Are you looking for a tool that helps you understand how breast tumors respond to treatments? NCI-funded researchers at the University of Texas at Austin developed a mathematical model to understand how variations within and between tumors can influence treatment outcomes. The model shows that local conditions within the tumor can affect the effectiveness and distribution of therapies.

Building on earlier work, the team refined their predictive approach using data assimilation, a method commonly used in weather forecasting. The researchers applied it to forecast the development of major spatial patterns in MCF7 breast cancer cell populations after treatment with doxorubicin in vitro. By choosing models that balance prediction accuracy with simplicity, the research team created a powerful spatiotemporal model that efficiently captures the biological dynamics at play within solid tumors.

The publicly available data sets used in this study offer spatial and temporal resolution beyond what is typically available in the clinical setting. This creates opportunities for translating these methods into clinical applications as long as the necessary data are available. For instance, you can apply their prediction pipeline to clinical MRI data gathered before and during treatment, enabling you to optimize treatment procedures to positively impact cancer patients.

First author Mr. Hugo Miniere shared, “Our platform combines experiments and computer models to accurately predict how tumor cells behave and change over time, both before and after chemotherapy.”

Corresponding author Dr. Thomas Yankeelov added, “These results represent an important step in the effort to quantify the relationship between chemoresistance and tumor heterogeneity as well as their effects on treatment efficacy.”