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.

  • Article
  • Published:

Loss of p53 drives neuron reprogramming in head and neck cancer

Nature volume 578, pages 449–454 (2020)Cite this article

Subjects

Abstract

The solid tumour microenvironment includes nerve fibres that arise from the peripheral nervous system1,2. Recent work indicates that newly formed adrenergic nerve fibres promote tumour growth, but the origin of these nerves and the mechanism of their inception are unknown1,3. Here, by comparing the transcriptomes of cancer-associated trigeminal sensory neurons with those of endogenous neurons in mouse models of oral cancer, we identified an adrenergic differentiation signature. We show that loss of TP53 leads to adrenergic transdifferentiation of tumour-associated sensory nerves through loss of the microRNA miR-34a. Tumour growth was inhibited by sensory denervation or pharmacological blockade of adrenergic receptors, but not by chemical sympathectomy of pre-existing adrenergic nerves. A retrospective analysis of samples from oral cancer revealed that p53 status was associated with nerve density, which was in turn associated with poor clinical outcomes. This crosstalk between cancer cells and neurons represents mechanism by which tumour-associated neurons are reprogrammed towards an adrenergic phenotype that can stimulate tumour progression, and is a potential target for anticancer therapy.

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

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

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

Fig. 1: Loss of p53 alters the neural microenvironment throughout tumour evolution.
Fig. 2: p53-dependent alterations in miRNA populations control neuritogenesis.
Fig. 3: p53-deficient tumours are enriched with adrenergic nerve fibres.
Fig. 4: De novo transdifferentiated cancer-associated adrenergic nerves support tumour growth.
Fig. 5: Retrograde signalling by p53-deficient but not p53-sufficient cancer cells activates neural reprogramming.

Similar content being viewed by others

Data availability

Neuron RNA sequencing data from in vivo and in vitro experiments are available from the Gene Expression Omnibus (GEO) under accession number GSE134220. mRNA array data are available on GEO under accession number GSE140189, and miRNA array data are available on GEO under accession number GSE140324. All other data are available in the article and source data, or from the corresponding author upon reasonable request.

References

  1. Magnon, C. et al. Autonomic nerve development contributes to prostate cancer progression. Science 341, 1236361 (2013).

    Article  PubMed  Google Scholar 

  2. Ayala, G. E. et al. Cancer-related axonogenesis and neurogenesis in prostate cancer. Clin. Cancer Res. 14, 7593–7603 (2008).

    Article  CAS  PubMed  Google Scholar 

  3. Zahalka, A. H. et al. Adrenergic nerves activate an angio-metabolic switch in prostate cancer. Science 358, 321–326 (2017).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  4. Ravi, R. et al. Regulation of tumor angiogenesis by p53-induced degradation of hypoxia-inducible factor 1α. Genes Dev. 14, 34–44 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Schmid, J. O. et al. Cancer cells cue the p53 response of cancer-associated fibroblasts to cisplatin. Cancer Res. 72, 5824–5832 (2012).

    Article  CAS  PubMed  Google Scholar 

  6. Antoniades, H. N., Galanopoulos, T., Neville-Golden, J., Kiritsy, C. P. & Lynch, S. E. p53 expression during normal tissue regeneration in response to acute cutaneous injury in swine. J. Clin. Invest. 93, 2206–2214 (1994).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Yun, M. H., Gates, P. B. & Brockes, J. P. Regulation of p53 is critical for vertebrate limb regeneration. Proc. Natl Acad. Sci. USA 110, 17392–17397 (2013).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  8. Li, Z. et al. Cdkn2a suppresses metastasis in squamous cell carcinomas induced by the gain-of-function mutant p53(R172H). J. Pathol. 240, 224–234 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Joerger, A. C. & Fersht, A. R. Structural biology of the tumor suppressor p53. Annu. Rev. Biochem. 77, 557–582 (2008).

    Article  CAS  PubMed  Google Scholar 

  10. Ostrowski, M. et al. Rab27a and Rab27b control different steps of the exosome secretion pathway. Nat. Cell Biol. 12, 19–30 (2010).

    Article  CAS  PubMed  Google Scholar 

  11. Posner, R. et al. Neuronal small RNAs control behavior transgenerationally. Cell 177, 1814–1826.e1815 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Chen, W. & Qin, C. General hallmarks of microRNAs in brain evolution and development. RNA Biol. 12, 701–708 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  13. Pang, R. T. et al. MicroRNA-34a suppresses invasion through downregulation of Notch1 and Jagged1 in cervical carcinoma and choriocarcinoma cells. Carcinogenesis 31, 1037–1044 (2010).

