Estimating Treatment Sensitivity in Synthetic and In Vitro Tumors Using a Random Differential Equation Model

Resistance to treatment, which comes from the heterogeneity of cell types within tumors, is a leading cause of poor treatment outcomes in cancer patients. Previous mathematical work modeling cancer over time has neither emphasized the relationship between cell heterogeneity and treatment resistance nor depicted heterogeneity with sufficient nuance. To respond to the need to depict a wide range of resistance levels, we develop a random differential equation model of tumor growth. Random differential equations are differential equations in which the parameters are random variables. In the inverse problem, we aim to recover the sensitivity to treatment as a probability mass function. This allows us to observe what proportions of cells exist at different sensitivity levels. After validating the method with synthetic data, we apply it to monoclonal and mixture cell population data of isogenic Ba/F3 murine cell lines to uncover each tumor's levels of sensitivity to treatment as a probability mass function. We emphasize the applications of this project by fitting the model to patient prostate cancer data to recover changes in treatment sensitivity over multiple treatment cycles.

Estimating Treatment Sensitivity in Synthetic and In Vitro Tumors Using a Random Differential Equation Model

Monday, July 14 at 6:00pm in

SMB2025 Follow

Natalie Meacham

"Estimating Treatment Sensitivity in Synthetic and In Vitro Tumors Using a Random Differential Equation Model"

#SMB2025 Follow