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:

Comprehensive molecular profiling of UV-induced metastatic melanoma in Nme1/Nme2-deficient mice reveals novel markers of survival in human patients

Oncogene volume 40, pages 6329–6342 (2021)Cite this article

Subjects

A Correction to this article was published on 11 October 2021

This article has been updated

Abstract

Hepatocyte growth factor-overexpressing mice that harbor a deletion of the Ink4a/p16 locus (HP mice) form melanomas with low metastatic potential in response to UV irradiation. Here we report that these tumors become highly metastatic following hemizygous deletion of the Nme1 and Nme2 metastasis suppressor genes (HPN mice). Whole-genome sequencing of melanomas from HPN mice revealed a striking increase in lung metastatic activity that is associated with missense mutations in eight signature genes (Arhgap35, Atp8b4, Brca1, Ift172, Kif21b, Nckap5, Pcdha2, and Zfp869). RNA-seq analysis of transcriptomes from HP and HPN primary melanomas identified a 32-gene signature (HPN lung metastasis signature) for which decreased expression is strongly associated with lung metastatic potential. Analysis of transcriptome data from The Cancer Genome Atlas revealed expression profiles of these genes that predict improved survival of patients with cutaneous or uveal melanoma. Silencing of three representative HPN lung metastasis signature genes (ARRDC3, NYNRIN, RND3) in human melanoma cells resulted in increased invasive activity, consistent with roles for these genes as mediators of the metastasis suppressor function of NME1 and NME2. In conclusion, our studies have identified a family of genes that mediate suppression of melanoma lung metastasis, and which may serve as prognostic markers and/or therapeutic targets for clinical management of metastatic melanoma.

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: Hemizygous deletion of the Nme1/Nme2 locus increases metastatic activity of UVR-induced melanomas in the HP mouse model.
Fig. 2: Summary of nonsilent mutations identified in primary melanomas of HP and HPN mice.
Fig. 3: Identification of a 32-gene expression signature associated with lung metastasis.
Fig. 4: Expression of genes associated with four melanoma metastasis-relevant pathways is negatively correlated with metastatic activity of primary melanomas of HP and HPN mice.
Fig. 5: Genes enriched for missense mutations in metastatic melanomas of HP and HPN melanomas are frequently mutated in human melanomas.
Fig. 6: The HPN-LMS predicts survival in cutaneous melanoma patients (TCGA-SKCM).
Fig. 7: The HPN-LMS predicts survival in three subtypes of uveal melanoma patients (TCGA-UVM).
Fig. 8: Four HPN-LMS genes (ARRDC3, FLRT3, NYNRIN, and RND3) associated with higher overall survival in human melanoma patients exhibit metastasis-suppressor functions in human melanoma cells.

Similar content being viewed by others

Change history

References

  1. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2019. CA Cancer J Clin. 2019;69:7–34.

    Article  PubMed  Google Scholar 

  2. Smith SC, Theodorescu D. Learning therapeutic lessons from metastasis suppressor proteins. Nat Rev Cancer. 2009;9:253–64.

    CAS  PubMed  PubMed Central  Google Scholar 

  3. Steeg PS, Bevilacqua G, Kopper L, Thorgerisson UR, Talmadge JE, Liotta LA, et al. Evidence for a novel gene associated with low tumor metastatic potential. J Natl Cancer Inst. 1988;80:200–5.

    CAS  PubMed  Google Scholar 

  4. Zhang Q, McCorkle JR, Novak M, Yang M, Kaetzel DM. Metastasis suppressor function of NM23-H1 requires its 3’-5’ exonuclease activity. Int J Cancer. 2011;128:40–50.

    CAS  PubMed  PubMed Central  Google Scholar 

  5. Leone A, Flatow U, VanHoutte K, Steeg PS. Transfection of human nm23-H1 into the human MDA-MB-435 breast carcinoma cell line: effects on tumor metastatic potential, colonization and enzymatic activity. Oncogene. 1993;8:2325–33.

