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Midkine rewires the melanoma microenvironment toward a tolerogenic and immune-resistant state

Nature Medicine volume 26, pages 1865–1877 (2020)Cite this article

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Abstract

An open question in aggressive cancers such as melanoma is how malignant cells can shift the immune system to pro-tumorigenic functions. Here we identify midkine (MDK) as a melanoma-secreted driver of an inflamed, but immune evasive, microenvironment that defines poor patient prognosis and resistance to immune checkpoint blockade. Mechanistically, MDK was found to control the transcriptome of melanoma cells, allowing for coordinated activation of nuclear factor-κB and downregulation of interferon-associated pathways. The resulting MDK-modulated secretome educated macrophages towards tolerant phenotypes that promoted CD8+ T cell dysfunction. In contrast, genetic targeting of MDK sensitized melanoma cells to anti-PD-1/anti-PD-L1 treatment. Emphasizing the translational relevance of these findings, the expression profile of MDK-depleted tumors was enriched in key indicators of a good response to immune checkpoint blockers in independent patient cohorts. Together, these data reveal that MDK acts as an internal modulator of autocrine and paracrine signals that maintain immune suppression in aggressive melanomas.

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Fig. 1: MDK-driven gene expression changes in melanoma.
Fig. 2: MDK favors intratumoral recruitment of myeloid cells with suppressive features.
Fig. 3: Signaling networks underlying immunomodulatory roles of MDK.
Fig. 4: MDK induces resistance to ICB in mice.
Fig. 5: MDK-educated macrophages drive T cell dysfunction.
Fig. 6: MDK correlates with immunotherapy resistance in patients with melanoma.

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Data availability

The WM164 GOF and SK-Mel-147 LOF RNA-seq data have been uploaded to the Gene Expression Omnibus repository with the dataset identifier GSE131203. The identifier for B16R2L RNA-seq is GSE150401. Mass spectrometry proteomics data were deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD013843. Source data are provided with this paper.

References

  1. Spranger, S. & Gajewski, T. F. Impact of oncogenic pathways on evasion of antitumour immune responses. Nat. Rev. Cancer 18, 139–147 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  2. Grivennikov, S. I., Greten, F. R. & Karin, M. Immunity, inflammation, and cancer. Cell 140, 883–899 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. Herrscher, H. & Robert, C. Immune checkpoint inhibitors in melanoma in the metastatic, neoadjuvant, and adjuvant setting. Curr. Opin. Oncol. 32, 106–113 (2020).

    CAS  PubMed  Google Scholar 

  4. Havel, J. J., Chowell, D. & Chan, T. A. The evolving landscape of biomarkers for checkpoint inhibitor immunotherapy. Nat. Rev. Cancer 19, 133–150 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. Liu, D. et al. Integrative molecular and clinical modeling of clinical outcomes to PD1 blockade in patients with metastatic melanoma. Nat. Med. 25, 1916–1927 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. Riaz, N. et al. Tumor and microenvironment evolution during immunotherapy with nivolumab. Cell 171, 934–949.e16 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Gide, T. N. et al. Distinct immune cell populations define response to anti-PD-1 monotherapy and anti-PD-1/anti-CTLA-4 combined therapy. Cancer Cell 35, 238–255.e6 (2019).

    CAS  PubMed  Google Scholar 

  8. Chen, P. L. et al. Analysis of immune signatures in longitudinal tumor samples yields insight into biomarkers of response and mechanisms of resistance to immune checkpoint blockade. Cancer Discov. 6, 827–837 (2016).

    PubMed  PubMed Central  Google Scholar 

  9. Snyder, A. et al. Genetic basis for clinical response to CTLA-4 blockade in melanoma. N. Engl. J. Med. 371, 2189–2199 (2014).

    PubMed  PubMed Central  Google Scholar 

  10. Jerby-Arnon, L. et al. A cancer cell program promotes T cell exclusion and resistance to checkpoint blockade. Cell 175, 984–997.e24 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Jiang, P. et al. Signatures of T cell dysfunction and exclusion predict cancer immunotherapy response. Nat. Med. 24, 1550–1558 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Neubert, N. et al. T cell-induced CSF1 promotes melanoma resistance to PD1 blockade. Sci. Transl. Med. 10, eaan3311 (2018).

