The use of biologically based cancer risk models in radiation epidemiology

Invited Commentary: Mechanistic and Biologically Based Models in Epidemiology-A Powerful Underutilized Tool

Mechanistic and biologically based mathematical models of chronic and behavioral disease processes aim to capture the main mechanistic or biological features of the disease development and to connect these with epidemiologic outcomes. These approaches have a long history in epidemiologic research and are complementary to traditional epidemiologic or statistical approaches to investigate the role of risk factor exposures on disease risk. Simonetto et al. (Am J Epidemiol. 2022;191(10):1766-1775) present a mechanistic, process-oriented model to investigate the role of smoking, hypertension, and dyslipidemia in the development of atherosclerotic lesions and their progression to myocardial infarction. Their approach builds on and brings to cardiovascular disease the ideas and perspectives of earlier mechanistic and biologically based models for the epidemiology of cancer and other chronic diseases, providing important insights into the mechanisms and epidemiology of smoking related myocardial infarction. We argue that although mechanistic modeling approaches have demonstrated their value and place in epidemiology, they are highly underutilized. We call for efforts to grow mechanistic and biologically based modeling research, expertise, and awareness in epidemiology, including the development of training and collaboration opportunities to attract more students and researchers from science, technology, engineering, and medical field into the epidemiology field.

Radon as a Tracer of Lung Changes Induced by Smoking

After smoking, exposure to radon and its progeny is the second leading cause of lung cancer. The probability of inducing lung carcinomas by inhaled radon progeny depends on the deposited radiation dose, and is significantly affected by physiological and morphometric changes induced by smoking. Due to irritation of the airways, the inhalation of cigarette smoke leads to the hyperproduction of mucus. Two concurrent processes occur: on one hand, increased production of mucus protects the target cells against radiation damage; on the other hand, in the case of long-term smokers, a chronic lung obstruction develops, causing an increase in the radiation dose to the lungs. Depending on the duration and intensity of smoking, these processes contribute to the final radiation dose with different weights. The primary objective of this study was to investigate to what extent these smoke-induced changes can modify the resulting absorbed dose of inhaled radon progeny relative to healthy nonsmokers. Since the bronchial dose depends on the degree of lung tissue damage, we have used this dose as a tool for detecting the effects of smoking on the lung epithelium. In other words, the biological effect of radon served as a tracer of changes induced by smoking.

The role of dose rate in radiation cancer risk: evaluating the effect of dose rate at the molecular, cellular and tissue levels using key events in critical pathways following exposure to low LET radiation

PURPOSE

This review evaluates the role of dose rate on cell and molecular responses. It focuses on the influence of dose rate on key events in critical pathways in the development of cancer. This approach is similar to that used by the U.S. EPA and others to evaluate risk from chemicals. It provides a mechanistic method to account for the influence of the dose rate from low-LET radiation, especially in the low-dose region on cancer risk assessment. Molecular, cellular, and tissues changes are observed in many key events and change as a function of dose rate. The magnitude and direction of change can be used to help establish an appropriate dose rate effectiveness factor (DREF).

CONCLUSIONS

Extensive data on key events suggest that exposure to low dose-rates are less effective in producing changes than high dose rates. Most of these data at the molecular and cellular level support a large (2-30) DREF. In addition, some evidence suggests that doses delivered at a low dose rate decrease damage to levels below that observed in the controls. However, there are some data human and mechanistic data that support a dose-rate effectiveness factor of 1. In summary, a review of the available molecular, cellular and tissue data indicates that not only is dose rate an important variable in understanding radiation risk but it also supports the selection of a DREF greater than one as currently recommended by ICRP ( 2007 ) and BEIR VII (NRC/NAS 2006 ).

Use of threshold-specific energy model for the prediction of effects of smoking and radon exposure on the risk of lung cancer

Lung cancer is the leading cause of cancer death in both men and women. Smoking causes 80-90% of cases of lung cancer. In this study, an attempt was made to assess the impact of cigarette smoking on the risk of lung cancer by the so-called threshold-specific energy model. This model allows to analyse the biological effects of radon daughter products on the lung tissue, and is based on the assumption that the biological effect (i.e. cell inactivation) will manifest itself after the threshold-specific energy z0 deposited in the sensitive volume of the cell is exceeded. Cigarette smoking causes, among others, an increase in the synthesis of the survivin protein that protects cells from apoptosis and thereby reduces their radiosensitivity. Based on these facts, an attempt was made to estimate the shape of the curves describing the increase in the oncological effect of radiation as a function of daily cigarette consumption.