    Article  CAS  PubMed  Google Scholar 

  14. Prueitt, R. L. et al. Expression of microRNAs and protein-coding genes associated with perineural invasion in prostate cancer. Prostate 68, 1152–1164 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Lubov, J. et al. Meta-analysis of microRNAs expression in head and neck cancer: uncovering association with outcome and mechanisms. Oncotarget 8, 55511–55524 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  16. Ramdas, L. et al. miRNA expression profiles in head and neck squamous cell carcinoma and adjacent normal tissue. Head Neck 31, 642–654 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  17. Sousa, L. O. et al. Lymph node or perineural invasion is associated with low miR-15a, miR-34c and miR-199b levels in head and neck squamous cell carcinoma. BBA Clin. 6, 159–164 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  18. North, R. Y. et al. Electrophysiological and transcriptomic correlates of neuropathic pain in human dorsal root ganglion neurons. Brain 142, 1215–1226 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  19. Choi, Y. J. et al. miR-34 miRNAs provide a barrier for somatic cell reprogramming. Nat. Cell Biol. 13, 1353–1360 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Choi, Y. J. et al. Deficiency of microRNA miR-34a expands cell fate potential in pluripotent stem cells. Science 355, eaag1927 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  21. Agostini, M. et al. Neuronal differentiation by TAp73 is mediated by microRNA-34a regulation of synaptic protein targets. Proc. Natl Acad. Sci. USA 108, 21093–21098 (2011).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  22. Jauhari, A., Singh, T., Singh, P., Parmar, D. & Yadav, S. Regulation of miR-34 family in neuronal development. Mol. Neurobiol. 55, 936–945 (2018).

    Article  CAS  PubMed  Google Scholar 

  23. Dennis, G. Jr et al. DAVID: database for annotation, visualization, and integrated discovery. Genome Biol. 4, 3 (2003).

    Article  Google Scholar 

  24. Tsunemoto, R. et al. Diverse reprogramming codes for neuronal identity. Nature 557, 375–380 (2018).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  25. Vierbuchen, T. et al. Direct conversion of fibroblasts to functional neurons by defined factors. Nature 463, 1035–1041 (2010).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  26. Hsu, S. D. et al. miRTarBase update 2014: an information resource for experimentally validated miRNA-target interactions. Nucleic Acids Res. 42, D78–D85 (2014).

    Article  CAS  PubMed  Google Scholar 

  27. Miranda, K. C. et al. A pattern-based method for the identification of microRNA binding sites and their corresponding heteroduplexes. Cell 126, 1203–1217 (2006).

    Article  CAS  PubMed  Google Scholar 

  28. Lewis, B. P., Shih, I. H., Jones-Rhoades, M. W., Bartel, D. P. & Burge, C. B. Prediction of mammalian microRNA targets. Cell 115, 787–798 (2003).

    Article  CAS  PubMed  Google Scholar 

  29. Kim, J. et al. Direct reprogramming of mouse fibroblasts to neural progenitors. Proc. Natl Acad. Sci. USA 108, 7838–7843 (2011).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  30. Yao, H. et al. Transdifferentiation-induced neural stem cells promote recovery of middle cerebral artery stroke rats. PLoS One 10, e0137211 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  31. Wapinski, O. L. et al. Hierarchical mechanisms for direct reprogramming of fibroblasts to neurons. Cell 155, 621–635 (2013).

    Article  CAS  PubMed  Google Scholar 

  32. Axelson, H. The Notch signaling cascade in neuroblastoma: role of the basic helix-loop-helix proteins HASH-1 and HES-1. Cancer Lett. 204, 171–178 (2004).

    Article  CAS  PubMed  Google Scholar 

  33. Mauffrey, P. et al. Progenitors from the central nervous system drive neurogenesis in cancer. Nature 569, 672–678 (2019).

    Article  ADS  CAS  PubMed  Google Scholar 

  34. Zhao, C. M. et al. Denervation suppresses gastric tumorigenesis. Sci. Transl. Med. 6, 250ra115 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  35. Melhem-Bertrandt, A. et al. Beta-blocker use is associated with improved relapse-free survival in patients with triple-negative breast cancer. J. Clin. Oncol. 29, 2645–2652 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Faulkner, S., Jobling, P., March, B., Jiang, C. C. & Hondermarck, H. Tumor neurobiology and the war of nerves in cancer. Cancer Discov. 9, 702–710 (2019).

    Article  PubMed  CAS  Google Scholar 

  37. Lin, C. S., Lin, W. S., Lin, C. L. & Kao, C. H. Carvedilol use is associated with reduced cancer risk: a nationwide population-based cohort study. Int. J. Cardiol. 184, 9–13 (2015).

