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:

BAP1 mutant uveal melanoma is stratified by metabolic phenotypes with distinct vulnerability to metabolic inhibitors

Oncogene volume 40, pages 618–632 (2021)Cite this article

Subjects

A Correction to this article was published on 28 January 2021

This article has been updated

Abstract

Cancer cell metabolism is a targetable vulnerability; however, a precise understanding of metabolic heterogeneity is required. Inactivating mutations in BRCA1-associated protein 1 (BAP1) are associated with metastasis in uveal melanoma (UM), the deadliest adult eye cancer. BAP1 functions in UM remain unclear. UM patient sample analysis divided BAP1 mutant UM tumors into two subgroups based on oxidative phosphorylation (OXPHOS) gene expression suggesting metabolic heterogeneity. Consistent with patient data, transcriptomic analysis of BAP1 mutant UM cell lines also showed OXPHOShigh or OXPHOSlow subgroups. Integrated RNA sequencing, metabolomics, and molecular analyses showed that OXPHOShigh BAP1 mutant UM cells utilize glycolytic and nucleotide biosynthesis pathways, whereas OXPHOSlow BAP1 mutant UM cells employ fatty acid oxidation. Furthermore, the two subgroups responded to different classes of metabolic suppressors. Our findings indicate that targeting cancer metabolism is a promising therapeutic option for BAP1 mutant UM; however, tailored approaches may be required due to metabolic heterogeneities.

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: BAP1 mutant UM samples are divided into two distinct metabolic subpopulations based on OXPHOS gene set.
Fig. 2: BAP1 status alters the metabolic pathways in UM cells.
Fig. 3: OXPHOShigh BAP1 mutant phenotype is linked to increased glycolytic-nucleotide biosynthetic pathway.
Fig. 4: OXPHOSlow BAP1 mutant phenotype is associated with an elevated FA oxidation pathway.
Fig. 5: The two metabolic phenotypes respond differently to metabolic stress.
Fig. 6: Nucleotide and FA metabolism gene expressions separate BAP1 mutant samples into two distinct subgroups.
Fig. 7: Two distinct metabolic phenotypes in BAP1 mutant UM.

Similar content being viewed by others

Data availability

Bulk RNA-Sequencing data have been deposited to the Gene Expression Omnibus (GEO) database with accession code GSE149920.

Code availability

Computational analyses were done using publicly-available software and R packages.

Change history

  • 14 January 2021

    The original published version was the given name from J. William Harbour incorrect tagged. It was given as J. (given name) William Harbour (family name) it was corrected to J. William (given name) Harbour (family name).

  • 28 January 2021

    A Correction to this paper has been published: https://doi.org/10.1038/s41388-021-01645-4

References

  1. Pacella I, Procaccini C, Focaccetti C, Miacci S, Timperi E, Faicchia D, et al. Fatty acid metabolism complements glycolysis in the selective regulatory T cell expansion during tumor growth. PNAS. 2018;115:E6546–E55.

    PubMed  PubMed Central  Google Scholar 

  2. Phan LM, Yeung S-CJ, Lee M-H. Cancer metabolic reprogramming: importance, main features, and potentials for precise targeted anti-cancer therapies. Cancer Biol Med. 2014;11:1.

    PubMed  PubMed Central  Google Scholar 

  3. DeBerardinis RJ, Chandel NS. Fundamentals of cancer metabolism. Sci Adv. 2016;2:e1600200.

    PubMed  PubMed Central  Google Scholar 

  4. Jones NP, Schulze A. Targeting cancer metabolism–aiming at a tumour’s sweet-spot. Drug Discov Today. 2012;17:232–41.

    PubMed  Google Scholar 

  5. Stine ZE, Walton ZE, Altman BJ, Hsieh AL, Dang CV. MYC, metabolism, and cancer. Cancer Discov. 2015;5:1024–39.

    PubMed  PubMed Central  Google Scholar 

  6. Kruiswijk F, Labuschagne CF, Vousden KH. p53 in survival, death and metabolic health: a lifeguard with a licence to kill. Nat Rev Mol Cell Biol. 2015;16:393.

    PubMed  Google Scholar 

  7. Vivas-García Y, Falletta P, Liebing J, Louphrasitthiphol P, Feng Y, Chauhan J, et al. Lineage-restricted regulation of SCD and fatty acid saturation by MITF controls melanoma phenotypic plasticity. Mol Cell. 2020;77:120–37 e9.