Diagnostic medical radiation in inflammatory bowel disease: how to limit risk and maximize benefit

Diagnosis and management of inflammatory bowel disease (IBD) requires repeat diagnostic imaging for monitoring of disease activity. Recent evidence has suggested that patients with IBD are at increased risk of radiation exposure from repeat imaging. The aim of this article was to highlight risks associated with increasing radiation exposure and identify alternatives to minimize exposure. The increasing use of computed tomography (CT) in both Crohn's disease and ulcerative colitis has brought additional benefits to guiding management through non-invasive measures. However, the massive increase in use of CT scans poses a risk of exposing patients with IBD to high levels of diagnostic medical radiation. High levels of diagnostic medical radiation are associated with an increased risk of malignancy in several studies. Numerous studies have identified particular risk factors in IBD associated with high levels of diagnostic medical radiation which are also associated with a more severe disease course. Imaging techniques such as magnetic resonance enterography, ultrasound, small bowel follow through, and capsule endoscopy are alternatives to CT scans as they do not utilize radiation. Gastroenterologists managing patients with IBD, particularly Crohn's disease, should be aware of the increased risk of high cumulative doses of radiation exposure, particularly from CT scanning. Alternative forms of imaging should be carefully considered when evaluating patients, in particularly those with identifiable risk factors for an aggressive disease course.

Canadian National Dose Registry of radiation workers: overview of research from 1951 through 2007

The National Dose Registry (NDR) of Canada is a unique resource for a direct estimation of the potential health risks associated with low doses of ionizing radiation. This is the largest national occupational radiation exposure database, comprising records for about 600,000 nuclear, industrial, medical and dental workers. An analysis of the NDR data based on a cohort of about 200,000 workers first exposed before 1984 and followed through 1987 and 1988 for mortality and cancer incidence, respectively, revealed that the mortality from most causes of death considered was lower than that in the general population, which is typical of occupational cohorts. Although the same was also observed for cancer incidence, there was a significant increase in the incidence of thyroid cancer and melanoma which, however, was not clearly related to radiation exposure. A significant dose-response was found for mortality from all causes, all cancers, lung cancer, cardiovascular diseases, accidents, for incidence of all cancers, cancers of the rectum and lung, leukaemia, all cancers except lung, and all cancers except leukaemia. In addition, in male workers, a significant dose-response was found for the incidence of colon, pancreatic, and testicular cancers. The estimates of cancer risks (mortality and incidence) were higher than those in most other occupational cohorts and in the studies on atomic bomb survivors. The biologically based dose-response models used to describe lung cancer incidence in the NDR showed that for a protracted exposure to low radiation doses there was a significant radiation effect on the promotion and malignant conversion, but not on the initiation stage of carcinogenesis. This stands in contrast to the findings for high-dose acute exposures in A-bomb survivors, where the initiation and possibly promotion were found to be affected by radiation exposure. Evidence of an inverse dose-rate effect (i.e. an increase in the risk with a protraction of a given cumulative dose) was found in the NDR cohort.

The effect of censoring on cancer risk estimates based on the Canadian National Dose Registry of occupational radiation exposure

Cohort studies represent an important epidemiological tool for exploring the potential adverse health effects of low-dose exposure to ionizing radiation in the workplace. Analyses of data from the National Dose Registry of Canada have suggested that occupational radiation exposure leads to increased risk of several specific types of cancer, as well as increased overall risk of cancer. An important aspect of such studies is the censoring in recorded exposures induced by dosimetry detection limits. Such a censoring effect can lead to significant underestimation of cumulative doses which, in turn, can result in overestimation of the excess cancer risk associated with occupational radiation exposure. In this article, we present analytic results, supported by a simulation study, on the magnitude of overestimation of risk based on the additive relative risk model used in the analysis of the NDR data that can occur due to censoring. Our results indicate that overestimation of risk is modest, being less than 20% in all situations considered here. Because censoring also results in ovestimation of the precision of the risk estimates, the significance levels of Wald-type statistical tests for increased risk based on the ratio of the estimate to its standard error are virtually unaffected by censoring. These results suggest that although the application of the additive excess relative risk model in the presence of censoring may lead to some overestimation of risk, the model does not lead to invalid conclusions regarding the association between occupational radiation exposure and cancer risk based on data from the NDR.

Modeling Intercellular Interactions during Carcinogenesis

By modulating the microenvironment of malignant or premalignant cells, inhibitory or stimulatory signals from nearby cells can play a key role in carcinogenesis. However, current commonly used quantitative models for induction of cancers by ionizing radiation focus on single cells and their progeny. Intercellular interactions are neglected or assumed to be confined to unidirectional radiation bystander effect signals from cells of the same tissue type. We here formulate a parsimoniously parameterized two-stage logistic (TSL) carcinogenesis model that incorporates some effects of intercellular interactions during the growth of premalignant cells. We show that for baseline tumor rates, involving no radiation apart from background radiation, this TSL model gives acceptable fits to a number of data sets. Specifically, it gives the same baseline hazard function, using the same number of adjustable parameters, as does the commonly used two-stage clonal expansion (TSCE) model, so it is automatically applicable to the many data sets on baseline cancer that have been analyzed using the TSCE model. For perturbations of baseline rates due to radiation, the models differ. We argue from epidemiological and laboratory evidence, especially results for the atomic bomb survivors, that for radiation carcinogenesis the TSL model gives results at least as realistic as the TSCE or similar models, despite involving fewer adjustable parameters in many cases.