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Mitochondrial copper depletion suppresses triple-negative breast cancer in mice

Abstract

Depletion of mitochondrial copper, which shifts metabolism from respiration to glycolysis and reduces energy production, is known to be effective against cancer types that depend on oxidative phosphorylation. However, existing copper chelators are too toxic or ineffective for cancer treatment. Here we develop a safe, mitochondria-targeted, copper-depleting nanoparticle (CDN) and test it against triple-negative breast cancer (TNBC). We show that CDNs decrease oxygen consumption and oxidative phosphorylation, cause a metabolic switch to glycolysis and reduce ATP production in TNBC cells. This energy deficiency, together with compromised mitochondrial membrane potential and elevated oxidative stress, results in apoptosis. CDNs should be less toxic than existing copper chelators because they favorably deprive copper in the mitochondria in cancer cells instead of systemic depletion. Indeed, we demonstrate low toxicity of CDNs in healthy mice. In three mouse models of TNBC, CDN administration inhibits tumor growth and substantially improves survival. The efficacy and safety of CDNs suggest the potential clinical relevance of this approach.

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Fig. 1: Design and characterization of CDN.
Fig. 2: CDN induces TNBC cell death via intracellular copper depletion.
Fig. 3: CDN inhibits mitochondria complex IV.
Fig. 4: CDN-induced inhibition of mitochondrial OXPHOS changes metabolisms of TNBC cells.
Fig. 5: CDN depletes copper efficiently in vivo.
Fig. 6: CDN inhibits TNBC tumor growth.

Data availability

All important data generated or analyzed during this study are included in this article and its Supplementary Information files. Additional data that support the findings of the study are available from the corresponding author upon reasonable request. RNA-sequencing data are available from the NCBI Sequence Read Archive under accession number PRJNA587318. Source data are provided with this paper.

Code availability

Custom code described in this work for the RNA-sequencing data analysis is available for academic research upon request from the authors.

References

  1. 1.

    Foulkes, W. D., Smith, I. E. & Reis-Filho, J. S. Triple-negative breast cancer. N. Engl. J. Med. 363, 1938–1948 (2010).

    CAS  Google Scholar 

  2. 2.

    Bianchini, G., Balko, J. M., Mayer, I. A., Sanders, M. E. & Gianni, L. Triple-negative breast cancer: challenges and opportunities of a heterogeneous disease. Nat. Rev. Clin. Oncol. 13, 674–690 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. 3.

    Park, J. H. H. et al. Fatty acid oxidation-driven Src links mitochondrial energy reprogramming and oncogenic properties in triple-negative breast cancer. Cell Rep. 14, 2154–2165 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. 4.

    Camarda, R. et al. Inhibition of fatty acid oxidation as a therapy for MYC-overexpressing triple-negative breast cancer. Nat. Med. 22, 427–432 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. 5.

    van Weverwijk, A. et al. Metabolic adaptability in metastatic breast cancer by AKR1B10-dependent balancing of glycolysis and fatty acid oxidation. Nat. Commun. 10, 2698 (2019).

    PubMed  PubMed Central  Google Scholar 

  6. 6.

    Srirangam, A. et al. Effects of HIV protease inhibitor ritonavir on Akt-regulated cell proliferation in breast cancer. Clin. Cancer Res. 12, 1883–1896 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. 7.

    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).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. 8.

    Gouw, A. M. et al. The MYC oncogene cooperates with sterol-regulated element-binding protein to regulate lipogenesis essential for neoplastic growth. Cell Metab. 30, 556–572 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. 9.

    Nagaraja, G. M. et al. Gene expression signatures and biomarkers of noninvasive and invasive breast cancer cells: comprehensive profiles by representational difference analysis, microarrays and proteomics. Oncogene 25, 2328–2338 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. 10.

    Blockhuys, S. et al. Defining the human copper proteome and analysis of its expression variation in cancers. Metallomics 9, 112–123 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. 11.

    Hargreaves, I. P. et al. Inhibition of mitochondrial complex IV leads to secondary loss complex II–III activity: implications for the pathogenesis and treatment of mitochondrial encephalomyopathies. Mitochondrion 7, 284–287 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Li, Y. et al. An assembled complex IV maintains the stability and activity of complex I in mammalian mitochondria. J. Biol. Chem. 282, 17557–17562 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. 13.

