A PGC1α-mediated transcriptional axis suppresses melanoma metastasis (original) (raw)

References

  1. Chin, L., Garraway, L. A. & Fisher, D. E. Malignant melanoma: genetics and therapeutics in the genomic era. Genes Dev. 20, 2149–2182 (2006)
    Article CAS Google Scholar
  2. Gupta, P. B. et al. The melanocyte differentiation program predisposes to metastasis after neoplastic transformation. Nat Genet. 37, 1047–1054 (2005)
    Article CAS Google Scholar
  3. Puigserver, P. & Spiegelman, B. M. Peroxisome proliferator-activated receptor-gamma coactivator 1 alpha (PGC-1 alpha): transcriptional coactivator and metabolic regulator. Endocr. Rev. 24, 78–90 (2003)
    Article CAS Google Scholar
  4. Vazquez, F. et al. PGC1α expression defines a subset of human melanoma tumors with increased mitochondrial capacity and resistance to oxidative stress. Cancer Cell 23, 287–301 (2013)
    Article CAS Google Scholar
  5. Haq, R. et al. Oncogenic BRAF regulates oxidative metabolism via PGC1α and MITF. Cancer Cell 23, 302–315 (2013)
    Article CAS Google Scholar
  6. Plantefaber, L. C. & Hynes, R. O. Changes in integrin receptors on oncogenically transformed cells. Cell 56, 281–290 (1989)
    Article CAS Google Scholar
  7. Naba, A. et al. The matrisome: in silico definition and in vivo characterization by proteomics of normal and tumor extracellular matrices. Mol. Cell Proteomics 11, M111 014647-1–18 (2012)
    Article Google Scholar
  8. Nguyen, D. X., Bos, P. D. & Massague, J. Metastasis: from dissemination to organ-specific colonization. Nat. Rev. Cancer 9, 274–284 (2009)
    Article CAS Google Scholar
  9. Bollag, G. et al. Clinical efficacy of a RAF inhibitor needs broad target blockade in BRAF-mutant melanoma. Nature 467, 596–599 (2010)
    Article CAS ADS Google Scholar
  10. Cancer Genome Atlas Network. Genomic classification of cutaneous melanoma. Cell 161, 1681–1696 (2015)
  11. Hodis, E. et al. A landscape of driver mutations in melanoma. Cell 150, 251–263 (2012)
    Article CAS Google Scholar
  12. Fidler, I. J. The pathogenesis of cancer metastasis: the ‘seed and soil’ hypothesis revisited. Nat. Rev. Cancer 3, 453–458 (2003)
    Article CAS Google Scholar
  13. Hoek, K. S. et al. In vivo switching of human melanoma cells between proliferative and invasive states. Cancer Res. 68, 650–656 (2008)
    Article CAS Google Scholar
  14. Pinon, P. & Wehrle-Haller, B. Integrins: versatile receptors controlling melanocyte adhesion, migration and proliferation. Pigment Cell Melanoma Res. 24, 282–294 (2011)
    Article CAS Google Scholar
  15. Busse, A. & Keilholz, U. Role of TGF-β in melanoma. Curr. Pharm. Biotechnol. 12, 2165–2175 (2011)
    Article CAS Google Scholar
  16. Damsky, W. E. et al. β-catenin signaling controls metastasis in Braf-activated Pten-deficient melanomas. Cancer Cell 20, 741–754 (2011)
    Article CAS Google Scholar
  17. Foley, C. J. et al. Matrix metalloprotease-1a promotes tumorigenesis and metastasis. J. Biol. Chem. 287, 24330–24338 (2012)
    Article CAS Google Scholar
  18. Parsons, J. T. Focal adhesion kinase: the first ten years. J. Cell Sci. 116, 1409–1416 (2003)
    Article CAS Google Scholar
