AOP-Wiki

AOP ID and Title:

AOP 272: Deposition of energy leading to lung cancer
Short Title: Deposition of energy leading to lung cancer

Graphical Representation

Authors

Samantha Sherman1, Zakara Said1, Baki Sadi1, Carole Yauk1,2, Danielle Beaton3, Ruth Wilkins1 Robert Stainforth1, Nadine Adam1,  Vinita Chauhan1,*

Consumer and Clinical Radiation Protection Bureau, Health Canada, Ottawa, ON, Canada

2 Department of Biology, University of Ottawa, Ottawa, ON, Canada

Canadian Nuclear Laboratories, Chalk River, ON, Canada

*Corresponding author: Vinita Chauhan (vinita.chauhan@canada.ca)

Status

Author status OECD status OECD project SAAOP status
Under development: Not open for comment. Do not cite EAGMST Under Review 1.56 Included in OECD Work Plan

Abstract

Despite its widespread recognition in chemical toxicology, the adverse outcome pathway (AOP) framework has not been fully explored in the radiation field to guide relevant research and subsequent risk assessment.  Development of a radiation relevant AOP is described here using a case example of lung cancer.  Lung cancer is a major public health problem world-wide, causing the deaths of an estimated 1.5 million people annually; it imposes a major health-care burden. Numerous environmental factors are known contributors including both chemical (e.g.. asbestos, air pollution and arsenic) and radiation stressors (e.g.. radon  gas).  Radon gas is the second leading cause of lung cancer in North America. Evidence suggests that environmental and indoor radon exposure constitutes a significant public health problem. The mechanism of lung cancer development from exposure to radon gas is unclear. Data suggest that cytogenetic damage from radon decay progeny may be an important contributor.  This AOP defines a path to cancer using key events  related to DNA damage response and repair. The molecular initiating event (MIE)  which represents the first chemical interaction with the cell is identified as the  deposition of ionizing energy.  Energy deposited onto a cell can lead to multiple ionization events to targets such as DNA. This energy will break DNA double strands (KE1) and initiate double strand break (DSB) repair machinery.  In higher eukaryotes, this occurs through non-homologous end joining (NHEJ) which is a quick and efficient, but error-prone process (KE2). If DSBs occur in regions of the DNA transcribing critical genes, then mutations (KE3) generated through faulty repair may alter the function of these genes or may cause chromosomal aberrations (KE4), resulting in genomic instability. These events will alter the functions of many gene products and impact cellular pathways such as cell growth, cell cycling, and apoptosis. With these alterations, cell proliferation (KE5) will be promoted by escaping the regulatory control and form hyperplasia in lung epithelial cells, leading eventually to lung cancer (AO) induction and metastasis . The overall weight of evidence for this AOP is strong.   The uncertainties and inconsistencies surrounding this AOP are centred on dose-response relationships associated with dose, dose-rates and radiation quality. The proposed AOP will act as a case example to motivate more researchers in the radiation field to use the AOP framework to effectively exchange knowledge and identify research gaps in the area of low dose risk assessment.

Background

According to the World Cancer Research Fund, lung cancer is a disease that poses a significant healthcare burden world-wide. (https://www.wcrf.org/dietandcancer/cancer-trends/worldwide-cancer-data (https://www.wcrf.org/dietandcancer/cancer-trends/worldwide-cancer-data)). It is the most commonly diagnosed cancer with the highest incidence of occurrence on a global scale (excluding non-melanoma skin cancers). It is a multi-faceted disease exhibiting various genetic lesions and involving the accumulation of multiple molecular abnormalities over time. It is responsible for 1.5 million deaths annually. There is convincing evidence to show that smoking is an important risk modulating factor to lung cancer development.  This risk is increased by age at which one starts, the total number of years  and number of cigarettes smoked/day.  Studies highlight smoking leads to the largest (relative) increases for small cell carcinoma and squamous cell carcinoma and (Sobue et al., 1999 and Janssen-Heijnen et al., 2001). Other risk factors include lack of physical activity, genetic mutations, dietary factors, asbestos, air pollution (de Groot et al., 2012). Although the link between smoking and lung cancer has been well-established, environmental and indoor radiation exposure are also significant contributors. Risk assessment measures for defining acceptable exposure levels of radiation exposure still remain uncertain; including the scientific research to support the justifications. This is partially due to the assumption of a non-threshold and linear model at low doses with no consideration that cellular/tissue effects of low dose radiation exposure remain poorly understood.

Efforts were focused on developing a simple, unidirectional AOP to lung cancer using predominantly available data from radiation studies. Decades of research suggest that energy in the form of ionizing radiation can break DNA molecules. In vitro mutagenicity studies suggest that alterations in genes in the form of mutations, chromosomal aberrations and micronuclei formation may be important for cancer cell differentiation/proliferation and eventually neoplastic transformation (Harris, 1987). The MIE was selected to be “deposition of energy” as it is the initial measurable interaction at the macro-molecular level within an organism that can lead to a perturbation that initiates the AOP. The term accurately defines the initiating phenomena that manifest from any type of radiation insult (e.g. alpha- and beta-particles, photons, neutrons and heavy ions) and is distinguishable from chemical-based initiation events.  Although the “deposition of energy” is itself a physical phenomenon (not biological) it is essential to describe the causal relationship between radiation insults and the stochastic onset of associated downstream biological damage. Historically, this relationship has been empirically observed and reported in the form of dose-response data. In addition, this MIE encapsulates the known varieties of radiation and their differing physical properties while still adhering to the stressor agnostic principles of the AOP framework.

 This AOP has brought together molecular and cellular based research in the radiation realm and defined a modular, simplistic path towards lung cancer. It has used data–rich key events to a classic targeted response onto a cell that is applicable to multiple radiation stressors (e.g. X-rays, gamma rays, alpha particles, beta particles, heavy ions, neutrons) and well supported thorough empirical evidence. The proposed AOP is not the only route to lung cancer it is likely to be one linear path in a network of multiple pathways that may include other critical events.  This hypothetical AOP will be networked to AOP-296, AOP- 322, AOP-293, AOP-294 and AOP-303 forming a larger network of KEs related inflammation, apoptosis, and oxidative stress, providing a more complete path to lung cancer. This AOP is also a case example of how existing evidence from radiation stressors can stregthen empirical evidence surrounding key events that may be non-radiation specific and vice versa. By using a radiation centric molecular initiating event (MIE), networks can be developed for multiple adverse outcomes distinct to a radiation response. As different radiation stressors can trigger the MIE, the AOP will have wide applicability.

It is our goal, with the development of this AOP to motivate radiation researchers to use this framework for bringing together research data, exchanging knowledge, identifying priority areas and better co-ordinating research in the low-dose ionizing radiation field.

Summary of the AOP

Events

Molecular Initiating Events (MIE), Key Events (KE), Adverse Outcomes (AO)

Sequence Type Event ID Title Short name
1 MIE 1686 Direct Deposition of Energy Energy Deposition
2 KE 1635 Increase, DNA strand breaks Increase, DNA strand breaks
3 KE 155 N/A, Inadequate DNA repair N/A, Inadequate DNA repair
4 KE 185 Increase, Mutations Increase, Mutations
5 KE 1636 Increase, Chromosomal aberrations Increase, Chromosomal aberrations
6 KE 870 Increase, Cell Proliferation Increase, Cell Proliferation
AO 1556 Increase, lung cancer Increase, lung cancer

Key Event Relationships

Upstream Event Relationship Type Downstream Event Evidence Quantitative Understanding
Direct Deposition of Energy adjacent Increase, DNA strand breaks High High
Increase, DNA strand breaks adjacent N/A, Inadequate DNA repair Moderate Moderate
N/A, Inadequate DNA repair adjacent Increase, Mutations Moderate Moderate
N/A, Inadequate DNA repair adjacent Increase, Chromosomal aberrations High Low
Increase, Mutations adjacent Increase, Cell Proliferation High Low
Increase, Chromosomal aberrations adjacent Increase, Cell Proliferation Moderate Low
Increase, Cell Proliferation adjacent Increase, lung cancer High Low
Direct Deposition of Energy non-adjacent Increase, Mutations High High
Direct Deposition of Energy non-adjacent Increase, Chromosomal aberrations High High
Direct Deposition of Energy non-adjacent Increase, lung cancer Moderate Moderate
Increase, DNA strand breaks non-adjacent Increase, Mutations High Low
Increase, DNA strand breaks non-adjacent Increase, Chromosomal aberrations High Low
Increase, Mutations non-adjacent Increase, lung cancer High Low
Increase, Chromosomal aberrations non-adjacent Increase, lung cancer Moderate Moderate

Stressors

Name Evidence
Ionizing Radiation High

Overall Assessment of the AOP

Considerable mechanistic dose-response data has been generated in the radiation field, particularly in the area of clastogenic lesions.  This data has been compiled and captured in this AOP in the most simplified, modular path to lung cancer from a molecular initiating event of deposited energy onto DNA.  This AOP is supported through KERs for which there is biological plausibility and available empirical evidence.  Although it is clear that our proposed AOP is not the only route to the AO, it does represent a classic targeted response of radiation insult on a cell.  The empirical evidence to support this pathway is strong and probabilistic.  As per AOP conventions, the pathway does not describe every mechanism and alteration that is ultimately involved in radiation-associated carcinogenesis. Instead, KEs that are routinely measured using modern and conventional assays are described. For this reason, not all of the KEs that are hallmarks of cancer (i.e. evasion, angiogenesis etc.) are mapped out, but as they are critical events they can be developed separately, and possibly networked in later. This AOP will be the first to use a MIE that is radiation-specific and therefore can act as a foundational AOP to build networks of radiation-specific responses.  Networks can evolve to multiple AOs with additional KEs that incorporate non-targeted effects, immune and adaptive responses, in parallel.   

While this AOP is applicable to other types of radiation-induced cancers, lung cancer was selected as the AO due to its relevance to radon risk assessment and its broader applicability to the chemical field. Lung cancer is a major public health problem world-wide, killing an estimated 1.5 million people annually (https://www.wcrf.org/dietandcancer/cancer-trends/worldwide-cancer-data).  Although smoking is the leading cause of lung cancer, numerous environmental sources are also important contributors including radon, asbestos, air pollution and arsenic (Hubaux et al., 2012).  Some of these stressors can act synergistically to increase risk, particularly among smokers. It has been shown that the histological lung profile of individuals that are smokers is quite different from non-smokers exposed to high radon levels (Kreuzer et al., 2000).  This is in part due to the complexity of each stressor, in terms of its interaction with cells at the molecular level. As radon is the second leading cause of cancer, distinguishing its mode of action at the cellular level from smoking becomes important. Environmental and indoor radon exposures are significant contributors to lung cancer and risk assessment measures for defining acceptable exposure levels of radon exposure still remain uncertain, including the scientific research to support the justification of these levels (Samet et al., 2000 and 2006).  This is partially due to the assumption of a non-threshold and linear model with no consideration that cellular/tissue effects of low dose radiation exposure remain poorly understood (Ruhm et al., 2016; Shore et al., 2018).

 

Despite the decades of research in the area of radiation and DNA damage, a major challenge in developing this AOP was in finding the required components (i.e. essentiality, temporal, incidence and dose concordance) to provide strong empirical evidence to help support the KERs.  Across all KERs, studies using a broard dose range were lacking that used of a broad dose-range.  Most studies conducted analysis at one time-point and there were limited studies that supported the essentiality criteria.  This was particularly evident for the KERs of inadequate repair to mutations/CA and mutations/CA to cellular proliferation. The non-adjacent KERs (i.e. DDOE to CA or DDOE to mutations), were generally more well supported.  Furthermore, no single study encompassed all the KERs proposed in this AOP.  In addition, there were considerable discordant results across KEs simply due to the MIE, as its outcome is dependent on factors such as cell type, dose, dose-rate, and radiation quality. These factors can influence the amount and type of damage, which in turn can affect the probability to drive a path forward to cancer. The principle knowledge gap arose from the lack of data in the form of essentiality studies, using inhibitors and knock-in genes. Furthermore, for a number of KERs, there was minimal dose-response and temporal response data in well-conducted animal studies. There is also a range of uncertainty on how confounders such as lifestyle, health status, and radiosensitivities affect an individual’s path to the AO.  Additional KEs may need to be added in parallel as our knowledge in these areas becomes better understood. These challenges can drive research priorities in the future.

An overall assessment of this AOP shows that there is strong biological plausibility and moderate empirical evidence to suggest a qualitative link between the deposition of energy on DNA to the final AO of lung cancer. This evidence has been derived predominately from decades of research using laboratory studies and through mathematical simulations of cell-based models.  These studies have shown both dose- and temporal-response relationships for select KEs. The quantitative thresholds to initiate each of the KEs have been shown to vary with factors such as the cell type, dose-rate of exposure and radiation quality. Thus, an absolute amount of deposited energy (MIE) to drive a KE forward to a path of cancer is not yet definable. This is particularly relevant to low doses and low dose-rates of radiation exposure where the biology is interplayed with conflicting concepts of hormesis, hypersensitivity and the linear no threshold theory. Furthermore, due to the stochastic nature of the MIE, it remains difficult to identify specific threshold values of DSBs needed to overwhelm the DNA repair machinery to cause “inadequate” DNA repair leading to downstream genetic abnormalities and eventually cancer. With a radiation stressor, a single hit to the DNA molecule could drive a path forward to lung cancer; however this is with low probability. Studies have shown that more than 1 hit is required for tumorigenesis (reviewed in Loeb et al. 2003). Conversely, at much higher doses, a cell will induce apoptosis and may not be driven to cancer induction.  Although empirical modeling of cancer probability vs. mean radiation dose and time to lethality, does provide a good visualization of the effective thresholds (Raabe et al., 2011), practically, there is still considerable uncertainty surrounding the connection of biologically contingent observations and stochastic energy deposition. Future work may focus on developing more precise quantitative and predictive models to help address these types of uncertainties. 

This foundational AOP will initiate the building of networks, and feedback loops that will further the essential events towards lung cancer, including genome alterations, oxidative stress, and metabolomics effectors.  This will require efforts from the larger radiation community.  As the empirical evidence to support these areas becomes stronger, a better representation of events leading to lung cancer will emerge. By identifying uncertainties and inconsistencies in the literature, research can be directed to address knowledge gaps, which can later help refine the pathway.  It is our goal, with this AOP to motivate radiation researchers to use this framework for bringing together research data, exchanging knowledge and identifying research priority areas in the low-dose ionizing radiation field.  Long-term, this AOP alongside others in the radiation field will help to identify key events common to chemical stressors and multiple adverse outcomes, which will be important to help refine risk assessment. In all, by building more radiation-relevant AOPs, the AOP framework will have a bigger role in supporting radiation practice.

Domain of Applicability

Life Stage Applicability
Life Stage Evidence
All life stages High
Taxonomic Applicability
Term Scientific Term Evidence Links
human Homo sapiens High NCBI
rat Rattus norvegicus High NCBI
mouse Mus musculus High NCBI
Sex Applicability
Sex Evidence
Unspecific High

This AOP is relevant to mammals (Eymin & Gazzeri, 2009; Barron et al., 2014; Kurgan et al., 2017). The pathway leading to the development of lung cancer often occurs during adulthood but may be applicable at earlier life stages (Liu et al., 2015) and is independent of sex. In humans, however, genetic abnormalities/mutations suggestive of lung cancer risk seem to be influenced by ethnicity (Lloyd et al., 2013), smoking history (Lim et al., 2009; Sanders & Albitar, 2010; Paik et al., 2012; Lloyd et al., 2013; Cortot et al., 2014; Minina et al., 2017, Cahoon et al., 2017), age (Lloyd et al., 2013), sex (Lim et al., 2009; Cortot et al., 2014) and genotype (Lim et al., 2009; Sanders & Albitar, 2010; Kim et al., 2012; Paik et al., 2012; Leng et al. 2013; Cortot et al., 2014; Minina et al., 2017). Evidence supporting this AOP comes primarily from studies using bacterial DNA (Sutherland et al., 2000; Jorge et al., 2012), human fibroblast cells (Rothkamm & Lo, 2003; Kuhne et al., 2005; Rydberg et al., 2005a), mice (Duan et al., 2008; Zhang & Jasin, 2011), hamsters (Bracalente et al., 2013; Lin et al., 2014), lung cancer cell lines (Sato, Melville B. Vaughan, et al. 2006; Kurgan et al., 2017; Tu et al., 2018), and tissue samples (both with and without lung cancer) Sun et al., 2016; Tu et al., 2018 Warth et al., 2014.

Essentiality of the Key Events

Support for Essentiality of KEs

Defining Question

Strong

Moderate

Weak

Are downstream KEs and/or the AO prevented if an upstream KE is blocked?

Direct evidence from specifically designed experimental studies illustrating essentiality for at least one of the important KEs

Indirect evidence that sufficient modification of an expected modulating factor attenuates or augments a KE

No or contradictory experimental evidence of the essentiality of any of the KEs

MIE:

Direct Deposition of Energy

Evidence for Essentiality of KE: Strong

This event is difficult to test for essentiality as deposition of energy is a physical stressor and cannot be blocked/decreased using chemicals.  However, there are a number of antioxidant studies demonstrating that treatment with various antioxidants prior to irradiation decreases the number of radiation-induced DSBs (results summarized in a review by Kuefner et al. 2015; Smith et al. 2017).

