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Unchecked oxidative stress in skeletal muscle prevents outgrowth of disseminated tumour cells

Nature Cell Biology volume 24, pages 538–553 (2022)Cite this article

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A Publisher Correction to this article was published on 24 May 2022

This article has been updated

Abstract

Skeletal muscle has long been recognized as an inhospitable site for disseminated tumour cells (DTCs). Yet its antimetastatic nature has eluded a thorough mechanistic examination. Here, we show that DTCs traffic to and persist within skeletal muscle in mice and in humans, which raises the question of how this tissue suppresses colonization. Results from mouse and organotypic culture models along with metabolomic profiling suggested that skeletal muscle imposes a sustained oxidative stress on DTCs that impairs their proliferation. Functional studies demonstrated that disrupting reduction–oxidation homeostasis via chemogenetic induction of reactive oxygen species slowed proliferation in a more fertile organ: the lung. Conversely, enhancement of the antioxidant potential of tumour cells through ectopic expression of catalase in the tumour or host mitochondria allowed robust colonization of skeletal muscle. These findings reveal a profound metabolic bottleneck imposed on DTCs and sustained by skeletal muscle. A thorough understanding of this biology could reveal previously undocumented DTC vulnerabilities that can be exploited to prevent metastasis in other more susceptible tissues.

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Fig. 1: Breast cancer cells traffic to and persist within SkM.
Fig. 2: Myotubes suppress breast and mammary cancer cells in culture, but not through a secreted or deposited factor.
Fig. 3: GSH metabolism is enriched in SkM metastases.
Fig. 4: The SkMc niche causes unchecked oxidative stress in DTCs.
Fig. 5: Sustained oxidative stress prevents DTC outgrowth in lung and muscle.
Fig. 6: Enhanced tumoural antioxidant capacity enables SkM colonization in culture and in vivo.
Fig. 7: Reduction of tumoural or environmental mtROS stimulates SkM colonization but hinders lung colonization.
Fig. 8: mtROS induced by the SkM niche resolves with mitochondrial catalase expression in tumour cells or in myocytes.

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Data availability

Raw data for Figs. 1e,f,h, 2c,e–h, 3d,e,g–i, 4b–d,f–h,j, 5a,b,e,g,j, 6a,b,d,g,i,n, 7c–f,h,j,k and 8a,c,d,f–h, and Extended Data Figs. 2b,d, 3b–e, 4b–c,e and 5b,d–f,h–j have been provided as individual source data files. Full metabolomics data pertaining to Fig. 3 and Extended Data Fig. 3 can be found in Supplementary Tables 1–4. Metabolomics data have been deposited in Metabolomics Workbench (study number ST002058) and can be accessed directly via its project: https://doi.org/10.21228/M89135. Additional details pertaining to the AluYb8 qPCR method and the studies described in Fig. 1 and Extended Data Fig. 2 are provided as Supplementary Table 5. Source data are provided with this paper.

Code availability

Code used in this study (for example, ImageJ macros for image analysis) are freely available from the corresponding author upon request.

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Acknowledgements

We are grateful to M. J. Bissell (Lawrence Berkeley National Laboratory) and to H. Blau (Stanford University) for inspiring this work. We remain indebted to the Breast Origin Cancer Tissue Donated after Death (BROCADE) programme, funded by the National Breast Cancer Foundation of Australia, for their hard work and especially to the women with MBC who selflessly donated to this study. We would like to thank P. Rabinovitch (University of Washington) and his group for the mCAT transgenics that enabled generation of the C57BL/6 myoblasts used within this manuscript. We thank W. J. Valente and J. Bielas (FHCRC) for providing the pBMN-IRES-GFP-mCAT and -cCAT vectors, and S. Beronja (FHCRC) for his critical feedback. This study was supported principally by the W. M. Keck Foundation (to C.M.G., K.C.H. and P.S.N.), and by start-up funds from the FHCRC (to C.M.G.). The Comparative Medicine and Proteomics Shared Resources of the FHCRC/University of Washington Cancer Consortium helped support this work and are funded by the NCI (P30 CA015704). BioRender.com was used to create mouse/human schematics. S.B.C. was supported by a Cellular and Molecular Biology Training Grant from the NIH (T32GM007270). J.D. was supported by a Postdoctoral Breakthrough award (W81XWH-18-1-0028) by the DoD Breast Cancer Research Program. P.S.N., L.D.T. and R.F.D. are also supported by P50CA097186.