    PubMed  PubMed Central  Google Scholar 

  13. Mantovani, A., Marchesi, F., Malesci, A., Laghi, L. & Allavena, P. Tumour-associated macrophages as treatment targets in oncology. Nat. Rev. Clin. Oncol. 14, 399–416 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Thommen, D. S. & Schumacher, T. N. T cell dysfunction in cancer. Cancer Cell 33, 547–562 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Olmeda, D. et al. Whole-body imaging of lymphovascular niches identifies pre-metastatic roles of midkine. Nature 546, 676–680 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Sorrelle, N., Dominguez, A. T. A. & Brekken, R. A. From top to bottom: midkine and pleiotrophin as emerging players in immune regulation. J. Leukoc. Biol. 102, 277–286 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Jones, D. R. Measuring midkine: the utility of midkine as a biomarker in cancer and other diseases. Br. J. Pharmacol. 171, 2925–2939 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Sakamoto, K. & Kadomatsu, K. Midkine in the pathology of cancer, neural disease, and inflammation. Pathol. Int. 62, 445–455 (2012).

    CAS  PubMed  Google Scholar 

  19. Kadomatsu, K. The midkine family in cancer, inflammation and neural development. Nagoya J. Med. Sci. 67, 71–82 (2005).

    CAS  PubMed  Google Scholar 

  20. Zhao, S. et al. Midkine upregulates MICA/B expression in human gastric cancer cells and decreases natural killer cell cytotoxicity. Cancer Immunol. Immunother. 61, 1745–1753 (2012).

    CAS  PubMed  Google Scholar 

  21. Guo, X. et al. Midkine activation of CD8+ T cells establishes a neuron–immune–cancer axis responsible for low-grade glioma growth. Nat. Commun. 11, 2177 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Ohuchida, T. et al. Midkine protects hepatocellular carcinoma cells against TRAIL-mediated apoptosis through down-regulation of caspase-3 activity. Cancer 100, 2430–2436 (2004).

    CAS  PubMed  Google Scholar 

  23. Ribas, A. & Wolchok, J. D. Cancer immunotherapy using checkpoint blockade. Science 359, 1350–1355 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Herradon, G., Ramos-Alvarez, M. P. & Gramage, E. Connecting metainflammation and neuroinflammation through the PTN–MK–RPTPβ/ζ axis: relevance in therapeutic development. Front. Pharmacol. 10, 377 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Weckbach, L. T., Preissner, K. T. & Deindl, E. The role of midkine in arteriogenesis, involving mechanosensing, endothelial cell proliferation, and vasodilation. Int. J. Mol. Sci. 19, 2559 (2018).

    PubMed Central  Google Scholar 

  26. Weckbach, L. T. et al. The cytokine midkine supports neutrophil trafficking during acute inflammation by promoting adhesion via β2 integrins (CD11/CD18). Blood 123, 1887–1896 (2014).

    CAS  PubMed  Google Scholar 

  27. Masuda, T. et al. Growth factor midkine promotes T-cell activation through nuclear factor of activated T cells signaling and TH1 cell differentiation in lupus nephritis. Am. J. Pathol. 187, 740–751 (2017).

    CAS  PubMed  Google Scholar 

  28. TCGA Genomic classification of cutaneous melanoma. Cell 161, 1681–1696 (2015).

  29. Liberzon, A. et al. The Molecular Signatures Database (MSigDB) Hallmark gene set collection. Cell Syst. 1, 417–425 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Shimoni, Y. Association between expression of random gene sets and survival is evident in multiple cancer types and may be explained by sub-classification. PLoS Comput. Biol. 14, e1006026 (2018).

    PubMed  PubMed Central  Google Scholar 

  31. Yoshihara, K. et al. Inferring tumour purity and stromal and immune cell admixture from expression data. Nat. Commun. 4, 2612 (2013).

    PubMed  Google Scholar 

  32. Thorsson, V. et al. The immune landscape of cancer. Immunity 48, 812–830.e14 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Weckbach, L. T., Muramatsu, T. & Walzog, B. Midkine in inflammation. ScientificWorldJournal 11, 2491–2505 (2011).

    PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Poh, A. R. & Ernst, M. Targeting macrophages in cancer: from bench to bedside. Front. Oncol. 8, 49 (2018).

    PubMed  PubMed Central  Google Scholar 

  36. Jewell, R. et al. The clinicopathological and gene expression patterns associated with ulceration of primary melanoma. Pigment Cell Melanoma Res. 28, 94–104 (2015).

    CAS  PubMed  Google Scholar 

  37. Perrotta, C. et al. Nitric oxide generated by tumor-associated macrophages is responsible for cancer resistance to cisplatin and correlated with syntaxin 4 and acid sphingomyelinase inhibition. Front. Immunol. 9, 1186 (2018).

