TAM-F - Special Session: Translational Approaches to Improve Health Effects Knowledge in Support of Radiation Protection Guidance Orange C 08:30 - 12:00
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| Chair(s): Isaf Al-Nabulsi, Daniela Stricklin
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TAM-F.1
08:30 The Framework for an Adverse Outcome Pathway for Radiation Carcinogenesis DL Stricklin*, DOE
Abstract: An adverse outcome pathway (AOP) describes the step-wise process by which an exposure leads to a deleterious health effect. AOP provides a structure for integrating biological observations associated with adverse events into a construct relevant to risk assessment, and therefore, may also help guide research needed to characterize key processes in the AOP. Since an exposure can lead to a wide array of biological responses, the AOP can help focus attention on the specific key events that lead to adverse health outcomes out of the vast number of possible biological observations. The structure provide by the AOP approach can help sort the myriad system responses to low dose radiation to focus on those that dictate overall outcome. As an initial step in establishing an AOP for low dose radiation, we have proposed a framework for the pathway of radiation in general to carcinogenesis. The AOP can involve multiple molecular initiating events (MIEs) and key events (KEs) that lead to an overall observation of cancer. For radiation, multiple MIEs and KEs are proposed, each of which has a dose and dose rate-dependence. Establishing the dose and dose-rate function of each of these processes may provide the foundation for developing a computational model to describe the differences in radiation risks from different types of radiation exposure. This talk will describe the proposed framework for radiation carcinogenesis and how this framework can be used to integrate radiobiological data, to guide future research, and to develop a computational model that describes dose and dose-rate differences in radiation risk that can be validated through molecular epidemiological studies.
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TAM-F.2
09:00 Review of Modern Molecular and Cellular Low Dose Radiation Literature Reveals Need for Paradigm Shifts in Radiation Biology S Tharmalingam*, Northern Ontario School of Medicine
; S Sreetharan, McMaster University; AL Brooks, Washington State University (retired); DR Boreham, Northern Ontario School of Medicine
Abstract: The linear no-threshold (LNT) model currently utilized for radiation protection is based on several outdated paradigms that do not reflect the biology at the low dose range. The overall aim of this study was to identify molecular paradigms that better represent the current data in low dose radiation (LDR) biology. This was achieved by performing a systematic literature review of LDR studies that have been published from 2012 – 2018. Using NCBI PubMed, we reviewed 320 LDR publications according to the following criteria: inclusion of mammalian studies with low LET (linear energy transfer) LDR (<500 mGy) exposures, while excluding low dose radiation therapy and occupational/environmental studies. Summary of molecular signaling mechanisms revealed that LDR exposure promotes DNA repair mechanisms, activates cell cycle arrest to allow repair machinery to fix the damaged macromolecules, induces apoptosis when damage is unrepairable, and increases expression of transcription factors and antioxidant enzymes. Overall, these findings demonstrate that LDR enhances global molecular defense systems. This presentation will discuss ten LDR paradigms that summarize the key findings from this systematic literature review. In addition, possible methods of integrating these mechanistic data with epidemiology modeling for establishing radiation protection limits will also be discussed.
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TAM-F.3
09:30 Exploring the Adverse Outcome Pathway Framework in Radiation Risk Assessment: A Case Example of Radon-Induced Lung Carcinogenesis V Chauhan*, Health Canada
Abstract: In 2012, the Organisation for Economic Cooperation and Development formally launched the Adverse Outcome Pathway (AOP) framework. It is increasingly being used in chemical risk assessment to support regulation policies; however, it is not used in the radiation field. It is a useful programme that has potential for predictive utility in identifying early endpoints linked to adverse effects. By using the weight of correlative evidence, a minimal set of measurable key events can be identified that link a molecular initiating event to an adverse outcome at an individual level. Each AOP is comprised of four main components 1) a Molecular Initiating Event (MIE), 2) Key Events (KEs), 3) Key Event Relationships (KERs), and 4) an Adverse Outcome (AO). AOPs are envisioned to provide high value in reducing animal testing by defining alternative in vitro measures that can be linked to adverse outcomes. They have the capability to strategically identify knowledge gaps and priority areas for future research based on relevance to risk assessment. Here, we illustrate the utility of the AOP concept through a case example in the field of ionizing radiation. Radon is well-established as a carcinogen based on extensive epidemiological studies of occupational and residential exposures. While evidence suggests that environmental and indoor radon exposure constitutes a significant public health problem, the mechanism of lung cancer development from exposure to radon gas is unclear. By using the AOP framework, a sequential chain of causally linked events from the MIE, to several measurable KEs and KERs related to DNA damage response were identified. The AOP framework provided an effective means to organize the scientific knowledge surrounding the KEs and identified those with quantifiable dose-response relationships. The majority of the data used to support this AOP were derived from published animal and in vitro based studies. This case study is an example of how the AOP methodology can be applied to sources of ionizing radiation.
