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PGC-1α mediates mitochondrial biogenesis and oxidative phosphorylation in cancer cells to promote metastasis

A Corrigendum to this article was published on 31 October 2014

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Abstract

Cancer cells can divert metabolites into anabolic pathways to support their rapid proliferation and to accumulate the cellular building blocks required for tumour growth. However, the specific bioenergetic profile of invasive and metastatic cancer cells is unknown. Here we report that migratory/invasive cancer cells specifically favour mitochondrial respiration and increased ATP production. Invasive cancer cells use the transcription coactivator peroxisome proliferator-activated receptor gamma, coactivator 1 alpha (PPARGC1A, also known as PGC-1α) to enhance oxidative phosphorylation, mitochondrial biogenesis and the oxygen consumption rate. Clinical analysis of human invasive breast cancers revealed a strong correlation between PGC-1α expression in invasive cancer cells and the formation of distant metastases. Silencing of PGC-1α in cancer cells suspended their invasive potential and attenuated metastasis without affecting proliferation, primary tumour growth or the epithelial-to-mesenchymal program. Inherent genetics of cancer cells can determine the transcriptome framework associated with invasion and metastasis, and mitochondrial biogenesis and respiration induced by PGC-1α are also essential for functional motility of cancer cells and metastasis.

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Figure 1: CCCs exhibit enhanced oxidative phosphorylation.
Figure 2: CCCs exhibit an increased OCR associated with PGC-1α expression and mitochondrial biogenesis.
Figure 3: PGC-1α expression is associated with mitochondrial respiration and biogenesis in cancer cells.
Figure 4: PGC-1α expression modulates complex-I-driven oxidative phosphorylation in 4T1 cancer cells.
Figure 5: PGC-1α expression modulates cancer cell invasion and migration.
Figure 6: Suppression of PGC-1α expression suppresses cancer cell dissemination and metastasis.
Figure 7: PGC-1α expression is co-induced with an EMT program.
Figure 8: PGC-1α expression in CCCs correlates with invasion and distant metastasis in patients with IDCs.

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  • 25 September 2014

    In the version of this Article originally published, the number of patients who were PGC-1α- with detected CTCs in Fig. 8f should have read 'n = 2 (18.2%)'. This error has now been corrected in the online version of the Article.

References

  1. Warburg, O. On the origin of cancer cells. Science 123, 309–314 (1956).

    Article  CAS  PubMed  Google Scholar 

  2. Vander Heiden, M. G., Cantley, L. C. & Thompson, C. B. Understanding the Warburg effect: the metabolic requirements of cell proliferation. Science 324, 1029–1033 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Ward, P. S. & Thompson, C. B. Metabolic reprogramming: a cancer hallmark even Warburg did not anticipate. Cancer Cell 21, 297–308 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. DeBerardinis, R. J., Lum, J. J., Hatzivassiliou, G. & Thompson, C. B. The biology of cancer: metabolic reprogramming fuels cell growth and proliferation. Cell Metab. 7, 11–20 (2008).

    Article  CAS  PubMed  Google Scholar 

  5. Locasale, J. W. & Cantley, L. C. Metabolic flux and the regulation of mammalian cell growth. Cell Metab. 14, 443–451 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Gatenby, R. A. & Gillies, R. J. Why do cancers have high aerobic glycolysis? Nat. Rev. Cancer 4, 891–899 (2004).

    Article  CAS  PubMed  Google Scholar 

  7. Aslakson, C. J. & Miller, F. R. Selective events in the metastatic process defined by analysis of the sequential dissemination of subpopulations of a mouse mammary tumor. Cancer Res. 52, 1399–1405 (1992).

    CAS  PubMed  Google Scholar 

  8. Kalluri, R. & Weinberg, R. A. The basics of epithelial–mesenchymal transition. J. Clin. Invest. 119, 1420–1428 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Thiery, J. P. Epithelial–mesenchymal transitions in tumour progression. Nat. Rev. Cancer 2, 442–454 (2002).