    PubMed  PubMed Central  Google Scholar 

  38. Liou, G.-Y. et al. The presence of interleukin-13 at pancreatic ADM/PanIN lesions alters macrophage populations and mediates pancreatic tumorigenesis. Cell Rep. 19, 1322–1333 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Meeth, K., Wang, J. X., Micevic, G., Damsky, W. & Bosenberg, M. W. The YUMM lines: a series of congenic mouse melanoma cell lines with defined genetic alterations. Pigment Cell Melanoma Res. 29, 590–597 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Szklarczyk, D. et al. STRING v10: protein–protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 43, D447–D452 (2015).

    CAS  PubMed  Google Scholar 

  41. Kessenbrock, K., Plaks, V. & Werb, Z. Matrix metalloproteinases: regulators of the tumor microenvironment. Cell 141, 52–67 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Dejonckheere, E., Vandenbroucke, R. E. & Libert, C. Matrix metalloproteinase8 has a central role in inflammatory disorders and cancer progression. Cytokine Growth Factor Rev. 22, 73–81 (2011).

    CAS  PubMed  Google Scholar 

  43. Shabani, F., Farasat, A., Mahdavi, M. & Gheibi, N. Calprotectin (S100A8/S100A9): a key protein between inflammation and cancer. Inflamm. Res. 67, 801–812 (2018).

    CAS  PubMed  Google Scholar 

  44. Jones, S. A. & Jenkins, B. J. Recent insights into targeting the IL-6 cytokine family in inflammatory diseases and cancer. Nat. Rev. Immunol. 18, 773–789 (2018).

    CAS  PubMed  Google Scholar 

  45. Lachmann, A. et al. ChEA: transcription factor regulation inferred from integrating genome-wide ChIP-X experiments. Bioinformatics 26, 2438–2444 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Sancho, D. et al. Identification of a dendritic cell receptor that couples sensing of necrosis to immunity. Nature 458, 899–903 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Enamorado, M. et al. Enhanced anti-tumour immunity requires the interplay between resident and circulating memory CD8+ T cells. Nat. Commun. 8, 16073 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. Ayers, M. et al. IFN-γ-related mRNA profile predicts clinical response to PD-1 blockade. J. Clin. Invest. 127, 2930–2940 (2017).

    PubMed  PubMed Central  Google Scholar 

  49. Curtsinger, J. M., Agarwal, P., Lins, D. C. & Mescher, M. F. Autocrine IFN-γ promotes naive CD8 T cell differentiation and synergizes with IFN-α to stimulate strong function. J. Immunol. 189, 659–668 (2012).

    CAS  PubMed  Google Scholar 

  50. Orecchioni, M., Ghosheh, Y., Pramod, A. B. & Ley, K. Macrophage polarization: different gene signatures in M1(LPS+) vs. classically and M2(LPS−) vs. alternatively activated macrophages. Front. Immunol. 10, 1084 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. Li, H. et al. Dysfunctional CD8 T cells form a proliferative, dynamically regulated compartment within human melanoma. Cell 176, 775–789.e18 (2019).

    CAS  PubMed  Google Scholar 

  52. Ascierto, M. L. et al. Transcriptional mechanisms of resistance to anti-PD-1 therapy. Clin. Cancer Res. 23, 3168–3180 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. Hugo, W. et al. Genomic and transcriptomic features of response to anti-PD-1 therapy in metastatic melanoma. Cell 165, 35–44 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. Van Sllen, E. M. et al. Genomic correlates of response to CTLA-4 blockade in metastatic melanoma. Science 350, 207–211 (2015).

    Google Scholar 

  55. 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  Google Scholar 

  56. Wei, S. C., Duffy, C. R. & Allison, J. P. Fundamental mechanisms of immune checkpoint blockade therapy. Cancer Discov. 8, 1069–1086 (2018).

    PubMed  Google Scholar 

  57. Cristescu, R. et al. Pan-tumor genomic biomarkers for PD-1 checkpoint blockade-based immunotherapy. Science 362, eaar3593 (2018).

    PubMed  PubMed Central  Google Scholar 

  58. Gajewski, T., Schreiber, H. & Fu, Y.-X. Innate and adaptive immune cells in the tumor microenvironment. Nat. Immunol. 14, 1014–1022 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. Vasan, N., Baselga, J. & Hyman, D. M. A view on drug resistance in cancer. Nature 575, 299–309 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  60. Liersch, R. et al. Analysis of a novel highly metastatic melanoma cell line identifies osteopontin as a new lymphangiogenic factor. Int. J. Oncol. 41, 1455–1463 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. Berghaus, L. J. et al. Innate immune responses of primary murine macrophage-lineage cells and RAW 264.7 cells to ligands of Toll-like receptors 2, 3, and 4. Comp. Immunol. Microbiol. Infect. Dis. 33, 443–454 (2010).

