1. health and fitness
2. disease
3. cancer

NCRP SC 1-21
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NOT TO BE DISSEMINATED OR REFERENCED
NCRP DRAFT SC 1-21 COMMENTARY
(Council and Board Draft) (3-31-15)
HEALTH EFFECTS OF LOW DOSES OF
RADIATION: INTEGRATING RADIATION
BIOLOGY AND EPIDEMIOLOGY
March 31, 2015
Note: Copyright permission is being sought for the figures and tables requiring such permission prior to
their use in the final NCRP publication.
National Council on Radiation Protection and Measurements
7910 Woodmont Avenue, Suite 400, Bethesda, Maryland 20814
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Preface
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In order to gain a greater understanding of the biological interactions and health effects of low
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doses of ionizing radiation, the National Council on Radiation Protection and Measurements
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(NCRP) has increased its activities on this subject in recent years. The NCRP 2008 Annual Meeting
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was held on the subject of Low Dose and Low Dose-Rate Radiation Effects and Models (NCRP,
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2009a). In December 2008 NCRP held a workshop that involved 30 participants with expertise in the
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effects of low doses of radiation. After the workshop summary was completed,1 a decision was made
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to convene a panel of experts to provide advice on a new NCRP Commentary related to critical
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issues and research needs for gaining a better understanding of effects of low doses of radiation. The
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advisory panel met in August 2010, and NCRP began the preparation of a Commentary in 2012. The
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focus of the Commentary is on integration of results of basic science studies, including biomarkers
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and bioindicators of cancer and other diseases, with epidemiologic studies on health effects of low
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doses of radiation. The Committee members have expertise and experience in the following areas
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related to low doses of ionizing radiation: epidemiology, radiation biology, radiation oncology,
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biostatistics, health physics and dosimetry.
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This Commentary was prepared by Scientific Committee 1-21 on Multiplatform National
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Program for Providing Guidance on Integrating Basic Science and Epidemiological Studies on Low-
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Dose Radiation Biological and Health Effects.
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Serving on Scientific Committee 1-21 were:
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Sally A. Amundson, Co-Chair
Columbia University Medical Center
New York, New York
Jonine L. Bernstein, Co-Chair
Memorial Sloan-Kettering Cancer Center
New York, New York
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NCRP (2008). National Council on Radiation Protection and Measurements. Developing a Framework for
Incorporating New Information in the Radiation Sciences to Understanding Potential Health Risks for Low Doses of
Ionizing Radiation: Summary of NCRP Workshop held on December 1-2, 2008 (National Council on Radiation
Protection and Measurements, Bethesda, Maryland).
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Members
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John D. Boice, Jr.
Vanderbilt University
Nashville, Tennessee
Raymond A. Guilmette
Lovelace Respiratory Research
Institute
Albuquerque, New Mexico
Amy Kronenberg
Lawrence Berkeley National
Laboratory
Berkeley, California
Mark P. Little
National Cancer Institute
Bethesda, Maryland
William F. Morgan
Pacific Northwest National Laboratory
Richland, Washington
Jac A. Nickoloff
Colorado State University
Fort Collins, Colorado
Simon N. Powell
Memorial Sloan-Kettering Cancer Center
New York, New York
Daniel O. Stram
University of Southern California
Los Angeles, California
Consultant
R. Julian Preston
U.S. Environmental Protection Agency
Research Triangle Park, North Carolina
NCRP Secretariat
Marvin Rosenstein, Staff Consultant (2013 –)
Terry Pellmar, Staff Consultant (2012)
Cindy L. O’Brien, Managing Editor
Laura J. Atwell, Office Manager
David A. Smith, Executive Director (2014 –)
James R. Cassata, Executive Director (2012 – 2014)
The Council expresses appreciation to the Committee members for the time and effort
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devoted to the preparation of this Commentary. NCRP also gratefully acknowledges the financial
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support provided by the Centers for Disease Control and Prevention.
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John D. Boice, Jr.
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President
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Contents
Preface ............................................................................................................................................2
Executive Summary ......................................................................................................................6
1. Introduction ................................................................................................................................9
1.1 Low Doses and Low Dose Rates ......................................................................................11
1.2 Use of Radiation Biology Data to Reduce Uncertainty in Risk Estimates ........................12
2. General Approaches to Risk Assessment ..............................................................................19
2.1 Risk Assessment Process and Associated Uncertainties ...................................................19
2.2 Modeling Uncertainties: Dosimetric, Epidemiologic and Biologic ...................................20
2.3 Uncertainties Related to Transfer of Radiation Risk between Populations and
Interactions with Carcinogens ...........................................................................................22
2.4 Extrapolating Low-Dose Effects from In Vitro to In Vivo; Animal to Human ................23
3. Studies Integrating Biology and Epidemiology ....................................................................27
3.1 Epidemiologic Studies Focused on Cancer Outcomes .....................................................27
3.1.1 Chromosome-Aberration Studies ............................................................................27
3.1.2 Atomic-Bomb Survivors ..........................................................................................28
3.1.3 Pelvic Irradiation Studies .........................................................................................30
3.1.4 Occupational Studies ...............................................................................................31
3.1.5 Environmental Background Studies ........................................................................33
3.1.6 Transgenerational Studies ........................................................................................34
3.1.7 Gene and Radiation Interaction: The Women’s Environmental Cancer and
Radiation Epidemiology Study ................................................................................35
3.1.8 Retinoblastoma ........................................................................................................36
3.2 Epidemiologic Studies Focused on Noncancer Outcomes ...............................................37
4. Bioindicators from Radiation Biology Studies .....................................................................39
4.1 Deoxyribonucleic Acid Damage and Repair ....................................................................39
4.2 Cellular Signaling at Low Doses ......................................................................................40
4.3 Chromosome Alterations ..................................................................................................41
4.4 Mutations ..........................................................................................................................42
4.5 Radiation-Induced Genomic Instability ............................................................................46
4.6 Modulators of Response ...................................................................................................49
4.6.1 Adaptive Responses .................................................................................................49
4.6.2 Bystander Effects .....................................................................................................51
4.6.3 Genetic Susceptibility and Interactions with Radiation ...........................................52
4.6.4 Interactions with Environmental and Lifestyle Factors ...........................................53
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5. Recommendations for Closing the Gaps towards an Integrated Approach ......................55
5.1 Targeted Research to Identify Adverse Outcome Pathways and Bioindicators of
Response ...........................................................................................................................55
5.2 Biologically-Based Modeling ...........................................................................................57
5.2.1 Biologically-Based Dose-Response Approach ........................................................58
5.2.2 Systems Biology ......................................................................................................60
5.3 Registries of Medical-Imaging Exposures Linked to Outcomes and Biological
Materials ............................................................................................................................62
5.4 Facilities and Interdisciplinary Training ...........................................................................63
5.4.1 Facilities ....................................................................................................................63
5.4.2 Interdisciplinary Training ........................................................................................63
Glossary .......................................................................................................................................64
Symbols, Abbreviations and Acronyms ....................................................................................70
References ....................................................................................................................................71
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Executive Summary
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For decades, epidemiologic studies have assessed the health effects of radiation exposure from
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multiple sources: occupational, accidental, environmental and medical. These efforts have been most
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successful in evaluating radiation-related adverse effects (particularly cancer) at or above ~100 mGy
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(absorbed dose). Since there are greater uncertainties inherent in epidemiologic studies of exposed
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individuals at lower doses, it is generally agreed that the effects observed at 100 mGy and above are
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the more reliable than those observed at <100 mGy. Despite a history of over 70 y, the field of
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radiation risk estimation has not, or perhaps more correctly, has not been able to make much direct
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use of radiation biology data from laboratory animal, cellular and molecular studies. Rather, such
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data have been used extensively in a supportive role for the epidemiology-based risk estimates.
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The current state of knowledge on the effects of low-dose and low dose-rate radiation (defined
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for the purpose of this Commentary as a dose <100 mGy and a dose rate <5 mGy h–1) is incomplete
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and uncertain with respect to understanding the shape of the dose-response relationship and the level
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of risk at low doses. These deficiencies have long-term and profound consequences with regard to
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radiation protection guidance, compensation programs, and environmental contamination issues.
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There is a need to identify novel approaches to reduce these uncertainties.
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The focus of this Commentary is on identifying further means to integrate results of basic
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science studies in radiation biology, including biomarkers and bioindicators of cancer and other
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diseases, with epidemiologic studies on health effects of low doses of radiation. A biomarker is a
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biological phenotype (e.g., chromosome alteration, DNA adduct, gene expression change, specific
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metabolite) that can be used to indicate a response at the cell or tissue level to an exposure. A
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bioindicator is a cellular alteration that is on the pathway to the disease endpoint itself (a key event),
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such as a specific mutation in a target cell that is associated with tumor formation. Towards this end,
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the Commentary reviews general approaches to risk assessment (Section 2), existing epidemiologic
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studies that have incorporated biological endpoints (Section 3), and areas of radiation biology with
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potential relevance to low-dose risk (Section 4).
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Given major advances in our understanding of the etiology of diseases, host susceptibility, and
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the cellular processes affected by radiation, coupled with the rapid development of new
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technologies, there now is the opportunity to integrate information from multiple disciplines in risk
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assessment. For example, radiation biology data can be incorporated into the process of
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extrapolating from epidemiologic data at higher doses to predict responses at low doses (<100 mGy)
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and low dose rates (<5 mGy h–1). The key to successfully doing this is to identify and select
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appropriate endpoints that inform the process of extrapolation of risk from the higher doses to low
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doses. Such biological endpoints utilized for extrapolation purposes need to be predictive of the
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apical endpoint for which risk is being estimated (i.e., a bioindicator predictive specifically of
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radiation-related cancer or noncancer outcome). While some approaches have been suggested, a key
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problem is the lack of bioindicators (key events) that are specific for radiogenic disease. It is not yet
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feasible to assess the robustness of key events in the radiation risk assessment process because too
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little reliable information has been developed.
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Biologically-based dose-response models have been suggested as a way forward for low-dose
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risk estimation. However, developing such models requires a fuller understanding of the relevant
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biology than we currently have, as well as the collection of sufficient data for parameterization of the
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models. Awareness of such modeling needs should be integrated with experimental design, to ensure
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the collection of relevant data.
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Radiation-induced genomic instability is one example of a specific biological response that may
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have relevance to modeling of low-dose radiation risk. It is important to determine if specific types
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of genomic instability are capable of transforming a cell from a normal to a cancer cell, or furthering
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the progression of a premalignant cell towards a more aggressive cancer phenotype. Chromosome
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instability has been shown to persist for as long as a year, but there is little information concerning
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how cells pass the unstable phenotype through succeeding somatic cell generations.
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Another gap in our understanding of radiation risk is the role of individual variation in genetic
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susceptibility and interaction with radiation. Clinical studies over the years have identified genetic
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disorders and a few high penetrance alleles that convey increased risk of radiation-related cancer and
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other endpoints. Such alleles are often demonstrated in studies of second cancers, but the magnitude
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of effect is unknown and likely to vary. As each person is exposed to a different array of lifestyle
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and environmental factors in addition to any low-dose radiation exposure, the potential impact of
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interactions between these factors, genetic factors, and radiation exposure must also be considered.
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The nascent field of systems biology, in which global information from multiple levels (e.g.,
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transcriptomic, proteomic, metabolomic) is integrated to develop a clearer picture of a process, for
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example, response to radiation, may also prove informative for risk prediction. A systems level
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understanding of radiation response could lead to a more comprehensive understanding of the
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mechanisms involved, and the interactions between mechanisms, thus supporting more robust
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biologically-based models of risk.
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The following recommendations are made for closing the gaps towards an integrated approach of
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basic science studies in radiation biology with epidemiologic studies on health effects of low doses
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of radiation:
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focus on key events (i.e., bioindicators) and modifying factors in adverse outcomes of
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ionizing radiation exposure [as outlined in the parallelogram approach presented in
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Figure 1.1 (for mechanistic studies) and Figure 1.3 (specific for bioindicators)], rather than
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simple biomarkers of exposure;
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develop biologically-based dose-response models to provide a path forward in low-dose
radiation risk assessment;
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assemble registries of patients with extensive exposure to current and emerging medical-
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imaging procedures (particularly those associated with higher patient doses) linked with
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biological specimens and outcome data, in order to provide for standardized research
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planning and data collection for prospective risk assessment;
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maintain and develop the specialized facilities required for studies conducted at low doses
and low dose rates; and
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promote and expand interdisciplinary training and integrated cross-professional research
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programs devoted to understanding and quantifying radiation health effects at low doses in
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order to meet the growing needs of radiation science for the next generation of radiation
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researchers.
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1. Introduction
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We live in an era of increased public exposure to ionizing radiation, from the proliferation of its
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use in diagnostic and interventional medical procedures to its nonmedical use for security screening.
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With an increasing need for nuclear power, we also see continuing concern surrounding the public
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exposures that may result from accidents, as dramatically illustrated by the recent Fukushima-
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Daiichi disaster. In such an environment, meaningful risk assessment for low-dose and low dose-rate
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ionizing radiation exposures is more critical than ever for the improvement of radiation protection,
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environmental protection and remediation, and compensation. A fundamental issue in deriving risks
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at low levels of dose and dose rate is the shape of the dose response for endpoints relevant to human
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health outcomes and possible modifications by dose rate. This Commentary outlines a framework to
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integrate data from biological studies with epidemiologic data to illuminate this issue. This
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framework is broadly based on a parallelogram approach for mechanistic studies shown in
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Figure 1.1 and made more specific by the use of bioindicators of response described in Figure 1.3
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(Section 1.2). The bioindicators proposed are developed from an adverse outcome pathway/key
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events approach initially described in Section 1.2 (Preston, 2015).
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While epidemiologic studies remain the gold standard for determining risk and setting exposure
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limits, a number of practical considerations limit the usefulness of such studies in the low-dose
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range. In recent years, however, considerable progress has been made in low-dose radiation biology
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at the cellular and molecular levels. Ideally, such work should contribute to our understanding of risk
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and should help shape radiation protection guidance. In order to effectively integrate the results of
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radiobiology studies into epidemiology and risk analysis, it is necessary to make multiple
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comparisons, from cell and molecular studies to in vivo studies, and from animal to human on both
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the cell and molecular level, and on the in vivo level (Figure 1.1).
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Conceptually, this approach has proven useful particularly at the cellular level (e.g., Brewen
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et al., 1973). The intercomparisons illustrated in Figure 1.1 are critical for developing a robust
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analysis of the risk of human exposure to low-dose radiation. Studies with biological models that
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more closely approximate tissue architecture are of particular use as a bridge between simple cellular
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studies in vitro and organismal studies (Bissell et al., 1997). Application of extrapolation to the
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Fig. 1.1. A parallelogram approach for utilizing laboratory animal and in vitro cell data to
estimate human cancer and noncancer risks for ionizing radiation.
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assessment of human risk has been reviewed in NCRP Report No. 150 (NCRP, 2005), which
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concluded that an understanding of the molecular mechanisms of radiation carcinogenesis in
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different animal species was needed for the most robust extrapolation. Although species-specific
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differences in critical biological processes need to be kept in mind, a focus on conservation of
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mechanism will be useful when making intercomparisons to inform risk in humans.
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The major goal of this Commentary is to develop a framework to support the useful
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incorporation of biological endpoints into epidemiologic studies in order to enhance our
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understanding of the risks of low-dose and low dose-rate ionizing radiation exposures.
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Towards this end, this Commentary reviews general approaches to risk assessment, areas of
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radiation biology with potential relevance to low-dose risk, and existing epidemiologic studies that
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have incorporated biological endpoints. The Commentary suggests that focusing on key events and
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modifying factors for adverse outcomes pathways from ionizing radiation exposure, rather than
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simple biomarkers of exposure, in concert with the development of biologically-based dose-response
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models will provide the best path forward in low-dose radiation risk assessment. Finally, we identify
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the major gaps in knowledge that need to be addressed to enable the integration of basic biology and
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epidemiologic studies for the advancement of low-dose ionizing radiation risk assessment and
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radiation protection.
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1.1 Low Doses and Low Dose Rates
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Studies have been conducted over a broad range of radiation doses and dose rates referred to as
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low, and what constitutes a low dose and a low dose rate is often due to the context in which dose
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and dose rate are being considered (e.g., radiobiological research, epidemiologic studies, medical
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exposure, environmental exposure). For the purposes of this Commentary, the context is the levels
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experienced by individuals or populations to various sources of ionizing radiation, and single values
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of low dose and low dose rate are useful for general discussion.
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In assessing human health effects (in particular cancer), ICRP (1991) applied a DDREF of two to
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obtain risk estimates for absorbed doses below 200 mGy and from higher doses when the dose rate is
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<100 mGy h–1. The U.K. National Radiological Protection Board (NRPB) concluded that for the
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purposes of applying a dose-rate effectiveness factor in radiation protection, dose rates of
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<0.1 mGy min–1 (when averaged over ~1 h) and acute doses of <100 mGy may be regarded as low
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(Muirhead et al., 1993). This dose rate is equivalent to 6 mGy h–1. Wakeford and Tawn (2010) more
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recently agreed with the NRPB guidance and suggested that a low dose is <100 mGy delivered
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acutely and a low dose rate is <5 mGy h–1. The rationale for selecting such dose rates appears to be
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driven by the desire to link them to what is believed to be the linear portion of a dose-response
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relationship. A dose of <100 mGy and a dose rate of <5 mGy h–1 are used for low doses and low
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dose rates in this Commentary.
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It is recognized that these specified values for low dose and low dose rate are most relevant to
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exposure scenarios related to occupations in which sources of radiation and radioactive material are
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used. In comparison, ubiquitous background dose rates tend to occur at levels of ~1 µGy h–1, which
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is a factor of 5,000 less than the value of 5 mGy h–1 used here. Additionally, regulatory limits for
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doses to members of the public from environmental radiation sources are typically set at levels that
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equate to a fraction of the ubiquitous background dose rate [e.g., the NRC limit for members of the
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public from nuclear reactor sites is 1 mSv y–1 (total effective dose equivalent) or 0.11 µSv h–1].
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These dose rates assume chronic exposure to relatively constant radiation fields. In any case, it is
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difficult to discern whether differences in dose rate on the order of 103 to 104 can be understood in
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terms of mechanisms of radiation action, given our current state of knowledge.
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1.2 Use of Radiation Biology Data to Reduce the Uncertainty in Risk Estimates
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There is an extensive literature on the health effects of radiation exposures (occupational,
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accidental, environmental and medical) in human populations. Many of these involve relatively high
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acute doses, although associations at lower doses (around a few tens of milligray) and lower dose
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rates have been reported. Given the greater uncertainties inherent in epidemiologic studies of
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exposed individuals at low doses than at higher doses, it is generally agreed that effects observed at
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~100 mGy and above are the more reliable than those observed at < 100 mGy. In this regard, despite
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a history of over 70 y, the field of risk estimation has not, or perhaps more correctly, has not been
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able to make much direct use of radiation biology data from laboratory animal, cellular and
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molecular studies. Rather such data have been used in a more supportive role for the epidemiology-
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based risk estimates.
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Risk estimates for radiation-related cancer and noncancer effects at low doses (chronic and
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acute) have relied almost exclusively on epidemiologic data. Exceptions are for the estimation of
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DDREF and relative biological effectiveness of high-LET radiation that have relied quite
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significantly on data from biological studies, although also supported by epidemiologic data. Given
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the major advances in our understanding of the etiology of chronic diseases (especially cancer,
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although with an increasing emphasis also on certain noncancer diseases) and of the fundamental
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cellular processes affected by radiation (both targeted and nontargeted), it is prudent to consider how
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this information can be incorporated into the risk assessment process. In particular, it is critical that
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the use of data from biological studies be incorporated into the process for extrapolating from
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epidemiologic data at medium/high doses to predict responses at low doses (<100 mGy) and low
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dose rates (<5 mGy h–1) (Section 1.1). Given these limitations, two issues to be addressed herein are:
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how can informative endpoints be selected that can enhance the process of extrapolation of
“risk” from high/medium to low doses; and
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how can data from laboratory animal, cellular and molecular studies best be integrated with
epidemiologic studies to better predict risk.
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Assistance with the task of estimating the health effects of radiation exposures at low doses and
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low dose rates can be found in the approach being developed for estimating risks from exposures to
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chemicals (e.g., Boobis et al., 2006; EPA, 2005; Julien et al., 2009; Preston, 2015; Seed et al., 2005;
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Thomas et al., 2012; Vinken, 2013). The use of mechanistic and other biological data is necessary
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for the risk assessment process for chemicals because of the lack of epidemiologic data for almost all
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chemicals (environmental or occupational exposure). The process is built upon a framework of
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adverse outcome pathways and key events for the development of adverse health effects following
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exposure to a chemical or chemical mixture. An adverse outcome pathway for both cancer and
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noncancer endpoints is defined as a sequence of key events and processes starting with interaction of
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an agent with a cell, proceeding through operational and anatomical changes, and resulting in
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formation of an adverse outcome (EPA, 2005). It should be noted that this adverse outcome
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pathway/key event approach has not been used for an entire risk assessment process for any
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chemical. A worked example has recently been developed by Adeleye et al. (2014).
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For ionizing radiation, for example, cancer is considered to be produced by a DNA-reactive
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mode of action. A key event is defined as an empirically observable precursor step that is itself a
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necessary element of the mode of action or is a biologically based marker for such an element (EPA,
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2005). Preston (2015) has recently presented a proposed adverse outcome pathway and associated
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key events for low doses and low dose rates of ionizing radiation. It is more difficult to utilize such a
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general approach for noncancer effects given the wide range of mechanisms whereby adverse
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outcomes (e.g., neurological, reproductive, cardiovascular) can be produced. However, an initial
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approach is described by Seed et al. (2005) for a range of chemical classes and noncancer endpoints.
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Preston and Williams (2005) developed a set of key events by which a DNA-reactive chemical
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can produce a metastatic cancer. A modified version of this scheme can be considered as being
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applicable to ionizing radiation. As a starting point for the development of an adverse outcome
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pathway for radiation-related cancer, an initial key event is considered to be a DNA double-strand
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break, if in turn it can be converted into a mutation. The set of key events in Figure 1.2 is a linear
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progression from the initial key event to a malignant tumor (i.e., an adverse outcome pathway)
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(Preston, 2015). It is possible to expand this linear approach to include radiation effects on
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preneoplastic lesions that may have developed prior to exposure. Another approach considers
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radiation effects on any of the critical processes that are considered hallmarks of cancer development
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(Boss et al., 2014; Hanahan and Weinberg, 2011).
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For the purpose of maximizing the use of biological data in the dose-response assessment for
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adverse health outcomes, it is informative to distinguish between biomarkers and bioindicators. A
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biomarker is a biological phenotype (e.g., chromosome alteration, DNA adduct, gene expression
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change, specific metabolite) that can be used to indicate a response to an exposure at the cell or
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tissue level. In this regard, it is generally a measure of the potential for development of an adverse
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outcome such as cancer (e.g., a predictor of exposure level). A bioindicator is defined as a cellular
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alteration that is on the pathway to the disease endpoint itself, such as a specific mutation in a target
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cell that is associated with tumor formation. Thus, a bioindicator can be perceived as informing on
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Adverse Outcome
Pathway Steps
Interaction with Radiation
Energy Deposition
Macro-Molecular Alterations
Key Events

