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Breaking NGF–TrkA immunosuppression in melanoma sensitizes immunotherapy for durable memory T cell protection

Nature Immunology volume 25, pages 268–281 (2024)Cite this article

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Abstract

Melanoma cells, deriving from neuroectodermal melanocytes, may exploit the nervous system’s immune privilege for growth. Here we show that nerve growth factor (NGF) has both melanoma cell intrinsic and extrinsic immunosuppressive functions. Autocrine NGF engages tropomyosin receptor kinase A (TrkA) on melanoma cells to desensitize interferon γ signaling, leading to T and natural killer cell exclusion. In effector T cells that upregulate surface TrkA expression upon T cell receptor activation, paracrine NGF dampens T cell receptor signaling and effector function. Inhibiting NGF, either through genetic modification or with the tropomyosin receptor kinase inhibitor larotrectinib, renders melanomas susceptible to immune checkpoint blockade therapy and fosters long-term immunity by activating memory T cells with low affinity. These results identify the NGF–TrkA axis as an important suppressor of anti-tumor immunity and suggest larotrectinib might be repurposed for immune sensitization. Moreover, by enlisting low-affinity T cells, anti-NGF reduces acquired resistance to immune checkpoint blockade and prevents melanoma recurrence.

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Fig. 1: NGF expression is predictive of poor prognosis and response to immunotherapy in patients.
Fig. 2: NGF establishes an immune-excluded TME in melanoma.
Fig. 3: Autocrine NGF signaling suppresses melanoma cell chemokine expression.
Fig. 4: The NGF–TrkA axis restricts CD8+ T cell activation and function.
Fig. 5: Targeting NGF enhances anti-melanoma immunotherapy.
Fig. 6: NGF inhibits the expansion and function of low-affinity T cells.

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Data availability

Bulk RNA-seq, single-cell RNA-seq and TCRβ-seq data have been deposited in the Gene Expression Omnibus (GEO) under the accession code GSE236682. Patients’ data were extracted from the TCGA research network (https://portal.gdc.cancer.gov/). Source data are provided with this paper.

References

  1. Sharma, P., Hu-Lieskovan, S., Wargo, J. A. & Ribas, A. Primary, adaptive, and acquired resistance to cancer immunotherapy. Cell 168, 707–723 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Joyce, J. A. & Fearon, D. T. T cell exclusion, immune privilege, and the tumor microenvironment. Science 348, 74–80 (2015).

    Article  CAS  PubMed  Google Scholar 

  3. Tumeh, P. C. et al. PD-1 blockade induces responses by inhibiting adaptive immune resistance. Nature 515, 568–571 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. 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.e236 (2019).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Scheper, W. et al. Low and variable tumor reactivity of the intratumoral TCR repertoire in human cancers. Nat. Med. 25, 89–94 (2019).

    Article  CAS  PubMed  Google Scholar 

  7. Han, J., Khatwani, N., Searles, T. G., Turk, M. J. & Angeles, C. V. Memory CD8+ T cell responses to cancer. Semin. Immunol. 49, 101435 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Aleksic, M. et al. Different affinity windows for virus and cancer-specific T-cell receptors: implications for therapeutic strategies. Eur. J. Immunol. 42, 3174–3179 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Stone, J. D., Harris, D. T. & Kranz, D. M. TCR affinity for p/MHC formed by tumor antigens that are self-proteins: impact on efficacy and toxicity. Curr. Opin. Immunol. 33, 16–22 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Zhong, S. et al. T-cell receptor affinity and avidity defines antitumor response and autoimmunity in T-cell immunotherapy. Proc. Natl Acad. Sci. USA 110, 6973–6978 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Knudson, K. M., Goplen, N. P., Cunningham, C. A., Daniels, M. A. & Teixeiro, E. Low-affinity T cells are programmed to maintain normal primary responses but are impaired in their recall to low-affinity ligands. Cell Rep. 4, 554–565 (2013).