    PubMed  Google Scholar 

  62. Olmeda, D. et al. Antimetastatic dsRNA mimics identified by live imaging of pathogenic neolymphangiogenesis. Preprint at bioRxiv https://doi.org/10.1101/2019.12.26.887943 (2019).

  63. Graña, O., Rubio-Camarillo, M., Fdez-Riverola, F., Pisano, D. G. & Glez-Peña, D. Nextpresso: next generation sequencing expression analysis pipeline. Curr. Bioinformatics 13, 583–591 (2018).

    Google Scholar 

  64. Wiśniewski, J. R., Zougman, A., Nagaraj, N. & Mann, M. Universal sample preparation method for proteome analysis. Nat. Methods 6, 3–7 (2009).

    Google Scholar 

  65. Hänzelmann, S., Castelo, R. & Guinney, J. GSVA: gene set variation analysis for microarray and RNA-Seq data. BMC Bioinformatics 14, 7 (2013).

    PubMed  PubMed Central  Google Scholar 

  66. Tirosh, I. et al. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science 352, 189–196 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank the colleagues at the CNIO Melanoma Group, as well as those at the laboratories of H. Peinado and Manuel V. (CNIO), for help and support, I. Blanco, S. Ruiz, V. Granda, S. Rueda (CNIO) and the Animal Facility, Histopathological Unit, Confocal Microscopy Unit and Crystallography and Protein Engineering Unit of CNIO for assistance with the mouse colonies and histopathological and protein analyses, and D. Sancho (CNIC) for the B16–OVAGFP cells and OT-I mouse strain, and for scientific guidance. P. Turko (University of Zurich) provided advice on the statistical analyses of tissue microarrays. We also thank the donors and the Biobank Hospital Universitario Puerta De Hierro Majadahonda (HUPHM)/Instituto De Investigación Sanitaria Puerta De Hierro-Segovia De Arana (IDIPHISA) (PT17/0015/0020 in the Spanish National Biobanks Network) for the human specimens used in this study. M.S.S. is funded by grants from the Spanish Ministry of Economy and Innovation (SAF2017-89533-R), Team Science and Established Investigator awards by the Melanoma Research Alliance, and grants from Worldwide Cancer Research and Fundación ‘La Caixa’ Health Research 2019. M.S.S., P.O.-R. and J.L.R.-P. are funded by a collaborative grant from the Asociación Española Contra el Cáncer (AECC). D.O. is funded by grants from the Spanish Ministry of Health (AES-PIS PI18/1057) and ‘Fundación BBVA-Becas Leonardo a Investigadores y Creadores Culturales 2018’. D.C.-W. was a recipient of a predoctoral fellowship from Fundación ‘La Caixa’ and is currently funded by the AECC. The CNIO Proteomics Unit belongs to ProteoRed, PRB2-ISCIII, supported by grant PT13/0001. N.I. and J.M. are funded by SAF2013-45504-R (MINECO). J.M. is also supported by Ramón y Cajal Programme (MINECO) RYC-2012-10651. M.C.-A. and X.C. were funded by the Immutrain Marie Skłodowska-Curie ITN Grant. S.H. received funding from the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement number 641458.

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Authors and Affiliations

  1. Melanoma Laboratory, Molecular Oncology Programme, Spanish National Cancer Research Centre (CNIO), Madrid, Spain

    Daniela Cerezo-Wallis, Marta Contreras-Alcalde, Xavier Catena, Cynthia Mucientes, Tonantzin G. Calvo, Estela Cañón, Cristina Tejedo, Paula C. Pennacchi, David Olmeda & María S. Soengas

  2. Bioinformatics Unit, Spanish National Cancer Research Centre (CNIO), Madrid, Spain

    Kevin Troulé, Héctor Tejero, Osvaldo Graña-Castro, Gonzalo Gómez-López & Fátima Al-Shahrour

  3. Department of Dermatology, University of Zurich Hospital, Zurich, Switzerland

    Sabrina Hogan, Peter Kölblinger & Mitchell P. Levesque

  4. Program for Mathematical Genomics, Departament of Systems Biology, Departament of Biomedical Informatics, Columbia University College of Physicians and Surgeons, New York, NY, USA

    Andrew X. Chen & Raúl Rabadán

  5. Proteomics Unit, Biotechnology Programme, Spanish National Cancer Research Centre (CNIO) and ProteoRed-ISCIII, Madrid, Madrid, Spain