Exposure of Target
Tissue

Single, double and multiple DNA
breaks
Base Modifications
Protein Oxidation
Free Radical Formation
Chromosome Alterations
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Gene Activation
Protein Production
Altered Signaling
Cell killing and Tissue
Disruption
Organ Responses
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Altered Physiology
Disrupted Homeostasis
Altered Tissue
Development/Function
Adverse Outcome
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
Impaired Development
Impaired Reproduction
Cancer and Noncancer
Effects
Cellular Responses
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Fig. 1.2. Schematic representation of an adverse outcome pathway for ionizing radiation-related
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cancer and noncancer diseases showing each step along the proposed pathway and the associated key
372
events (Preston, 2015).
373
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374
the shape of dose-response curve for the disease outcome or on cancer frequency itself, and
375
therefore, is equivalent to a key event. In a logical way, the more proximate a key event/bioindicator
376
is to the apical endpoint (e.g., cancer), the more predictive of this outcome it will be. Thus, for the
377
purposes of risk assessment, it is most pertinent to design research studies to assess bioindicators of
378
response rather than simply biomarkers of an exposure. In addition, it is essential to describe the
379
predicted human relevance of key events/bioindicators developed in nonhuman assay systems
380
(Boobis et al., 2006). Figure 1.1 can be modified (Figure 1.3) to show how bioindicators can be used
381
as the data input for a typical parallelogram approach (Sobels, 1993). The types of informative
382
bioindicators for cancer risk assessment include: cancer-specific (target tissue) chromosome
383
alterations and gene mutations, target tissue cell killing and regenerative cell proliferation, and target
384
tissue inflammatory responses. Genovese et al. (2014) has provided data that suggest that “a subset
385
of genes that are mutated in patients with myeloid cancers is frequently mutated in apparently
386
healthy persons; these mutations may represent characteristic early events in the development of
387
hematologic cancers.” This type of DNA sequencing approach can be extended to investigate other
388
389
390
cancer types.
391
and/or modulating factors can influence the nature of the overall response for both cancer and
392
noncancer outcomes. Consideration of such events and factors is particularly important for
393
394
395
quantitative assessments of dose response.
396
host factors that can modulate the dose-response relationship of one or more key events, thus altering
397
the probability or magnitude of the adverse outcome. As such, some modulating factors might be
398
considered as “inevitable” (fixed) because all humans are subject to their influence; examples are
399
sex, age, and ethnicity. Other modulating factors are more aptly described as “optional”
400
(controllable): these include smoking, diet, and occupation. It is relatively straightforward to
401
incorporate the inevitable modulators into a risk assessment process but much more problematic to
402
adequately account for the optional ones without considering individual risk values. Laboratory
403
animal and cellular and molecular data might be developed that can account for the magnitude of the
404
influence of modulating factors on risk but currently this is very rarely done and frequently it is not
405
possible.
Key events provide the critical input for dose-response modeling, so-called associated events
A modulating factor can be considered as a biological feature, including control mechanisms or
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406
407
408
409
410
Fig. 1.3. Modified Figure 1.1, showing how bioindicators can be used as the data input for a
parallelogram approach (Sobels, 1993).
411
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412
413
An associative event is a biological process that is not itself a key event but is a reliable
414
biomarker for a key event(s). Thus, an associative event can be a biomarker for exposure or closely
415
linked to the mechanism of disease formation. Recent research has identified two novel classes of
416
events, epigenetic changes and nontargeted events [e.g., bystander effects (Section 4.6.2) and
417
genomic instability (Section 4.5)] that are sometimes considered as associative events. However, at
418
present it is not clear whether epigenetic events are associative, but based on the evidence to date it
419
appears unlikely that nontargeted events are associative, since not all radiation exposures are
420
associated with instability in human subjects (Tawn et al., 2000; 2011; 2015; Whitehouse and Tawn,
421
422
423
2001).
424
health effects, it is necessary to utilize or develop some form of a biologically-based dose-response
425
model. Several approaches that have been described suggest the viability of this type of modeling
426
(Eidemuller et al., 2011; Kaiser et al., 2014; Little, 2010; Luebeck et al., 2013; Moolgavkar and
427
Knudson, 1981; Moolgavkar and Luebeck, 1992; Moolgavkar and Venzon, 1979; Shuryak et al.,
428
2010), although perhaps limited in their value to reliably predict cancer risk because they do not
429
incorporate any biological data other than the generalized mutational evidence supporting the
430
multistage model. Some recent approaches have perhaps provided a greater reliance on auxiliary
431
biological data (Heidenreich and Rosemann, 2012; Heidenreich et al., 2013). The further
432
development of truly informative models along these lines has to be a research priority given the
433
importance of accurate estimates of radiation-related health effects at low doses and low dose rates.
To fully use such key event/bioindicator data to inform dose-response assessment for adverse
434
435
The discussion herein describes an approach for integrating data from radiation biology and
436
epidemiology in the dose-response assessment phase of risk assessment for radiation-related cancer
437
and noncancer effects (Morgan and Bair, 2013). It proposes the development and application of
438
mode of action/key events/bioindicators as providing a link between epidemiology and radiation
439
biology data at the animal, cell and molecular levels.
440
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441
442
443
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2. General Approaches to Risk Assessment
2.1 Risk Assessment Process and Associated Uncertainties
444
445
Risk estimates for radiation-related cancer mortality and incidence and those for noncancer
446
diseases can play a critical role in the development of dose limits used for radiation protection
447
purposes. A recent example of the use of risk estimates for cancer is provided by the approach for
448
establishing the standards used by NASA for limits on days in space for astronauts (Cucinotta et al.,
449
2013; NA/NRC, 2012). The estimates of radiation risk currently used by international and national
450
bodies such as the International Commission on Radiological Protection (ICRP), the United Nations
451
Scientific Committee on the Effects of Atomic Radiation (UNSCEAR) , the National
452
Academies/National Research Council (NA/NRC), National Council on Radiation Protection and
453
Measurements (NCRP), and the U.S. Environmental Protection Agency (EPA) rely heavily upon
454
epidemiologic data on cancer and noncancer diseases in a variety of exposed populations
455
(particularly atomic-bomb survivors and people with estimated or known doses from occupational,
456
medical and environmental exposures). The general approach to risk assessment is to extrapolate
457
from health effects measured at medium to high doses to predict those expected at low doses.
458
Currently, for cancer this extrapolation utilizes a linear-nonthreshold model (ICRP, 2007; NA/NRC,
459
2006; NCRP, 2001). The estimates obtained in this way are corrected for a DDREF to allow for
460
calculated reductions in effect from high to low dose and from high to low dose rate. There are
461
challenges to this approach largely because of the reliance on the linear-nonthreshold model and the
462
uncertainty in a chosen value for DDREF. For noncancer diseases, the approach employed by ICRP
463
(2007; 2012) is to calculate a dose for a practical threshold level of disease response (<1 % chance of
464
occurrence) (ICRP, 2012) based on effects at medium to high doses. Because of the reliance on
465
epidemiologic data, it is extremely important to utilize the most recent and relevant data for
466
developing risk estimates and to take advantage of enhancements in computational modeling
467
approaches. However, at the same time, it is essential to appreciate that any approach used will
468
inevitably have associated uncertainties that should be considered when applying risk estimates for
469
protection purposes. NCRP Report No. 171 (NCRP, 2012) considers the types and magnitude of the
470
several uncertainties that are a component of the risk assessment process for cancer, noncancer
471
effects and heritable effects following radiation exposure. Consideration of this issue is timely
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472
because new data have recently become available for cancer incidence, noncancer occurrence
473
(particularly for cataracts and cardiovascular disease) and heritable effects (reviewed in ICRP, 2007;
474
2012; UNSCEAR, 2001). NCRP (2012) builds upon the analyses in other recent NCRP reports
475
(NCRP, 2007; 2009b; 2009c) of the sources and magnitude of uncertainties in the estimation of
476
organ doses from exposure to external and internal sources of radiation. Given the heavy reliance on
477
epidemiologic studies, a significant component of uncertainty associated with these risk estimates
478
will be accounted for by comprehensive assessments of uncertainties in these epidemiological
479
studies. It is of note that little direct use has been made of the extensive data from ~70 y of
480
radiobiological study in estimating radiation-related health risks (ICRP, 2007; NA/NRC, 2006;
481
NCRP, 2005); the data have generally provided support for the epidemiology-based approach
482
currently used. The most significant uses of laboratory animal and cellular data have been for
483
developing a value for DDREF and values for quality factors and radiation weighting factors used in
484
radiation protection. However, even with the use of these experimental data, uncertainties associated
485
with these factors are large, and contribute significantly to the overall uncertainty in risk estimates
486
(Cucinotta et al., 2013). Sections 2.2 through 2.4 consider the types of uncertainty, their magnitude
487
488
489
490
491
and potential approaches for reducing these uncertainties.
492
(2012), the precision of epidemiologic risk estimates is driven primarily by the:
2.2 Modeling Uncertainties: Dosimetric, Epidemiologic and Biologic
Epidemiology is by its nature observational rather than experimental. As discussed in NCRP
493
494

range and distribution of doses;
495

sample size;
496

duration of and ages at observation;
497

baseline frequencies of the health endpoint of interest;
498

“true” strength of the radiation-disease association;
499

various types of dose uncertainties; and
500

degree of accuracy of ascertainment (or, conversely, misclassification or underascertainment)
501
of the disease of interest.
502
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503
However, the degree of systematic errors (i.e., bias) of the epidemiologic risk estimates depends on a
504
different set of factors, such as:
505
506

507
systematic personal reporting errors (with respect to dose-related items, disease experience,
or risk-factor covariates);
508

insufficient statistical adjustment for other risk factors;
509

dose-related inequalities in disease ascertainment;
510

errors in assigning average values for shared dosimetry factors (e.g., an incorrect estimate of
511
the magnitude of a radioactive release);
512

failure to correct for individual measurement errors; or
513

failure to adjust for the effects of disease-related covariates.
514
515
The importance of these sources of uncertainty varies from study to study.
516
517
Uncertainties and biases resulting from design and methodologic issues in epidemiologic studies
518
have been considered by a number of scientific committees (NCRP, 2012; UNSCEAR, 2008a). Of
519
special importance are biases, that is, any process at any stage in the conduct of the study that tends
520
to produce results or conclusions that differ systematically from the truth (Sackett, 1979), and
521
include:
522
523

follow-up bias:
524

ascertainment bias:
525

recall bias; and
526

confounding.
527
528
Statistical methods to deal with the complexities of measurement and ascertainment error corrections
529
are still evolving (Carroll et al., 2006). A new generation of epidemiologic studies that incorporate
530
biologic measures for estimating radiation risk will help sharpen assessments and correct for some
531
uncertainties and biases.
532
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533
2.3 Uncertainties Related to Transfer of Radiation Risk between Populations
534
535
536
and Interactions with Carcinogens
Despite the relatively large number of data on radiation risk, the question of how to transfer risk
537
estimates derived from one population to a different population remains unanswered. It should not
538
be surprising that the relationship between radiation-related and underlying risk in different
539
populations is not consistent for different cancer sites. The available data suggest that there is no
540
541
542
simple solution to the problem (Muirhead et al., 1993; UNSCEAR, 1994),
543
transfer of risk between populations, or indeed the use of some sort of hybrid approach, such as that
544
employed by Little et al. (1999a) and Muirhead and Darby (1987), may be appropriate. The principal
545
scientific committees implicitly assume that risk transfer is intermediate between additive and
546
multiplicative (ICRP, 2007; NA/NRC, 2006). The subject is discussed in NCRP (2012), highlighting
547
the complexity of the evidence, so that for example for breast cancer a more nearly additive transfer
548
549
550
was suggested, whereas for liver cancer relative risk transfer was more likely to be valid.
551
exposed group to another, it is necessary to also consider the influence of factors other than radiation
552
on the cancer rates. Thus, much of environmental, nutritional and occupational cancer epidemiology
553
is concerned with identifying risk factors that might account for some part of the variation of site-
554
specific underlying cancer rates among populations. While there has been much progress, the
555
problem is complex and there is only limited information on the interaction between radiation dose
556
and lifestyle, or constitutional factors, in terms of cancer risk. Thus it is likely that, for the
557
foreseeable future, the most useful information relevant to transferring radiation-related risk
558
coefficients from one population to another will come from multinational comparisons of site-
559
specific radiation-related risk, rather than from investigations of underlying cancer risk factors and
560
their interactions with radiation dose. There has been some work comparing aggregate radiation-
561
associated cancer mortality risks in humans and in various animals (Carnes et al., 2003), suggesting
562
that a simple scaling of radiation-related mortality may be appropriate. However, without more
563
detailed investigation taking account of the different spectrum of tumors in humans and in these
564
experimental systems, and their distinct etiology, this approach may not be very productive.
The available information suggests that, depending on circumstances, relative or absolute
When considering the issue of how to transfer site-specific cancer rates from one population or
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565
566
NOT TO BE DISSEMINATED OR REFERENCED
Sometimes a meta-analysis, based on published findings from several studies, may be performed.
567
Where feasible, as noted below, it is preferable to combine the original data and analyse them using
568
a common format, in other words to perform a pooled analysis. Pooled analyses have been
569
conducted of various cohorts of radiation workers (Cardis et al., 2007) to assess:
570
571