    Article  CAS  PubMed  Google Scholar 

  12. Yu, W. et al. Clonal deletion prunes but does not eliminate self-specific αβ CD8+ T lymphocytes. Immunity 42, 929–941 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Shakiba, M. et al. TCR signal strength defines distinct mechanisms of T cell dysfunction and cancer evasion. J. Exp. Med. https://doi.org/10.1084/jem.20201966 (2022).

  14. Kho, A. T. et al. Conserved mechanisms across development and tumorigenesis revealed by a mouse development perspective of human cancers. Genes Dev. 18, 629–640 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Ali, Z., Yousaf, N. & Larkin, J. Melanoma epidemiology, biology and prognosis. EJC Suppl. 11, 81–91 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Shakhova, O. et al. Sox10 promotes the formation and maintenance of giant congenital naevi and melanoma. Nat. Cell Biol. 14, 882–890 (2012).

    Article  CAS  PubMed  Google Scholar 

  17. Varum, S. et al. Yin Yang 1 orchestrates a metabolic program required for both neural crest development and melanoma formation. Cell Stem Cell 24, 637–653.e639 (2019).

    Article  CAS  PubMed  Google Scholar 

  18. Ransohoff, R. M. & Engelhardt, B. The anatomical and cellular basis of immune surveillance in the central nervous system. Nat. Rev. Immunol. 12, 623–635 (2012).

    Article  CAS  PubMed  Google Scholar 

  19. Naba, A. et al. The matrisome: in silico definition and in vivo characterization by proteomics of normal and tumor extracellular matrices. Mol. Cell Proteom. 11, M111.014647 (2012).

    Article  Google Scholar 

  20. Truzzi, F. et al. Neurotrophins and their receptors stimulate melanoma cell proliferation and migration. J. Invest Dermatol 128, 2031–2040 (2008).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Gu, S. S. et al. Therapeutically increasing MHC-I expression potentiates immune checkpoint blockade. Cancer Discov. 11, 1524–1541 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Riaz, N. et al. Tumor and microenvironment evolution during immunotherapy with nivolumab. Cell 171, 934–949.e916 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Pender, A. et al. Genome and transcriptome biomarkers of response to immune checkpoint inhibitors in advanced solid tumors. Clin. Cancer Res. 27, 202–212 (2021).

    Article  CAS  PubMed  Google Scholar 

  25. Jia, Q. et al. Local mutational diversity drives intratumoral immune heterogeneity in non-small cell lung cancer. Nat. Commun. 9, 5361 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Pan, J. et al. CXCR3/CXCR3 ligand biological axis impairs RENCA tumor growth by a mechanism of immunoangiostasis. J. Immunol. 176, 1456–1464 (2006).

    Article  CAS  PubMed  Google Scholar 

  28. Colvin, R. A., Campanella, G. S., Sun, J. & Luster, A. D. Intracellular domains of CXCR3 that mediate CXCL9, CXCL10, and CXCL11 function. J. Biol. Chem. 279, 30219–30227 (2004).

    Article  CAS  PubMed  Google Scholar 

  29. Tomellini, E. et al. Nerve growth factor and proNGF simultaneously promote symmetric self-renewal, quiescence, and epithelial to mesenchymal transition to enlarge the breast cancer stem cell compartment. Stem Cells 33, 342–353 (2015).

    Article  CAS  PubMed  Google Scholar 

  30. Yoshimura, A., Naka, T. & Kubo, M. SOCS proteins, cytokine signalling and immune regulation. Nat. Rev. Immunol. 7, 454–465 (2007).

    Article  CAS  PubMed  Google Scholar 

  31. Song, M. M. & Shuai, K. The suppressor of cytokine signaling (SOCS) 1 and SOCS3 but not SOCS2 proteins inhibit interferon-mediated antiviral and antiproliferative activities. J. Biol. Chem. 273, 35056–35062 (1998).

