Lack of Correlation between Stem-Cell Proliferation and Radiation- or Smoking-Associated Cancer Risk.

BACKGROUND: A recent paper by Tomasetti and Vogelstein (Science 2015 347 78-81) suggested that the variation in natural cancer risk was largely explained by the total number of stem-cell divisions, and that most cancers arose by chance. They proposed an extra-risk score as way of distinguishing the effects of the stochastic, replicative component of cancer risk from other causative factors, specifically those due to the external environment and inherited mutations. OBJECTIVES: We tested the hypothesis raised by Tomasetti and Vogelstein by assessing the degree of correlation of stem cell divisions and their extra-risk score with radiation- and tobacco-associated cancer risk. METHODS: We fitted a variety of linear and log-linear models to data on stem cell divisions per year and cumulative stem cell divisions over lifetime and natural cancer risk, some taken from the paper of Tomasetti and Vogelstein, augmented using current US lifetime cancer risk data, and also radiation- and tobacco-associated cancer risk. RESULTS: The data assembled by Tomasetti and Vogelstein, as augmented here, are inconsistent with the power-of-age relationship commonly observed for cancer incidence and the predictions of a multistage carcinogenesis model, if one makes the strong assumption of homogeneity of numbers of driver mutations across cancer sites. Analysis of the extra-risk score and various other measures (number of stem cell divisions per year, cumulative number of stem cell divisions over life) considered by Tomasetti and Vogelstein suggests that these are poorly predictive of currently available estimates of radiation- or smoking-associated cancer risk-for only one out of 37 measures or logarithmic transformations thereof is there a statistically significant correlation (p<0.05) with radiation- or smoking-associated risk. CONCLUSIONS: The data used by Tomasetti and Vogelstein are in conflict with predictions of a multistage model of carcinogenesis, under the assumption of homogeneity of numbers of driver mutations across most cancer sites. Their hypothesis that if the extra-risk score for a tissue type is high then one would expect that environmental factors would play a relatively more important role in that cancer's risk is in conflict with the lack of correlation between the extra-risk score and other stem-cell proliferation indices and radiation- or smoking-related cancer risk.

Perspective on the use of LNT for radiation protection and risk assessment by the U.S. Environmental Protection Agency.

The U.S. Environmental Protection Agency (EPA) bases its risk assessments, regulatory limits, and nonregulatory guidelines for population exposures to low level ionizing radiation on the linear no-threshold (LNT) hypothesis, which assumes that the risk of cancer due to a low dose exposure is proportional to dose, with no threshold. The use of LNT for radiation protection purposes has been repeatedly endorsed by authoritative scientific advisory bodies, including the National Academy of Sciences' BEIR Committees, whose recommendations form a primary basis of EPA's risk assessment methodology. Although recent radiobiological findings indicate novel damage and repair processes at low doses, LNT is supported by data from both epidemiology and radiobiology. Given the current state of the science, the consensus positions of key scientific and governmental bodies, as well as the conservatism and calculational convenience of the LNT assumption, it is unlikely that EPA will modify this approach in the near future.

Radon exposure assessment and dosimetry applied to epidemiology and risk estimation.

Epidemiological studies of underground miners provide the primary basis for radon risk estimates for indoor exposures as well as mine exposures. A major source of uncertainty in these risk estimates is the uncertainty in radon progeny exposure estimates for the miners. Often the exposure information is very incomplete, and exposure estimation must rely on interpolations, extrapolations and reconstruction of mining conditions decades before, which might differ markedly from those in more recent times. Many of the measurements that were carried out-commonly for health protection purposes-are not likely to be representative of actual exposures. Early monitoring was often of radon gas rather than of the progeny, so that quantifying exposure requires an estimate of the equilibrium fraction under the conditions existing at the time of the reported measurement. In addition to the uncertainty in radon progeny exposure, doses from gamma radiation, inhaled radioactive dust, and thoron progeny have historically been neglected. These may induce a systematic bias in risk estimates and add to the overall uncertainty in risk estimates derived from the miner studies. Unlike other radiogenic cancer risk estimates, numerical risk estimates derived for radon from epidemiology are usually expressed as a risk per unit exposure rather than as a risk per unit dose to a target tissue. Nevertheless, dosimetric considerations are important when trying to compare risks under different exposure conditions, e.g. in mines and homes. A recent comparative assessment of exposure conditions indicates that, for equal radon progeny exposures, the dose in homes is about the same as in mines. Thus, neglecting other possible differences, such as the presence in mines of other potential airborne carcinogens, the risk per unit progeny exposure should be about the same for indoor exposures as observed in miners. Results of case-control studies of lung cancer incidence in homes monitored for radon are reasonably consistent with what would be projected from miner studies. Measurements of exposure in these indoor case-control studies rely on different types of detectors than those used in mines, and the estimates of exposure are again a major source of uncertainty in these studies.

Cancer risks attributable to low doses of ionizing radiation: assessing what we really know.

High doses of ionizing radiation clearly produce deleterious consequences in humans, including, but not exclusively, cancer induction. At very low radiation doses the situation is much less clear, but the risks of low-dose radiation are of societal importance in relation to issues as varied as screening tests for cancer, the future of nuclear power, occupational radiation exposure, frequent-flyer risks, manned space exploration, and radiological terrorism. We review the difficulties involved in quantifying the risks of low-dose radiation and address two specific questions. First, what is the lowest dose of x- or gamma-radiation for which good evidence exists of increased cancer risks in humans? The epidemiological data suggest that it is approximately 10-50 mSv for an acute exposure and approximately 50-100 mSv for a protracted exposure. Second, what is the most appropriate way to extrapolate such cancer risk estimates to still lower doses? Given that it is supported by experimentally grounded, quantifiable, biophysical arguments, a linear extrapolation of cancer risks from intermediate to very low doses currently appears to be the most appropriate methodology. This linearity assumption is not necessarily the most conservative approach, and it is likely that it will result in an underestimate of some radiation-induced cancer risks and an overestimate of others.