    Article  PubMed  Google Scholar 

  38. Caulin, C. et al. An inducible mouse model for skin cancer reveals distinct roles for gain- and loss-of-function p53 mutations. J. Clin. Invest. 117, 1893–1901 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Myers, J. N., Holsinger, F. C., Jasser, S. A., Bekele, B. N. & Fiddler, I. J. An orthotopic nude mouse model of oral tongue squamous cell carcinoma. Clin. Cancer. Res. 8, 293–298 (2002).

  40. Foy, J. P. et al. The dynamics of gene expression changes in a mouse model of oral tumorigenesis may help refine prevention and treatment strategies in patients with oral cancer. Oncotarget 7, 35932–35945 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  41. Tang, X. H., Knudsen, B., Bemis, D., Tickoo, S. & Gudas, L. J. Oral cavity and esophageal carcinogenesis modeled in carcinogen-treated mice. Clin. Cancer Res. 10, 301–313 (2004).

    Article  CAS  PubMed  Google Scholar 

  42. Guagliardo, N. A. & Hill, D. L. Fungiform taste bud degeneration in C57BL/6J mice following chorda-lingual nerve transection. J. Comp. Neurol. 504, 206–216 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  43. Ricklefs, F. et al. Extracellular vesicles from high-grade glioma exchange diverse pro-oncogenic signals that maintain intratumoral heterogeneity. Cancer Res. 76, 2876–2881 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Haderk, F. et al. Extracellular vesicles in chronic lymphocytic leukemia. Leuk. Lymphoma 54, 1826–1830 (2013).

    Article  CAS  PubMed  Google Scholar 

  45. FASTQC: a quality control tool for high throughput sequence data (Babraham Bioinformatics, 2011).

  46. Kim, D. et al. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 14, R36 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  47. Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  48. Anders, S., Pyl, P. T. & Huber, W. HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015).

    Article  CAS  PubMed  Google Scholar 

  49. Robinson, M. D. & Oshlack, A. A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol. 11, R25 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  50. Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

    Article  CAS  PubMed  Google Scholar 

  51. Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal 17, 10–12 (2011).

    Article  Google Scholar 

  52. Li, H. & Durbin, R. Fast and accurate long-read alignment with Burrows-Wheeler transform. Bioinformatics 26, 589–595 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  53. 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 

  54. Bandyopadhyay, S. & Mitra, R. TargetMiner: microRNA target prediction with systematic identification of tissue-specific negative examples. Bioinformatics 25, 2625–2631 (2009).

    Article  CAS  PubMed  Google Scholar 

  55. Agarwal, V., Bell, G. W., Nam, J. W. & Bartel, D. P. Predicting effective microRNA target sites in mammalian mRNAs. eLife 4, (2015).

  56. Wong, N. & Wang, X. miRDB: an online resource for microRNA target prediction and functional annotations. Nucleic Acids Res. 43, D146–D152 (2015).

    Article  CAS  PubMed  Google Scholar 

  57. Wang, X. Improving microRNA target prediction by modeling with unambiguously identified microRNA-target pairs from CLIP-ligation studies. Bioinformatics 32, 1316–1322 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Kozomara, A. & Griffiths-Jones, S. miRBase: annotating high confidence microRNAs using deep sequencing data. Nucleic Acids Res. 42, D68–D73 (2014).

    Article  CAS  PubMed  Google Scholar 

  59. Chou, C. H. et al. miRTarBase update 2018: a resource for experimentally validated microRNA-target interactions. Nucleic Acids Res. 46, D296–D302 (2018).

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

G.A.C. is the Felix L. Haas Endowed Professor in Basic Science. Work in the Calin laboratory is supported by National Institutes of Health (NIH/NCATS) grant UH3TR00943-01 through the NIH Common Fund, Office of Strategic Coordination (OSC), NCI grants 1R01CA182905-01 and 1R01CA222007-01A1, National Institute of General Medical Sciences (NIGMS) grant 1R01GM122775-01, U54 grant CA096297/CA096300 – UPR/MDACC Partnership for Excellence in Cancer Research 2016 Pilot Project, US Department of Defense grant CA160445P1, a Chronic Lymphocytic Leukemia Moonshot Flagship Project, a Sister Institution Network Fund (SINF) 2017 grant, and the Estate of C. G. Johnson Jr. Work in the Dougherty laboratory is supported by NIH grant CA200263, Thompson Family Foundation Initiative; P.M.D. is the H.E.B. Endowed Professor in Basic Science. The NIH Cancer Center Support Grant P30CA016672 supports the High Resolution Electron Microscopy Facility (K. Dunner Jr) and the Advanced Technology Genomics Core (core grant CA016672) at The University of Texas MD Anderson Cancer Center. We thank M. Sushnitha for assistance with miRNA encapsulation; A. Patel for technical assistance; J. K. Burks for discussions and technical assistance with image analysis; C. M. Johnston; H. Kimhi; S. J. Bronson; E. Kimhi and D. M. Aten for artistic work; and our patients and their families. This work was supported by National Institute of Dental and Craniofacial Research grant 5R01 DE014613 12 (J.N.M.) and by S.I.A. funds (G.A.C.). Y.C. and R.W. were funded by the National Natural Science Foundation of China (NSFC, 81741082). D.A.S. is supported by NCI fellowship NIH/NCI F30CA228258.