    Nuria Ibarz & Lola Martinez

  6. Flow Cytometry Unit, Biotechnology Programme, Spanish National Cancer Research Centre (CNIO), Madrid, Madrid, Spain

    Javier Muñoz

  7. Dermatology Service, Hospital 12 de Octubre, Universidad Complutense Madrid Medical School, Madrid, Spain

    Pablo Ortiz-Romero

  8. Instituto de Investigación i+12, Hospital 12 de Octubre, Universidad Complutense Madrid Medical School, Madrid, Spain

    José L. Rodriguez-Peralto

  9. Pathology Service, Hospital 12 de Octubre, Universidad Complutense Madrid Medical School, Madrid, Spain

    José L. Rodriguez-Peralto

Authors
  1. Daniela Cerezo-Wallis
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  2. Marta Contreras-Alcalde
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Contributions

M.S.S., D.O. and D.C.-W. conceived of and designed the expression studies and mechanistic analyses in this work. D.C.-W. developed the protocols to define MDK-induced immunophenotyping in vivo, performed the histological assessment of MDK and other immune markers and characterized MDK effects in human and mouse cell lines. D.C.-W. and D.O. performed in vivo anti-PD-1 and anti-PD-L1 treatments in all settings (that is, MDKi and anti-CSF1R), as well as human histological validations, and contributed to the proteomic and transcriptomic data analyses. D.O. overexpressed and depleted MDK, performed the proteomics and transcriptomics analyses of the melanoma cell lines, as well as the computational characterization of MDK-associated gene expression and ICB responses, and coordinated additional bioinformatics analyses. M.P.L., S.H., J.L.R.-P., P.O.-R. and P.K. provided paraffin-embedded specimens for pathological analyses of MDK expression in human specimens and coordinated and interpreted the univariate and multivariate estimations of MDK-associated correlations to macrophage markers. M.C.-A. helped with validation of the MDK effect in melanoma cells in vitro, performed flow cytometry and histological analysis and contributed to the animal experiments. X.C. contributed to the animal experiments and flow cytometry analysis and prepared cell lines for in vitro experiments with other cancer cell lines and OVA models. P.C.P. helped with the in vitro experiments and human histological quantifications. K.T. performed the ssGSEA in TCGA cohorts and corresponding controls and helped with interpretation of the bioinformatics data. H.T. helped with interpretation of the bioinformatics data. O.G.-C. analyzed the RNA-seq data. G.G.-L. and F.A.-S. supervised ssGSEA in TCGA cohorts and helped with interpretation of the bioinformatics data. C.M. helped with the in vitro experiments and qRT-PCR validations. C.T. helped with OT-1 cytotoxicity assays in vitro and MDK mRNA levels in mouse melanoma cell lines. A.X.C. and R.R. performed the single-cell RNA-seq analysis and helped with interpretation of the results. L.M. helped with the flow cytometry analysis. N.I. and J.M. performed the proteomics analyses. T.G.C. helped with the animal sample processing and mouse colony maintenance. E.C. helped with the tissue culture experiments and western blot validations. The manuscript was written by M.S.S., D.C.-W. and D.O. and approved by all authors. M.S.S. and D.O. supervised the project.

Corresponding authors

Correspondence to David Olmeda or María S. Soengas.

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Competing interests

The authors declare no competing interests.

Additional information

Peer review information Javier Carmona 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 Bioinformatic analyses of MDK-associated gene expression profile in TCGA patients.

a, Kaplan-Meier analysis showing the survival probability of the patients expressing high (n = 54) and low (n = 55) MDK mRNA levels (top and bottom 15th percentile in the metastatic melanoma TCGA dataset, respectively). Two-sided Log-Rank (Mantel-Cox) test. b, Heatmap showing relative mRNA levels (defined by RNAseq) in the indicated cell populations corresponding to gain or loss of function of MDK (GOF and LOF). Data are presented as normalized RPKMs per gene (row) with min and max set to 0 and 1, respectively. See Supplementary Tables 1 and 2 for complete gene lists. c-f, Controls to rule out random bias in the MDK-associated gene profile. 10,000 gene sets with the same size as the original MDK GOF and LOF profiles, were tested by ssGSEA for enrichment in TCGA-metastatic melanoma as in Fig. 1b. Pearson correlation coefficient, −0.56 < r <0.52, mean: −0.002 (c); R squared correlation index (b); P-value (d) and FDR (e). g, Kaplan-Meier curves of overall survival in patients within the 15th percentile with an MDKHigh-p (red) or a MDKLow-p (blue) in TCGA patient cohorts of Lung Squamous Cell Carcinoma (LUSC, MDKHigh-p n = 33; MDKLow-p = 30), Glioblastoma (GBM, MDKHigh-p n = 10; MDKLow-p = 13) or Kidney Renal Clear Cell Carcinoma (KIRC, MDKHigh-p n = 53; MDKLow-p = 48). Log-rank (Mantel-Cox) test.

