Exploring approaches for predictive cancer patient digital twins: Opportunities for collaboration and innovation

Eric A. Stahlberg, Mohamed Abdel-Rahman, Boris Aguilar, Alireza Asadpoure, Robert A. Beckman, Lynn L. Borkon, Jeffrey N. Bryan, Colleen M. Cebulla, Young Hwan Chang, Ansu Chatterjee, Jun Deng, Sepideh Dolatshahi, Olivier Gevaert, Emily J. Greenspan, Wenrui Hao, Tina Hernandez-Boussard, Pamela R. Jackson, Marieke Kuijjer, Adrian Lee, Paul MacklinSubha Madhavan, Matthew D. McCoy, Navid Mohammad Mirzaei, Talayeh Razzaghi, Heber L. Rocha, Leili Shahriyari, Ilya Shmulevich, Daniel G. Stover, Yi Sun, Tanveer Syeda-Mahmood, Jinhua Wang, Qi Wang, Ioannis Zervantonakis

Research output: Contribution to journalArticlepeer-review


We are rapidly approaching a future in which cancer patient digital twins will reach their potential to predict cancer prevention, diagnosis, and treatment in individual patients. This will be realized based on advances in high performance computing, computational modeling, and an expanding repertoire of observational data across multiple scales and modalities. In 2020, the US National Cancer Institute, and the US Department of Energy, through a trans-disciplinary research community at the intersection of advanced computing and cancer research, initiated team science collaborative projects to explore the development and implementation of predictive Cancer Patient Digital Twins. Several diverse pilot projects were launched to provide key insights into important features of this emerging landscape and to determine the requirements for the development and adoption of cancer patient digital twins. Projects included exploring approaches to using a large cohort of digital twins to perform deep phenotyping and plan treatments at the individual level, prototyping self-learning digital twin platforms, using adaptive digital twin approaches to monitor treatment response and resistance, developing methods to integrate and fuse data and observations across multiple scales, and personalizing treatment based on cancer type. Collectively these efforts have yielded increased insights into the opportunities and challenges facing cancer patient digital twin approaches and helped define a path forward. Given the rapidly growing interest in patient digital twins, this manuscript provides a valuable early progress report of several CPDT pilot projects commenced in common, their overall aims, early progress, lessons learned and future directions that will increasingly involve the broader research community.

Original languageEnglish (US)
Article number1007784
JournalFrontiers in Digital Health
StatePublished - Oct 6 2022

Bibliographical note

Funding Information:
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. RB's work was supported by the Department of Defense Breakthrough Award (BC161497), which is aimed at applying models to a preclinical model of breast cancer. This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research under Award Numbers DE-SC0021655, DE-SC0021630, and DE-SC0021631. Acknowledgments

Publisher Copyright:
2022 Stahlberg, Abdel-Rahman, Aguilar, Asadpoure, Beckman, Borkon, Bryan, Cebulla, Chang, Chatterjee, Deng, Dolatshahi, Gevaert, Greenspan, Hao, Hernandez-Boussard, Jackson, Kuijjer, Lee, Macklin, Madhavan, McCoy, Mohammed Mirzaei, Razzaghi, Rocha, Shahriyari, Shmulevich, Stover, Sun, Syeda-Mahmood, Wang, Wang and Zervantonakis.


  • artificial intelligence
  • cancer patient
  • digital twins
  • machine learning
  • mathematical modeling
  • oncology
  • predictive medicine


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