  19. Pencheva, N., Buss, C. G., Posada, J., Merghoub, T. & Tavazoie, S. F. Broad-spectrum therapeutic suppression of metastatic melanoma through nuclear hormone receptor activation. Cell 156, 986–1001 (2014)
    Article CAS Google Scholar
  20. Lim, J. H., Luo, C., Vazquez, F. & Puigserver, P. Targeting mitochondrial oxidative metabolism in melanoma causes metabolic compensation through glucose and glutamine utilization. Cancer Res. 74, 3535–3545 (2014)
    Article CAS Google Scholar
  21. Langlands, K., Yin, X., Anand, G. & Prochownik, E. V. Differential interactions of Id proteins with basic-helix-loop-helix transcription factors. J. Biol. Chem. 272, 19785–19793 (1997)
    Article CAS Google Scholar
  22. Norton, J. D. ID helix-loop-helix proteins in cell growth, differentiation and tumorigenesis. J. Cell Sci. 113, 3897–3905 (2000)
    CAS Google Scholar
  23. Zebedee, Z. & Hara, E. Id proteins in cell cycle control and cellular senescence. Oncogene 20, 8317–8325 (2001)
    Article CAS Google Scholar
  24. Chatr-Aryamontri, A. et al. The BioGRID interaction database: 2013 update. Nucleic Acids Res. 41, D816–823 (2013)
    Article CAS Google Scholar
  25. Franceschini, A. et al. STRING v9.1: protein-protein interaction networks, with increased coverage and integration. Nucleic Acids Res. 41, D808–815 (2013)
    Article CAS Google Scholar
  26. Luo, C. et al. Loss of ARF sensitizes transgenic BRAFV600E mice to UV-induced melanoma via suppression of XPC. Cancer Res. 73, 4337–4348 (2013)
    Article CAS Google Scholar
  27. Yang, H. et al. RG7204 (PLX4032), a selective BRAFV600E inhibitor, displays potent antitumor activity in preclinical melanoma models. Cancer Res. 70, 5518–5527 (2010)
    Article CAS Google Scholar
  28. Sanchez-Laorden, B. et al. BRAF inhibitors induce metastasis in RAS mutant or inhibitor-resistant melanoma cells by reactivating MEK and ERK signaling. Sci. Signal. 7, ra30 (2014)
    Article Google Scholar
  29. Torrano, V. et al. The metabolic co-regulator PGC1α suppresses prostate cancer metastasis. Nat. Cell Biol. 18, 645–656 (2016)
    Article CAS Google Scholar
  30. Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005)
    Article CAS ADS Google Scholar
  31. Talantov, D. et al. Novel genes associated with malignant melanoma but not benign melanocytic lesions. Clin Cancer Res 11, 7234–7242 (2005)
    Article CAS Google Scholar
  32. Scatolini, M. et al. Altered molecular pathways in melanocytic lesions. Int. J. Cancer 126, 1869–1881 (2010)
    Article CAS Google Scholar
  33. Barbie, D. A. et al. Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1. Nature 462, 108–112 (2009)
    Article CAS ADS Google Scholar
  34. Cancer Genome Atlas Network. Genomic classification of cutaneous melanoma. Cell 161, 1681–1696 (2015)
  35. Gao, J. et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci. Signal. 6, pl1 (2013)
    Article Google Scholar
  36. Cerami, E. et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2, 401–404 (2012)
    Article Google Scholar
  37. Meerbrey, K. L. et al. The pINDUCER lentiviral toolkit for inducible RNA interference in vitro and in vivo. Proc. Natl Acad. Sci. U.S.A. 108, 3665–3670 (2011)
    Article CAS ADS Google Scholar

