Sachs Rainer K, Brenner David J
Department of Mathematics, University of California, Berkeley, CA 94720, USA.
Proc Natl Acad Sci U S A. 2005 Sep 13;102(37):13040-5. doi: 10.1073/pnas.0506648102. Epub 2005 Sep 6.
There is increasing concern regarding radiation-related second-cancer risks in long-term radiotherapy survivors and a corresponding need to be able to predict cancer risks at high radiation doses. Although cancer risks at moderately low radiation doses are reasonably understood from atomic bomb survivor studies, there is much more uncertainty at the high doses used in radiotherapy. It has generally been assumed that cancer induction decreases rapidly at high doses due to cell killing. However, recent studies of radiation-induced second cancers in the lung and breast, covering a very wide range of doses, contradict this assumption. A likely resolution of this disagreement comes from considering cellular repopulation during and after radiation exposure. Such repopulation tends to counteract cell killing and accounts for the large discrepancies between the current standard model for cancer induction at high doses and recent second-cancer data. We describe and apply a biologically based minimally parameterized model of dose-dependent cancer risks, incorporating carcinogenic effects, cell killing, and, additionally, proliferation/repopulation effects. Including stem-cell repopulation leads to risk estimates consistent with high-dose second-cancer data. A simplified version of the model provides a practical and parameter-free approach to predicting high-dose cancer risks, based only on data for atomic bomb survivors (who were exposed to lower total doses) and the demographic variables of the population of interest. Incorporating repopulation effects provides both a mechanistic understanding of cancer risks at high doses and a practical methodology for predicting cancer risks in organs exposed to high radiation doses, such as during radiotherapy.
长期放疗幸存者中与辐射相关的二次癌症风险日益受到关注，相应地需要能够预测高辐射剂量下的癌症风险。虽然从原子弹幸存者研究中可以合理地了解到中等低辐射剂量下的癌症风险，但放疗中使用的高剂量下存在更多不确定性。一般认为，由于细胞杀伤，高剂量下的癌症诱发率会迅速下降。然而，最近对涵盖非常广泛剂量范围的肺部和乳腺辐射诱发二次癌症的研究与这一假设相矛盾。这种分歧的一个可能解决方案来自于考虑辐射暴露期间和之后的细胞再增殖。这种再增殖往往会抵消细胞杀伤，并解释了当前高剂量癌症诱发标准模型与最近二次癌症数据之间的巨大差异。我们描述并应用了一个基于生物学的最小参数化剂量依赖性癌症风险模型，该模型纳入了致癌作用、细胞杀伤，此外还包括增殖/再增殖效应。纳入干细胞再增殖会导致风险估计与高剂量二次癌症数据一致。该模型的简化版本提供了一种实用且无参数的方法来预测高剂量癌症风险，仅基于原子弹幸存者的数据（他们暴露于较低的总剂量）以及感兴趣人群的人口统计学变量。纳入再增殖效应既提供了对高剂量癌症风险的机制理解，也提供了一种预测暴露于高辐射剂量器官（如放疗期间）癌症风险的实用方法。