    Article  CAS  PubMed  Google Scholar 

  32. Ji, R. R., Samad, T. A., Jin, S. X., Schmoll, R. & Woolf, C. J. p38 MAPK activation by NGF in primary sensory neurons after inflammation increases TRPV1 levels and maintains heat hyperalgesia. Neuron 36, 57–68 (2002).

    Article  CAS  PubMed  Google Scholar 

  33. Zhong, X. P. et al. Enhanced T cell responses due to diacylglycerol kinase zeta deficiency. Nat. Immunol. 4, 882–890 (2003).

    Article  CAS  PubMed  Google Scholar 

  34. Zehn, D., Lee, S. Y. & Bevan, M. J. Complete but curtailed T-cell response to very low-affinity antigen. Nature 458, 211–214 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Matloubian, M. et al. Lymphocyte egress from thymus and peripheral lymphoid organs is dependent on S1P receptor 1. Nature 427, 355–360 (2004).

    Article  CAS  PubMed  Google Scholar 

  36. Li, Q. J. et al. CD4 enhances T cell sensitivity to antigen by coordinating Lck accumulation at the immunological synapse. Nat. Immunol. 5, 791–799 (2004).

    Article  CAS  PubMed  Google Scholar 

  37. Plas, D. R. et al. Direct regulation of ZAP-70 by SHP-1 in T cell antigen receptor signaling. Science 272, 1173–1176 (1996).

    Article  CAS  PubMed  Google Scholar 

  38. Grasso, C. S. et al. Conserved interferon-γ signaling drives clinical response to immune checkpoint blockade therapy in melanoma. Cancer Cell 38, 500–515.e503 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Drilon, A. et al. Efficacy of Larotrectinib in TRK fusion-positive cancers in adults and children. N. Engl. J. Med. 378, 731–739 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Cocco, E., Scaltriti, M. & Drilon, A. NTRK fusion-positive cancers and TRK inhibitor therapy. Nat. Rev. Clin. Oncol. 15, 731–747 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Moynihan, K. D. et al. Eradication of large established tumors in mice by combination immunotherapy that engages innate and adaptive immune responses. Nat. Med. 22, 1402–1410 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Li, Y. et al. Analysis of the Rab GTPase interactome in dendritic cells reveals anti-microbial functions of the Rab32 complex in bacterial containment. Immunity 44, 422–437 (2016).

    Article  CAS  PubMed  Google Scholar 

  43. Brundage, R. A., Smith, G. A., Camilli, A., Theriot, J. A. & Portnoy, D. A. Expression and phosphorylation of the Listeria monocytogenes ActA protein in mammalian cells. Proc. Natl Acad. Sci. USA 90, 11890–11894 (1993).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Levi-Montalcini, R. The nerve growth factor 35 years later. Science 237, 1154–1162 (1987).

    Article  CAS  PubMed  Google Scholar 

  45. Aloe, L., Rocco, M. L., Bianchi, P. & Manni, L. Nerve growth factor: from the early discoveries to the potential clinical use. J. Transl. Med 10, 239 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Laudiero, L. B. et al. Multiple sclerosis patients express increased levels of β-nerve growth factor in cerebrospinal fluid. Neurosci. Lett. 147, 9–12 (1992).

    Article  CAS  PubMed  Google Scholar 

  47. Sofroniew, M. V., Howe, C. L. & Mobley, W. C. Nerve growth factor signaling, neuroprotection, and neural repair. Annu. Rev. Neurosci. 24, 1217–1281 (2001).

    Article  CAS  PubMed  Google Scholar 

  48. Villoslada, P. et al. Human nerve growth factor protects common marmosets against autoimmune encephalomyelitis by switching the balance of T helper cell type 1 and 2 cytokines within the central nervous system. J. Exp. Med. 191, 1799–1806 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Arredondo, L. R. et al. Role of nerve growth factor in experimental autoimmune encephalomyelitis. Eur. J. Immunol. 31, 625–633 (2001).