Author information

Author notes

  1. These authors contributed equally: Moran Amit, Hideaki Takahashi

Authors and Affiliations

  1. Department of Head and Neck Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Moran Amit, Hideaki Takahashi, Antje Lindemann, Frederico O. Gleber-Netto, Curtis R. Pickering, Abdullah A. Osman, Yu Cai, Rong Wang, Samantha Tam, Mei Zhao & Jeffrey N. Myers

  2. Department of Otorhinolaryngology Head and Neck Surgery, Yokohama City University, Yokohama, Japan

    Hideaki Takahashi

  3. Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Mihnea Paul Dragomir, Simone Anfossi, Erik Knutsen, Masayoshi Shimizu, Cristina Ivan & George A. Calin

  4. Department of Medical Biology, Faculty of Health Sciences, UiT, The Arctic University of Norway, Tromsø, Norway

    Erik Knutsen

  5. Center for RNA Interference and Non-coding RNAs, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Masayoshi Shimizu, Cristina Ivan & George A. Calin

  6. Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Xiayu Rao & Jing Wang

  7. Department of Melanoma Medical Oncology, Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Deborah A. Silverman

  8. Department of Otolaryngology, Head and Neck Surgery, University of Arizona, Tucson, AZ, USA

    Carlos Caulin

  9. University of Arizona Cancer Center, University of Arizona, Tucson, AZ, USA

    Carlos Caulin

  10. Regenerative Medicine Program, Houston Methodist Research Institute, Houston, TX, USA

    Assaf Zinger & Ennio Tasciotti

  11. Department of Orthopedics and Sports Medicine, Houston Methodist Hospital, Houston, TX, USA

    Assaf Zinger & Ennio Tasciotti

  12. Department of Pain Medicine, Division of Anesthesiology, Critical Care, and Pain Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Patrick M. Dougherty

  13. Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Adel El-Naggar

  14. Department of Cancer Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Jeffrey N. Myers

Authors
  1. Moran Amit
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hideaki Takahashi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mihnea Paul Dragomir
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Antje Lindemann
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Frederico O. Gleber-Netto
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Curtis R. Pickering
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Simone Anfossi
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Abdullah A. Osman
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Yu Cai
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Rong Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Erik Knutsen
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Masayoshi Shimizu
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Cristina Ivan
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Xiayu Rao
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Jing Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Deborah A. Silverman
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Samantha Tam
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Mei Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Carlos Caulin
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Assaf Zinger
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Ennio Tasciotti
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Patrick M. Dougherty
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. Adel El-Naggar
    View author publications

    You can also search for this author in PubMed Google Scholar

  24. George A. Calin
    View author publications

    You can also search for this author in PubMed Google Scholar

  25. Jeffrey N. Myers
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

J.W. and X.R. analysed the RNA and miRNA sequencing and performed statistical analysis; M.S. performed the ncRNA array experiments; C.I. analysed the RNA and miRNA arrays and performed statistical analysis; F.O.G.-N. analysed the TCGA data and performed statistical analysis; M.Z. produced and cultured oral keratinocytes; M.A., H.T., M.P.D., A.L. and S.A. performed in vivo orthotopic model experiments; M.A. and H.T. performed the animal surgery; Y.C. and R.W. performed in vivo carcinogen-induced genetically engineered mouse model experiments; H.T., M.P.D., A.L. and S.A. performed western blots; A.E.-N. and M.A. reviewed human and mouse pathology; P.M.D. provided and cultured human DRG neurons; M.A., H.T. and A.L. performed in vitro neuron growth studies and catecholamine measurements; A.L. performed miRNA qPCR; E.K. and M.P.D. designed and prepared CRISPR knockout cells; S.A. performed EV characterization and quantification; A.A.O. provided p53-isogenic cells; C.C. provided transgenic animals; D.A.S. interpreted p53 mutational data, analysed miRNA target gene pathways, and revised the manuscript; S.T. and C.R.P. provided the cohort of patients; A.Z. and E.T. provided miRNA-loaded nanoparticles (liposomes); M.A. wrote the manuscript with input from all authors; H.T. designed the figures; and G.A.C. and J.N.M. designed and supervised all experiments, prepared figures, and wrote the manuscript.