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Acknowledgements

We thank R. Bronson for his critical analysis of the mouse histology, the Nikon Imaging Center at Harvard Medical School for help with light microscopy and members of the Puigserver laboratory for discussions. J.-H.L was supported in part by a postdoctoral fellowship from the American Heart Association (13POST14750008) and the National Research Foundation from the South-Korean government (2015R1A2A2A01002483). These studies were funded in part by the Claudia Adams Barr Program in Cancer Research (to P.P.), Dana-Farber Cancer Institute internal funds (to P.P.) and NIH R01CA181217 (to P.P.), as well as the Friends of Dana-Farber Award (to C.L.).

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Author notes

  1. Ji-Hong Lim
    Present address: †Present address: Department of Biomedical Chemistry, College of Biomedical and Health Science, Konkuk University, Chungbuk, Chungju, Chungcheongbuk-do 380-701, South Korea.,
  2. Chi Luo and Ji-Hong Lim: These authors contributed equally to this work.

Authors and Affiliations

  1. Department of Cancer Biology, Dana-Farber Cancer Institute and Department of Cell Biology, Harvard Medical School, Boston, 02115, Massachusetts, USA
    Chi Luo, Ji-Hong Lim, Yoonjin Lee, Ajith Thomas, Francisca Vazquez & Pere Puigserver
  2. Department of Chemistry and Chemical Biology, Harvard University, Cambridge, 02138, Massachusetts, USA
    Yoonjin Lee
  3. Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, 02115, Massachusetts, USA
    Scott R. Granter
  4. Broad Institute of Harvard and MIT, Cambridge, 02142, Massachusetts, USA
    Francisca Vazquez
  5. Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, 02115, Massachusetts, USA
    Francisca Vazquez
  6. Department of Dermatology, Brigham and Women’s Hospital, Harvard Medical School, Boston, 02115, Massachusetts, USA
    Hans R. Widlund

Authors

  1. Chi Luo
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  2. Ji-Hong Lim
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  3. Yoonjin Lee
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  4. Scott R. Granter
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  5. Ajith Thomas
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  6. Francisca Vazquez
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  7. Hans R. Widlund
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  8. Pere Puigserver
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Contributions

P.P. and F.V. conceived the project. C.L. contributed to immunoblots for Figs 1d, 2a, 3h, 4d,4c and Extended Data Figs 2g, 5e, 5i, 6a, 7c, 8d and 8f; qPCR for Figs 2, 3j, Extended Data Figs 2g, 4, 5e, 5i, and 8b; in vitro migration for Fig. 2c, Extended Data Figs 2h, 2i; in vivo experiments for Figs 1g 1h 2, and Extended Data Fig. 3. J.-H.L. contributed to all the other western blots, qPCR for Figs 1, 3 and 4, Extended Data Figs 2, 5, 6, 7, 8, and 9; all the in vitro migration and invasion experiments except Fig. 2c, Extended Data Figs 2h, 2i; all the in vivo experiments except Figs 1g, 1h, 2, and Extended Data Fig. 3. Y.L. contributed to all in vivo experiments and edited the manuscript. A.T. contributed to immunoblotting experiments for Figs 1d, 2a, 3h, 4d and 4c. F.V. and H.R.W. designed and performed the bioinformatic analyses for Extended Data Figs 1, 8g, and Fig. 1a and 1c, respectively. S.R.G. performed immunohistochemistry experiments. P.P., F.V., J.-H.L., H.R.W. and C.L. prepared the manuscript.

Corresponding author

Correspondence toPere Puigserver.

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The authors declare no competing financial interests.

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Reviewer Information

Nature thanks G. Bollag and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Extended data figures and tables

Extended Data Figure 1 GSEA analysis of PGC1α expression and deletion in melanoma cell lines.

a, b, Representative GSEA plots (a) and list of gene sets (b) enriched in A375P cells upon PGC1α knockdown from data set GSE36879, with the significance defined by false discovery rate (FDR) q < 0.25. c, d, Plots (c) and list of (d) the top gene sets in which expression is negatively correlated with PGC1α in 61 melanoma cell lines from CCLE, with the significance defined by FDR q < 0.15.

Extended Data Figure 2 PGC1α depletion activates integrin, TGFβ and Wnt pathways.

a – d, PGC1α knockdown increases expression of integrin genes (a, b), as well as genes in the TGFβ (c) and Wnt (d) pathways. e, f, Ectopic expression of PGC1α by adenoviruses (Ad) inhibits integrin gene expression. g, CRISPR-mediated PGC1α depletion increases gene expression linked to integrin, TGFβ and Wnt pathways in A375P cells. Depletion of PGC1α was confirmed by immunoblotting. h, i, FAK inhibition blunted the increased migration induced by PGC1α depletion. A375P (h) and G361 (i) cells were subjected to 24 h transwell migration assays in the presence of DMSO or various doses of FAK inhibitor PF-573228. Images represent three pictures captured with scale bar representing 100 μm. j, The cytotoxic effects of the FAK inhibitor on A375P melanoma cells were comparable between the various doses used in the migration assay within the 24 h time frame. The relative level of dead cells in the culture supernatant was quantified by ToxiLight Bioassay. Values in all panels represent mean ± s.d. of independent biological triplicates; *P < 0.05, **P < 0.01 and ***P < 0.001 by Student’s _t_-test in all panels.

