origin and development of dendritic cells. Immunol. Rev. 234, 45–54 (2010).

    CAS  PubMed  Google Scholar 

  14. 14.

    Engelhardt, J. J. et al. Marginating dendritic cells of the tumor microenvironment cross-present tumor antigens and stably engage tumor-specific T cells. Cancer Cell. 21, 402–417 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Böttcher, J. P. et al. NK cells stimulate recruitment of cDC1 into the tumor microenvironment promoting cancer immune control. Cell 172, 1022–1037.e14 (2018).

    PubMed  PubMed Central  Google Scholar 

  16. 16.

    Du, X. et al. Genomic profiles for human peripheral blood T cells, B cells, natural killer cells, monocytes, and polymorphonuclear cells: comparisons to ischemic stroke, migraine, and Tourette syndrome. Genomics 87, 693–703 (2006).

    CAS  PubMed  Google Scholar 

  17. 17.

    Bezman, N. A. et al. Molecular definition of the identity and activation of natural killer cells. Nat. Immunol. 13, 1000–1009 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Loo, K. et al. Partially exhausted tumor-infiltrating lymphocytes predict response to combination immunotherapy. JCI Insight 2, 93433 (2017).

    PubMed  Google Scholar 

  19. 19.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  20. 20.

    Krieg, C. et al. High-dimensional single-cell analysis predicts response to anti-PD-1 immunotherapy. Nat. Med. 24, 144–153 (2018).

    CAS  PubMed  Google Scholar 

  21. 21.

    Zitvogel, L. Dendritic and natural killer cells cooperate in the control/switch of innate immunity. J. Exp. Med. 195, F9–F14 (2002).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. 22.

    Fernandez, N. C. et al. Dendritic cells directly trigger NK cell functions: cross-talk relevant in innate anti-tumor immune responses in vivo. Nat. Med. 5, 405–411 (1999).

    CAS  PubMed  Google Scholar 

  23. 23.

    Solanilla, A. et al. Expression of Flt3-ligand by the endothelial cell. Leukemia 14, 153–162 (2000).

    CAS  PubMed  Google Scholar 

  24. 24.

    Miloud, T., Fiegler, N., Suffner, J., Hämmerling, G. J. & Garbi, N. Organ-specific cellular requirements for in vivo dendritic cell generation. J. Immunol. 188, 1125–1135 (2012).

    CAS  PubMed  Google Scholar 

  25. 25.

    Goding, S. R., Yu, S., Bailey, L. M., Lotze, M. T. & Basse, P. H. Adoptive transfer of natural killer cells promotes the anti-tumor efficacy of T cells. Clin. Immunol. 177, 76–86 (2017).

    CAS  PubMed  Google Scholar 

  26. 26.

    Blake, S. J. et al. Suppression of metastases using a new lymphocyte checkpoint target for cancer immunotherapy. Cancer Discov. 6, 446–459 (2016).

    CAS  PubMed  Google Scholar 

  27. 27.

    Dougall, W. C., Kurtulus, S., Smyth, M. J. & Anderson, A. C. TIGIT and CD96: new checkpoint receptor targets for cancer immunotherapy. Immunol. Rev. 276, 112–120 (2017).

    CAS  PubMed  Google Scholar 

  28. 28.

    Cheng, P. F., Dummer, R. & Levesque, M. P. Data mining The Cancer Genome Atlas in the era of precision cancer medicine. Swiss Med. Wkly. 145, w14183 (2015).

    PubMed  Google Scholar 

  29. 29.

    Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B Stat. Methodol. 57, 289–300 (1995).

    Google Scholar 

  30. 30.

    Wu, C., Jin, X., Tsueng, G., Afrasiabi, C. & Su, A. I. BioGPS: building your own mash-up of gene annotations and expression profiles. Nucleic Acids Res. 44 D1, D313–D316 (2016).

  31. 31.

    Ruffell, B. et al. Leukocyte composition of human breast cancer. Proc. Natl. Acad. Sci. USA 109, 2796–2801 (2012).

    CAS  PubMed  Google Scholar 

  32. 32.

    Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. 33.

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

    CAS  Google Scholar 

  34. 34.

    Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. 35.

    McKenna, H. J. et al. Mice lacking flt3 ligand have deficient hematopoiesis affecting hematopoietic progenitor cells, dendritic cells, and natural killer cells. Blood 95, 3489–3497 (2000).

    CAS  PubMed  Google Scholar 

  36. 36.

    Cao, X. et al. Defective lymphoid development in mice lacking expression of the common cytokine receptor γ chain. Immunity 2, 223–238 (1995).

    CAS  Google Scholar 

  37. 37.

    Gazit, R. et al. Lethal influenza infection in the absence of the natural killer cell receptor gene Ncr1. Nat. Immunol. 7, 517–523 (2006).