Source data

Extended Data Fig. 2 Immune profiles associated to MDK in TCGA-patients.

a, b, Box-plots depicting proliferation score (a), ImmuneScore (b, left) and Leukocyte Score (b, right) in MDKHigh-p vs MDKLow-p patients. Low MDK, n= 30; High MDK = 42 (defined as in Fig. 1b). Unpaired Two-Tailed T-test. c, Scores of the indicated immune cell populations in MDKHigh-p vs MDKLow-p samples. Low MDK, n= 30; High MDK = 42. Multiple comparison using two-tailed t-test with Holm-Sidak correction. d, Violin plots showing the enrichment of the MDKHigh-p score within the indicated cell populations, generated from scRNA-seq of melanoma samples66 (n =2887 cells) and defined by ssGSEA. Statistical differences among cell populations compared as indicated were estimated by two-tail Mann-Whitney test. e, UMAP showing the identification of the different populations of cells (left panel), and their corresponding Enrichment Score Rank (Right panel), for data from (d). f, TGF-β, Tregs and macrophage score, for patients within the 15th percentile of MDKHigh-p (red) or MDKLow-p (blue) of Lung Squamous Cell Carcinoma (LUSC, left), Glioblastoma (GBM, middle) or Kidney Renal Clear Cell Carcinoma (KIRC, right) from TCGA. Information was extracted from previously reported immunogenomic data32. Unpaired two-Tailed t-test. g, Representative histological staining showing levels of CD163 (level 1–3) and MDK in these patients. CD163 L1 (n = 32), CD163 L2 (n = 85) and L3 groups (n = 68). h, Summary of multivariate analysis of MDK expression and the indicated tumour-associated features in patients from an independent cohort of stage III melanoma patients. In all boxplots in this figure, the median is indicated by the horizontal line and the first and third quartiles by the box edges. The lower and upper whiskers extend from the hinges to the smallest and largest values, respectively, with individual values included.

Source data

Extended Data Fig. 3 MDK favours the intratumoural recruitment of Arg1+ cells.

a, Immunofluorescence microscopy for MDK (green), TAM marker YM1 (red), and iNOS (grey) in WM164-MDK vs Control xenografts. b, Immunofluorescence microscopy for MDK (green) and Arg1 (red) in B16F10-MDK vs Control tumours. c, MDK mRNA levels assessed by qRT-PCR in indicated mouse melanoma cell lines. YM2.1 (YUMM2.1), YM1.1 (YUMM 1.1) and YUMMER 1.7 (YMR1.7). Data in graphs correspond to mean ± s.d. n = 3 biological replicates. d, MDK secretion in human melanoma cell lines SK-Mel-147 (shCtrl vs shMDK), and WM164 (Control vs MDK); mouse melanoma cell lines YM2.1 (YUMM2.1), YM1.1 (YUMM1.1) and YUMMER1.7 (YMR1.7) upon MDK overexpression (red); human LUSC cell lines (green); and human GBM (Yellow), as assessed by ELISA. Data in graphs correspond to mean ± s.d. n = 3 biological replicates. e-g, Gating strategy for flow cytometry assays in this study: Arg1+ myeloid cells (e); myeloid cells, including dendritic cell populations (f), or lymphocytes (g). Neu = neutrophils; Mo = monocytes; TAMs = tumour associated macrophages; DC1 = dendritic cells type 1; DC2 = dendritic cells type 2; CD4+ T cells = T helper cells; CD8+ T =T cytotoxic cells; Tregs = regulatory T cells.

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Extended Data Fig. 4 Immunophenotyping of MDK-expressing tumours.

