The Risk Function of Breast and Ovarian Cancers in the Avrami-Dobrzynski Cellular Phase-Transition Model.

Specifying the role of genetic mutations in cancer development is crucial for effective screening or targeted treatments for people with hereditary cancer predispositions. Our goal here is to find the relationship between a number of cancerogenic mutations and the probability of cancer induction over the lifetime of cancer patients. We believe that the Avrami-Dobrzynski biophysical model can be used to describe this mechanism. Therefore, clinical data from breast and ovarian cancer patients were used to validate this model of cancer induction, which is based on a purely physical concept of the phase-transition process with an analogy to the neoplastic transformation. The obtained values of model parameters established using clinical data confirm the hypothesis that the carcinogenic process strongly follows fractal dynamics. We found that the model's theoretical prediction and population clinical data slightly differed for patients with the age below 30 years old, and that might point to the existence of an ancillary protection mechanism against cancer development. Additionally, we reveal that the existing clinical data predict breast or ovarian cancers onset two years earlier for patients with BRCA1/2 mutations.

Biophysical Modeling of the Ionizing Radiation Influence on Cells Using the Stochastic (Monte Carlo) and Deterministic (Analytical) Approaches.

This review article describes our simplified biophysical model for the response of a group of cells to ionizing radiation. The model, which is a product of 10 years of studies, acts as (a) a comprehensive stochastic approach based on the Monte Carlo simulation with a probability tree and (b) the thereof derived detailed deterministic models describing the selected biophysical and radiobiological phenomena in an analytical manner. Specifically, the presented model describes effects such as the risk of neoplastic transformation of cells relative to the absorbed radiation dose, the dynamics of tumor development, the priming dose effect (also called the Raper-Yonezawa effect) based on the introduced adaptive response approach, and the bystander effect. The model is also modifiable depending on users' potential needs.

The radiation adaptive response and priming dose influence: the quantification of the Raper-Yonezawa effect and its three-parameter model for postradiation DNA lesions and mutations.

The priming dose effect, called also the Raper-Yonezawa effect or simply the Yonezawa effect, is a special case of the radiation adaptive response phenomenon (radioadaptation), which refers to: (a) faster repair of direct DNA lesions (damage), and (b) DNA mutation frequency reduction after irradiation, by applying a small priming (conditioning) dose prior to the high detrimental (challenging) one. This effect is observed in many (but not all) radiobiological experiments which present the reduction of lesion, mutation or even mortality frequency of the irradiated cells or species. Additionally, the multi-parameter model created by Dr. Yonezawa and collaborators tried to explain it theoretically based on experimental data on the mortality of mice with chronic internal irradiation. The presented paper proposes a new theoretical approach to understanding and explaining the priming dose effect: it starts from the radiation adaptive response theory and moves to the three-parameter model, separately for two previously mentioned situations: creation of fast (lesions) and delayed damage (mutations). The proposed biophysical model was applied to experimental data-lesions in human lymphocytes and chromosomal inversions in mice-and was shown to be able to predict the Yonezawa effect for future investigations. It was also found that the strongest radioadaptation is correlated with the weakest cellular radiosensitivity. Additional discussions were focussed on more general situations where many small priming doses are used.

Modeling of single cell cancer transformation using phase transition theory: application of the Avrami equation.

The nucleation and growth theory, described by the Avrami equation (also called Johnson-Mehl-Avrami-Kolmogorov equation), and usually used to describe crystallization and nucleation processes in condensed matter physics, was applied in the present paper to cancer physics. This can enhance the popular multi-hit model of carcinogenesis to volumetric processes of single cell's DNA neoplastic transformation. The presented approach assumes the transforming system as a DNA chain including many oncogenic mutations. Finally, the probability function of the cell's cancer transformation is directly related to the number of oncogenic mutations. This creates a universal sigmoidal probability function of cancer transformation of single cells, as observed in the kinetics of nucleation and growth, a special case of a phase transition process. The proposed model, which represents a different view on the multi-hit carcinogenesis approach, is tested on clinical data concerning gastric cancer. The results also show that cancer transformation follows DNA fractal geometry.

Radiation adaptive response and cancer: From the statistical physics point of view.

Elements of statistical physics formalism were applied to mutagenic and carcinogenic processes associated with cellular DNA; these are lesion (damage) creation, mutation creation, and cellular neoplastic (cancer) transformation. The probabilities of all state changes were strictly related to potential barrier heights between energetic states of DNA molecules. Barriers can be modified when radiation adaptive response mechanisms are applied, which are associated with a radiobiological quantity called radiosensitivity. It was discussed that radiosensitivity is determined by the cell's response to radiation resulting in three potential dose-response scenarios: linear, threshold, or hormetic. The type of dose-response is of critical importance in the development of radiation protection standards and individual radiation risk assessment. It is shown that the different scenarios describe different limits of the same underlying phenomena and the cell can respond in a linear, threshold, or hormetic way regarding its radiosensitivity. Finally, the dissipative adaptation mechanism is discussed in the context of proliferating cancer cells.

