Epigenetic activation of a cryptic TBC1D16 transcript enhances melanoma progression by targeting EGFR

Journal name:
Nature Medicine
Volume:
21,
Pages:
741–750
Year published:
DOI:
doi:10.1038/nm.3863
Received
Accepted
Published online

Abstract

Metastasis is responsible for most cancer-related deaths, and, among common tumor types, melanoma is one with great potential to metastasize. Here we study the contribution of epigenetic changes to the dissemination process by analyzing the changes that occur at the DNA methylation level between primary cancer cells and metastases. We found a hypomethylation event that reactivates a cryptic transcript of the Rab GTPase activating protein TBC1D16 (TBC1D16-47 kDa; referred to hereafter as TBC1D16-47KD) to be a characteristic feature of the metastatic cascade. This short isoform of TBC1D16 exacerbates melanoma growth and metastasis both in vitro and in vivo. By combining immunoprecipitation and mass spectrometry, we identified RAB5C as a new TBC1D16 target and showed that it regulates EGFR in melanoma cells. We also found that epigenetic reactivation of TBC1D16-47KD is associated with poor clinical outcome in melanoma, while conferring greater sensitivity to BRAF and MEK inhibitors.

At a glance

Figures

  1. DNA hypomethylation-associated transcriptional activation of a TBC1D16 cryptic isoform in metastatic cancer cells.
    Figure 1: DNA hypomethylation-associated transcriptional activation of a TBC1D16 cryptic isoform in metastatic cancer cells.

    (a) DNA methylation heatmap clustering of 2,620 CpG sites showing DNA methylation events that distinguish primary (IGR39, MDA-MB-468PT and SW480) or metastatic-derived cell lines (IGR37, MDA-MB-468LN and SW620). (b) Representation of the correlation between DNA methylation microarray data and gene expression microarray values for TBC1D16 and SBNO2. Expression is represented as the average intensity of three probes for TBC1D16 and two probes for SBNO2. (c) Genomic structure of TBC1D16. Transcription start sites (TSSs) of the isoforms are denoted by small black arrows. Locations of CpG islands (green bars) and designed bisulfite sequencing primers (black horizontal arrows) are shown. Bisulfite genomic sequencing was carried out in eight individual clones. The presence of a methylated or unmethylated cytosine is indicated by a black or white square, respectively. (d) Protein expression levels for the TBC1D16-47KD and TBC1D16-86KD isoforms analyzed by western blot in the paired primary/metastasis cancer cell lines. (e) Conventional (top) and quantitative RT-PCR (middle) analyses of TBC1D16 (47 kDa) expression in paired primary and metastasis cancer cell lines and normal tissues. Bottom, reactivation of the TBC1D16 (47 kDa) transcript upon use of the DNA demethylating agent 5-aza-2′-deoxycytidine (aza). NS, nonsignificant; *P < 0.05; **P < 0.01, using Student's t-test. Error bars show means ± s.d.

  2. TBC1D16-47KD enhances melanoma progression in vitro and in vivo.
    Figure 2: TBC1D16-47KD enhances melanoma progression in vitro and in vivo.

    (a) Western blot of TBC1D16-47KD shRNA interference in IGR37. (b) Colony formation and 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assays in TBC1D16-47KD shRNA-depleted IGR37 cells. Values derived from two experiments with 12 replicates (mean ± s.d.); significance according to 10,000-permutation t-test with Holm correction. (c) Cellular proliferation upon TBC1D16-47KD ectopic expression in IGR39. (d) Invasion capacity upon shRNA-mediated depletion in IGR37 and TBC1D16-47KD transfection in IGR39 cells. (e) Top, tumor volume in subcutaneous implanted shRNA TBC1D16-47KD-depleted IGR37. Bottom, TBC1D16-47KD and TBC1D16-86KD protein levels (22 d; n = 5 for group). Right, illustrative samples obtained at the end point and mean ± s.d. weights. (f) Left, orthotopic volumes from TBC1D16-47KD shRNA-depleted IGR37 cells. Bottom, TBC1D16-47KD and TBC1D16-86KD protein levels (30 d, n = 8 for group). Right, illustrative endpoint tumor samples and mean ± s.d. tumor weights. (g) Left, model of orthotopic melanoma implantation for studying metastasis. Right, H&E staining of lymph nodes showing metastases (M). Percentage of metastatic lymph nodes in orthotopics derived from IGR37 (eight mice) and IGR39 (six mice). (h) H&E staining. Percentage of metastatic lymph nodes upon TBC1D16-47KD shRNA depletion in IGR37-derived orthotopics (eight mice). (i) H&E staining of lungs showing metastases. Percentage of metastases upon TBC1D16-47KD depletion in IGR37-derived orthotopics (eight mice) (left) and in IGR39 (eight mice) tumors with TBC1D16-47KD ectopic expression (right). (j) Number of liver metastases (black stars) upon direct spleen injection of TBC1D16-47KD shRNA-depleted IGR37 cells (ten mice). NS, nonsignificant; *P < 0.05; **P < 0.01, ***P < 0.001; Student's t-test. Error bars show means ± s.d.