KE1:

Double-Strand Breaks, Increase

Evidence for Essentiality of KE: Weak

A variety of different studies demonstrate that organisms with compromised DNA repair tend to have an increased incidence of DSBs. Inhibition studies have shown that addition of a DNA repair antagonist results in significant increases in DSBs at 6 and 12 hours post-irradiation (Dong et al. 2017). Similarly, knock-outs/knock-downs of DNA repair proteins also results in persisting DSBs post-irradiation (Rothkamm and Lo 2003; Bracalente et al. 2013; Mcmahon et al. 2016; Dong et al. 2017), with one DNA ligase IV-deficient human cell line showing DSBs 240 - 340 hours after radiation exposure (Mcmahon et al. 2016). Studies by Tatsumi-Miyajima et al., (1993) note the increased rate of supF mutation frequencies following the use of a restriction enzyme, Aval, which induces DSBs in different human fibroblast cell lines transfected with plasmids containing the Aval restriction site.  Kurashige et al. (2017) have demonstrated a decrease in MN frequency following the reduction in DSBs by regulating NAC pre-treatment.

KE2: Inadequate DNA Repair, Increase

Evidence for Essentiality of KE: Strong

There is extensive evidence to demonstrate the essentiality of inadequate repair to downstream events. Studies show that inhibiting DNA repair results in a lack of DNA repair foci post-irradiation (Paull et al. 2000), while cells deficient in ATM (involved in DNA repair) show increased levels of incorrectly rejoined DSBs (Lobrich et al. 2000; Bucher et al. 2021). Similarly, chromosomal aberrations were more frequent after inhibition of various proteins involved in DNA repair (Chernikova et al. 1999; Heterodimer et al. 2002; Wilhelm et al. 2014). Furthermore, when knock-out cell lines (i.e., knock-out of genes involved in DNA repair to increase the incidence of ‘inadequate’ repair)  were examined for genomic abnormalities, increased incidence of chromosomal aberrations were clearly evident (Karanjawala et al. 1999; Cornforth and Bedford 1994; Patel et al. 1998; Simsek and Jasin 2010; Lin et al. 2014; Wilhelm et al. 2014; Mcmahon et al. 2016).  Deficiencies in proteins involved in DNA repair also resulted in altered mutation frequencies relative to wild-type cases (Amundson and Chen 1996; Feldmann et al. 2000; Smith et al. 2003; Wessendorf et al. 2014; Perera et al. 2016). Mutation frequency increased following knocked-down BER-initiating glycosylases (OGG1, NEIL1, MYH, NTH1) in HEK293T human embryonic kidney cells transfected with plasmids that were either positive or negative for 8-oxodG (Suzuki et al., 2010). Moreover, G:C to T:A transversion frequency increased in all analyzed cells. Nallanthighal et al. (2017) demonstrated that inadequate DNA repair impacts MN induction in irradiated Ogg1-deficienct mice (compared to Oggff1+/+ mice).

KE3: Mutations, Increase

Evidence for Essentiality of KE: Strong

Numerous studies show a strong correlation between inadequate DNA repair and mutation incidence, as altered mutation frequencies were evident when there were deficiencies in the proteins involved in DNA repair (Amundson and Chen 1996; Feldmann et al. 2000; Smith et al. 2003; Wessendorf et al. 2014; Perera et al. 2016). Mutations in several different genes, including tumour suppressor gene TP53, have also been shown to increase cell proliferation rates (Hundley et al. 1997; Lang et al. 2004; Ventura et al. 2007; Welcker and Clurman 2008; Duan et al. 2008; Geng et al. 2017; Li and Xiong 2017); mutant or absent TP53 has likewise been implicated in carcinogenesis (Iwakuma and Lozano 2007; Muller et al. 2011; Kim and Lozano 2018). In terms of lung cancer specifically, there are many different studies showing that mutations in TP53, KRAS, and EGFR  are associated with lung carcinogenesis. The conceptual ‘removal’ or ‘blocking’ of these mutations using conditional knock out models, inducible mutation models, and treatment with various antagonizing and agonizing compounds has been observed to reverse or prevent lung tumourigenesis in vivo (Roth et al. 1996; Fisher et al. 2001; Ventura et al. 2007; Iwakuma and Lozano 2007; Jia et al. 2016; Luo et al. 2019, Krasinski 2012). The lung tumourigenesis process was also observed to be expedited by exposure of Gprc5a knock-out mice to a known pulmonary carcinogen; this resulted in more somatic mutations and an increased tumour burden in a much shorter time frame relative to unexposed mice (Fujimoto et al. 2017).   

KE4: Chromosomal Aberrations, Increase

Evidence for Essentiality of KE: Weak

Many studies using a model with inadequate DNA repair (in the form of knock-out cell lines and DNA repair inhibitor studies) demonstrated that chromosomal aberrations were significantly increased when DNA repair was inadequate (Karanjawala et al.; Patel et al. 1998; Deniz Simsek and Jasin 2010; Lin et al. 2014; Wilhelm et al. 2014; Mcmahon et al. 2016, Cornforth 1994). The presence of chromosomal aberrations, particularly gene fusions and translocations, has also been associated with high rates of cellular proliferation (Li et al. 2007; Soda et al. 2007; Guarnerio et al. 2016).There also is support for the essentiality of CAs in the induction of cancer. There were significant increases in CAs (micronuclei, nucleoplasmic bridges and nuclear buds) in peripheral blood lymphocyte cultures after addition of a known pulmonary carcinogen to the cells (Lloyd et al. 2013). Furthermore, introduction of the BCR/ABL translocation in mice resulted in chronic myelogenous leukemia; this was accomplished by lethally irradiating the mice and performing a bone marrow transplant with cells that contained a retrovirus carrying the BCR/ABL translocation (Pear et al. 1998). Furthermore, tumour-inducing A549 cells, which are deficient in TSCL1 due to a loss of heterozygosity at chromosome 11, can induce detectable tumours within 3 weeks of injection; transfection of these A549 cells with genes to correct the TSCL1 deficiency and subsequent injection into mice results in fewer and slower-growing tumours (Kuramochi et al. 2001).

KE5:

Cell Proliferation, Increase

Evidence for Essentiality of KE: Strong

Rates of cellular proliferation have been shown to be increased when there are mutations in key genes associated with cell cycle control, including tumour suppressor gene TP53 (Hundley et al. 1997; Lang et al. 2004; Ventura et al. 2007; Welcker and Clurman 2008; Duan et al. 2008; Geng et al. 2017; Li and Xiong 2017). Cells transformed with various oncogenic mutations that suppressed tumour suppressor genes and enhanced activity of proto-oncogenes also showed increased cellular proliferation rates in the form of higher tumour volumes (Sato et al. 2017). Addition of inhibitors that blocked the pro-proliferative signaling pathway associated with KRAS and EGFR in these oncogenically-transformed cells resulted in lower rates of cellular proliferation (Sato et al. 2017). Similarly, several specific chromosomal gene fusions and translocations have also been associated with increasing the rate of cellular proliferation (Li et al. 2007; Soda et al. 2007; Guarnerio et al. 2016). In cancer cells known to harbor the Philadelphia chromosome (a translocation heavily implicated in the pathogenesis of acute lymphoblastic leukemia), addition of an ERB inhibitor resulted in decreased cellular proliferation rates in the cancer cells (Irwin et al. 2013). In another experiment where human ovarian cancer cells were treated with estrogen, there was an increase in the levels of micronuclei and a corresponding increase in the proliferation rates; addition of an antagonist maintained micronuclei frequencies and cell proliferation rates at control cell levels (Stopper et al. 2003). Cellular proliferation rates were decreased using both in vitro and in vivo carcinogenic models exposed to anti-cancer compounds, which highlights the importance of high cellular proliferation for carcinogenesis (Kassie et al. 2008; Lv et al. 2012; Wanitchakool et al. 2012; Pal et al. 2013; Warin et al. 2014; Tu et al. 2018). Genetic manipulations of genes involved in proliferation also resulted in modified cellular proliferation rates (Lv et al. 2012; Sun et al. 2016).

 

Weight of Evidence Summary

Support for Biological Plausibility of KERs

Defining Question

Strong

Moderate

Weak

Is there a mechanistic relationship between KEup and KEdown consistent with established biological knowledge?

Extensive understanding of the KER based on extensive previous documentation and broad acceptance; Established mechanistic basis

KER is plausible based on analogy to accepted biological relationships, but scientific understanding is  not completely established

There is empirical support for  statistical association between KEs, but the structural or functional relationship between them is not understood

Direct Deposition of Energy (MIE)    --> Double-Strand Breaks, Increase (KE1)

Evidence for Biological Plausibility of KER: Strong

It is well established that ionizing radiation can cause various types of DNA damage including single-strand and double-strand breaks (DSBs) (reviewed in Lomax et al. 2013). In particular, there is evidence for the direct deposition of energy and a resulting increase in DSBs (Ward 1988; Terato and Ide 2005; Goodhead 2006; Hada and Georgakilas 2008; Asaithamby and Chen 2011; Okayasu 2012; Lomax et al. 2013; Moore et al. 2014; Desouky et al. 2015; Sage and Shikazono 2017; Chadwick 2017; Franken et al., 2012; Frankenberg et al., 1999; Rydberg et al., 2002; Belli et al., 2000). Structural damage from the deposited energy can induce chemical modifications in the form of breaks to the phosphodiester backbone of both strands of the DNA. (Joiner 2009). DSBs are also often formed by indirect interactions with radiation through water molecules. Energy deposited on water molecules by radiation results in the production of reactive oxygen species that can then damage the DNA (Ward 1988; Desouky et al. 2015; Maier et al. 2016).

Direct Deposition of Energy (MIE)    --> Mutations, Increase (KE3)

Evidence for Biological Plausibility of KER: Strong

Many studies across a variety of different models provide evidence that direct deposition of energy by ionizing radiation results in increased mutation frequencies (Russell et al. 1957; Winegar et al. 1994; Gossen et al. 1995; Suzuki and Hei 1996; Albertini et al. 1997; Dubrova et al. 1998; Dubrova et al. 2000; Canova et al. 2002; Dubrova et al. 2002; Dubrova and Plumb 2002; Masumura et al. 2002; Somers et al. 2004; Burr et al. 2007; Ali et al. 2012; Adewoye et al. 2015; Wilson et al. 2015; Bolsunovsky et al. 2016; Mcmahon et al. 2016; Matuo et al. 2018; Nagashima et al. 2018; Wu et al., 1999; Hei et al., 1997; Nagasawa and Little, 1999; Barnhart and Cox, 1979; Thacker at al., 1982; Zhu et al., 1982; Metting et al., 1992; Schwartz et al., 1991; Chen et al., 1984). Radiation-specific mutational signatures have been identified in a variety of radiation-induced tumours (Sherborne et al. 2015; Behjati et al. 2016), and there is extensive evidence that radiation increases germline mutations in both mice (Dubrova et al. 1998; Dubrova et al. 2000; Dubrova et al. 2002; Somers et al. 2004; Barber et al. 2009; Ali et al. 2012; Adewoye et al. 2015; Wilson et al. 2015) and humans (Dubrova et al. 2002; Dubrova and Plumb 2002).

Direct Deposition of Energy (MIE)    --> Chromosomal Aberrations, Increase (KE4)

Evidence for Biological Plausibility of KER: Strong

Extensive and diverse data from human, animal and in vitro-based studies show ionizing radiation induces a rich variety of chromosomal aberrations (Bauchinger et al. 1994; Schmid et al. 2002; Thomas et al. 2003; Maffei et al. 2004; Tucker et al. 2005a; Tucker et al. 2005b; George et al. 2009; Meenakshi and Mohankumar 2013; Santovito et al. 2013; Arlt et al. 2014; Balajee et al. 2014; Han et al. 2014; Vellingiri et al. 2014; Suto et al. 2015; Adewoye et al. 2015; Cheki et al. 2016; Mcmahon et al. 2016; Morishita et al. 2016; Qian et al. 2016; Basheerudeen et al. 2017; Meenakshi et al. 2017; Abe et al. 2018; Jang et al. 2019; Puig et al., 2016; Barquinero et al., 2004; Curwen et al., 2012; Testa et al., 2018; Franken et al., 2012; Cornforth et al., 2002; Loucas et al., 2013; Nagasawa et al., 1990a; Nagasawa et al., 1990b; Edwards et al., 1980; Themis et al., 2013; Schmid et al., 1996; Mestres et al., 2004; Bilbao et al., 1989; Mill et al., 1996; Brooks, 1975; Tawn and Thierens, 2009; Durante et al., 1992; Hamza and Mohankumar, 2009; Takatsuji and Sasaki, 1984; Moquet et al., 2001; Purrott et al., 1980; duFrain et al., 1979).The mechanism leading from direct deposition of energy to chromosomal aberrations has been described in several reviews (Smith et al. 2003; Christensen 2014; Sage and Shikazono 2017). Other evidence derives from studies examining the mechanism of copy number variant formation (Arlt et al. 2014) and induction of radiation-induced chromothripsis (Morishita et al. 2016).

Double-Strand Breaks, Increase (KE1) --> Inadequate DNA Repair, Increase (KE2)

Evidence for Biological Plausibility of KER: Strong

 It is well recognized that almost all types of DNA lesions will result in recruitment of repair enzymes and factors to the site of damage, and the pathway involved in the repair of DSBs has been well-documented in a number of reviews, many of which also discuss the error-prone nature of DNA repair (Van Gent et al. 2001; Hoeijmakers 2001a; Khanna and Jackson 2001; Lieber et al. 2003; San Filippo et al. 2008; Lieber et al. 2010; Polo and Jackson 2011; Schipler and Iliakis 2013; Vignard et al. 2013; Betermier et al. 2014; Mehta and Haber 2014; Moore et al. 2014; Rothkamm et al. 2015; Jeggo and Markus 2015; Chang et al. 2017; Lobrich and Jeggo 2017; Sage and Shikazono 2017) Error-prone repair processes are particularly important when DSBs are biologically induced and repaired during V(D)J recombination of developing lymphocytes(Jeggo et al. 1995; Malu et al. 2012) and during meiotic divisions to generate gametes (Murakami and Keeney 2008).

Inadequate DNA Repair, Increase (KE2) --> Mutations, Increase (KE3)

Evidence for Biological Plausibility of KER: Strong

Decades of research have shown that DNA repair pathways are error prone and can cause mutations inherently (such as the error prone NHEJ) (Sishc and Davis 2017). This error-prone repair, however, may be due more to the structure of the DSB ends rather than the repair machinery; more complex breaks require more processing, increasing the likelihood that there will be errors in the DNA sequence upon completion of repair (Betermier et al. 2014; Waters et al. 2014). After being exposed to ionizing radiation, approximately 25 – 50% of double-strand breaks have been shown to be incorrectly repaired (Löbrich et al. 1998; Kuhne et al. 2000; Lobrich et al. 2000).

Inadequate DNA Repair, Increase (KE2) --> Chromosomal Aberrations, Increase (KE4)

Evidence for Biological Plausibility of KER: Strong

DSBs are repaired by non-homologous end joining (NHEJ) and homologous recombination (HR). HR uses a template DNA strand to repair DNA damage, while the more error-prone NHEJ simply religates broken ends back together without the use of a template (van Gent et al. 2001; Hoeijmakers 2001; Jeggo and Markus 2015; Sishc and Davis 2017). Chromosomal aberrations may result if DNA repair is inadequate, meaning that the double-strand breaks are misrepaired or not repaired at all (Bignold, 2009; Danford, 2012; Schipler & Iliakis, 2013). A multitude of different types of chromosomal aberrations can occur, depending on the timing and type of erroneous repair. Examples of chromosomal aberrations include copy number variants, deletions, translocations, inversions, dicentric chromosomes, nucleoplasmic bridges, nuclear buds, micronuclei, centric rings, and acentric fragments. A multitude of publications are available that provide details on how these various chromosomal aberrations are formed in the context of inadequate repair (Ferguson and Alt 2001; Venkitaraman 2002; Povirk 2006; Weinstock et al. 2006; Denis Simsek and Jasin 2010; Lieber et al. 2010; Fenech and Natarajan 2011; Danford 2012; Schipler and Iliakis 2013; Mizukami et al. 2014; Russo et al. 2015; Leibowitz et al. 2015; Rode et al. 2016; Vodicka et al. 2018).  

Mutations, Increase (KE3) -->  Cell Proliferation, Increase (KE5)

Evidence for Biological Plausibility of KER: Strong

It is clearly documented that when enough mutations accumulate in critical genes associated with cell cycling or proliferation, there is potential for uncontrollable cell proliferation to occur, which in some cases leads to carcinogenesis (Bertram 2001; Vogelstein and Kinzler 2004; Panov 2005, Lee and Muller 2010). In fact, one of the hallmarks of cancer is sustained proliferative signalling, and one of the enabling characteristics of this increased proliferation is genomic instability/mutations (Hanahan and Weinberg 2011). Thus mutations are particularly dangerous if they occur in proteins controlling the cell cycle checkpoint for entry into proliferation, such as RB and p53 (Lee and Muller 2010). Activating mutations in proto-oncogenes (Bertram 2001; Vogelstein and Kinzler 2004; Larsen and Minna 2011; Lee and Muller 2010) Lee and Muller 2010, inactivating mutations in tumour suppressor genes (Bertram 2001; Vogelstein and Kinzler 2004; Lee and Muller 2010; Fernandez-Antoran et al. 2019) and inactivating mutations in caretaker/stability genes (Vogelstein and Kinzler 2004; Hanahan and Weinberg 2011) are all associated with abnormal increases the rate of cellular proliferation.