Corresponding authors

Correspondence to Moran Amit, George A. Calin or Jeffrey N. Myers.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature thanks Elsa Flores, Hector Peinado and Hongjun Song for their contribution to the peer review of this work.

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

Extended data figures and tables

Extended Data Fig. 1 High nerve density was associated with the presence of p53 mutation.

a, Representative haematoxylin and eosin image of OCSCC samples from TCGA demonstrating low (top) and high (bottom) nerve densities; data independently replicated in 231 samples. Asterisks represent neural structures. b, Overall survival of patients with OCSCC with high (≥4 neurofilaments per field) and low nerve densities. Two-sided log-rank test. c, Quantification of nerve density in TCGA OCSCC patient cohort (n = 231). Bar graphs represent mean ± s.e.m. Unpaired two-tailed t-test. d, Serial in vivo analyses of PCI-13 cell engraftment and growth in BALB/c (nu/nu) mice (n = 6 per group). Tumour growth curves represent mean tumour volume ± s.e.m. Unpaired two-tailed t-test. e, Representative immunofluorescence of glossectomy specimens taken 4 weeks after orthotopic injection of isogenic p53WT or p53null PCI-13 cells; data independently replicated in 16 mice. f, Quantification of nerve area in p53WT and p53null OCSCC xenografts (n = 8 mice per group). g, Quantification of neuritogenesis in DRG co-cultured with p53-isogenic PCI-13 cells or normal oral keratinocytes (n = 6 biologically independent ganglia per cell line). h, Immunoblots demonstrating the knockdown of TP53 in human HN30 OCSCC cells; data replicated in two independent experiments. i, Representative immunofluorescence staining of neo-neurites (β3-tubulin+) in DRG co-cultured with HN30 (left) and HN30-shp53 (right) OCSCC cells. Data independently replicated in 13 samples. j–l, In vitro quantification of number (j), branching (k), and length (l) of neurofilaments protruding from ex vivo DRG co-cultured with HN30 (n = 8 ganglia) or HN30-shp53 (n = 5 ganglia) OCSCC cells. Bar graphs represent mean ± s.e.m. One-way ANOVA with Tukey multiple comparison.

Source data

Extended Data Fig. 2 OCSCC-derived EVs and neuritogenesis.

a, Representative transmission electron microscopy image of EVs from isogenic PCI-13, HN30, and HN31 OCSCC cells; data replicated in two independent experiments. b–d, Size distribution from nanoparticle tracking analysis of particles derived from p53WT (b) or p53null (c) PCI-13 cells or HN30 and HN31 (d) cells; data replicated in two independent experiments. e, Western blot of the EV marker CD63 in EVs from PCI-13 and HN30/HN31 cells; data replicated in two independent experiments. f, Western blot of the p53 and controls (HSP70) in p53WT or p53Null PCI-13 and HN31 cells and their corresponding cell-derived EVs; data replicated in three independent experiments. g, Confocal immunofluorescence images showing EVs (lipophilic DiI-labelled, red) in the cytoplasm of a neuron (labelled with β3-tubulin, green) 8 h after application of EVs derived from p53WT or p53null PCI-13 cells. Percentage represents the proportion of DiI+ β3-tubulin+ neurons out of all β3-tubulin+ neurons (n = 6 ganglia per condition). h, Immunoblots demonstrating the knockout (KO) of RAB27A and RAB27B in OCSCC cells edited with sgRNAs targeting RAB27A and RAB27B (HN31 clones 11 and 18, respectively) compared with HN31 controls; data replicated in two independent experiments. i, Nanoparticle tracking analysis of EV particle number in conditioned medium from HN31 clones 11 (n = 5 biologically independent samples) and 18 (n = 7 biologically independent samples) compared with HN31 controls (n = 6 biologically independent samples). Number of EVs was adjusted to cell number; bars represent mean ± s.e.m. Unpaired two-tailed t-test. j, k, In vitro quantification of branching (j) and neurofilament length (k) in freshly collected DRGs cultured with conditioned medium from HN31 RAB27A+/+RAB27B+/+ (n = 8) and HN31 RAB27A−/−RAB27B−/− (n = 5) isogenic human OCSCC cells. Bar graphs and tumour growth curves represent mean tumour volume ± s.e.m. Unpaired two-tailed t-test.