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Authors and Affiliations

  1. Public Health Sciences Division/Translational Research Program, Fred Hutchinson Cancer Research Center, Seattle, WA, USA

    Sarah B. Crist, Jinxiang Dai & Cyrus M. Ghajar

  2. Graduate Program in Molecular and Cellular Biology, University of Washington, Seattle, WA, USA

    Sarah B. Crist

  3. Department of Biochemistry and Molecular Genetics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA

    Travis Nemkov & Kirk C. Hansen

  4. Human Biology Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA

    Ruth F. Dumpit, Stephen J. Tapscott, Lucas B. Sullivan, Peter S. Nelson & Cyrus M. Ghajar

  5. Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA

    Stephen J. Tapscott & Peter S. Nelson

  6. Department of Neurology, University of Washington, Seattle, WA, USA

    Stephen J. Tapscott

  7. Department of Urology, University of Washington, Seattle, WA, USA

    Lawrence D. True & Peter S. Nelson

  8. Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA

    Lawrence D. True & Peter S. Nelson

  9. The Kinghorn Cancer Centre and Cancer Research Theme, Garvan Institute of Medical Research, Darlinghurst, New South Wales, Australia

    Alexander Swarbrick

  10. St Vincent’s Clinical School, Faculty of Medicine, UNSW Sydney, Sydney, New South Wales, Australia

    Alexander Swarbrick

  11. Department of Medicine, University of Washington, Seattle, WA, USA

    Peter S. Nelson

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Contributions

S.B.C. performed experiments and analysed and interpreted data. T.N. and K.C.H. conducted metabolomics and associated data analysis and interpretation. J.D. assisted with animal experiments. A.S. facilitated the collection of human SkM specimens. R.F.D. stained and analysed the human SkM specimens (findings were confirmed by L.D.T.). S.B.C., L.B.S., S.J.T., P.S.N. and K.C.H. provided scientific insight. C.M.G. conceived the study. S.B.C. and C.M.G. wrote the manuscript. All authors read and provided feedback on the manuscript.

Corresponding author

Correspondence to Cyrus M. Ghajar.

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Extended data

Extended Data Fig. 1 Human SkM from a metastatic breast cancer patient harbours panCK+/ER+/PR+ breast cancer cells.

a) Representative multiplex IHC images from an ER+/PR+ primary breast tumour specimen, used as a positive staining control. ER/PR (breast cell marker) staining in purple, pan-cytokeratin (panCK, epithelial cell marker) staining in yellow. Scale bar: 1 mm (left), 100 μm (centre) and 10 μm (right). b) Representative multiplex IHC images from human SkM, used as a negative staining control. ER/PR staining in purple, panCK staining in yellow. Scale bar, 10 μm. c) Representative IHC image of the three human SkM sites sampled (tibialis anterior, quadriceps and gastrocnemius) from a patient with metastatic breast cancer (MBC). Scale bar, 1 cm. d) Multiplex IHC panels of three panCK+/ER+/PR+ cells located inside the quadriceps muscle. ER/PR staining in purple, pan-cytokeratin staining in yellow. Scale bar 100 μm, Inset 10 μm. e-f) Two micropictographs of serial sections (4 µm) of FFPE SkM tissue from an individual with metastatic breast cancer. The slides were stained with hematoxylin and eosin or sequentially stained with a multiplex IHC panel consisting of ER/PR (purple) and panCK (yellow). Two ER+/PR+/panCK+ breast cancer cells were identified in a vessel located within the quadriceps muscle. Scale bar, 10 μm.

Extended Data Fig. 2 AluYb8 qPCR reveals that DTCs travel to and persist within multiple mouse SkMs following intracardiac injection of breast cancer cells.

a) Schematic of mouse study to examine frequency of DTCs across organ site at early and late timepoints. b) AluYb8 amplification (fold change) at Day 3 post ic injection. n = 11 inoculated and 3 uninoculated mice. Lung, brain and TA, P < 0.0001 when multiple unpaired two-tailed t-tests, followed by the Holm-Sidak method, were run for comparison of tumour-bearing tissues to uninoculated controls. c) Representative IF images of matched brain, lung, SkM and bone marrow (BoMa) with DTCs at the early timepoint. Scale bar: 100 μm for lung, brain and BoMa. 10 μm for muscle. d) AluYb8 amplification (fold change) at Week 7. n = 11 inoculated and 3 uninoculated mice. Lung, brain, liver, BoMa and TA, **** P < 0.0001 when multiple unpaired two-tailed t-tests, followed by Holm-Sidak method, were run for comparison of tumour-bearing tissues to uninoculated controls. e) Representative IF images of matched brain, lung, SkM and BoMa with DTCs at the late timepoint. Scale bar: 100 μm for lung, brain and BoMa; 10 μm for muscle. f) Representative IF images of DTCs or clusters of tumour cells in the brain, lung or SkM. Ki67+ marks proliferating cells. Scale bar: 100 μm for lung, brain and BoMa; 10 μm for muscle. n = 15 cells each. For c, e and g, centre line represents the mean, and error bars the standard error of the mean (s.e.m.).