    Article  CAS  PubMed  Google Scholar 

  10. Wu, Z. et al. Mechanisms controlling mitochondrial biogenesis and respiration through the thermogenic coactivator PGC-1. Cell 98, 115–124 (1999).

    Article  CAS  PubMed  Google Scholar 

  11. Puigserver, P. et al. A cold-inducible coactivator of nuclear receptors linked to adaptive thermogenesis. Cell 92, 829–839 (1998).

    Article  CAS  PubMed  Google Scholar 

  12. Girnun, G. D. The diverse role of the PPARgamma coactivator 1 family of transcriptional coactivators in cancer. Semin. Cell Dev. Biol. 23, 381–388 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Bhalla, K. et al. PGC1α promotes tumor growth by inducing gene expression programs supporting lipogenesis. Cancer Res. 71, 6888–6898 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. D’Errico, I. et al. Peroxisome proliferator-activated receptor-gamma coactivator 1-alpha (PGC1α) is a metabolic regulator of intestinal epithelial cell fate. Proc. Natl Acad. Sci. USA 108, 6603–6608 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  15. Guy, C. T., Cardiff, R. D. & Muller, W. J. Induction of mammary tumors by expression of polyomavirus middle T oncogene: a transgenic mouse model for metastatic disease. Mol. Cell Biol. 12, 954–961 (1992).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Cooke, V. G. et al. Pericyte depletion results in hypoxia-associated epithelial-to-mesenchymal transition and metastasis mediated by met signaling pathway. Cancer Cell 21, 66–81 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Rae, J. M., Creighton, C. J., Meck, J. M., Haddad, B. R. & Johnson, M. D. MDA-MB-435 cells are derived from M14 melanoma cells—a loss for breast cancer, but a boon for melanoma research. Breast Cancer Res. Treat. 104, 13–19 (2007).

    Article  PubMed  Google Scholar 

  18. Yuan, M., Breitkopf, S. B., Yang, X. & Asara, J. M. A positive/negative ion-switching, targeted mass spectrometry-based metabolomics platform for bodily fluids, cells, and fresh and fixed tissue. Nat. Protoc. 7, 872–881 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Schreiber, S. N., Knutti, D., Brogli, K., Uhlmann, T. & Kralli, A. The transcriptional coactivator PGC-1 regulates the expression and activity of the orphan nuclear receptor estrogen-related receptor alpha (ERRα). J. Biol. Chem. 278, 9013–9018 (2003).

    Article  CAS  PubMed  Google Scholar 

  20. Woelfle, U. et al. Molecular signature associated with bone marrow micrometastasis in human breast cancer. Cancer Res. 63, 5679–5684 (2003).

    CAS  PubMed  Google Scholar 

  21. Jose, C., Bellance, N. & Rossignol, R. Choosing between glycolysis and oxidative phosphorylation: a tumor’s dilemma? Biochim. Biophys. Acta 1807, 552–561 (2011).

    Article  CAS  PubMed  Google Scholar 

  22. Koppenol, W. H., Bounds, P. L. & Dang, C. V. Otto Warburg’s contributions to current concepts of cancer metabolism. Nat. Rev. Cancer 11, 325–337 (2011).

    Article  CAS  PubMed  Google Scholar 

  23. Hu, J. et al. Antitelomerase therapy provokes ALT and mitochondrial adaptive mechanisms in cancer. Cell 148, 651–663 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. St-Pierre, J. et al. Suppression of reactive oxygen species and neurodegeneration by the PGC-1 transcriptional coactivators. Cell 127, 397–408 (2006).

    Article  CAS  PubMed  Google Scholar 

  25. Lin, M. T. & Beal, M. F. Mitochondrial dysfunction and oxidative stress in neurodegenerative diseases. Nature 443, 787–795 (2006).