Source data

Extended Data Fig. 3 p53-dependent miRNA in OCSCC.

a, OCSCC RNA transfer to neurons via EVs. Representative confocal immunofluorescence image demonstrating PCI-13 cell-derived RNA labelled with SYTO RNASelect (green) in the perinuclear cytoplasm of a neuron (labelled with β3-tubulin, red). Images were captured 12 h after application of EVs derived from PCI-13 cells labelled with SYTO RNASelect; data replicated in two independent experiments. b, EVs derived from PCI-13 cells contained mainly small RNA species. Bioanalyzer results showing presence of RNA in EVs from PCI-13 cells. Representative band of EV RNA by Agilent RNA Pico Chips; data independently replicated in ten experiments. c, An unsupervised hierarchical clustering heat map showing differentially expressed EV miRNAs between p53-isogenic PCI-13 cells. p53WT, n = 3 biologically independent samples; p53null and p53mut, n = 14 biologically independent samples. d, Heat map of differentially expressed miRNA, arranged by unsupervised hierarchical clustering, presenting the miRNA sequencing for EVs derived from isogenic PCI-13 cells expressing p53WT versus no p53 (p53null) or mutant p53 (p53C238F, p53G245D, and p53R273H). The Pearson distance and Ward’s minimum variance method were used for pairwise clustering (c, d). Red and green indicate increased and decreased expression levels, respectively (n = 2 to 5 per group). e, Fold change in hsa-miR-141-5p and hsa-miR-34a-5p in EVs derived from p53WT PCI-13 cells (blue, n = 3 biologically independent samples) compared with p53null or p53mut cells (red, n = 14 biologically independent samples). Results are log2 normalized. f, Real-time PCR quantification of miR-34a and miR-141 in ventral tongues from Trp53flox/flox and Krt5CreTrp53flox/flox mice (n = 7 per group). g, Real-time PCR quantification of CDK6 (miR-34a target) and ZEB1 (miR-141 target) in neurons treated with antagomiR-34 or antagomiR-141 compared with nonspecific antagomiR-treated controls (n = 3 biologically independent samples per group). h, Quantitative validation of miR-34a and miR-141 overexpression after transfection with miR-34a and miR-141 mimics, respectively. TG neurons were transfected with miR-34a mimic, miR-141 mimic, or scramble miR, and overexpression of miR-34a and miR-141 was confirmed by real-time PCR (n = 7 biologically independent samples per group). i, Real-time PCR quantification of miR-34a in orthotopic tumour xenografts of HN30 OCSCC cells treated with shControl (blue) or shmiR-34a (purple). n = 4 biologically independent samples per group. j, k, Western blot of NOTCH1 (confirmed miR-34a target) in OCSCC transfected with lentiviral miR-34a inhibitor or scramble miRNA inhibitor (j). Bar graph quantification of the blots demonstrates no impact of miR-34a inhibition on p53 expression and is normalized to the total amount of β-actin (n = 4 biologically independent samples per group, j). Unpaired two-tailed t-test; bars and dot plots represent mean ± s.e.m. (e–i, k).

Source data

Extended Data Fig. 4 microRNAs modulate neuritogenesis.

a, Screening of candidate neuritogenesis-associated miRNAs. Quantification of neuritogenesis 72 h after neuron–EV co-culture. Eight hours after transfection with 13 different antagomiRs, TG neurons were incubated with EVs derived from p53null PCI-13 cells (n = 4 biologically independent samples per condition). One-way ANOVA with Tukey multiple comparison. b, Quantification of neuritogenesis in TG neurons 72 h after transfection with miR-21 mimic, miR-197 mimic, or miR-324 mimic or co-transfection with their combinations (n = 3 biologically independent samples per condition). c, Representative fluorescent–bright-field overlay images demonstrating the lack of response of TG neurons exposed to EVs derived from p53null PCI-13 cells after co-transfection with antagomiR-21 and antagomiR-324 and the response of TG neurons after miR-21 and miR-324 mimic co-transfection. Data replicated across six independent samples. d, Quantitative validation of miR-21 and miR-324 overexpression in TG neurons incubated with liposomes containing miR-21, miR-324, and scramble miRNA (n = 3 biologically independent samples per group). e, Representative fluorescence–bright-field overlay images of TG neurons exposed to liposomes containing miR-21, miR-324, and scramble miRNA or liposomes containing miR-21, miR-324, and miR-34a; data independently replicated in 20 wells. f, Quantification of neuritogenesis in TG neurons 72 h after neuron–liposome co-culture (n = 5 biologically independent samples per condition). Unpaired two-tailed t-test; bars represent mean ± s.e.m. (a, b, d) or one-way ANOVA with Tukey multiple comparisons (f).

