A generalized state-vector model for radiation-induced cellular transformation

Internal microdosimetry of alpha-emitting radionuclides

At the tissue level, energy deposition in cells is determined by the microdistribution of alpha-emitting radionuclides in relation to sensitive target cells. Furthermore, the highly localized energy deposition of alpha particle tracks and the limited range of alpha particles in tissue produce a highly inhomogeneous energy deposition in traversed cell nuclei. Thus, energy deposition in cell nuclei in a given tissue is characterized by the probability of alpha particle hits and, in the case of a hit, by the energy deposited there. In classical microdosimetry, the randomness of energy deposition in cellular sites is described by a stochastic quantity, the specific energy, which approximates the macroscopic dose for a sufficiently large number of energy deposition events. Typical examples of the alpha-emitting radionuclides in internal microdosimetry are radon progeny and plutonium in the lungs, plutonium and americium in bones, and radium in targeted radionuclide therapy. Several microdosimetric approaches have been proposed to relate specific energy distributions to radiobiological effects, such as hit-related concepts, LET and track length-based models, effect-specific interpretations of specific energy distributions, such as the dual radiation action theory or the hit-size effectiveness function, and finally track structure models. Since microdosimetry characterizes only the initial step of energy deposition, microdosimetric concepts are most successful in exposure situations where biological effects are dominated by energy deposition, but not by subsequently operating biological mechanisms. Indeed, the simulation of the combined action of physical and biological factors may eventually require the application of track structure models at the nanometer scale.

The effect of non-targeted cellular mechanisms on lung cancer risk for chronic, low level radon exposures

PURPOSE

The goal of the present study was to investigate the effect of non-targeted mechanisms on the shape of the lung cancer risk function at chronic, low level radon exposures relative to direct cellular radiation effects. This includes detrimental and protective bystander effects, radio-adaptive bystander response, genomic instability and induction of apoptosis by surrounding cells.

METHODS

To quantify the dependence of these mechanisms on dose, analytical functions were derived from the experimental evidence presently available. Alpha particle intersections of bronchial target cells during a given exposure period were simulated by a Transformation Frequency-Tissue Response (TF-TR) model, formulated in terms of cellular hits within the cycle time of the cell and then integrated over the whole exposure period.

RESULTS

In general, non-targeted effects like genomic instability and bystander effects amplify the biological effectiveness of a given radiation dose, while induction of apoptosis and adaptive response will decrease the risk values. While these observations are related to the absolute number of lung cancer cases, normalization to the epidemiologically observed risk at 0.675 Gy suggests that the effect of such mechanisms on the shape of the dose-response relationship may be different. Indeed, genomic instability and adaptive response cause a substantial reduction of the risk at low doses, while induction of apoptosis and detrimental bystander effects slightly increase the risk.

CONCLUSIONS

Predictions of lung cancer risk, including these mechanisms, exhibit a distinct sublinear dose-response relationship at low exposures, particularly for very low exposure rates. However, the relatively large error bars of the epidemiological data do not currently allow the prediction of a statistically significant deviation from the Linear - No Threshold (LNT) assumption.

MIRD Pamphlet No. 22 (abridged): radiobiology and dosimetry of alpha-particle emitters for targeted radionuclide therapy

The potential of alpha-particle emitters to treat cancer has been recognized since the early 1900s. Advances in the targeted delivery of radionuclides and radionuclide conjugation chemistry, and the increased availability of alpha-emitters appropriate for clinical use, have recently led to patient trials of radiopharmaceuticals labeled with alpha-particle emitters. Although alpha-emitters have been studied for many decades, their current use in humans for targeted therapy is an important milestone. The objective of this work is to review those aspects of the field that are pertinent to targeted alpha-particle emitter therapy and to provide guidance and recommendations for human alpha-particle emitter dosimetry.

Radon: Current Challenges in Cellular Radiobiology

Radon is by far the most important contributor to the collective dose equivalent. Most of what is known about the hazards of radon daughters comes from epidemiological studies of miners. There are a few well defined areas in which in vitro research can complement such studies: First, more data on the relative effects of differing energy (LET) alpha-particles would help: (1) understand the significance of the depth of sensitive cells in the bronchial epithelium--which varies between individuals, as well as between smokers and non-smokers, and between miners and non-miners; (2) understand the relative hazards of radon and thoron daughters. Second, reliable methods for predicting high LET responses from low LET response, would enable Japanese A-bomb survivor data to be applied with confidence. Third, understanding the effects of single-particle traversals of cells relative to multiple traversals could allow reliable extrapolation of epidemiological miner data to low exposures. Fourth, a better understanding of the nature of the interaction between tobacco and radiation damage would help predict the effect of radon on non-smokers.