  3. TBC1D16-47KD regulates Rab GTPases and EGFR activation in melanoma cells.
    Figure 3: TBC1D16-47KD regulates Rab GTPases and EGFR activation in melanoma cells.

    (a) Co-immunoprecipitation experiments show the interaction of TBC1D16-47KD with the Rab GTPases, RAB4A and RAB27A. Immunoprecipitation (IP) and western blotting (WB) were carried out using anti-FLAG and anti-RAB antibodies. Normal mouse IgG was used as a negative control. UB, unbound fraction; B, bound fraction. (b) Strategy used to identify TBC1D16-47KD novel interacting RABs based on MS analysis comparing the protein immunoprecipitates obtained in IGR37 cells from the empty vector–transfected condition versus the TBC1D16-47KD-FLAG–transfected cells. (c) Data from a representative RAB5C peptide identified by MS. (d) Co-immunoprecipitation experiments confirmed the interaction between TBC1D16-47KD and RAB5C. Immunoprecipitation and western blotting were performed using anti-FLAG and anti-RAB5C. UB, unbound fraction; B, bound fraction. (e) Examples of multicolored immunofluorescence images show colocalization of the TBC1D16-47KD and RAB5C proteins. (f) Western blot analyses indicate that IGR39 (methylated and silenced for TBC1D16-47KD) had large amounts of EGFR and its phosphorylated Y1068 form, whereas IGR37 cells (unmethylated and expressing TBC1D16-47KD) had minimal amounts of EGFR and is hypophosphorylated. Protein levels were determined at 0, 15, 30 and 60 min after EGF stimulation. (g) Western blot analyses show that shRNA-mediated downregulation of TBC1D16-47KD induces higher levels of EGFR and phospho-EGFR compared with control cells.

  4. Epigenetic reactivation of TBC1D16-47KD predicts response to BRAF inhibition.
    Figure 4: Epigenetic reactivation of TBC1D16-47KD predicts response to BRAF inhibition.

    (a) MTT and sulforhodamine B (SRB) assays for IGR37 (TBC1D16-47KD unmethylated and expressed) and IGR39 (TBC1D16-47KD methylated and repressed) cells upon the use of the BRAF inhibitor dabrafenib. Corresponding half-maximal inhibitory concentration (IC50) values are also shown. R = resistance to drug inhibition. Values are the mean ± s.e.m (n = 3 for each group). (b) MTT and SRB assays in TBC1D16-47KD shRNA-downregulated IGR37 cells upon the use of dabrafenib. Values are the mean ± s.e.m (n = 3 for each group). (c) Volume of TBC1D16-47KD methylated orthotopic tumors derived from IGR39 upon daily BRAF inhibitor (30 mg/kg) or vehicle treatment. Values are the mean ± s.d. (n = 5 for each group.) Right, representative images of the studied melanoma grafts. Tn/T0 (%), tumor size on day “n” in relation to day 0 of drug treatment. (d) Left, volume of TBC1D16-47KD unmethylated orthotopic tumors derived from scrambled shRNA IGR37 upon daily treatment with BRAF inhibitor (30 mg/kg) (n = 9) or vehicle (n = 5). Right, TBC1D16-47KD shRNA-depleted IGR37-derived orthotopic tumors (n = 7) are less responsive to the BRAF inhibitor than the shRNA scrambled IGR37-derived tumors. Tn/T0 (%), tumor size on day “n” in relation to day 0 of drug treatment. Values are the mean ± s.d. (n = 7 for each group). Significance was determined from a 10,000-permutation t-test with Holm correction of the difference between growth or volume curves. NS, nonsignificant; *P < 0.05; **P < 0.01, ***P < 0.001. Representative images of the studied melanoma grafts are shown.