    Article  CAS  PubMed  Google Scholar 

  50. Flugel, A. et al. Anti-inflammatory activity of nerve growth factor in experimental autoimmune encephalomyelitis: inhibition of monocyte transendothelial migration. Eur. J. Immunol. 31, 11–22 (2001).

    Article  CAS  PubMed  Google Scholar 

  51. Micera, A., Properzi, F., Triaca, V. & Aloe, L. Nerve growth factor antibody exacerbates neuropathological signs of experimental allergic encephalomyelitis in adult lewis rats. J. Neuroimmunol. 104, 116–123 (2000).

    Article  CAS  PubMed  Google Scholar 

  52. Koike, S. & Yamasaki, K. Melanogenesis connection with innate immunity and toll-like receptors. Int. J. Mol. Sci. https://doi.org/10.3390/ijms21249769 (2020).

  53. Cohen, S., Levi-Montalcini, R. & Hamburger, V. A nerve growth-stimulating factor isolated from sarcom as 37 and 180. Proc. Natl Acad. Sci. USA 40, 1014–1018 (1954).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Aloe, L., Tuveri, M. A., Carcassi, U. & Levi-Montalcini, R. Nerve growth factor in the synovial fluid of patients with chronic arthritis. Arthritis Rheum. 35, 351–355 (1992).

    Article  CAS  PubMed  Google Scholar 

  55. Falcini, F. et al. Increased circulating nerve growth factor is directly correlated with disease activity in juvenile chronic arthritis. Ann. Rheum. Dis. 55, 745–748 (1996).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Holtzhausen, A. et al. Melanoma-derived Wnt5a promotes local dendritic-cell expression of IDO and immunotolerance: opportunities for pharmacologic enhancement of immunotherapy. Cancer Immunol. Res. 3, 1082–1095 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. The Genetic Perturbation Platform. Broad Institute (2021); https://portals.broadinstitute.org/gpp/public/

  58. Chen, S., Zhou, Y., Chen, Y. & Gu, J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 34, i884–i890 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  59. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    Article  CAS  PubMed  Google Scholar 

  60. Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  61. Liao, Y., Smyth, G. K. & Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).

    Article  CAS  PubMed  Google Scholar 

  62. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  63. Yu, G., Wang, L. G., Han, Y. & He, Q. Y. clusterProfiler: an R package for comparing biological themes among gene clusters. Omics 16, 284–287 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Feature barcode overview. 10x Genomics (2020); https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/latest/feature-bc

  65. Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587.e3529 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 16, 1289–1296 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Sample type codes. National Cancer Institute (2023); https://gdc.cancer.gov/resources-tcga-users/tcga-code-tables/sample-type-codes

  68. Kim, D., Paggi, J. M., Park, C., Bennett, C. & Salzberg, S. L. Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nat. Biotechnol. 37, 907–915 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Liao, Y., Smyth, G. K. & Shi, W. The Subread aligner: fast, accurate and scalable read mapping by seed-and-vote. Nucleic Acids Res. 41, e108 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  70. Cunningham, F. et al. Ensembl 2022. Nucleic Acids Res. 50, D988–d995 (2022).

    Article  CAS  PubMed  Google Scholar 

  71. Leek, J. T., Johnson, W. E., Parker, H. S., Jaffe, A. E. & Storey, J. D. The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics 28, 882–883 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).

    Article  Google Scholar 

  73. Robin, X. et al. pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinf. 12, 77 (2011).

    Article  Google Scholar 

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Acknowledgements

We thank L. Yang from Fudan University for assistance with human melanoma bioinformatic analysis. We thank the staff of the Duke University Flow Cytometry Shared Resource, the Light Microscopy Core Facility and the Duke Molecular Physiology Institute Molecular Genomics Core for help with data acquisition. This work was supported by R01-CA249726 (X.-F.W. and Q.-J.L.), P01-CA225622 (to D. M. Ashley and Q.-J.L.) and P50-CA190991 (to D. M. Ashley and Q.-J.L.), from the National Cancer Institute. Y.-H.L. was supported by UIBR and core funds provided by IMCB and the Biomedical Research Council (BMRC), A*STAR. Q.-J.L. was supported by core research grants provided to the IMCB and SIgN by the BMRC, A*STAR. We thank Z. Zhang from Duke University for assistance with drawing illustrations. Schematics in Figs. 1j,o, 3r, 4o and 6b,f,i,l and Extended Data Figs. 7e and 9 were created with BioRender.com.