Analytical and quasi-Bayesian methods as development of the iterative approach for mixed radiation biodosimetry.

The present paper proposes two methods of calculating components of the dose absorbed by the human body after exposure to a mixed neutron and gamma radiation field. The article presents a novel approach to replace the common iterative method in its analytical form, thus reducing the calculation time. It also shows a possibility of estimating the neutron and gamma doses when their ratio in a mixed beam is not precisely known.

Meta-analysis of thirty-two case-control and two ecological radon studies of lung cancer.

A re-analysis has been carried out of thirty-two case-control and two ecological studies concerning the influence of radon, a radioactive gas, on the risk of lung cancer. Three mathematically simplest dose-response relationships (models) were tested: constant (zero health effect), linear, and parabolic (linear-quadratic). Health effect end-points reported in the analysed studies are odds ratios or relative risk ratios, related either to morbidity or mortality. In our preliminary analysis, we show that the results of dose-response fitting are qualitatively (within uncertainties, given as error bars) the same, whichever of these health effect end-points are applied. Therefore, we deemed it reasonable to aggregate all response data into the so-called Relative Health Factor and jointly analysed such mixed data, to obtain better statistical power. In the second part of our analysis, robust Bayesian and classical methods of analysis were applied to this combined dataset. In this part of our analysis, we selected different subranges of radon concentrations. In view of substantial differences between the methodology used by the authors of case-control and ecological studies, the mathematical relationships (models) were applied mainly to the thirty-two case-control studies. The degree to which the two ecological studies, analysed separately, affect the overall results when combined with the thirty-two case-control studies, has also been evaluated. In all, as a result of our meta-analysis of the combined cohort, we conclude that the analysed data concerning radon concentrations below ~1000 Bq/m3 (~20 mSv/year of effective dose to the whole body) do not support the thesis that radon may be a cause of any statistically significant increase in lung cancer incidence.

Modeling of Irradiated Cell Transformation: Dose- and Time-Dependent Effects.

We report here on various biophysical aspects of irradiated cells, beginning with a phenomenological description of radiation-induced cancer cells. This description includes detrimental factors such as chromosomal aberrations, as well as beneficial factors, such as adaptive response. Also discussed here is the dose- and time-dependent evolution of cancer cells using a purely mathematical approach. The general dose-response shape, which is sigmoidal, is shown to be modified by such mechanisms as adaptive response or bystander effect. The many aspects of the sigmoid function, which most appropriately demonstrates the relationships among irradiated organisms, are discussed here as well. Finally, the balance equation is presented as the most general relationship for irradiated cell behavior.

Cancer Mortality Among People Living in Areas With Various Levels of Natural Background Radiation.

There are many places on the earth, where natural background radiation exposures are elevated significantly above about 2.5 mSv/year. The studies of health effects on populations living in such places are crucially important for understanding the impact of low doses of ionizing radiation. This article critically reviews some recent representative literature that addresses the likelihood of radiation-induced cancer and early childhood death in regions with high natural background radiation. The comparative and Bayesian analysis of the published data shows that the linear no-threshold hypothesis does not likely explain the results of these recent studies, whereas they favor the model of threshold or hormesis. Neither cancers nor early childhood deaths positively correlate with dose rates in regions with elevated natural background radiation.

Pooled Bayesian meta-analysis of two Polish studies on radiation-induced cancers.

The robust Bayesian regression method was applied to perform meta-analysis of two independent studies on influence of low ionising radiation doses on the occurrence of fatal cancers. The re-analysed data come from occupational exposure analysis of nuclear workers in Swierk (Poland) and from ecological study of cancer risk from natural background radiation in Poland. Such two different types of data were analysed, and three popular models were tested: constant, linear and quadratic dose-response dependencies. The Bayesian model selection algorithm was used for all models. The Bayesian statistics clearly indicates that the popular linear no-threshold (LNT) assumption is not valid for presented cancer risks in the range of low doses of ionising radiation. The subject of LNT hypothesis use in radiation risk prediction and assessment is also discussed.

The healthy worker effect and nuclear industry workers.

The linear no-threshold (LNT) dose-effect relationship has been consistently used by most radiation epidemiologists to estimate cancer mortality risk. The large scattering of data by International Agency for Research on Cancer, IARC (Vrijheid et al. 2007; Therry-Chef et al. 2007; Cardis et al. 2007), interpreted in accordance with LNT, has been previously demonstrated (Fornalski and Dobrzynski 2009). Using conventional and Bayesian methods the present paper demonstrates that the standard mortality ratios (SMRs), lower in the IARC cohort of exposed nuclear workers than in the non exposed group, should be considered as a hormetic effect, rather than a healthy worker effect (HWE) as claimed by the IARC group.