    CAS  PubMed  Google Scholar 

  6. Hartsough MT, Steeg PS. Nm23/nucleoside diphosphate kinase in human cancers. J Bioenerg Biomembr. 2000;32:301–8.

    CAS  PubMed  Google Scholar 

  7. Boissan M, Wendum D, Arnaud-Dabernat S, Munier A, Debray M, Lascu I, et al. Increased lung metastasis in transgenic NM23-Null/SV40 mice with hepatocellular carcinoma. J Natl Cancer Inst. 2005;97:836–45.

    CAS  PubMed  Google Scholar 

  8. Pamidimukkala N, Puts GS, Leonard MK, Snyder D, Dabernat S, De Fabo EC, Noonan FP, Slominski A, Merlino G, Kaetzel DM. Nme1 and Nme2 genes exert metastasis suppressor activities in a genetically-engineered mouse model of UV-induced melanoma. Br J Cancer. 2021;124:161–5.

    CAS  PubMed  Google Scholar 

  9. Marino N, Nakayama J, Collins JW, Steeg PS. Insights into the biology and prevention of tumor metastasis provided by the Nm23 metastasis suppressor gene. Cancer Metastasis Rev. 2012;31:593–603.

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Horak CE, Lee JH, Elkahloun AG, Boissan M, Dumont S, Maga TK, et al. Nm23-H1 suppresses tumor cell motility by down-regulating the lysophosphatidic acid receptor EDG2. Cancer Res. 2007;67:7238–46.

    CAS  PubMed  Google Scholar 

  11. Leonard MK, McCorkle JR, Snyder DE, Novak M, Zhang Q, Shetty AC, et al. Identification of a gene expression signature associated with the metastasis suppressor function of NME1: prognostic value in human melanoma. Lab Investig. 2017;98:327–38.

    PubMed  Google Scholar 

  12. Horak CE, Mendoza A, Vega-Valle E, Albaugh M, Graff-Cherry C, McDermott WG, et al. Nm23-H1 suppresses metastasis by inhibiting expression of the lysophosphatidic acid receptor EDG2. Cancer Res. 2007;67:11751–9.

    CAS  PubMed  Google Scholar 

  13. Leonard MK, Novak M, Snyder D, Snow G, Pamidimukkala N, McCorkle JR, et al. The metastasis suppressor NME1 inhibits melanoma cell motility via direct transcriptional induction of the integrin beta-3 gene. Exp Cell Res. 2019;374:85–93.

    CAS  PubMed  Google Scholar 

  14. Kaetzel DM, Leonard MK, Cook GS, Novak M, Jarrett SG, Yang X, et al. Dual functions of NME1 in suppression of cell motility and enhancement of genomic stability in melanoma. Naunyn Schmiedebergs Arch Pharmacol. 2015;388:199–206.

    CAS  PubMed  Google Scholar 

  15. Yang M, Jarrett SG, Craven R, Kaetzel DM. YNK1, the yeast homolog of human metastasis suppressor NM23, is required for repair of UV radiation- and etoposide-induced DNA damage. Mutat Res. 2009;660:74–8.

    CAS  PubMed  Google Scholar 

  16. Sheng Y, Xu M, Li C, Xiong Y, Yang Y, Kuang X, et al. Nm23-H1 is involved in the repair of ionizing radiation-induced DNA double-strand breaks in the A549 lung cancer cell line. BMC Cancer. 2018;18:710.

    PubMed  PubMed Central  Google Scholar 

  17. Xue R, Peng Y, Han B, Li X, Chen Y, Pei H. Metastasis suppressor NME1 promotes non-homologous end joining of DNA double-strand breaks. DNA Repair. 2019;77:27–35.

    CAS  PubMed  Google Scholar 

  18. Recio JA, Noonan FP, Takayama H, Anver MR, Duray P, Rush WL, et al. Ink4a/arf deficiency promotes ultraviolet radiation-induced melanomagenesis. Cancer Res. 2002;62:6724–30.

    CAS  PubMed  Google Scholar 

  19. Noonan FP, Recio JA, Takayama H, Duray P, Anver MR, Rush WL, et al. Neonatal sunburn and melanoma in mice. Nature. 2001;413:271–2.

