Purpose: To develop and validate a simulation pipeline to investigate the effect of subcellular scale chromosome conformation from Hi-C data on radiation-induced DNA damage and consequent repair.
Methods: A semi-automated simulation pipeline was developed to analyze outcomes of radiation exposure on varying chromosome conformations in terms of DNA damage and repair processes. The pipeline used the G-NOME code to process Hi-C data for modeling chromosome structures in a cell nucleus, TOPAS-nBio for radiation transport, and MEDRAS-MC for DNA repair and repair kinetics models. We modeled two cell types, BJ-5ta (fibroblasts) and GM12878 (lymphoblasts), and irradiated them with 160-kV x-rays and 100-MeV protons. The resulting distribution of double-strand breaks (DSBs) was input into the MEDRAS-MC DNA repair model to determine the kinetics of DNA repair and the likelihood of misrepair. X-ray results were validated against experimental data.
Results: This framework estimated DNA damage and repair kinetics in cell nuclei with varying chromosome conformations. For both x-ray and proton irradiation, the probability of DSB misrepair was greater on average and more variable for GM12878 cells than for BJ-5ta cells. Consistent with in vitro experimental findings, GM12878 cells had a more pronounced response to radiation than BJ-5ta cells and matched the calculated ratios of foci 30 minutes after exposure to 24 hours after exposure. This high-performance computation from creating models to generating outcomes took on average 18 hours.
Conclusion: The developed simulation pipeline models varying and specific chromosome conformations from different cell nuclei and biological endpoints such as DSB misrepair and chromosome aberrations. The framework can model differences in radiation response corresponding to differences in chromosome structures of cells. In future work we will integrate this pipeline into our multiscale computational framework to estimate radiation effects from DNA damage and repair to whole body absorbed dose for applications in precision oncology.
Funding Support, Disclosures, and Conflict of Interest: This work was supported by the Office of Biological and Environmental Research's, Biological Systems Science Division and Laboratory Directed Research and Development Program of ORNL, managed by UT-Battelle, LLC, for the U. S. Department of Energy. This manuscript has been authored by UT-Battelle, LLC (Contract#. DE-AC05-00OR22725) with the U.S DOE.
Modeling, Simulation, Radiobiology