    Article  CAS  PubMed  Google Scholar 

  26. Yang, M. H. et al. Direct regulation of TWIST by HIF-1α promotes metastasis. Nat. Cell Biol. 10, 295–305 (2008).

    Article  CAS  PubMed  Google Scholar 

  27. Yu, M. et al. Circulating breast tumor cells exhibit dynamic changes in epithelial and mesenchymal composition. Science 339, 580–584 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Jones, A. W., Yao, Z., Vicencio, J. M., Karkucinska-Wieckowska, A. & Szabadkai, G. PGC-1 family coactivators and cell fate: roles in cancer, neurodegeneration, cardiovascular disease and retrograde mitochondria-nucleus signalling. Mitochondrion 12, 86–99 (2012).

    Article  CAS  PubMed  Google Scholar 

  29. Gibbins, J. R. Epithelial cell motility: the effect of 2-deoxyglucose on cell migration, ATP production, and the structure of the cytoplasmic ground substance in lamellipodia of epithelial cells in culture. Cell Motil. 2, 25–46 (1982).

    Article  CAS  PubMed  Google Scholar 

  30. Schafer, Z. T. et al. Antioxidant and oncogene rescue of metabolic defects caused by loss of matrix attachment. Nature 461, 109–113 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Schumacher, D., Strilic, B., Sivaraj, K. K., Wettschureck, N. & Offermanns, S. Platelet-derived nucleotides promote tumor-cell transendothelial migration and metastasis via P2Y2 receptor. Cancer Cell 24, 130–137 (2013).

    Article  CAS  PubMed  Google Scholar 

  32. Suzuki, T. et al. Estrogen-related receptor α in human breast carcinoma as a potent prognostic factor. Cancer Res. 64, 4670–4676 (2004).

    Article  CAS  PubMed  Google Scholar 

  33. O’Connell, J. T. et al. VEGF-A and Tenascin-C produced by S100A4 + stromal cells are important for metastatic colonization. Proc. Natl Acad. Sci. USA 108, 16002–16007 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  34. Castle, J. C. et al. Exploiting the mutanome for tumor vaccination. Cancer Res. 72, 1081–1091 (2012).

    Article  CAS  PubMed  Google Scholar 

  35. Barretina, J. et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483, 603–607 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Boehm, E. A., Jones, B. E., Radda, G. K., Veech, R. L. & Clarke, K. Increased uncoupling proteins and decreased efficiency in palmitate-perfused hyperthyroid rat heart. Am. J. Physiol. Heart Circ. Physiol. 280, H977–H983 (2001).

    Article  CAS  PubMed  Google Scholar 

  37. Woelfle, U. et al. Molecular signature associated with bone marrow micrometastasis in human breast cancer. Cancer Res. 63, 5679–5684 (2003).

    CAS  PubMed  Google Scholar 

  38. Braun, S. et al. Cytokeratin-positive cells in the bone marrow and survival of patients with stage I, II, or III breast cancer. N. Engl. J. Med. 342, 525–533 (2000).

    Article  CAS  PubMed  Google Scholar 

  39. Fehm, T. et al. A concept for the standardized detection of disseminated tumor cells in bone marrow from patients with primary breast cancer and its clinical implementation. Cancer 107, 885–892 (2006).

    Article  PubMed  Google Scholar 

  40. Hofman, V. et al. Morphological analysis of circulating tumour cells in patients undergoing surgery for non-small cell lung carcinoma using the isolation by size of epithelial tumour cell (ISET) method. Cytopathology 23, 30–38 (2012).