    CAS  PubMed  Google Scholar 

  38. 38.

    Hadjantonakis, A.-K., Macmaster, S. & Nagy, A. Embryonic stem cells and mice expressing different GFP variants for multiple non-invasive reporter usage within a single animal. BMC Biotechnol. 2, 11 (2002).

    PubMed  PubMed Central  Google Scholar 

  39. 39.

    Hogquist, K. A. et al. T cell receptor antagonist peptides induce positive selection. Cell 76, 17–27 (1994).

    CAS  Google Scholar 

  40. 40.

    Yamazaki, C. et al. Critical roles of a dendritic cell subset expressing a chemokine receptor, XCR1. J. Immunol. 190, 6071–6082 (2013).

    CAS  PubMed  Google Scholar 

  41. 41.

    Khanna, K. M. et al. T cell and APC dynamics in situ control the outcome of vaccination. J. Immunol. 185, 239–252 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. 42.

    Fidler, I. J. Biological behavior of malignant melanoma cells correlated to their survival in vivo. Cancer Res. 35, 218–224 (1975).

    CAS  PubMed  Google Scholar 

  43. 43.

    Graf, L. H. Jr., Kaplan, P. & Silagi, S. Efficient DNA-mediated transfer of selectable genes and unselected sequences into differentiated and undifferentiated mouse melanoma clones. Somat. Cell. Mol. Genet. 10, 139–151 (1984).

    CAS  PubMed  Google Scholar 

  44. 44.

    Corbett, T. H., Griswold, D. P. Jr., Roberts, B. J., Peckham, J. C. & Schabel, F. M. Jr. Tumor induction relationships in development of transplantable cancers of the colon in mice for chemotherapy assays, with a note on carcinogen structure. Cancer Res. 35, 2434–2439 (1975).

    CAS  PubMed  Google Scholar 

  45. 45.

    Pinkard, H., Stuurman, N., Corbin, K., Vale, R. & Krummel, M. F. Micro-Magellan: open-source, sample-adaptive, acquisition software for optical microscopy. Nat. Methods 13, 807–809 (2016).

    CAS  PubMed  Google Scholar 

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We thank L. Lanier, J. Roose and L. Fong for advice, and we thank M. Spasic and N. Khurana for support with response data. This work was supported by National Institutes of Health (NIH) grant R01CA197363, awarded to M.F.K. Acquisition and processing of human melanoma samples in cohort A described in this study was funded in part by contributions from AbbVie, Amgen, and Bristol-Myers Squibb as members of the Immunoprofiler Consortium. Further support came from NIH grant 5P30CA082103, awarded to the University of California, San Francisco (UCSF) Hellen Diller Family Comprehensive Cancer Center. M.B. was supported by the Genentech Predoctoral Research Fellowship, the Margaret A. Cunningham Immune Mechanisms in Cancer Research Fellowship Award, and the Achievement Reward for College Scientists Scholarship. K.C.B. was supported by a postdoctoral fellowship from the Cancer Research Institute and Fibrolamellar Cancer Foundation.

Author information




K.C.B. designed and performed the experiments and wrote and edited the manuscript. J.H. assisted in analysis of tumor-infiltrating myeloid populations and data analysis. M.L.B. designed and performed experiments with melanoma cohort B. F.J.C. assisted in imaging data analysis. M.B. assisted in analysis of tumor-infiltrating myeloid populations and T cell–depletion experiments. A.J.C. assisted in analysis of HNSCC tumor samples. R.K. and A.E.N. assisted in analysis of tumor-infiltrating myeloid populations. K.L. and A.I.D. provided human melanoma biopsies, clinical data, and edited the manuscript. M.D.R. read the manuscript and provided useful discussion. M.D.A. provided human melanoma biopsies and clinical data. D.B. and N.B. provided metastatic melanoma data, and D.B. edited the manuscript. D.M.W. performed statistical analyses. P.K.H and W.R.R provided human HNSCC samples. J.L.P., B.S., and S.A. provided bioinformatics analyses. V.C. managed sample collection, assisted in analysis of tumor-infiltrating myeloid populations, read the manuscript, and provided useful discussion. M.F.K. conceived the project and wrote and edited the manuscript.

Corresponding author

Correspondence to Matthew F. Krummel.

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Competing interests

J.L.P. was an employee at Pionyr Immunotherapeutics at the time of manuscript writing. The other authors declare no competing interests.

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Supplementary Text and Figures

Supplementary Figures 1–7 and Supplementary Tables 1–3

Reporting Summary

Supplementary Video 1

Stable interaction of NK cells and XCR1+ DCs

Supplementary Video 2

NK cells distant from XCR1+ DCs have increased motility

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

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