    Carr, H. S. & Winge, D. R. Assembly of cytochrome c oxidase within the mitochondrion. Acc. Chem. Res. 36, 309–316 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. 14.

    Wang, J. et al. Inhibition of human copper trafficking by a small molecule significantly attenuates cancer cell proliferation. Nat. Chem. 7, 968–979 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Ishida, S., Andreux, P., Poitry-Yamate, C., Auwerx, J. & Hanahan, D. Bioavailable copper modulates oxidative phosphorylation and growth of tumors. Proc. Natl Acad. Sci. USA 110, 19507–19512 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. 16.

    Kim, K. K. et al. Tetrathiomolybdate inhibits mitochondrial complex IV and mediates degradation of hypoxia-inducible factor-1α in cancer cells. Sci. Rep. 5, 14296 (2015).

    PubMed  PubMed Central  Google Scholar 

  17. 17.

    Pu, K. et al. Semiconducting polymer nanoparticles as photoacoustic molecular imaging probes in living mice. Nat. Nanotechnol. 9, 233–239 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Heffern, M. C. et al. In vivo bioluminescence imaging reveals copper deficiency in a murine model of nonalcoholic fatty liver disease. Proc. Natl. Acad. Sci. USA 113, 14219–14224 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. 19.

    Ellinghaus, P. et al. BAY 87-2243, a highly potent and selective inhibitor of hypoxia-induced gene activation has antitumor activities by inhibition of mitochondrial complex I. Cancer Med. 2, 611–624 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. 20.

    Molina, J. R. et al. An inhibitor of oxidative phosphorylation exploits cancer vulnerability. Nat. Med. 24, 1036–1046 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. 21.

    Yasuta, N., Takenaka, N. & Takemura, T. Mechanism of photosensitized chemiluminescence of 2-methyl-6-phenylimidazo[1,2-a]pyrazin-3(7 H)-one (CLA) in aqueous solution. Chem. Lett. 28, 451–452 (1999).

    Google Scholar 

  22. 22.

    Bazhin, A. A. et al. A bioluminescent probe for longitudinal monitoring of mitochondrial membrane potential. Nat. Chem. Biol. https://doi.org/10.1038/s41589-020-0602-1 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  23. 23.

    Weinberg, S. E. & Chandel, N. S. Targeting mitochondria metabolism for cancer therapy. Nat. Chem. Biol. 11, 9–15 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. 24.

    Vyas, S., Zaganjor, E. & Haigis, M. C. Mitochondria and cancer. Cell 166, 555–566 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. 25.

    Hoy, A. J., Balaban, S. & Saunders, D. N. Adipocyte–tumor cell metabolic crosstalk in breast cancer. Trends Mol. Med. 23, 381–392 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. 26.

    Liu, Y. Fatty acid oxidation is a dominant bioenergetic pathway in prostate cancer. Prostate Cancer Prostatic Dis. 9, 230–234 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. 27.

    Nieman, K. M. et al. Adipocytes promote ovarian cancer metastasis and provide energy for rapid tumor growth. Nat. Med. 17, 1498–1503 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. 28.

    Schild, T., Low, V., Blenis, J. & Gomes, A. P. Unique metabolic adaptations dictate distal organ-specific metastatic colonization. Cancer Cell 33, 347–354 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Duman, C. et al. Acyl-CoA-binding protein drives glioblastoma tumorigenesis by sustaining fatty acid oxidation. Cell Metab. 30, 274–289 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. 30.

    Israelsen, W. J. et al. PKM2 isoform-specific deletion reveals a differential requirement for pyruvate kinase in tumor cells. Cell 155, 397–409 (2013).

    CAS  Google Scholar 

  31. 31.

    Bridges, H. R., Jones, A. J. Y., Pollak, M. N. & Hirst, J. Effects of metformin and other biguanides on oxidative phosphorylation in mitochondria. Biochem. J. 462, 475–487 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. 32.

    Ariaans, G., Jalving, M., Vries, E. G. Ede & Jong, Sde Anti-tumor effects of everolimus and metformin are complementary and glucose-dependent in breast cancer cells. BMC Cancer 17, 232 (2017). 1–13.