    CAS  PubMed  Google Scholar 

  20. Garibyan L, Fisher DE. How sunlight causes melanoma. Curr Oncol Rep. 2010;12:319–26.

    CAS  PubMed  Google Scholar 

  21. Postel EH, Wohlman I, Zou X, Juan T, Sun N, D’Agostin D, et al. Targeted deletion of Nm23/nucleoside diphosphate kinase A and B reveals their requirement for definitive erythropoiesis in the mouse embryo. Dev Dyn. 2009;238:775–87.

    CAS  PubMed  Google Scholar 

  22. Pérez-Guijarro E, Yang HH, Araya RE, El Meskini R, Michael HT, Vodnala SK, et al. Multimodel preclinical platform predicts clinical response of melanoma to immunotherapy. Nat Med. 2020;26:781–91.

    PubMed  PubMed Central  Google Scholar 

  23. Van Raamsdonk CD, Bezrookove V, Green G, Bauer J, Gaugler L, O’Brien JM, et al. Frequent somatic mutations of GNAQ in uveal melanoma and blue naevi. Nature. 2009;457:599–602.

    PubMed  Google Scholar 

  24. Van Raamsdonk CD, Griewank KG, Crosby MB, Garrido MC, Vemula S, Wiesner T, et al. Mutations in GNA11 in uveal melanoma. N Engl J Med. 2010;363:2191–99.

    PubMed  PubMed Central  Google Scholar 

  25. Diskin SJ, Eck T, Greshock J, Mosse YP, Naylor T, Stoeckert CJ Jr., et al. STAC: A method for testing the significance of DNA copy number aberrations across multiple array-CGH experiments. Genome Res. 2006;16:1149–58.

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Beroukhim R, Getz G, Nghiemphu L, Barretina J, Hsueh T, Linhart D, et al. Assessing the significance of chromosomal aberrations in cancer: methodology and application to glioma. Proc Natl Acad Sci USA. 2007;104:20007–12.

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Ahn A, Chatterjee A, Eccles MR. The slow cycling phenotype: a growing problem for treatment resistance in melanoma. Mol Cancer Ther. 2017;16:1002–9.

    CAS  PubMed  Google Scholar 

  28. Hugo W, Zaretsky JM, Sun L, Song C, Moreno BH, Hu-Lieskovan S, et al. Genomic and transcriptomic features of response to anti-PD-1 therapy in metastatic melanoma. Cell. 2016;165:35–44.

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Van Allen EM, Miao D, Schilling B, Shukla SA, Blank C, Zimmer L, et al. Genomic correlates of response to CTLA-4 blockade in metastatic melanoma. Science. 2015;350:207–11.

    PubMed  PubMed Central  Google Scholar 

  30. Boissan M, Lacombe M-L. NM23, an example of a metastasis suppressor gene. Bull Cancer. 2012;99:431–40.

    CAS  PubMed  Google Scholar 

  31. Snyder D, Wang Y, Kaetzel DM. A rare subpopulation of melanoma cells with low expression of metastasis suppressor NME1 is highly metastatic in vivo. Sci Rep. 2020;10:1971.

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Abkevich V, Timms KM, Hennessy BT, Potter J, Carey MS, Meyer LA, et al. Patterns of genomic loss of heterozygosity predict homologous recombination repair defects in epithelial ovarian cancer. Br J Cancer. 2012;107:1776–82.

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Popova T, Manié E, Rieunier G, Caux-Moncoutier V, Tirapo C, Dubois T, et al. Ploidy and large-scale genomic instability consistently identify basal-like breast carcinomas with BRCA1/2 inactivation. Cancer Res. 2012;72:5454–62.

    CAS  PubMed  Google Scholar 

  34. Molina-Ortiz I, Bartolome RA, Hernandez-Varas P, Colo GP, Teixido J. Overexpression of E-cadherin on melanoma cells inhibits chemokine-promoted invasion involving p190RhoGAP/p120ctn-dependent inactivation of RhoA. J Biol Chem. 2009;284:15147–57.