572
effects of radon daughter exposure in relation to lung cancer risk in underground miners
(UNSCEAR, 2000);
573

thyroid cancer risk in various (mainly medically exposed) cohorts (Ron et al., 1995); and
574

breast cancer risk in various populations (Howe and McLaughlin, 1996; Little and Boice,
575
1999; Preston et al., 2002).
576
577
Less commonly, analyses combining cohort and case-control data, for example in relation to
578
579
580
581
582
leukaemia risk (Little et al., 1999b), have been conducted.
583
events that occur in response to acute moderate to high doses of radiation, including activation of
584
DNA damage checkpoints, DNA repair pathways, mutagenesis, genomic instability, cell
585
transformation, and the regulation of cell death pathways (Hall and Giaccia, 2011). The radiation-
586
induced genetic changes observed in cells in vitro are similar to those observed in radiation-induced
587
tumors in animal models. As a result, studies of model genetic loci are frequently extrapolated to
588
cancer relevant genetic changes seen in animals and humans. Such genetic events include oncogene
589
activation via chromosome translocation or gene amplification and inactivation of tumor suppressor
590
genes via point mutations or larger scale changes such as deletions, insertions and inversions.
2.4 Extrapolating Low-Dose Effects from In Vitro to In Vivo; Animal to Human
Several decades of laboratory research have given us a detailed view of molecular and cellular
591
592
The complexities of extrapolation between animal models and humans has been reviewed in
593
depth in NCRP Report No. 150 (NCRP, 2005), which concluded that studies of life shortening in
594
animals may provide reliable estimates of radiation risks to humans. The strength of life shortening
595
data is that it integrates more completely the detrimental effects of radiation in the entire animal,
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596
rather than focusing on just one endpoint such as cancer induction. NCRP (2005) further concluded
597
that risk extrapolation from animal models to humans will:
598
599

600
require robust, well annotated databases that must be widely disseminated to investigators
interested in radiation risk estimation;
601

include experimental data from animal studies and data from human epidemiologic studies;
602

further develop our understanding of molecular mechanisms of cancer and other disease
603
604
605
induction; and