    PubMed  PubMed Central  Google Scholar 

  33. 33.

    Sanchez, M., Gastaldi, L., Remedi, M., Cáceres, A. & Landa, C. Rotenone-induced toxicity is mediated by Rho-GTPases in hippocampal neurons. Toxicol. Sci. 104, 352–361 (2008).

    CAS  Google Scholar 

  34. 34.

    Modica-Napolitano, JosephineS. & Aprille, J. R. Delocalized lipophilic cations selectively target the mitochondria of carcinoma cells. Adv. Drug Deliv. Rev. 49, 63–70 (2001).

    CAS  Google Scholar 

  35. 35.

    Heerdt, B. G., Houston, M. A. & Augenlicht, L. H. The intrinsic mitochondrial membrane potential of colonic carcinoma cells is linked to the probability of tumor progression. Cancer Res. 65, 9861–9867 (2005).

    CAS  Google Scholar 

  36. 36.

    Shi, Y. et al. Gboxin is an oxidative phosphorylation inhibitor that targets glioblastoma. Nature 567, 341–346 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. 37.

    Krebs, H. A. The Pasteur effect and the relations between respiration and fermentation. Essays Biochem. 8, 1–34 (1972).

    CAS  Google Scholar 

  38. 38.

    McGarry, J. D., Mannaerts, G. P. & Foster, D. W. A possible role for malonyl-CoA in the regulation of hepatic fatty acid oxidation and ketogenesis. J. Clin. Invest. 60, 265–270 (1977).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. 39.

    McGarry, J. D., Leatherman, G. F. & Foster, D. W. Carnitine palmitoyltransferase I. The site of inhibition of hepatic fatty acid oxidation by malonyl-CoA. J. Biol. Chem. 253, 4128–4136 (1978).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. 40.

    Ackerman, C. M. & Chang, C. J. Copper signaling in the brain and beyond. J. Biol. Chem. 293, 4628–4635 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. 41.

    Krishnamoorthy, L. et al. Copper regulates cyclic-AMP-dependent lipolysis. Nat. Chem. Biol. 12, 586–592 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. 42.

    Turski, M. L. & Thiele, D. J. New roles for copper metabolism in cell proliferation, signaling, and disease. J. Biol. Chem. 284, 717–721 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. 43.

    Blockhuys, S. & Wittung-Stafshede, P. Roles of copper-binding proteins in breast cancer. Int. J. Mol. Sci. 18, 871 (2017).

    Google Scholar 

  44. 44.

    Denoyer, D., Masaldan, S., La Fontaine, S. & Cater, M. A. Targeting copper in cancer therapy: ‘Copper That Cancer’. Metallomics 7, 1459–1476 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. 45.

    Hanahan, D. & Weinberg, R. A. Hallmarks of cancer: the next generation. Cell 144, 646–674 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. 46.

    Grubman, A. & White, A. R. Copper as a key regulator of cell signalling pathways. Expert Rev. Mol. Med. 16, e11 (2014).

    PubMed  PubMed Central  Google Scholar 

  47. 47.

    Matson Dzebo, M., Ariöz, C. & Wittung-Stafshede, P. Extended functional repertoire for human copper chaperones. Biomol. Concepts 7, 29–39 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. 48.

    Shao, S. et al. A non-cytotoxic dendrimer with innate and potent anticancer and anti-metastatic activities. Nat. Biomed. Eng. 1, 745–757 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. 49.

    Pan, Q. et al. Copper deficiency induced by tetrathiomolybdate suppresses tumor growth and angiogenesis. Cancer Res. 62, 4854–4859 (2002).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. 50.

    Doñate, F. et al. Identification of biomarkers for the antiangiogenic and antitumour activity of the superoxide dismutase 1 (SOD1) inhibitor tetrathiomolybdate (ATN-224). Br. J. Cancer 98, 776–783 (2008).

    PubMed  PubMed Central  Google Scholar 

  51. 51.

    Chisholm, C. L. et al. Ammonium tetrathiomolybdate treatment targets the copper transporter ATP7A and enhances sensitivity of breast cancer to cisplatin. Oncotarget 7, 84439–84452 (2016).