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Chen D, Li Y, Zhang X, Wu H, Wang Q, Cai J, et al. Ubiquitin ligase TRIM65 promotes colorectal cancer metastasis by targeting ARHGAP35 for protein degradation. Oncogene. 2019;38:6429–44.

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Markby DW, Onrust R, Bourne HR. Separate GTP binding and GTPase activating domains of a G alpha subunit. Science. 1993;262:1895–901.

    CAS  PubMed  Google Scholar 

  37. Spivak G. Transcription-coupled repair: an update. Arch Toxicol. 2016;90:2583–94.

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Heraud C, Pinault M, Lagree V, Moreau V. p190RhoGAPs, the ARHGAP35- and ARHGAP5-encoded proteins, in health and disease. Cells. 2019;8:351.

    PubMed Central  Google Scholar 

  39. McIlwain DR, Berger T, Mak TW. Caspase functions in cell death and disease. Cold Spring Harbor Perspectives. Cold Spring Harbor Perspect Biol. 2013;5:a008656.

    Google Scholar 

  40. Hamidi H, Ivaska J. Every step of the way: integrins in cancer progression and metastasis. Nat Rev Cancer. 2018;18:533–48.

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Balint K, Xiao M, Pinnix CC, Soma A, Veres I, Juhasz I, et al. Activation of Notch1 signaling is required for beta-catenin-mediated human primary melanoma progression. J Clin Investig. 2005;115:3166–76.

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Zhang K, Wong P, Salvaggio C, Salhi A, Osman I, Bedogni B. Synchronized targeting of notch and ERBB signaling suppresses melanoma tumor growth through inhibition of Notch1 and ERBB3. J Investig Dermatol. 2016;136:464–72.

    CAS  PubMed  Google Scholar 

  43. Fei F, Qu J, Zhang M, Li Y, Zhang S. S100A4 in cancer progression and metastasis: a systematic review. Oncotarget. 2017;8:73219–39.

    PubMed  PubMed Central  Google Scholar 

  44. Bektic J, Pfeil K, Berger AP, Ramoner R, Pelzer A, Schafer G, et al. Small G-protein RhoE is underexpressed in prostate cancer and induces cell cycle arrest and apoptosis. Prostate. 2005;64:332–40.

    CAS  PubMed  Google Scholar 

  45. Rubenstein NM, Chan JF, Kim JY, Hansen SH, Firestone GL. Rnd3/RhoE induces tight junction formation in mammary epithelial tumor cells. Exp Cell Res. 2005;305:74–82.

    CAS  PubMed  Google Scholar 

  46. Chen LH, Kuo WH, Tsai MH, Chen PC, Hsiao CK, Chuang EY, et al. Identification of prognostic genes for recurrent risk prediction in triple negative breast cancer patients in Taiwan. PLoS ONE. 2011;6:e28222.

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Ekizoglu S, Seven D, Ulutin T, Guliyev J, Buyru N. Investigation of the SLC22A23 gene in laryngeal squamous cell carcinoma. BMC Cancer. 2018;18:477.

    PubMed  PubMed Central  Google Scholar 

  48. Tamura K, Makino A, Hullin-Matsuda F, Kobayashi T, Furihata M, Chung S, et al. Novel lipogenic enzyme ELOVL7 is involved in prostate cancer growth through saturated long-chain fatty acid metabolism. Cancer Res. 2009;69:8133–40.

    CAS  PubMed  Google Scholar 

  49. Arakaki AKS, Pan WA, Trejo J. GPCRs in cancer: protease-activated receptors, endocytic adaptors and signaling. Int J Mol Sci. 2018;19:1886.

    PubMed Central  Google Scholar 

  50. Draheim KM, Chen HB, Tao Q, Moore N, Roche M, Lyle S. ARRDC3 suppresses breast cancer progression by negatively regulating integrin beta4. Oncogene. 2010;29:5032–47.

