A potential fingerprint of α-particle damage at the whole-chromosome level has been suggested. Because of the physical distribution of α-particle tracks, there might be a much lower ratio of interchromosomal exchange aberrations to intrachromosomal exchanges compared to the ratio of these exchanges induced by either low-LET radiation or chemically induced damage (Brenner and Sachs 1994). Again, after the scrambling of the genotype associated with tumor progression, this ratio would be extremely difficult to assess in advanced tumors.

There have been only a few analyses of tumors known to be induced by radon or other α-particle exposure. One set of results is from miners who experienced high radiation doses and dose rates—doses that might not correspond to the exposures expected from domestic situations. The evidence of signature mutations is not strong. A report described point mutations in codon-249 and 250 of the p53 gene, but these could have been spontaneous events or induced by the molds or cigarette-smoking associated with miners' working conditions (Vahakangas and others 1992). The presence of signature mutations in the p53 gene therefore remains to be established. Alternatively, if p53 is not the critical, rate-limiting gatekeeper gene for lung carcinogenesis, signature mutations might yet be identified when the appropriate genes are known and investigated.

Epidemiologic, Biophysical, And Cell-Based Models Of Radon-Induced Carcinogenesis

To obtain estimates of risk posed by exposure to radon in air or drinking water, it would be ideal to trace the complete process from α-particle exposure to cancer, on a quantitative, biologic, and molecular basis and to incorporate such difficult issues as individual and subpopulation variations in susceptibilities (see BEIR VI, National Research Council 1999). Unfortunately that is not yet feasible. Instead, the problem of risk estimation has been approached from a variety of avenues. One is through strict epidemiologic relationships between numbers of cancers and exposure and the use of the linear no-threshold dose-response curves used commonly in radiation risk estimates. Another approach introduces biophysical models of radiation action based on radiation tracks, total doses and dose rates, damaged sites in DNA, and breaks and their rejoining and, from these considerations, reaches interpretations of risk versus dose. Most often, this approach has been used to explain such phenomena as the inverse dose-rate effects relevant at doses higher than those that would arise from domestic exposures (Brenner 1994; Elkind 1994). The approach still does not pay specific attention to the particular genetic changes involved in cancers, and a more detailed attempt to interpret carcinogenesis on a quantitative basis has incorporated changes in cell cycles, proliferation kinetics, cell-killing (Luebeck and others 1994), and other biological processes alluded to in this chapter (Luebeck and others 1994). Because of the large numbers of variables involved, these approaches are still too difficult, computationally, to incorporate into a completely predictive model.

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