    PubMed  PubMed Central  Google Scholar 

  52. 52.

    Lin, J. et al. A non-comparative randomized phase II study of 2 doses of ATN-224, a copper/zinc superoxide dismutase inhibitor, in patients with biochemically recurrent hormone-naïve prostate cancer. Urol. Oncol. Semin. Orig. Investig. 31, 581–588 (2013).

    CAS  Google Scholar 

  53. 53.

    Redman, B. G. et al. Phase II trial of tetrathiomolybdate in patients with advanced kidney cancer. Clin. Cancer Res. 9, 1666–1672 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. 54.

    Schneider, B. J. et al. Pre-operative chemoradiation followed by post-operative adjuvant therapy with tetrathiomolybdate, a novel copper chelator, for patients with resectable esophageal cancer. Invest. New Drugs 31, 435–442 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. 55.

    Garber, K. Targeting copper to treat breast cancer. Science 349, 128–129 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. 56.

    Chan, N. et al. Influencing the tumor microenvironment: a phase II study of copper depletion using tetrathiomolybdate in patients with breast cancer at high risk for recurrence and in preclinical models of lung metastases. Clin. Cancer Res. 23, 666–676 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. 57.

    Sahota, S. et al. A phase II study of copper-depletion using tetrathiomolybdate (TM) in patients (pts) with breast cancer (BC) at high risk for recurrence: updated results. J. Clin. Oncol. 35, 2557–2557 (2017).

    Google Scholar 

  58. 58.

    Heffern, M. C. et al. In vivo bioluminescence imaging reveals copper deficiency in a murine model of nonalcoholic fatty liver disease. Proc. Natl Acad. Sci. USA 113, 14219–14224 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. 59.

    Nguyen, T. et al. Uncovering the role of N-acetyl-aspartyl-glutamate as a glutamate reservoir in cancer. Cell Reports 27, 491–501 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  60. 60.

    Udupa, S. et al. Upregulation of the glutaminase II pathway contributes to glutamate production upon glutaminase 1 inhibition in pancreatic cancer. Proteomics 19, 1800451 (2019).

    CAS  Google Scholar 

  61. 61.

    Elgogary, A. et al. Combination therapy with BPTES nanoparticles and metformin targets the metabolic heterogeneity of pancreatic cancer. Proc. Natl Acad. Sci. USA 113, E5328–E5336 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

L.C. acknowledges support from the Office of the Assistant Secretary of Defense for Health Affairs through the Breast Cancer Research Program under Award W81XWH-18-1-0591. A.M.G. and M.C. acknowledge support by the Stanford Cancer Translational Nanotechnology Training T32 training grant funded by the National Cancer Institute (grant T32 CA196585). This work was also supported by the US National Institutes of Health (NIH) National Cancer Institute grant R01CA243033 (to J.R.), grant R01CA184384 (to D.W.F.), grant R01CA208735 (to D.W.F.), grant R01CA193895 (A.L.), grant R35CA197713 (to A.J.G.) and the Shared Instrument Grant (1S10OD025226-01 to A.L.) funded by the NIH. We acknowledge the use of the Mass Spectrometry Facility, the Department of Chemistry NMR Facility, the SCi3 Core Facility, the Neuroscience Microscopy Service Facility (NIH grant NS069375), the Cell Sciences Imaging Facility, the Animal Histology Services and Diagnostic Lab at the Veterinary Service Center and the Genetics Bioinformatics Service Center at Stanford University. We thank A. Olson for his expertise with tissue preparation and imaging by confocal microscopy, and J. Rosenburg and T. Liang for their assistance in biostatistical analysis.

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Contributions

L.C. and J.R. conceived of the project. L.C. formulated and characterized the nanoparticle in solution and in vitro. L.C., E.L.L. and A.J.G. discussed and validated the OXPHOS inhibition in vitro. A.M.G. and D.W.F. initiated the metabolomic study and contributed to data analysis and manuscript writing. Y.T. and W.Z. synthesized the CDM molecule. J.Q. and Z.W. synthesized and characterized the semiconducting polymer (SP). L.C. and N.A. conducted the metabolic study and data acquisition. S.G. and C.Z. performed metabolic data analysis and prepared related figures. A.L. supervised the metabolism study and data interpretation. L.C., Y.-S.C. and S.S.G designed and performed near-infrared II photoacoustic imaging. Z.G. analyzed the mRNA sequencing data. A.A.B. and E.A.G. conducted imaging of mitochondrial membrane potential in vivo. L.C. and M.C. performed the animal study. L.H. performed in vitro study on 4T1 cells. J.X. performed western blotting experiments. M.F. acquired NMR spectra for the CDM molecule. Q.Z. synthesized the CCL-1 molecule. L.C. and J.R. prepared the figures and wrote the manuscript with contributions from co-authors.