Author information

Author notes

  1. These authors contributed equally: Tao Yin, Guoping Wang.

Authors and Affiliations

  1. Department of Pharmacology and Cancer Biology, Duke University School of Medicine, Durham, NC, USA

    Tao Yin, Ergang Wang, Christopher C. Pan, Yaosi Liang, Lianmei Tan, De Huang, Mengyang Chong, Kun Xiang & Xiao-Fan Wang

  2. Department of Immunology, Duke University School of Medicine, Durham, NC, USA

    Tao Yin, Guoping Wang, Bryan Jian Wei Lim & Qi-Jing Li

  3. Department of Molecular Genetics and Microbiology, Duke University School of Medicine, Durham, NC, USA

    Liuyang Wang

  4. TCRCure Biopharma, Durham, NC, USA

    Poorva Mudgal & Peter B. Alexander

  5. TCRCure Biopharma, Chongqing, China

    Haiyang Wu & Chengjie Cao

  6. Hervor Therapeutics, Hangzhou, China

    Rui Chen

  7. Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China

    Wei Xue

  8. Department of Molecular Oncology and Cutaneous Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA

    Lixin Wan

  9. Zhejiang University School of Brain Science and Brain Medicine, Hangzhou, China

    Hailan Hu

  10. Institute of Molecular and Cell Biology, Agency for Science, Technology and Research (A*STAR), Singapore, Singapore

    Yuin-Han Loh & Qi-Jing Li

  11. Singapore Immunology Network, Agency for Science, Technology and Research (A*STAR), Singapore, Singapore

    Qi-Jing Li

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  1. Tao Yin
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Contributions

Conceptualization, T.Y., G.W., H.H., X.-F.W. and Q.-J.L.; methodology, T.Y., G.W., L.W., P.M., E.W., H.W., C.C., H.H., Y.-H.L. and Q.-J.L.; experimental study, T.Y., G.W., C.C.P., Y.L., L.T., D.H., M.C., R.C., B.J.W.L., K.X., L.W. and Q.-J.L.; bioinformatics analysis, L.W., P.M., E.W., H.W., C.C., W.X., Y.-H.L. and Q.-J.L.; data analysis, T.Y., G.W., Y.-H.L., X.-F.W. and Q.-J.L.; writing and editing, T.Y., P.B.A., X.-F.W. and Q.-J.L.; supervision, X.-F.W. and Q.-J.L.

Corresponding authors

Correspondence to Xiao-Fan Wang or Qi-Jing Li.

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Competing interests

Q.-J.L. declares being a scientific co-founder and shareholder of TCRCure Biopharma and Hervor Therapeutics. The other authors declare no competing interests.

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Nature Immunology thanks the anonymous reviewers for their contribution to the peer review of this work. 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. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 NGF expression in melanoma and its effect on melanoma cell proliferation.