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TAM-F.4
10:00 Radiation Effects on Neurogenesis: A Mechanistic Modeling Approach E Cacao*, University of Nevada Las Vegas
; FA Cucinotta, University of Nevada Las Vegas
Abstract: High doses of ionizing radiation can cause damage in living cells, and there is uncertainty about health effects at very low doses. Understanding the mechanisms by which radiation affects biological systems is challenging because as the number of components and interactions in a biological network increases, it becomes difficult to maintain intuitive understanding of the overall system behavior, especially when feedback is involved. By developing and analyzing mathematical models that simulate the behavior of a biological network, investigations of dynamic systems can be carried out and model components can be used to predict system behavior under certain conditions. Mathematical models provide effective tools that contribute valuable insights to the role of radiation-induced changes in producing functional deficits in the human body and are also useful for extrapolation to other conditions that are often constrained in experiments. For instance, we have developed predictive models to study radiation-induced changes to neurogenesis using a system of nonlinear ordinary differential equations (ODEs) to represent age, time after exposure and dose-dependent changes to several cell populations involved in neurogenesis as reported in experiments utilizing rodent models. With these models, we obtained a description of the age-related dynamics of hippocampal neurogenesis and the effects of a variety of radiation [1] in altering neurogenesis of a diverse strain of rodent models [2], and make predictions of threshold doses where recovery fails for given radiation types and sub-lethal damage repair for various rat strains. In conclusion, mathematical models and simulations provide description of biological systems and recapitulate and predict its behavior under given conditions. These insights can serve as valuable guides to experimental design and to recognize radiation health risks needed to develop protection standards and policy.
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TAM-F.5
11:00 Integrating Molecular Biology And Radioepidemiology For Biologically-Based Risk Estimation With Mechanistic Models Of Carcinogenesis JC Kaiser*, Helmholtz Zentrum Muenchen
Abstract: Statistical association between exposure and disease is convincingly revealed with state-of-the-art risk models of radioepidemiology. However, limitations of the epidemiological approach to explore health risks especially at low doses appear obvious. Statistical fluctuations due to low case numbers dominate the uncertainties of risk estimates. On the other hand, growing insight into pathogenic molecular processes has been produced with advanced omics technologies. This knowledge can be harnessed to explain obervational data with process-oriented disease models. Mechanistic models, informed by adequate bioassays in addition to classical covariables, can characterize health risks with improved accuracy. The efficiency of such models is demonstrated in two recent examples. For papillary thyroid cancer (PTC) after the Chernobyl accident overexpression of the CLIP2 gene has been proposed as a molecular radiation marker. With a mechanistic model PTC development is described as a sequence of rate-limiting events in two distinct pathways of CLIP2-associated and multistage carcinogenesis. Radiation risk is calculated with two different approaches from marker measurements and from PTC incidence in the Ukrainian-American cohort [1]. In lung adenocarcinoma (LADC) KRAS mutations are associated with smoking but little is known on radiation-oncogene associations. Examination of genomic signatures for environmental exposures in the large Campbell dataset revealed two main molecular pathways with characteristic driver mutations: one unique to transmembrane receptor-mutant (Rmut) patients displaying radiation signatures, and one dominated by submembrane transducer-mutant (Tmut) patients carrying a smoking signature. The design of a mechanistic risk model for LADC incidence in Japanese a-bomb survivors accounts for pathways Tmut and Rmut. In contrast to previous studies the model suggests that smokers possess the same radiation risk as never smokers [2].
[1] Kaiser et al. (2016) Integration of a radiation biomarker into modeling of thyroid carcinogenesis and post-Chernobyl risk assessment. Carcinogenesis 37(12)
[2] Castelletti et al. (2019) Risk of lung adenocarcinoma from smoking and radiation
arises in distinct molecular pathways. Carcinogenesis, accepted
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TAM-F.0
11:30 Panel Discussion
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