Asparagine bioavailability governs metastasis in a model of breast cancer

  • Nature volume 554, pages 378381 (15 February 2018)
  • doi:10.1038/nature25465
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Using a functional model of breast cancer heterogeneity, we previously showed that clonal sub-populations proficient at generating circulating tumour cells were not all equally capable of forming metastases at secondary sites1. A combination of differential expression and focused in vitro and in vivo RNA interference screens revealed candidate drivers of metastasis that discriminated metastatic clones. Among these, asparagine synthetase expression in a patient’s primary tumour was most strongly correlated with later metastatic relapse. Here we show that asparagine bioavailability strongly influences metastatic potential. Limiting asparagine by knockdown of asparagine synthetase, treatment with l-asparaginase, or dietary asparagine restriction reduces metastasis without affecting growth of the primary tumour, whereas increased dietary asparagine or enforced asparagine synthetase expression promotes metastatic progression. Altering asparagine availability in vitro strongly influences invasive potential, which is correlated with an effect on proteins that promote the epithelial-to-mesenchymal transition. This provides at least one potential mechanism for how the bioavailability of a single amino acid could regulate metastatic progression.

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Change history

  • Corrected online 04 April 2018

    Please see accompanying Erratum (http://doi.org/10.1038/nature26162). In Fig. 3d, the blue bars should be ‘L-asparaginase’ rather than ‘L-asparagine; ‘orthotropic’ should be ‘orthotopic’ in the Extended Data Fig. 6 legend; and the legend to Supplementary Table 4 was repeated for Supplementary Table 5 in the HTML. These and some other minor wording changes have been corrected online.


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This work was performed with assistance from CSHL Shared Resources, which are funded, in part, by the Cancer Center Support Grant 5P30CA045508. We thank M. Mosquera, M. Cahn, J. Coblentz, and L. Bianco for support with mouse work; K. Cheng, J. Bourbonniere, D. Hoppe, A. Nourjanova, and R. Puzis for support with histology; E. Hodges and E. Lee for support with next-generation sequencing; and J. Johnson for assistance with HPLC. This work was also performed with the assistance of the Cancer Research UK, Cambridge Institute Proteomics Core Facility. S.R.V.K. is supported by a fellowship from The Hope Funds for Cancer Research. E.W. is supported by a long-term fellowship from the Human Frontier Science Program. L.A.C. is supported by the Susan G. Komen Foundation (SAC110006) and the NCI Breast SPORE program (P50-CA58223-09A1). J.C.H. and C.M.P. are supported by funds from the NCI Breast SPORE program (P50-CA58223-09A1), the Breast Cancer Research Foundation, and the Triple Negative Breast Cancer Foundation. H.G. is supported by a grant from the National Institutes of Health (NIH) (NCI R00 CA194077). Work in the G.P. laboratory is supported by the Institute of Cancer Research, London and a Cancer Research UK grand challenge award (C59824/A25044). G.J.H. is the Royal Society Wolfson Research Professor and is supported by core funding from Cancer Research UK, by a Program Project grant from the NIH (5 P01 CA013106-44), and by a grant from the Department of Defense Breast Cancer Research Program (W81XWH-12-1-0300).

Author information

Author notes

    • Simon R. V. Knott
    •  & Elvin Wagenblast

    These authors contributed equally to this work.


  1. Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, UK

    • Simon R. V. Knott
    • , Nicolas Erard
    • , Evangelia K. Papachristou
    • , Clive S. D’Santos
    •  & Gregory J. Hannon
  2. Watson School of Biological Sciences, Howard Hughes Medical Institute, Cold Spring Harbor Laboratory, 1 Bungtown Road, Cold Spring Harbor, New York 11724, USA

    • Simon R. V. Knott
    • , Elvin Wagenblast
    • , Showkhin Khan
    • , Sun Y. Kim
    • , Mar Soto
    • , Annika L. Gable
    • , Ashley R. Maceli
    • , Steffen Dickopf
    •  & Gregory J. Hannon
  3. Center for Bioinformatics and Functional Genomics, Department of Biomedical Sciences, Cedars-Sinai Medical Center, 8700 Beverly Boulevard, Los Angeles, California 90048, USA

    • Simon R. V. Knott
  4. Princess Margaret Cancer Centre, University Health Network, University of Toronto, Toronto, Ontario M5G 1L7, Canada.