A computational model for radiation-induced cellular transformation to in vitro irradiation of cells by acute doses of X-rays

This research incorporates new biological concepts to improve the predictive ability of a state-vector model with respect to dose-response data on in vitro oncogenic transformation, including mechanisms of DNA damage, DNA repair, cell death, cell proliferation and intercellular communication. Experimentally recognized biological processes, including background transformation, compensatory proliferation and bystander cell-killing effect were formulated mathematically and included as model parameters. These were then adjusted with an optimization method to reproduce in vitro transformation frequency data from C3H10T1/2 mouse cells exposed to acute doses of X-rays. A plateau observed in the data at low doses is reproduced well and a dose-dependent increase above 1 Gy is predicted almost precisely. Extension of the model predictions to the dose range 0-100 mGy indicates that transformation frequencies are practically constant over this low dose region. Results suggest a protective, rather than detrimental, bystander cell-killing effect. Further analysis of model sensitivity to this bystander parameter, though, revealed uncertainties with respect to its biological plausibility in the model.

Detrimental and protective bystander effects: a model approach

This work integrates two important cellular responses to low doses, detrimental bystander effects and apoptosis-mediated protective bystander effects, into a multistage model for chromosome aberrations and in vitro neoplastic transformation: the State-Vector Model. The new models were tested on representative data sets that show supralinear or U-shaped dose responses. The original model without the new low-dose features was also tested for consistency with LNT-shaped dose responses. Reductions of in vitro neoplastic transformation frequencies below the spontaneous level have been reported after exposure of cells to low doses of low-LET radiation. In the current study, this protective effect is explained with bystander-induced apoptosis. An important data set that shows a low-dose detrimental bystander effect for chromosome aberrations was successfully fitted by additional terms within the cell initiation stage. It was found that this approach is equivalent to bystander-induced clonal expansion of initiated cells. This study is an important step toward a comprehensive model that contains all essential biological mechanisms that can influence dose-response curves at low doses.

Second cancers after fractionated radiotherapy: stochastic population dynamics effects

When ionizing radiation is used in cancer therapy it can induce second cancers in nearby organs. Mainly due to longer patient survival times, these second cancers have become of increasing concern. Estimating the risk of solid second cancers involves modeling: because of long latency times, available data is usually for older, obsolescent treatment regimens. Moreover, modeling second cancers gives unique insights into human carcinogenesis, since the therapy involves administering well-characterized doses of a well-studied carcinogen, followed by long-term monitoring. In addition to putative radiation initiation that produces pre-malignant cells, inactivation (i.e. cell killing), and subsequent cell repopulation by proliferation, can be important at the doses relevant to second cancer situations. A recent initiation/inactivation/proliferation (IIP) model characterized quantitatively the observed occurrence of second breast and lung cancers, using a deterministic cell population dynamics approach. To analyze if radiation-initiated pre-malignant clones become extinct before full repopulation can occur, we here give a stochastic version of this IIP model. Combining Monte-Carlo simulations with standard solutions for time-inhomogeneous birth-death equations, we show that repeated cycles of inactivation and repopulation, as occur during fractionated radiation therapy, can lead to distributions of pre-malignant cells per patient with variance>>mean, even when pre-malignant clones are Poisson-distributed. Thus fewer patients would be affected, but with a higher probability, than a deterministic model, tracking average pre-malignant cell numbers, would predict. Our results are applied to data on breast cancers after radiotherapy for Hodgkin disease. The stochastic IIP analysis, unlike the deterministic one, indicates: (a) initiated, pre-malignant cells can have a growth advantage during repopulation, not just during the longer tumor latency period that follows; (b) weekend treatment gaps during radiotherapy, apart from decreasing the probability of eradicating the primary cancer, substantially increase the risk of later second cancers.