Source data

Extended Data Fig. 3 Metabolic comparison of SkM-metastatic 4T1 cells pre- and post-injection reveal that 4T1-SkM adapt to SkM.

a) PLS-DA score plot comparing 4T1-parental and 4T1-SkM cells in tissue culture against 4T1-SkM SkM metastases, 4T1-SkM lung metastases, healthy SkM and healthy lung. b) Fold change enrichment of GSH-related metabolites (defined by KEGG’s GSH metabolism set) for 4T1-SkM v. 4T1-parental in culture, SkM metastases v. 4T1-SkM in culture, and lung metastases v. 4T1-SkM in culture. c) Metabolite Set Enrichment of the metabolites that were 2-fold enriched in healthy SkM versus healthy lung. Over Representation Analysis used the hypergeometric test; one-tailed P-values were provided after adjusting for multiple testing. d) Table displaying the mean values for the GSH-related metabolites in healthy SkM and healthy lung, followed by the fold-change difference between the two. e) Dot-plot of the ratio of reduced to oxidized glutathione (GSH:GSSG) for 4T1-SkM and 4T1-parental in culture, healthy SkM and healthy lung, and SkM- and lung- metastases. n = 3 replicates for 4T1-parental and 4T1-SkM culture samples. n = 4 muscle- and 3 lung- metastases, 6 healthy muscle and 3 healthy lung. An one-way ANOVA, followed by uncorrected Fisher’s LSD, was performed where *** P = 0.0004 for 4T1-parental v 4T1-SkM, **** P < 0.0001 for healthy SkM v. lung, * P = 0.037 for SkM metastases v. lung metastases. For e, centre line represents the mean, and error bars the standard error of the mean (s.e.m.).

Source data

Extended Data Fig. 4 H2O2 is generated upon D-alanine treatment in a dose-dependent fashion in DAAO-expressing tumour cells and lung fibroblasts.

a) Workflow to test if D-/L-alanine stimulated H2O2 in MDA231-WT and -DAAO. b) Extracellular H2O2 of MDA231-WT and -DAAO treated with D-/L-alanine. n = 3 replicates, performed twice. Two-way ANOVA was used, followed by Dunnett’s test. Compared against untreated MDA231-DAAO mean- 1 h: *P = 0.028,100 mM D-ala. 4 h: ***P = 0.0001, 100 mM D-ala; **P = 0.0017, 20 mM D-ala; **P = 0.0019, 100 mM D-ala MDA231-WT. 8 h: ***P = 0.0001, 100 mM L-ala, 20 mM L-ala. *P = 0.023, 100 mM D-ala MDA231-WT; * P = 0.018, 20 mM D-ala MDA231-WT. 24 h: ***P = 0.000, 100 mM D-ala; *P = 0.011, 100 mM L-ala. 48 h: ***P = 0.000, 100 mM D-ala. All other P > 0.05. c) MDA231-WT and -DAAO outgrowth with D-/L-alanine. n = 3 replicates, performed twice. Two-way ANOVA, followed by Dunnett’s test: compared against untreated MDA231-DAAO- 48 h: ***P = 0.0001, 100 mM D-ala; **P = 0.0011, 100 mM D-ala MDA231-WT; **P = 0.0036, 20 mM D-ala MDA231-WT; *P = 0.04, untreated MDA231-WT. 72 h: ***P = 0.0001, 100 mM D-ala, untreated MDA231-WT, 20 mM L-ala MDA231-WT. All others P > 0.05. d) Workflow to test if D-/L-alanine stimulates H2O2 in LF-DAAO. e) Extracellular H2O2 in LF-DAAO and LF treated with D-/L-alanine. n = 3 replicates per condition, performed twice. Two-way ANOVA, followed by uncorrected Fisher’s LSD: ****P < 0.0001, LF-DAAO 100 mM D-ala v. LF 100 mM D-ala, LF-DAAO 100 mM D-ala v. LF-DAAO 100 mM L-ala (4 h). 8 h: *P = 0.03, LF-DAAO 50 mM D-ala v. LF 50 mM D-ala; ****P < 0.0001, 70-, 100-mM D-ala LF-DAAO v. LF. 24 h: * P = 0.033, LF-DAAO 20 mM D-ala v. LF 20 mM D-ala; ** P = 0.004, LF-DAAO 30 mM D-ala v. LF 30 mM D-ala. **** P < 0.0001, 40-, 50-, 70-, 100-mM D-ala LF-DAAO v. LF. 48 h: *** P = 0.0006, LF-DAAO 30 mM D-ala v. LF 30 mM D-ala. **** P < 0.0001, 40-, 50-, 70-, 100-mM D-ala LF-DAAO v. LF. Other LF-DAAO v. LF comparisons with D-ala P > 0.05. For b-c and e, error bars represent the s.e.m.

