A new view of radiation-induced cancer: Integrating short- and long-term processes. Part I: Approach

Center for Radiological Research, Columbia University Medical Center, New York, NY 10032, USA.
Biophysik (Impact Factor: 1.53). 07/2009; 48(3):263-74. DOI: 10.1007/s00411-009-0230-3
Source: PubMed


Mathematical models of radiation carcinogenesis are important for understanding mechanisms and for interpreting or extrapolating risk. There are two classes of such models: (1) long-term formalisms that track pre-malignant cell numbers throughout an entire lifetime but treat initial radiation dose-response simplistically and (2) short-term formalisms that provide a detailed initial dose-response even for complicated radiation protocols, but address its modulation during the subsequent cancer latency period only indirectly. We argue that integrating short- and long-term models is needed. As an example of this novel approach, we integrate a stochastic short-term initiation/inactivation/repopulation model with a deterministic two-stage long-term model. Within this new formalism, the following assumptions are implemented: radiation initiates, promotes, or kills pre-malignant cells; a pre-malignant cell generates a clone, which, if it survives, quickly reaches a size limitation; the clone subsequently grows more slowly and can eventually generate a malignant cell; the carcinogenic potential of pre-malignant cells decreases with age.

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Available from: Igor Shuryak, Oct 08, 2015
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    • "Importantly, it is now recognized that cancer involves the cooption of normal cellular pathways (Hanahan and Weinberg, 2011). Again, radiation is thought to be a " complete carcinogen " as it is both an initiator and a promoter (Fry et al., 1982; Shuryak et al., 2009). With increasing age at radiation exposure, cancer promotion and progression assume more importance due to the larger number of already-initiated, premalignant cells in older individuals. "
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    ABSTRACT: Cancer is an important long-term risk for astronauts exposed to protons and high-energy charged particles during travel and residence on asteroids, the moon, and other planets. NASA's Biomedical Critical Path Roadmap defines the carcinogenic risks of radiation exposure as one of four type I risks. A type I risk represents a demonstrated, serious problem with no countermeasure concepts, and may be a potential "show-stopper" for long duration spaceflight. Estimating the carcinogenic risks for humans who will be exposed to heavy ions during deep space exploration has very large uncertainties at present. There are no human data that address risk from extended exposure to complex radiation fields. The overarching goal in this area to improve risk modeling is to provide biological insight and mechanistic analysis of radiation quality effects on carcinogenesis. Understanding mechanisms will provide routes to modeling and predicting risk and designing countermeasures. This white paper reviews broad issues related to experimental models and concepts in space radiation carcinogenesis as well as the current state of the field to place into context recent findings and concepts derived from the NASA Space Radiation Program. Copyright © 2015 The Committee on Space Research (COSPAR). Published by Elsevier Ltd. All rights reserved.
    07/2015; 6:92-103. DOI:10.1016/j.lssr.2015.07.006
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    • "An influence of ionizing radiation on single cells or cell colonies can be described by many deterministic and stochastic models. All such models can be easily implemented as computational algorithms, as shown for example in (UNSCEAR 1986; Moolgavkar and Luebeck 1990; Feinendegen et al. 2000, 2010; Brenner et al. 2001; Calabrese and Baldwin 2003; Feinendegen 2005; Scott et al. 2007, 2013; Leonard 2008; Shuryak et al. 2009; Bogen 2011; Leonard et al. 2011a, 2011b; Scott 2011, 2013; Tavares and Tavares 2013; Wodarz et al. 2014). "
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    ABSTRACT: The paper presents a computational stochastic model of virtual cells irradiation, based on Quasi-Markov Chain Monte Carlo method and using biophysical input. The model is based on a stochastic tree of probabilities for each cell of the entire colony. Biophysics of the cells is described by probabilities and probability distributions provided as the input. The adaptation of nucleation and catastrophe theories, well known in physics, yields sigmoidal relationships for carcinogenic risk as a function of the irradiation. Adaptive response and bystander effect, incorporated into the model, improves its application. The results show that behavior of virtual cells can be successfully modeled, e.g. cancer transformation, creation of mutations, radioadaptation or radiotherapy. The used methodology makes the model universal and practical for simulations of general processes. Potential biophysical curves and relationships are also widely discussed in the paper. However, the presented theoretical model does not describe the real cells and tissues. Also the exposure geometry (e.g., uniform or non-uniform exposure), type of radiation (e.g., X-rays, gamma rays, neutrons, heavy ions, etc.) as well as microdosimetry are not presently addressed. The model is focused mainly on creation of general and maximal wide mathematical description of irradiated hypothetical cells treated as complex physical systems.
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    • "Using the model described above, (i) the time-dependence of the excess relative risks of radiation for second cancer, and (ii) the effective mutation rate of pre-malignant cells resulting from radiation therapy were calculated. The model was fitted with the background incidences for colon, breast and lung cancers using data extracted from (Shuryak et al, 2009a)(Shuryak et al, 2009b). "
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    ABSTRACT: Although the survival rate of cancer patients has significantly increased due to advances in anti-cancer therapeutics, one of the major side effects of these therapies, particularly radiotherapy, is the potential manifestation of radiation-induced secondary malignancies. In this work, a novel evolutionary stochastic model is introduced that couples short-term formalism (during radiotherapy) and long-term formalism (post treatment). This framework is used to estimate the risks of second cancer as a function of spontaneous background and radiation-induced mutation rates of normal and pre-malignant cells. By fitting the model to available clinical data for spontaneous background risk together with data of Hodgkins lymphoma survivors (for various organs), the second cancer mutation rate is estimated. The model predicts a significant increase in mutation rate for some cancer types, which may be a sign of genomic instability. Finally, it is shown that the model results are in agreement with the measured results for excess relative risk (ERR) as a function of exposure age, and that the model predicts a negative correlation of ERR with increase in attained age. This novel approach can be used to analyze several radiotherapy protocols in current clinical practice, and to forecast the second cancer risks over time for individual patients.
    Biophysik 10/2014; DOI:10.1007/s00411-014-0576-z · 1.53 Impact Factor
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