account for effects of age and genetic heterogeneity (or lack thereof) of animal models versus
humans.
606
607
While the benefits of life shortening studies are clear, the most recent study that validated risk
608
estimates for humans using life shortening measures in mice and dogs demonstrated that there was
609
little effect on life span with acute radiation doses <1 Gy (Carnes et al., 2003). Earlier studies of life
610
shortening in mice indicated that acute radiation doses reduced life span by ~28 d Gy–1 (Grahn,
611
1960). However, with protracted, low dose-rate exposures (<0.1 Gy d–1), life shortening was greatly
612
reduced to ~4 d Gy–1 (Grahn and Sacher, 1968). Therefore, despite the advantages of life shortening
613
as an integrated endpoint of the detrimental effects of radiation, this approach does not appear to
614
have sufficient sensitivity to detect low-dose or low dose-rate effects on animal life span, and even
615
with strong tools to extrapolate risk from animal models to humans (Carnes et al., 2003) this lack of
616
sensitivity poses problems when attempting to use these animal data to extrapolate human risk to
617
618
619
low-dose or low dose-rate radiation exposures.
620
many challenges due to significant environmental and genetic differences among cells in culture
621
versus cells in animals or humans. For example, long-term studies of cells in culture have
622
historically focused on cancer cell lines, which are not only different from the normal cells from
623
which they were derived, but are often genetically unstable. Studies of normal (primary) cells in
624
culture are typically limited to ~50 cell divisions, and immortalization by expression of telomerase
625
reverse transcriptase (TERT, hTERT in humans) to maintain telomeres, introduces another departure
626
from normal tissues. Cells in culture are generally studied as monolayers, which is far different from
Extrapolations of in vitro cell culture results to animals and to human cancer risks also pose
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627
their in vivo three dimensional (3D) organization. The development of 3D artificial tissue models
628
has provided an important experimental bridge between traditional monolayer models and animal
629
models, and there is clear evidence of differential signaling and other cellular responses to radiation
630
in two dimensional (2D) monolayer versus 3D tissue models (Barcellos-Hoff and Costes, 2006). For
631
example, 3D tissue models have been used to investigate transcriptome and secretome changes in
632
response to low- versus high-dose radiation (Mezentsev and Amundson, 2011; Zhang et al., 2014),
633
and there is evidence of reduced high-dose radiation cytotoxicity in long-term 3D cultures versus 2D
634
cultures, reflecting differential apoptotic responses in 2D versus 3D cultures despite similar effects
635
on checkpoint delay (Sowa et al., 2010). In addition, cell signaling in vitro may also differ markedly
636
from the behavior of the parent cell in its normal tissue environment within an animal, where
637
638
639
communication among cells of different types may contribute to the response to radiation.
640
understanding low-dose radiation effects in humans, but inbred mice may not accurately reflect
641
effects in outbred human populations with diverse histories of radiation (and other genotoxin)
642
exposures, diet and lifestyles. There is clear evidence of variation in radiation responses in human
643
cells [e.g., cells that are heterozygous for ataxia-telangiectasia mutated (ATM), or the retinoblastoma
644
susceptibility gene (Fernet et al., 2004; Kato et al., 2009; Wilson et al., 2010a)], but at least in mice,
645
ATM heterozyosity does not enhance radiation carcinogenesis (Mao et al., 2008). The idea that low-
646
dose ionizing radiation may have significantly different effects in outbred populations is further
647
supported by a study of metabolic markers in outbred mice exposed to 100 mGy ionizing radiation
648
(Lee Do et al., 2012), and a study of mammary tissue responses to four weekly doses of 75 mGy
649
(Snijders et al., 2012). Similarly, Okayasu et al. (2000) and Yu et al. (2001) demonstrated clear
650
differences in (high-dose) radiation carcinogenesis in different strains of mice, tracing the effects to
651
hypomorphic mutations in the DNA-PKcs gene, a key factor in nonhomologous end joining repair of
652
ionizing radiation-induced double-strand breaks (DSBs). As rodents express DNA-PKcs at ~20-fold
653
lower levels than humans, this highlights yet another point to be considered when extrapolating
654
655
656
experimental results in mice to assess risk in humans.
657
mechanism. For instance, transgenerational effects have been seen in progeny of whole-body
658
irradiated male mice (Barber et al., 2002; Carls and Schiestl, 1999) but there is little evidence of
Animal studies represent a critical translational bridge between in vitro studies and
Extrapolation of risk from animal models to humans depends on conservation of the underlying
25
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659
transgenerational effects in exposed human populations (Dauer et al., 2010; Little et al., 2013),
660
suggesting that at least for this endpoint, there are fundamental mechanistic differences between
661
animal models and humans. Particularly pertinent to extrapolation of risk from animals to humans
662
with regard to effects of low doses and low dose rates are inverse dose-rate effects, which have been
663
described in several species (mouse, rat, human cells) and several endpoints (cytotoxicity,
664
mutagenesis, tumor induction) (Brenner, 1994; Lubin et al., 1995; Spiess and Mays, 1973; Ullrich,
665
1984), characterized by greater biological effects of equal doses delivered at low versus high dose
666
rates. While data on inverse dose-rate effects are limited, these effects have been explained in terms
667
of differential checkpoint responses at low- versus high-dose rates. Thus, low dose rates do not
668
activate checkpoint arrest, and low doses absorbed by cycling cells result in greater biological effects
669
per unit dose than higher dose rates which cause arrest and thereby reduce the number of cells in
670
sensitive S and G2 phases of the cell cycle (Hall and Giaccia, 2011). The generality of inverse dose-
671
rate effects across species and endpoints argues for conserved mechanisms, and such effects should
672
be further investigated in animal models to refine risk estimates of low-dose and low dose-rate
673
radiation exposures in humans.
674
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675
NOT TO BE DISSEMINATED OR REFERENCED
3. Studies Integrating Biology and Epidemiology
676
677
There are numerous epidemiologic studies documenting radiation-associated cancer
678
(UNSCEAR, 2008a) and certain noncancer effects (UNSCEAR, 2008b). Section 3.1 provides
679
examples of a few studies that successfully incorporate biological measures, sound epidemiologic
680
methods and radiation measures, which is the main focus of this Commentary. Section 3.2 discusses
681
studies of various noncancer diseases and effects, concentrating in particular on circulatory disease
682
and cataracts. Emerging epidemiologic studies focused on chronic low-dose radiation, such as the
683
Million Worker Study (Boice, 2012; 2014a; Bouville et al., 2015), incorporate radiation dosimetry
684
with biological and epidemiologic data, and are being designed to examine both cancer and
685
noncancer outcomes. Although currently in preliminary phases, the integrative approach combining
686
multiple measures, including individual dosimetry measures, and the planned analyses of multiple
687
outcomes promise improvements over prior studies designed in singular contexts.
688
689
3.1 Epidemiologic Studies Focused on Cancer Outcomes
690
691
3.1.1 Chromosome-Aberration Studies
692
693
A small number of radiation epidemiologic studies have also incorporated measures of
694
chromosome aberrations on a subset of the study population, in particular for support of dose
695
estimation.
696
697
Chromosome aberrations in blood lymphocytes are the most thoroughly studied biological
698
sentinel of radiation-induced cellular injury. Unstable aberrations such as rings and dicentrics are
699
used to estimate exposures in the short term, and stable aberrations such as symmetrical
700
translocations are used to estimate exposures many years after they occurred (Ainsbury et al., 2011;
701
Kleinerman et al., 2006; Tucker, 2008; Tucker and Luckinbill, 2011). Epidemiologic observations
702
that have incorporated chromosome-aberration evaluations include atomic-bomb survivors (Kodama
703
et al., 2001), patients treated with radiation for ankylosing spondylitis (Buckton, 1983), cervical
704
cancer (Kleinerman et al., 1989), benign gynecological disorders (Kleinerman et al., 1994), enlarged
705
thymus gland or tonsils (Kleinerman et al., 1990); patients receiving diagnostic radiation for
27
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NOT TO BE DISSEMINATED OR REFERENCED
706
tuberculosis monitoring (Kleinerman et al., 1990; Littlefield et al., 1991) or given Thorostrast for
707
cerebral angiography (Littlefield et al., 1997); workers in radiation occupations (Bhatti et al., 2008;
708
Little et al., 2014a; Tucker et al., 1997a); and populations living in areas of high natural background
709
radiation (Wang et al., 1990).
710
711
While chromosome aberrations in lymphocytes are indirect indicators of radiation dose and thus
712
correlated with cancer risk, there is evidence that chromosome aberrations may predict human
713
cancer risk independently of such exposure and thus be relevant to the mechanistic and biological
714
understanding of the carcinogenic process (Bonassi et al., 2008; Hagmar et al., 2004; Tucker et al.,
715
1997b). Examples are given below for several epidemiologic investigations where concurrent studies
716
of chromosome aberrations have provided insights into the possible mechanisms of carcinogenesis
717
and other human health detriments.
718
719
3.1.2 Atomic-Bomb Survivors
720
721
The principal atomic-bomb survivor cohorts consist of the LSS, which provides follow-up for
722
mortality and cancer incidence of about 90,000 survivors with dose estimates, and the Adult Health
723
Study (AHS), which provides biennial medical follow-up for a subcohort of initially about 20,000
724
LSS members. These two studies are primary sources for investigating the relationship between
725
radiation exposure and cancer and noncancer outcomes. It is from the LSS, for example, that we
726
know that most types of cancer can be associated with radiation exposure, and that increased cancer
727
risk from an acute radiation exposure continues for more than 60 y (through 2009) from initial
728
exposure (Grant et al., 2014; Hsu et al., 2013; Ozasa et al., 2012; Preston et al., 2007).
729
730
There are few known biological effects or lifestyle variables apart from cancer, mortality from
731
various causes, and the standard socio-demographic variables (e.g., age, sex and city) determined in
732
the full LSS cohort. Much more comprehensive biological and lifestyle information was collected in
733
the AHS. The AHS includes a large fraction of LSS members with moderate to high radiation
734
exposures, as well as, for control purposes, survivors who were essentially unexposed. Within this
735
study group a large number of effects and biomarkers have been assessed, many of which have
736
focused on noncancer outcomes. The following presents an overview of those atomic-bomb survivor
28
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737
studies that have incorporated chromosome aberrations as a biological endpoint and/or a biomarker
738
of exposure. A significant fraction of the AHS members (~4,000 total) have been assessed for stable
739
chromosome aberrations, evaluated in lymphocytes either by traditional Giemsa methods or more
740
recently by fluorescent in situ hybridization. Stable chromosome aberrations (primarily
741
translocations) are clearly related to radiation dose but there is large inter-individual variability in
742
response to dose (Kodama et al., 2001).
743
744
Significant excesses of leukemia have been observed among children and adults exposed to the
745
Hiroshima and Nagasaki atomic bombs (Hsu et al., 2013), but not among survivors who were
746
exposed in utero (Jablon and Kato, 1970). Studies of chromosome aberrations mirror these
747
epidemiologic observations. There was little evidence of dose response for chromosome aberrations
748
among atomic-bomb survivors exposed in utero, although there was a small but significant increase
749
in chromosome-aberration rate at <100 mGy (weighted fetal dose)2, whereas dose-response
750
relationships of the expected magnitude were seen for translocation frequencies in lymphocytes of
751
the mothers of those exposed in utero (Ohtaki et al., 2004). Murine experimental data (using
752
B6C3F1 mice) confirmed the human observational data, indicating that chromosome aberrations in
753
lymphocytes do not persist after an in utero dose of 1 to 2 Gy (x rays) (Nakano et al., 2007). A
754
slightly different, although essentially compatible result was observed in a Sprague-Dawley rat
755
model, where mammary cells from animals irradiated in utero with a dose of 2 Gy (gamma rays)
756
appeared to show the normal chromosome-aberration dose response, but hemato-lymphoid spleen
757
cells did not (Nakano et al., 2014).
758
759
The lack of a dose response at high doses for chromosome aberrations among those exposed in
760
utero may provide insights into the differences seen in excess risks among those exposed in utero
761
and during early childhood for leukemia. However, it should be noted that for solid cancers there is
762
an appreciable risk following in utero exposure, manifest in adulthood, which is similar to that
763
among those survivors exposed in early childhood (Preston et al., 2008). As one possible explanation
764
for these human and experimental observations, Nakano et al., (2007) suggested that the abnormal
765
cells in the fetus were replaced by normal fetal stem cells during the postnatal growth period.
766
Another possibility, suggested by Nakano et al. (2014) is that following relatively high doses, the
2
The estimated fetal dose from gamma rays plus 10 times the estimated fetal dose from neutrons, presented in milligray.
29
NCRP SC 1-21
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NOT TO BE DISSEMINATED OR REFERENCED
767
high degree of induced cell sterilization will leave too small a pool of hematopoietic stem cells for
768
any malignancy (i.e., leukemia) to be observed, given the relatively low expected mutation rate (10–5
769
to 10–4 per gene per gray). If this phenomenon is true in general, it might suggest that the fetus is less
770
vulnerable to the oncogenic effect of high-dose radiation exposure than the child, but that this may
771
not be the case at lower doses. However, the apparent contrast between the null findings in relation
772
to leukemia following in utero exposure in the atomic-bomb survivors and in the various studies of
773
leukemia following diagnostic obstetric exposure (Bithell and Stewart, 1975; Harvey et al., 1985;
774
Monson and MacMahon, 1984; Stewart et al., 1956) should not be overstated, since the results are in
775
fact statistically compatible (Wakeford and Little, 2003).
776
777
In addition to the atomic-bomb survivors’ cohort, other studies (Sections 3.1.3 through 3.1.8)
778
have incorporated chromosome aberration measures to evaluate further whether these markers are
779
predictive of dose and possibly cancer risk.
780
781
3.1.3 Pelvic Irradiation Studies
782
783
Significant excesses of leukemia have been observed in women treated with pelvic radiation
784
therapy for cervical cancer (Boice et al., 1987) and for nonmalignant gynecological bleeding
785
disorders (Sakata et al., 2012). Following radiation treatments for cervical cancer, the leukemia risk
786
increased up to doses of ~4 Gy, and then decreased at higher levels. A similar pattern was seen for
787
radiation-induced chromosomal aberrations suggesting that cell killing might predominate over
788
transformation at very high doses (Kleinerman et al., 1989; Littlefield et al., 1991). Excess leukemia
789
was also reported following lower dose partial-body radiation therapy for benign gynecological
790
disease. However, the level of risk was nearly the same as seen among the cervical cancer patients,
791
about twofold, despite a tenfold difference in average dose to active bone marrow (8 versus 0.7 Gy,
792
respectively) (Kleinerman et al., 1994). High-dose cell killing was again postulated as one
793
explanation for this apparent inconsistency. Remarkably, the rate of stable aberrations, which
794
reflects nonlethal damage in surviving stem cells, was also similar among the cancer patients and
795
benign gynecological disease patients. Apparently, the lower-dose treatments for benign disorders
796
resulted in much higher chromosomal-aberration yields per unit dose than seen following radiation
797
therapy for cervical cancer. Assuming that cytogenetically aberrant stem cells that survive radiation
30
NCRP SC 1-21
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NOT TO BE DISSEMINATED OR REFERENCED
798
therapy contribute to the leukemogenic process, the chromosome-aberration data in these two
799
irradiated populations are consistent with the leukemia outcomes in the two populations. In these
800
circumstances of partial-body radiation therapy, stable aberrations many years after exposure appear
801
to be indicators of effective risk rather than biomarkers of radiation dose.
802
803
3.1.4 Occupational Studies
804
805
Studies of chromosome aberrations among working populations with accurate measures of
806
chronic radiation exposures can provide insights into the ability of human cellular systems to repair
807
radiation damage incurred at a low dose rate. Understanding human biological responses to radiation
808
exposure has implications concerning radiation protection guidelines where a DDREF is applied to
809
scale the high dose-rate exposure risks among atomic-bomb survivors to low dose-rate exposure
810
circumstances (ICRP, 2007). Studies of workers have indicated that stable chromosome aberrations
811
can be detected and related to the amount of radiation experienced over time (Evans et al., 1979;
812
Little et al., 2014a; Tucker et al., 1997a).
813
814
The ability to detect a biological effect at the chromosome level does not mean that there will be
815
a biological consequence in the population exposed, and it may be informative to contrast
816
chromosome findings in studies for which effects have and have not been demonstrated. In studies of
817
Chernobyl cleanup workers from Estonia, increases in chromosome aberrations (Littlefield et al.,
818
1998) and in leukemia have not been observed (Rahu et al., 2013), suggesting that the population
819
size may have been too small and the doses too low to demonstrate effects following protracted
820
exposure had there been any. Other studies of low-dose occupational exposure and chromosome
821
aberrations suggested a consistency with the cytogenetic data among atomic-bomb survivors (Little
822
et al., 2014a) and there is also evidence for an increase in leukemia risk in the radiologic
823
technologist population, albeit based only on number of years worked before 1950, when doses were
824
highest (Liu et al., 2014).
825
826
Studies of workers at Sellafield nuclear facilities have revealed increased chromosome
827
aberrations that are dose related (Tucker et al., 1997a; Tawn et al., 2004; 2006). A separate study
828
found increased leukemia occurrences (Muirhead et al., 2009) that are also dose related. A similar
31
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NOT TO BE DISSEMINATED OR REFERENCED
829
dose-related excess of chromosome aberrations has been observed in the Mayak nuclear workers
830
(Burak et al., 2001), at a rate lower than, albeit statistically compatible with, those in the Japanese
831
atomic-bomb survivors (Kodama et al., 2001). This parallels a dose-related excess risk of leukemia
832
(Shilnikova et al., 2003) in that cohort, also compatible with that in the LSS (Hsu et al., 2013). The
833
cytogenetic data, however, indicated that DDREF for chromosome aberrations was about 6,
834
suggesting to the authors that “radiation-induced double-strand breaks may be repaired more
835
efficiently at low dose rates, either because there are fewer breaks competing for a limited number of
836
repair enzymes or because chronic exposure results in a higher steady state of induction of these
837
same enzymes.” However, the parallels between the independent studies of dose-related
838
chromosome aberrations and of dose-related leukemia in these studies are only suggestive, as there
839
is no linkage between the two; there is no evidence that the leukemia cases themselves had elevated
840
chromosome aberration rates before disease development. Inferences on DDREF for a disease
841
endpoint in humans should not be drawn from these data.
842
843
Studies of Chernobyl clean-up workers provide limited evidence for chromosome damage
844
among workers whose mean whole-body doses were on the order of 100 mGy (Jones et al., 2002;
845
Littlefield et al., 1997; Moore and Tucker, 1999). This may simply reflect the fact that excess stable
846
translocations cannot be detected at much lower doses than 100 mGy (Tucker and Luckinbill, 2011).
847
Related to this, among liquidators exposed at higher doses there is unambiguous evidence of excess
848
translocations (Sevankaev et al., 2005). The evidence for a leukemia excess in large cohorts of
849
Chernobyl recovery workers also remains equivocal. While recent reports suggest an increase in the
850
incidence of leukemia among the recovery operation workers (UNSCEAR, 2011; Zablotska et al.,
851
2013), the limitations of these studies are severe and include low participation rates, proxy
852
interviews for dose reconstruction, uncertainties in dose reconstruction and ascertainment of cases,
853
and internal inconsistencies. An example is a high radiation risk for chronic lymphocytic leukemia, a
854
malignancy that is not generally found to be increased in other exposed populations (UNSCEAR,
855
2008a), although there is significant (p < 0.05) excess in the latest LSS incidence data (Hsu et al.,
856
2013), a possibly fragile finding based on 12 cases. This suggests potential biases or confounding
857
factors (UNSCEAR, 2011). The chromosome-aberration data, although limited because of the low
858
doses involved, add additional caution to the interpretation of the reports of leukemia among
32
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859
Chernobyl recovery workers, and confirm that the worker exposure was generally relatively low and
860
the risk of exposure-related cancer accordingly low (Jones et al., 2002).
861
862
3.1.5 Environmental Background Studies
863
864
There are excess chromosome translocations in populations exposed to discharges from the
865
Mayak nuclear plant, living along the Techa River (Vozilova et al., 2012), at a rate lower than, albeit
866
statistically compatible with those in the Japanese atomic-bomb survivors (Kodama et al. 2001).
867
This group also exhibits excess risk of leukemia (Krestinina et al., 2013), consistent (per unit dose)
868
with those in the LSS (Hsu et al. 2013) and as observed also in various chronically exposed groups
869
(Ivanov et al., 2012, Zablotska et al., 2013).
870
871
Excess prevalence of micronuclei has been observed in a group of people exposed to 137Cs
872
contamination at near background dose rates from a research reactor in Taiwan (Jen et al., 2002).
873
Another population in Taiwan was exposed to prolonged, near background dose-rate 60Co
874
contamination, and again excess prevalence of chromosome translocations and deletions were
875
observed (Hsieh et al., 2002). This population has also been followed up for cancer, and significant
876
excess incidence of leukemia, and a borderline significant excess incidence of breast cancer
877
observed (Hwang et al., 2008), in both cases consistent with, although slightly less than, risks that
878
can be derived by linear extrapolation from the Japanese atomic-bomb survivors. Reduced fertility
879
has also been observed in this cohort, with fertility decreasing according to dose rate, especially of
880
mothers, but not in relation to cumulative dose (Lin et al., 2010); fertility recovered to normal among
881
those who moved out of the 60Co contaminated buildings.
882
883
In contrast, studies of populations living in areas of high natural background radiation have
884
failed to reveal convincing evidence for increases in leukemia, thyroid cancer or other malignancies
885
(Nair et al., 2009; Tao et al., 2012; Wang et al., 1990), yet increased chromosome aberrations are
886
observed in the resident populations (Hayata et al., 2004; Wang et al., 1990). Such data, limited by
887
both sample size and low doses, indicate caution when interpreting the biological significance of
888
chromosome aberrations in circulating lymphocytes as well as the absence of an effect in exposed
889
populations. Nonetheless, the absence of a leukemia response and a lower chromosome-aberration
33
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NOT TO BE DISSEMINATED OR REFERENCED
890
yield than anticipated from studies of acute exposures (Tucker et al., 1997a) conceivably might be
891
attributable to a decrease in the tumorigenic effectiveness of low-LET radiation with decreasing dose
892
rate, as has been observed for tumorigenic effects on many tissues in laboratory animals (NCRP,
893
1980; Upton, 1990).
894
895
3.1.6 Transgenerational Studies
896
897
To date, no radiation-related genetic effects (i.e., hereditary diseases) have been
898
demonstrated in a human population exposed to ionizing radiation. A number of long-term studies of
899
the children of survivors of the atomic bombings in Hiroshima and Nagasaki have not detected any
900
transmitted genetic effects (transgenerational effects) of radiation exposure (Neel et al., 1980; Satoh
901
et al., 1996).
902
903
Children of cancer survivors have also been studied to learn of possible adverse genetic effects
904
associated with curative radiation treatments of the parents (Boice et al., 2003; Green et al., 2009;
905
Winther and Olsen, 2012). Blood samples taken from survivors of cancer, their spouses or partners
906
and their children have been analyzed for genomic instability, inherited mutations in minisatellite
907
DNA and in mitochondrial DNA, chromosome radiosensitivity and DNA polymorphic variation, and
908
the occurrence of cytogenetic abnormalities. Molecular studies found no evidence for inheritance of
909
minisatellite DNA mutations or mitochondrial DNA changes related to gonadal radiation (Guo et al.,
910
2012; Tawn et al., 2011). There was no evidence for genomic instability (Tawn et al., 2005) or
911
consistent evidence for G2 chromosome radiosensitivity (Wilding et al., 2007), although inheritance
912
of chromosome radiosensitivity was evident (Curwen et al., 2010). The molecular analyses
913
supported the epidemiologic findings of no detectable heritable genetic changes in the risk of
914
cytogenetic abnormalities, single gene disorders, birth defects, stillbirths, neonatal deaths and cancer
915
in the children of men exposed to testicular irradiation or women exposed to ovarian irradiation
916
(Signorello et al., 2012; Winther et al., 2012). These epidemiologic and cellular studies of the
917
children of cancer survivors are consistent with the studies of the children of atomic-bomb survivors
918
in Japan (Schull, 2003).
919
34
NCRP SC 1-21
Draft of March 31, 2015 [MR]
920
NOT TO BE DISSEMINATED OR REFERENCED
In contrast however, Dubrova and colleagues have described an elevated minisatellite mutation
921
rate in the offspring of workers involved in radiation cleanup after the Chernobyl accident (Dubrova
922
et al., 1996; 1997), as well as populations living along the Techa River (Dubrova et al., 2006).
923
However, these studies have been somewhat controversial (Livshits et al., 2001; Slebos et al., 2004).
924
Possible reasons for the discrepancy between these observations have been considered by Bouffler
925
et al. (2006), Little (2014) and Tawn et al. (2015).
926
927
928
3.1.7 Gene and Radiation Interaction: The Women’s Environmental Cancer and Radiation
Epidemiology Study
929
930
The Women’s Environmental Cancer and Radiation Epidemiology (WECARE) Study is an
931
international multicenter, population-based case-control study of breast cancer designed specifically
932
to examine the joint roles of interaction of gene carrier status and radiation exposure in the etiology
933
of breast cancer (Stovall et al., 2008). The underlying hypothesis is that a woman who carries certain
934
genetic mutations will be more susceptible to radiation-associated breast cancer than a woman who
935
is not a carrier. The study is nested within seven population-based cancer registries. 1,508 women
936
with asynchronous contralateral breast cancer were individually matched to 2,200 controls with
937
unilateral breast cancer on date and age at diagnosis of the first breast cancer, race, and registry
938
region.
939
940
Only young women with breast cancer were included because they were more likely to be
941
genetically predisposed to breast cancer and to have had a greater susceptibility to radiation-related
942
cancer. All study subjects were identified, recruited, and interviewed through seven population-
943
based cancer registries. The average age of cases and controls was 45 y and the average time
944
between primaries for cases was 5 y. All women were interviewed using a questionnaire that
945
included known and suspected risk factors for breast cancer and detailed treatment. Tumor
946
characteristic information and biospecimens were also collected. For each patient who received
947
radiation therapy, absorbed dose was estimated for each of the four quadrants and nipple area for the
948
contralateral breast. The average dose to the contralateral breast was 1 Gy (Stovall et al., 2008).
949
35
NCRP SC 1-21
Draft of March 31, 2015 [MR]
NOT TO BE DISSEMINATED OR REFERENCED
950
To date, the WECARE Study participants have been screened for candidate breast cancer
951
susceptibility genes, genes that lie in the ATM-CHEK2 pathway and across the genome using an
952
agnostic approach in order to determine genetic variation associated with contralateral breast cancer
953
and radiation-associated contralateral breast cancer in particular. Screening 2,100 WECARE Study
954
participants for the ATM gene yielded an elevated risk among carriers who were treated with
955
radiation [RR for carrying missense mutations and receiving >1 Gy (3.3, 95 % CI 1.4 to 8.0)]. The
956
risk was stronger among those who were treated at young ages and who carried mutations predicted
957
to be deleterious [RR for those under age 45 y at exposure (10.4, 95 % CI 2.3 to 47.2) (Stovall et al.,
958
2008)]. There was no elevated risk in the absence of radiation. The WECARE Study incorporates
959
epidemiologic methods, genetic data, and individual dosimetry for each participant, making the
960
study a paradigm for future interdisciplinary integrated genetic radiation research.
961
962
3.1.8 Retinoblastoma
963
964
Retinoblastoma is an embryonic childhood cancer of the eye that originates in the cells of the
965
retina (Gallie et al., 1999). It has both a heritable and a nonheritable etiology (Knudson, 1971).
966
Similar to other familial susceptibility syndromes, hereditary retinoblastoma is an autosomal-
967
dominant cancer resulting from deleterious mutations of a single gene of high penetrance, the RB1
968
tumor-suppressor gene (Taylor et al., 2007). Most patients with hereditary retinoblastoma develop
969
cancer in both eyes, but carriers of low-penetrance alleles may develop cancer in one eye only or not
970
at all. Nonhereditary retinoblastoma rises sporadically as a result of biallelic somatic mutation and
