Skip to main content
SpringerLink
Log in

Digital twins for predictive oncology will be a paradigm shift for precision cancer care

  • Correspondence
  • Published:

From Nature Medicine

View current issue Submit your manuscript

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1: The Cancer Patient Digital Twin life cycle.

References

  1. Wilkinson, M. D. et al. Sci. Data 3, 160018 (2016).

    Article  Google Scholar 

  2. Fagnan, K. et al. Data and Models: a Framework for Advancing AI in Science (Office of Scientific and Technical Information, 2019); https://doi.org/10.2172/1579323

  3. Bozkurt, S. et al. J. Am. Med. Inform. Assoc. 27, 1878–1884 (2020).

    Article  Google Scholar 

  4. Kapteyn, M. G., Pretorius, J. V. & Willcox, K. E. Nat. Comput. Sci. 1, 337–347 (2021).

    Article  Google Scholar 

  5. Taschuk, M. & Wilson, G. PLoS Comput. Biol. 13, e1005412 (2017).

    Article  Google Scholar 

  6. Zhang, J., Cunningham, J. J., Brown, J. S. & Gatenby, R. A. Nat. Commun. 8, 1816 (2017).

    Article  Google Scholar 

  7. Zou, J. & Schiebinger, L. Nature 559, 324–326 (2018).

    Article  CAS  Google Scholar 

  8. Corbett-Davies, S. & Goel, S. Preprint at https://arxiv.org/abs/1808.00023v2 (2018).

  9. Hernandez-Boussard, T., Bozkurt, S., Ioannidis, J. P. A. & Shah, N. H. J. Am. Med. Inform. Assoc. 27, 2011–2015 (2020).

    Article  Google Scholar 

  10. Greenspan, E. et al. CAFCW 113 Digital Twins for predictive cancer care: an HPC-enabled community initiative (2019); https://ncihub.org/resources/2296

Download references

Acknowledgements

We thank Jeannette Yusko (LLNL) and Janelle Cataldo (LLNL) for their contributions to the development of Fig. 1. This work was supported in part by Lawrence Livermore National Laboratory (LLNL). Lawrence Livermore National Laboratory is operated by Lawrence Livermore National Security, LLC, for the US Department of Energy, National Nuclear Security Administration, under contract DE-AC52-07NA27344. LLNL-JRNL-823517. This work has been supported in part by the Joint Design of Advanced Computing Solutions for Cancer (JDACS4C) program established by the US Department of Energy (DOE) and the National Cancer Institute (NCI) of the National Institutes of Health NIH), Leidos Biomedical Research contract no. 75N91019D00024. T.H.-B., P.M., I.S. and T.S.-M. were supported in part by Cancer Moonshot funds from the National Cancer Institute, Leidos Biomedical Research Subcontract 21X126F. T.H.-B. was supported in part by the National Cancer Institute of the National Institutes of Health under award number R01CA183962. The content of this article is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. This document was prepared as an account of work sponsored by an agency of the US government. Neither the US government nor Lawrence Livermore National Security, LLC, nor any of their employees makes any warranty, expressed or implied, or assumes any legal liability or responsibility for the accuracy, completeness or usefulness of any information, apparatus, product or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer or otherwise does not necessarily constitute or imply its endorsement, recommendation or favoring by the US government or Lawrence Livermore National Security, LLC. The views and opinions of authors expressed herein do not necessarily state or reflect those of the US government or Lawrence Livermore National Security, LLC, and shall not be used for advertising or product endorsement purposes.

Author information

Author notes

  1. These authors contributed equally to this work: Tina Hernandez-Boussard, Paul Macklin, Emily J. Greenspan

Authors and Affiliations

  1. Department of Medicine, Stanford University, Stanford, CA, USA

    Tina Hernandez-Boussard

  2. Department of Medicine, Indiana University, Stanford, CA, USA

    Paul Macklin

  3. Center for Biomedical Informatics and Information Technology, National Cancer Institute, Rockville, MD, USA

    Emily J. Greenspan

  4. Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, CA, USA

    Amy L. Gryshuk

  5. Biomedical Informatics and Data Science Directorate, Frederick National Laboratory for Cancer Research, Frederick, MD, USA

    Eric Stahlberg

  6. IBM Almaden Research Center, San Jose, CA, USA

    Tanveer Syeda-Mahmood

  7. Institute for Systems Biology, Seattle, WA, USA

    Ilya Shmulevich

Authors
  1. Tina Hernandez-Boussard
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Paul Macklin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Emily J. Greenspan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Amy L. Gryshuk
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Eric Stahlberg
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Tanveer Syeda-Mahmood
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Ilya Shmulevich
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

T.H.B., P.M., E.G., A.G. and I.S. drafted the manuscript; all authors were responsible for the concept, design and critical revision of the manuscript.

Corresponding author

Correspondence to Tina Hernandez-Boussard.

Ethics declarations

Competing interests

The authors declare no competing interests.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hernandez-Boussard, T., Macklin, P., Greenspan, E.J. et al. Digital twins for predictive oncology will be a paradigm shift for precision cancer care. Nat Med 27, 2065–2066 (2021). https://doi.org/10.1038/s41591-021-01558-5

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41591-021-01558-5

  • Springer Nature America, Inc.

This article is cited by

Search

Navigation