What should personalized, precision treatment of cancer look like in the future?
We know that people are different, their tumors are different, and they respond differently to different therapies. Medical teams of the future might be able to create a ?“virtual twin” of a person and their tumor. Then, by tapping supercomputers, physician-led teams could simulate how tumor cells behave to test millions (or billions) of possible treatment combinations. Ultimately, the best combinations might offer clues towards a personalized, effective treatment plan.
Sound like wishful thinking? The first steps towards this vision have been undertaken by a multi-institution research collaboration that includes Jonathan Ozik and Nicholson Collier, computational scientists at the U.S. Department of Energy’s Argonne National Laboratory.
The research team, which includes collaborators at Indiana University and the University of Vermont Medical Center, brought the power of high-performance computing to the thorny challenge of improving cancer immunotherapy. The team tapped twin supercomputers at Argonne and the University of Chicago, finding that high-performance computing can yield clues in fighting cancer, as discussed in a June 7 article published in Molecular Systems Design and Engineering.
“With this new approach, researchers can use agent-based modeling in more scientifically robust ways.” — Nicholson Collier, computational scientist at Argonne and the University of Chicago
Standing up to cancer
Cancer immunotherapy is a promising treatment that realigns your immune system to reduce or eliminate cancer cells. The therapy, however, helps only 10 to 20 percent of patients — partly because the way in which cancer cells and immune cells mingle is complex and poorly understood. Proven rules are scarce.
To begin uncovering the rules of immunotherapy, the team turned to a set of three tools:
- Agent-based modeling, which predicted the behavior of individual ?“agents” – cancer and immune cells, in this case
- Argonne’s award-winning workflow technology to take full advantage of the supercomputers
- A guiding framework to explore models and dynamically direct and track results
The trio operate in a hierarchy. The framework, developed by Ozik, Collier, Argonne colleagues, and Gary An, a surgeon and professor at the University of Vermont Medical Center, is called Extreme-scale Model Exploration with Swift (EMEWS). It oversees the agent-based model and the workflow system, the Swift/T parallel scripting language, developed at Argonne and the University of Chicago.
What is unique about this combination of tools? ?“We are helping more people in a variety of computational science fields to do large-scale experimentation with their models,” said Ozik, who — like Collier — holds a joint appointment at the University of Chicago. ?“Building a model is fun. But without supercomputers, it is difficult to really understand the full potential of how models can behave.”
The Latest on: Personalized medicine
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