    Article  CAS  PubMed  Google Scholar 

  41. Balakrishanan, N. & Rao, C. R. Handbook of Statistics Vol. 23, 1st edn (North Holland, 2004).

    Google Scholar 

Download references

Acknowledgements

This study was primarily supported by funds from the Cancer Prevention and Research Institute of Texas and funds from MD Anderson Cancer Center (MDACC). J.T.O’C. was financially supported by the DoD Breast Cancer Research Predoctoral Traineeship Award (W81XWH-09-1-0008). R.K. is supported by NIH Grants CA125550, CA155370, CA151925, DK081576 and DK055001. Mass spectrometry work was partially supported by CA12096405 (J.M.A.) and CA00651646 (J.M.A.). We wish to thank B. Spiegelman and J. Estall (Dana Farber Cancer Institute, Boston, Massachusetts, USA) for providing us with reagents related to PGC-1α. We thank M. Protopopova (MDACC, Houston, Texas) and F. Muller (MDACC, Houston, Texas) for their help with the Seahorse experiments. We thank L. Cantley (BIDMC, Boston, Massachusetts) for his critical reading of the manuscript. We also thank M. Yuan and S. Breitkopf (BIDMC, Boston, Massachusetts) for their help with mass spectrometry experiments, and G. Buruzula and J. LaVecchio at the Joslin Flow Cytometry Core Facility (Joslin Diabetes Center, Boston, Massachusetts) for helping with flow cytometry experiments. For electron microscopy imaging, the High Resolution Electron Microscopy Facility at UTMDACC is supported by the Institutional Core Grant CA16672. We thank R. Langley for help in editing of the manuscript.

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Authors and Affiliations

Authors

Contributions

J.T.O’C. performed data analyses and helped with the preparation of figures; K.N.G.H. performed experiments; H.W., K.P. and M.C.H. helped with data analyses; F.M.d.C., L.T.D.C., R.M.R. and J.M.A. performed experiments and analysed data, A.D. performed statistical analyses, V.S.L. performed experiments, analysed the data and contributed to the design of the experiment, writing of the manuscript and preparation of figures, R.K. contributed to the conceptual design of the study and provided advice regarding experiments and writing of the manuscript.

Corresponding author

Correspondence to Raghu Kalluri.

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Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Microarray heat maps of differentially regulated genes.

Heat maps rendering of the indicated metabolism pathways in PCC, CCC and MCC.

Supplementary Figure 2 CCC display increased mitochondria biogenesis associated with PGC-1α expression in multiple models of metastasis.

A. Quantitative PCR analyses of relative expression of indicated genes in CCC and MCC normalized to PCC (arbitrarily set to 1). Expression of ACC, FASN, CK8 in CCC and ACC in MCC was not detected (no bars) (n = 5 RNA samples from 5 mice, unpaired two-tailed Student’s t-test, see also Fig. 2a). B. PGC-1α expression in PCC (n = 5), CCC (n = 5) and MCC (n = 4) from MMTV-PyMT mice. n = RNA samples from n mice. C. PGC-1α expression (n = 5 RNA samples from 5 mice) and D. mitochondrial DNA (mtDNA) content (n = 3 DNA samples from 3 mice) in PCC, CCC and MCC from MDA-MB-231 orthotopic tumour model. E. PGC-1α expression (n = 5 RNA samples from 5 mice) and F. mitochondrial DNA (mtDNA) content (n = 3 DNA samples from 3 mice) in PCC, CCC and MCC from B16F10 orthotopic tumour model. Data is represented as mean ± s.e.m. Unless otherwise specified, one-way ANOVA was used. P < 0.05,P < 0.01,P < 0.001,P < 0.0001.

Supplementary Figure 3 PGC-1α knockdown is associated with decreased mitochondria number and impairs invasion and migration of cancer cells in a complex I dependent manner.

AB. Transmission electron microscopy images of MDAMB231 shScrbl and shPGC-1α cells (A) and B16F10 shScrbl and shPG1α, (B) white arrowheads and ‘M’ identify mitochondria. Scale bar upper panel: 2 μm, insert and lower panel: 500 nm. CD. Quantification of the number of mitochondria per cell in MDAMB231 (shScrbl, n = 3 cells; shPGC-1α, n = 4 cells) (C) and B16F10 (shScrbl, n = 4 cells; shPGC-1α, n = 3 cells), unpaired two-tailed Student’s t-test. Scale bar upper panel: 2 μm, insert and lower panel: 500 nm. E. Hematoxylin stained 4T1 cells following invasion, and light microscopy imaging of migrated cells in scratch assay migration, quantitation of invasion assay (n = 3 wells/group) and migration assay (n = 3 wells/group). Scale bar, 50 μm. One-way ANOVA. Ad. PGC-1α: adenoviral induction of PGC-1α expression. Data is represented as mean ± s.e.m.P < 0.05,P < 0.01,P < 0.001.