    CAS  PubMed  PubMed Central  Google Scholar 

  51. Soung YH, Pruitt K, Chung J. Epigenetic silencing of ARRDC3 expression in basal-like breast cancer cells. Sci Rep. 2014;4:3846.

    PubMed  PubMed Central  Google Scholar 

  52. Zheng Y, Lin ZY, Xie JJ, Jiang FN, Chen CJ, Li JX, et al. ARRDC3 inhibits the progression of human prostate cancer through ARRDC3-ITGβ4 pathway. Curr Mol Med. 2017;17:221–9.

    CAS  PubMed  Google Scholar 

  53. Xiao J, Shi Q, Li W, Mu X, Peng J, Li M, et al. ARRDC1 and ARRDC3 act as tumor suppressors in renal cell carcinoma by facilitating YAP1 degradation. Am J Cancer Res. 2018;8:132–43.

    CAS  PubMed  PubMed Central  Google Scholar 

  54. Yang Z, Shang J, Li N, Zhang L, Tang T, Tian G, et al. Development and validation of a 10-gene prognostic signature for acute myeloid leukaemia. J Cell Mol Med. 2020;24:4510–23.

    CAS  PubMed  PubMed Central  Google Scholar 

  55. Mahamdallie S, Yost S, Poyastro-Pearson E, Holt E, Zachariou A, Seal S, et al. Identification of new Wilms tumour predisposition genes: an exome sequencing study. Lancet Child Adolesc Health. 2019;3:322–31.

    PubMed  PubMed Central  Google Scholar 

  56. Paysan L, Piquet L, Saltel F, Moreau V. Rnd3 in cancer: a review of the evidence for tumor promoter or suppressor. Mol Cancer Res. 2016;14:1033–44.

    CAS  PubMed  Google Scholar 

  57. Klein RM, Aplin AE. Rnd3 regulation of the actin cytoskeleton promotes melanoma migration and invasive outgrowth in three dimensions. Cancer Res. 2009;69:2224–33.

    CAS  PubMed  PubMed Central  Google Scholar 

  58. Tomayko MM, Reynolds CP. Determination of subcutaneous tumor size in athymic (nude) mice. Cancer Chemother Pharmacol. 1989;24:148–54.

    CAS  PubMed  Google Scholar 

  59. Aaron KA, Manojlovic Z, Tu N, Xu Y, Jin Y, Chang S, et al. What genes can tell: a closer look at vestibular schwannoma. Otol Neurotol. 2020;41:522–9.

    PubMed  Google Scholar 

  60. Manojlovic Z, Christofferson A, Liang WS, Aldrich J, Washington M, Wong S, et al. Comprehensive molecular profiling of 718 Multiple Myelomas reveals significant differences in mutation frequencies between African and European descent cases. PLoS Genet. 2017;13:e1007087.

    PubMed  PubMed Central  Google Scholar 

  61. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550.

    PubMed  PubMed Central  Google Scholar 

  62. Morrison GJ, Cunha AT, Jojo N, Xu Y, Xu Y, Kwok E, et al. Cancer transcriptomic profiling from rapidly enriched circulating tumor cells. Int J Cancer. 2020;146:2845–54.

    CAS  PubMed  PubMed Central  Google Scholar 

  63. Patro R, Duggal G, Love MI, Irizarry RA, Kingsford C. Salmon provides fast and bias-aware quantification of transcript expression. Nat Methods. 2017;14:417–9.

    CAS  PubMed  PubMed Central  Google Scholar 

  64. Roberts A, Trapnell C, Donaghey J, Rinn JL, Pachter L. Improving RNA-Seq expression estimates by correcting for fragment bias. Genome Biol. 2011;12:r22.

    CAS  PubMed  PubMed Central  Google Scholar 

  65. Roberts A, Pachter L. Streaming fragment assignment for real-time analysis of sequencing experiments. Nat Methods. 2013;10:71–3.