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Extended Data Fig. 5 Targeting catalase to the tumour cell mitochondria does not promote lung colonization.

a) Representative Western blots of MDA231 and EO771 -Ctl, -mCAT and -cCAT. b) Dot-plots of MDA231 and EO771 -Ctl, -mCAT or -cCAT outgrowth on LF. n = 10-15 replicates, performed in triplicate. One-way ANOVA, followed by Dunnett’s test, was performed. Mean of each condition compared against Ctl mean: P = 0.43, MDA231-Ctl v mCAT; P = 0.35, MDA231-Ctl v cCAT,; **** P < 0.0001, EO771-Ctl v mCAT, EO771-Ctl v cCAT. c) NOD-SCID study to examine if ectopic catalase promoted lung colonization. d) BLI measurements (total flux, photon/second) for MDA231-Ctl, -mCAT and -cCAT. n = 5 mice per cohort. Two-way ANOVA, followed by Tukey’s test, was performed. Week5- *P = 0.032, MDA231-Ctl v mCAT; *P = 0.027, MDA231-Ctl v cCAT. Week6- ****P < 0.0001, MDA231-Ctl v. mCAT, Ctl v. cCAT, e) BLI total flux for MDA231-Ctl, -mCAT and -cCAT in lung ex vivo. n = 5 mice per cohort. One-way ANOVA, followed by Tukey’s test, was performed: * P = 0.028, Ctl v mCAT; *P = 0.026, Ctl v cCAT. f) Quantification of MDA231-Ctl, -mCAT and -cCAT lesions in lung, with representative images of tumour burden. Arrows point to small GFP+ lesions. n = 5 mice per cohort. Scale bar, 1 mm. One-way ANOVA was performed, followed by Dunnett’s test, to determine **P = 0.0044, MDA231-Ctl v. mCAT; *** P = 0.0008, MDA231-Ctl v. cCAT. g) C57BL/6 study to examine if ectopic catalase promoted lung colonization. h) BLI total flux for EO771-Ctl and -mCAT. n = 5 mice per cohort. Two-way ANOVA, followed by Sidak’s test, was performed. Week2- *P = 0.039, EO771-Ctl v mCAT. i) BLI total flux for EO771-Ctl and -mCAT tumours in lung ex vivo. n = 5 mice per cohort. Unpaired two-tailed t-test was performed, where EO771-Ctl v mCAT, *P = 0.026. j) Quantification of EO771-Ctl and -mCAT lesions in lung, with representative images of tumour burden. Scale bar, 1 mm. Unpaired two-tailed t-test was performed where **P = 0.0036. For b, e-f and i-j, centre line represents mean, and error bars the s.e.m.

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Supplementary Table 1: Details of the metabolomics study; glutathione ratios. Supplementary Table 2: Raw dataset for the metabolomics study (first run: paired metastases and healthy tissues). Supplementary Table 3: Raw dataset for the metabolomics study (second run: parental-4T1 cells and 4T1-SkM cells cultured on tissue culture plastic). Supplementary Table 4: Combined raw datasets of the metabolomics studies (cells cultured on tissue culture plastic combined with metastases and healthy tissues). Supplementary Table 5: Technical details and validation for the AluYb8 qPCR method.

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Crist, S.B., Nemkov, T., Dumpit, R.F. et al. Unchecked oxidative stress in skeletal muscle prevents outgrowth of disseminated tumour cells. Nat Cell Biol 24, 538–553 (2022). https://doi.org/10.1038/s41556-022-00881-4

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