Supplementary Figure 4 Knockdown of PGC-1α in B16F10 cells suppresses their mitochondria function and invasive properties.

A. Relative PGC-1α expression in B16F10shPGC-1α cells, normalized to B16F10shScrbl cells (n = 3 RNA samples/cell line, unpaired two-tailed Student’s t-test). B. Western blot for PGC-1α in B16F10shPGC-1α and B16F10shScrbl cells. See also Supplementary Fig. 9. C. Relative mitochondrial DNA (mtDNA) content (n = 3 DNA samples/cell line, unpaired two-tailed Student’s t-test) and D. mitochondrial protein content (n = 2 lysates/cell line) relative to total cell protein content in B16F10shPGC-1α normalized to B16F10shScrbl cells. E. Intracellular ATP levels in B16F10shPGC-1α normalized to B16F10shScrbl cells (n = 3 lysates/cell line, unpaired two-tailed Student’s t-test). F. Oxygen consumption rate (OCR) in B16F10shPGC-1α normalized to B16F10shScrbl cells (n = 4 wells/cell line). G. Hematoxylin stained B16F10 cells following invasion (scale bar, 50 μm), and H. quantitation of invasion assay (n = 6 wells/group, one-way ANOVA). Ad. PGC-1α: adenoviral induction of PGC-1α expression. I. Light microscopy imaging (scale bar, 50 μm) of migrated cells in scratch assay and J. quantitation of migration assay (n = 5 wells/group, one-way ANOVA). K. Type I collagen gel area reflecting gel contraction by indicated cells (n = 4 wells/group, unpaired two-tailed Student’s t-test). Data is represented as mean ± s.e.m.P < 0.05,P < 0.01,P < 0.001,P < 0.0001.

Supplementary Figure 5 Knockdown of PGC-1α in MDA-MB-231 cells suppresses their mitochondria function and invasive properties.

A. Relative PGC-1α expression in MDA-MB-231shPGC-1α cells, normalized to MDA-MB-231shScrbl cells (n = 3 RNA samples/cell line, unpaired two-tailed Student’s t-test). B. Western blot for PGC-1α in MDA-MB-231shPGC-1α and MDA-MB-231shScrbl cells. See also Supplementary Fig. 9. C. Relative mitochondrial DNA (mtDNA) (n = 3 DNA samples/cell line, unpaired two-tailed Student’s t-test) and D. mitochondrial protein content relative to total cell protein content (n = 2 lysates/cell line) in MDA-MB-231shPGC-1α normalized to MDA-MB-231shScrbl cells. E. Intracellular ATP levels in MDA-MB-231shPGC-1α normalized to MDA-MB-231shScrbl cells (n = 3 lysates/cell line, unpaired two-tailed Student’s t-test). F. Oxygen consumption rate (OCR) in MDA-MB-231shPGC-1α (n = 3 wells) normalized to MDA-MB-231shScrbl cells (n = 4 wells). G. Hematoxylin stained MDA-MB-231 cells following invasion (scale bar, 50 μm), and H. quantitation of invasion assay (n = 4 wells/group, one-way ANOVA). Ad. PGC-1α: adenoviral induction of PGC-1α expression. I. Light microscopy imaging (scale bar, 50 μm) of migrated cells in scratch assay and J. quantitation of migration assay (n = 3 wells/group, one-way ANOVA). K. Type I collagen gel area reflecting gel contraction by indicated cells (n = 4 wells/group, unpaired two-tailed Student’s t-test). Data is represented as mean ± s.e.m.P < 0.05,P < 0.01,P < 0.001,P < 0.0001. NS, not significant.

Supplementary Figure 6 Changes in metabolites associated with PGC-1α suppression.

Heat map rendering of the metabolites measured by targeted metabolomics analyses in the indicated metabolism pathways of 4T1sh PGC-1α normalized to 4T1shScrbl cells (arbitrarily set to 0).

Supplementary Figure 7 PGC-1α suppression minimally impact glycolysis but impairs metastasis.