971
usually affects only one eye (Little et al., 2012a; Lohmann, 2010). Unlike patients with sporadic
972
(nonhereditary) disease, children with hereditary retinoblastoma are at a notable increased risk for
973
developing a second cancer later in life (Eng et al., 1993; Kleinerman et al., 2007). Radiation therapy
974
further increases the risk of sarcoma and second cancer development (Kleinerman et al., 2005;
975
NCRP, 2011; Wong et al., 1997; Yu et al., 2009). Radiation-related sarcomas occur most frequently
976
around the orbit of the eye that received curative treatments, whereas the nonradiation associated
977
sarcomas occur in other parts of the body. For germline (hereditary) retinoblastoma, every cell in the
978
body contains a deleterious mutation of the retinoblastoma tumor-suppressor gene and studies have
979
attempted to evaluate the possible interaction between high-dose radiation and underlying genetic
980
susceptibilities in causing second primary cancers (Kleinerman, 2009). While there is compelling
36
NCRP SC 1-21
Draft of March 31, 2015 [MR]
NOT TO BE DISSEMINATED OR REFERENCED
981
evidence for an enhancement of risk of certain types of second cancer following radiation therapy
982
among children with heritable retinoblastoma contrasted with children with the nonheritable type of
983
retinoblastoma (i.e., for a gene and radiation interaction), the data are limited by small numbers and
984
statistical uncertainties (Draper et al., 1986; Wong et al., 1997; Yu et al., 2009). For some cancer
985
sites (e.g., breast) there appears to be little or no excess radiation risk among those with heritable
986
retinoblastoma, in contrast to the much larger risk in those without the heritable disease (Little et al.,
987
2014b). Nonetheless, it is interesting that studies of cells from retinoblastoma families involving the
988
-H2AX assay (Kato et al., 2007) and the G2 chromosome-radiosensitivity assay (Wilson et al.,
989
2010) indicate elevated radiation sensitivities with respect to cell killing and DNA repair or DNA
990
damage processing functions. Conceivably these cellular studies point to a defect in genome
991
maintenance, including the capacity to process radiation-induced DNA damage, that results in
992
increased germline mutation rates, predisposition to cancer, and increased susceptibility to radiation-
993
related cancer. Identification of such genes or factors involved could provide insights into
994
995
996
mechanisms leading to retinoblastoma, cancer predisposition and radiation carcinogenesis.
997
radiation therapy-related cancers (Travis et al., 2006). Germline mutations in the RB1 tumor-
998
suppressor gene predispose children to a high risk of osteosarcomas, soft-tissue sarcomas, and a
999
number of other cancers. Radiation therapy further enhances the risk of tumors arising in the
Retinoblastoma provides an example of how genetic mutations may influence the risk of
1000
radiation field. Mutations in the RB1 gene and perhaps other genes appear important in the cellular
1001
response to DNA damage and may confer an increased risk of radiation-related cancer. It is unclear,
1002
however, the extent to which radiation therapy for hereditary retinoblastoma, a genetic disorder due
1003
to mutations in a gene of high penetrance, can be generalized to genetic conditions of lower
1004
penetrance (or to polygenic circumstances) following much lower absorbed doses at or below
1005
1006
1007
1008
100 mGy.
1009
3.2. Epidemiologic Studies Focused on Noncancer Outcomes
There is emerging evidence of radiation-associated excess risk of circulatory disease in a number
1010
of groups exposed at moderate doses (e.g., 500 mGy) (Little et al., 2012b). In particular, radiation-
1011
associated excess risk of mortality and morbidity for a number of circulatory disease endpoints has
1012
been observed in the Japanese atomic-bomb survivor data (Shimizu et al., 2010); Yamada et al. (2004).
37
NCRP SC 1-21
Draft of March 31, 2015 [MR]
NOT TO BE DISSEMINATED OR REFERENCED
1013
There is very rich covariate information available within the AHS subcohort of atomic-bomb
1014
survivors, and the radiation associations for a number of these have been analyzed in a number of
1015
studies. For example, elevated levels of the pro-inflammatory cytokines IL6, CRP, TNF-α and INF-γ,
1016
but also increased levels of the (generally) anti-inflammatory cytokine IL10, have been observed in
1017
the Japanese atomic-bomb survivors (Hayashi et al., 2003; 2005). There was also a dose-related
1018
elevation in the erythrocyte sedimentation rate and in the levels of IgG, IgA and total
1019
immunoglobulins, all markers of systemic inflammation, in this cohort (Hayashi et al., 2005).
1020
Certain T-cell and B-cell population numbers are known to vary with radiation dose among the
1021
Japanese atomic-bomb survivors (Kusunoki et al., 1998), suggesting a role for the immune system.
1022
The atomic-bomb survivors also demonstrate dose-dependent decreases in levels of CD4+ helper T
1023
cells (Hayashi et al., 2003); decreased levels of helper T cells have also been found in blood samples
1024
from Japanese atomic-bomb survivors with myocardial infarction (Kusunoki et al., 1999). However,
1025
none of this covariate data has been analyzed together with circulatory disease outcome data. There
1026
is known to be similarly rich covariate information in the Mayak nuclear worker data, in which there
1027
is also significant radiation-associated excess risk of circulatory disease (Azizova et al., 2010a;
1028
2010b; 2011; 2014; Moseeva et al., 2014). However, this covariate data has not been separately
1029
1030
analyzed, nor is there any analysis in which this has been combined with health outcome data.
1031
The literature on radiation and cataract and other lenticular opacities has been the subject of a
1032
number of recent reviews (Ainsbury et al., 2009; ICRP, 2012; Little, 2013; Shore et al., 2010),
1033
including a recent systematic review (Hammer et al., 2013), all suggesting that there may be
1034
radiation-associated risk at moderate and low doses. However, there appear to be no studies
1035
combining analysis of cataract outcome data with intermediate bioindicator or biomarker covariates.
1036
For all other noncancer disease endpoints, the evidence for radiation etiology is much weaker,
1037
whether in directly exposed populations (Little, 2013) or in the offspring of exposed individuals
1038
(Little et al., 2013).
1039
38
NCRP SC 1-21
Draft of March 31, 2015 [MR]
NOT TO BE DISSEMINATED OR REFERENCED
1040
1041
1042
4. Bioindicators from Radiation Biology Studies
1043
outcomes (and to a lesser extent noncancer outcomes) was introduced in Section 1.2. This included a
1044
description of how bioindicators of effect could be used as the key events for such an approach. In
1045
Section 4, a discussion is presented of the types of bioindicators that can be measured in cellular,
1046
laboratory animal and human systems, with a parallelogram approach for linking these
1047
measurements to adverse health outcomes in humans. In this way, it is feasible to both describe the
1048
shape of the dose response at low doses and low dose rates as well as the risk estimates themselves
1049
for these exposure scenarios. The rapid development of new technologies that permit whole-genome
1050
sequencing in a very short time has and will continue to enhance the ability to identify and measure
1051
many of these types of informative bioindicators. To fully utilize this type of approach to develop a
1052
bioindicator of response, it is likely that a systems biology type approach (Section 5.2.2) will be
1053
1054
1055
1056
1057
needed.
1058
indirectly through production of reactive oxygen species, producing a plethora of lesions, including
1059
many types of base damage such as broken rings, oxidized bases, small and large adducts including
1060
DNA-DNA and DNA-protein crosslinks, single-strand breaks (SSBs), and DSBs, including
1061
relatively reparable simple DSBs, and more difficult to repair complex DNA lesions. Under typical
1062
conditions, ~40 DSBs are created per cell per gray, and many more base lesions and SSBs are
1063
produced than DSBs (Ward, 1998). However, DSBs have the greatest biologic significance since
1064
DSBs correlate with cytotoxicity, chromosome aberration formation, and cellular transformation
1065
1066
1067
(Frankenberg-Schwager, 1990).
1068
2011). Specific lesions are typically repaired by a specific pathway or subpathway, but if the primary
1069
pathway fails, repair may be attempted by another pathway due to redundancies in the process. DNA
1070
damage and repair do not occur in a static environment, but rather in the context of other dynamic
1071
processes including DNA replication and transcription, and in a structurally complex and dynamic
The concept of using a key event/adverse outcome pathway approach for estimating cancer
4.1 Deoxyribonucleic Acid Damage and Repair
Ionizing radiation causes damage to DNA by direct energy absorption and to a larger extent,
There are at least five types of DNA repair pathways, each with subpathways (Shaheen et al.,
39
NCRP SC 1-21
Draft of March 31, 2015 [MR]
NOT TO BE DISSEMINATED OR REFERENCED
1072
chromatin environment. DNA repair systems maintain genome stability and confer resistance to
1073
ionizing radiation and other DNA damaging agents, and defects in DNA repair systems typically
1074
predispose to cancer and other diseases (Friedberg et al., 2005). Three sets of pathways act on
1075
single-strand damage, including base-excision repair (nonbulky lesions) (Krokan and Bjoras, 2013),
1076
nucleotide excision repair [bulky, helix distorting lesions (Kamileri et al., 2012)], and mismatch
1077
repair (Hsieh and Yamane, 2008). Classical and alternative nonhomologous end joining, and
1078
homologous recombination, are redundant DSB repair pathways that can restore the chemical
1079
1080
1081
integrity of DNA, but with lesser or greater capacity to accurately restore genetic sequences.
1082
when forks encounter other types of lesions (single-strand breaks, many types of base damage, most
1083
DNA adducts, pyrimidine dimers, and intra- and inter-strand crosslinks) (Allen et al., 2011;
1084
Budzowska and Kanaar, 2009). DSBs are marked by phosphorylated histone H2AX (-H2AX)
1085
(Rogakou et al., 1998), which plays important roles in DNA damage checkpoint signaling and DSB
1086
repair (Chanoux et al., 2008; Downey and Durocher, 2006). -H2AX is a fairly reliable biomarker of
1087
DSBs, detected by fluorescence microscopy using phospho-specific antibodies, hence -H2AX is
1088
often used in kinetic studies to monitor both DSB induction and repair (Lobrich et al., 2010), and as
1089
a radiation biodosimeter (Rothkamm et al., 2013). Proteins involved in nonhomologous end joining
1090
and homologous recombination migrate to DSBs, such as the early DNA damage response protein
1091
53BP1, and the late acting homologous recombination protein RAD51. The resulting protein
1092
aggregates (“repair factories”) can be detected with fluorescence microscopy using cognate
1093
antibodies as subnuclear foci. These foci thus complement -H2AX as additional biomarkers of
1094
DSBs and their repair (Allen et al., 2011). -H2AX foci represent a robust biomarker of DNA
1095
damage by radiation, and induced foci or residual (unrepaired) -H2AX foci show promise as
1096
potential biomarkers of risk for various cancers (Fernandez et al., 2013; He et al., 2013; Matthaios
1097
1098
1099
et al., 2013).
DSBs are produced immediately by radiation, or they can arise later during DNA replication
4.2 Cellular Signaling at Low Doses
1100
1101
1102
Cells respond to DNA damage in part through the protein kinase mediated activation of signaling
pathways that can culminate in cell-cycle arrest, stimulation of DNA repair, induction of cell death,
40
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NOT TO BE DISSEMINATED OR REFERENCED
1103
or terminal differentiation and senescence. The cellular DNA-damage response includes two major
1104
checkpoint signaling pathways; one responding to DSBs through ATM activation that leads to
1105
CHEK2 activation and p53 stabilization (Postiglione et al., 2010), and one centered on ATM and
1106
Rad3-related activation, which activates CHEK1 in response to single-strand breaks and gaps (i.e., at
1107
replication forks) (Fokas et al., 2014). Damage to cellular components in addition to DNA can also
1108
trigger signal transduction pathways, such as the ceramide pathway that responds to damage to the
1109
cell membrane (Zeidan and Hannun, 2010), redox signaling from mitochondria (Hei et al., 2008), as
1110
well as signaling between directly hit and non-hit bystander cells (Section 4.6.2).
1111
1112
Checkpoint pathways are not “on or off” but show different responses depending on the level of
1113
damage. This is reflected in different gene expression responses at different levels of radiation
1114
exposure and in different physiological outcomes (Ding et al., 2005; Gridley et al., 2009; Lu et al.,
1115
2010; Mezentsev and Amundson, 2011). DNA damage response thresholds are genetically regulated
1116
(Peng et al., 2010; Putnam et al., 2009), and thresholds may vary for each pathway (Fernet et al.,
1117
2010). With minimal DNA damage, as with low-dose ionizing radiation, some cells may not activate
1118
the DNA repair process (Rothkamm and Lobrich, 2003) while others may activate repair pathways
1119
but not cell-cycle arrest or death pathways. Activation of repair or other damage signaling pathways
1120
by low-dose ionizing radiation probably underlies adaptive responses that enhance survival, prevent
1121
genomic instability, or limit deleterious effects when cells are subsequently exposed to large ionizing
1122
radiation doses (Section 4.6.1). DNA repair efficiency may vary with the level of damage, with more
1123
efficient repair at low ionizing radiation doses than at high doses (Neumaier et al., 2012). At higher
1124
levels of damage, cells arrest at G1, S, and/or G2/M phases to prevent cell-cycle progression with
1125
DNA damage to reduce the chance of mutations, aneuploidy, and mitotic catastrophe, and above
1126
threshold levels of damage (which vary with cell type), cell death and nonproliferation pathways are
1127
activated, including apoptosis, autophagy, necrosis and senescence (Branzei and Foiani, 2009;
1128
Budzowska and Kanaar, 2009; Eriksson and Stigbrand, 2010; Galluzzi and Kroemer, 2008;
1129
1130
1131
1132
1133
Vakifahmetoglu et al., 2008; Vandenabeele et al., 2010).
1134
of radiation dose, dose rate and radiation quality, and are the best-documented biomarker of
4.3 Chromosome Alterations
Following exposure to ionizing radiation, chromosomal rearrangements are induced as a function
41
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1135
radiation exposure, even to doses as low as 200 mGy (Lloyd et al., 1992; Pohl-Ruling et al., 1983;
1136
Tucker and Luckinbill, 2011). Chromosomal rearrangements arise as a consequence of the
1137
processing of directly and indirectly induced DNA DSBs and at high dose rates increase in a linear-
1138
quadratic fashion for sparsely ionizing radiation, suggesting that the observed rearrangements are the
1139
result of two or more interacting molecular events. There is a long and well-documented literature on
1140
the dose-response relationships for the induction of chromosomal aberrations evaluated after Giemsa
1141
staining, or by fluorescence in situ hybridization using chromosome-specific molecular probes, and
1142
more recently by combinatorial hybridization techniques (reviewed in Cornforth, 2001).
1143
1144
Using these more modern technologies it appears that the shape of the dose-response profile for
1145
chromosomal rearrangements is influenced by the production of complex rearrangements involving
1146
three or more chromosomes (Loucas et al., 2013), especially following exposure to low dose-rate
1147
radiation exposures (Loucas et al., 2004).
1148
1149
While chromosomal alterations are a well-documented biomarker of exposure and dose, and
1150
remain the gold standard in this area following potential radiation exposure (Tucker, 2008), it should
1151
be noted that rearrangement frequency can vary significantly between tissues (Brooks et al., 2003).
1152
Ongoing studies suggest that chromosomal aberrations might predict human cancer independently of
1153
exposure to carcinogens (Bonassi et al., 2000; Hagmar et al., 1994). However, it remains to be seen
1154
if a molecular, biochemical, or cellular signature of radiation exposure can be correlated with
1155
chromosomal aberration formation (a biomarker of radiation exposure) and be used as a predictor of
1156
health risk(s) associated with irradiation and predict adverse effects, particularly at low radiation
1157
doses.
1158
1159
4.4 Mutations
1160
1161
Mutations are essential features of cancer (Hanahan and Weinberg, 2011), and ionizing
1162
radiation was the first environmental mutagen identified (Muller, 1927). Mutations can serve as
1163
bioindicators of radiation effects as a function of dose, dose rate and radiation quality, and most
1164
mutation studies to date have made use of model systems that assess changes at reporter loci that are
1165
not directly linked to tumor formation.
42
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1166
1167
Most mutation studies to date have utilized model reporter gene systems in which changes in the
1168
DNA sequence of a gene result in the failure to produce a functional protein product, allowing
1169
chemical selection of the mutant cells. Such mutations can reflect small changes in the protein
1170
product that render it unable to perform its normal function, or the failure to produce any of the
1171
protein, but the proteins involved generally are not related to cancer development.. However,
1172
information learned from these studies can be useful in helping to guide our understanding of
1173
mutational mechanisms and the types of mutational events likely to occur at loci that are directly
1174
linked to tumor formation following exposure to ionizing radiation. All of the studies presented in
1175
Section 4.4 focus on the identification of mutations that are thought to be directly induced by the
1176
radiation at short times after exposure.
1177
1178
Tumor suppressor genes generally require mutation of two alleles, often involving a point
1179
mutation and subsequent loss of the second allele to enable tumor formation. Model systems have
1180
been engineered to allow quantification of mutations at such heterozygous autosomal loci, and
1181
analysis of the molecular changes in DNA. In these models, one copy of a target gene, such as
1182
thymidine kinase (TK1, or Tk) or adenine phosphoribosyltransferase (APRT, or Aprt), has been
1183
inactivated and a mutagenic event that results in the loss of production of any functional protein
1184
from the second copy of the gene can be selected using a chemical that will kill only the normal
1185
cells. Mutations can also occur in dominantly acting oncogenes. In this case, mutation of a single
1186
copy of the gene can lead to tumorigenesis, but the mutational mechanisms are the same.
1187
1188
Radiation has been shown to induce mutations by many mechanisms, including all types of
1189
single basepair substitutions (Grosovsky et al., 1988), small insertions and deletions within the gene
1190
itself (intragenic events), deletion of the whole gene, multi-locus deletions, recombination-mediated
1191
events leading to loss of heterozygosity, and loss of whole chromosomes (a form of aneuploidy)
1192
(reviewed in Turker et al., 2009; Wiese et al., 2001).
1193
1194
Studies using single copy genes, such as the X-linked hypoxanthine phosphoribosyltransferase
1195
(HPRT, or Hprt) gene, can detect a subset of the radiation-induced mutational mechanisms that can
1196
occur at an autosomal locus (Liber et al., 1989). Similarly, the S1/MIC1/CD59 locus in artificially
43
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NOT TO BE DISSEMINATED OR REFERENCED
1197
constructed human x hamster hybrid cells has been used to demonstrate that ionizing radiation often
1198
produces chromosomal scale events, including whole chromosome loss, although such events are
1199
often lethal in normal human cells (Kraemer et al., 2001).
1200
1201
There are also data on radiation mutagenesis at low doses and/or low dose rates. In one study,
1202
induction of HPRT mutations in a human lymphoblast cell line by protracted exposure could not be
1203
distinguished from the mutant frequencies measured in samples exposed to the same total doses in a
1204
single acute exposure, suggesting that there was no dose-rate effect (Grosovsky and Little, 1985).
1205
Other studies using the same cell line have found an inverse dose rate effect for TK1 mutation that
1206
was dependent on dose rate (Amundson and Chen, 1996; Brenner et al., 1996).
1207
1208
The Aprt heterozygous mouse model provides a good example of translation of mutagenesis
1209
studies from in vitro to in vivo. Mutations have been characterized in kidney cells irradiated in vitro,
1210
or in cells retrieved from irradiated kidneys in mice that had total body exposures. All of the types of
1211
mutational events that occurred in kidney cells exposed in vitro also occurred in vivo, from small
1212
intragenic events to whole chromosome loss (Ponomareva et al., 2002; Turker et al., 2009; 2013).
1213
The chromosomal milieu of the Aprt locus in the mouse is similar to that for the TK1 locus in human
1214
cells. This is important for cross-species extrapolations of mutational risk, although one class of
1215
radiation-induced mutations (whole chromosome loss) that can be observed in the engineered mouse
1216
model is not detected in the human cells, most likely due to functional hemizygosity of linked loci in
1217
the “outbred” human genome that would result in cell death from such an event in the human cells.
1218
1219
Recent studies on radiation-related acute myeloid leukemia (AML) in mice have examined
1220
mutations at the PU.1 locus on mouse chromosome 2. Deletion of one allele of PU.1 was proposed
1221
as a candidate bioindicator for radiogenic AML in a susceptible mouse strain (Peng et al. 2009).
1222
Further work indicates that point mutation of the second PU.1 allele is not radiation related, and that
1223
mutation of other genes located near PU.1 may be particularly important in radiogenic AML in
1224
different mouse strains (Genik et al., 2014).
1225
1226
Radiation-induced autosomal mutations have also been studied in Dlb-1 heterozygous mice.
1227
Mutations were measured in intestinal stem cells and their progeny (cells which are of particular
44
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NOT TO BE DISSEMINATED OR REFERENCED
1228
interest since mutations in stem cells are thought to be important in the carcinogenic process). Fewer
1229
gamma ray-induced mutations were observed at the Dlb-1 locus in the mouse small Muintestine
1230
when the dose rate was lowered to 0.01 Gy min–1 as compared with acute exposures at 1.8 Gy min–1
1231
(Winton et al., 1989). However, this study was limited to high total doses.
1232
1233
In the early 1950s and 1960s hybrid male mice were irradiated and then mated to unirradiated
1234
females that harbored a series of seven recessive loci and mutations were assessed in these marker
1235
loci in the offspring. Mutant frequency results indicated that low-LET radiation delivered to stem
1236
cell spermatogonia at exposure rates of 0.8 R min–1 and below induced only about one-third as many
1237
mutations in the offspring as those delivered as single acute exposures (Russell and Kelly, 1982;
1238
Russell et al., 1958). While the total exposures in these experiments were not low (86 to 1,000 R),
1239
the exposure rates were as low as 1 mR min–1 and lower in some cases. The data gathered in these
1240
very large-scale studies were re-examined in light of the availability of fine-structure genetic or
1241
molecular analyses of the seven marker loci and their surrounding chromosomal regions (Russell
1242
and Hunsicker, 2012), and indicated that the reduction in mutant frequency at the lower exposure-
1243
rates is associated with a decline in “large lesion” mutations. Since these data reflect radiation effects
1244
on the irradiated male germline, it is more challenging to extrapolate these findings to somatic cells
1245
in adult humans, where most human cancers develop.
1246
1247
New approaches to the analysis of mutations have become available with the advent of next-
1248
generation DNA sequencing, in addition to molecular cytogenetic approaches that can detect copy
1249
number variations with increasing precision. These methods are being considered now for use in the
1250
analysis of irradiated cells in culture as well as in irradiated tissues. Associating radiation-induced
1251
changes with particular loss of heterozygosity or other events that may be causal to tumor formation
1252
will be challenging, given the wide variety of molecular events that lead to radiation-induced
1253
mutations and the large number of driver and passenger mutations found in human tumors (Bozic
1254
et al., 2010; Vogelstein et al., 2013). An example of the use of such an approach comes from
1255
experimental studies of genetic and chemically-induced lung cancers in mice (Westcott et al., 2014).
1256
In this study, the carcinogen-induced tumors display signatures of the initiating chemical
1257
carcinogens. The situation may be more challenging for ionizing radiation-induced events, where
1258
specific radiation-signature events have been more difficult to identify.
45
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1259
1260
4.5 Radiation-Induced Genomic Instability
1261
1262
Genomic instability is characterized by an increased rate of genetic change in the progeny of an
1263
irradiated cell. Instability manifests as alterations in chromosomal rearrangements, micronuclei,
1264
increased gene copy number, minisatellite and microsatellite instabilities in DNA repeat sequences,
1265
transformation, and cell killing and can be induced through both targeted and nontargeted bystander-
1266
like processes (Little, 2000; Morgan et al., 2002). Genome instability is a key feature of cancer cells
1267
and it is now well established that defects in DNA repair and DNA damage response systems
1268
predispose to cancer. Defects in DNA repair or DNA damage response systems can allow more rapid
1269
accumulation of instability that underlies cancer development. However it is still controversial
1270
whether there is a causal association between instability and cancer (Little, 2010). Genomic
1271
instability can be produced from endogenous or induced DNA damage, and as for nontargeted
1272
bystander effects, the mechanisms of instability share features with inflammatory responses
1273
characterized by intercellular signaling, production of chemokines, cytokines, and reactive oxygen
1274
and nitrogen species.
1275
1276
Ionizing radiation causes DNA damage that directly leads to large- or small-scale genetic
1277
changes, ranging from single-base (point) mutations to gene rearrangements, translocations, and
1278
whole chromosome gain or loss (Section 4.4). Studies over the past decade have shown that ionizing
1279
radiation (low- and high-LET) and nonionizing radiation (e.g., ultraviolet light) also induce genome
1280
instability many cell generations after the exposure (Durant et al., 2006; Huang et al., 2004; 2007;
1281
Morgan, 2003a; 2003b). Some of these instability phenotypes are correlated and therefore likely
1282
share a common means of induction or are otherwise related mechanistically (Limoli et al., 1997).
1283
For example, cells that display ionizing radiation-induced chromosome instability typically show
1284
delayed death, probably reflecting the low viability of cells with large-scale karyotypic changes and
1285
aneuploidy as a result of massive gene expression imbalances. In contrast, ionizing radiation-
1286
induced delayed hyper-recombination is not associated with delayed death; therefore chromosome
1287
instability and delayed hyper-recombination are mechanistically distinct processes (Huang et al.,
1288
2004).
1289
46
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NOT TO BE DISSEMINATED OR REFERENCED
1290
One commonality among the many instabilities investigated to date is the fact that they can be
1291
induced by both high (1 to 10 Gy) and low (0.01 to 0.1 Gy) ionizing radiation doses. However, in
1292
some instances a threshold dose has been observed and there are also examples of the effect
1293
saturating at a specific radiation dose (Limoli et al., 1999). Like many immediate effects of
1294
radiation, the induction of genome instability is also subject to adaptive responses. As an example,
1295
exposing cells to low doses of ionizing radiation offered some protection for induction of
1296
instabilities by a subsequent higher dose (Huang et al., 2007). However, the adaptive response was
1297
obviously not a complete or even a strong protective mechanism, since even low doses were
1298
sufficient to induce instabilities.
1299
1300
Two other features common to ionizing radiation-induced instabilities is that they occur at high
1301
frequencies (often 10 % or more of cells that survive low to moderate doses) and they typically do
1302
not show a dose response (Little, 2000; Morgan, 2003a). These features suggest that the target for
1303
induced instability is large. It is difficult to imagine instabilities arising from gene inactivation (even
1304
if only one of a large family of genes such as those in the DNA damage response pathway needed to
1305
be inactivated). Instead, the target is likely to be as large as the nucleus or even the whole cell.
1306
1307
There is considerable variation in the manifestation of induced instability between cell lines in
1308
vitro, and organisms in vivo. This variation in response has a genetic component (Ponnaiya et al.,
1309
1997) and may reflect the age of the organism at the time of irradiation as well as the cells/organisms
1310
ability to detoxify cellular reactive oxygen/nitrogen species (Spitz et al., 2004). Once initiated
1311
however, there is evidence that instability can be perpetuated over time by dicentric chromosome
1312
formation followed by bridge breakage fusion cycles (Marder and Morgan, 1993) as well as
1313
recombinational events involving interstitial telomere like repeat sequences (Day et al., 1998). There
1314
is also increasing evidence that epigenetic factors (Aypar et al., 2011; Koturbash et al., 2006),
1315
dysfunctional mitochondria (Kim et al., 2006a; 2006b), and inflammatory type reactions (Lorimore
1316
and Wright, 2003; Mukherjee et al., 2012), presumably involving reactive oxygen and nitrogen
1317
species as well as cytokines and chemokines might be involved in driving the unstable phenotype
1318
(Laiakis et al., 2007; Hei et al., 2008). To this end there is convincing evidence for such reactions
1319
being involved in another nontargeted effect associated with ionizing radiation, the bystander effect
1320
(Morgan et al., 2002). Clearly the link between induced instability and bystander effects suggests
47
NCRP SC 1-21
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NOT TO BE DISSEMINATED OR REFERENCED
1321
common processes and inflammatory type reactions will likely be the subject of future investigation,
1322
as will the clinical implications of induced instability in terms of a marker for increased cancer
1323
risk/early detection, and as a consequence of radiation therapy that may result in the induction of a
1324
therapy-related second malignancy (Goldberg, 2003).
1325
1326
While the precise molecular, cellular, organ, and organismal mechanisms contributing to
1327
radiation-induced genomic instability are not known, the phenotype is well documented in a number
1328
of in vivo model systems (Morgan, 2003b; Watson et al., 2001). This raises a number of questions
1329
regarding this intriguing phenotype.
1330
1331