a, Representative histograms of intratumoural myeloid cells (CD11b+) in B16-R2L implants upon MDK depletion (shMDK, LOF), and in B16F1 tumours upon MDK expression (GOF). b, Quantification of total Ly6G+Ly6C+ granulocytic/neutrophil cells (left) and Arg1+ neutrophils in B16F10 tumours upon MDK expression, as measured by flow cytometry. n = 5 mice per condition. Unpaired two-tailed t-test. c, Quantification of the indicated cell populations within immune cells infiltrating B16F1 tumours upon MDK-expression, as measured by flow cytometry. Control, n = 8; MDK = 7 tumours. Unpaired Two-Tailed T-test. d, Arg1 mRNA expression in tumours from (c). Unpaired two-tailed t-test . e, Pie-charts indicating the percentage of indicated cell populations from total intratumoural immune cells (CD45+) in B16R2L tumours upon MDK depletion, as measured by flow cytometry. Control, n = 4; MDK = 4 tumours. Indicated with an asterisk are significantly altered populations P<0.05. Two-tailed T-test. DC1 (CD11b+CD11c+MHCII+CD24+CD103+), DC2 (CD11b+CD11c+MHCII+CD24+CD103-F4/80-), monocytes (CD11b+Ly6C+Ly6G-, P = 0.0217), TAMs (CD11b+Ly6G-F4/80+, P = 0.0405), neutrophils (CD11b+Ly6C+Ly6G+), CD8+ T cells (CD3+CD8+); CD4+ T cells (CD3+CD4+FoxP3-), Tregs (CD3+CD4+CD25+FoxP3+), NK (CD3-NK1.1+), NKT (CD3+NK1.1+). f, Quantification of indicated cell populations from (e), as measured by flow cytometry. n = 4 tumours, unpaired two-tailed t-test . Data shown correspond to a representative example out of 3 independent experiments. All box-plots in the figure represent minimum, maximum, median 25th/75th 5th/75th percentiles, and contain individual points.

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Extended Data Fig. 5 Transcriptomic and proteomic analyses of downstream effectors of MDK.

a, MSigDB Hallmark Gene set collection identified by GSEA to be differentially expressed in the RNAseq of WM164-MDK vs WM164-Control melanoma cells. Data are plotted with respect to Normalized Enriched Scores (NES), with those representing downregulation and upregulation in blue and red, respectively. FDR q-value < 0.25. b, Heatmap representing differentially expressed immunomodulatory proteins secreted by WM164 MDK melanoma cells with respect to their isogenic control. Relative expression values are represented as in the scale drawn in the bottom part of the figure.

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Extended Data Fig. 6 MDK and NF-κB associated signaling.

a, Predicted Transcription Factors (TF) (Enrichr-ChEA2016) regulating the MDK-induced secretome. Data represent Log10 of the P - value of the top 10 TF identified. b, Western blot analysis of total and phosphorylated p65 (p-p65) at Ser536, in starved WM164 melanoma cells upon treatment with recombinant MDK (10 ng/ml), or with TNF (10 ng/ml) as a control. Representative example of three independent replicates. c, Relative Arg1 and Il6 mRNA levels in BMDM cultured with the secretome from WM164 cells (blue) or the WM164-MDK isogenic pair control (red), previously treated with vehicle or anti-Calprotectin (ɑS100A8/A9) antibody, as assessed by qRT-PCR. n = 3 independent experiments; two-way ANOVA with Tukey post-test. d, Relative RelA mRNA levels in WM164 (left panel) and B16-OVA (right panel) 36h after RelA targeting by means of siRNA. Two-way ANOVA with Tukey Post-test. Data represent average ± s.d.; n = 3. e, Cell viability in B16-OVA melanoma cells 48h after RelA targeting by means of siRNA, as assessed by Flow Cytometry analysis of Annexin V and Dapi. Left= representative dot plots, Righ= quantification. Two-way ANOVA with Tukey Post-test. Data represents average ± s.d; n = 3 independent experiments. Box plots show median with 25th/75th percentiles and whiskers from minimum to maximum, plotting all individual values.

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Extended Data Fig. 7 MDK promotes αPD1/PDL1 therapy resistance in mice.

a, Experiment setup to assess the impact of MDK on an OVA-based vaccination assay. Shown are the different steps to vaccinate mice with the OVA protein, followed by subcutaneous implantation of parental B16-OVA melanoma cells (blue) or their isogenic MDK-expressing counterparts (red). 5 days after cell implantation, mice were rechallenged with LPS in the absence of OVA (non immunized, NI) or in the presence of OVA (immunized, I). b, Immunohistochemical staining for MDK (red, top panels) and Arg1 (pink, bottom panels) in B16-OVA Control vs B16-OVA-MDK xenografts in non-immunized or immunized settings as indicated. c, Growth curves of B16-OVA-MDK tumours (vs isogenic pair control) treated with IgG2a isotype control (10 mg/Kg) or αPD1 (clone RMP1–14, 10 mg/Kg) at the indicated days (arrows). Data correspond to average ± s.e.m.; n = 5 mice per condition. Statistical difference (P - value, two-way ANOVA with Tukey Post-test) between MDK vs Control B16-OVA tumours upon αPD1 treatment is indicated. d, Quantification of intratumoural macrophages (CD11b+Ly6G-F4/80+), CD8+PD1+ T cells, Treg (CD4+FoxP3+CD25+), and CD8/Treg ratio by flow cytometry at the endpoint of (c). n = 4 tumours per condition. e, Growth curves of B16-OVA-MDK tumours (vs isogenic pair control) treated with IgG2b isotype control (10 mg/Kg) or αPDL1 blocking monoclonal antibody (clone 10F.9G2, 10 mg/Kg) at the indicated days (arrows). Data represent mean ± s.e.m. n = 5 mice per condition, with significant differences between MDK vs Control B16-OVA tumours upon αPDL1 treatment as indicated. f, Circulating CD8+PD1+ T cells, Treg, and estimation of CD8/Treg ratio (mean ± s.e.m) defined by flow cytometry at the endpoint of the experiment in (e). n = 5 tumours per group. Data in c-f were analysed by two-way ANOVA with Bonferroni post-test. Box plots show median with 25th/75th percentiles and whiskers from minimum to maximum, plotting all individual values.