Chromosomal Aberrations, Increase (KE4) --> Cell Proliferation, Increase (KE5)

Evidence for Biological Plausibility of KER: Strong

Chromosomal aberrations are formed when there is inadequate DNA repair (Bignold 2009; Danford 2012; Schipler and Iliakis 2013) or errors during mitosis (Levine and Holland 2018). Chromosomal aberrations have been shown to increase cell proliferation when the aberrations result in the activation of proto-oncogenes (Bertram 2001; Vogelstein and Kinzler 2004), the inactivation of tumour suppressor genes (Bertram 2001; Vogelstein and Kinzler 2004),, or the modification of caretaker/stability genes (Vogelstein and Kinzler 2004). Reviews documenting the contribution of CAs to cellular proliferation and/or cancer development (which implies high rates of cellular proliferation) are available (Mes-Masson and Witte 1987; Bertram 2001; Vogelstein and Kinzler 2004; Ghazavi et al. 2015; Kang et al. 2016). The link between chromosomal instability (CIN), which describes the rate of chromosome gains and losses, and cancer development has also been reviewed (Thompson et al. 2017; Gronroos 2018; Targa and Rancati 2018; Lepage et al. 2019).

Cell Proliferation, Increase (KE5) -->  Lung Cancer, Increase (AO)

Evidence for Biological Plausibility of KER: Strong

The means by  which dysregulation of cell proliferation promotes the transformation of normal to carcinogenic cells has been heavily reviewed (Pucci et al. 2000; Bertram 2001; Panov 2005; Eymin and Gazzeri 2009; Hanahan and Weinberg 2011; Larsen and Minna 2011). The cell cycle is essential in controlling cellular proliferation rates, and requires a series of checkpoints to be passed before the cell can fully commit to the process of cell division (Pucci et al. 2000; Bertram 2001; Eymin and Gazzeri 2009; Hanahan and Weinberg 2011). One of the most important checkpoints requires the proper functioning of p53, RB, CDK4 and CDK6. The tumour suppressor p53  plays a particularly important role in stopping the cell cycle when there is DNA damage, and for triggering apoptosis when damage is too severe to be repaired (Bertram 2001; Hanahan and Weinberg 2011; Larsen and Minna 2011). Telomeres also play a role in controlling cell proliferation; when the telomeres become too short to protect the coding DNA, the cell enters into a state of replicative senescence (Bertram 2001; Hanahan and Weinberg 2011). All of these processes play a role in controlling the rate of cellular proliferation within a cell. Cancer may occur when these processes became dysregulated such that cells begin to proliferate at excessively high rates. High rates of proliferation are in fact one of the strongest hallmarks of cancer (Hanahan and Weinberg 2011), and uncontrolled proliferation can be accomplished through sustained proliferative signalling through activation of proto-oncogenes (Bertram 2001; Vogelstein and Kinzler 2004; Eymin and Gazzeri 2009; Hanahan and Weinberg 2011; Larsen and Minna 2011), evading growth suppressors and resisting cell death through suppression of tumour suppressor genes (Bertram 2001; Vogelstein and Kinzler 2004; Eymin and Gazzeri 2009; Hanahan and Weinberg 2011; Larsen and Minna 2011), and overcoming replicative senescence through expression of the telomere-lengthening enzyme telomerase (Bertram 2001; Panov 2005; Hanahan and Weinberg 2011; Larsen and Minna 2011). In lung cancer specifically, commonly activated proto-oncogenes include EGFR, ERBB2, MYC, KRAS, MET, CCND1, CDK4 and BCL2, while commonly inactivated tumour suppressor genes are TP53, RB1, STK11, CDKN2A, FHIT, RASSF1A, and PTEN (Larsen and Minna 2011). Telomerase is also activated in nearly all small cell lung cancer (SCLC) cases, and in over three-quarters of non-small cell lung cancer (NSCLC) cases (Panov 2005; Larsen and Minna 2011).

 

Double-Strand Breaks, Increase (KE1) --> Mutations, Increase (KE3)

Evidence for Biological Plausibility of KER: Strong

Mechanisms of DNA strand break repair have been extensively studied. It is accepted that non-homologous joining of broken ends can introduce deletions, insertions, or base substitution. In mamalian and yeast cells, both HR and NHEJ can lead to alteration in DNA sequence (Hicks & Haber, 2010; Butning & Nussenzweig, 2013; Byrne et al., 2014; Rodgers & McVey, 2016; Dwivedi & Haver, 2018).

 

Double-Strand Breaks, Increase (KE1) --> Chromosomal Aberrations, Increase (KE4)

Evidence for Biological Plausibility of KER: Strong

DNA strand breaks must occur for chromosomal aberrations to occur. Studies have shown DSBs leading to irreversible damage. The links between DSBs and the role DSB repairs has in preventing chromosomal aberrations is widely discussed, with several reviews available: (van Gent et al., 2001; Ferguson & Alt, 2001; Hoeijmakers, 2001; Iliakis et al., 2004; Povirik, 2006; Weinstock et al., 2006; Natarajan & Palitti, 2008; Lieber et al., 2010; Mehta & Haber, 2014; Ceccaldi et al., 2016; Chang et al., 2017; Sishc & Davis, 2017; Brunet & Jasin, 2018).

Mutations, Increase (KE3) --> Lung Cancer, Increase (AO)

Evidence for Biological Plausibility of KER: Moderate

There is strong biological plausibility for the relationship between mutations and lung cancer. Bioinformatics studies have identified unique mutation signature profiles associated with specific types of cancer, including lung adenocarcinoma, lung squamous cell carcinoma and lung small cell carcinoma (Alexandrov et al. 2013; Jia et al. 2014; George et al. 2015). Moreover, mutations/genome instability have been implicated as one of the ‘enabling characteristics’ underlying the hallmarks of cancer (Hanahan and Weinberg 2011). Mutations are thought to promote tumourigenesis by modifying the expression of tumour suppressor genes, proto-oncogenes, and caretaker/stability genes in such a way that promotes cell proliferation and/or suppresses apoptosis (Vogelstein and Kinzler 2004; Panov 2005; Sanders and Albitar 2010; Hanahan and Weinberg 2011; Larsen and Minna 2011).  Commonly mutated genes in lung cancer include TP53, KRAS and EGFR. Mutations in these genes, along with known lung cancer driver mutations, are thought to promote tumourigenesis by stimulating pro-proliferation signalling pathways such as the PI3K-AKT-mTOR pathway and RAS-REF-MEK pathway (Varella-garcia 2009; Sanders and Albitar 2010; Larsen and Minna 2011McCubrey 2006).

Chromosomal Aberrations, Increase (KE4) --> Lung Cancer, Increase (AO)

Evidence for Biological Plausibility of KER: Moderate

Chromosomal aberrations, encompassing chromosome-type aberrations, chromatid-type aberrations, micronuclei, and nucleoplasmic bridges, have all been found to be predictive of cancer risk in various human cohorts (Bonassi et al. 2000; Smerhovsky et al. 2002; Hagmar et al. 2004; Norppa et al. 2006; Boffetta et al. 2007; Bonassi et al. 2008; Lloyd et al. 2013; El-zein et al. 2014; Vodenkova et al. 2015; El-zein et al. 2017). Specific categories of CAs, including CNVs (Wrage et al. 2009; Shlien and Malkin 2009; Liu et al. 2013; Mukherjee et al. 2016; Zhang et al. 2016; Ohshima et al. 2017) and gene rearrangements (Bartova et al. 2000; Trask 2002; Sanders and Albitar 2010; Sasaki et al. 2010; Mao et al. 2011), have also been associated with cancer development. Chromosomal aberrations promote tumourigenesis through the alteration of pathways controlling cellular growth and apoptosis (Albertson et al. 2003; Sanders and Albitar 2010). The chromosomal aberration burden may be increased by factors such as aberrant centromeres, telomerase deficiencies paired with poor cell surveillance (Albertson et al. 2003), ionizing radiation (Hei et al. 1994; Weaver et al. 1997; Weaver et al. 2000), and the interplay between non-clonal and clonal CAs (Heng, Bremer, et al. 2006; Heng, Stevens, et al. 2006).

Direct Deposition of Energy (MIE)    --> Lung Cancer, Increase (AO)

Evidence for Biological Plausibility of KER: Strong

The direct deposition of energy, particularly by radon gas, has been associated heavily with lung cancer (Axelson 1995; Jostes 1996; Beir 1999; Kendall and Smith 2002a; Al-Zoughool and Krewski 2009; Robertson et al. 2013). Deposition of energy that triggers lung carcinogenesis in particular is thought to enter the body through inhalation (Beir 1999; Kendall and Smith 2002b). The inhaled particles are thought to deposit on lung tissue and decay, producing ionizing radiation (Axelson 1995; Beir 1999; Kendall and Smith 2002b; Al-Zoughool and Krewski 2009) that can direct the cell towards carcinogenesis (Axelson 1995; Beir 1999; Robertson et al. 2013). The process of radiation-induced carcinogenesis often follows three steps: initiation, promotion and progression. Initiation refers to the interaction between the radiation and the cell, and results in irreversible genetic changes. Promotion occurs when non-carcinogenic promoter is added to the initiated cells such that it synergistically increases oncogenesis, often through receptor-mediated epigenetic changes. Progression occurs at the point when the cells convert from benign to malignant, and is associated with rapid growth and further accumulation of genomic aberrations (NRC 1990; Pitot 1993).

 

Support for Empirical Evidence of KERs

Defining Question

Strong

Moderate

Weak

Does empirical evidence support that a change in KEup leads to an appropriate change in KEdown? Does KEup occur at lower doses and earlier time points than KEdown and is the incidence of KEup > than that for KEdown?

 

Inconsistencies?

Multiple studies showing dependent change in both events following exposure to a wide range of specific stressors (Extensive evidence for temporal, dose-response and incidence concordance);  No or few critical data gaps or conflicting data

Demonstrated dependent change in both events following exposure to a small number of specific stressors; Some evidence inconsistent with expected pattern that can be explained by factors such as the experimental design, technical considerations, differences between laboratories, etc.

Limited or no studies reporting dependent change in both events following exposure to a specific stressor (i.e. endpoints never measured in the same study or not at all); And/or significant inconsistencies in empirical support across taxa and species that don’t align with expected pattern for hypothesized AOP

Direct Deposition of Energy (MIE) --> Double-Strand Breaks, Increase (KE1)

Evidence for Empirical Support of KER: Strong

Evidence exists for dose/incidence and temporal concordance between deposition of energy and the resultant formation of DNA double-strand breaks. With increasing ionizing radiation, there is an increase in the frequency of double-strand breaks (Charlton et al. 1989; Rogakou et al. 1999; Sutherland et al. 2000; Lara et al. 2001; Rothkamm and Lo 2003; Kuhne et al. 2005; Sudprasert et al. 2006; Rube et al. 2008; Beels et al. 2009; Grudzenski et al. 2010; Flegal et al. 2015; Shelke and Das 2015; Antonelli et al. 2015; Franken et al., 2012; Frankenberg et al., 1999; Rydberg et al., 2002; Belli et al., 2000; Chadwick 2017). However, dose-rate and radiation quality play a crucial role in determining the degree of DNA damage. Temporally, DSBs have been evident at 3 - 30 minutes post-irradiation (Rogakou et al. 1999; Rothkamm and Lo 2003; Rube et al. 2008; Beels et al. 2009; Kuefner et al. 2009; Grudzenski et al. 2010; Antonelli et al. 2015). A significant proportion of the DSBs are resolved within 5 hours of radiation (Rogakou et al. 1999; Rube et al. 2008; Kuefner et al. 2009; Grudzenski et al. 2010; Shelke and Das 2015), with a return to baseline levels by 24 hours in most cases (Rothkamm and Lo 2003; Rube et al. 2008; Grudzenski et al. 2010; Antonelli et al. 2015).

Direct Deposition of Energy (MIE) --> Mutations, Increase (KE3)

Evidence for Empirical Support of KER: Strong

Evidence exists for dose/incidence concordance between deposition of energy by ionizing radiation and a corresponding dose-dependent increase in mutation frequency (Suzuki and Hei 1996; Schmidt and Kiefer 1998; Kraemer et al. 2000; Canova et al. 2002; Bolsunovsky et al. 2016; Mcmahon et al. 2016; Matuo et al. 2018; Nagashima et al. 2018). The linear energy transfer of the radiation (Dubrova and Plumb 2002; Matuo et al. 2018), whether the radiation is chronic or acute (Russell 1958), the radiation type (Schmidt and Kiefer 1998; Masumura 2002), and the tissue being irradiated (Masumura 2002, Gossen 1995) all affect this dose-dependent increase. Temporally, it is well established that an increased incidence of mutations is reported after the deposition of energy by radiation (Winegar 1994, Gossen 1995, Albertini 1997, Dubrova 2002A, Matuo 2018, Canova 2002, Nagashima 2018, Masumura 2002, Russell 1958). Most of these studies, however, span over days and weeks, thus making it difficult to pinpoint exactly when mutations occur. Several studies report the manifestation of mutations within 2 - 3 days of irradiation (Winegar 1994, Masumura 2002, Gossen 1995), with an increased mutation frequency still elevated at 14 (Winegar 1994) and 21 days (Gossen 1995) after radiation exposure. At low doses (<1 Gy) the induction of mutations in cells has been observed for high-LET radiation such as alpha particles (Wu et al., 1999; Hei et al., 1997; Nagasawa and Little, 1999; Barnhart and Cox, 1979; Thacker at al., 1982; Zhu et al., 1982; Metting et al., 1992; Schwartz et al., 1991; Chen et al., 1984; Albertini et al., 1997).

Direct Deposition of Energy (MIE) -->  Chromosomal Aberrations, Increase (KE4)

Evidence for Empirical Support of KER: Strong

Results from many studies indicate dose/incidence and temporal concordance between the deposition of energy and the increased frequency of chromosomal aberrations. There is strong evidence of a dose-dependent increase in a wide range of chromosomal aberrations in response to increasing radiation dose (Schmid 2002, Hande et al. 2003, Thomas 2003, Jang 2019, Abe 2018, Suto 2015, McMahon 2016, Tucker 2005A, Tucker 2005B, Arlt 2014, McMahon 2016, Balajee 2014,George 2009, Maffei 2004, Qian 2015; Puig et al., 2016; Barquinero et al., 2004; Curwen et al., 2012; Testa et al., 2018; Franken et al., 2012; Cornforth et al., 2002; Loucas et al., 2013; Nagasawa et al., 1990a; Nagasawa et al., 1990b; Edwards et al., 1980; Themis et al., 2013; Schmid et al., 1996; Mestres et al., 2004; Bilbao et al., 1989; Mill et al., 1996; Brooks, 1975; Tawn and Thierens, 2009; Durante et al., 1992; Hamza and Mohankumar, 2009; Takatsuji and Sasaki, 1984; Moquet et al., 2001; Purrott et al., 1980; duFrain et al., 1979). Temporally, it is well-established that chromosomal aberrations occur after exposure to radiation (Schmid 2002, Thomas 2003, Balajee 2014, Arlt 2014, George 2009, Suto 2015, Basheerudeen 2017, Tucker 2005A, Tucker 2005B, Abe 2018, Jang 2019), though the exact timing is difficult to pinpoint because most assays take place hours or days after the radiation exposure. One notable study did, however, document the presence of chromosomal aberrations within the first 20 minutes of irradiation, with the frequency increasing sharply until approximately 40 minutes, followed by a plateau (McMahon 2016). By 7 days post-irradiation, the frequencies of most chromosomal aberrations had declined (Tucker 2005A, Tucker 2005B).  It should be noted that chromosomal aberrations induced by ionizing radiation are dependent on dose, dose-rate, and radiation type (Bender et al., 1988; Guerrero-Carbajal et al., 2003; Day et al., 2007, Suzuki 1996, Hande et al. 2003).  

Double-Strand Breaks, Increase (KE1) --> Inadequate DNA Repair, Increase (KE2)

Evidence for Empirical Support of KER: Moderate

Results from many studies indicate dose/incidence and temporal concordance between the frequency of double-strand breaks and the rate of inadequate repair. As DNA damage accumulates in organisms, the incidence of in adequate DNA repair activity (in the form of non-repaired or misrepaired DSBs) also increases (Dikomey 2000, McMahon 2016, Kuhne 2005, Rydberg 2005, Kuhne 2000, Lobrich 2000). DNA damage and its ensuing repair also follow a very similar time course, with both events documented within minutes of a radiation stressor (Pinto 2005, Rothkamm 2003, Asaithambly 2009, Dong 2017, Paull 2000). Uncertainties in this KER include controversy surrounding how error-prone NHEJ truly is (Betemier 2014), differences in responses depending on the level of exposure of a genotoxic substance (Marples 2004), and confounding factors (such as smoking) that affect double-strand break repair fidelity (Scott 2006, Leng 2008).

Inadequate DNA Repair, Increase (KE2) --> Mutations, Increase (KE3)

Evidence for Empirical Support of KER: Moderate

There are several studies that indicate a dose/incidence concordance between inadequate DNA repair and an increased frequency of mutations. Inadequate DNA repair (Ptácek et al. 2001; Mcmahon et al. 2016) and mutation frequencies (Mcmahon et al. 2016) have both been found to increase in a dose-dependent fashion with increasing doses of a radiation stressor. Moreover, specific genomic regions with inadequate DNA repair rates also were found to have increased mutation densities in cancer samples (Perera et al. 2016). Increased mutation frequencies have also been demonstrated in cases where more complex DNA repair is required (Smith et al. 2001). According to the results of this study, evidence of repaired DNA was present prior to the detection of mutations in cases of simple repair, whereas these two events occurred together at a later time point when more complex repair was required (Smith et al. 2001).

