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IU researchers using high-performance computing to improve cancer treatment

By creating “digital twins” of the human immune system, IU researchers are leveraging the university’s supercomputer and other high-performance computing resources to develop more effective treatments for a wide range of diseases.

Mar 29, 2024

By creating “digital twins” of the human immune system, Indiana University researchers are leveraging the university’s Big Red 200 supercomputer and other high-performance computing resources to develop more effective treatments for a wide range of diseases, including cancer and viral infections like COVID-19.

Digital twin research is an emerging field of research that uses virtual replicas of physical systems to simulate and analyze real-world scenarios.

Two men in front of IU Big Red 200 IU doctoral student Heber Rocha, left, and IU associate professor Paul Macklin aim to use real-world patient data to come up with individually tailored cancer therapeutics. Photo by James Brosher, Indiana University

A leader of this effort is Paul Macklin, an associate professor of intelligent systems engineering at the IU Luddy School of Informatics, Computing and Engineering and director of IU’s Math Cancer Lab, which develops pioneering computational technologies for use in patient-specific cancer simulators. He and Heber Rocha, a postdoctoral research associate in his lab, aim to integrate real-world patient data from multiple sources and use the simulations to come up with individually tailored cancer therapeutics.

“We are building digital twins based on mathematical models that simulate individual cancer and immune cells as they interact,” said Macklin, also the associate dean for undergraduate education at the Luddy School and an affiliate of the IU Simon Comprehensive Cancer Center. “Our goal is for doctors to be able to prescribe the best intervention strategies for their patients based on how different drug compounds and therapies affect cell behaviors in the cell simulations.”

Using IU’s high-performance computing resources, Macklin’s lab can simulate the dynamics and the interactions of hundreds of thousands or millions of cells to assess, optimize and customize patient treatments.

A big challenge is how cancer and immune cells behave, Macklin said.

“It is very stochastic, which means that there’s an element of random chance,” he said. “Today an immune cell may go right. Tomorrow that immune cell might have gone left, and compounding those left-right random choices over time can lead to really big changes in long-term effects.”

Big Red 200 helps the lab solve that challenge by allowing researchers to model this behavior at a fine level of detail. By running these virtual experiments over many starting conditions, different immune cell parameters and simulated drug combinations, the team can find the best treatment strategies and estimate how robust they are to the unknowns of the clinic.

“We have different cell types with many different behaviors,” Macklin said. “To understand how that particular model of that patient is going to react for that specific therapeutic intervention, we have to run that model many times. Having a resource like Big Red 200 makes a big difference because we have the freedom to run many, many simulations.”

Macklin’s team is working with a coalition of researchers in the Johns Hopkins University Sidney Kimmel Comprehensive Cancer Center’s Convergence Institute and the Oregon Health & Science University Knight Cancer Institute to develop and test technologies that could power future digital twins for cancer immunotherapy, currently focusing on breast and pancreatic cancers. Collaborators include:

  • Elana Fertig, director of the division of oncology quantitative sciences and co-director of the Convergence Institute, whose team aims to extend Macklin’s models to trace pancreatic cancer by applying data assimilation methods used in weather prediction and forecasting.
  • Jackie Zimmerman, assistant professor of oncology and a member of the Convergence Institute. An expert in growing “cancer organoids,” which are mini-tumors grown from a patient’s own cells, she uses these organoids to provide data on how cancer and immune cells response to drugs, allowing Macklin’s team to build models.
  • Elizabeth Jaffee, deputy director of the Kimmel Comprehensive Cancer Center and co-director of the Convergence Institute, who provides clinical trial datasets to validate the predictions from these integrated biological and mathematical models in human tumors.
  • Assistant professor of oncology Atul Deshpande and assistant professor of neuroscience Genevieve Stein-O’Brien, who are applying expertise in data assimilation, bioinformatics and cancer genomics to calibrate Macklin’s simulation models to the organoid experiments and clinical assays. This includes seeding simulations with virtual versions of the patient’s own cancer and immune cells.
  • Laura Heiser, vice chair of biomedical engineering at Oregon Health & Science University, who is helping expand their digital twins approach to additional cancer types with cutting-edge measurement imaging technologies.

Together, the researchers construct a digital twin for each pancreatic cancer patient. They then run “virtual experiments” on each patient’s digital twin with IU’s high-performance computing resources, identifying which therapies are most likely to be effective for a patient. They can even virtually test which strategies will slow the metastatic spread to distant organs.

The approach will allow the team to predict which strategies do the best to control or eliminate tumors, without the risk of trying unsuccessful treatments on the patient.

IU’s high-performance computing resources also allow Macklin’s team to create “a computational untreated control group,” he added.

“You have an untreated virtual patient, you have a real patient, and you have a predicted treatment,” he said. “Does that patient lie closer to the predicted treatment, which means the treatment worked, or closer to the predicted untreated control, which means it didn’t make a difference? Digital twins give us the opportunity to answer nuanced clinical questions that we would otherwise not know.”

Typical treatment trials will run for years before there are enough events to measure an effect, Macklin said. With computational models, simulating months of a virtual trial can occur in a matter of hours.

Macklin’s lab’s open-source code is also being used to advance research on other medical fields and conditions beyond cancer, such as cryobiology, cryopreservation, tissue development, macular degeneration, angiogenesis and blood vessel development.

Although digital-twin cancer treatments are making tremendous progress, Macklin said these techniques are a long way from use in clinical trials.

In addition to this work, IU has many other efforts underway that rely upon the university’s high-performance computing resources provided by University Information Technology Services’ Research Technologies.

Digital twin modeling at IU has been used to develop COVID-19 treatments. The IU Biocomplexity Institute has developed an open-source software environment that simplifies the construction of digital twins. The IU Luddy School’s Department of Computer Science in Bloomington is making significant contributions to the field of digital twin research by developing new programming techniques and educating the next generation of researchers.

Other research centers and labs that are focused on digital twin research include the Center for Complex Networks and Systems Research and the Center for Simulation and Analysis of Complex Networks. Along with UITS Research Technologies, these centers and labs provide IU researchers with access to state-of-the-art computing resources and expertise in a variety of fields.


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