Source data

Extended Data Fig. 5 TP53 deficiency does not change parasympathetic nerve fibre densities in human OCSCC specimens.

a, b, Representation of vesicular acetylcholine transporter (VAChT)+ nerve densities in both TP53WT and TP53mut OCSCC tissues; data independently replicated in 24 patient specimens (a). Quantification of cholinergic VAChT+ neural areas in TP53-sufficient (TP53WT, blue, n = 12) and TP53-deficient (TP53mut, red, n = 12) human OCSCC tissues. Each dot represents the mean for one patient (b). NF-H, neurofilament heavy. c, d, TP53 deficiency increases the sympathetic nerve fibre density in normal tongue tissue surrounding OCSCC in humans. Representative images showing TH+ adrenergic neural fibres in human normal tongue tissue surrounding OCSCC with TP53WT(left) or TP53mut (right) (TH, green; neurofilament light (NF-L), red; DAPI, blue). Data independently replicated in 24 patient specimens (c). Quantification of adrenergic TH+ areas in TP53-sufficient (TP53WT, blue, n = 12) and TP53-deficient (TP53mut, red, n = 12) human OCSCC samples. Each dot represents the mean for a patient (d). e, Correlation of TH and NF-L expression levels. Linear regression (r2, n = 24 biologically independent samples). f–h, Representative images of TG neurons labelled with anti-TH antibody after incubation with EVs derived from p53WT or p53null PCI-13 cells; data independently replicated in 14 wells (f). Quantification of TH+ neurons (n = 7 biologically independent samples per condition, g), and noradrenaline levels (n = 4 biologically independent samples per condition, h). i–k, Co-culture of TG neurons with p53null EVs for 72 h induced TH coexpression in TRPV1+ but not IB4+ neurons; TH expression remained stable 72 h after washout of the EVs. n = 4 biologically independent samples per condition (k). l, TH+ neural areas in PCI-13–p53null orthotopic tumours injected daily with no EVs or with EVs derived from p53null or p53WT PCI-13 cells for 3 weeks; data independently replicated in 15 mice. m, Co-culture of TG with liposomes containing miR-21 and miR-324 but not miR-34a increases catecholamine synthesis. Noradrenaline levels in neurons cultured with nano-liposomes containing miR-21 + miR-34a + miR-324 or miR-21 + miR-324 controls, quantified by enzyme-linked immunosorbent assay. n = 3 biologically independent samples per condition. n, Heat map of differentially expressed genes in mouse TG neurons co-cultured with p53-isogenic EVs. Enriched Gene Ontology terms of the neurons were plotted at fold enrichment with the associated log P value (Fisher’s exact algorithm for functional gene set enrichment); n = 3 biologically independent samples for p53WT and n = 4 biologically independent samples for p53null. Mean ± s.e.m.; unpaired two-tailed t-test (b, d, g, h, k, m).

Source data

Extended Data Fig. 6 Target analysis for miR-21, miR-34a, and miR-324.

a, Schematic of Ingenuity pathway analysis of pain signalling (silenced, green) and noradrenaline biosynthesis (activated, red). AADC, aromatic l-amino acid decarboxylase; DBH, dopamine β-hydroxylase; PNMT, phenylethanolamine N-methyltransferase. b, Total RNA was collected to assess global gene changes after TG neurons were transfected with miR-21 (red), miR-324 (blue), miR-34a (green), or scrambled miRNA. Data show fold change in expression of potential targets involved in neural identity determination (left), neural growth (middle), and neural function (right) between neurons transfected with different miRNAs (n = 3 biologically independent samples per condition). P values obtained from the moderated t-statistic for the presented genes were <0.05. Forest plots displaying fold change and P values for genes on different neuron pathways that are significantly differentiated between TG neurons transfected with miR-21, miR-34a or miR-324 and scramble miRNA. Linear models and empirical Bayes methods were used for obtaining the statistics and assessing differential gene expression between two conditions.