Inadequate DNA Repair, Increase (KE2) --> Chromosomal Aberrations, Increase (KE4)

Evidence for Empirical Support of KER:  Moderate

There is little empirical evidence available that directly examines the dose and incidence concordance between DNA repair and CAs within the same study. However, comparison of results from studies that measure either radiation-induced DNA repair or radiation-induced chromosomal aberrations demonstrate that the rate of double-strand break misrepair increases in a dose-dependent fashion with radiation doses between 0 - 80 Gy (Mcmahon et al. 2016), as does the incidence of chromosomal aberrations between doses of 0 - 10 Gy (Thomas et al. 2003; Tucker et al. 2005a; Tucker et al. 2005b; George et al. 2009; Arlt et al. 2014; Balajee et al. 2014; Han et al. 2014; Suto et al. 2015; Mcmahon et al. 2016). Similarly, there is not clear evidence of a temporal concordance between these two events. One study examining DNA repair and micronuclei in irradiated cells pre-treated with a DNA repair inhibitor found that both repair and micronuclei were present at 3 hours and 24 hours post-irradiation. This suggests that there may be temporal concordance (Chernikova et al. 1999). More research, however, is required to establish empirical evidence for this KER.

Mutations, Increase (KE3)    --> Cell Proliferation, Increase (KE5)

Evidence for Empirical Support of KER: Moderate

There is little empirical evidence available that assesses the dose and incidence concordance between mutation frequency and cellular proliferation rates. The correlation between these two events is clear in human epidemiology studies examining the incidence between mutations in specific genes, such as TP53 and BRCA1, and the proliferative status of human tumours (M Jarvis et al. 1998; Schabath et al. 2016). Another study introducing oncogenic mutations into mouse lung epithelial cells demonstrated that the addition of multiple oncogenic mutations to the cells resulted in increased tumour volumes over 40 days (suggestive of cell proliferation); in contrast, cells containing only one of these mutations did not show significant changes in tumour volumes (Sato et al. 2017). Unsurprisingly, there is also little empirical evidence available supporting a temporal concordance between these two events. One review explores the timing between these two events by comparing the somatic mutation theory of cancer and the stem cell division theory of cancer. In the somatic mutation theory, it is suggested that mutations accumulate and result in increased rates of cellular proliferation; the stem cell theory, however, states that high proliferation in stem cells allows the accumulation of mutations (López-lázaro 2018). More research is thus required to establish empirical evidence for this KER.

 

Chromosomal Aberrations, Increase (KE4)    --> Cell Proliferation, Increase (KE5)

Evidence for Empirical Support of KER: Moderate

There is little empirical evidence available that assesses the dose and incidence concordance between chromosomal aberration frequency and cellular proliferation rates. There are several reviews available that discuss the structure and function of specific human cancer-associated chromosomal aberrations, including BCR-ABL1, ALK fusions, and ETV6-RUNX1 (Mes-Masson and Witte 1987; Ghazavi et al. 2015; Kang et al. 2016). There was no identified evidence supporting dose and incidence concordance. Details from a study where estrogen-responsive cancer cells were treated with estrogen suggested the possibility of a temporal concordance, as both micronuclei levels and proliferation rates were higher in the estrogen-treated cells at 140 and 216 hours post-treatment (Stopper et al. 2003). Overall, however, more empirical evidence is required to support this KER.

 

Cell Proliferation, Increase (KE5)    --> Lung Cancer, Increase (AO)

Evidence for Empirical Support of KER: Moderate

There is some empirical evidence of a dose and incidence concordance between cell proliferation and lung carcinogenesis. In a few experiments, rodent lungs exposed to various carcinogens showed increased levels of proliferation and developed squamous metaplasia (Zhong et al. 2005) or full-blown tumours (Kassie et al. 2008). Furthermore, nude mice injected with carcinogenic human NSCLC cells also developed tumours within a few weeks of the injection (Pal et al. 2013; Warin et al. 2014; Sun et al. 2016; Tu et al. 2018)(Sun 2016, Pal 2013, Tu 2018, Warin 2014). In terms of temporal concordance between these two events, studies are also limited. Multiple tumour xenograft experiments found that nude mice injected with NSCLC cells develop detectable tumours within two weeks of inoculation, which continued to increase in size over time (Sun 2016, Pal 2013, Tu 2018, Warin 2014). Examination of lung squamous metaplasia after 14 weeks of exposure to high levels of tobacco smoke showed increased cell proliferation markers in comparison to lungs from rats exposed to filtered air (Zhong et al. 2005). Similarly, lung tumours from mice that received carcinogens NNK and BaP orally over 4 weeks were also found to express proliferation markers when examined 27 weeks after the start of the experiment (Kassie et al. 2008).

 

 

Double-Strand Breaks, Increase (KE1) --> Mutations, Increase (KE3)

Evidence for Empirical Support of KER: Moderate

There is some evidence demonstrating dose and temporal concordance between the two KEs, both in-viv and in-vitro. These studies used a variety of sources of ionizing radiation as stressors. The types of radiation testing this relationship include X-rays, gamma-rays, alpha particles and heavy ions. Example studies include: (in vitro) Rydberg et al., 2005; Kuhne et al., 2005, 2000; Dikomey et al., 2000; Lobrich et al., 2000, (in vivo) Ptacek et al., 2001. For a discussion of chemical stressors affecting this relationship, see AOP 296.

Double-Strand Breaks, Increase (KE1) --> Chromosomal Aberrations, Increase (KE4)

Evidence for Empirical Support of KER: Moderate

Temporal concordance is clear in both in vitro and in vivo data. However, due to the differences in the methods used to measure strand breaks and chromosomal aberrations, the dose-response of these events often appear to be discordant. Examples of studies relating the links between DSBs and chromosomal aberrations include an in-vitro study of gamma-radiated lymphoblasted cell lines (Trenz et al. 2003) isolated lymphocytes and whole blood samples (Sudpresert et al., 2006) and PL61 cells (Chernikova et al., 1999). Source of high linear energy transfer have also been probed, see Iliakis et al. (2019).

Mutations, Increase (KE3)    --> Lung Cancer, Increase (AO)

Evidence for Empirical Support of KER: Moderate

Evidence for dose/incidence concordance comes from studies with similar radiological and biological conditions that assessed either the relationship between radiation exposure and mutations, or radiation exposure and cancer. Using various in vitro  models, there was a dose-dependent relationship found for mutation induction and radiation dose (Suzuki and Hei 1996; Weaver et al. 1997; Canova et al. 2002), and for oncogenic transformations and radiation dose (Hei et al. 1994; Miller et al. 1995; Miller et al. 1999). Analyses of lung cancer incidences in radon-exposed rats and uranium miners echo these results (Monchaux et al. 1994; Lubin et al. 1995; Ramkissoon et al. 2018). Likewise, administration of a known pulmonary carcinogen to Gprc5a knock-out mice resulted in an increased rate of tumourigenesis and increased mutation accumulation relative to saline-treated mice (Fujimoto et al. 2017). Increasing the number of mutations in vitro  and  in vivo resulted in cells becoming increasingly oncogenic (Sato, Melville B Vaughan, et al. 2006; Sasai et al. 2011) and mice sporting a faster rate of lung tumourigenesis (Fisher et al. 2001; Kasinski and Slack 2012), respectively. In terms of temporal concordance, there is some evidence from separate studies indicating that mutations precede tumourigenesis (Hei et al. 1994; Lubin et al. 1995; Hei et al. 1997; Miller et al. 1999; Fujimoto et al. 2017) , particulary in Cre-inducible models where Cre expression must be induced for the mutations to be expressed (Fisher et al. 2001; Kasinski and Slack 2012).

Chromosomal Aberrations, Increase (KE4)    --> Lung Cancer, Increase (AO)

Evidence for Empirical Support of KER: Moderate

Evidence for dose/incidence concordance comes from epidemiological studies of radon-exposed uranium miners that found there was an increased CA load with increasing radon exposure (Smerhovsky et al. 2002), and an increased risk of lung cancer with increased cumulative radon exposure (Tirmarchel et al. 1993; Smerhovsky et al. 2002; Vacquier et al. 2008; Walsh et al. 2010). In vivo and in vitro studies have also shown a dose-dependent increase in CAs in lung and non-lung cell lines (Nagasawa et al. 1990; Deshpande et al. 1996; Yamada et al. 2002; Stevens et al. 2014) and lung cells of rodents with increasing radiation dose (A.L. Brooks et al. 1995; Khan et al. 1995; Werner et al. 2017), and a dose-dependent increase in oncogenic transformation in non-lung cells lines (Robertson et al. 1983; Miller et al. 1996)  and in rodent lung tumours with increasing radiation dose (Monchaux et al. 1994; Yamada et al. 2017) Furthermore, there are several published reviews that provide evidence for associations between radon exposure and the appearance of CAs, and radon exposure and the incidence of lung cancer (Jostes 1996; Al-Zoughool and Krewski 2009; Robertson et al. 2013). Likewise, more CAs were found to accumulate in larger tumours (To et al. 2011) and in increasingly more oncogenic lung tissue lesions (Thibervile et al. 1995; Wistuba et al. 1999). There is also evidence for temporal concordance as, the time gap between radiation exposure and the increased incidence of CAs is hours to days (Nagasawa et al. 1990; A.A.L. Brooks et al. 1995; Deshpande et al. 1996; Yamada et al. 2002; Stevens et al. 2014; Werner et al. 2017), while the time gap between radiation exposure and the development of oncogenic transformations or lung tumours is weeks, months or years (Robertson et al. 1983; Tirmarchel et al. 1993; Miller et al. 1996; Pear et al. 1998; Kuramochi et al. 2001; Yamada et al. 2017).

Direct Deposition of Energy (MIE)  --> Lung Cancer, Increase (AO)

Evidence for Empirical Support of KER: Moderate

There is strong evidence of the relationship between radiation exposure and lung carcinogenesis in human epidemiological studies that assess radon exposure and the risk of lung cancer. Results from numerous studies assessing indoor residential radon exposure and outdoor radon exposure in miners suggest that there is a positive association between cumulative radon exposure and lung cancer risk (Lubin et al. 1995; Hazelton et al. 2001; Darby et al. 2005; Krewski et al. 2005; Krewski et al. 2006; TAl-Zoughool and Krewski 2009; Torres-Durán et al. 2014; Kreuzer et al. 2015; Sheen et al. 2016; Rodríguez-Martínez et al. 2018; Ramkissoon et al. 2018; Rage et al. 2020). Several in vitro studies showed that cells could be induced to obtain oncogenic characteristics through radiation exposure (Hei et al. 1994; Miller et al. 1995). Likewise, irradiation of rats at radon levels comparable to those experienced by uranium miners resulted in a dose-dependent increase in lung carcinoma incidence (Monchaux et al. 1994). There is also evidence of temporal concordance, as the oncogenic characteristics of the radon-exposed cells were not evident until weeks after the irradiation (Hei et al. 1994; Miller et al. 1995), while tumours took months to years to grow (Hei et al. 1994; Monchaux et al. 1994). In humans, the risk of lung cancer was also found to increase with increasing time since exposure (Hazelton et al. 2001) at a mean time of 15 years (Aßenmacher et al. 2019) and with longer periods of exposure (Lubin et al.1995).

Quantitative Consideration

There is strong biological plausibility and empirical evidence to suggest a qualitative link between the deposition of energy on DNA to the final adverse outcome of lung cancer. This evidence has been derived predominately from laboratory studies and through mathematical simulations using cell-based models. The studies show both dose and temporal-response relationships for a select KEs. The quantitative thresholds to initiate each of the KEs are not definitive and have been shown to vary with factors such as the cell type, dose-rate of exposure and radiation quality. Thus, an absolute amount of deposited energy (MIE) to drive a key event forward to a path of cancer is not yet definable. This is particularly relevant to low doses and low dose-rates of radiation exposure where the biology is interplayed with conflicting concepts of hormesis, hypersensitivity and the linear no threshold theory. Furthermore due to the stochastic nature of the MIE, it remains difficult to identify specific threshold values of DSBs needed to overwhelm the DNA repair machinery to cause “inadequate” DNA repair leading to downstream genetic abnormalities and eventually cancer. With a radiation stressor, a single hit to the DNA molecule could drive a path forward to lung cancer; however this is with low probability.  Empirical modeling of cancer probability vs. mean radiation dose and time to lethality, does provide a good visualization of the effective thresholds (Raabe 2011). However, in general there is considerable uncertainty surrounding the connection of biologically contingent observations and stochastic energy deposition.

Raabe OG. Toward improved ionizing radiation safety standards. Health Phys 101: 84–93; 2011.

Support for Quantitative Understanding of KERs

Defining Question

Strong

Moderate

Weak

What is the extent to which a change in KEdown can be predicted from KEup? What is the precision with which uncertainty in the prediction of KEdown can be quantified? What is the extent to which known modulating factors or feedback mechanisms can be accounted for? What is the extent to which the relationships can be reliably generalized across the applicability domain of the KER?

Change in KEdown can be precisely predicted based on a relevant measure of KEup; Uncertainty in the quantitative prediction can be precisely estimated from the variability in the relevant KEup measure; Known modulating factors and feedback/ feedforward mechanisms are accounted for in the quantitative description; Evidence that the quantitative relationship between the KEs generalizes across the relevant applicability domain of the KER

Change in KEdown can be precisely predicted based on relevant measure of KEup; Uncertainty in the quantitative prediction is influenced by factors other than the variability in the relevant KEup measure; Quantitative description does not account for all known modulating factors and/or known feedback/ feedforward mechanisms; Quantitative relationship has only been demonstrated for a subset of the overall applicability domain of the KER

Only a qualitative or semi-quantitative prediction of the change in KEdown can be determined from a measure of KEup; Known modulating factors and feedback/ feedforward mechanisms are not accounted for; Quantitative relationship has only been demonstrated for a narrow subset of the overall applicability domain of the KER

Direct Deposition of Energy (MIE) --> Double-Strand Breaks, Increase (KE1)

Evidence for Quantitative Understanding of KER: Strong

The vast majority of studies examining energy deposition and incidence of DSBs suggest a positive, linear relationship between these two events (Sutherland et al. 2000; Lara et al. 2001; Rothkamm and Lo 2003; Kuhne et al. 2005; Rube et al. 2008; Grudzenski et al. 2010; Shelke and Das 2015; Antonelli et al. 2015). Predicting the exact number of DSBs from the deposition of energy, however, appears to be highly dependent on the biological model, the type of radiation and the radiation dose range, as evidenced by the differing calculated DSB rates across studies (Charlton et al. 1989; Rogakou et al. 1999; Sutherland et al. 2000; Lara et al. 2001; Rothkamm and Lo 2003; Kuhne et al. 2005; Rube et al. 2008; Grudzenski et al. 2010; Antonelli et al. 2015) .

Direct Deposition of Energy (MIE) --> Mutations, Increase (KE3)

Evidence for Quantitative Understanding of KER: Strong

Most studies indicate a positive, linear relationship between the radiation dose and the mutation frequency (Russell et al. 1957; Albertini et al. 1997; Canova et al. 2002; Dubrova et al. 2002; Nagashima et al. 2018). In order to predict the number of mutations induced by a particular dose of radiation, parameters such as the type of radiation, the radiation’s LET, and the type of model system being used should be taken into account (Albertini et al. 1997; Dubrova et al. 2002; Matuo et al. 2018; Nagashima et al. 2018). Predicting the mutation frequency at particular time-points, however, would be very difficult owing to our limited time scale knowledge.

Direct Deposition of Energy (MIE) -->  Chromosomal Aberrations, Increase (KE4)

Evidence for Quantitative Understanding of KER: Strong

Most studies indicate a positive, linear-quadratic relationship between the deposition of energy by ionizing radiation and the frequency of chromosomal aberrations (Schmid et al. 2002; Suto et al. 2015; Abe et al. 2018; Jang et al. 2019). Equations describing this relationship were given in a number of studies (Schmid et al. 2002; George et al. 2009; Suto et al. 2015; Abe et al. 2018; Jang et al. 2019), with validation of the dose-response curve performed in one particular case (Suto et al. 2015). In terms of time scale predictions, this may still be difficult owing to the often-lengthy cell cultures required to assess chromosomal aberrations post-irradiation. For translocations in particular, however, one study defined a linear relationship between time and translocation frequency at lower radiation doses (0 - 0.5 Gy) and a linear quadratic relationship at higher doses (0.5 - 4 Gy) (Tucker et al. 2005b).

Double-Strand Breaks, Increase (KE1)  --> Inadequate DNA Repair, Increase (KE2)

Evidence for Quantitative Understanding of KER: Moderate

According to studies examining DSBs and DNA repair after exposure to a radiation stressor, there was a positive linear relationship between DSBs and radiation dose (Lobrich et al. 2000; Rothkamm and Lo 2003; Kuhne et al. 2005; Asaithamby and Chen 2009), and a linear-quadratic relationship between the number of misrejoined DSBs and radiation dose (Kuhne et al. 2005) which varied according to LET (Rydberg et al. 2005b) and dose-rate (Dikomey and Brammer 2000) of the radiation. Overall, 1 Gy of radiation may induce between 35 and 70 DSBs (Dubrova et al. 2002; Rothkamm and Lo 2003), with 10 - 15% being misrepaired at 10 Gy (Mcmahon et al. 2016) and 50 - 60% being misrepaired at 80 Gy (Lobrich et al. 2000; Mcmahon et al. 2016). Twenty-four hours after radiation exposure the frequency of misrepair appeared to remain relatively constant around 80%, a rate that was maintained for the next ten days of monitoring (Kuhne et al. 2000).

