An analysis of the effects of chronic low dose-rate radiation exposure on cancer focusing on the differences among cancer types.

PURPOSE: The effect of chronic low dose-rate radiation exposure on cancers was investigated by analyzing the data of mice experiments conducted at the Institute for Environmental Sciences (IES). This analysis focuses on the differences between malignant lymphomas and solid cancers. MATERIALS AND METHODS: The analysis is conducted based on the mathematical model introduced in our previous work. The model is expanded to analyze malignant lymphomas and solid cancers separately. Using the expanded model, the effect of chronic low dose-rate radiation on malignant lymphomas and solid cancers are discussed based on their occurrences, progressions, and mortalities. RESULTS: Non-irradiated control group and 20 mGy/day × 400 days irradiated groups are analyzed. The analysis showed that radiation exposure shortened mean life expectancy for both malignant lymphomas and solid cancers (shorter by 89.6 days for malignant lymphomas and 149.3 days for solid cancers). For malignant lymphomas, both the occurrence and the progression are affected by radiation exposure. The mean age at which malignant lymphoma developed in mice was shortened by 32.7 days and the mean progression period was shortened by 57.3 days. The occurrence of solid cancer is also affected by radiation exposure, wherein the mean age at which solid cancer develops was shortened by 147.9 days. However, no significant change in progression period of solid cancers was seen in the analysis. CONCLUSIONS: The analysis showed that the occurrence and mean lifespan are affected in both malignant lymphomas and solid cancers. The shortening of the progression period is only seen in malignant lymphoma, no significant change was observed in solid cancers.

A mathematical model for radiation-induced life-shortening attributed to cancer.

PURPOSE: In this paper, we described our mathematical model for radiation-induced life shortening in detail and applied the model to the experimental data on mice to investigate the effect of radiation on cancer-related life-shortening. MATERIALS AND METHODS: Our mathematical model incorporates the following components: (i) occurrence of cancer, (ii) progression of cancer over time, and (iii) death from cancer. We evaluated the progression of cancer over time by analyzing the cancer incidence data and cumulative mortalities data obtained from mice experiments conducted at the Institute for Environmental Sciences (IES). RESULTS: We analyzed non-irradiated control and 20 mGy/day × 400 days irradiated groups. In the analysis, all malignant neoplasms were lumped together and referred to as 'cancer'. Our analysis showed that the reduction in lifespan (104 days in median) was the result of the early onset of cancer (68 days in median) and the shortening of the cancer progression period (48 days in median). CONCLUSIONS: We described in detail our mathematical model for radiation-induced life-shortening attributed to cancer. We analyzed the mice data obtained from the experiment conducted at the IES using our model. We decomposed radiation-induced life-shortening into the early onset of cancer and the shortening of the cancer progression period.

COMBINED ANALYSIS OF CANCER INCIDENCE AND LIFESPAN IN MICE EXPOSED TO CHRONIC LOW DOSE RATE RADIATION.

The authors performed a combined analysis using the data obtained from continuous low dose rate irradiation experiments on mice conducted at the Institute for Environmental Sciences, namely, cancer incidence data and lifespan data. They estimated the length of cancer progression period, which is difficult to assess experimentally. The combined analysis showed that the mean cancer progression period is 173 d in the control group and 103 d in the irradiated group.

ON RADIATION-INDUCED AGING: ACCELERATED OR PREMATURE AGING.

The concept of radiation-induced aging is revisited from the viewpoint of a mathematical model. The effect of radiation on carcinogenesis is treated based on the Armitage-Doll multi-stage theory. The formula obtained for cancer incidence rate indicates that radiation dose can be explained in terms of time. Radiation-induced aging for acute and chronic exposures is described using age-specific cancer incidence rates as a measure of aging. It shows that accelerated aging is related to the dose rate, whereas premature aging is related to the cumulative dose, providing a simple and natural interpretation of radiation-induced aging. The usefulness of this approach is demonstrated by applying the formula to cancer prevalence data from mice chronically exposed to low dose-rate radiation.

Study of mutation from DNA to biological evolution.

Purpose: This is a paper based on a talk given in the BER2018 conference by M. Bando. We first emphasize the importance of collaborations among scientists in various fields for the low dose/dose-rate effects on biological body. We make comparisons of quantitative estimations of mutation caused by the radiation exposure on various animals and plants using one mathematical model. We derive the importance of the spontaneous mutation at the DNA level, which provides the key to understand the biological evolution. We try to make a guide map to solve this problem and find that the mutation is an important stage of the pathway from the DNA damage to the macroscopic biological evolution. Materials and methods: We construct a mathematical model for the mutation, named as 'WAM' model, which takes into account the recovery effect. The model setting is regarded as an extension of the survival and the hazard functions. The WAM model is used to reproduce accumulated data of mutation frequency of animals and plants. Especially the model analysis shows that the dose-rate dependence is important to understand various mutation data. Results and conclusions: The WAM model is successful in reproducing various mutation data of animals and plants. We find that the inclusion of the dose rate is important to understand all the mutation data. Hence, we are able to develop the 'scaling law' to make the cross-species comparison of mutation frequency data. With this finding, we can extract the dominant effect on the mutation to be caused by the spontaneous mutation, and quantify this amount. We are able to write then the artificial radiation frequency by subtracting the spontaneous mutation. With this success, we estimate the origin of the spontaneous mutation as due to ROS, the order of which agrees to the spontaneous mutation.