Extended Data Fig. 7 Loss of p53 in OCSCC induces adrenergic switch proximally in TG neurons.

a, Flow cytometry quantification of neurotrophin-3-positive (NT3+), TH+ neurons in freshly collected ipsilateral TG neurons 3 weeks after orthotopic inoculation of p53null PCI-13 cells to the tongues of sympathectomized mice. Non-tumour-bearing, sympathectomized mice were used as controls (n = 6). b, Representative immunohistochemical analysis for TH+ in orthotopic xenografts; data independently replicated in 16 mice. c, Flow cytometry quantification of NT3+TH+ neurons in ipsilateral TG neurons (n = 6 mice per condition). d, Representative images of TH+ TG neurons in mice without tumours (left) and 3 weeks after injection of p53WT (middle) or p53null (right) PCI-13 cells to the ipsilateral tongue; data independently replicated in nine mice. e, Flow cytometry quantification of NT3+ TH+ neurons in freshly collected ipsilateral TG 3 weeks after orthotopic inoculation of p53WT (middle) and p53null (right) PCI-13 cells to the tongue. Non-tumour-bearing mice were used as controls (left, n = 12 per group). f, Serial in vivo analyses of tumour growth after engraftment of HN30 transfected with either control lentivirus (HN30-lenti) or shmiR34a (HN30-shmiR34a) into BALB/c (nu/nu) mice. Mice were randomized and underwent lingual denervation or sham surgery 1 week before cell injection (n = 8 per group). Tumour growth curves represent mean tumour volume ± s.e.m.; unpaired two-tailed t-test. g–i, Neural density (g), TH+ area (h), and noradrenaline levels in vivo (i) in HN30-lenti and HN30-shmiR34a orthotopic xenografts with and without lingual denervation (n = 5 biologically independent samples per condition). Bars indicate mean ± s.e.m.; unpaired two-tailed t-test.

Source data

Extended Data Fig. 8 Characterization of OCSCC-induced neural transcriptional program.

a, Heat map of differentially expressed genes arranged by unsupervised hierarchical clustering in TG neurons 3 weeks after orthotopic injection of p53null or p53WT PCI-13 cells (n = 3 biologically independent samples per condition) and enriched Gene Ontology terms plotted by fold enrichment with the associated log P value (right; Fisher’s exact algorithm for functional gene set enrichment). b, Flow cytometry quantification of NeuroFluor-positive (NeuO+), POU5F1+ (left), NeuO+KLF4+ (middle), and NeuO+ASCL1+ (right) neurons in ipsilateral TGs after orthotopic injection of p53null or p53WT PCI-13 cells; data independently replicated in six mice. c, Representative images in freshly collected TG neurons (red, NF-H+) from BALB/c (nu/nu) mice after orthotopic injection of either p53null or p53WT PCI-13 cells to the ipsilateral tongue, demonstrating lack of nuclear expression of the transcription factors SOX2, TBR2, and DCX and similar neurogenin2 expression between groups. Data independently replicated in six mice; DAPI, blue. d, e, Representative necropsy photograph (TG delineated by dashed line in c) and flow cytometry quantification of NeuO+ neurons in freshly collected TG 3 weeks after tumour injection to the left side of the tongue; ipsilateral (c, right, black arrowhead) and contralateral (c, left, white arrowhead) ganglia were similar in size. None of the tumours crossed the midline of the tongue (n = 6). f, g, Mice were treated daily with either β-adrenergic receptor blocker carvedilol or vehicle via oral gavage. On day 5, mice were orthotopically xenografted with human p53-deficient (p53null PCI-13 or HN30-shp53) cells to the tongue. Serial in vivo tumour volume measurement (n = 12 per group except for p53null PCI-13 tumour-bearing mice with carvedilol treatment, n = 13; f). h, Adrenergic inhibition decreases OCSCC proliferation in vivo. Carvedilol injections inhibited the proliferation of p53null PCI-13 cells orthotopically implanted into the tongue, as determined by Ki-67 expression (n = 6 biologically independent samples per condition). i, Kaplan–Meier curves showing the recurrence-free survival of patients with high (>2,000 μm2 per field) and low (≤2,000 μm2 per field) TH+ adrenergic nerve densities. Mean ± s.e.m.; unpaired two-tailed t-test.

Source data

Supplementary information

Supplementary Information

This file contains Supplementary Figure 1: Uncropped western blot scans; Supplementary Table 1: Cardiovascular hemodynamics in mice treated with carvedilol; Supplementary Table 2: Patient characteristics; Supplementary Table 3. Multivariate analysis of survival; Materials and Methods; and additional references.

Reporting Summary

Source data

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Amit, M., Takahashi, H., Dragomir, M.P. et al. Loss of p53 drives neuron reprogramming in head and neck cancer. Nature 578, 449–454 (2020). https://doi.org/10.1038/s41586-020-1996-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41586-020-1996-3

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Advanced search

Quick links

Nature Briefing: Cancer

Sign up for the Nature Briefing: Cancer newsletter — what matters in cancer research, free to your inbox weekly.

Get what matters in cancer research, free to your inbox weekly. Sign up for Nature Briefing: Cancer