Protective bystander effects simulated with the state-vector model

Apoptosis induced in non-hit bystander cells is an important biological mechanism which operates after exposure to low doses of low-LET radiation. This process was implemented into a deterministic multistage model for in vitro neoplastic transformation: the State-Vector Model (SVM). The new model is tested on two data sets that show a reduction of the transformation frequency below the spontaneous level after exposure of the human hybrid cell line CGL1 to low doses of gamma-radiation. Stronger protective effects are visible in the data for delayed plating while the data for immediate plating show more of an LNT-like dose-response curve. It is shown that the model can describe both data sets. The calculation of the time-dependent numerical solution of the model also allows to obtain information about the time-dependence of the protective apoptosis-mediated process after low dose exposures. These findings are compared with experimental observations after high dose exposures.

Analysis of epidemiological cohort data on smoking effects and lung cancer with a multi-stage cancer model

A stochastic two-stage cancer model is used to analyse the relation between lung cancer and cigarette smoking. The model contains the main rate-limiting stages of carcinogenesis, which include initiation, promotion (clonal expansion of initiated cells), malignant transformation and a lag time for tumour formation. Various data sets were used to test the model. These include the data of a large prospective collaborative project carried out in 10 different European countries, the European Prospective Investigation into Cancer and Nutrition (EPIC). This new data set has not been modelled before. The model is also tested on other published data from CPS-II (Cancer Prevention Study II) of the American Cancer Society and the British doctors' study. The analyses indicate that the EPIC data are best described with smoking dependence on the rates of malignant transformation and clonal expansion. With increasing smoking rates, saturation effects in the two exposure rate-dependent model parameters were observed. The results find confirmation in the biological literature, where both mutational effects and promotional effects of cigarette smoke are documented.

A model for the induction of chromosome aberrations through direct and bystander mechanisms

A state vector model (SVM) for chromosome aberrations and neoplastic transformation has been adapted to describe detrimental bystander effects. The model describes initiation (formation of translocations) and promotion (clonal expansion and loss of contact inhibition of initiated cells). Additional terms either in the initiation model or in the rate of clonal expansion of initiated cells, describe detrimental bystander effects for chromosome aberrations as reported in the scientific literature. In the present study, the SVM with bystander effects is tested on a suitable dataset. In addition to the simulation of non-linear effects, a classical dataset for neoplastic transformation in C3H 10T1/2 cells after alpha particle irradiation is used to show that the model without bystander features can also describe LNT-like dose responses. A published model for bystander induced neoplastic transformation was adapted for chromosome aberration induction and used to compare the results obtained with the different models.

Modeling lung cancer incidence in rats following exposure to radon progeny

Lung cancer incidence in Sprague-Dawley rats was simulated by a biologically based carcinogenesis model, which is formulated mathematically in terms of a stochastic state-vector model. Doses to the sensitive target cells in the bronchial epithelium of the rat lung were calculated by a stochastic dosimetry model, considering the distinct monopodial branching structure and the crossfire of alpha particles from alveolar tissue to bronchial epithelium. Bronchial and alveolar cellular doses could reasonably be approximated by lognormal distributions, with geometric standard deviations (GSD) between 7 and 10, depending on exposure conditions. Based on a dose-exposure conversion factor of 8.5 mGy WLM(-1) and a GSD of 8, lung cancer incidences were calculated for each cumulative exposure category in the rat inhalation study, consisting of different exposure rates and exposure times. The fair agreement between theoretical predictions and experimental data over the whole exposure range emphasises the necessity to incorporate the full cellular dose distributions rather than their mean values.

Incorporation of microdosimetric concepts into a biologically-based model of radiation carcinogenesis

The generalised state-vector model of radiation carcinogenesis (SVM) simulates radiation induced biological effects by expressing the transition rates between the various initiation and promotion stages in terms of dose rate for low and high linear energy transfer (LET) particles. In the present work, the SVM has been reformulated to incorporate single track characteristics of particles with varying LET. Transition rates of the initiation phase were expressed as functions of LET by describing the complexity and clustering of DNA double strand breaks (DSBs) and its effect on repair kinetics, while the promotion phase was reformulated based on a multi-target single-hit hypothesis. Such an approach allows the consideration of hit frequencies and the variability of the specific energy and LET spectra of radon progeny alpha particles in bronchial target cells for different exposure conditions.