    PubMed  Google Scholar 

  8. Singh N, Bergman L, Seregard S, Singh AD. Uveal melanoma: epidemiologic aspects. Ophthalmol Clin North Am. 2005;18:75–84.

    PubMed  Google Scholar 

  9. Shields JA, Shields CL, Donoso LA. Management of posterior uveal melanoma. Surv Ophthalmol. 1991;36:161–95.

    PubMed  Google Scholar 

  10. Singh AD, Turell ME, Topham AK. Uveal melanoma: trends in incidence, treatment, and survival. Ophthalmology. 2011;118:1881–5.

    PubMed  Google Scholar 

  11. Jager MJ, Shields CL, Cebulla CM, Abdel-Rahman MH, Grossniklaus HE, Stern M-H, et al. Uveal melanoma. Nat Rev Dis Prim. 2020;6:1–25.

    Google Scholar 

  12. Luke JJ, Triozzi PL, McKenna KC, Van Meir EG, Gershenwald JE, Bastian BC, et al. Biology of advanced uveal melanoma and next steps for clinical therapeutics. Pigment Cell Melanoma Res. 2015;28:135–47.

    PubMed  Google Scholar 

  13. Postow MA, Kuk D, Bogatch K, Carvajal RD. Assessment of overall survival from time of metastastasis in mucosal, uveal, and cutaneous melanoma. Am Soc Clin Oncol. 2014;32(Suppl):9074.

    Google Scholar 

  14. Seedor RS, Eschelman DJ, Gonsalves CF, Adamo RD, Orloff MM, Amjad A, et al. Liver-directed treatment for patients with uveal melanoma hepatic metastasis: a retrospective analysis of overall survival. Am Soc Clin Oncol. 2018;36(Suppl):9592.

    Google Scholar 

  15. Harbour JW, Onken MD, Roberson ED, Duan S, Cao L, Worley LA, et al. Frequent mutation of BAP1 in metastasizing uveal melanomas. Science 2010;330:1410–3.

    PubMed  PubMed Central  Google Scholar 

  16. Kalirai H, Dodson A, Faqir S, Damato B, Coupland S. Lack of BAP1 protein expression in uveal melanoma is associated with increased metastatic risk and has utility in routine prognostic testing. Br J Cancer. 2014;111:1373.

    PubMed  PubMed Central  Google Scholar 

  17. Wang A, Papneja A, Hyrcza M, Al-Habeeb A, Ghazarian D. BAP1: gene of the month. J Clin Pathol. 2016;69:750–3.

    PubMed  Google Scholar 

  18. Klebe S, Driml J, Nasu M, Pastorino S, Zangiabadi A, Henderson D, et al. BAP1 hereditary cancer predisposition syndrome: a case report and review of literature. Biomark Res. 2015;3:14.

    PubMed  PubMed Central  Google Scholar 

  19. Rai K, Pilarski R, Cebulla C, Abdel‐Rahman M. Comprehensive review of BAP1 tumor predisposition syndrome with report of two new cases. Clin Genet. 2016;89:285–94.

    PubMed  Google Scholar 

  20. Bononi A, Yang H, Giorgi C, Patergnani S, Pellegrini L, Su M, et al. Germline BAP1 mutations induce a Warburg effect. Cell Death Differ. 2017;24:1694.

    PubMed  PubMed Central  Google Scholar 

  21. Baughman JM, Rose CM, Kolumam G, Webster JD, Wilkerson EM, Merrill AE, et al. NeuCode proteomics reveals Bap1 regulation of metabolism. Cell Rep. 2016;16:583–95.

    PubMed  PubMed Central  Google Scholar 

  22. Dai F, Lee H, Zhang Y, Zhuang L, Yao H, Xi Y, et al. BAP1 inhibits the ER stress gene regulatory network and modulates metabolic stress response. PNAS. 2017;114:3192–7.

    PubMed  PubMed Central  Google Scholar 

  23. Porporato PE, Payen VL, Pérez-Escuredo J, De Saedeleer CJ, Danhier P, Copetti T, et al. A mitochondrial switch promotes tumor metastasis. Cell Rep. 2014;8:754–66.

