In contrast to the classic view of static DNA double-strand breaks (DSBs) being repaired at the site of damage, we hypothesize that DSBs move and merge with each other over large distances (μm). As X-ray dose increases, the probability of having DSB clusters increases as does the probability of misrepair and cell death. Experimental work characterizing the X-ray dose dependence of radiation-induced foci (RIF) in nonmalignant human mammary epithelial cells (MCF10A) is used here to validate a DSB clustering model. We then use the principles of the local effect model (LEM) to predict the yield of DSBs at the submicron level. Two mechanisms for DSB clustering, namely random coalescence of DSBs versus active movement of DSBs into repair domains are compared and tested. Simulations that best predicted both RIF dose dependence and cell survival after X-ray irradiation favored the repair domain hypothesis, suggesting the nucleus is divided into an array of regularly spaced repair domains of ∼1.55 μm sides. Applying the same approach to high-linear energy transfer (LET) ion tracks, we are able to predict experimental RIF/μm along tracks with an overall relative error of 12%, for LET ranging between 30–350 keV/μm and for three different ions. Finally, cell death was predicted by assuming an exponential dependence on the total number of DSBs and of all possible combinations of paired DSBs within each simulated RIF. Relative biological effectiveness (RBE) predictions for cell survival of MCF10A exposed to high-LET showed an LET dependence that matches previous experimental results for similar cell types. Overall, this work suggests that microdosimetric properties of ion tracks at the submicron level are sufficient to explain both RIF data and survival curves for any LET, similarly to the LEM assumption. Conversely, high-LET death mechanism does not have to infer linear-quadratic dose formalism as done in the LEM. In addition, the size of repair domains derived in our model are based on experimental RIF and are three times larger than the hypothetical LEM voxel used to fit survival curves. Our model is therefore an alternative to previous approaches that provides a testable biological mechanism (i.e., RIF). In addition, we propose that DSB pairing will help develop more accurate alternatives to the linear cancer risk model (LNT) currently used for regulating exposure to very low levels of ionizing radiation.

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