Junyan Liu1, David A Hormuth2,3, Jianchen Yang1, Thomas E Yankeelov1,2,3,4,5,6. 1. Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, United States. 2. Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX, United States. 3. Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX, United States. 4. Department of Diagnostic Medicine, The University of Texas at Austin, Austin, TX, United States. 5. Department of Oncology, The University of Texas at Austin, Austin, TX, United States. 6. Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States.
Abstract
PURPOSE: Conventional 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. METHODS: We 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). RESULTS: For 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. CONCLUSION: By 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.
PURPOSE: Conventional 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. METHODS: We 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). RESULTS: For 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. CONCLUSION: By 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.
Authors: Junyan Liu; David A Hormuth; Tessa Davis; Jianchen Yang; Matthew T McKenna; Angela M Jarrett; Heiko Enderling; Amy Brock; Thomas E Yankeelov Journal: Integr Biol (Camb) Date: 2021-07-08 Impact factor: 3.177
Authors: Luca G Mariotti; Giacomo Pirovano; Kienan I Savage; Mihaela Ghita; Andrea Ottolenghi; Kevin M Prise; Giuseppe Schettino Journal: PLoS One Date: 2013-11-29 Impact factor: 3.240
Authors: David A Hormuth; Karine A Al Feghali; Andrew M Elliott; Thomas E Yankeelov; Caroline Chung Journal: Sci Rep Date: 2021-04-19 Impact factor: 4.379
Authors: Sotiris Prokopiou; Eduardo G Moros; Jan Poleszczuk; Jimmy Caudell; Javier F Torres-Roca; Kujtim Latifi; Jae K Lee; Robert Myerson; Louis B Harrison; Heiko Enderling Journal: Radiat Oncol Date: 2015-07-31 Impact factor: 3.481
Authors: S Brüningk; G Powathil; P Ziegenhein; J Ijaz; I Rivens; S Nill; M Chaplain; U Oelfke; G Ter Haar Journal: J R Soc Interface Date: 2018-01 Impact factor: 4.118