A Multi-Compartment Model of Glioma Response to Fractionated Radiation Therapy Parameterized via Time-Resolved Microscopy Data

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.

Modeling of chemo-radiotherapy targeting growing vascular tumors: a continuum-level approach

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Cancer cell sedimentation in 3D cultures reveals active migration regulated by self-generated gradients and adhesion sites

“…Although there is growing literature on spatiotemporal models of cancer, their validation using experimental data is still relatively limited, yet important for quantitatively describing cancer-related mechanisms [32][33][34][35][36][37][38][39][40]. Model validation in cancer has previously been investigated in both forecasting [32,41] and exploratory studies [42][43][44][45][46][47][48] of cancer growth using ODEs and PDEs. Recently, we also expanded validation to hybrid models using an integrated experimental and computational framework for the calibration and validation of hybrid models with 3D cell culture data using Bayesian inference and spatial statistical analysis techniques [49].…”

3D Cell Models in Radiobiology: Improving the Predictive Value of In Vitro Research

“…Two-dimensional (2D) cell cultures have historically been used for radiobiological studies and for modelling interactions between radiation and tissues, and so many of the dogmas of radiobiology are based on cellular and molecular responses of cells grown attached to a plastic surface [ 1 , 2 , 3 ]. In particular, the “gold standards” used to assess sensitivity towards radiotherapy are represented by the “clonogenic assay”, a test used to measure reproductive cell survival in vitro [ 4 , 5 ], and DNA damage evaluation in established cell lines [ 6 , 7 ].…”