An examination of radiation hormesis mechanisms using a multistage carcinogenesis model

A multistage cancer model that describes the putative rate-limiting steps in carcinogenesis is developed and used to investigate the potential impact on cumulative lung cancer incidence of the hormesis mechanisms suggested by Feinendegen and Pollycove. In the model, radiation and endogenous processes damage the DNA of target cells in the lung. Some fraction of the misrepaired or unrepaired DNA damage induces genomic instability and, ultimately, leads to the accumulation of malignant cells. The model explicitly accounts for cell birth and death processes, the clonal expansion of initiated cells, malignant conversion, and a lag period for tumor formation. Radioprotective mechanisms are incorporated into the model by postulating dose and dose-rate-dependent radical scavenging. The accuracy of DNA damage repair also depends on dose and dose rate. As currently formulated, the model is most applicable to low-linear-energy-transfer (LET) radiation delivered at low dose rates. Sensitivity studies are conducted to identify critical model inputs and to help define the shapes of the cumulative lung cancer incidence curves that may arise when dose and dose-rate-dependent cellular defense mechanisms are incorporated into a multistage cancer model. For lung cancer, both linear no-threshold (LNT-), and non-LNT-shaped responses can be obtained. If experiments demonstrate that the effects of DNA damage repair and radical scavenging are enhanced at least three-fold under low-dose conditions, our studies would support the existence of U-shaped responses. The overall fidelity of the DNA damage repair process may have a large impact on the cumulative incidence of lung cancer. The reported studies also highlight the need to know whether or not (or to what extent) multiply damaged DNA sites are formed by endogenous processes. Model inputs that give rise to U-shaped responses are consistent with an effective cumulative lung cancer incidence threshold that may be as high as 300 mGy (4 mGy per year for 75 years) for low-LET radiation.

Mechanistic basis for nonlinear dose-response relationships for low-dose radiation-induced stochastic effects

The linear nonthreshold (LNT) model plays a central role in low-dose radiation risk assessment for humans. With the LNT model, any radiation exposure is assumed to increase one's risk of cancer. Based on the LNT model, others have predicted tens of thousands of deaths related to environmental exposure to radioactive material from nuclear accidents (e.g., Chernobyl) and fallout from nuclear weapons testing. Here, we introduce a mechanism-based model for low-dose, radiation-induced, stochastic effects (genomic instability, apoptosis, mutations, neoplastic transformation) that leads to a LNT relationship between the risk for neoplastic transformation and dose only in special cases. It is shown that nonlinear dose-response relationships for risk of stochastic effects (problematic nonlethal mutations, neoplastic transformation) should be expected based on known biological mechanisms. Further, for low-dose, low-dose rate, low-LET radiation, large thresholds may exist for cancer induction. We summarize previously published data demonstrating large thresholds for cancer induction. We also provide evidence for low-dose-radiation-induced, protection (assumed via apoptosis) from neoplastic transformation. We speculate based on work of others (Chung 2002) that such protection may also be induced to operate on existing cancer cells and may be amplified by apoptosis-inducing agents such as dietary isothiocyanates.

Analysis of radon-induced lung cancer risk by a stochastic state-vector model of radiation carcinogenesis

A biologically based state-vector model (SVM) of radiation carcinogenesis has been extended to incorporate stochasticity of cellular transitions and specific in vivo irradiation conditions in the lungs. Dose-rate-dependent cellular transitions related to the formation of double-stranded DNA breaks, repair of breaks, interactions (translocations) between breaks, fixation of breaks, cellular inactivation, stimulated mitosis and promotion through loss of intercellular communication are simulated by Monte Carlo methods. The stochastic SVM has been applied to the analysis of lung cancer incidence in uranium miners exposed to alpha-emitting radon progeny. When incorporating in vivo features of cell differentiation, stimulated cell division and heterogeneity of cellular doses into the model, excellent agreement between epidemiological data and modelling results could be obtained. At low doses, the model predicts a nonlinear dose-response relationship; e.g., computed lung cancerrisk at 20WLM is about half of current lung cancer estimates based on the linear hypothesis. The model also predicts a slight dose rate effect; e.g., at a cumulative exposure of 20 WLM, calculated lung cancer incidence for an exposure rate 0.27 WLM/year (assuming an exposure time of 73 years) is smaller by a factor of 1.2 than that for an exposure rate of 10 WLM/year.

Nonlinear dose-response relationships and inducible cellular defence mechanisms

With the inclusion of inducible radioprotective mechanisms in a radiobiological state-vector model it was possible to explain plateaus in dose-response relationships for neoplastic transformation produced by in vitro irradiation of different cell lines with low-LET irradiation at high dose rates. The current study repeated the simulation of one data set that contains a plateau at mid doses. In contrast to earlier studies, the new one did not model the repair of double-strand breaks (DSBs) located in bulk DNA (likely via non-homologous end joining) as being inducible. Repair of specific DSBs located in actively transcribed genes was assumed to occur via homologous recombination and was considered to be inducible. This reduced the number of parameters that have to be determined by fitting the model to data. In addition, all types of radical scavengers were formerly considered to be inducible by radiation. This was redefined in the current work and the effectiveness of scavengers was implemented in a refined way. The current work investigated whether these and other model adjustments lead to an improved fit of the data set.