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Correspondence to Jianghong Rao.

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Extended data

Extended Data Fig. 1 Cytotoxicity of CDN measured with trypan blue exclusion method.

Cell viability of (a) MDA-MB-231 and (b) MDA-MB-468 cells after treatment with various concentrations of CDN, TPA or ATN224 (mean ± s.e.m., n=3 independent samples). (c) Metal remedy experiment measuring the cell viability of MDA-MB-231 cells after 24 h treatment with CDN (CDM: 1 μM) with (n=3 independent samples,) or without n=6 independent samples) various metal ion supplement (mean ± s.e.m., P value from unpaired t test, two-tailed). (d) Titration study showing the remedy effect of various concentrations of copper ions on MDA-MB-231 cell viability measured by trypan blue method (mean ± s.e.m., n=6 independent samples for CDN group, n=3 independent samples for copper addition group, P value from unpaired t test, two-tailed).

Extended Data Fig. 2 Acute OCR response of MDA-MB-231 cells after 1 h treatment with CDN.

OCR was measured with a serial injection of oligomycin (1 μM), FCCP (1 μM) and rotenone and antimycin A (0.5 μM) (mean ± s.e.m., n=6 biologically independent samples).

Extended Data Fig. 3 Comparison on the cytotoxicity, OCR inhibition and mitochondria membrane potential damaging effect between CDN and established complex I inhibitors.

MDA-MB-231 or MDA-MB-468 cells were incubated with 1 μM of CDN, IACS-010759 or BAY 87-2243. (a) Cell viability after 24 h of treatment was measured by MTS assay (mean ± s.e.m., n=3 biologically independent samples, P value from unpaired t test, two-tailed). (b) OCR and (c) ECAR were determined via Seahorse assay at 1 h after incubation. The results were normalized by cell number (mean ± s.e.m., n=6 biologically independent samples, P value from unpaired t test, two-tailed)). (d) Representative confocal microscopy images of cells stained with MitoTracker Green (MT-G, green) and DAPI (blue) after 24 h treatment. (scale bar: 50 μm). Three experiments were repeated independently with similar results.

Extended Data Fig. 4 CDN-enabled copper depletion elevated cellular oxidative stress.

(a) SOD1 levels of MDA-MB-231 after treatment with TPA and CDN at concentrations indicated by western blot analysis. Two experiments were repeated independently with similar results. (b) SOD1 activity of TNBC cells treated with CDN (1 μM) or ATN224 (5 μM) (mean ± s.e.m., n=3 biologically independent samples, P>0.05 for MDA-MB-468, P value from unpaired t test, two-tailed). (c) Cellular superoxide level measured by the chemiluminescence signal of CLA after treatment of SPN (50 μg/ml), CDN treatment (CDM: 1 μM, SPN: 50 μg/ml), TM (1 μM) and TPA (1 μM), shown as a ratio of treated to non-treated control (mean ± s.e.m., n=3 biologically independent samples, P value from unpaired t test, two-tailed). (d) Immunofluorescence staining of 𝛾H2AX (left) and 4-hydroxynonenal (4-HNE, right) on MDA-MB 231 cells at 24 h after incubation with SPN (50 μg/ml), CDN (CDM: 1 μM, SPN: 50 μg/ml), TPA (1 μM) or ATN224 (1 μM). (Scale bar=50 μm) Three experiments were repeated independently with similar results.

Source data

Extended Data Fig. 5 Intensities of isotopologues of metabolites (glutamine, cysteinyl glycine, glutathione (reduced), 4-oxoproline, hydroxyproline) produced from 13C515N2-labeled glutamine in vitro.

