Room: Exhibit Hall
Purpose: To propose and validate a mechanism based mathematical model that characterizes the radiation response of 9L glioma cell in vitro by explicitly incorporating biological interactions with radiation including DNA repair and cell death pathways.
Methods: In vitro: 9L glioma cells are seeded at 200 cells per well on a 24-well plate, and are irradiated at 8 Gy and 16 Gy using a Faxitron irradiator (Faxitron, Arizona). Immediately after irradiation, phase-contrast images are acquired via the Incucyte Live-Cell imaging system (Essen Bioscience, Michigan) for 150 hours. The images are used to estimate cell confluence over time through a histogram-based image segmentation algorithm. Additionally, DNA damage is measured and visualized using gamma-H2AX immunofluorescence method by flow cytometry (Accuri, Michigan) and fluorescence microscope (Leica, Germany). In silico: In our current formulation, the temporal evolution of tumor growth is described by an ordinary differential equation consisting of a logistic cell proliferation term, one death term accounting for cells which suffer extensive DNA damage and therefore undergo rapid apoptosis, and a second death term describing reproductive cell death. We also propose a bi-exponential decay DNA damage repair model, which accounts for Non-Homologous End Joining and Homologous Recombination. We then fit in vitro data to the model to estimate model parameters related to DNA repair rate, cell death rate, radiation efficacy, and death delay.
Results: Four parameters are estimated from phase-contrast data to show the radiation responses. For the 8 Gy and 16 Gy groups, the rapid death rates are 6.2e-05 hoursâ?»Â¹ and 1e-03 hoursâ?»Â¹, respectively. The reproductive death rates are 3.1e-06 hoursâ?»Â¹ and 3.6e-6 hoursâ?»Â¹ for the 8 Gy and 16 Gy groups, respectively.
Conclusion: A preliminary version of mechanism-based model effort demonstrates a promising steps towards a time-resolved model of radiation response, which could, eventually, be used to predict, and optimize, tumor response.
Funding Support, Disclosures, and Conflict of Interest: We thank the National Institutes of Health for funding through NCI R01CA186193, and U01CA174706. We thank the Cancer Prevention and Research Institute of Texas for support through CPRIT RR160005.