A data assimilation framework to predict the response of glioma cells to radiation

Junyan Liu; David A. Hormuth II; Jianchen Yang; Thomas E. Yankeelov; Junyan Liu; David A. Hormuth II; Jianchen Yang; Thomas E. Yankeelov

doi:10.3934/mbe.2023015

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 1: 318-336. doi: 10.3934/mbe.2023015

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A data assimilation framework to predict the response of glioma cells to radiation

1.
Department of Biomedical Engineering, The University of Texas at Austin, 107 W Dean Keeton Street Stop C0800, Austin, TX 78712, USA
2.
Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, 201 E. 24^th Street POB 4.102 Stop C0200, Austin, TX 78712, USA
3.
Livestrong Cancer Institutes, The University of Texas at Austin, 1601 Trinity St. Bldg. B Mail Stop Z1100, TX 78712, USA
4.
Department of Diagnostic Medicine, The University of Texas at Austin, 1601 Trinity St Bldg. B Stop Z0300, Austin, TX 78712, USA
5.
Department of Oncology, The University of Texas at Austin, 1601 Trinity St Bldg. B, Austin, TX 78712, USA
6.
Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, PO Box 301402, Houston, TX, 77230-1402, USA

Academic Editor: Wenrui Hao

Received: 19 July 2022 Revised: 19 September 2022 Accepted: 22 September 2022 Published: 08 October 2022

We incorporate a practical data assimilation methodology into our previously established experimental-computational framework to predict the heterogeneous response of glioma cells receiving fractionated radiation treatment. Replicates of 9L and C6 glioma cells grown in 96-well plates were irradiated with six different fractionation schemes and imaged via time-resolved microscopy to yield 360- and 286-time courses for the 9L and C6 lines, respectively. These data were used to calibrate a biology-based mathematical model and then make predictions within two different scenarios. For Scenario 1, 70% of the time courses are fit to the model and the resulting parameter values are averaged. These average values, along with the initial cell number, initialize the model to predict the temporal evolution for each test time course (10% of the data). In Scenario 2, the predictions for the test cases are made with model parameters initially assigned from the training data, but then updated with new measurements every 24 hours via four versions of a data assimilation framework. We then compare the predictions made from Scenario 1 and the best version of Scenario 2 to the experimentally measured microscopy measurements using the concordance correlation coefficient (CCC). Across all fractionation schemes, Scenario 1 achieved a CCC value (mean ± standard deviation) of 0.845 ± 0.185 and 0.726 ± 0.195 for the 9L and C6 cell lines, respectively. For the best data assimilation version from Scenario 2 (validated with the last 20% of the data), the CCC values significantly increased to 0.954 ± 0.056 (p = 0.002) and 0.901 ± 0.061 (p = 8.9e-5) for the 9L and C6 cell lines, respectively. Thus, we have developed a data assimilation approach that incorporates an experimental-computational system to accurately predict the in vitro response of glioma cells to fractionated radiation therapy.
- mathematical model,
- mechanism-based,
- biology-based model,
- calibration,
- time-resolved microscopy,
- in vitro,
- brain cancer,
- glioblastoma
Citation: Junyan Liu, David A. Hormuth II, Jianchen Yang, Thomas E. Yankeelov. A data assimilation framework to predict the response of glioma cells to radiation[J]. Mathematical Biosciences and Engineering, 2023, 20(1): 318-336. doi: 10.3934/mbe.2023015

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Abstract

We incorporate a practical data assimilation methodology into our previously established experimental-computational framework to predict the heterogeneous response of glioma cells receiving fractionated radiation treatment. Replicates of 9L and C6 glioma cells grown in 96-well plates were irradiated with six different fractionation schemes and imaged via time-resolved microscopy to yield 360- and 286-time courses for the 9L and C6 lines, respectively. These data were used to calibrate a biology-based mathematical model and then make predictions within two different scenarios. For Scenario 1, 70% of the time courses are fit to the model and the resulting parameter values are averaged. These average values, along with the initial cell number, initialize the model to predict the temporal evolution for each test time course (10% of the data). In Scenario 2, the predictions for the test cases are made with model parameters initially assigned from the training data, but then updated with new measurements every 24 hours via four versions of a data assimilation framework. We then compare the predictions made from Scenario 1 and the best version of Scenario 2 to the experimentally measured microscopy measurements using the concordance correlation coefficient (CCC). Across all fractionation schemes, Scenario 1 achieved a CCC value (mean ± standard deviation) of 0.845 ± 0.185 and 0.726 ± 0.195 for the 9L and C6 cell lines, respectively. For the best data assimilation version from Scenario 2 (validated with the last 20% of the data), the CCC values significantly increased to 0.954 ± 0.056 (p = 0.002) and 0.901 ± 0.061 (p = 8.9e-5) for the 9L and C6 cell lines, respectively. Thus, we have developed a data assimilation approach that incorporates an experimental-computational system to accurately predict the in vitro response of glioma cells to fractionated radiation therapy.

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