    • Elvin Wagenblast
  5. Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5G 1L7, Canada

    • Elvin Wagenblast
  6. New York Genome Center, 101 6th Avenue, New York, New York 10013, USA

    • Showkhin Khan
    •  & Gregory J. Hannon
  7. Division of Cancer Biology, The Institute of Cancer Research, 237 Fulham Road, London SW3 6JB, UK

    • Michel Wagner
    • , Marc-Olivier Turgeon
    •  & George Poulogiannis
  8. Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, California 94158, USA

    • Lisa Fish
    •  & Hani Goodarzi
  9. Department of Urology, University of California, San Francisco, San Francisco, California 94158, USA

    • Lisa Fish
    •  & Hani Goodarzi
  10. Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, California 94158, USA

    • Lisa Fish
    •  & Hani Goodarzi
  11. Division of Hematology and Oncology, University of North Carolina at Chapel Hill, 170 Manning Drive, CB7305, Chapel Hill, North Carolina 27599, USA

    • Lisa A. Carey
  12. Department of Pathology, University of Michigan School of Medicine, Ann Arbor, Michigan 48109, USA

    • John E. Wilkinson
  13. Department of Pathology, Virginia Commonwealth University, Richmond, Virginia 23284, USA.

    • J. Chuck Harrell
  14. Department of Genetics and Pathology, Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA

    • Charles M. Perou
  15. Division of Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London, London SW7 2AZ, UK

    • George Poulogiannis


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S.R.V.K., E.W., and G.J.H. conceived the project, supervised research, and wrote the paper. S.R.V.K. and E.W. analysed experiments. S.R.V.K., E.W., S.K., S.Y.K., and M.S. performed in vitro and in vivo experiments. N.E., A.L.G., A.R.M., and S.D. assisted with virus production, shRNA cloning, and library preparation. L.A.C., J.C.H., and C.M.P. assisted with human expression data. J.E.W. performed histological analyses. E.K.P. and C.S.D. assisted with proteomic analyses. L.F. and H.G. assisted with ribosomal profiling analyses. M.W., M.T., and G.P. performed metabolite profiling experiments.

Competing interests

C.M.P. is an equity stock holder of BioClassifier LLC and University Genomics, and has filed a patent on the PAM50 subtyping assay. S.R.V.K., E.W., and G.J.H. are seeking patent protection on the manipulation of asparagine availability for patient benefit in the metastatic setting. The remaining authors declare no competing financial interests.

Corresponding author

Correspondence to Gregory J. Hannon.

Reviewer Information Nature thanks R. Agami and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Supplementary information

PDF files

  1. 1.

    Life Sciences Reporting Summary

Excel files

  1. 1.

    Supplementary Table 1

    Genes identified as over-expressed in 4T1-T. Genes identified as over-expressed in 4T1-T cells as compared to 4T1-E cells by differential expression analysis of RNA sequencing data. For each line, cells were grown in vitro and two biological replicates were processed for RNA sequencing. Genes were identified in 4T1-T using a fold-change cutoff of 2 and a DESeq FDR cut-off of 0.05.

  2. 2.

    Supplementary Table 2

    Gene Ontology analysis of genes over-expressed in 4T1-T. The genes listed in Supplementary Table 1 were analysed to identify enriched cellular processes, functions and components. Each Gene Ontology term that was associated with the genes in the subset was compared in its number of associates within the list to its number of associations to the entire murine Refseq gene list via a hypergeometic test. Ontology terms that were enriched with a q-value < 0.05 are listed.

  3. 3.

    Supplementary Table 3

    Raw RNAi screening data and shRNA depletion scores. Represented are the Illumina sequence reads that were assigned to each of ~6 shRNAs targeting protein coding members of the genes listed in Supplementary Table 1, both in the pre-injection infected cell population and in the cell populations that were removed from the lungs of mice in the in vivo screen or were removed from the matrigel invaded cells in the in vitro screen. Also listed are the log-fold enrichment and depletion scores of each shRNA and false-discovery rates as assigned using an Emperical-bayes moderated t-test.

  4. 4.

    Supplementary Table 4

    Expression changes induced by shRNA silencing or cDNA induced over-expression. Relative expression values for each cell line produced using shRNAs for silencing or cDNAs for enforcing expression.

  5. 5.

    Supplementary Table 5

    Amino acid composition of serum with and without ʟ-asparaginase treatment. 4T1-T cells harbouring the non-targeting Renilla shRNA were injected into immunocompromised mice. Five mice each were either injected with 60 U l -asparaginase or PBS 5 days per week. After blood collection and serum isolation, free amino acids were quantified using High Performance Liquid Chromatography (HPLC) and a fluorometric detector.

  6. 6.

    Supplementary Table 6

    Mouse orthologues of human genes that were identified as differentially expressed during EMT. Listed are genes that are up- or down-regulated when cells were enforced for the expression of Tgf-ß, Twist, Gsc or Snail or when E-cadherin was silenced (EMT-up and –down genes, respectively). EMT-up genes whose protein-level log-fold changes in Asns-silenced cells fell within the bottom or top 10% are annotated as Down-regulated and Up-regulated, respectively.


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