Sachs Rainer K, Shuryak Igor, Brenner David, Fakir Hatim, Hlatky Lynn, Hahnfeldt Philip
Departments of Mathematics and of Physics, University of California, 970 Evans Hall, MC 3840, Berkeley, CA 94720, USA.
J Theor Biol. 2007 Dec 7;249(3):518-31. doi: 10.1016/j.jtbi.2007.07.034. Epub 2007 Aug 12.
When ionizing radiation is used in cancer therapy it can induce second cancers in nearby organs. Mainly due to longer patient survival times, these second cancers have become of increasing concern. Estimating the risk of solid second cancers involves modeling: because of long latency times, available data is usually for older, obsolescent treatment regimens. Moreover, modeling second cancers gives unique insights into human carcinogenesis, since the therapy involves administering well-characterized doses of a well-studied carcinogen, followed by long-term monitoring. In addition to putative radiation initiation that produces pre-malignant cells, inactivation (i.e. cell killing), and subsequent cell repopulation by proliferation, can be important at the doses relevant to second cancer situations. A recent initiation/inactivation/proliferation (IIP) model characterized quantitatively the observed occurrence of second breast and lung cancers, using a deterministic cell population dynamics approach. To analyze if radiation-initiated pre-malignant clones become extinct before full repopulation can occur, we here give a stochastic version of this IIP model. Combining Monte-Carlo simulations with standard solutions for time-inhomogeneous birth-death equations, we show that repeated cycles of inactivation and repopulation, as occur during fractionated radiation therapy, can lead to distributions of pre-malignant cells per patient with variance>>mean, even when pre-malignant clones are Poisson-distributed. Thus fewer patients would be affected, but with a higher probability, than a deterministic model, tracking average pre-malignant cell numbers, would predict. Our results are applied to data on breast cancers after radiotherapy for Hodgkin disease. The stochastic IIP analysis, unlike the deterministic one, indicates: (a) initiated, pre-malignant cells can have a growth advantage during repopulation, not just during the longer tumor latency period that follows; (b) weekend treatment gaps during radiotherapy, apart from decreasing the probability of eradicating the primary cancer, substantially increase the risk of later second cancers.
当电离辐射用于癌症治疗时,它会诱发附近器官产生二次癌症。主要由于患者存活时间延长,这些二次癌症已越来越受到关注。估计实体二次癌症的风险需要进行建模:由于潜伏期较长,可用数据通常来自较旧的、过时的治疗方案。此外,对二次癌症进行建模能为人类致癌作用提供独特的见解,因为该治疗涉及给予特征明确的剂量的一种经过充分研究的致癌物,然后进行长期监测。除了假定的辐射引发产生癌前细胞外,失活(即细胞杀伤)以及随后通过增殖进行的细胞再增殖,在与二次癌症情况相关的剂量下也可能很重要。最近的引发/失活/增殖(IIP)模型使用确定性细胞群体动力学方法对观察到的二次乳腺癌和肺癌的发生情况进行了定量表征。为了分析辐射引发的癌前克隆在完全再增殖发生之前是否会灭绝,我们在此给出该IIP模型的一个随机版本。通过将蒙特卡罗模拟与非齐次生死方程的标准解相结合,我们表明,在分次放射治疗期间发生的失活和再增殖的重复循环,即使癌前克隆呈泊松分布,也会导致每位患者癌前细胞的分布出现方差>>均值的情况。因此,与追踪平均癌前细胞数量的确定性模型预测相比,受影响的患者会更少,但概率更高。我们的结果应用于霍奇金病放疗后乳腺癌的数据。与确定性分析不同,随机IIP分析表明:(a)引发的癌前细胞不仅在随后较长的肿瘤潜伏期内,而且在再增殖期间也可能具有生长优势;(b)放疗期间周末的治疗间隙,除了会降低根除原发性癌症的概率外,还会大幅增加后期发生二次癌症的风险。