    PubMed  Google Scholar 

  24. Yu L, Lu M, Jia D, Ma J, Ben-Jacob E, Levine H, et al. Modeling the genetic regulation of cancer metabolism: interplay between glycolysis and oxidative phosphorylation. Cancer Res. 2017;77:1564–74.

    PubMed  PubMed Central  Google Scholar 

  25. Robertson AG, Shih J, Yau C, Gibb EA, Oba J, Mungall KL, et al. Integrative analysis identifies four molecular and clinical subsets in uveal melanoma. Cancer Cell. 2017;32:204–20.e15.

    PubMed  PubMed Central  Google Scholar 

  26. Hsu PP, Sabatini DM. Cancer cell metabolism: Warburg and beyond. Cell. 2008;134:703–7.

    PubMed  Google Scholar 

  27. Carracedo A, Cantley LC, Pandolfi PP. Cancer metabolism: fatty acid oxidation in the limelight. Nat Rev Cancer. 2013;13:227.

    PubMed  PubMed Central  Google Scholar 

  28. Molina JR, Sun Y, Protopopova M, Gera S, Bandi M, Bristow C, et al. An inhibitor of oxidative phosphorylation exploits cancer vulnerability. Nat Med. 2018;24:1036–46.

    PubMed  Google Scholar 

  29. Jones RG, Thompson CB. Tumor suppressors and cell metabolism: a recipe for cancer growth. Genes Dev. 2009;23:537–48.

    PubMed  PubMed Central  Google Scholar 

  30. Lehuédé C, Dupuy F, Rabinovitch R, Jones RG, Siegel PM. Metabolic plasticity as a determinant of tumor growth and metastasis. Cancer Res. 2016;76:5201–8.

    PubMed  Google Scholar 

  31. Desler C, Hansen TL, Frederiksen JB, Marcker ML, Singh KK, Juel Rasmussen L. Is there a link between mitochondrial reserve respiratory capacity and aging? J Aging Res. 2012;2012:192503.

    PubMed  PubMed Central  Google Scholar 

  32. Johansson PA, Brooks K, Newell F, Palmer JM, Wilmott JS, Pritchard AL, et al. Whole genome landscapes of uveal melanoma show an ultraviolet radiation signature in iris tumours. Nat Commun. 2020;11:1–8.

    Google Scholar 

  33. Chua V, Aplin AE. Novel therapeutic strategies and targets in advanced uveal melanoma. Curr Opin Oncol. 2018;30:134–41.

    PubMed  Google Scholar 

  34. Karlsson J, Nilsson LM, Mitra S, Alsén S, Shelke GV, Sah VR, et al. Molecular profiling of driver events in metastatic uveal melanoma. Nat Commun. 2020;11:1–13.

    Google Scholar 

  35. Hebert L, Bellanger D, Guillas C, Campagne A, Dingli F, Loew D, et al. Modulating BAP1 expression affects ROS homeostasis, cell motility and mitochondrial function. Oncotarget. 2017;8:72513.

    PubMed  PubMed Central  Google Scholar 

  36. Dey A, Seshasayee D, Noubade R, French DM, Liu J, Chaurushiya MS, et al. Loss of the tumor suppressor BAP1 causes myeloid transformation. Science. 2012;337:1541–6.

    PubMed  PubMed Central  Google Scholar 

  37. Misaghi S, Ottosen S, Izrael-Tomasevic A, Arnott D, Lamkanfi M, Lee J, et al. Association of C-terminal ubiquitin hydrolase BRCA1-associated protein 1 with cell cycle regulator host cell factor 1. Mol Cell Biol. 2009;29:2181–92.

    PubMed  PubMed Central  Google Scholar 

  38. Scheuermann JC, de Ayala Alonso AG, Oktaba K, Ly-Hartig N, McGinty RK, Fraterman S, et al. Histone H2A deubiquitinase activity of the Polycomb repressive complex PR-DUB. Nature. 2010;465:243–7.

    PubMed  PubMed Central  Google Scholar 

  39. Yu H, Mashtalir N, Daou S, Hammond-Martel I, Ross J, Sui G, et al. The ubiquitin carboxyl hydrolase BAP1 forms a ternary complex with YY1 and HCF-1 and is a critical regulator of gene expression. Mol Cell Biol. 2010;30:5071–85.

