Radiation-induced cell cycle perturbations: a computational tool validated with flow-cytometry data
AbstractCell cycle progression can be studied with computational models that allow to describe and predict its perturbation by agents as ionizing radiation or drugs. Such models can then be integrated in tools for pre-clinical/clinical use, e.g. to optimize kinetically-based administration protocols of radiation therapy and chemotherapy.We present a deterministic compartmental model, specifically reproducing how cells that survive radiation exposure are distributed in the cell cycle as a function of dose and time after exposure. Model compartments represent the four cell-cycle phases, as a fuction of DNA content and time. A system of differential equations, whose parameters represent transition rates, division rate and DNA synthesis rate, describes the temporal evolution. Initial model inputs are data from unexposed cells in exponential growth. Perturbation is implemented as an alteration of model parameters that allows to best reproduce cell-cycle profiles post-irradiation. The model is validated with dedicated in vitro measurements on human lung fibroblasts (IMR90). Cells were irradiated with 2 and 5 Gy with a Varian 6 MV Clinac at IRCCS Maugeri. Flow cytometry analysis was performed at the RadBioPhys Laboratory (University of Pavia), obtaining cell percentages in each of the four phases in all studied conditions up to 72 hours post-irradiation.Cells show early G2-phase block (increasing in duration as dose increases) and later G1-phase accumulation. For each condition, we identified the best sets of model parameters that lead to a good agreement between model and experimental data, varying transition rates from G1- to S- and from G2- to M-phase.This work offers a proof-of-concept validation of the new computational tool, opening to its future development and, in perspective, to its integration in a wider framework for clinical use.Author summaryWe implemented a computational model able to describe how the progression in the cell cycle is perturbed when cells are exposed to ionizing radiation. It is known that radiation causes delays or arrest in cell cycle progression, and also that cells that are in different phases of the cycle at the time of exposure show different sensitivity to radiation. Chemotherapeutic drugs also affect cell cycle, and their action can be phase-specific. These findings can be exploited to find the optimal protocol of a combined radiotherapy/chemotherapy cancer treatment: to this aim, we need to know not only the effectiveness of an agent (dose/drug) in terms of cell killing, but also how surviving cells are distributed in the cell cycle. With the model we present, this information can be reproduced as a function of dose and time after radiation exposure. To test the model performance we measured distributions of cells in different phases of the cycle (using flow-cytometry) for human healthy fibroblast cells exposed to X-rays. The results of this work constitute a first step for further development of our model and its future integration in a tool for pre-clinical/clinical use.
