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Mitochondrial copper depletion suppresses triple-negative breast cancer in mice

Nature Biotechnology volume 39, pages 357–367 (2021)Cite this article

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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.

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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.

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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.

Author information

Authors and Affiliations

  1. Department of Radiology, Molecular Imaging Program at Stanford, Stanford University School of Medicine, Stanford, CA, USA

    Liyang Cui, Yun-Sheng Chen, Min Chen, Jinghang Xie, Mingxi Fang, Qihua Zhu, Sanjiv Sam Gambhir & Jianghong Rao

  2. Division of Oncology, Departments of Medicine and Pathology, Stanford University School of Medicine, Stanford, CA, USA

    Arvin M. Gouw & Dean W. Felsher

  3. Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA

    Edward L. LaGory & Amato J. Giaccia

  4. Departments of Pathology and Oncology, and ChemBE, Johns Hopkins University School of Medicine, Baltimore, MD, USA

    Shenghao Guo, Nabeel Attarwala, Cissy Zhang & Anne Le

  5. State Key Laboratory of Bioreactor Engineering, Shanghai Key Laboratory of Chemical Biology, School of Pharmacy, East China University of Science and Technology, Shanghai, P. R. China

    Yao Tang & Weiping Zhu

  6. State Key Laboratory of Polymer Physics and Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun, P. R. China

    Ji Qi & Zhiyuan Wang

  7. Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA

    Yun-Sheng Chen

  8. Genetics Bioinformatics Service Center, Stanford University, Stanford, CA, USA

    Zhou Gao

  9. Department of Comparative Medicine, Stanford University School of Medicine, Stanford, CA, USA

    Kerriann M. Casey

  10. Institute of Chemical Sciences and Engineering, School of Basic Sciences, Swiss Federal Institute of Technology Lausanne (EPFL), Lausanne, Switzerland

    Arkadiy A. Bazhin & Elena A. Goun

  11. Salk Institute for Biological Studies, San Diego, CA, USA

    Leeann Hu

  12. Department of Medicinal Chemistry, China Pharmaceutical University, Nanjing, P. R. China

    Qihua Zhu

  13. Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA

    Mark D. Pegram

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  1. Liyang Cui
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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.

Corresponding author

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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