Inadequate DNA Repair, Increase (KE2) --> Mutations, Increase (KE3)

Evidence for Quantitative Understanding of KER: Moderate

Positive relationships have been reported between radiation stressor and inadequate DNA repair, radiation stressor and mutation frequency (Mcmahon et al. 2016), and inadequate DNA repair and mutation frequency (Perera et al. 2016). It has been found that 10 - 15% of DSBs are misrepaired at 10 Gy (Mcmahon et al. 2016) and 50 - 60% at 80 Gy (Lobrich et al. 2000; Mcmahon et al. 2016), with mutation rates varying from 0.1 - 0.2 mutation per 104 cells at 1 Gy and 0.4 - 1.5 mutation per 104 cells at 6 Gy (Mcmahon et al. 2016).

Inadequate DNA Repair, Increase (KE2) --> Chromosomal Aberrations, Increase (KE4)

Evidence for Quantitative Understanding of KER: Weak

A direct quantitative understanding of the relationship between inadequate DNA repair and chromosomal aberrations has not been established. However, some data has been generated using studies from radiation stressor studies. At a radiation dose of 10 Gy, the rate of DSB misrepair was found to be approximately 10 - 15% (Lobrich et al. 2000); this rate increased to 50 - 60% at a radiation exposure of 80 Gy (Kuhne et al. 2000; Lobrich et al. 2000; Mcmahon et al. 2016). It is not known, however, how this rate of misrepair relates to chromosomal aberration frequency. Results from one study using a DNA repair inhibitor suggested that as adequate DNA repair declines, the chromosomal aberration frequency increases (Chernikova et al. 1999).  The time scale between inadequate repair and chromosomal aberration frequency has also not been well established.

Mutations, Increase (KE3)    --> Cell Proliferation, Increase (KE5)

Evidence for Quantitative Understanding of KER: Weak

 Quantitative understanding of the relationship between these two events has not been well established. There are, however, some studies that have examined how cellular proliferation changes over time in the presence of mutations. In cells harbouring mutations in critical genes, higher proliferation rates were evident by the fourth day in culture (Lang et al. 2004; Li and Xiong 2017) and higher rates of population doublings were evident by passage 7 (Li and Xiong 2017) relative to wild-type cells. DNA synthesis (which could be indicative of cellular proliferation) was higher in p53-/- cells than in wild-type cells for the first 6 days of culture, and increased to drastically higher levels in the knock-out cells until the end of the experiment at day 10 (Lang et al. 2004). In vivo, mice injected with oncogenically-transformed cells containing multiple mutations had detectable tumour growth by 10 - 12 days post-inoculation. These volumes continued increasing over the 40-day experiment (Sato et al. 2017).  

Chromosomal Aberrations, Increase (KE4)    --> Cell Proliferation, Increase (KE5)

Evidence for Quantitative Understanding of KER: Weak

Quantitative understanding of the relationship between these two events has not been well established. . Although studies that directly assessed the time scale between chromosomal aberrations and cell proliferation rates were not identified, differences in cellular proliferation rates for cells with different CA-related manipulations or treatments were evident within the first 3 days of culture (Stopper et al. 2003; Li et al. 2007; Soda et al. 2007; Irwin et al. 2013; Guarnerio et al. 2016).

Cell Proliferation, Increase (KE5)    --> Lung Cancer, Increase (AO)

Evidence for Quantitative Understanding of KER: Weak

Quantitative understanding of the relationship between these two events has not been well established. Human non-carcinogenic cells are thought to undergo 50 – 70 cell divisions before the telomeres can no longer support cell division (Panov 2005); this number would presumably be higher in cancer cells, but  quantitative data was not able to be identified. There are some studies available, however, that provide some details regarding the timing between these two events. In vitro experiments using lung cancer cell lines demonstrated that expression levels of key proteins involved in the regulation of the cell cycle and/or proliferation were modified by chemical inhibitors within the first 48 hours of treatment (Lv et al. 2012; Wanitchakool et al. 2012; Pal et al. 2013; Sun et al. 2016). In vivo studies using xenograft nude mice found that tumours were detected within two weeks of NSCLC-cell inoculation, and continued to grow over the experimental period (Pal et al. 2013; Warin et al. 2014; Sun et al. 2016; Tu et al. 2018). Differences in tumour growth rates between mice treated with an anti-cancer drug and those left untreated were also evident within 13 - 27 days (Pal et al. 2013; Sun et al. 2016; Tu et al. 2018), with significant differences in cell proliferation markers and tumour numbers or sizes at time of harvest (22 days - 27 weeks) (Kassie et al. 2008; Pal et al. 2013; Warin et al. 2014; Sun et al. 2016; Tu et al. 2018).

Double-Strand Breaks, Increase (KE1) --> Mutations, Increase (KE3)

Evidence for Quantitative Understanding of KER: Weak

There is overall limited quantitiative understanding of the relationship between DSBs and increased mutation rates. McMahon et al., 2016 compiled data from multiple studies spanning different human and mouse cell lines to model the IR dose-dependent increase in mutation rate. However, further quantitiative studies into this relationship are required to provide a better quantitiative understanding.

Double-Strand Breaks, Increase (KE1) --> Chromosomal Aberrations, Increase (KE4)

Evidence for Quantitative Understanding of KER: Weak

Similarly to the non-adjacent relationship above (KE1 -> KE4), there is overall limited quantitiative understanding of the relationship between DSBs and increased rates of chromosomal aberrations. McMahon et al., 2016 compiled data from multiple studies spanning different human and mouse cell lines to model the IR dose-dependent increase in the rate of chromosomal aberrations. However, further quantitiative studies into this relationship are required to provide a better quantitiative understanding.

Mutations, Increase (KE3)    --> Lung Cancer, Increase (AO)

Evidence for Quantitative Understanding of KER: Weak

Finding studies addressing the quantitative relationship between mutations and cancer directly was particularly challenging. However, many studies indicated that there was a positive, dose-dependent increase in mutations with increasing radiation dose (Suzuki and Hei 1996; Canova et al. 2002). A similar positive, dose-dependent relationship was found for the oncogenic transformations in cell and the radiation dose (Miller et al. 1995), and the incidence of lung cancer in rats and their cumulative radon exposure (Monchaux et al. 1994). Epidemiological studies examining lung cancer in radon-exposed uranium miners found a positive, linear relationship between lung cancer and cumulative radon exposure (Lubin et al. 1995; Ramkissoon et al. 2018). In terms of time-scale, mutations were evident in 2 weeks following irradiation (Hei et al. 1997), whereas oncogenic transformations took 7 weeks to develop following radiation exposure (Miller et al. 1999). In vivo models with injected tumour cells, inherent mutations, exposure to carcinogens, or Cre-induced mutations showed tumour growth months after exposure to the tumour-inducing insult (Hei et al. 1994; Fisher et al. 2001; Kasinski and Slack 2012; Fujimoto et al. 2017).

Chromosomal Aberrations, Increase (KE4)    --> Lung Cancer, Increase (AO)

Evidence for Quantitative Understanding of KER: Moderate

There is evidence of a positive, linear relationship between radiation dose and CAs (Nagasawa et al. 1990; A.L. Brooks et al. 1995; Khan et al. 1995; Yamada et al. 2002; Stevens et al. 2014), radiation dose and oncogenic transformations (Miller et al. 1996), as well as radon exposure and the risk of lung cancer mortality (Tirmarchel et al. 1993; Walsh et al. 2010). The latter relationship was found to be exponentially modified, however, by factors such as the age at median exposure, the time since median exposure, and the radon exposure rate (Walsh et al. 2010). Equations defining these relationships were derived in a number of different studies (Tirmarchel et al. 1993; A.L. Brooks et al. 1995; Khan et al. 1995; Miller et al. 1996; Girard et al. 2000; Yamada et al. 2002; Walsh et al. 2010; Stevens et al. 2014). In terms of time scale, micronuclei were documented in cells of the rodent lung as early as 0.2 days (Khan et al. 1995), and were found to persist for days to weeks (Khan et al. 1995; Deshpande et al. 1996; Werner et al. 2017). Oncogenic transformations, on the other hand, took weeks to develop (Robertson et al. 1983; Miller et al. 1996), while lung tumours took months or years to develop following radiation exposure (Tirmarchel et al. 1993; Yamada et al. 2017). Delivery of an agent carrying a cancer-related CA resulted in tumour growth within 21 - 31 days of its injection into mice (Pear et al. 1998; Kuramochi et al. 2001).

Direct Deposition of Energy (MIE) --> Lung Cancer, Increase (AO)

Evidence for Quantitative Understanding of KER: Moderate

Quantitative understanding has been well-established for this KER. According to current Canadian guidelines developed by Health Canada, annual residential radon levels should not exceed 200 Bq/m3. Similarly, the WHO recommends that the national annual residential radon levels not exceed 100 Bq/m3 where possible; if there are geographic or national constraints that make this target unachievable, the national standard should not be higher than 300 Bq/m3 (World Health Organization - Radon Guide 2009). Positive relationships between radon exposure and lung cancer have been established using in vitro models (Miller 1995), in vivo models(Monchaux et al. 1994) and results from human epidemiological studies (Lubin et al. 1995; Hazelton et al. 2001; Darby et al. 2005; Krewski et al. 2005; Krewski et al. 2006; Rodríguez-Martínez et al. 2018; Ramkissoon et al. 2018). Unsurprisingly, oncogenic transformation in cells were found weeks after radiation exposure (Miller et al. 1995), sizable tumours developed months after irradiation in mice (Hei et al. 1994) and lung cancer was found years after exposure in humans (Lubin et al. 1995; Darby et al. 2005; Torres-Durán et al. 2014; Rodríguez-Martínez et al. 2018; Ramkissoon et al. 2018).

Quantification of AOP KERs

The development of quantitative AOPs (qAOPs) has been demonstrated in other fields such as chemical toxicology (Zgheib et al., 2019) and similar objectives are warranted for AOPs with ionizing radiation stressors. The quantification of an AOP can help expedite the development of an AOP by reducing the original long-form and qualitative nature of an AOP to tables and graphs that summarize particular features e.g. dose ranges considered, radiation types included etc. Quantification is achieved by extracting numerical information from the underlying supporting evidence of KERs. The quantification of four key event relationships (KERs) from this AOP has been completed. The KERs which have been quantified are as follows:

  1. Energy deposition leads to Increase, DNA strand breaks (Ad-KER1)

  2. Energy deposition leads to Increase, mutations (NAd-KER1)

  3. Energy deposition leads to Increase, Chromosomal aberrations (NAd-KER2)

  4. Energy deposition leads to Increase, lung cancer (NAd-KER7)

For each of the KERs listed above, all relevant publications were considered for quantification. In some cases, the measure of dose-response featured in one publication could not be reconciled with the measure adopted by another. For example, in the study of energy deposition leading to an increase in DNA strand breaks, Sudprasert et al. (2006) use a measure of olive moment from the Comet assay technique, whereas Sutherland et al. (2000) measure the relative site frequency compared to a benchmark instance of DNA damage. Due to variations such as these, not all studies that contribute qualitatively to supporting the weight of evidence of a given KER is eligible for quantification. In the case of the four KERs considered above, the most common measure of response across studies was adopted ensure the largest data sample possible. These response measured were as follows (in same order for each KER listed above):

  1. Ad-KER1 - DNA DSBs / cell

  2. NAd-KER1 - Mutations / 106 cells

  3. NAd-KER2 - Chromosomal aberrations / 100 cells

  4. NAd-KER7 - Relative risk (RR) of lung cancer

The process of quantification first involves digitizing data from publications. Results provided from tables were used directly. For figures (e.g. scatter or bar-charts) information was obtained by using the WebPlotDigitizer-4.2 authored by Rohatgi (2019). Full information of all quantified studies and respective references can be found in Tables 1-7, here.

The two dominant radiation types featured in the AOP are from photon and alpha-particle sources, see Table 1 below. Upstream KERs describing Ad-KER1, NAd-KER1 and NAd-KER2 are respectively composed of datasets with 298, 176 and 629 data points with 59%, 39% and 57% from photon sources and 35%, 52% and 42% from alpha-particle sources. The AO (NAd-KER7) is 100% characterized by radon (alpha-particle emitter) with a total of 33 data points.

A graphical representation of the four quantified KERs is shown in Figure 1. This AOP is best documented for alpha-particles but could potentially support further data relevant to lung cancer incidence from photon radiation sources. The scope of the AOP could be extended with additional data from proton and heavy ion sources. This would encapsulate research areas such as space-travel where galactic radiation is predominantly composed of protons, and to a lesser extent, heavy ions (Chancellor et al., 2014). Overall, Figure 1 and Table 1 demonstrate how reviewing supporting empirical evidence through a quantitative lens reduces the description of an AOP to tables and graphs that can be used to identify inconsistencies and potential missing information across KERs and radiation types.
 

 

Radiation quality

Photons

Protons

Alpha-particles

Heavy ions

KER

Values of dose, response, time and dose rate quoted as [minimum, maximum, average]

 

Dose [Gy]

Ad-KER1

[1.2x103, 80, 7.9]

[0.5, 0.5, 0.5]

[0.1, 713, 203]

[0.5, -, -]

NAd-KER1

[1.7x10-5, 14, 2.4]

[1.24, 3.74, 2.5]

[3.4x10-5, 2.4, 0.6]

[10, 20, 11.8]

NAd-KER2

[6.3x10-4, 10, 1.8]

N/A

[4.3x10-4, 6.9, 0.7]

[0.15, 1.5, 0.7]

NAd-KER7

[4.8x10-2, 2.63, 0.9]

N/A

[7.89x10-3, 10.1, 0.63]

N/A

 

Response measures [DNA DSBs / cell (Ad-KER1), Mutant frequency / 106 cells (NAd-KER1), CAs / 100 cells (NAd-KER2), Increase in lung cancer RR [%] (NAd-KER7)]

Ad-KER1

[5x10-3, 2.8x103, 244]

[0.34, 10.1, 5.3]

[1.3, 3x104, 9.31x103]

[0.4, 8.8, 4.3]

NAd-KER1

[0.3, 1.9x103, 148]

N/A

[1.7, 3.8x103, 279]

[0.4, 19.4, 4]

NAd-KER2

[0.01, 584, 44.8]

N/A

[0.08, 314, 34.9]

[13.2, 138, 5.7]

NAd-KER7

[2.7, 166, 64.4]

N/A

[-17.9, 942, 84.4]

N/A

 

Time [hours (Ad-KER1), days (Ad-KER1, NAd-KER2), years (NAd-KER7)]

Ad-KER1

[0.02, 72, 10.6]

[0.03, 24, 6.5]

[0.02, 24, 0.5]

[0.25, 24, 6.5]

NAd-KER1

[6.9x10-4, 67, 5.3]

[6.9x10-4, -, -]

[6.94x10-4, 6, 1.4]

[6.94x10-4, 2, 0.1]

NAd-KER2

[6.9x10-4, 56, 1.2]

N/A

[6.94x10-4, 362, 23.6]

[6.94x10-4, -, -]

NAd-KER7

[40, -, -]

N/A

[5.7, 39.0, 18.5]

N/A

 

Dose rate [Gy/min]

Ad-KER1

[0.03, 2, 0.9]

N/A

[0.08, 100, 51.5]

N/A

NAd-KER1

[1.1x10-6, 1.2, 0.5]

N/A

[2x10-3, 3.6, 1.3]

[1, 5, 4.8]

NAd-KER2

[1.7x10-3, 5.9, 0.9]

N/A

[5.3x10-6, 2.3, 0.4]

[0.5, -, -]

NAd-KER7

[2.27x10-9, 1.25x10-7, 4.15x10-8]

N/A

[7.7x10-10, 3.4x10-6, 1.8x10-7]

N/A

 

% data points for KER dataset with valid dose and response values (number of data points)

Ad-KER1

59 (177)

3 (8)

35 (105)

3 (8)

NAd-KER1

40 (75)

3 (6)

48 (91)

9 (17)

NAd-KER2

56 (344)

0 (0)

43 (262)

1 (10)

NAd-KER7

12 (6)

0 (0)

88 (44)

0 (0)

 

Table 1: Summary  of the quantified datasets from four KERs of the AOP. Data is categorized by both KER and radiation type. Values of dose, response measure, time since irradiation and dose rate are quoted in terms of ‘[minimum, maximum, average]’ values. ‘N/A’ denotes fields where there was no data. The final set of rows denote the percentages of dose-response data of a given KER associated with a given radiation type.

 

Figure 1: Quantified datasets of the four KERs in graphical form. Each row of plots represents a KER in the following order from top to bottom: Ad-KER1, NAd-KER1, NAd-KER2 and NAd-KER7. The response measure for each KER is shown along the y-axis of each plot, and from left to right the dose, time and dose rate along the x-axes respectively.

 

Shown in Figure 2 below is a comparison between the two dominat radiation sources; alpha-particles (green) and photon radiation (back). For each of the response measures shown in Figure 2, different symbols denote different end-points or variants of the response as measured for each KER. In the case of chromsomal aberrations (bottom-left) there is a distinct difference in the response of different chromsomal aberration types among a given radiation type e.g. for alpha-particles PCC rings (solid stars) can be 10-100 times less abundant than dicentric chromsomal types (solid circles).