  5. Epigenetic reactivation of TBC1D16-47KD predicts response to BRAF and MEK inhibitors by targeting two signaling pathways.
    Figure 5: Epigenetic reactivation of TBC1D16-47KD predicts response to BRAF and MEK inhibitors by targeting two signaling pathways.

    (a) MTT and SRB assays in IGR37 and IGR39 cells upon treatment with MEK inhibitors (AZD6244 and CI-1040). Corresponding half-maximal inhibitory concentration (IC50) values are also shown. R = resistance to drug inhibition. (b) shRNA-mediated downregulation of TBC1D16-47KD induced higher resistance to MEK inhibitors compared with the shRNA-scrambled control cells. (c) TBC1D16-47KD CpG island unmethylated status is associated with enhanced sensitivity to MEK inhibitors in the Sanger panel of melanoma cell lines. y axis, drug IC50 values (natural log, μM); x axis, DNA methylation status. Values are means ± s.e.m. (d) MTT and SRB assays in the WM115 (methylated), WM793 (methylated), SK-MEL-28 (unmethylated) and WM266.4 (unmethylated) cell lines upon the use of BRAF or MEK inhibitors. (e) Western blot analyses of the RAS/BRAF/MEK/ERK and PI3K/AKT signaling pathways in TBC1D16-47KD unmethylated (U) or methylated (M) melanoma cells upon using the MEK inhibitor CI-1040. (f) Western blot analyses of the RAS/BRAF/MEK/ERK and PI3K/AKT signaling pathways in TBC1D16-47KD unmethylated (U) or methylated (M) melanoma cells upon using the BRAF inhibitor dabrafenib. Significance was determined from a 10,000-permutation t-test with Holm correction of the difference between growth curves. NS, nonsignificant; *P < 0.05; **P < 0.01, ***P < 0.001. Error bars show means ± s.e.m.

  6. TBC1D16-47KD promoter demethylation is an independent prognostic factor for poor clinical outcome in melanoma patients.
    Figure 6: TBC1D16-47KD promoter demethylation is an independent prognostic factor for poor clinical outcome in melanoma patients.

    (a) Unsupervised clustering analysis using the CpGs located in the TBC1D16-47KD promoter shows enrichment of the hypomethylated sites in the metastatic melanoma samples. Green, unmethylated; red, methylated. (b) Kaplan-Meier analysis of PFS and OS in the melanoma discovery cohort with respect to TBC1D16-47KD methylation status. Significance of the log-rank test is shown. Results of the univariate Cox regression analysis are represented by the HR and 95% CI. Twelve unmethylated and 22 methylated cases had a mean PFS of 1.6 and 8.8 years (yrs), respectively. 12 unmethylated and 24 methylated cases had a mean time OS of 2.8 and 9 years, respectively. (c) Kaplan-Meier analysis of PFS and OS among the melanoma validation cohort with respect to TBC1D16-47KD methylation status. Significance of the log-rank test is shown. Results of the univariate Cox regression analysis are represented by the HR and 95% CI. Thirty-two unmethylated and 39 methylated cases had a mean PFS of 8.1 and 11.6 years, respectively. Thirty-two unmethylated and 39 methylated cases had a mean OS of 4.0 and 4.9 years, respectively.