    PubMed  PubMed Central  Google Scholar 

  40. Ma J, Hart GW. Protein O-GlcNAcylation in diabetes and diabetic complications. Expert Rev Proteom. 2013;10:365–80.

    Google Scholar 

  41. Izawa T, Rohatgi N, Fukunaga T, Wang Q-T, Silva MJ, Gardner MJ, et al. ASXL2 regulates glucose, lipid, and skeletal homeostasis. Cell Rep. 2015;11:1625–37.

    PubMed  PubMed Central  Google Scholar 

  42. Goodwin J, Neugent ML, Lee SY, Choe JH, Choi H, Jenkins DM, et al. The distinct metabolic phenotype of lung squamous cell carcinoma defines selective vulnerability to glycolytic inhibition. Nat Commun. 2017;8:15503.

    PubMed  PubMed Central  Google Scholar 

  43. Daemen A, Peterson D, Sahu N, McCord R, Du X, Liu B, et al. Metabolite profiling stratifies pancreatic ductal adenocarcinomas into subtypes with distinct sensitivities to metabolic inhibitors. PNAS. 2015;112:E4410–E7.

    PubMed  PubMed Central  Google Scholar 

  44. DeBerardinis RJ, Mancuso A, Daikhin E, Nissim I, Yudkoff M, Wehrli S, et al. Beyond aerobic glycolysis: transformed cells can engage in glutamine metabolism that exceeds the requirement for protein and nucleotide synthesis. PNAS. 2007;104:19345–50.

    PubMed  PubMed Central  Google Scholar 

  45. Vander Heiden MG. Targeting cancer metabolism: a therapeutic window opens. Nat Rev Drug Discov. 2011;10:671.

    Google Scholar 

  46. Camarda R, Zhou AY, Kohnz RA, Balakrishnan S, Mahieu C, Anderton B, et al. Inhibition of fatty acid oxidation as a therapy for MYC-overexpressing triple-negative breast cancer. Nat Med. 2016;22:427.

    PubMed  PubMed Central  Google Scholar 

  47. Amirouchene-Angelozzi N, Nemati F, Gentien D, Nicolas A, Dumont A, Carita G, et al. Establishment of novel cell lines recapitulating the genetic landscape of uveal melanoma and preclinical validation of mTOR as a therapeutic target. Mol Oncol. 2014;8:1508–20.

    PubMed  PubMed Central  Google Scholar 

  48. Matatall KA, Agapova OA, Onken MD, Worley LA, Bowcock AM, Harbour JW. BAP1 deficiency causes loss of melanocytic cell identity in uveal melanoma. BMC Cancer. 2013;13:371.

    PubMed  PubMed Central  Google Scholar 

  49. Peng X, Chen Z, Farshidfar F, Xu X, Lorenzi PL, Wang Y, et al. Molecular characterization and clinical relevance of metabolic expression subtypes in human cancers. Cell Rep. 2018;23:255–69 e4.

    PubMed  PubMed Central  Google Scholar 

  50. Gaude E, Frezza C. Tissue-specific and convergent metabolic transformation of cancer correlates with metastatic potential and patient survival. Nat Commun. 2016;7:1–9.

    Google Scholar 

  51. Van Essen TH, van Pelt SI, Versluis M, Bronkhorst IH, Van Duinen SG, Marinkovic M, et al. Prognostic parameters in uveal melanoma and their association with BAP1 expression. Br J Ophthalmol. 2014;98:1738–43.

    PubMed  Google Scholar 

  52. 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 

  53. Liberzon A, Birger C, Thorvaldsdóttir H, Ghandi M, Mesirov JP, Tamayo P. The molecular signatures database hallmark gene set collection. Cell Syst. 2015;1:417–25.

    PubMed  PubMed Central  Google Scholar 

  54. Wilkerson MD, Hayes DN. ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking. Bioinformatics. 2010;26:1572–3.

    PubMed  PubMed Central  Google Scholar 

  55. Cheng H, Chua V, Liao C, Purwin TJ, Terai M, Kageyama K, et al. Co-targeting HGF/cMET signaling with MEK Inhibitors in Metastatic Uveal Melanoma. Mol Cancer Ther. 2017;16:516–28.

    PubMed  PubMed Central  Google Scholar 

  56. Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29:15–21.

    PubMed  Google Scholar 

  57. Li B, Dewey CN. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinforma. 2011;12:323.

    Google Scholar 

  58. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. PNAS. 2005;102:15545–50.