While these differences and variations are embraced by the standard AOP construction, it should be questioned if the quantitative form of these variations is of use for constructing predictive models, and whether such an application is limited only to those of direct response-response relationships where the level of variation may be reduced. Even then, such response-response relationships would need to account for radiation type effects between each KE e.g. differing cell survival rates and the fraction of total DNA damage attributable to single strand breaks (SSBs), DSBs and complex/clustered damage. These are both very different between photon and alpha-particle sources (Franken et al., 2012; Nikjoo et al., 2001). This ultimately constrains any quantitative formalism of an AOP to be radiation type specific.

Figure 2: Quantified dose-response of the four KERs in graphical form. Data is focussed on the comparison between photon and alpha-particle radiation types, in addition to the response variants for each type of response. Data is evaluated for the low-dose range of 0-2 Gy for time periods following exposure < 60 minutes for Ad-KER1 (top-left), NAd-KER1 (top-right), and NAd-KER2 (bottom-left). No restriction on the time value for data points plotted for NAd-KER7 (bottom-right) has been made.

 

Considerations for Potential Applications of the AOP (optional)

At present the AOP framework is not readily used to support regulatory decision-making in radiation protection practices. The goal of developing this AOP is to bring attention to the framework to the radiation community as an effective means to organize knowledge,  identify gaps  and co-ordinate research.  We have used lung cancer as the case example due to its relevance to radon risk assessment and broadly because it can be represented as a simplified targeted path with a molecular initiating event that is specific to a radiation insult.  From this AOP, more complex networks can form which are relevant to both radiation and chemical exposure scenarios. Furthermore, as  mechanistic knowledge surrounding low dose radiation exposures becomes clear, this information can be incorporated into the AOP.  By developing this AOP, we have supported the necessary efforts highlighted by the international and national radiation protection agencies such as, the United Nations Scientific Committee on the Effects of Atomic Radiation, International Commission of Radiological Protection, International Dose Effect Alliance and the Electric Power Research Institute Radiation Program to consolidate and enhance the knowledge in understanding of low dose radiation exposures from the cellular to organelle levels within the biological system.

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Appendix 1

List of MIEs in this AOP

Event: 1686: Direct Deposition of Energy

Short Name: Energy Deposition

AOPs Including This Key Event

AOP ID and Name Event Type
Aop:272 - Deposition of energy leading to lung cancer MolecularInitiatingEvent

Stressors

Name
Ionizing Radiation

Biological Context

Level of Biological Organization
Molecular

Evidence for Perturbation by Stressor

Overview for Molecular Initiating Event

It is well documented that ionizing radiation( (eg. X-rays, gamma, photons, alpha, beta, neutrons, heavy ions) leads to energy deposition on the atoms and molecules of the substrate. Many studies, have demonstrated that the type of radiation and distance from source has an impact on the pattern of energy deposition (Alloni, et al. 2014). High linear energy transfer (LET) radiation has been associated with higher-energy deposits (Liamsuwan et al., 2014) that are more densely-packed and cause more complex effects within the particle track (Hada and Georgakilas, 2008; Okayasu, 2012b; Lorat et al., 2015; Nikitaki et al., 2016) in comparison to low LET radiation. Parameters such as mean lineal energy, dose mean lineal energy, frequency mean specific energy and dose mean specific energy can impact track structure of the traversed energy into a medium. The detection of energy deposition by ionizing radiation can be demonstrated with the use of fluorescent nuclear track detectors (FNTDs). FNTDs used in conjunction with fluorescent microscopy, are able to visualize radiation tracks produced by ionizing radiation (Niklas et al., 2013; Kodaira et al., 2015; Sawakuchi and Akselrod, 2016). In addition, these FNTD chips can quantify the LET of primary and secondary radiation tracks up to 0.47 keV/um (Sawakuchi and Akselrod, 2016). This co-visualization of the radiation tracks and the cell markers enable the mapping of the radiation trajectory to specific cellular compartments, and the identification of accrued damage (Niklas et al., 2013; Kodaira et al., 2015). There are no known chemical initiators or prototypes that can mimic the MIE.

Domain of Applicability

Taxonomic Applicability
Term Scientific Term Evidence Links
human Homo sapiens High NCBI
rat Rattus norvegicus High NCBI
mouse Mus musculus High NCBI
Life Stage Applicability
Life Stage Evidence
All life stages High
Sex Applicability
Sex Evidence
Unspecific High

Energy can be deposited into any substrate, both living and non-living; it is independent of age, taxa, sex, or life-stage.

Key Event Description

Direct deposition of energy refers to events where subatomic particles or electromagnetic waves of sufficient energy cause ionization in the media through which they transverse (Beir, 1999). The resulting energy can cause the ejection of electrons from atoms and molecules, thereby breaking chemical bonds and ionizing atoms and molecules. The energy of these subatomic particles or electromagnetic waves ranges from 124 KeV to 5.4 MeV, and is dependent on the source and type of radiation. Not all electromagnetic radiation is ionizing; as the incident radiation must have sufficient energy to free electrons from the atom or molecule’s electron orbitals. The energy can induce direct and indirect ionization events. Direct ionization is the principal path where charged particles interact with DNA to cause a biological damage. Photons, which are electromagenetic waves can also cause direct ionization. Indirect ionization produces free radicals of other molecules, specifically water, which can transform to damage critical targets such as DNA (Beir, 1999). There are no chemical mimetics or prototypes of energy deposition.

Given the fundamental nature of energy deposition by nuclei, nucleons or elementary particles in material, this process is universal to all biological contexts. It is a phenomenon dictated by radioactive decay laws. As such chemical initiators are also not applicable to this MIE.

How it is Measured or Detected

Assay Name

References

Description

OECD Approved Assay

Monte Carlo Simulations (Geant4)

Douglass et al., 2013; Douglass et al. 2012

Monte Carlo simulations are based on a computational algorithm that mathematically models the deposition of energy into materials.

N/A

Fluorescent Nuclear Track Detector (FNTD)

Sawakuchi, 2016; Niklas, 2013; Koaira et al., 2015

FNTDs are biocompatible chips with crystals of aluminium oxide doped with carbon and magnesium; used in conjuction with fluorescent microscopy, these FNTDs allow for the visualization and the linear energy transfer (LET) quantification of tracks produced by the deposition of energy into a material.

N/A

 

References

Alloni, AD. et al.(2014),” Modeling Dose Deposition and DNA Damage Due to Low-Energy β – Emitters.”, Radiation Research.182(3):322–330. doi:10.1667/RR13664.1.

Beir, V. et al. (1999), “ The Mechanistic Basis of Radon-Induced Lung Cancer.”, https://www.ncbi.nlm.nih.gov/books/NBK233261/.

Douglass, M. et al. (2013),” Monte Carlo investigation of the increased radiation deposition due to gold nanoparticles using kilovoltage and megavoltage photons in a 3D randomized cell model.”, Med Phys. 40(7), 071710. doi:10.1118/1.4808150.

Douglass, M. et al. (2012),” Development of a randomized 3D cell model for Monte Carlo microdosimetry simulations.”, Med Phys. 39(6):3509-3519, doi:10.1118/1.4719963.

Friedland, W. et al. (2017),” Comprehensive track-structure based evaluation of DNA damage by light ions from radiotherapy- relevant energies down to stopping.”, Nat Publ Gr.1–15. doi:10.1038/srep45161.

Hada, M. & Georgakilas, AG. (2008), “Formation of Clustered DNA Damage after High-LET Irradiation.” J Radiat Res. 49(3):203–210. doi:10.1269/jrr.07123.

Hunter, N. & Muirhead, CR. (2009).” Review of relative biological effectiveness dependence on linear energy transfer for low-LET radiations Review of relative biological effectiveness dependence.”, Journal of Radiological Protection. 29(1):5-21. doi:10.1088/0952-4746/29/1/R01.

Kodaira, S. & Konishi, T. (2015), “Co-visualization of DNA damage and ion traversals in live mammalian cells using a fluorescent nuclear track detector.” Journal of Radiation Research. 360–365. doi:10.1093/jrr/rru091.

Liamsuwan, T. (2014).” Microdosimetry of proton and carbon ions.”,  Med Phys. 41(8):081721. doi: 10.1118/1.4888338.

Lorat, Y. (2015),” Nanoscale analysis of clustered DNA damage after high-LET irradiation by quantitative electron microscopy – The heavy burden to repair.”,  DNA Repair (Amst). 28:93–106. doi:10.1016/j.dnarep.2015.01.007.

Nikitaki, Z. et al. (2016), “Measurement of complex DNA damage induction and repair in human cellular systems after exposure to ionizing radiations of varying linear energy transfer ( LET ).”,Free Radical Research. 50(sup1):S64-S78.doi:10.1080/10715762.2016.1232484.

Niklas, M. et al. (2013), “Engineering cell-fluorescent ion track hybrid detectors.”, Radiation Oncology. 8:141. doi: 10.1186/1748-717X-8-141.

Okayasu, R. (2012a), “heavy ions — a mini review.”, Int J Cancer. 1000:991–1000. doi:10.1002/ijc.26445.

Okayasu, R. (2012b), “Repair of DNA damage induced by accelerated heavy ions-A mini review.”,  Int J Cancer. 130(5):991–1000. doi:10.1002/ijc.26445.

Robertson,  A. et al. (2013), “The Cellular and Molecular Carcinogenic Effects of Radon Exposure.”,  Int J Mol Sci.14(7):14024-63. doi: 10.3390/ijms140714024.

Sawakuchi, GO. & Akselrod, MS. (2016),  “Nanoscale measurements of proton tracks using fluorescent nuclear track detectors.”,Med Phys. 43(5):2485–2490. doi:10.1118/1.4947128.

Wyrobek, A. J. et al. (2005), “Relative susceptibilities of male germ cells to genetic defects induced by cancer chemotherapies.”, J Natl Cancer Inst Monogr.(34)” 31-35. doi:10.1093/jncimonographs/lgi001.

List of Key Events in the AOP

Event: 1635: Increase, DNA strand breaks

Short Name: Increase, DNA strand breaks

AOPs Including This Key Event

Stressors

Name
Ionizing Radiation
Topoisomerase inhibitors
Radiomimetic compounds

Biological Context

Level of Biological Organization
Molecular

Domain of Applicability

Taxonomic Applicability
Term Scientific Term Evidence Links
human and other cells in culture human and other cells in culture NCBI
Life Stage Applicability
Life Stage Evidence
All life stages High
Sex Applicability
Sex Evidence
Unspecific High

DNA strand breaks can occur in any eukaryotic or prokaryotic cell.

Key Event Description

DNA strand breaks can occur on a single strand (SSB) or both strands (double strand breaks; DSB). SSBs arise when the phosphate backbone connecting adjacent nucleotides in DNA is broken on one strand. DSBs are generated when both strands are simultaneously broken at sites that are sufficiently close to one another that base-pairing and chromatin structure are insufficient to keep the two DNA ends juxtaposed. As a consequence, the two DNA ends generated by a DSB can physically dissociate from one another, becoming difficult to repair and increasing the chance of inappropriate recombination with other sites in the genome (Jackson, 2002). SSB can turn into DSB if the replication fork stalls at the lesion leading to fork collapse.

Strand breaks are intermediates in various biological events, including DNA repair (e.g., excision repair), V(D)J recombination in developing lymphoid cells and chromatin remodeling in both somatic cells and germ cells. Th spectrum of damage  can be complex, particularily if the stressor is from large amounts of deposited energy which can result in complex lesions and clustered damage defined as two or more oxidzed bases, abasic sites or starnd breaks on opposing DNA strands within a few helical turns. These lesions are more difficult to repair and have been studied in many types of models  (Barbieri et al., 2019 and Asaithamby et al., 2011). DSBs and complex lesions  are of particular concern, as they are considered the most lethal and deleterious type of DNA lesion. If misrepaired or left unrepaired, DSBs may drive the cell towards genomic instability, apoptosis or tumorigenesis (Beir, 1999).

How it is Measured or Detected

Please refer to the table below for details regarding these and other methodologies for detecting DNA DSBs.

 

Assay Name

References

Description

OECD Approved Assay

Comet Assay (Single Cell Gel Eletrophoresis - Alkaline)

Collins, 2004; Olive and Banath, 2006; Platel et al., 2011; Nikolova et al., 2017

To detect SSBs or DSBs, single cells are encapsulated in agarose on a slide, lysed, and subjected to gel electrophoresis at an alkaline pH (pH >13); DNA fragments are forced to move, forming a "comet"-like appearance

Yes (No. 489)

Comet Assay (Single Cell Gel Eltrophoresis - Neutral)

Collins, 2014; Olive and Banath, 2006; Anderson and Laubenthal, 2013; Nikolova et al., 2017

To detect DSBs, single cells are encapsulated in agarose on a slide, lysed, and subjected to gel electrophoresis at a neutral pH; DNA fragments, which are not denatured at the neutral pH, are forced to move, forming a "comet"-like appearance

N/A

γ-H2AX Foci Quantification - Flow Cytometry

Rothkamm and Horn, 2009; Bryce et al., 2016

Measurement of γ-H2AX immunostaining in cells by flow cytometry, normalized to total levels of H2AX

N/A

γ-H2AX Foci Quantification - Western Blot

Burma et al., 2001; Revet et al., 2011

Measurement of γ-H2AX immunostaining in cells by Western blotting, normalized to total levels of H2AX

N/A

γ-H2AX Foci Quantification - Microscopy

Redon et al., 2010; Mah et al., 2010; Garcia-Canton et al., 2013

Quantification of γ-H2AX immunostaining by counting γ- H2AX foci visualized with a microscope

N/A

γ-H2AX Foci Detection - ELISA and flow cytometry

Ji et al., 2017; Bryce et al., 2016

Detection of γ-H2AX in cells by ELISA, normalized to total levels of H2AX; γH2AX foci detection can be high-throughput and automated using flow cytometry-based immunodetection.

N/A

Pulsed Field Gel Electrophoresis (PFGE)

Ager et al., 1990; Gardiner et al., 1985; Herschleb et al., 2007; Kawashima et al., 2017

To detect DSBs, cells are embedded and lysed in agarose, and the released DNA undergoes gel electrophoresis in which the direction of the voltage is periodically alternated; Large DNA fragments are thus able to be separated by size

N/A

The TUNEL (Terminal Deoxynucleotidyl Transferase dUTP Nick End Labeling) Assay

Loo, 2011

To detect strand breaks, dUTPs added to the 3’OH end of a strand break by the DNA polymerase terminal deoxynucleotidyl transferase (TdT) are tagged with a fluorescent dye or a reporter enzyme to allow visualization (We note that this method is typically used to measure apoptosis)

N/A

In Vitro DNA Cleavage Assays using Topoisomerase

Nitiss, 2012

Cleavage of DNA can be achieved using purified topoisomerase; DNA strand breaks can then be separated and quantified using gel electrophoresis

N/A

References

Ager, D. D. et al. (1990). “Measurement of Radiation- Induced DNA Double-Strand Breaks by Pulsed-Field Gel Electrophoresis.” Radiat Res. 122(2), 181-7.

Anderson, D. & Laubenthal J. (2013), “Analysis of DNA Damage via Single-Cell Electrophoresis. In: Makovets S, editor. DNA Electrophoresis. Totowa.”, NJ: Humana Press. p 209-218.

Asaithamby, A., B. Hu and D.J. Chen. (2011) Unrepaired clustered DNA lesions induce chromosome breakage in human cells. Proc Natl Acad Sci U S A 108(20): 8293-8298 .

Barbieri, S., G. Babini, J. Morini et a l (2019). . Predicting DNA damage foci and their experimental readout with 2D microscopy: a unified approach applied to photon and neutron exposures. Scientific Reports 9(1): 14019

Bryce, S. et al. (2016), “Genotoxic mode of action predictions from a multiplexed flow cytometric assay and a machine learning approach.”, Environ Mol Mutagen. 57:171-189. Doi: 10.1002/em.21996.

Burma, S. et al. (2001), “ATM phosphorylates histone H2AX in response to DNA double-strand breaks.”, J Biol Chem, 276(45): 42462-42467. doi:10.1074/jbc.C100466200

Charlton, E. D. et al. (1989), “Calculation of Initial Yields of Single and Double Stranded Breaks in Cell Nuclei from Electrons, Protons, and Alpha Particles.”,  Int. J. Radiat. Biol. 56(1): 1-19. doi: 10.1080/09553008914551141.

Collins, R. A. (2004), “The Comet Assay for DNA Damage and Repair. Molecular Biotechnology.”, Mol Biotechnol. 26(3): 249-61. doi:10.1385/MB:26:3:249

Garcia-Canton, C. et al. (2013), “Assessment of the in vitro p-H2AX assay by High Content Screening asa novel genotoxicity test.”, Mutat Res. 757:158-166.  Doi:  10.1016/j.mrgentox.2013.08.002

Gardiner, K. et al. (1986), “Fractionation of Large Mammalian DNA Restriction Fragments Using Vertical Pulsed-Field Gradient Gel Electrophoresis.”,  Somatic Cell and Molecular Genetics. 12(2): 185-95.Doi: 10.1007/bf01560665.

