Heuskin A C, Osseiran A I, Tang J, Costes S V
a Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California.
c NAmur Research Institute for Life Sciences (NARILIS), Research Center for the Physics of Matter and Radiation (PMR), University of Namur, Namur, Belgium.
Radiat Res. 2016 Jul;186(1):27-38. doi: 10.1667/RR14338.1. Epub 2016 Jun 22.
Estimating cancer risk from space radiation has been an ongoing challenge for decades primarily because most of the reported epidemiological data on radiation-induced risks are derived from studies of atomic bomb survivors who were exposed to an acute dose of gamma rays instead of chronic high-LET cosmic radiation. In this study, we introduce a formalism using cellular automata to model the long-term effects of ionizing radiation in human breast for different radiation qualities. We first validated and tuned parameters for an automata-based two-stage clonal expansion model simulating the age dependence of spontaneous breast cancer incidence in an unexposed U.S.
We then tested the impact of radiation perturbation in the model by modifying parameters to reflect both targeted and nontargeted radiation effects. Targeted effects (TE) reflect the immediate impact of radiation on a cell's DNA with classic end points being gene mutations and cell death. They are well known and are directly derived from experimental data. In contrast, nontargeted effects (NTE) are persistent and affect both damaged and undamaged cells, are nonlinear with dose and are not well characterized in the literature. In this study, we introduced TE in our model and compared predictions against epidemiologic data of the atomic bomb survivor cohort. TE alone are not sufficient for inducing enough cancer. NTE independent of dose and lasting ∼100 days postirradiation need to be added to accurately predict dose dependence of breast cancer induced by gamma rays. Finally, by integrating experimental relative biological effectiveness (RBE) for TE and keeping NTE (i.e., radiation-induced genomic instability) constant with dose and LET, the model predicts that RBE for breast cancer induced by cosmic radiation would be maximum at 220 keV/μm. This approach lays the groundwork for further investigation into the impact of chronic low-dose exposure, inter-individual variation and more complex space radiation scenarios.
几十年来，估算太空辐射致癌风险一直是一项持续存在的挑战，主要是因为大多数已报道的辐射诱发风险的流行病学数据来自原子弹幸存者研究，这些幸存者遭受的是急性剂量的伽马射线照射，而非慢性高传能线密度的宇宙辐射。在本研究中，我们引入一种使用细胞自动机的形式体系，以模拟不同辐射性质对人类乳腺电离辐射的长期影响。我们首先验证并调整了基于自动机的两阶段克隆扩增模型的参数，该模型模拟了未受辐射的美国人群中自发性乳腺癌发病率的年龄依赖性。
然后，我们通过修改参数来反映靶向和非靶向辐射效应，测试了模型中辐射扰动的影响。靶向效应（TE）反映了辐射对细胞DNA的直接影响，典型终点是基因突变和细胞死亡。它们是众所周知的，并且直接来自实验数据。相比之下，非靶向效应（NTE）具有持续性，会影响受损和未受损细胞，与剂量呈非线性关系，且在文献中没有很好的特征描述。在本研究中，我们在模型中引入了TE，并将预测结果与原子弹幸存者队列的流行病学数据进行了比较。仅TE不足以诱发足够的癌症。需要添加与剂量无关且在照射后持续约100天的NTE，以准确预测伽马射线诱发乳腺癌的剂量依赖性。最后，通过整合TE的实验相对生物效应（RBE）并使NTE（即辐射诱发的基因组不稳定）随剂量和传能线密度保持恒定，该模型预测宇宙辐射诱发乳腺癌的RBE在220 keV/μm时将达到最大值。这种方法为进一步研究慢性低剂量暴露、个体间差异以及更复杂的太空辐射场景的影响奠定了基础。