a-f, 1,000,000 B16, BrafV600EPten−/−, or YUMM1.7 melanoma cells were subcutaneously injected into C57BL/6 J mice. Melanoma tissues were harvested when tumors grew to 100 mm3. Expression of Ngf, Bdnf, Ntf3, Ntf4, Cntf, and Lif was assessed by quantitative RT-PCR. Spleen, lung, and skin tissues from normal C57BL/6 J mice were as controls. Data are pooled from four independent experiments. g, Bioinformatic assessment of NGF expression in primary and metastatic melanoma. Box plots: the horizontal lines indicate the first, second (median) and third quartiles; the whiskers extend to ±1.5× the interquartile range. h, 100,000 B16 or YUMM1.7 melanoma cells were subcutaneously injected into female C57BL/6 J mice. NGF concentration in serum of tumor-bearing mice was assessed by ELISA at indicated time. n = 6 (day 0), n = 3 (day 10), n = 3 (day 14), n = 3 (day 21 for B16), n = 6 (day 21 for YUMM1.7). i-l, Knockout efficiency of NGF sgRNA in B16 (i) and YUMM1.7 (k) cells. In vitro proliferation of B16 (j) and YUMM1.7 (l) cells after NGF knockout. Hereafter we termed sgRNA-2 as NGF sgRNA. n = 3 for each time point. m, 100,000 B16 cells were subcutaneously injected into NSG mice. Data are presented as means ± s.d. n = 6 (WT) and 8 (KO). Western blot, one of two independent experiments is shown. P values were determined using two-tailed Student’s t-test (a), one-way ANOVA (j, l), two-sided Wilcoxon test (g), and two-sided Mann-Whitney test (m, h).

Source data

Extended Data Fig. 2 NGF deficiency remodels the melanoma microenvironment.

a, b, 1,000,000 B16 cells were subcutaneously injected into male C57BL/6 J mice. Total RNA was extracted from melanoma tissues on day 15 and analyzed by RNA-seq. t-distributed stochastic neighbor embedding (t-SNE) plot cluster visualization (a) showing segregation of tumors. GO pathway analysis (b) of NGF sgRNA versus Ctrl sgRNA tumors. Red arrows indicated immune-related pathways. P value was calculated by one-tailed fisher exact test, and adjusted using Benjamini-Hochberg false discovey rate (FDR). c-d, 1,000,000 YUMM1.7 (c) and B16 (d) cells were subcutaneously injected into C57BL/6 J mice. Melanoma tissues were harvested on day 10, and proportions of myeloid populations were analyzed by flow cytometry in YUMM1.7 (c) and B16 (d) tumors. n = 6 (WT) and 7 (KO) in (c). n = 7 (WT) and 7 (KO) for PMN and Mϕ in (d), n = 6 (WT) and 6 (KO) for DC in (d). e-h, 1,000,000 YUMM1.7 Ctrl sgRNA and 2,000,000 YUMM1.7 NGF sgRNA cells were subcutaneously injected into female C57BL/6 J mice. Tumors with similar tumor size (e) were collected on day 6 (Ctrl sgRNA) and day 15 (NGF sgRNA) after inoculation. The proportion of CD4+ T (f), CD8+ T (g) and NK (h) cells were analyzed by flow cytometry. n = 8 (WT) and n = 7 (KO). i-l, 1,000,000 Ctrl sgRNA and NGF sgRNA B16 cells were subcutaneously injected into male C57BL/6 J mice. Tumors with similar tumor size (i) were collected on day 8 (Ctrl sgRNA) and day 15 (NGF sgRNA) after inoculation. The absolute number of CD4+ T (j), CD8+ T (k) and NK (l) cells were analyzed by flow cytometry. n = 4 (WT) and 6 (KO). Data are presented as means ± s.d. Two-sided Mann-Whitney test (c, e, f, g, h, i, j, k, l), one-tailed fisher exact test (b) and two-tailed Student’s t-test (d).

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Extended Data Fig. 3 NGF decreases IFNγ sensitivity of melanoma cells.