Herschleb, J. et al. (2007), “Pulsed-field gel electrophoresis.”,  Nat Protoc. 2(3): 677-684. doi:10.1038/nprot.2007.94

Iliakis, G. et al. (2015), “Alternative End-Joining Repair Pathways Are the Ultimate Backup for Abrogated Classical Non-Homologous End-Joining and Homologous Recombination Repair: Implications for the Formation of Chromosome Translocations.”,  Mutation Research/Genetic Toxicology and Environmental Mutagenesis. 2(3): 677-84. doi: 10.1038/nprot.2007.94

Jackson, S. (2002). “Sensing and repairing DNA double-strand breaks.”,  Carcinogenesis. 23:687-696. Doi:10.1093/carcin/23.5.687.

Ji, J. et al. (2017), “Phosphorylated fraction of H2AX as a measurement for DNA damage in cancer cells and potential applications of a novel assay.”,  PLoS One. 12(2): e0171582. doi:10.1371/journal.pone.0171582

Kawashima, Y.(2017), “Detection of DNA double-strand breaks by pulsed-field gel electrophoresis.”,  Genes Cells 22:84-93. Doi: 10.1111/gtc.12457.

Khoury, L. et al. (2013), “Validation of high-throughput genotoxicity assay screening using cH2AX in-cell Western assay on HepG2 cells.”, Environ Mol Mutagen, 54:737-746. Doi:  10.1002/em.21817.

Khoury, L. et al. (2016), “Evaluation of four human cell lines with distinct biotransformation properties for genotoxic screening.”, Mutagenesis, 31:83-96. Doi: 10.1093/mutage/gev058.

Loo, DT. (2011), “In Situ Detection of Apoptosis by the TUNEL Assay: An Overview of Techniques. In: Didenko V, editor. DNA Damage Detection In Situ, Ex Vivo, and In Vivo. Totowa.”, NJ: Humana Press. p 3-13.doi: 10.1007/978-1-60327-409-8_1.

Mah, L. J. et al. (2010), “Quantification of gammaH2AX foci in response to ionising radiation.”,  J Vis Exp(38). doi:10.3791/1957.

Nikolova, T., F. et al. (2017), “Genotoxicity testing: Comparison of the γH2AX focus assay with the alkaline and neutral comet assays.”,  Mutat Res 822:10-18. Doi: 10.1016/j.mrgentox.2017.07.004.

Nitiss, J. L. et al. (2012), “Topoisomerase assays. ”, Curr Protoc Pharmacol. Chapter 3: Unit 3 3.

OECD. (2014). Test No. 489: “In vivo mammalian alkaline comet assay.”  OECD Guideline for the Testing of Chemicals, Section 4 .

Olive, P. L., & Banáth, J. P. (2006), “The comet assay: a method to measure DNA damage in individual cells.”,  Nature Protocols. 1(1): 23-29. doi:10.1038/nprot.2006.5.

Platel A. et al. (2011), “Study of oxidative DNA damage in TK6 human lymphoblastoid cells by use of the thymidine kinase gene-mutation assay and the in vitro modified comet assay: Determination of No-Observed-Genotoxic-Effect-Levels.”,  Mutat Res 726:151-159. Doi: 10.1016/j.mrgentox.2011.09.003.

Redon, C. et al. (2010), “The use of gamma-H2AX as a biodosimeter for total-body radiation exposure in non-human primates.”,  PLoS One. 5(11): e15544. doi:10.1371/journal.pone.0015544

Revet, I. et al. (2011), “Functional relevance of the histone γH2Ax in the response to DNA damaging agents.” Proc Natl Acad Sci USA.108:8663-8667. Doi: 10.1073/pnas.1105866108

Rogakou, E.P. et al. (1998), “DNA Double-stranded Breaks Induce Histone H2AX Phosphorylation on Serine 139.” , J Biol Chem, 273:5858-5868. Doi: 10.1074/jbc.273.10.5858

Rothkamm, K. & Horn, S. (2009), “γ-H2AX as protein biomarker for radiation exposure.”,  Ann Ist Super Sanità, 45(3): 265-71.

Event: 155: N/A, Inadequate DNA repair

Short Name: N/A, Inadequate DNA repair

Key Event Component

Process Object Action
DNA repair deoxyribonucleic acid functional change

AOPs Including This Key Event

Stressors

Name
Ionizing Radiation

Biological Context

Level of Biological Organization
Cellular

Domain of Applicability

Taxonomic Applicability
Term Scientific Term Evidence Links
mouse Mus musculus High NCBI
rat Rattus norvegicus Moderate NCBI
Syrian golden hamster Mesocricetus auratus Moderate NCBI
Homo sapiens Homo sapiens High NCBI
Life Stage Applicability
Life Stage Evidence
All life stages High
Sex Applicability
Sex Evidence
Unspecific High

The retention of adducts has been directly measured in many different types of eukaryotic somatic cells (in vitro and in vivo). In male germ cells, work has been done on hamsters, rats and mice. The accumulation of mutation and changes in mutation spectrum has been measured in mice and human cells in culture. Theoretically, saturation of DNA repair occurs in every species (prokaryotic and eukaryotic). The principles of this work were established in prokaryotic models. Nagel et al. (2014) have produced an assay that directly measures DNA repair in human cells in culture.

NHEJ is primarily used by vertebrate multicellular eukaryotes, but it also been observed in plants. Furthermore, it has recently been discovered that some bacteria (Matthews et al., 2014) and yeast (Emerson et al., 2016) also use NHEJ. In terms of invertebrates, most lack the core DNA-PKcs and Artemis proteins; they accomplish end joining by using the RA50:MRE11:NBS1 complex (Chen et al., 2001).  HR occurs naturally in eukaryotes, bacteria, and some viruses (Bhatti et al., 2016).

Key Event Description

DNA lesions may result from the formation of DNA adducts (i.e., covalent modification of DNA by chemicals), or by the action of agents such as radiation that may produce strand breaks or modified nucleotides within the DNA molecule. These DNA lesions are repaired through several mechanistically distinct pathways that can be categorized as follows:

  1. Damage reversal acts to reverse the damage without breaking any bonds within the sugar phosphate backbone of the DNA. The most prominent enzymes associated with damage reversal are photolyases (Sancar, 2003) that can repair UV dimers in some organisms, and O6-alkylguanine-DNA alkyltransferase (AGT) (Pegg 2011) and oxidative demethylases (Sundheim et al., 2008), which can repair some types of alkylated bases.
  2. Excision repair involves the removal of a damaged nucleotide(s) through cleavage of the sugar phosphate backbone followed by re-synthesis of DNA within the resultant gap. Excision repair of DNA lesions can be mechanistically divided into: 

    a) Base excision repair (BER) (Dianov and Hübscher, 2013), in which the damaged base is removed by a damage-specific glycosylase prior to incision of the phosphodiester backbone at the resulting abasic site.

    b) Nucleotide excision repair (NER) (Schärer, 2013), in which the DNA strand containing the damaged nucleotide is incised at sites several nucleotides 5’ and 3’ to the site of damage, and a polynucleotide containing the damaged nucleotide is removed prior to DNA resynthesis within the resultant gap. 

    c) Mismatch repair (MMR) (Li et al., 2016)  which does not act on DNA lesions but does recognize mispaired bases resulting from replication errors. In MMR the strand containing the misincorporated base is removed prior to DNA resynthesis.

    The major pathway that removes oxidative DNA damage is base excision repair (BER), which can be either monofunctional or bifunctional; in mammals, a specific DNA glycosylase (OGG1: 8-Oxoguanine glycosylase) is responsible for excision of 8-oxoguanine (8-oxoG) and other oxidative lesions (Hu et al., 2005; Scott et al., 2014; Whitaker et al., 2017). We note that long-patch BER is used for the repair of clustered oxidative lesions, which uses several enzymes from DNA replication pathways (Klungland and Lindahl, 1997). These pathways are described in detail in various reviews e.g., (Whitaker et al., 2017). 

  3. Single strand break repair (SSBR) involves different proteins and enzymes depending on the origin of the SSB (e.g., produced as an intermediate in excision repair or due to direct chemical insult) but the same general steps of repair are taken for all SSBs: detection, DNA end processing, synthesis, and ligation (Caldecott, 2014). Poly-ADP-ribose polymerase1 (PARP1) detects and binds unscheduled SSBs (i.e., not deliberately induced during excision repair) and synthesizes PAR as a signal to the downstream factors in repair. PARP1 is not required to initiate SSBR of BER intermediates. The XRCC1 protein complex is then recruited to the site of damage and acts as a scaffold for proteins and enzymes required for repair. Depending on the nature of the damaged termini of the DNA strand, different enzymes are required for end processing to generate the substrates that DNA polymerase β (Polβ; short patch repair) or Pol δ/ε (long patch repair) can bind to synthesize over the gap. Synthesis in long-patch repair displaces a single stranded flap which is excised by flap endonuclease 1 (FEN1). In short-patch repair, the XRCC1/Lig3α complex joins the two ends after synthesis. In long-patch repair, the PCNA/Lig1 complex ligates the ends. (Caldecott, 2014). 
  4. Double strand break repair (DSBR) is necessary to preserve genomic integrity when breaks occur in both strands of a DNA molecule. There are two major pathways for DSBR: homologous recombination (HR), which operates primarily during S phase in dividing cells, and nonhomologous end joining (NHEJ), which can function in both dividing and non-dividing cells (Teruaki Iyama and David M. Wilson III, 2013). 

In higher eukaryotes such as mammals, NHEJ is usually the preferred pathway for DNA DSBR. Its use, however, is dependent on the cell type, the gene locus, and the nuclease platform (Miyaoka et al., 2016). The use of NHEJ is also dependent on the cell cycle; NHEJ is generally not the pathway of choice when the cell is in the late S or G2 phase of the cell cycle, or in mitotic cells when the sister chromatid is directly adjacent to the double-strand break (DSB) (Lieber et al., 2003). In these cases, the HR pathway is commonly used for repair of DSBs. Despite this, NHEJ is still used more commonly than HR in human cells. Classical NHEJ (C-NHEJ) is the most common NHEJ repair mechanism, but alternative NHEJ (alt-NHEJ) can also occur, especially in the absence of C-NHEJ and HR.

The process of C-NHEJ in humans requires at least seven core proteins: Ku70, Ku86, DNA-dependent protein kinase complex (DNA-PKcs ), Artemis, X-ray cross-complementing protein 4 (XRCC4), XRCC4-like factor (XLF), and DNA ligase IV (Boboila et al., 2012). When DSBs occur, the Ku proteins, which have a high affinity for DNA ends, will bind to the break site and form a heterodimer. This protects the DNA from exonucleolytic attack and acts to recruit DNA-PKcs, thus forming a trimeric complex on the ends of the DNA strands. The kinase activity of DNA-PKcs is then triggered, causing DNA-PKcs to auto-phosphorylate and thereby lose its kinase activity; the now phosphorylated DNA-PKcs dissociates from the DNA-bound Ku proteins. The free DNA-PKcs phosphorylates Artemis, an enzyme that possesses 5’-3’ exonuclease and endonuclease activity in the presence of DNA-PKcs and ATP. Artemis is responsible for ‘cleaning up’ the ends of the DNA. For 5’ overhangs, Artemis nicks the overhang, generally leaving a blunt duplex end. For 3’ overhangs, Artemis will often leave a four- or five-nucleotide single stranded overhang (Pardo et al., 2009; Fattah et al., 2010; Lieber et al., 2010). Next, the XLF and XRCC4 proteins form a complex which makes a channel to bind DNA and aligns the ends for efficient ligation via DNA ligase IV (Hammel et al., 2011).

The process of alt-NHEJ is less well understood than C-NHEJ.  Alt-NHEJ is known to involve slightly different core proteins than C-NHEJ, but the steps of the pathway are essentially the same between the two processes (reviewed in Chiruvella et al., 2013). It is established, however, that alt-NHEJ is more error-prone in nature than C-NHEJ, which contributes to incorrect DNA repair. Alt-NHEJ is thus considered primarily to be a backup repair mechanism (reviewed in Chiruvella et al., 2013). 

In contrast to NHEJ, HR takes advantage of similar or identical DNA sequences to repair DSBs (Sung and Klein, 2006). The initiating step of HR is the creation of a 3’ single strand DNA (ss-DNA) overhang. Combinases such as RecA and Rad51 then bind to the ss-DNA overhang, and other accessory factors, including Rad54, help recognize and invade the homologous region on another DNA strand. From there, DNA polymerases are able to elongate the 3’ invading single strand and resynthesize the broken DNA strand using the corresponding sequence on the homologous strand.

 

Fidelity of DNA Repair


Most DNA repair pathways are extremely efficient. However, in principal, all DNA repair pathways can be overwhelmed when the DNA lesion burden exceeds the capacity of a given DNA repair pathway to recognize and remove the lesion. Exceeded repair capacity may lead to toxicity or mutagenesis following DNA damage. Apart from extremely high DNA lesion burden, inadequate repair may arise through several different specific mechanisms. For example, during repair of DNA containing O6-alkylguanine adducts, AGT irreversibly binds a single O6-alkylguanine lesion and as a result is inactivated (this is termed suicide inactivation, as its own action causes it to become inactivated). Thus, the capacity of AGT to carry out alkylation repair can become rapidly saturated when the DNA repair rate exceeds the de novo synthesis of AGT (Pegg, 2011).

A second mechanism relates to cell specific differences in the cellular levels or activity of some DNA repair proteins. For example, XPA is an essential component of the NER complex. The level of XPA that is active in NER is low in the testes, which may reduce the efficiency of NER in testes as compared to other tissues (Köberle et al., 1999). Likewise, both NER and BER have been reported to be deficient in cells lacking functional p53 (Adimoolam and Ford, 2003; Hanawalt et al., 2003; Seo and Jung, 2004). A third mechanism relates to the importance of the DNA sequence context of a lesion in its recognition by DNA repair enzymes. For example, 8-oxoguanine (8-oxoG) is repaired primarily by BER; the lesion is initially acted upon by a bifunctional glycosylase, OGG1, which carries out the initial damage recognition and excision steps of 8-oxoG repair. However, the rate of excision of 8-oxoG is modulated strongly by both chromatin components (Menoni et al., 2012) and DNA sequence context (Allgayer et al., 2013) leading to significant differences in the repair of lesions situated in different chromosomal locations.

DNA repair is also remarkably error-free. However, misrepair can arise during repair under some circumstances. DSBR is notably error prone, particularly when breaks are processed through NHEJ, during which partial loss of genome information is common at the site of the double strand break (Iyama and Wilson, 2013). This is because NHEJ rejoins broken DNA ends without the use of extensive homology; instead, it uses the microhomology present between the two ends of the DNA strand break to ligate the strand back into one. When the overhangs are not compatible, however, indels (insertion or deletion events), duplications, translocations, and inversions in the DNA can occur. These changes in the DNA may lead to significant issues within the cell, including alterations in the gene determinants for cellular fatality (Moore et al., 1996).

Activation of mutagenic DNA repair pathways to withstand cellular or replication stress either from endogenous or exogenous sources can promote cellular viability, albeit at a cost of increased genome instability and mutagenesis (Fitzgerald et al., 2017). These salvage DNA repair pathways including, Break-induced Replication (BIR) and Microhomology-mediated Break-induced Replication (MMBIR). BIR repairs one-ended DSBs and has been extensively studied in yeast as well as in mammalian systems. BIR and MMBIR are linked with heightened levels of mutagenesis, chromosomal rearrangements and ensuing genome instability (Deem et al., 2011; Sakofsky et al., 2015; Saini et al., 2017; Kramara et al., 2018). In mammalian genomes BIR-like synthesis has been proposed to be involved in late stage Mitotic DNA Synthesis (MiDAS) that predominantly occurs at so-called Common Fragile Sites (CFSs) and maintains telomere length under s conditions of replication stress that serve to promote cell viability (Minocherhomji et al., 2015; Bhowmick et al., 2016; Dilley et al., 2016).       

Misrepair may also occur through other repair pathways. Excision repair pathways require the resynthesis of DNA and rare DNA polymerase errors during gap resynthesis will result in mutations (Brown et al., 2011). Errors may also arise during gap resynthesis when the strand that is being used as a template for DNA synthesis contains DNA lesions (Kozmin and Jinks-Robertson, 2013). In addition, it has been shown that sequences that contain tandemly repeated sequences, such as CAG triplet repeats, are subject to expansion during gap resynthesis that occurs during BER of 8-oxoG damage (Liu et al., 2009).

How it is Measured or Detected

There is no test guideline for this event. The event is usually inferred from measuring the retention of DNA adducts or the creation of mutations as a measure of lack of repair or incorrect repair. These ‘indirect’ measures of its occurrence are crucial to determining the mechanisms of genotoxic chemicals and for regulatory applications (i.e., determining the best approach for deriving a point of departure). More recently, a fluorescence-based multiplex flow-cytometric host cell reactivation assay (FM-HCR) has been developed to directly measure the ability of human cells to repair plasmid reporters (Nagel et al., 2014).

Indirect Measurement

In somatic and spermatogenic cells, measurement of DNA repair is usually inferred by measuring DNA adduct formation/removal. Insufficient repair is inferred from the retention of adducts and from increasing adduct formation with dose. Insufficient DNA repair is also measured by the formation of increased numbers of mutations and alterations in mutation spectrum. The methods will be specific to the type of DNA adduct that is under study.

Some EXAMPLES are given below for alkylated DNA.

