Cancer Patient Digital Twins—A Progress Report and Next Steps
Collaboration, cooperation, and the need for multimodal, multiscale data were central themes in a recent article, “Exploring approaches for predictive cancer patient digital twins: Opportunities for collaboration and innovation,” published in Frontiers in Digital Health.
The article, authored by a diverse team of scientists, including CBIIT’s Dr. Emily Greenspan and the NCI’s Frederick National Laboratory for Cancer Research’s (FNLCR) Dr. Eric Stahlberg and Lynn Borkon, gives an update on NCI’s efforts to develop a cancer research digital twin.
In particular, the article outlines five projects that combine biological and clinical information with mechanistic modeling, machine learning, and advanced computing. These include:
- Project 1: simulating one million pancreatic cancer patients to guide treatment, led by Georgetown University.
- Project 2: self-learning platforms for personalized treatment of melanoma, led by Indiana University.
- Project 3: an adaptive digital twin approach for monitoring treatment response and resistance, led by Stanford University.
- Project 4: a patient-specific multiscale digital twin for exploring optimal treatment pathways for non-small cell lung cancer, led by the University of South Carolina.
- Project 5: virtual cancer digital twin approaches, led by the University of Massachusetts, Amherst.
The five seed projects are the fruits of a collaboration between NCI and the U.S. Department of Energy (DOE). This work is being coordinated through the Envisioning Computational Innovations for Cancer Challenges (ECICC) community, which first helped to conceptualize the projects in an Ideas Lab, titled “Towards Building a Cancer Patient Digital Twin.”
“These projects are an important first step in tackling the development of cancer patient digital twins and show us how critical it is to work across different scientific disciplines to move this research area forward,” said Dr. Emily Greenspan, NCI’s federal program lead for the NCI-DOE Collaboration.
“We formed the ECICC community to bring together a diverse community of interdisciplinary scientists from every career stage, working in both the public and private sectors,” she added.
The ECICC community now has more than 200 members from academia, the government, and industry who are looking for new ways to combine cancer research with computational science and artificial intelligence. ECICC features a dedicated cancer patient digital twin resource hub to foster collaboration. Interested in joining the ECICC community? For information, contact the community via email.