What, if any, specific types of radiation-induced genomic instability are capable of
1332
transforming a cell from a normal to a cancer cell, or furthering the progression of a
1333
premalignant cell towards a more aggressive cancer phenotype?
1334

1335
1336
What, if any, is the role of induced instability in secondary cancers observed in radiation
therapy patients?

Could analysis of radiation-induced genomic instability in peripheral blood lymphocytes be
1337
used as a biomarker of an individual’s radiation sensitivity and potential radiation-related
1338
cancer risk?
1339
1340
Recently the integration of radiobiological effects in a two-step clonal expansion model of
1341
carcinogenesis and applications to radioepidemiologic data have been proposed (Jacob et al., 2010).
1342
First, a model version with radiation-induced genomic instability was shown to be a possible
1343
explanation for the age dependence of the radiation-related cancer mortality in the Techa River
1344
Cohort. Second, it was demonstrated that inclusion of a bystander effect with a dose threshold allows
1345
an improved description of the lung cancer mortality risk for the Mayak workers cohort due to
1346
incorporation of plutonium. It is envisaged that a further combination of epidemiologic, biological
1347
and modeling interactions will address these questions in the future (Eidemuller et al., 2011; Kaiser
1348
1349
et al., 2014).
48
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1350
NOT TO BE DISSEMINATED OR REFERENCED
4.6 Modulators of Response
1351
1352
Classical radiation biology was built on the concept of target theory wherein the deleterious
1353
effects of exposure including chromosome aberrations, mutations and carcinogenesis are assumed to
1354
arise from damage to a cellular target (e.g., nuclear DNA) via energy deposition in the cell at risk
1355
(Hall and Giaccia, 2011; Lea, 1946; UNSCEAR, 1993). The deposition of energy after irradiation is
1356
well understood, but the biological processes that modify response to that damage may vary between
1357
individuals and in response to low versus high dose and dose rate.
1358
1359
In recent years, much research has focused on responses to doses of 100 mGy and below.
1360
While some responses appear to follow a continuous dose-response relationship from high to low
1361
doses, some other responses may only occur at either low or high doses, thus complicating the
1362
extrapolation from high dose to low dose. Current radiation risk extrapolations to low doses assume
1363
that:
1364
1365