    CAS  PubMed  Google Scholar 

  66. Kim D, Salzberg SL. TopHat-Fusion: an algorithm for discovery of novel fusion transcripts. Genome Biol. 2011;12:r72.

    CAS  PubMed  PubMed Central  Google Scholar 

  67. Fisher ML, Ciavattone N, Grun D, Adhikary G, Eckert RL. Sulforaphane reduces YAP/∆Np63α signaling to reduce cancer stem cell survival and tumor formation. Oncotarget. 2017;8:73407–18.

    PubMed  PubMed Central  Google Scholar 

  68. Krauthammer M, Kong Y, Ha BH, Evans P, Bacchiocchi A, McCusker JP, et al. Exome sequencing identifies recurrent somatic RAC1 mutations in melanoma. Nat Genet. 2012;44:1006–14.

    CAS  PubMed  PubMed Central  Google Scholar 

  69. Hodis E, Watson IR, Kryukov GV, Arold ST, Imielinski M, Theurillat JP, et al. A landscape of driver mutations in melanoma. Cell. 2012;150:251–63.

    CAS  PubMed  PubMed Central  Google Scholar 

  70. Van Allen EM, Wagle N, Sucker A, Treacy DJ, Johannessen CM, Goetz EM, et al. The genetic landscape of clinical resistance to RAF inhibition in metastatic melanoma. Cancer Discov. 2014;4:94–109.

    PubMed  Google Scholar 

  71. Snyder A, Makarov V, Merghoub T, Yuan J, Zaretsky JM, Desrichard A, et al. Genetic basis for clinical response to CTLA-4 blockade in melanoma. N Engl J Med. 2014;371:2189–99.

    PubMed  PubMed Central  Google Scholar 

  72. Berger MF, Hodis E, Heffernan TP, Deribe YL, Lawrence MS, Protopopov A, et al. Melanoma genome sequencing reveals frequent PREX2 mutations. Nature. 2012;485:502–6.

    CAS  PubMed  PubMed Central  Google Scholar 

  73. Liang WS, Hendricks W, Kiefer J, Schmidt J, Sekar S, Carpten J, et al. Integrated genomic analyses reveal frequent TERT aberrations in acral melanoma. Genome Res. 2017;27:524–32.

    CAS  PubMed  PubMed Central  Google Scholar 

  74. Shain AH, Garrido M, Botton T, Talevich E, Yeh I, Sanborn JZ, et al. Exome sequencing of desmoplastic melanoma identifies recurrent NFKBIE promoter mutations and diverse activating mutations in the MAPK pathway. Nat Genet. 2015;47:1194–9.

    CAS  PubMed  PubMed Central  Google Scholar 

  75. Johansson P, Aoude LG, Wadt K, Glasson WJ, Warrier SK, Hewitt AW, et al. Deep sequencing of uveal melanoma identifies a recurrent mutation in PLCB4. Oncotarget. 2016;7:4624–31.

    PubMed  Google Scholar 

  76. Kandasamy S, Adhikary G, Rorke EA, Friedberg JS, Mickle MB, Alexander HR, et al. The YAP1 signaling inhibitors, verteporfin and CA3, suppress the mesothelioma cancer stem cell phenotype. Mol. Cancer Res. 2020;18:343–51.

    CAS  Google Scholar 

Download references

Acknowledgements

The authors gratefully acknowledge the expert technical assistance of A. Greenawalt and B. Hazzard, and to I. Violich for providing guidance in development of the mouse computational pipeline. This work was supported by the National Institutes of Health/National Cancer Institute through research grants R01 CA83237, R01 CA159871 and R01 CA159871-S1 (DMK), R01 CA211909 (RLE), center core grant P30 CA134274 and training grant T32 CA154274, by research grant R01 AR071189 (ATS) from the National Institutes of Health/National Institute of Arthritis and Musculoskeletal and Skin Diseases, and by education grant R25 GM055036 from the National Institutes of Health/National Institute of General Medical Sciences. The study was also supported by funding from the Maryland Stem Cell Research Foundation (MSCRFI-1638, DMK), the Veterans Administration (VA Merit Grant 1I01BX004293, ATS), and the Baltimore Research and Education Foundation, VA Maryland Health Care System.

