Abstract
PurposeConventional radiobiology models, including the linear-quadratic model, do not explicitly account for the temporal effects of radiation, thereby making it difficult to make time-resolved predictions of tumor response to fractionated radiation. To overcome this limitation, we propose and validate an experimental-computational approach that predicts the changes in cell number over time in response to fractionated radiation.MethodsWe irradiated 9L and C6 glioma cells with six different fractionation schemes yielding a total dose of either 16 Gy or 20 Gy, and then observed their response via time-resolved microscopy. Phase-contrast images and Cytotox Red images (to label dead cells) were collected every 4 to 6 hours up to 330 hours post-radiation. Using 75% of the total data (i.e., 262 9L curves and 211 C6 curves), we calibrated a two-species model describing proliferative and senescent cells. We then applied the calibrated parameters to a validation dataset (the remaining 25% of the data, i.e., 91 9L curves and 74 C6 curves) to predict radiation response. Model predictions were compared to the microscopy measurements using the Pearson correlation coefficient (PCC) and the concordance correlation coefficient (CCC).ResultsFor the 9L cells, we observed PCCs and CCCs between the model predictions and validation data of (mean ± standard error) 0.96 ± 0.007 and 0.88 ± 0.013, respectively, across all fractionation schemes. For the C6 cells, we observed PCCs and CCCs between model predictions and the validation data were 0.89 ± 0.008 and 0.75 ± 0.017, respectively, across all fractionation schemes.ConclusionBy proposing a time-resolved mathematical model of fractionated radiation response that can be experimentally verified in vitro, this study is the first to establish a framework for quantitative characterization and prediction of the dynamic radiobiological response of 9L and C6 gliomas to fractionated radiotherapy.
Highlights
Radiation therapy is a central component of the standard-of-care for treating malignant gliomas [1], especially when the tumor is located near sensitive brain regions with important functions that are unresectable by surgery
The currently accepted model for evaluating radiation response given a specific dose is the linear quadratic (LQ) model which was originally developed empirically more than 40 years ago [5]
To avoid cells reaching the carrying capacity at later timepoints, we do not seed at a confluence higher than 10,000 total cells
Summary
Radiation therapy is a central component of the standard-of-care for treating malignant gliomas [1], especially when the tumor is located near sensitive brain regions with important functions that are unresectable by surgery. Though various dose escalation and fractionation schemes (i.e., hyper- and hypo- fractionation) have been investigated, none have shown definitive improvement on the long-term survival for glioblastoma patients [2] One reason for this limitation is that the efficacy of radiation therapy varies between patients due to heterogeneous radiosensitivity of the cells within each individual’s tumor [3]. Interpretation of the two main parameters in the LQ model (alpha and beta) is fraught with difficult, thereby clouding their biological meaning [7] This is despite the vast biological knowledge that exists regarding DNA repair [8] and radiation-induced cell death pathways [9]. We seek to extend this model to account for multiple-fraction treatment regimens
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