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References

  1. Siegel, R., Naishadham, D. & Jemal, A. Cancer statistics. CA Cancer J. Clin. 63, 1130 (2013).
  2. Jones, P.A. & Baylin, S.B. The epigenomics of cancer. Cell 128, 683692 (2007).
  3. Heyn, H. & Esteller, M. DNA methylation profiling in the clinic: applications and challenges. Nat. Rev. Genet. 13, 679692 (2012).
  4. Timp, W. & Feinberg, A.P. Cancer as a dysregulated epigenome allowing cellular growth advantage at the expense of the host. Nat. Rev. Cancer 13, 497510 (2013).
  5. Fang, F. et al. Breast cancer methylomes establish an epigenomic foundation for metastasis. Sci. Transl. Med. 3, 75ra25 (2011).
  6. Cunha, S. et al. The RON receptor tyrosine kinase promotes metastasis by triggering MBD4-dependent DNA methylation reprogramming. Cell Reports 6, 141154 (2014).
  7. Carmona, F.J. et al. A comprehensive DNA methylation profile of epithelial-to-mesenchymal transition. Cancer Res. 74, 56085619 (2014).
  8. Lujambio, A. et al. A microRNA DNA methylation signature for human cancer metastasis. Proc. Natl. Acad. Sci. USA 105, 1355613561 (2008).
  9. Harbst, K. et al. Multiple metastases from cutaneous malignant melanoma patients may display heterogeneous genomic and epigenomic patterns. Melanoma Res. 20, 381391 (2010).
  10. Marzese, D.M. et al. Epigenome-wide DNA methylation landscape of melanoma progression to brain metastasis reveals aberrations on homeobox D cluster associated with prognosis. Hum. Mol. Genet. 23, 226238 (2014).
  11. MacKie, R.M., Hauschild, A. & Eggermont, A.M. Epidemiology of invasive cutaneous melanoma. Ann. Oncol. 20 (suppl. 6), vi1vi7 (2009).
  12. Villanueva, M.T. Skin cancer: in melanoma ulceration, size matters. Nat. Rev. Clin. Oncol 9, 370 (2012).
  13. Mandalà, M. & Massi, D. Tissue prognostic biomarkers in primary cutaneous melanoma. Virchows Arch. 464, 265281 (2014).
  14. Tsao, H., Chin, L., Garraway, L.A. & Fisher, D.E. Melanoma: from mutations to medicine. Genes Dev. 26, 11311155 (2012).
  15. Kaufman, H.L. et al. The Society for Immunotherapy of Cancer consensus statement on tumour immunotherapy for the treatment of cutaneous melanoma. Nat. Rev. Clin. Oncol 10, 588598 (2013).
  16. Kaufman, H.L. Melanoma as a model for precision medicine in oncology. Lancet Oncol. 15, 251253 (2014).
  17. Weinreb, A. & Travo, P. Discrimination between human melanoma cell lines by fluorescence anisotropy. Eur. J. Cancer Clin. Oncol. 20, 673677 (1984).
  18. Sandoval, J. et al. Validation of a DNA methylation microarray for 450,000 CpG sites in the human genome. Epigenetics 6, 692702 (2011).
  19. Akavia, U.D. et al. An integrated approach to uncover drivers of cancer. Cell 143, 10051017 (2010).
  20. Goueli, B.S., Powell, M.B., Finger, E.C. & Pfeffer, S.R. TBC1D16 is a Rab4A GTPase activating protein that regulates receptor recycling and EGF receptor signaling. Proc. Natl. Acad. Sci. USA 109, 1578715792 (2012).
  21. El Kasmi, K.C. et al. Cutting edge: a transcriptional repressor and corepressor induced by the STAT3-regulated anti-inflammatory signalling pathway. J. Immunol. 179, 72157219 (2007).
  22. Eisenberg, M.C. et al. Mechanistic modeling of the effects of myoferlin on tumor cell invasion. Proc. Natl. Acad. Sci. USA 108, 2007820083 (2011).
  23. Frasa, M.A., Koessmeier, K.T., Ahmadian, M.R. & Braga, V.M. Illuminating the functional and structural repertoire of human TBC/RABGAPs. Nat. Rev. Mol. Cell Biol. 13, 6773 (2012).