Depleted uranium-catalyzed oxidative DNA damage: absence of significant alpha particle decay

Depleted uranium (DU) is a dense heavy metal used primarily in military applications. Published data from our laboratory have demonstrated that DU exposure in vitro to immortalized human osteoblast cells (HOS) is both neoplastically transforming and genotoxic. DU possesses both a radiological (alpha particle) and a chemical (metal) component. Since DU has a low-specific activity in comparison to natural uranium, it is not considered to be a significant radiological hazard. In the current study we demonstrate that DU can generate oxidative DNA damage and can also catalyze reactions that induce hydroxyl radicals in the absence of significant alpha particle decay. Experiments were conducted under conditions in which chemical generation of hydroxyl radicals was calculated to exceed the radiolytic generation by one million-fold. The data showed that markers of oxidative DNA base damage, thymine glycol and 8-deoxyguanosine could be induced from DU-catalyzed reactions of hydrogen peroxide and ascorbate similarly to those occurring in the presence of iron catalysts. DU was 6-fold more efficient than iron at catalyzing the oxidation of ascorbate at pH 7. These data not only demonstrate that DU at pH 7 can induced oxidative DNA damage in the absence of significant alpha particle decay, but also suggest that DU can induce carcinogenic lesions, e.g. oxidative DNA lesions, through interaction with a cellular oxygen species.

Testing extrapolation of a biologically based exposure-response model from in vitro to in vivo conditions

Models of carcinogenesis may become so flexible as to preclude the possibility of being falsified by data. This problem is removed in part by stronger biophysical specification of processes and parameters within the model prior to fitting to in vivo data on the relationship between exposure and cancer incidence. This paper explores the use of a biophysical model of chromosomal damage, cellular transformation, repair, mitosis, initiation, promotion, progression, and cytotoxicity in developing exposure-response models for radiation-induced cancer. Many of the aspects of model form and parameter values are developed from in vitro data, and the model then is extrapolated to the in vivo setting using a dosimetric model to account for dose inhomogeneity within the lung tissue of rats exposed to radon progeny in air. The ability of the model to predict cancer incidence in the rats is assessed and is shown to be problematic at higher doses. This calls into question whether a full claim may be made about the ability of first-principle models to fully constrain models applied to in vivo data at present. Possible explanations for the discrepancy, and implications for extrapolation, are provided.

Modeling energy deposition and cellular radiation effects in human bronchial epithelium by radon progeny alpha particles

Energy deposition and cellular radiation effects arising from the interaction of single 218Po and 214Po alpha particles with basal and secretory cell nuclei were simulated for different target cell depths in the bronchial epithelium of human airway generations 2, 4, 6, and 10. To relate the random chord lengths of alpha particle tracks through spherical cell nuclei to the resulting biological endpoints, probabilities per unit track length for different cellular radiation effects as functions of LET were derived from in vitro experiments. The radiobiological data employed in the present study were inactivation and mutation (mutant frequency at the HPRT gene) in V79 Chinese hamster cells and inactivation and transformation in C3H 10T1/2 cells. Based on computed LET spectra and relative frequencies of target cells, probabilities for transformation, mutation, and cell killing in basal and secretory cells were computed for a lifetime exposure of 20 WLM. While predicted transformation probabilities were about two orders of magnitude higher than mutation probabilities, they were still about two orders of magnitude lower than inactivation probabilities. Furthermore transformation probabilities for basal cells are generally higher than those for secretory cells, and 214Po alpha particles are primarily responsible for transformations in bronchial target cells.

