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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.
The authors declare no competing interests.
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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
Nature Medicine (2022)