  24. Onodera, Y. et al. Rab5c promotes AMAP1–PRKD2 complex formation to enhance β1 integrin recycling in EGF-induced cancer invasion. J. Cell Biol. 197, 983996 (2012).
  25. Corcoran, R.B. et al. EGFR-mediated re-activation of MAPK signaling contributes to insensitivity of BRAF mutant colorectal cancers to RAF inhibition with vemurafenib. Cancer Discov. 2, 227235 (2012).
  26. Prahallad, A. et al. Unresponsiveness of colon cancer to BRAF(V600E) inhibition through feedback activation of EGFR. Nature 483, 100103 (2012).
  27. Yoon, Y.K. et al. Combination of EGFR and MEK1/2 inhibitor shows synergistic effects by suppressing EGFR/HER3-dependent AKT activation in human gastric cancer cells. Mol. Cancer Ther. 8, 25262536 (2009).
  28. Mirzoeva, O.K. et al. Basal subtype and MAPK/ERK kinase (MEK)-phosphoinositide 3-kinase feedback signaling determine susceptibility of breast cancer cells to MEK inhibition. Cancer Res. 69, 565572 (2009).
  29. Garnett, M.J. et al. Systematic identification of genomic markers of drug sensitivity in cancer cells. Nature 483, 570575 (2012).
  30. Hansen, K.D. et al. Increased methylation variation in epigenetic domains across cancer types. Nat. Genet. 43, 768775 (2011).
  31. Hon, G.C. et al. Global DNA hypomethylation coupled to repressive chromatin domain formation and gene silencing in breast cancer. Genome Res. 22, 246258 (2012).
  32. Bert, S.A. et al. Regional activation of the cancer genome by long-range epigenetic remodeling. Cancer Cell 23, 922 (2013).
  33. Kedlaya, R. et al. Interactions between GIPC-APPL and GIPC-TRP1 regulate melanosomal protein trafficking and melanogenesis in human melanocytes. Arch. Biochem. Biophys. 508, 227233 (2011).
  34. Huang, Z.M. et al. Targeting protein-trafficking pathways alters melanoma treatment sensitivity. Proc. Natl. Acad. Sci. USA 109, 553558 (2012).
  35. Gray-Schopfer, V., Wellbrock, C. & Marais, R. Melanoma biology and new targeted therapy. Nature 445, 851857 (2007).
  36. Middleton, M.R. et al. Randomized phase III study of temozolomide versus dacarbazine in the treatment of patients with advanced metastatic malignant melanoma. J. Clin. Oncol. 18, 158166 (2000).
  37. Eggermont, A.M., Spatz, A. & Robert, C. Cutaneous melanoma. Lancet 383, 816827 (2014).
  38. Catalanotti, F. et al. Phase II trial of MEK inhibitor selumetinib (AZD6244, ARRY-142886) in patients with BRAFV600E/K-mutated melanoma. Clin. Cancer Res. 19, 22572264 (2013).
  39. Girotti, M.R. et al. Inhibiting EGF receptor or SRC family kinase signaling overcomes BRAF inhibitor resistance in melanoma. Cancer Discov. 3, 158167 (2013).
  40. Altman, D.G. et al. Reporting Recommendations for Tumor Marker Prognostic Studies (REMARK): explanation and elaboration. PLoS Med. 9, e1001216 (2012).
  41. Heyn, H. et al. Distinct DNA methylomes of newborns and centenarians. Proc. Natl. Acad. Sci. USA 109, 1052210527 (2012).
  42. Moutinho, C. et al. Epigenetic inactivation of the BRCA1 interactor SRBC and resistance to oxaliplatin in colorectal cancer. J. Natl. Cancer Inst. 106, djt322 (2014).
  43. Lopez-Serra, P. et al. A DERL3-associated defect in the degradation of SLC2A1 mediates the Warburg effect. Nat. Commun. 5, 3608 (2014).
  44. Howard-Jones, N. A CIOMS ethical code for animal experimentation. WHO Chron. 39, 5156 (1985).

