Tumor Organoids Modeling Reveals Timed Responses and Interplay of Radiotherapy and Chemotherapy In Pancreatic Cancer
Abstract
Purpose
Model the therapeutic effects of chemotherapy, radiotherapy, and chemoradiotherapy to study the heterogeneous responses of pancreatic cancer patient-derived tumor organoids (PDTOs).
Methods
We proposed novel mathematical forecasting frameworks based on logistic growth ordinary differential equations and the characteristics of tumor responses to model the therapeutic effects of chemo- and radiation-induced killing in PDTOs. To validate the models, we cultured PDTOs from different patients and treated them with radiation (4 Gy and 8 Gy), chemotherapy (FOLFIRINOX), and combined regimens, respectively. The diameters of 20-40 organoids per patient were tracked and measured using brightfield images up to 7 or 9 days following each treatment to capture organoid growth dynamics data, which was used for model fitting. The accuracy of the modeling was evaluated by the average normalized mean squared errors (NMSE) of data fitting.
Results
The proposed mathematical modeling frameworks accurately captured the observed growth dynamics of three PDTO samples after chemotherapy, radiotherapy, and chemoradiotherapy. The fitted parameters, including killing pattern, effect window, and peak killing timing, quantitatively revealed significant heterogeneities in treatment responses across different PDTOs. Chemotherapy shows pronounced effectiveness in the early stage, while radiotherapy exhibits the effect later in the first week (around day 4), and a significantly stronger secondary response occurs one week after radiation treatment (around day 8). Chemoradiotherapy combines the strengths of both modalities, producing pronounced effects in both response phases, with modeling results suggesting that the radiation-induced killing effect may play a dominant role in the combined interaction.
Conclusion
Our modeling frameworks demonstrated high accuracy in modeling the heterogeneous therapeutic responses of PDTOs and provided insights into the dynamic killing effects and interplay between chemotherapy and radiotherapy. The modeling of therapeutic responses of PDTOs provides a valuable tool for optimizing treatment regimens and informing clinical trial design, thereby improving the efficacy of personalized medicine.