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

Nature Medicine volume 27, pages 2065–2066 (2021)Cite this article

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The proposed CPDT framework integrates individual-level data, such as proteome and clinical characteristics, with other factors like clinical trials and population studies to create a multiscale and multimodal data set for model training. To ensure rapid and comprehensive data integration, data must be captured under FAIR (Findability, Accessibility, Interoperability, Reusability) principles1,2 and across diverse populations to make sure that all patients equally benefit3.

A revolutionary concept of the proposed CPDT will be its ability to bridge size and time scales of biological organization to address changes spanning the full patient experience, from the molecular level over nanoseconds to the population levels across decades. As a patient’s physical state evolves, their CPDT must incorporate observational data to represent the patient’s current state and reliably forecast future state transitions. A range of multiscale models exist for various cancer-related processes. The envisioned CPDT will need to connect scales and processes by adapting existing techniques for simulation, model inference, data assimilation and high-performance computing to build and test real-time, dynamic models at scale4. Throughout its development and once it is complete, technical validation and rigorous software engineering best practices will be critical to ensuring that the future CPDT system is trustworthy5.

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

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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.

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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

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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.

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Correspondence to Tina Hernandez-Boussard.

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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

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