a, 1,000,000 B16 cells were subcutaneously injected into male C57BL/6 J mice. Melanoma tissues were harvested on day 10 and submitted for scRNA-seq. Uniform manifold approximation and projection (UMAP) plots. Phenotypic clusters are represented in distinct colors. b, Fraction of immune cells. c, Significantly enriched hallmark gene sets in the melanoma population from scRNA-seq. d, IFNγ response gene signature in melanoma cells. e, The apoptosis score was compared in melanoma cell population after NGF inactivation. Box plots in e: the horizontal lines indicate the first, second (median) and third quartiles; the whiskers extend to ±1.5× the interquartile range. f, Ctrl or NGF sgRNA B16 cells were treated with IFNγ for 30 min. Western blotting was used to assess the level of p-Stat1 (Y701) and total Stat1. g, B16 tumor cells were treated with 10 μM p75NTR inhibitor LM11A-31 for 24 h, followed by 20 ng/ml IFNγ stimulation for 30 min. Western blots show levels of p-Stat1 (Y701) and total Stat1. h, shTrkA knockdown efficiency in B16 cells as determined by flow cytometry. i, Rescued expression of Socs1 in NGF sgRNA B16 cells, as assessed by RT-PCR. Cell line samples were from one well of 6-well plate for each group, with 3 technical replicates. Data are presented as means ± s.d. Western blot, one of two independent experiments is shown. Two-sided Wilcoxon test (e) and two-tailed Student’s t-test (i). OE, overexpression.

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Extended Data Fig. 4 NGF decreases IFNγ sensitivity through the MEK/MAPK/SOCS1 pathway.

NGF sgRNA B16 tumor cells were treated with NGF at the indicated concentrations for 30 min. Western blots show Jak-Stat (a) and MAPK signaling (c) pathway proteins. NGF sgRNA B16 cells were serum-starved for 5 hours, and then treated with NGF for another 2 h. NGF sgRNA B16 cells were also treated by Jak1, Stat3 (b), MEK, ERK, p38 MAPK and JNK inhibitors (d) 1 h before NGF treatment, as indicated. Socs1 expression was evaluated by RT-PCR (b, d). Samples were pooled from 3 biological replicates with 3 technical replicates. Data are presented as means ± s.d. Western blot data pooled from two independent experiments. P values were determined using one-way ANOVA.

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Extended Data Fig. 5 NGF suppresses IFNγ signaling in the melanoma microenvironment.

a-l, 1,000,000 B16 cells were subcutaneously injected into male C57BL/6 J mice. Melanoma tissues were harvested on day 10 for scRNA-seq. Figure shows GO pathway analysis of each cluster in NGF sgRNA versus Ctrl sgRNA tumors. m, 1,000,000 B16 cells were subcutaneously injected into male and female C57BL/6 J mice. Melanoma tissues were harvested on day 15. The level of IFNγ in B16 tumor lysates was analyzed by western blot using antibody against IFNγ. n= 3.

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Extended Data Fig. 6 Lymphocyte depletion experiments in melanoma models.

1,000,000 control or NGF sgRNA B16 cells were subcutaneously injected into male C57BL/6 J mice. Lymphocyte depletion efficiencies by anti-CD4, anti-CD8, and anti-NK1.1 antibodies in B16 (a,b,d,e) and YUMM1.7 (c,e)-bearing mice were assessed by flow cytometry. Representative plots (a,d) and statistics (b,c,e) are shown. n = 10 (IgG) and n = 9 (anti-CD4 and anti-CD8) in (b), n = 6 (IgG), 6 (anti-CD4) and 7 (anti-CD8) in (c), n = 10 (IgG) and 9 (anti-NK1.1) for B16, n = 6 for YUMM1.7 in (e). Data are presented as means ± s.d. Two-tailed Student’s t-test.

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Extended Data Fig. 7 NGF-TrkA suppresses T cell activation and function.