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Extended Data Fig. 8 MDK depletion enhances ICB efficacy.

a, Growth curves of B16R2L tumours generated by control (shCtrl) or MDK-depleted (shMDK), in mice treated with IgG2a isotype control (10 mg/Kg) or αPD1 (clone RMP1-14, 10 mg/Kg) at the indicated days (arrows). n = 6 mice per condition; average ± s.e.m. b, Spider-plots depicting individual tumour growth from (a). c,d, MSigDB Hallmark Gene set collection identified by GSEA to be differentially expressed in the RNAseq of B16R2L shMDK vs shCtrl tumors in mice without treatment (c) and upon αPD1 treatment (d). Data are plotted with respect to Normalized Enriched Scores (NES), with those representing downregulation and upregulation in red and blue, respectively. FDR q - value <0.25. e, Enrichment score for the GO ‘Acute Immune Response’ (GO:0002526), in the transcriptome of MDK-depleted B16R2L tumours treated with IgG2a isotype control (left) or αPD1 (right).

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Extended Data Fig. 9 Impact of monocyte/macrophage depletion in MDK-associated responses and correlations with ICB signatures.

a, Experimental procedure for the depletion of inflammatory monocytes using αCSF1R blocking monoclonal antibody (clone AFS98, 20 mg/Kg), and treatment with αPD1 (clone RMP1–14, 10 mg/Kg) at the indicated days. b, Gene expression signatures correlating with resistance to ICB in the indicated studies. Highlighted in red are genes differentially upregulated between MDKHigh-p and MDKLow-p metastatic melanoma TCGA patients. Indicated are enrichment (FDR q-values) for each gene signature in MDKHigh-p. c, Correlation between the TIDE dysfunctional score11 and the MDKGOF score (from differentially expressed genes upon MDK depletion in melanoma cells, Supplementary Table 2) in patients with metastatic melanoma (SKCM; n = 316), glioblastoma (GBM; n = 151), lung squamous cell carcinoma (LUSC; n = 484), and kidney renal clear cell carcinoma (KIRC; n = 153) from the TCGA database.

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Extended Data Fig. 10 MDK correlates with ICB resistance in melanoma patient cohorts.

a, Enrichment score for the ‘good prognosis to ICB’ Signature 5 (ref. 7, responders to αPD1+αCTLA4) in the differentially expressed genes of the shMDK-ICB combination, identified in B16R2L LOF experiments as in Fig. 6a. b, Enrichment score for the indicated ‘good prognosis to ICB’ signatures listed in the text (see Supplementary Table 7) in shMDK-depleted B16R2L tumours (data with respect to shC). c, Kaplan-Meier analysis of overall survival in the indicated patient cohorts comparing patients within the top 25th percentile (red) and the bottom 25th percentile (blue) of MDK-ICB-Score. Two-sided Log-Rank test. d, Progression-free survival in the indicated patient cohorts generated as in (c). Two-sided Log-rank (Mantel-Cox) test. Cohort 1 (ref. 7), Cohort 2 (ref. 6), Cohort 3 (ref. 5), Cohort 4 (ref. 53), Cohort 5 (ref. 54), and Cohort 6 (ref. 9).

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Supplementary information

Reporting Summary

Supplementary Tables

Supplementary Tables 1–8.

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Cerezo-Wallis, D., Contreras-Alcalde, M., Troulé, K. et al. Midkine rewires the melanoma microenvironment toward a tolerogenic and immune-resistant state. Nat Med 26, 1865–1877 (2020). https://doi.org/10.1038/s41591-020-1073-3

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