Illustration of the corresponding pathways through which these metabolites are produced is also shown. Metabolites with isotopologues shown are written in red. Other important intermediate metabolites are written in black. Non-labeled 12C and 14N are shown as black-filled circles while labeled 13C are shown as red-filled circles and labeled 15N are shown as green-filled circles. Raw intensities are normalized by the protein concentration. Data are shown as mean ± s.e.m. (n = 5 for biologically independent samples, P value was from unpaired t test, two-tailed).

Extended Data Fig. 6 Intensities of isotopologues of metabolites (lactic acid, glutamate and taurine) produced from 13C515N2-labeled glutamine in vitro and illustration of the corresponding pathways through which these metabolites are produced.

Metabolites with isotopologues shown are written in red. Other important intermediate metabolites are written in black. Non-labeled 12C and 14N are shown as black-filled circles while labeled 13C are shown as red-filled circles and labeled 15N are shown as green-filled circles. Raw intensities are normalized by both the protein concentration. Data are shown as mean ± s.e.m. (n = 5 for biologically independent samples, P value was from unpaired t test, two-tailed).

Extended Data Fig. 7 Ex vivo imaging of MDA-MB-231 tumor bearing mice administered with fCDN.

Nude mice bearing orthotopic MDA-MB-231 tumors were injected i.v. with fCDN (CDM dose: 1.35 mg/kg, n=6 independent animals). At 24 h after injection, mice were sacrificed, and major organs were collected and imaged with IVIS. (a). Representative images of ex vivo imaging emitted at 540 nm and 740 nm (excited at 500 nm). The ratiometric fluorescence graphs were shown as Em540/Em740. Average fluorescence efficiency from all organs was quantified at the (b) 540 nm emission (from the SPN group) and (c) 740 nm emission (from CDM group). (d) The ratio of fluorescence emission between 540 nm and 740 nm. For all the boxplots: center line, median; box limits, first and third quartiles; whiskers, min to max values.

Extended Data Fig. 8 Cumulative toxicity profile for CDN.

(a) Hematological (i-vii) and liver panel (viii-x) analysis of mice after receiving either saline or CDN (CDM dose: 1.35 mg/kg, intravenous administration weekly, 7 doses in total). (mean ± s.e.m., n=5 independent animals for blood test, n=3 independent animals for liver panel test). (b) Representative haematoxylin and eosin (H&E) staining of normal tissue slice from mice treated with saline or treatment strategy shown in Fig. 6a for CDN (scale bar: 50 μm). Slides from 5 independent animals were imaged and showed similar results.

Extended Data Fig. 9 Acute toxicity profile for CDN.

(a) Body weight changes of mice after i.v. injection of saline or single large dose of CDN (CDM: 100 mg/kg, n=3 independent animals). (b) Blood test parameters of treated mice comparing to control mice (mean ± s.e.m., n=3 independent animals). (c) representative H&E stained tissue slices of indicated organs from CDN treated and control mice (scale bar: 20 μm). Slides from 3 independent animals were imaged and showed similar results.

Extended Data Fig. 10 Cytotoxicity of CDN to receptor-positive breast cancer and prostate cancer cells.

(a) Viability of receptor-positive breast cancer cell lines (HCC1428, MCF7, T47D) and TNBC cells (MDA-MB-231, MDA-MB-468 and BT-20) after 24 h of treatment with CDN measured by MTS assay. (mean ± s.e.m., n=3 biologically independent samples). (b) Prostate cancer cell lines, PC3 and 22Rv1, also responded to CDN treatment. Viability after 24 h of CDN treatment was presented as percentage of cell control without treatment (mean ± s.e.m., n=3 biologically independent samples).

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Supplementary Figs. 1–20, Notes and Source Data for Supplementary Fig. 5.

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Source Data Extended Data Fig. 4

Unprocessed western blots full scan for GAPDH in Extended Data Fig. 4.

Source Data Extended Data Fig. 4

Unprocessed western blots full scan for SOD1 in Extended Data Fig. 4.

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Cui, L., Gouw, A.M., LaGory, E.L. et al. Mitochondrial copper depletion suppresses triple-negative breast cancer in mice. Nat Biotechnol 39, 357–367 (2021). https://doi.org/10.1038/s41587-020-0707-9

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