A. Percent 13C labelled metabolites derived from labelled glucose fed to 4T1shPGC-1α and 4T1shScrbl cells. Metabolites are clustered with respect to the listed metabolic pathways they are associated with n = 3 wells/cell line. PPP, pentose phosphate pathway; Polysac., polysaccharides; AA (amino acids) and FA (fatty acids) synthesis. Statistics source data can be found in Supplementary Table 6. B. Relative PGC-1α expression in two clones of 4T1shPGC-1α normalized to 4T1shScrbl cells (shScrbl: n = 3, shPGC-1α clone 1: n = 3, shPGC-1α clone 2: n = 4 RNA samples/cell line, unpaired two-tailed Student’s t-test). C. Tumour volume measured over time and D. Tumour weight at experimental endpoint (shScrbl, n = 6 mice; shPGC-1α clone 1, n = 7 mice; shPGC-1α clone 1 n = 5 mice). E. Number of surface lung nodules in 4T1 orthotopic tumour model (shScrbl, n = 6 mice; shPGC-1α clone 1, n = 7 mice; shPGC-1α clone 1 n = 5 mice, one-way ANOVA). Data is represented as mean ± s.e.m.P < 0.001,P < 0.0001.

Supplementary Figure 8 Knockdown of PGC-1α in MDA-MB-231 and B16F10 cells impairs metastasis.

A. MDA-MB-231shScrbl and MDA-MB-231shPGC-1α cells were implanted in the mammary fat pad of nude mice. Tumour volume measured over time. B. Tumour weight at experimental endpoint. C. Number of CCC colonies formed. D. Percent of GFP+ cancer cells per 200 μl blood collected at experimental endpoint. E. Representative images of H&E stained lung sections and quantitation of per cent metastatic lung surface area relative to total lung surface area. Metastatic lung nodules are encircled. Scale bar, 50 μm. F. Number of lung surface nodules. For A–F: MDA-MB-231shScrbl, n = 5 mice; MDA-MB-231shPGC-1α, n = 5 mice, unpaired two-tailed Student’s t-test. G. Representative images of H&E stained lung sections of mice with i.v. injection of indicated cells and per cent metastatic surface area relative to total lung surface area. Lung nodules are encircled. Scale bar, 50 μm. H. Number of lung surface nodules following i.v. injection of indicated cells. For G–H: MDA-MB-231shScrbl, n = 5 mice; MDA-MB-231shPGC-1α, n = 5 mice, unpaired two-tailed Student’s t-test. I. B16F10shScrbl and B16F10shPGC-1α cells were implanted subcutaneously in C57Bl/6 mice. Tumour volume measured over time. J. Tumour weight at experimental endpoint. K. Number of CCC colonies formed. L. Percent of GFP+ cancer cells per 200 μl blood collected at experimental endpoint. M. Representative images of H&E stained lung sections and quantitation of per cent metastatic lung surface area relative to total lung surface area. Metastatic lung nodules are encircled. Scale bar, 50 μm. N. Number of lung surface nodules. For I–N: B16F10shScrbl, n = 5 mice; B16F10shPGC-1α: n = 5 mice, unpaired two-tailed Student’s t-test. O. Representative images of H&E stained lung sections of mice with i.v. injection of indicated cells and per cent metastatic surface area relative to total lung surface area. Lung nodules are encircled. Scale bar, 50 μm. P. Number of lung surface nodules following i.v. injection of indicated cells. For O–P: B16F10shScrbl, n = 5 mice; B16F10shPGC-1α: n = 5 mice, unpaired two-tailed Student’s t-test. Data is represented as mean ± s.e.m.P < 0.05,P < 0.01,P < 0.0001.

Supplementary Figure 9 Uncropped western blots.

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LeBleu, V., O’Connell, J., Gonzalez Herrera, K. et al. PGC-1α mediates mitochondrial biogenesis and oxidative phosphorylation in cancer cells to promote metastasis. Nat Cell Biol 16, 992–1003 (2014). https://doi.org/10.1038/ncb3039

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