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Author information

Affiliations

  1. Cancer Epigenetics and Biology Program, Bellvitge Biomedical Research Institute, L'Hospitalet, Barcelona, Catalonia, Spain.

    • Miguel Vizoso,
    • Humberto J Ferreira,
    • Paula Lopez-Serra,
    • F Javier Carmona,
    • Anna Martínez-Cardús,
    • Sonia Guil,
    • Catia Moutinho,
    • Julia Liz,
    • Anna Portela,
    • Holger Heyn,
    • Sebastian Moran &
    • Manel Esteller
  2. Molecular Oncology Group, Cancer Research UK Manchester Institute, Manchester, UK.

    • Maria Romina Girotti &
    • Richard Marais
  3. Translational Research Laboratory, Catalan Institute of Oncology, Bellvitge Biomedical Research Institute, L'Hospitalet, Barcelona, Catalonia, Spain.

    • Alberto Villanueva &
    • Maria Martinez-Iniesta
  4. Department of Pathological Anatomy, Bellvitge University Hospital, Bellvitge Biomedical Research Institute, L'Hospitalet, Barcelona, Catalonia, Spain.

    • August Vidal
  5. Medical Oncology Service, Catalan Institute of Oncology, Germans Trias i Pujol University Hospital, Badalona, Catalonia, Spain.

    • Jose L Manzano
  6. Pathology Department, Germans Trias i Pujol University Hospital, Badalona, Catalonia, Spain.

    • Maria Teresa Fernandez-Figueras
  7. Medical Oncology Service, Vall d'Hebron University Hospital, Barcelona, Catalonia, Spain.

    • Elena Elez &
    • Eva Muñoz-Couselo
  8. Dermatology Service, Hospital La Fe, Universidad de Valencia, Valencia, Spain.

    • Rafael Botella-Estrada
  9. Medical Oncology Service, Hospital General, Valencia, Spain.

    • Alfonso Berrocal
  10. Department of Pathology, University Hospital of Uppsala, Uppsala, Sweden.

    • Fredrik Pontén
  11. Translational Cell & Tissue Pathology, Katholieke Universiteit Leuven, Leuven, Belgium.

    • Joost van den Oord
  12. University College Dublin School of Biomolecular and Biomedical Science, University College Dublin Conway Institute, University College Dublin, Belfield, Dublin, Ireland.

    • William M Gallagher
  13. Center for Melanoma, Massachusetts General Hospital Cancer Center, Boston, Massachusetts.

    • Dennie T Frederick &
    • Keith T Flaherty
  14. Cancer Genome Project, Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK.

    • Ultan McDermott
  15. University of Manchester, Christie National Health Service Foundation Trust, Manchester, UK.

    • Paul Lorigan
  16. Department of Physiological Sciences II, School of Medicine, University of Barcelona, Barcelona, Catalonia, Spain.

    • Manel Esteller
  17. Institucio Catalana de Recerca i Estudis Avançats, Barcelona, Catalonia, Spain.

    • Manel Esteller

Contributions

M.V. and M.E. conceived the study and wrote the manuscript. M.V. performed most experiments with the help of H.J.F., P.L.-S., F.J.C., S.G., C.M., J.L., A.P., H.H. and S.M. A.Vidal and A. Villanueva, together with M.M.-I., performed the mouse studies. A.M.-C., M.R.G., J.L.M., M.T.F.-F., E.E., E.M.-C., R.B.-E., A.B., F.P., J.v.d.O., W.M.G., D.T.F., K.T.F., U.M., P.L. and R.M. analyzed the clinical outcome and drug response data and provided conceptual input. All authors discussed the results and commented on the manuscript.

Competing financial interests

The authors declare no competing financial interests.

Corresponding author

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Supplementary information

PDF files

  1. Supplementary Figures and Text (3,547 KB)

    Supplementary Figures 1–12 & Supplementary Tables 1–3

Excel files

  1. Supplementary Data 1 (330 KB)

    2,620 CpGs most divergent between primary and metatatic tumor cell lines.

  2. Supplementary Data 2 (13 KB)

    CpGs located outside CpG islands most divergent between primary and metastatic tumor cell lines.

  3. Supplementary Data 3 (14 KB)

    DNA methylation profile of the TBC1D16-45/47KD promoter CpG island according to the DNA methylation microarray values in 36 melanoma cell lines.

Additional data