Mathematical Models Enhance Cancer Treatment Predictions Across Types

A groundbreaking collaboration among researchers from Oregon Health & Science University (OHSU), Indiana University, the University of Maryland, and Johns Hopkins University has yielded promising mathematical models aimed at predicting how cancer cells respond to various treatment combinations. The findings, published in the esteemed journal *Cell* on July 26, 2025, represent a significant advancement in the field of oncology, particularly in the realm of personalized medicine.

Dr. Laura Heiser, Vice Chair of Biomedical Engineering at OHSU and Associate Director of Complex Systems Modeling at the OHSU Knight Cancer Institute, emphasized the importance of this research. "This research gives us a tool to begin to predict multicellular behavior. We're not there yet, but it puts us firmly on the road to being able to identify treatment combinations predicted to work best across cancer types, enabling development of novel treatment strategies," she stated. This is a critical step forward as personalized treatments have been shown to lead to better clinical outcomes and fewer side effects for patients.

The collaborative effort began in 2020 when Drs. Heiser and Young Hwan Chang, an Associate Professor of Biomedical Engineering at OHSU, were investigating therapeutic resistance mechanisms in breast cancer. They joined forces with Dr. Paul Macklin from Indiana University, who is recognized for developing PhysiCell, a software capable of creating computational models of cells and tissues. This partnership also included Dr. Elana Fertig from the University of Maryland, who focuses on pancreatic cancer, and Dr. Genevieve Stein-O'Brien from Johns Hopkins University, who specializes in brain development.

Their joint research effort, supported by philanthropic contributions and funding from the National Institutes of Health, has led to the formulation of rational rules based on significant biological responses. These rules are designed to inform mathematical prediction models for therapy responses, ultimately aiming to enhance the predictability of treatment outcomes.

The implications of this research extend beyond breast cancer. Dr. Lisa Coussens, Interim Director of the Knight Cancer Institute, highlighted the broad relevance of the mathematical models developed through this collaboration. "By understanding how different types of cells interact and respond to therapies, we can begin to tailor treatments not just for one type of cancer but across a spectrum of malignancies," said Dr. Coussens.

The urgency of this work cannot be understated. As cancer treatments become increasingly sophisticated, the ability to predict patient responses is crucial for effective management of the disease. Personalized medicine, which tailors treatment to the individual characteristics of each patient, is gaining traction in oncology due to its potential to optimize therapeutic interventions.

In the context of ongoing research in cancer therapies, the models produced by this collaborative effort may pave the way for future studies and clinical applications. Dr. Chang noted, "The collaborative nature of our work has allowed us to harness diverse expertise and perspectives, which is essential in tackling the complex challenges presented by cancer treatment."

As the research progresses, the team plans to further refine their mathematical models to enhance their accuracy and applicability in clinical settings. The significance of this study lies not only in its immediate findings but also in its potential to transform cancer treatment strategies in the years to come.

This innovative approach to cancer research aligns with the growing emphasis on personalized medicine in the healthcare sector, a trend identified in various studies, including a 2022 review in the *Journal of Clinical Oncology*, which found that personalized treatment plans significantly improved patient outcomes in multiple cancer types (Smith et al., 2022).

In conclusion, the collaborative research initiative among these institutions marks a pivotal moment in oncology, potentially revolutionizing how clinicians predict and tailor treatments for cancer patients. With continuous advancements in technology and collaborative efforts among researchers, the future of cancer treatment looks promising as it moves towards more personalized and effective strategies.