a, CD25 expression on CD8+ T cells as measured by flow cytometry after NGF treatment for 72 h. Data are presented as means ± s.d. b, Cytokine production by effector CD8+ T cells after NGF treatment for 72 h. Data are presented as means ± s.d. c, OT-I cells were activated with 1 μg/ml OVA N4 peptide for 48 h. Expression of cell surface p75NTR was measured by flow cytometry. d, OT-I cells were stimulated with the indicated concentrations of OVA N4 and OVA Q4 peptides for 16 h. CD69 expression was assessed by flow cytometry. Data are presented as means ± s.d. e, Schematic representation of competitive T cell transfer experiments. f, Knockout efficiency of TrkA on CD8+ T cells by CRISPR/Cas 9 editing technology. g-i, 36 hr after intravenous transfer, the percentage of ZsGreen+ sgNT and BFP+ sgTrkA Pmel-1:Cas 9 T cells in B16 melanoma was analyzed by flow cytometry (g). The ratio between BFP+ sgTrkA and ZsGreen+ sgNT Pmel-1:Cas 9 T cells was calculated (h). The percentage of IFNγ-producing CD8+ T cells in ZsGreen+ sgNT and BFP+ sgTrkA Pmel-1:Cas 9 T cells was analyzed (i). Data were pooled from two independent experiments. Data are presented as means ± s.d. n = 7. j, A375 melanoma cells were treated with 10 μM larotrectinib for 16 hr, followed by treatment with 20 ng/ml IFNγ for 4 h. CXCL10 expression was assessed by RT-PCR. k, SK-MEL-2 melanoma cells were treated with 10 μM larotrectinib for 16 hr, followed by treatment with 100 ng/ml IFNγ for 5 h. CXCL10 expression was assessed by RT-PCR. Samples were pooled from 3 biological replicates in 3 independent experiments, with 2−3 technical replicates for each experiment. Data are presented as means ± s.d. Two-tailed Student’s t-test (a,b,d,i),one-way ANOVA (j,k). FMO, Fluorescence Minus One.

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Extended Data Fig. 8 NGF does not suppress non-antigen-specific T cells and strategies for sorting high- and low-affinity T cells.

a, Tumor growth in indicated groups of male C57BL/6 mice that rejected B16 tumors and were rechallenged with LLC lung cancer cells, as compared with naive male C57BL/6 J mice. Data are combined from two independent experiments (n = 3–5). Data are presented as means ± s.d. b, Tumor growth in female C57BL/6 mice that rejected YUMM1.7 NGF sgRNA tumors and were rechallenged with LLC lung cancer cells, as compared with naive female C57BL/6 J mice. n = 4. Data are presented as means ± s.d. c, Tumor growth of female C57BL/6 mice that rejected B16 NGF sgRNA tumors and were rechallenged with LLC lung cancer cells, as compared with naive female C57BL/6 J mice. n = 4. Data are presented as means ± s.d. d, Shannon entropy of TCRs in YUMM1.7 tumors. n = 4. Box plots: the horizontal lines indicate the first, second (median) and third quartiles; the whiskers extend to ±1.5× the interquartile range. e, Female C57BL/6 mice were vaccinated i.v. with 0.1 LD50 of attenuated recombinant Listeria-OVA. OVA-tetramer-high and –low CD8+ T cells were FACS-sorted from spleen on day 7 after a single vaccination. Representative dot blots gated on OVA-tetramer+ CD8+ T cells are shown. f, Female C57BL/6 J mice were subcutaneously injected with 50,000 Ctrl or NGF sgRNA B16-OVA cells. All mice in Ctrl sgRNA group were sacrificed due to excessive tumor growth. Tumor-free mice in the NGF sgRNA group were rechallenged with B16-OVA cells on day 78. 9 days later, OVA-tetramer+ CD8+ T cells were sorted from tumor draining-lymph nodes and submitted for TCRβ-seq. Representative dot blots gated on OVA-tetramer+ CD8+ T cells are shown.

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Extended Data Fig. 9 Schematic of tumor cell-derived NGF restricting anti-tumor immunity in melanoma.

NGF exerts both melanoma cell intrinsic and extrinsic immunosuppressive functions.

Supplementary information

Supplementary Information

Supplementary Table 1. NGF-deficiency gene signature. Supplementary Table 2. Primer sequences for real-time PCR. Supplementary Table 3. Primer sequences for TCRβ-seq.

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Yin, T., Wang, G., Wang, L. et al. Breaking NGF–TrkA immunosuppression in melanoma sensitizes immunotherapy for durable memory T cell protection. Nat Immunol 25, 268–281 (2024). https://doi.org/10.1038/s41590-023-01723-7

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