Extended Data Figure 3 PGC1α suppresses metastasis in melanoma cells.

a, b, Knockdown of PGC1α increases the metastatic capacity of PGC1α-positive G361 (a, n = 3 mice per group) and MeWo (b, n = 3 mice per group) cells. Quantification of the number and size of lung metastatic nodules is shown. Metastatic size was quantified by measuring the longest diameter of each nodule. Values represent mean ± s.d., *P < 0.05 by Student’s _t_-test. Images in b represent one picture captured per H&E slide; scale bar represents 200 μm if not otherwise indicated. c, Ectopic expression level of PGC1α in the PGC1α-negative A375 and A2058 cell lines. d, Restoration of PGC1α suppresses integrin signalling, as indicated by p-FAK (Y397), in A375-derived lung metastatic nodules. The melanoma diagnostic marker HMB45 was used to distinguish the tumour nodules from the lung tissues. Images represent three pictures captured per slide with the scale bar representing 100 μm.

Extended Data Figure 4 Melanoma cells contain heterogeneous levels of mitochondria and PGC1α.

a, The mitochondrial content in melanoma cells is dynamically regulated. After 24 h in culture, the sorted mito-high and -low A375P subpopulations re-establish normal mitochondrial content distribution. b, Within the PGC1α-positive G361 line, the cells with higher migratory ability express lower PGC1α and elevated pro-metastatic genes. Values represent mean ± s.d. of triplicates; *P < 0.05 and **P < 0.01 by Student’s _t_-test. c, Isolation of circulating tumour cells (CTCs) from a tumour-bearing mouse. Two months post-injection, when the subcutaneous MeWo tumours became detectable, whole blood was collected by cardiac perfusion, followed by FACS based on surface protein staining with mouse CD31 and CD45 to exclude endothelial cells and lymphocytes and human HLAabc to purify human tumour cells. The primary subcutaneous tumours were enzymatically digested into single-cell suspension and subjected to the same sorting strategy. d, Gene expression in A375P melanoma cells after PGC1α induction. Values represent mean ± s.d. of independent biological triplicates; *P < 0.05, ***P < 0.01 and ***P < 0.001 versus shScr/DMSO; #P < 0.05 and ##P < 0.01 versus shPGC1α/DMSO by Student’s _t_-test.

Extended Data Figure 5 ID2, but not ID3, is downstream of PGC1α in the suppression of the pro-metastatic program.

a, b, PGC1α knockdown inhibits ID2 expression in PGC1α-positive cells. c, Ectopic expression of PGC1α increases ID2 levels in PGC1α-negative cells. d, PGC1α occupies the ID2 promoter region in A375P cells. e–g, Inhibition of ID2, but not ID3, increases expression and activation of integrin signalling in melanoma cell lines. h, i, Inhibition of ID2 by either shRNA or CRISPR/Cas9 increases expression of integrins. j, Quantification of in vitro migration and invasion induced by ID2 knockdown as shown in Fig. 3b. Values in all panels except h represent mean ± s.d. of independent biological triplicates; *P < 0.05, **P < 0.01 and ***P < 0.001 by Student’s _t_-test in all panels except h.