Author information

Author notes

  1. M. Kathryn Leonard

    Present address: American Association for Cancer Research, Philadelphia, PA, USA

  2. Edward C. De Fabo is deceased. Permission to include Dr. DeFabo as a co-author was provided by co-author Dr. Frances Noonan, his next-of-kin (wife).

Authors and Affiliations

  1. Department of Biochemistry and Molecular Biology, School of Medicine, University of Maryland-Baltimore, Baltimore, MD, USA

    M. Kathryn Leonard, Gemma S. Puts, Nidhi Pamidimukkala, Gautam Adhikary, Devin Snyder, Nicolette Matsangos, Marián Novak, Richard L. Eckert & David M. Kaetzel

  2. Marlene and Stewart Greenebaum Comprehensive Cancer Center, School of Medicine, University of Maryland-Baltimore, Baltimore, MD, USA

    M. Kathryn Leonard, Gemma S. Puts, Nidhi Pamidimukkala, Gautam Adhikary, Devin Snyder, Richard L. Eckert & David M. Kaetzel

  3. Department of Translational Genomics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA

    Yili Xu, Eric Kwok, Yuxin Jin, Michelle G. Webb, David W. Craig, John D. Carpten & Zarko Manojlovic

  4. Institute for Genome Sciences, School of Medicine, University of Maryland-Baltimore, Baltimore, MD, USA

    Anup Mahurkar & Amol C. Shetty

  5. Department of Dermatology, University of Alabama at Birmingham, Birmingham, AL, USA

    Radomir M. Slominski

  6. The George Washington University Medical Center, Washington, DC, USA

    Edward C. De Fabo & Frances P. Noonan

  7. Laboratory of Cancer Biology and Genetics, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA

    Chi-Ping Day & Glenn Merlino

  8. Department of Pathology, University of Alabama at Birmingham, Birmingham, AL, USA

    Mohammed Rigi & Andrzej T. Slominski

  9. Research and Development Service, VA Maryland Health Care System, Baltimore, MA, USA

    David M. Kaetzel

Authors
  1. M. Kathryn Leonard
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gemma S. Puts
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nidhi Pamidimukkala
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Gautam Adhikary
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yili Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Eric Kwok
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Yuxin Jin
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Devin Snyder
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Nicolette Matsangos
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Marián Novak
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Anup Mahurkar
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Amol C. Shetty
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Radomir M. Slominski
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Edward C. De Fabo
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Frances P. Noonan
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Chi-Ping Day
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Mohammed Rigi
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Andrzej T. Slominski
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Michelle G. Webb
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. David W. Craig
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Glenn Merlino
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Richard L. Eckert
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. John D. Carpten
    View author publications

    You can also search for this author in PubMed Google Scholar

  24. Zarko Manojlovic
    View author publications

    You can also search for this author in PubMed Google Scholar

  25. David M. Kaetzel
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conceptualization: JDC and DMK. Methodology: MKL, ECD-F, FPN, MGW, DWC, ZM, GM, RLE, and DMK. Formal analysis: MKL, ATS, ZM, and DMK. Investigation: MKL, GSP, NP, DS, GA, YX, EK, YJ, NM, MN, RMS, ACS, C-PD, MR, ATS, MGW, and DMK. Data curation: MKL, AM, ACS, and DMK. Writing (original draft): DMK; Writing (review and editing): MKL, RLE, GM, JDC, ZM, and DMK. Visualization: ATS, ZM, and DMK. Supervision: JDC, ZM, ATS, RLE, and DMK. Project administration: RLE, JDC, ZM, and DMK. Funding acquisition: RLE and DMK.

Corresponding author

Correspondence to David M. Kaetzel.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

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

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Leonard, M.K., Puts, G.S., Pamidimukkala, N. et al. Comprehensive molecular profiling of UV-induced metastatic melanoma in Nme1/Nme2-deficient mice reveals novel markers of survival in human patients. Oncogene 40, 6329–6342 (2021). https://doi.org/10.1038/s41388-021-01998-w

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41388-021-01998-w

This article is cited by

Search

Advanced search

Quick links