Modeling radioprotective mechanisms in the dose effect relation at low doses and low dose rates of ionizing radiation

A new model (Random Coincidence Model--Radiation Adapted (RCM-RA)) is proposed which explains a possible pseudo threshold for stochastic radiation effects. It describes the formation of cancer in the case of multistep fixation of lesions in the critical regions of tumor associated genes such as proto-oncogenes or tumor-suppressor genes. The RCM-RA contains two different possibilities of DNA damage to complementary nucleotides. The damage may be caused either by radiation or by natural processes such as cellular radicals or thermal damage or by chemical cytotoxins. The model is based on the premise that radiation initially is bionegative, damaging organisms at their different levels of organization. The radiation, however, also induces various cellular radioprotective mechanisms which decrease the damage by natural processes. Considering both effects together, the theory explains apparent thresholds in the dose-response relation for radiation carcinogenesis without contradiction to the classical assumption that radiation is predominantly bionegative at doses typically found in occupational exposures.

Effects of in vitro alpha-particle irradiation on osteogenic bone marrow cultures

Murine bone marrow contains osteogenic precursor cells that undergo differentiation during in vitro cultivation. In vitro these cells are potential target cells for alpha-irradiation-induced bone tumour formation. Under defined tissue culture conditions these differentiating cells were directly exposed to alpha-particle irradiation from the radon daughter 210Po. Po deposits in soft tissue and it was shown to be associated with marrow cells and with the extracellular marrow tissue formed in vitro. These differentiating marrow cultures showed high sensitivity to alpha-irradiation. Cell death was observed at 210Po concentrations in tissue culture medium (TCM) > 7 Bq 210Po/ml. At lower concentrations (between 1 and 5 Bq 210Po per ml TCM) proliferation was enhanced as measured by uptake of 3H-thymidine, also differentiation was stimulated as measured by alkaline phosphatase activity and incorporation of 3H-proline in newly synthesized collagen. At several times of culture, the association of 210Po with the extracellular matrix and cells was measured. These retention data enabled us to calculate the daily alpha-particle fluence. At 1 Bq 210Po present per ml tissue culture, a daily alpha-particle fluence as low as 3-6 per 1000 cells seemed very efficient in changing the expression of osteogenic differentiation of marrow cells.

Osteosarcomagenic doses of radium (224Ra) and infectious endogenous retroviruses enhance proliferation and osteogenic differentiation of skeletal tissue differentiating in vitro

Cartilage tissue from embryonic mice which undergoes osteogenic differentiation during in vitro cultivation was used to study the effect of osteosarcomagenic doses of alpha-irradiation and bone-tumor-inducing retroviruses on proliferation and phenotypic differentiation of skeletal cells in a defined tissue culture model. Irradiated mandibular condyles showed dose-dependent enhancement of cell proliferation at day 7 of the culture and increased osteogenic differentiation at day 14. Maximal effects were found with 7.4 Bq/ml of 224Ra-labeled medium. Doses of 740 and 7400 Bq/ml of 224Ra-labeled medium induced increasing cell death. Retrovirus infection enhanced osteogenic differentiation and extended the viability of irradiated cells. After transplantation none of the treated tissues developed tumors in syngeneic mice.

Extension of a generalized state-vector model of radiation carcinogenesis to consideration of dose rate

Mathematical models for radiation carcinogenesis typically employ transition rates that either are a function of the dose to specific cells or are purely empirical constructs unrelated to biophysical theory. These functions either ignore or do not explicitly model interactions between the fates of cells in a community. This paper extends a model of mitosis, cell transformation, promotion, and progression to cases in which interacting cellular communities are irradiated at specific dose rates. The model predicts that lower dose rates are less effective at producing cancer when irradiation is by X- or gamma rays but are generally more effective in instances of irradiation by alpha particles up to a dose rate in excess of 0.01 Gy/day. The resulting predictions are compared with existing experimental data.

Job factors, radiation and cancer mortality at Oak Ridge National Laboratory: follow-up through 1984

A previous study of mortality among white men hired at Oak Ridge National Laboratory between 1943 and 1972 (n = 8,318) revealed an association between low-dose external penetrating ionizing radiation and cancer mortality in follow-up through 1984. The association was not observed in follow-up through 1977. This report considers the role of possible selection and confounding factors not previously studied. Control for hire during the World War II era and employment duration of less than 1 year had little effect on the radiation risk estimates. Risks associated with length of time spent in 15 job categories were considered as proxies for the effects of other occupational carcinogens. Adjustment for employment duration in each job category one at a time produced only small changes in the radiation risk estimate. Adjustment for potential exposures to beryllium, lead, and mercury also had little effect on the radiation risk estimates. These analyses suggest that selection factors and potential for chemical exposure do not account for the previously noted association of external radiation dose with cancer mortality. However, power to detect effects of chemical exposures is limited by a lack of individual exposure measures.