the primary mode of action is linearly related to dose; and
1366

cancer is clonal in origin (i.e., the individual cell is the unit of risk), although cellular
1367
responses are likely modulated by the tissue microenvironment.
1368
1369
Although the targeted effects of radiation (i.e., responses in hit cells) may be proportional to dose,
1370
other processes, including effects in non-hit cells (e.g., induced genomic instability and bystander
1371
effects) may show nonlinear responses. Some of these low-dose responses are highlighted in
1372
Section 4.6.
1373
1374
4.6.1 Adaptive Responses
1375
1376
Two different adaptive responses have been described. The classical adaptive response was first
1377
described by Olivieri et al. (1984). They demonstrated that when human lymphocytes were exposed
1378
to a very low dose of radiation, a priming dose, followed a short time later with a larger higher dose,
1379
the challenge dose, the frequency of chromosomal aberrations induced by the challenge dose was
1380
less than that from the challenge dose given alone. A second adaptive response suggests that when
49
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NOT TO BE DISSEMINATED OR REFERENCED
1381
cells with a high background frequency of phenotypic alterations (e.g., mutations or transformation)
1382
were exposed to very low doses of ionizing radiation, these doses can actually reduce that frequency
1383
to below background (Azzam et al., 1996; Redpath et al., 2001).
1384
1385
While adaptive responses are well characterized in certain situations in vitro, there is also some
1386
evidence that they can occur in humans occupationally (Barquinero et al., 1995) or clinically
1387
(Monsieurs et al., 2000) exposed to ionizing radiation. As always in a biological system there is
1388
considerable variation both in vitro (Bosi and Olivieri, 1989) and in vivo (Padovani et al., 1995;
1389
Tedeschi et al., 1995). In fact, not only are reduced levels of DNA alterations observed in some
1390
systems, in other individuals no response has been observed, while in others an additive increase in
1391
the manifestations of irradiation has been observed (Tapio and Jacob, 2007; UNSCEAR, 1994;
1392
2012).
1393
1394
Adaptive response(s), if reproducibly induced by low doses of ionizing radiation, have potential
1395
clinical and radiation protection implications. For example, that radiation signals danger to the
1396
immune system through the Toll-like receptor family can cause cells to release damage-associated
1397
molecular patterns to activate canonical inflammatory pathways and/or initiate immunity (McBride
1398
et al., 2004). Through induction of superoxide dismutase and anti-inflammatory responses, low-dose
1399
ionizing radiation could also be used prior to surgery or other therapies that cause inflammation or
1400
ischemia. Howell et al. (2013) indicated that exposure of mice in utero to a dose rate of 10 to
1401
13 mGy d–1 [Chernobyl soils (primary radionuclides being 137Cs and 90Sr) embedded into bioplastic]
1402
for 10 d had no deleterious effects on litter size, bone marrow stem cells, and white blood cell
1403
counts, yet this exposure had significant protective effects when pups were later exposed to an acute
1404
dose of 2.4 Gy (calibrated 137Cs source). Alternatively, if low dose radiation exposures can induce an
1405
adaptive response then a CT scan for treatment planning purposes may result in the subsequent
1406
fractionated radiation dose being less effective in tumor cell kill, thus limiting the impact of radiation
1407
therapy. These outcomes are likely to vary between individuals and a future challenge is to
1408
determine which individuals will manifest one or other of these alternatives.
1409
50
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1410
NOT TO BE DISSEMINATED OR REFERENCED
4.6.2 Bystander Effects
1411
1412
Bystander effects occur in nonirradiated cells that receive a signal from an irradiated cell. Such
1413
signals may be secreted and/or shed by irradiated cells or communicated via molecules transported
1414
between cells by cell-to-cell gap junction communication. Not all cells are able to produce bystander
1415
signals or respond to them. The bystander effect has been the subject of a number of recent reviews
1416
(Blyth and Sykes, 2011; Butterworth et al., 2013; Ilnytskyy and Kovalchuk, 2011; Mancuso et al.,
1417
2012).
1418
1419
The key question in the context of this Commentary is whether bystander effects can be
1420
beneficial or detrimental to the nonirradiated cell, tissue, or organism. On the cellular level there is
1421
evidence of both potentially protective effects, such as apoptosis (Belyakov et al., 2005) and
1422
differentiation (Belyakov et al., 2006), and detrimental effects, such as DNA damage (Dickey et al.,
1423
2009; Nagasawa and Little, 1992), chromosomal instability (Dickey et al., 2009), mutation (Zhang
1424
et al., 2009; Zhou et al., 2000), and transformation (Sawant et al., 2001). More recently, bystander
1425
responses including mutagenesis (Chai et al., 2013a; 2013b) and carcinogenesis (Mancuso et al.,
1426
2008; 2011; 2013) have been convincingly demonstrated in animals in vivo. Neither the net
1427
protective or detrimental effect of the different processes affected, nor their relevance to human risk
1428
are yet fully understood.
1429
1430
It is sometimes thought that the implications of bystander effects for radiation protection and
1431
ultimately for interpreting epidemiologic studies are not likely to be significant – in that they are
1432
likely already “built into” organ risks and consequent tissue weighting factors. However, at low
1433
doses and/or dose rates this is probably incorrect. The bystander effect would not be observed (e.g.,
1434
in the LSS data) because most cells are traversed by at least 100 electron tracks (= 0.1 Gy). But this
1435
is not the case extrapolating to an occupational setting, for example, where, with low-LET doses of
1436
1 mGy y–1, most cells would receive only one electron track per year. By definition, bystander
1437
effects imply that the target for radiation effects in low-dose exposures will be greater than the target
1438
volume actually irradiated.
1439
51
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NOT TO BE DISSEMINATED OR REFERENCED
1440
A number of bystander effect models have been fitted to experimental (Brenner et al., 2001;
1441
Little et al., 2005; Nikjoo and Khvostunov, 2003) and epidemiologic (Brenner and Sachs, 2003;
1442
Little, 2004; Little and Wakeford, 2001) data. The available data suggest that despite compelling
1443
experimental findings there is not strong evidence of the involvement of the bystander effect as a
1444
modifier of radiation-related cancer risk in humans (Little, 2004; Little and Wakeford, 2001; Little
1445
et al., 2009). However, as bystander responses could alter the shape of the dose-response curve at
1446
low doses, an understanding of this area could add confidence to biological models and
1447
extrapolations.
1448
1449
4.6.3 Genetic Susceptibility and Interactions with Radiation
1450
1451
Over the past decade or so, there has been an extensive increase in knowledge of the genetic
1452
basis of diseases, especially cancers of many different types (Weinberg, 2013). For example, for so-
1453
called high penetrance genes, excess spontaneous cancers are expressed in a large proportion of
1454
carriers. In addition, there are gene-gene and gene-environment interactions that can lead to
1455
variations in the likelihood of cancer across the population. How such information might convert
1456
into inter-individual differences in susceptibility to radiation-related cancer has been quite
1457
extensively discussed by ICRP (1998), NA/NRC (2006), and UNSCEAR (2001; 2008a). However, it
1458
remains unclear as to the magnitude of the effect of any such sensitivity even at the individual level,
1459
let alone a population level.
1460
1461
Sankaranarayanan and Chakraborty (2001) conducted an extensive analysis of potential impacts
1462
at the individual and population levels. They used a Mendelian one-locus, two allele autosomal
1463
dominant model for predicting the impact of cancer predisposition and increased radiosensitivity on
1464
the risk of radiation-related cancers in the population and in relatives of affected individuals using
1465
breast cancer due to BRCA-1 mutations. The general conclusions from their study were that, when
1466
the proportion of cancers due to the susceptible genotype is small (<10 %), the attributable risks are
1467
small. These risks only become large when cancer susceptibility is very large (>10-fold). On the
1468
other hand, when the proportion of cancers resulting from the susceptible genotypes is large (10 %),
1469
there can be significant increases in attributable risk for relatively small increases in cancer
1470
susceptibility (>10-fold) and radiosensitivity (>100-fold) in the susceptible populations. This means
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1471
that the increase in cancer risk to a heterogeneous population of susceptible and nonsusceptible
1472
genotypes will generally be quite small if the proportion of susceptible individuals is small and the
1473
radiation sensitivity relatively small. On the other hand, cancer risks assessed on the basis of an
1474
individual (in radiation therapy situations, for example) can be greatly influenced by genotype. For
1475
the purposes of radiation protection, dose limits for the public will not be significantly influenced by
1476
genetic radiosensitivity whereas they certainly could be in some occupational settings and for
1477
individual assessments for defined medical exposures.
1478
1479
There is evidence to suggest that efficiency in DNA DSB-repair modulates radiation toxicity risk
1480
because patients with genetic syndromes involving DNA DSB-repair factors, such as ataxia
1481
telangiectasia, have well recognized acute and late sensitivities to radiation. However, apart from
1482
these rare genetic syndromes, the genetic determinants of radiation toxicity are largely unknown.
1483
However, progress is being made in demonstrating experimentally that there are complex
1484
interactions that underlie the expression of cancer-predisposing genes of lower penetrance
1485
(NA/NRC, 2006). This knowledge highlights the difficulty that will be encountered in trying to
1486
incorporate facets of genetic radiosensitivity into the radiation risk assessment process, especially at
1487
low doses and doses rates, and determinants of clinical endpoints.
1488
1489
4.6.4 Interactions with Environmental and Lifestyle Factors
1490
1491
Lifetime risk estimates are reported for radiation exposures essentially considering the radiation
1492
exposure as the single stressor. Modulating factors have been identified to some extent and a few
1493
have been incorporated into the risk paradigm (e.g., age at exposure, sex, smoking for radon
1494
exposures) (Section 2.2). A major issue is to be able to identify those cancers associated with
1495
radiation exposure among the high frequency of background cancers, so that the impact of any
1496
modulating factor on the radiation component can be ascertained. This is currently an intractable
1497
problem since radiation-specific disease signatures are not known. The issue has been addressed by
1498
UNSCEAR (2000) and by Wakeford (2012). However, there is no simple approach to the problem.
1499
As a starting point it is necessary to be able to describe the nature of exposures to which individuals
1500
or populations are subject. Because the U.S. Environmental Protection Agency’s guidelines for
1501
estimation of risks to environmental stressors (chemicals and others) requires a consideration of
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1502
aggregate or cumulative risk, methods have been proposed and are being developed for this purpose
1503
(EPA, 2003). A novel approach that takes advantage of current technical advances in exposure
1504
science, including biomonitoring, is the concept of a so-called exposome [see an overview at NIOSH
1505
(2014) and reviews by Rappaport (2011) and Vrijheid (2014)]. The exposome is broadly defined as a
1506
measure of the total exposure of an individual in their lifetime and the relationship of this exposure
1507
to health outcomes. In this context, exposure begins before birth and includes both environmental
1508
and occupational exposures. As noted by NIOSH (2014), “Understanding how exposures from our
1509
environment, diet, lifestyle etc. interact with our own unique characteristics such as genetics,
1510
physiology, and epigenetics impact our health” is how the exposome will be articulated. This issue is
1511
very pertinent to this Commentary for which ionizing radiation is just one of the environmental/
1512
occupational exposures and considerations of risk from radiation will need to be placed in the
1513
purview of total exposure and total risk. Exposomics is the name given to the study of the exposome
1514
and utilizes internal and external exposure methods.
1515
1516
As discussed by Rappaport (2011), it is appropriate for the application of an exposome-based
1517
approach for exposure assessment, to regard the “environment” as the body’s internal chemical (and
1518
physical) environment and to define “exposures” as levels of biologically active chemical and
1519
physical agents in and impacting this environment. He further proposes that to assess the exposome,
1520
the more applicable approach might be a top down one based on biomonitoring, rather than a
1521
bottom-up one that relies on analysis of exposures from external sources (e.g., air water, food). This
1522
general approach is extended by Wild et al. (2013) to provide a view of how an understanding of the
1523
complete exposure profiles of individuals in large populations can be linked to a detailed analysis of
1524
their omics profiles. As noted such approaches are in their early stages of development but already
1525
indicate the reasonable likelihood of linking total exposure measures to biological effects and
1526
subsequently adverse health outcomes. In the context of this Commentary, the “omics” component is
1527
intended to indicate that molecular methods based on omics techniques (e.g., genomics,
1528
transcriptomics, proteomics, and metabolomics) can be used in the selection of informative
1529
biomarkers and bioindicators of external exposure and internal dose. The more that is known about
1530
the etiology of diseases such as cancer, the more readily such informative biological surrogates can
1531
be identified.
1532
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1533
NOT TO BE DISSEMINATED OR REFERENCED
5. Recommendations for Closing the Gaps towards an Integrated Approach
1534
1535
The current state of knowledge on the effects of low-dose radiation is incomplete and uncertain
1536
with respect to understanding the shape of the dose-response relationship and the level of risk at low
1537
doses. These deficiencies have long-term and profound consequences with regard to radiation
1538
protection guidance, compensation programs, and environmental contamination issues. There is a
1539
need to identify novel approaches to reduce these uncertainties. Research is needed to identify,
1540
evaluate, and utilize informative bioindicators of the health effects of interest (i.e., cancer and
1541
noncancer effects). Ideally, a bioindicator would be a quantitative predictor of the adverse health
1542
effect and measureable over the dose range of interest, particularly including low doses (i.e.,
1543
<100 mGy). Such information would extend the knowledge of the dose-response relationship at
1544
doses below those that can be observed through epidemiologic studies.
1545
1546
To be able to utilize an approach for incorporating radiation biology data into the risk assessment
1547
process for low-dose and low dose-rate exposures, it is necessary to establish a framework for
1548
research planning and data collection. Such a framework has been presented in this Commentary and
1549
is basically one that involves the use of adverse outcome pathways and key events defining such
1550
pathways. Research should address this need in a prospective manner rather than in a retrospective
1551
one. Lastly, to ensure progress in multi-disciplinary radiation research and continuity of low-dose
1552
radiation research in general, it is essential that low-dose facilities are maintained and broadened and
1553
educational training programs are expanded to meet the growing needs of cross-disciplinary
1554
radiation science in the next generation of radiation researchers.
1555
1556
5.1 Targeted Research to Identify Adverse Outcome Pathways and Bioindicators of Response
1557
1558
To utilize such an approach outlined above, it is essential to identify adverse outcome pathways
1559
for radiation-related adverse health outcomes and the key events that characterize these. The types of
1560
bioindicators that will be informative in this regard are discussed above in Section 4. It remains very
1561
difficult to develop the required information for human tumors because of the lack of ability to
1562
accurately identify radiation-related tumors. Thus, it is necessary, in part, to use animal models of
1563
human tumors together with cellular systems to support the evaluation of the potential predictive
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1564
value of any prospective bioindicator. This general approach is depicted in the parallelogram
1565
presented in Figure 1.3. It has been proposed that adverse outcome pathways are likely to be
1566
conserved across species in support of integrated approaches to testing for chemical risks (Tollefsen
1567
et al., 2014)
1568
1569
It is initially necessary to establish the robustness of specific bioindicators and other intermediate
1570
endpoints for predicting the adverse outcome under study. Thus, it is required that any biological
1571
endpoint utilized for extrapolation purposes be predictive of the apical endpoint for which risk is
1572
being estimated. Such predictions can describe the shape of the dose-response curve or an estimate
1573
of the risk itself, depending on the nature of the specific biological endpoint. Of course, it is
1574
pertinent to establish, whenever possible, if there are additional dose-dependent modifications
1575
beyond the position of a particular bioindicator along the AOP. This can, in part, be overcome by
1576
using a suite of key-event bioindicators for steps along the AOP. As discussed in Sections 1 and 4,
1577
biomarkers are considered to be surrogates for key events in the formation of the apical endpoint,
1578
whereas bioindicators are defined as being key events themselves in an adverse outcome pathway for
1579
a particular adverse health outcome. For a quantitative assessment of risk, it is necessary to make use
1580
of bioindicators at dose levels below those at which the apical endpoint itself can be assessed with
1581
any degree of accuracy. In the case of cancer, it is also feasible to assess pre-neoplastic lesions as
1582
predictors of cancer outcome in some human studies. These are likely to be the most accurate
1583
predictors of a cancer outcome, given their proximity to the malignant cancer itself.
1584
1585
There are basically two classes of predictive biomarkers or bioindicators: those that can be
1586
predictive of the adverse health outcome (cancer or noncancer) and those that are predictive
1587
specifically of radiation-related cancer or noncancer outcomes. Knowledge of the cancer process has
1588
increased dramatically leading to the description of the Hallmarks of Cancer by Hanahan and
1589
Weinberg (2000; 2011) and the concept of gene alterations in cancer cells being classified as
1590
“drivers” and “passengers” (Pleasance et al., 2010). The use of this type of information forms the
1591
basis for the U.S. Environmental Protection Agency’s Guidelines for Carcinogen Risk Assessment
1592
(EPA, 2005). The approach is predicated on the fact that for the majority of chemicals (and exposure
1593
scenarios) there are very few epidemiologic data sets available other than those for occupational
1594
exposures. Thus, data from other sources (laboratory animal and in vitro cell studies) are used to
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1595
support the epidemiology. The specific approach relies upon the assessment of key events in the
1596
cancer process to be used as bioindicators in a qualitative manner today, but is anticipated to be more
1597
quantitative in the near future. A similar use of key events has been proposed for noncancer
1598
endpoints using toxicity data (Julien et al., 2009; Seed et al., 2005). It would seem feasible for the
1599
general principles developed for these diverse set of exposures and disease endpoints to be applied
1600
for the enhancement of the radiation risk assessment process. The aim, in this case, would be to
1601
design research that specifically measured key events for a particular disease outcome — in the first
1602
instance, likely to be cancer given the wealth of knowledge on the underlying molecular mechanisms
1603
of cancer development.
1604
1605
At this time, it is not readily feasible to assess the robustness of surrogate markers or key events
1606
in the radiation (or chemical) risk assessment process because too little reliable information has been
1607
developed. There is some developing information that will prove useful with enhancement. For
1608
example, Jarabek et al. (2009) describe the use of specific DNA adducts as bioindicators for use in
1609
the cancer risk assessment process, and chromosome alterations have been used to predict cancer
1610
outcomes at the group (not the individual) level (Bonassi et al., 2008). The real problem is the lack
1611
of bioindicators that are specific for radiation exposure. The advent of ultra-high-throughput DNA
1612
sequencing and the identification of a limited set of genes that can be mutated in cancer cells [about
1613
120 according to Greenman et al. (2007)] could lead to the identification of radiation-specific
1614
signature alterations.
1615
1616
5.2 Biologically-Based Modeling
1617
1618
The limitations of the direct use of epidemiologic data for estimating cancer and noncancer risks
1619
at low radiation doses were discussed in (Sections 2.1, 2.2 and 2.3). In cases where such direct use
1620
has been presented (e.g., Kendall et al. 2013; Mathews et al., 2013; Pearce et al., 2012), possible
1621
confounders [in particular in relation to the brain cancer findings in the study of Pearce et al. (2012)]
1622
and modest power [in the case of the study of Kendall et al. (2013)] make interpretation somewhat
1623
problematic. The present Commentary is predicated on the potential use of biological data to support
1624
the current risk assessment approach. The specific application would be to enhance the extrapolation
1625
from available high/medium dose epidemiologic data to dose levels and dose rates that are necessary
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1626
for radiation protection purposes (e.g., <100 mGy). In this regard, it has been proposed that some
1627
form of biologically-based dose-response modeling would meet this need (e.g., Conolly et al., 2004;
1628
Curtis et al., 2002; Little et al., 2008a; NCRP, 2012; Shuryak et al., 2010). However, here also,
1629
modeling on such a biological basis is uncertain outside the range of the available data that are used
1630
as input parameters to a biologically-based model.
1631
1632
5.2.1 Biologically-Based Dose-Response Approach
1633
1634
For risk assessment for environmental chemicals, because of the paucity of epidemiologic data, it
1635
is recommended that a biologically-based dose-response (BBDR) approach be used for predicting
1636
adverse health outcomes under low-dose chronic exposure scenarios (EPA, 2005). However, the
1637
approach has been used very sparingly in the regulatory process itself, largely because of the lack of
1638
availability of the appropriate data sets to provide input parameters to a BBDR model. The need for
1639
expanded use of BBDR models has been concisely expressed in Section 5.7 of NCRP (2012):
1640
1641
“The challenge of developing a biologically-based computational model to minimize
1642
uncertainty in dose-response modeling can be summarized as: understanding a
1643
sufficient amount of the relevant biology; acquiring enough data to parameterize the
1644
1645
1646
model; and developing the computational model.”
Of course, this simple statement hides a degree of complexity that must be addressed. For
1647
example, how much of the detailed biology do we need to know and when are there sufficient data to
1648
sustain model development? This situation can be significantly alleviated if research is directed
1649
towards the needs identified by BBDR models. Research targeted to the risk assessment process is
1650
needed.
1651
1652
The initial development of BBDR as applied to carcinogenesis is described by Moolgavkar and
1653
Knudson (1981) and Moolgavkar and Venzon (1979) in their two-stage cancer model [usually
1654
referred to as the Moolgavkar-Venzon-Knudson model]. This simplified model has been expanded
1655
upon in subsequent years, as reviewed in Little (2010). The basic principle is that carcinogenesis can
1656
be considered as a two-step process. These two critical steps in carcinogenesis are considered to be
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1657
specific, irreversible, and inherited (at the cell level). The first step is assumed to give a small growth
1658
advantage through partial alteration of growth control, and the second is assumed to lead to total
1659
abrogation of growth control. Other assumptions include: that cancer arises from a single cell;
1660
transformation of stem cells are independent events; and the time from transformation of a single
1661
cell to a tumor is constant. Thus, what is developed is essentially a “working” model rather than one
1662
that is truly representative of the cancer process as described, for example, by Hanahan and
1663
Weinberg (2000; 2011).
1664
1665
The two-stage carcinogenesis model has been applied to a number of situations, for example, by
1666
Heidenreich et al. (2004) and Krewski et al. (2003) for radiation and by Conolly et al. (2004) for
1667
formaldehyde exposures. There remain some concerns that the use of this two-stage model does not
1668
make use of the considerable body of data that has been generated over the past 20 y on the
1669
understanding of cancer development and, in the present context, of radiation-related effects and
1670
radiation carcinogenesis. Multi-stage generalizations of the two-stage model have been developed
1671
(Little, 1995), and applied to model ionizing radiation exposure (Little, 1996; Little et al., 2002).
1672
Further generalizations of the model have been developed to incorporate genomic instability, and
1673
used for modeling population colon cancer data (Little and Li, 2007; Little and Wright, 2003; Little
1674
et al., 2008b). However, it is equally fair to note that no viable alternative BBDR has been proposed
1675
and applied to radiation risk assessment, with the possible exception of the multistage model
1676
developed by Shuryak et al. (2010) for the analysis of cancer risk patterns as a function of age-at-
1677
exposure in Japanese atomic-bomb survivors. The uncertainties inevitably associated with BBDR
1678
approaches are discussed in detail in NCRP (2012).
1679
1680
In addition to a simplified model approach (such as with the two-stage carcinogenesis model), it
1681
is feasible to use more realistic models of carcinogenesis, that is, models that establish if specific
1682
processes such as genomic instability or adaptive responses are predictive of the cancer response in
1683
specific studies (e.g., Eidemuller et al., 2011). Third, and perhaps the most informative type of
1684
BBDR model, are ones that are developed to examine the role of specific measured biological
1685
variables in the genesis of radiogenic cancer in a particular study. Examples of this informative
1686
approach have been reported by Heidenreich and Rosemann (2012) and Heidenreich et al. (2013).
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1687
Further development of this latter type of model using key-event bioindicators as input parameters is
1688
encouraged.
1689
1690
5.2.2 Systems Biology
1691
1692
Systems biology, defined here as the quantitative study of biological networks and pathways as
1693
integrated systems rather than as a set of isolated parts, is a young but rapidly evolving field that
1694
takes a data-intensive approach to biologically-based modeling. Systems radiation biology
1695
emphasizes the interactions of cell signaling pathways and networks, the distribution of effects and
1696
responses throughout these networks, as well as the redundancy between and within such networks
1697
for maintenance of cellular homeostasis and subsequent tissue function. This approach uses ‘omics-
1698
level data (e.g., genomics, transcriptomics, proteomics and metabolomics) to reverse engineer the
1699
biological circuitry of a cell, tissue, or organism with the goal of being able to predict the response to
1700
a perturbation, such as the mutation of a gene that effectively removes a node from the network, or
1701
an extrinsic stress, such as radiation exposure. Ultimately, systems biology also attempts to consider
1702
tissue responses in the context of the whole irradiated system rather than as the response of
1703
individual cells.
1704
1705
While much is known about the DNA damage induced by the deposition of energy from
1706
exposure to ionizing radiation and the subsequent cellular responses, there is a significant gap in our
1707
understanding of how these might lead to detrimental health effects years after exposure. Current
1708
radiation modeling and risk evaluation tend to be limited to simplistic models that do not reflect the
1709
inherent biological diversity of humans or the mechanisms that underlie radiation risks (Barcellos-
1710
Hoff, 2008). For instance, the interactions between irradiated cells within a tissue and the
1711
modulating effects of the tissue microenvironment (Barcellos-Hoff, 2005) are not accounted for in
1712
many models. Linking mechanisms of cellular and molecular responses to radiation exposure to
1713
macroscopic processes at the tissue, organ, and whole organism levels would improve our ability to
1714
predict cancer risk in response to irradiation. This is our multiscale systems challenge: to understand
1715
the sequence of events occurring in tissues following irradiation and why in some instances exposure
1716
can lead to cancer or other phenotypic alterations, and in other instances no long-term phenotypic
1717
changes are observed.
60
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1718
1719
Recent advances in high-throughput ‘omics technologies enable interrogation of induced
1720
radiation effects at the genomic [reviewed in Amundson (2008)], proteomic (Lin et al., 2009;
1721
Menard, et al. 2006; Park et al., 2006), and metabolomic (Lanz et al., 2009; Tyburski et al., 2008;
1722
2009; Zhang, et al., 2014) levels. Radiation systems biology attempts to integrate the resulting
1723
‘omics data to enable modeling of radiation dose and dose-rate effects in a complex tissue as a
1724
function of time, and relate these to observable functional physiological and /or physical changes in
1725
that tissue after irradiation.
1726
1727
It is clear that vast amounts of data can, and will, be generated. The usefulness of these data will
1728
depend on the development of computational modeling approaches to organize an abundance of
1729
complex biological data in a predictive, multicellular framework. The challenge is to understand the
1730
flow of information between cell types in a given tissue and why different tissues vary in their
1731
response to radiation. This demands approaches that can elucidate the connectivity among and
1732
between signaling networks rather than focusing on reducing analysis to individual signaling
1733
components.
1734
1735
Network construction of radiation responses will require quality, dose-dependent measurements
1736
over a protracted time frame (Eschrich et al., 2009). Multiple doses, dose rates and time points will
1737
need to be interrogated at the global level in order to address the question at hand. The impact of
1738
post-transcriptional and post-translational modifications as well as potential epigenetic modifications
1739
that influence cell signaling will have to be evaluated and incorporated into the model. In this way
1740
the combined behavior of interdependent processes that dictate cell responses and the fundamental
1741
properties of the networks involved, such as redundancy, modularity, robustness, and feedback
1742
1743
1744
1745
control can be assessed.
The systems biology approach for radiation risk assessment will require:
1746

multi-level ‘omics data from well-designed experiments;
1747

knowledge of the pathways involved;
1748

developing the computational modeling approaches to organize complex biological data in a
1749
predictive, multi-cellular framework that can be integrated with radiation risk models;
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1750