Extended Data Figure 6 Enforced expression of ID2 suppresses metastasis.

a, Ectopic expression of ID2 in A375P cells is higher than its endogenous level. b, Ectopic expression of ID2 attenuates integrin proteins and FAK (Y397) phosphorylation induced by PGC1α depletion. c, Quantification of invading cells as shown in Fig. 3e. d–f, Ectopic expression of ID2 suppresses integrin gene expression (d), invasion in vitro (e) and metastasis in vivo (f, n = 8 mice per group). Images in e and f represent one picture captured; scale bar represents 200 μm. g, ID2 does not affect cellular metabolism. Values in c, d, e and g represent mean ± s.d. of independent biological triplicates; values in f represent mean ± s.e.m. of the 8 mice; *P < 0.05 and **P < 0.01 by Student’s _t_-test in c, d, e, f and g.

Extended Data Figure 7 TCF4 is a putative ID2 partner in the regulation of integrin genes.

a, b, List of the top ID2-interacting proteins from the BioGRID (a) and STRING (b) databases. c, Knockdown efficiency of individual bHLH transcriptional factors by siRNAs in A375P cells was tested by immunoblotting. d, Inhibition of TCF4 attenuates PGC1α-knockdown-mediated integrin induction in A375P cells. e, TCF4 knockdown suppresses gene expression linked to integrin signalling. Values in d and e represent mean ± s.d. of independent biological triplicates; *P < 0.05 and **P < 0.01 by Student’s _t_-test in d and e.

Extended Data Figure 8 TCF4 induces integrin genes.

a, TCF4 is required for PGC1α-depletion-mediated induction of integrin genes in A375P cells. b, Depletion of TCF4 blunts the activation of integrin signalling by PGC1α or ID2 knockdown in A375P cells. c, Ectopic expression of ID2 blocks the binding of TCF4 to integrin promoters in A375P cells. A375P cells with indicated genetic manipulations that were stably overexpressing V5-TCF4 were subjected to ChIP and qPCR. Values in a and c represent mean ± s.d. of independent biological triplicates; *P < 0.05 and **_P_ < 0.01 versus shScr; #_P_ < 0.05 versus shPGC1α by Student’s _t_-test. **d**, Ectopic expression of TCF4 increases integrin proteins and signalling in A375P cells. **e**, TCF4 knockdown suppresses cell invasion in A375 and A2058 cells. Images represent one picture captured per membrane with the scale bar representing 200 μm. **f**, Expression of PGC1α and TCF4 in a panel of human melanoma cell lines. **g**, TCF4 and PGC1α expression in TCGA skin cutaneous melanoma dataset (471 samples with RNA-seq expression data). Tendency towards mutual exclusivity for samples with _Z_-scores >0 (represented by dotted lines), P = 0.00016 by Fisher’s exact test. h, TCF4 level does not affect cellular metabolism. Values in e and h represent mean ± s.d. of independent biological triplicates; *P < 0.05 by Student’s _t_-test.

Extended Data Figure 9 BRAFV600E inhibitor suppresses melanoma invasion independent of its cytostatic effect.

a, The BRAFV600E inhibitor, PLX4032, and the MEK1/2 inhibitor, PD98059, decrease integrin gene expression in melanoma cell lines. Gene expression was quantified 6 h post-treatment of inhibitors. b, PLX4032-induced PGC1α occupancy at the ID2 promoter. A375P cells were incubated with 2 μM of PLX4032 for 6 h before ChIP analysis. c, PLX4032 inhibits invasion of BRAFV600E-containing melanoma cells. Cells were incubated with 1 μM PLX4032 for 10 h in matrigel-coated transwell chambers, followed by quantification. Images represent one picture captured per membrane with the scale bar representing 200 μm. d, PGC1α and ID2 double knockdown does not affect sensitivity to PLX4032. Values in a, b and d represent mean ± s.d. of independent biological triplicates; *P < 0.05 and **P < 0.01 by Student’s _t_-test in a, b and d.

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Supplementary Information

This file contains the uncropped blots for Figures 1d, 2b, 3h, 4c, 4d, and Extended Data Figures 2g, 3c, 5a, 5e, 5h, 5i, 6a, 6b, 6d, 7c, 8c, 8d, 8f. (PDF 10886 kb)

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Luo, C., Lim, JH., Lee, Y. et al. A PGC1α-mediated transcriptional axis suppresses melanoma metastasis.Nature 537, 422–426 (2016). https://doi.org/10.1038/nature19347

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