    PubMed  PubMed Central  Google Scholar 

  59. Mootha VK, Lindgren CM, Eriksson K-F, Subramanian A, Sihag S, Lehar J, et al. PGC-1α-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat Genet. 2003;34:267.

    PubMed  Google Scholar 

  60. Luo W, Brouwer C. Pathview: an R/Bioconductor package for pathway-based data integration and visualization. Bioinformatics. 2013;29:1830–1.

    PubMed  PubMed Central  Google Scholar 

  61. Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000;28:27–30.

    PubMed  PubMed Central  Google Scholar 

  62. Wishart DS, Feunang YD, Marcu A, Guo AC, Liang K, Vázquez-Fresno R, et al. HMDB 4.0: the human metabolome database for 2018. Nucleic Acids Res. 2018;46:D608–D17.

    PubMed  Google Scholar 

Download references

Acknowledgements

We thank Dr. Sergio Roman-Roman (Uveal Melanoma Translational Group, Department of Translational Research, Institute Curie, PSL Research University, Paris, France) for cell lines. We thank Dr. Michele Carbone (University of Hawaii Cancer Center, Honolulu, HI, USA) for BAP1 cDNA. This work was supported by a Melanoma Research Alliance team science award (#559058) to AEA and JWH. Further support was from National Institutes of Health (NIH)/National Cancer Institute (NCI), R01 CA196278 and R01 CA253977 to AEA, P50CA174523 to DWS and fellowships from the National Cancer Center and American Association for Cancer Research (AACR)/Ocular Melanoma Foundation (OMF) awarded to AH and VC. This work was also supported by grant from NCI P01 CA114046. The Wistar Proteomics and Metabolomics Facility was supported by P30CA010815 and S10OD023586.

Author information

Authors and Affiliations

  1. Department of Cancer Biology, Thomas Jefferson University, Philadelphia, PA, 19107, USA

    Anna Han, Timothy J. Purwin, Nelisa Bechtel, Connie Liao, Vivian Chua & Andrew E. Aplin

  2. Department of Pathology, Thomas Jefferson University, Philadelphia, PA, 19107, USA

    Erin Seifert

  3. Department of Medical Oncology, Thomas Jefferson University, Philadelphia, PA, 19107, USA

    Takami Sato

  4. Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA, 19107, USA

    Takami Sato & Andrew E. Aplin

  5. Molecular and Cellular Oncogenesis Program, The Wistar Institute, Philadelphia, PA, 19104, USA

    Zachary T. Schug & David W. Speicher

  6. Proteomics and Metabolomics Facility, The Wistar Institute, Philadelphia, PA, 19104, USA

    David W. Speicher

  7. Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, FL, 33101, USA

    J. William Harbour

  8. Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL, 33101, USA

    J. William Harbour

  9. Interdisciplinary Stem Cell Institute, University of Miami Miller School of Medicine, Miami, FL, 33101, USA

    J. William Harbour

Authors
  1. Anna Han
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Timothy J. Purwin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nelisa Bechtel
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Connie Liao
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Vivian Chua
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Erin Seifert
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Takami Sato
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Zachary T. Schug
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. David W. Speicher
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. J. William Harbour
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Andrew E. Aplin
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conceptualization: AH, AEA; formal analysis: AH and TJP; investigations and interpretations: AH, TJP, NB, VC, CL, ES, and ZTS; resources: ES, TS, DWS, and JWH; writing (original draft): AH; writing (review and editing): AH, TJP, VC, ES, ZTS, DWS. JWH and AEA; funding acquisition: DWS, JWH, and AEA; supervision: AEA.

Corresponding author

Correspondence to Andrew E. Aplin.

Ethics declarations

Conflict of interest

AEA reports receiving a commercial research grant from Pfizer Inc. (2013–2017) and has ownership interest in patent number 9880150. No potential conflicts of interest are disclosed by the other authors. JWH is the inventor of intellectual property related to prognostic testing for uveal melanoma. He is a paid consultant for Castle Biosciences, licensee of this intellectual property, and he receives royalties from its commercialization.

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

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Han, A., Purwin, T.J., Bechtel, N. et al. BAP1 mutant uveal melanoma is stratified by metabolic phenotypes with distinct vulnerability to metabolic inhibitors. Oncogene 40, 618–632 (2021). https://doi.org/10.1038/s41388-020-01554-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41388-020-01554-y

This article is cited by

Search

Advanced search

Quick links