a long-term commitment from federal funding agencies; and
1751

dedicated cross-disciplinary teams of investigators to link studies at all levels to relevant
1752
1753
1754
1755
1756
health outcomes.
5.3 Registries of Medical-Imaging Exposures Linked to Outcomes and Biological Materials
The use of ionizing radiation for medical imaging has grown significantly in recent years. In the
1757
United States, exposure to ionizing radiation from medical-imaging procedures3 in 2006 was
1758
approximately six times greater than in 1980 (NCRP, 2009d), with an average effective dose to a
1759
member of the U.S. population of ~3 mSv, comparable to the annual effective dose from natural
1760
background radiation in 2006; the dose from natural background was the same as in 1980. This
1761
increased dose from medical-imaging procedures was attributed to the greatly increased usage in
1762
2006 of higher-dose procedures particularly computed tomography (CT) and nuclear medicine
1763
compared to 1980 (NCRP, 2009d). Brenner and Hall (2007) reported that almost 82 million CT
1764
procedures were performed annually in the United States in 2011, up from 46 million in 2000 and
1765
13 million in 1990. Mettler et al. (2009), reported that cardiac diagnostic nuclear procedures
1766
increased from 1 % of the total number of diagnostic nuclear medicine examinations performed in
1767
1973 to 57 % in 2005). Further, Brenner and Hricak (2010) state that an increase in emerging
1768
imaging modalities such as positron-emission tomography CT, single-photon emission CT, and
1769
potentially CT for screening of high-risk asymptomatic patients (e.g., smokers screened for early
1770
1771
1772
lung cancer detection) are likely.
1773
patient demographics, as available in other countries (e.g., Nordic countries) could be useful for
1774
tracking long-term sequelae of these procedures, including the potential for carcinogenic risk
1775
associated with extensive medical imaging using these technologies. An ideal situation would also
1776
include the collection and storage of biospecimens. In addition, there may be other potentially
1777
informative radiation-exposed groups, such as astronauts, where follow-up and collection of samples
1778
would be valuable. However, such a comprehensive endeavor may not be economically feasible as
Therefore registries of medical-imaging procedures, including indication for examination and
3
Medical-imaging procedures included computed tomography, converntional radiography and fluoroscopy,
interventional fluoroscopy, and nuclear medicine.
62
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1779
the cost of establishing and maintaining the registry, harmonizing the input and follow-up
1780
1781
1782
1783
1784
1785
1786
procedures and the complex issues of sample collection and storage may be extensive.
1787
information and strong quantitative data to inform risk assessments for human exposure. This
1788
research requires the availability of facilities that can provide accurate dose delivery at the relevant
1789
dose-rates. The necessary facilities have variable requirements. Some must be able to accommodate
1790
cell culture systems, including tissue-mimetic models, and these require ancillary equipment to
1791
provide detailed dosimetry and to maintain defined oxygen tensions. Some animal work will be
1792
possible on a small scale using these facilities, but larger-scale, dedicated facilities with the
1793
capability to conduct long-term exposures at low dose rates are critical for some aspects of this
1794
work. Specialized facilities are also needed for studies with low-energy sources, charged-particle
1795
microbeams, and inhaled sources, and to investigate long-term low dose-rate effects. Support for the
1796
maintenance and development of this diverse group of facilities is essential to the conduct of a robust
1797
1798
1799
1800
1801
research program at low doses and low dose rates.
5.4.2 Interdisciplinary Training
1802
critical elements that are required to support this highly interdisciplinary field (Boice, 2014b; NCRP,
1803
2015). Individuals trained across disciplines are essential to meet the health needs of the nation as
1804
we evaluate radiation risk. Current training programs are often narrowly focused and rarely include
1805
the breadth of coursework and practical training needed to develop robust research programs.
1806
Integrated training programs with representation of the disciplines of radiation biology, molecular
1807
biology, pathology, radiation dosimetry, health physics, epidemiology, and statistics should be
1808
developed and supported to ensure the availability of a cadre of scientists who can advance our
1809
understanding of radiation health effects at low doses.
5.4 Facilities and Interdisciplinary Training
5.4.1 Facilities
Basic and applied research studies at low doses and low dose rates can provide mechanistic
The science of radiation protection will continue to develop only if individuals are trained in
1810
63
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1811
NOT TO BE DISSEMINATED OR REFERENCED
Glossary
1812
1813
absorbed dose (D): The quotient of dε by dm, where dε is the mean energy imparted to matter of mass dm
1814
(i.e., D = dε /dm). The unit for D is joule per kilogram (J kg–1) with the special name gray (Gy) (see also
1815
organ dose).
1816
1817
1818
1819
1820
1821
ascertainment bias: Arises when there is variation in ascertainment of disease status, perhaps correlated with
exposure variables.
adaptive response: In radiobiology, the theory that a low dose of ionizing radiation can reduce the effect of a
higher dose of ionizing radiation when the higher dose is administered after a short time delay.
Adult Health Study (AHS): A subcohort of the Life Span Study, which provides biennial medical follow-up
for a subcohort of about 20,000 members of the Life Span Study (see Life Span Study).
1822
as low as reasonably achievable (ALARA): A principle of radiation protection philosophy that requires that
1823
exposures to ionizing radiation should be kept as low as reasonably achievable, economic and social
1824
factors being taken into consideration. The protection from radiation exposure is ALARA when the
1825
expenditure of further resources would be unwarranted by the reduction in exposure that would be
1826
achieved.
1827
background radiation: The amount of radiation to which a member of the population is exposed from
1828
natural sources, such as terrestrial radiation from naturally-occurring radionuclides in the soil, cosmic
1829
radiation originating in outer space, and naturally-occurring radionuclides deposited in the human body.
1830
The natural background radiation received by an individual depends on geographic location and living
1831
habits. In the United States, the average effective dose from background radiation is ~1 mSv y–1,
1832
excluding indoor radon which amounts to ~2 mSv y–1 on average.
1833
Berkson error: The Berkson error model assumes that true dose is equal to the observed dose plus an error
1834
introduced by individual peculiarity, which is defined as a random variable that has a mean of zero and is
1835
independent of observed dose.
1836
bias (epidemiology): Any process at any stage in the conduct of the study that tends to produce results or
1837
conclusions that differ systematically from the truth (see follow-up bias, ascertainment bias, recall bias,
1838
and confounding factor).
1839
bioindicator: A cellular alteration that is on the pathway to the disease endpoint itself, such as a specific
1840
mutation in a target cell that is associated with tumor formation. Thus, a bioindicator can be perceived as
1841
informing on the shape of dose-response curve for the disease outcome or on cancer frequency itself, and
1842
therefore, is equivalent to a key event.
1843
1844
biomarker: A biological phenotype (e.g., chromosome alteration, DNA adduct, gene expression change,
specific metabolite) that can be used to indicate a response at the cell or tissue level to an exposure. In this
64
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NOT TO BE DISSEMINATED OR REFERENCED
1845
regard, it is generally a measure of the potential for development of an adverse outcome such as cancer
1846
(e.g., a predictor of exposure level).
1847
bystander effect: In radiobiology, the term is used to describe an effect on cells in which the energy had not
1848
been directly deposited. In most instances, the cells so affected were neighbors of the cells directly
1849
impacted by the radiation.
1850
1851
cancer: A general term for more than 100 diseases characterized by abnormal and uncontrolled growth of
cells.
1852
carcinogen: An agent that is associated with an increased risk of cancer.
1853
carcinogenesis: Induction of cancer by radiation or any other agent (a somatic effect).
1854
case-control study: An epidemiologic study in which people with disease and a similarly composed control
1855
1856
group are compared in terms of exposures to a putative causative agent.
Chernobyl recovery workers: The civil and military personnel cleanup workers who were called upon to
1857
deal with consequences of the 1986 Chernobyl nuclear disaster on the site of the event in the
1858
former Soviet Union.
1859
Classical measurement error: The Classical measurement error model assumes that observed dose equals
1860
true dose plus measurement error, where measurement error is a random variable that is independent of
1861
the true dose and has a mean of zero.
1862
cohort study: An epidemiologic study in which groups of people (the cohort) are identified with respect to
1863
the presence or absence of exposure to a disease-causing agent, and in which the outcomes of disease
1864
rates are compared; also called a follow-up study.
1865
confounding factor: In statistics, an extraneous variable in a statistical model that correlates (directly or
1866
inversely) with both the dependent variable and the independent variable (e.g., a factor that is correlated
1867
both with the disease under study and with an exposure of interest).
1868
1869
1870
1871
1872
chromosome aberration: A missing, extra, or irregular portion of chromosomal DNA. It can be from an
atypical number of chromosomes or a structural abnormality in one or more chromosomes.
cytogenetic: Relating to the branch of genetics that is concerned with the study of the structure and function
of the cell, especially the chromosomes.
deoxyribonucleic acid (DNA): Genetic material of cells; a complex molecule of high molecular weight
1873
consisting of deoxyribose, phosphoric acid, and four bases which are arranged as two long chains that
1874
twist around each other to form a double helix joined by hydrogen bonds between the complementary
1875
components.
1876
1877
DNA damage: An alteration in the chemical structure of DNA, such as a break in a strand of DNA, a base
missing from the backbone of DNA, or a chemically changed base.
65
NCRP SC 1-21
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1878
1879
1880
NOT TO BE DISSEMINATED OR REFERENCED
dose: A general term used when the context is not specific to a particular dose quantity. When the context is
specific, the name for the quantity is used (e.g., absorbed dose).
dose and dose-rate effectiveness factor (DDREF): A judged factor that generalizes the usually lower
1881
biological effectiveness (per unit of dose) of radiation exposures at low doses and low dose rates as
1882
compared with exposures at high doses and high dose rates.
1883
dose limit: A limit on radiation dose that is applied for exposure to individuals or groups of individuals in
1884
order to prevent the occurrence of radiation-induced tissue reactions or to limit the probability of
1885
radiation-related stochastic effects to an acceptable level (see tissue reaction and stochastic effect).
1886
dose rate: Dose delivered per unit time. Can refer to any dose quantity (e.g., organ dose).
1887
dose reconstruction: Retrospective assessment of dose to identifiable or representative individuals or
1888
populations by any means, especially absorbed dose to specific organs or tissues.
1889
dose response: The way in which an effect, or response, depends on dose.
1890
effective dose: The sum over specified tissues of the products of the equivalent dose in a tissue or organ and
1891
the tissue weighting factor for that tissue or organ. Equivalent dose is the product of the radiation
1892
weighting factor and the mean absorbed dose in a tissue or organ (organ dose).The SI unit for effective
1893
dose is joule per kilogram (J kg–1), with the special name sievert (Sv). A similar quantity used by the
1894
Nuclear Regulatory Commission is named total effective dose equivalent.
1895
1896
electron: Subatomic charged particle. Negatively charged particles are parts of stable atoms. Both negatively
and positively charged electrons may be expelled from the radioactive atom when it disintegrates.
1897
exposome: (see exposomics).
1898
exposomics: The name given to the study of the exposome. The exposome is broadly defined as a measure of
1899
the total exposure of an individual in a lifetime and the relationship of this exposure to health outcomes.
1900
The “omics” component is intended to indicate that molecular methods based on omics techniques (e.g.,
1901
genomics, transcriptomics, proteomics, and metabolomics) can be used in the selection of informative
1902
biomarkers and bioindicators of the exposure.
1903
excess absolute risk (EAR): The difference in absolute risk between exposed and control populations.
1904
excess relative risk (ERR): The ratio of the excess risk of a specified disease to the probability of the same
1905
effect in the unexposed population (i.e., the relative risk minus one). The relative is ratio of the incidence
1906
rate of a given disease in those exposed to the incidence rate of that disease in those not exposed.
1907
exposure: In this Commentary, exposure is used in a general sense meaning to be irradiated.
1908
follow-up bias: Arises when there is a lack of follow-up information, for example if persons have, unknown
1909
to the investigator, migrated outside of the study area, so that their health status cannot be reported.
1910
gamma rays: Short-wavelength electromagnetic radiation of nuclear origin (approximate range of energy:
1911
10 keV to 9 MeV).
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NOT TO BE DISSEMINATED OR REFERENCED
1912
genomic instability (delayed genetic effects): An increased rate of acquiring genetic change.
1913
genotoxin: Any substance capable of causing damage to cellular DNA and thus
1914
producing mutations or cancer.
1915
gray (Gy): The International System (SI) unit of absorbed dose of radiation, 1 Gy = 1 J kg–1.
1916
germline mutation: Any detectable and heritable variation in the lineage of germ cells. Mutations in these
1917
cells are transmitted to offspring,
1918
incidence: The rate of occurrence of a disease, usually expressed in number of cases per million.
1919
ionizing radiation: Any electromagnetic or particulate radiation capable of producing ions, directly or
1920
1921
1922
1923
1924
indirectly, in its passage through matter.
key event: An empirically observable precursor step that is itself a necessary element of the mode of action or
is a biologically-based marker for such an element (see also bioindicator).
Life Span Study (LSS): Study of the Japanese atomic-bomb survivors; the sample consists of 93,741 persons
(in city) of whom 86,572 survivors have a defined dose assigned to them.
1925
lifetime risk: The probability during one’s lifetime of expressing a given health outcome.
1926
linear energy transfer (LET): Average amount of energy lost per unit of particle track length and expressed
1927
in keV μm–1.
1928
low-LET: Radiation having a low linear energy transfer; for example, electrons, x rays, and gamma rays.
1929
high-LET: Radiation having a high linear energy transfer; for example, alpha particles and interaction
1930
products of fast neutrons.
1931
linear-nonthreshold model: In radiation protection, a model that assumes that the long term, biological
1932
damage caused by ionizing radiation (essentially the cancer risk) is directly proportional to the dose.
1933
linkage study: A study that looks for patterns of inheritance of genetic markers in large families in which a
1934
1935
particular condition is common.
mode of action: For both cancer and noncancer endpoints, a sequence of key events and processes starting
1936
with interaction of an agent with a cell, proceeding through operational and anatomical changes, and
1937
resulting in formation of an adverse outcome (see key event).
1938
Monte Carlo simulation: Computation of a probability distribution of an output of a model on the basis of
1939
repeated calculations using random sampling of input variables from specified probability distributions.
1940
natural background radiation (see background radiation):
1941
neutrons: Particles with a mass similar to that of a proton, but with no electrical charge. Because they are
1942
1943
1944
electrically neutral, they cannot be accelerated in an electrical field.
noncancer: Health effects other than cancer (e.g., cataracts, cardiovascular disease) that occur in the exposed
individual.
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NOT TO BE DISSEMINATED OR REFERENCED
1945
nonionizing radiation: Electromagnetic radiation that includes the ultraviolet, visible, infrared, microwave,
1946
radiofrequency, and extremely-low-frequency portions of the electromagnetic spectrum. Unlike ionizing
1947
radiation, nonionizing radiation is unable to ionize atoms in its interactions with matter.
1948
1949
1950
1951
1952
1953
1954
occupational exposure: The exposure received by an individual in a restricted area, or in the course of
employment in which the individual’s duties necessarily involve exposure to radiation.
’omics: Refers to molecular methods for measuring all of a certain molecular specie in a cell (e.g., genomics,
transcriptomics, proteomics, and metabolomics).
organ dose: The mean absorbed dose in an organ or tissue, obtained by integrating or averaging absorbed
doses at points in the organ or tissue.
parallelogram approach: As initially applied to the estimation of heritable risk: the principle that genetic
1955
damage in human germ cells can be estimated by measuring a common end point in humans and animals,
1956
such as mutations or chromosomal aberrations in lymphocytes, and a corresponding genetic end point in
1957
germ cells of animals, the desired target tissue generally inaccessible in man.
1958
partial body: Exposure to only part of a body, as opposed to exposure of the whole body.
1959
polygene: A group of nonallelic genes that together influence a phenotypic trait.
1960
quality factor: The factor by which absorbed dose at a point is modified in order to express the effectiveness
1961
of a given type of ionizing radiation in inducing stochastic effects.
1962
radiation: (1) The energy propagated as waves; radiation or radiant energy, when unqualified, usually refers
1963
to electromagnetic radiation; commonly classified by frequency (e.g., infrared, visible, ultraviolet, x rays,
1964
and gamma rays), and (2) corpuscular emission, such as alpha and beta particles.
1965
radiation weighting factor: A factor used to allow for differences in the biological effectiveness between
1966
different radiations when calculating equivalent dose. These factors are independent of the tissue or organ
1967
irradiated. Equivalent dose is the product of the radiation weighting factor and the mean absorbed dose in
1968
a tissue or organ (organ dose).
1969
1970
radiosensitivity: The relative susceptibility of cells, tissues, organs or organisms to the harmful effect
of ionizing radiation.
1971
recall bias: Arises when information, for example on exposure, is collected retrospectively, and patients, or
1972
their relatives, are subject to differential recall of this information, depending on their disease status.
1973
relative biological effectiveness: A factor used to compare the biological effectiveness of absorbed doses
1974
from different types of ionizing radiation, determined experimentally. Relative biological effectiveness is
1975
the ratio of the absorbed dose of a reference radiation (often taken as 250 kVp x rays) to the absorbed
1976
dose of the radiation in question required to produce an identical biological effect in a particular
1977
experimental organism or tissue.
1978
risk: The probability of a specified effect or response occurring.
68
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1979
1980
NOT TO BE DISSEMINATED OR REFERENCED
risk estimate: The number of cases (or deaths) that are projected to occur in a specified exposed population
per unit dose for a defined exposure regime and expression period.
1981
sievert (Sv): The special name for the unit of effective dose. 1 Sv = 1 J kg–1.
1982
somatic mutation: A mutation occurring in a somatic cell as opposed to a germ cell.
1983
stochastic effect: An effect for which the probability of occurrence, rather than its severity, is a function of
1984
1985
1986
1987
1988
1989
radiation dose without threshold (e.g., cancer).
systems biology: The quantitative study of biological networks and pathways as integrated systems rather
than as a set of isolated parts.
tissue reaction: Injury in population of cells, characterized by a threshold dose and an increase in the severity
of the reaction as the dose is increased further.
tissue weighting factor: A factor representing the ratio of risk of stochastic effects attributable to
1990
irradiation of a given organ or tissue to the total risk when the whole body is irradiated
1991
uniformly. The factor is assumed to be independent of the type of radiation or energy of the
1992
radiation.
1993
total effective dose equivalent: (see effective dose).
1994
uncertainty: Lack of sureness or confidence in predictions of models or results of measurements.
1995
Uncertainties may be categorized as those due to stochastic variation, or as those due to lack of
1996
knowledge founded on an incomplete characterization, understanding or measurement of a system.
1997
69
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1998
NOT TO BE DISSEMINATED OR REFERENCED
Symbols, Abbreviations and Acronyms
1999
2000
AHS
Adult Health Study (Japanese atomic-bomb survivors)
2001
ALARA
as low as reasonably achievable
2002
AML
acute myeloid leukemia
2003
ATM
ataxia-telangiectasia mutated
2004
BBDR
biologically-based dose-response
2005
BEIR
Committee on the Biological Effects of Ionizing Radiation
2006
CT
computed tomography
2007
DDREF
dose and dose-rate effectiveness factor
2008
DNA
deoxyribonucleic acid
2009
DSBs
double-strand breaks (DNA)
2010
EAR
excess absolute risk
2011
ERR
excess relative risk
2012
GWAS
genome-wide association study(ies)
2013
LET
linear energy transfer
2014
LSS
Life Span Study (Japanese atomic-bomb survivors)
2015
SNP
single nucleotide polymorphism
2016
SSBs
single-strand breaks (DNA)
2017
TERT, hTERT
telomerase reverse transcriptase (hTERT in humans)
2018
WARP
Where are the Radiation Professionals?
2019
WECARE
Women’s Environmental Cancer and Radiation Epidemiology (Study)
2020
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2021
NOT TO BE DISSEMINATED OR REFERENCED
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