DOSE-RESPONSE CURVE FOR ALKYL ADDUCTS/MUTATIONS: It is important to consider that some adducts are not mutagenic at all because they are very effectively repaired. Others are effectively repaired, but if these repair processes become overwhelmed mutations begin to occur. The relationship between exposure to mutagenic agents and the presence of adducts (determined as adducts per nucleotide) provide an indication of whether the removal of adducts occurs, and whether it is more efficient at low doses. A sub-linear DNA adduct curve suggests that less effective repair occurs at higher doses (i.e., repair processes are becoming saturated). A sub-linear shape for the dose-response curves for mutation induction is also suggestive of repair of adducts at low doses, followed by saturation of repair at higher doses. Measurement of a clear point of inflection in the dose-response curve for mutations suggests that repair does occur, at least to some extent, but reduced repair efficiency arises above the breakpoint. A lack of increase in mutation frequencies (i.e., flat line for dose-response) for a compound showing a dose-dependent increase in adducts would imply that the adducts formed are either not mutagenic or are effectively repaired.

RETENTION OF ALKYL ADDUCTS: Alkylated DNA can be found in cells long after exposure has occurred. This indicates that repair has not effectively removed the adducts. For example, DNA adducts have been measured in hamster and rat spermatogonia several days following exposure to alkylating agents, indicating lack of repair (Seiler et al., 1997; Scherer et al., 1987).

MUTATION SPECTRUM: Shifts in mutation spectrum (i.e., the specific changes in the DNA sequence) following a chemical exposure (relative to non-exposed mutation spectrum) indicates that repair was not operating effectively to remove specific types of lesions. The shift in mutation spectrum is indicative of the types of DNA lesions (target nucleotides and DNA sequence context) that were not repaired. For example, if a greater proportion of mutations occur at guanine nucleotides in exposed cells, it can be assumed that the chemical causes DNA adducts on guanine that are not effectively repaired.


Direct Measurement

Nagel et al. (2014) we developed a fluorescence-based multiplex flow-cytometric host cell reactivation assay (FM-HCR) to measures the ability of human cells to repair plasmid reporters. These reporters contain different types and amounts of DNA damage and can be used to measure repair through by NER, MMR, BER, NHEJ, HR and MGMT.

Please refer to the table below for additional details and methodologies for detecting DNA damage and repair.

Assay Name References Description DNA Damage/Repair Being Measured OECD Approved Assay
Dose-Response Curve for Alkyl Adducts/ Mutations

Lutz 1991

 

Clewell 2016

Creation of a curve plotting the stressor dose and the abundance of adducts/mutations; Characteristics of the resulting curve can provide information on the efficiency of DNA repair

Alkylation,

oxidative damage, or DSBs

N/A
Retention of Alkyl Adducts

Seiler 1997

 

Scherer 1987

Examination of DNA for alkylation after exposure to an alkylating agent; Presence of alkylation suggests a lack of repair Alkylation N/A
Mutation Spectrum Wyrick 2015 Shifts in the mutation spectrum after exposure to a chemical/mutagen relative to an unexposed subject can provide an indication of DNA repair efficiency, and can inform as to the type of DNA lesions present

Alkylation,

oxidative damage, or DSBs

N/A
DSB Repair Assay (Reporter constructs) Mao et al., 2011 Transfection of a GFP reporter construct (and DsRed control) where the GFP signal is only detected if the DSB is repaired; GFP signal  is quantified using fluorescence microscopy or flow cytometry DSBs N/A
Primary Rat Hepatocyte DNA Repair Assay

Jeffrey and Williams, 2000

 

Butterworth et al., 1987

Rat primary hepatocytes are cultured with a 3H-thymidine solution in order to measure DNA synthesis in response to a stressor in non-replicating cells; Autoradiography is used to measure the amount of 3H incorporated in the DNA post-repair Unscheduled DNA synthesis in response to DNA damage N/A
Repair synthesis measurement by 3H-thymine incorporation Iyama and Wilson, 2013 Measure DNA synthesis in non-dividing cells as indication of gap filling during excision repair Excision repair N/A
Comet Assay with Time-Course

Olive et al., 1990

 

Trucco et al., 1998

Comet assay is performed with a time-course; Quantity of DNA in the tail should decrease as DNA repair progresses DSBs  Yes (No. 489)
Pulsed Field Gel Electro-phoresis (PFGE) with Time-Course Biedermann et al., 1991 PFGE assay with a time-course; Quantity of small DNA fragments should decrease as DNA repair  progresses DSBs N/A

Fluorescence -Based Multiplex Flow-Cytometric Host Reactivation Assay

(FM-HCR)

Nagel et al., 2014 Measures the ability of human cells to repair plasma reporters, which contain different types and amounts of DNA damage; Used to measure repair processes including HR, NHEJ, BER, NER, MMR, and MGMT HR, NHEJ, BER, NER, MMR, or MGMT N/A

 

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Event: 185: Increase, Mutations

Short Name: Increase, Mutations

Key Event Component

Process Object Action
mutation deoxyribonucleic acid increased

AOPs Including This Key Event

Stressors

Name
Ionizing Radiation

Biological Context

Level of Biological Organization
Molecular

Domain of Applicability

Taxonomic Applicability
Term Scientific Term Evidence Links
Mus musculus Mus musculus High NCBI
medaka Oryzias latipes Moderate NCBI
rat Rattus norvegicus High NCBI
Homo sapiens Homo sapiens Moderate NCBI
Life Stage Applicability
Life Stage Evidence
All life stages High
Sex Applicability
Sex Evidence
Unspecific High

Mutations can occur in any organism and in any cell type, and are the fundamental material of evolution. The test guidelines described above range from analysis from prokaryotes, to rodents, to human cells in vitro. Mutations have been measured in virtually every human tissue sampled in vivo.

Key Event Description

A mutation is a change in DNA sequence. Mutations can thus alter the coding sequence of genes, potentially leading to malformed or truncated proteins. Mutations can also occur in promoter regions, splice junctions, non-coding RNA, DNA segments, and other functional locations in the genome. These mutations can lead to various downstream consequences, including alterations in gene expression. There are several different types of mutations including missense, nonsense, insertion, deletion, duplication, and frameshift mutations, all of which can impact the genome and its expression in unique ways.

Mutations can be propagated to daughter cells upon cellular replication. Mutations in stem cells (versus terminally differentiated non-replicating cells) are the most concerning, as these will persist in the organism. The consequence of the mutation, and thus the fate of the cell, depends on the location (e.g., coding versus non-coding) and the type (e.g., nonsense versus silent) of mutation.

Mutations can occur in somatic cells or germ cells (sperm or egg).

How it is Measured or Detected

Mutations can be measured using a variety of both OECD and non-OECD mutagenicity tests. Some examples are given below.

Somatic cells: The Salmonella mutagenicity test (Ames Test) is generally used as part of a first tier screen to determine if a chemical can cause gene mutations. This well-established test has an OECD test guideline (OECD TG 471, 2020). A variety of bacterial strains are used, in the presence and absence of a metabolic activation system (e.g., rat liver microsomal S9 fraction), to determine the mutagenic potency of chemicals by dose-response analysis. A full description is found in Test No. 471: Bacterial Reverse Mutation Test (OECD, 2016).

A variety of in vitro mammalian cell gene mutation tests are described in OECD’s Test Guidelines 476 (2016) and 490 (2015). TG 476 (2016) is used to identify substances that induce gene mutations at the hprt (hypoxanthine-guanine phosphoribosyl transferase) gene, or the transgenic xprt (xanthine-guanine phosphoribosyl transferase) reporter locus. The most commonly used cells for the HPRT test include the CHO, CHL and V79 lines of Chinese hamster cells, L5178Y mouse lymphoma cells, and TK6 human lymphoblastoid cells. The only cells suitable for the XPRT test are AS52 cells containing the bacterial xprt (or gpt) transgene (from which the hprt gene was deleted).

The new OECD TG 490 (2015) describes two distinct in vitro mammalian gene mutation assays using the thymidine kinase (tk) locus and requiring two specific tk heterozygous cells lines: L5178Y tk+/-3.7.2C cells for the mouse lymphoma assay (MLA) and TK6 tk+/- cells for the TK6 assay. The autosomal and heterozygous nature of the thymidine kinase gene in the two cell lines enables the detection of cells deficient in the enzyme thymidine kinase following mutation from tk+/- to tk-/-.

It is important to consider that different mutation spectra are detected by the different mutation endpoints assessed. The non-autosomal location of the hprt gene (X-chromosome) means that the types of mutations detected in this assay are point mutations, including base pair substitutions and frameshift mutations resulting from small insertions and deletions. Whereas, the autosomal location of the transgenic xprt, tk, or gpt locus allows the detection of large deletions not readily detected at the hemizygous hprt locus on X-chromosomes. Genetic events detected using the tk locus include both gene mutations (point mutations, frameshift mutations, small deletions) and large deletions.

The transgenic rodent mutation assay (OECD TG 488, 2020) is the only assay capable of measuring gene mutation in virtually all tissues in vivo. Specific details on the rodent transgenic mutation reporter assays are reviewed in Lambert et al. (2005, 2009). The transgenic reporter genes are used for detection of gene mutations and/or chromosomal deletions and rearrangements resulting in DNA size changes (the latter specifically in the lacZ plasmid and Spi- test models) induced in vivo by test substances (OECD, 2009, OECD, 2011; Lambert et al., 2005). Briefly, transgenic rodents (mouse or rat) are exposed to the chemical agent sub-chronically. Following a manifestation period, genomic DNA is extracted from tissues, transgenes are rescued from genomic DNA, and transfected into bacteria where the mutant frequency is measured using specific selection systems.

The Pig-a (phosphatidylinositol glycan, Class A) gene on the X chromosome codes for a catalytic subunit of the N-acetylglucosamine transferase complex that is involved in glycosylphosphatidyl inositol (GPI) cell surface anchor synthesis. Cells lacking GPI anchors, or GPI-anchored cell surface proteins are predominantly due to mutations in the Pig-a gene. Thus, flow cytometry of red blood cells expressing or not expressing the Pig-a gene has been developed for mutation analysis in blood cells from humans, rats, mice, and monkeys. The assay is described in detail in Dobrovolsky et al. (2010). Development of an OECD guideline for the Pig-a assay is underway. In addition, experiments determining precisely what proportion of cells expressing the Pig-a mutant phenotype have mutations in the Pig-a gene are in progress (e.g., Nicklas et al., 2015, Drobovolsky et al., 2015). A recent paper indicates that the majority of CD48 deficient cells from 7,12-dimethylbenz[a]anthracene-treated rats (78%) are indeed due to mutation in Pig-a (Drobovolsky et al., 2015).


Germ cells: Tandem repeat mutations can be measured in bone marrow, sperm, and other tissues using single-molecule PCR. This approach has been applied most frequently to measure repeat mutations occurring in sperm DNA. Isolation of sperm DNA is as described above for the transgenic rodent mutation assay, and analysis of tandem repeats is done using electrophoresis for size analysis of allele length using single-molecule PCR. For expanded simple tandem repeat this involved agarose gel electrophoresis and Southern blotting, whereas for microsatellites sizing is done by capillary electrophoresis. Detailed methodologies for this approach are found in Yauk et al. (2002) and Beal et al. (2015).

Mutations in rodent sperm can also be measured using the transgenic reporter model (OECD TG 488, 2020). A description of the approach is found within this published TG. Further modifications to this protocol have now been made for the analysis of germ cells. Detailed methodology for detecting mutant frequency arising in spermatogonia is described in Douglas et al. (1995), O'Brien et al. (2013); and O'Brien et al. (2014). Briefly, male mice are exposed to the mutagen and killed at varying times post-exposure to evaluate effects on different phases of spermatogenesis. Sperm are collected from the vas deferens or caudal epididymis (the latter preferred). Modified protocols have been developed for extraction of DNA from sperm.

A similar transgenic assay can be used in transgenic medaka (Norris and Winn, 2010).


Please note, gene mutations that occur in somatic cells in vivo (OECD Test. No. 488, 2020) or in vitro (OECD Test No. 476: In vitro Mammalian Cell Gene Mutation Test, 2016), or in bacterial cells (i.e., OECD Test No. 471, 2020) can be used as an indicator that mutations in male pre-meiotic germ cells may occur for a particular agent (sensitivity and specificity of other assays for male germ cell effects is given in Waters et al., 1994). However, given the very unique biological features of spermatogenesis relative to other cell types, known exceptions to this rule, and the small database on which this is based, inferring results from somatic cell or bacterial tests to male pre-meiotic germ cells must be done with caution. That mutational assays in somatic cells may predict mutations in germ cells has not been rigorously tested empirically (Singer and Yauk, 2010). The IWGT working group on germ cells specifically addressed this gap in knowledge in their report (Yauk et al., 2015) and recommended that additional research address this issue. Mutations can be directly measured in humans (and other species) through the application of next-generation sequencing. Although single-molecule approaches are growing in prevalence, the most robust approach to measure mutation using next-generation sequencing today requires clonal expansion of the mutation to a sizable proportion (e.g., sequencing tumours; Shen et al., 2015), or analysis of families to identify germline derived mutations (reviewed in Campbell and Eichler, 2013; Adewoye et al., 2015).

Please refer to the table below for additional details and methodologies for measuring mutations.

Assay Name References Description OECD Approved Assay
Assorted Gene Loci Mutation Assays

Tindall et al., 1989; Kruger et al., 2015

After exposure to a chemical/mutagen, mutations can be measured by the ability of exposed cells to form colonies in the presence of specific compounds that would normally inhibit colony growth; Usually only cells -/- for the gene of interest are able to form colonies N/A
TK Mutation Assay

Yamamoto et al., 2017; Liber et al., 1982; Lloyd and Kidd, 2012

After exposure to a chemical/mutagen, mutations are detected at the thymidine kinase (TK) loci of L5178Y wild-type mouse lymphoma TK (+/-) cells by measuring resistance to lethaltriflurothymidine (TFT); Only TK-/- cells are able to form colonies Yes (No. 490)
HPRT Mutation Assay

Ayres et al., 2006; Parry and Parry, 2012

Similar to TK Mutation Assay above, X-linked HPRT mutations produced in response to chemical/mutagen exposure can be measured through colony formation in the presence of 6-TG or 8-azoguanine; Only HPRT-/- cells are able to form colonies Yes (No. 476)
Salmonella Mutagenicity Test (Ames Test) OECD, 1997 After exposure to a chemical/mutagen, point mutations are detected by analyzing the growth capacity of different bacterial strains in the presence and absence of various metabolic activation systems  Yes (No. 471)
PIG-A / PIG-O Assay

Kruger et al., 2015; Nakamura, 2012; Chikura, 2019

After exposure to a chemical/mutagen, mutations  in PIG-A or PIG-O (which decrease the biosynthesis of the glycosylphosphatidylinositol (GPI) anchor protein) are assessed by the colony-forming capabilities of cells after in vitro exposure, or by flow cytometry of blood samples after in vivo exposure N/A
Single Molecule PCR

Kraytsberg, 2005; Yauk, 2002

This PCR technique uses a single DNA template, and is often employed for detection of mutations in microsatellites, recombination studies, and generation of polonies N/A
ACB-PCR

Myers et al., 2014 (Textbook, pg 345-363); Banda et al.,  2013; Banda et al.,  2015; Parsons et al., 2017

Using this PCR technique, single base pair substitution mutations within oncogenes or tumour suppressor genes can be detected by selectively amplifying specific point mutations within an allele and selectively blocking amplification of the wild-type allele N/A
Transgenic Rodent Mutation Assay

OECD 2013; Lambert 2005; Lambert 2009

This in vivo test detects gene mutations using transgenic rodents that possess transgenes and reporter genes; After in vivo exposure to a chemical/mutagen, the transgenes are analyzed by transfecting bacteria with the reporter gene and examining the resulting phenotype Yes (No. 488)
Conditionally inducible transgenic mouse models Parsons 2018 (Review) Inducible mutations linked to fluorescent tags are introduced into transgenic mice; Upon exposure of the transgenic mice to an inducing agent, the presence and functional assessment of the mutations can be easily ascertained due to expression of the linked fluorescent tags N/A
Error-Corrected Next Generation Sequencing (NGS) Salk 2018 (Review) This technique detects rare subclonal mutations within a pool of heterogeneous DNA samples through the application of new error-correction strategies to NGS; At present, few laboratories in the world are capable of doing this, but commercial services are becoming available (e.g., Duplex sequencing at TwinStrand BioSciences) N/A

 

References

Adewoye, A.B. et al. (2015), "The genome-wide effects of ionizing radiation on mutation induction in the mammalian germline", Nat. Commu., 6:6684. Doi: 10.1038/ncomms7684.

Ayres, M. F. et al. (2006),  “Low doses of gamma ionizing radiation increase hprt mutant frequencies of TK6 cells without triggering the mutator phenotype pathway”,  Genetics and Molecular Biology. 2(3): 558-561. Doi:10.1590/S1415-4757200600030002.

Banda M, Recio L, and Parsons BL. (2013), “ACB-PCR measurement of spontaneous and furan-induced H-ras codon 61 CAA to CTA and CAA to AAA mutation in B6C3F1 mouse liver”, Environ Mol Mutagen. 54(8):659-67. Doi:10.1002/em.21808.

Banda,  M. et al. (2015), “Quantification of Kras mutant fraction in the lung DNA of mice exposed to aerosolized particulate vanadium pentoxide by inhalation”,  Mutat Res Genet Toxicol Environ Mutagen. 789-790:53-60. Doi: 10.1016/j.mrgentox.2015.07.003

Campbell, C.D. & E.E. Eichler (2013), "Properties and rates of germline mutations in humans", Trends Genet., 29(10): 575-84. Doi:  10.1016/j.tig.2013.04.005

Chikura, S. et al. (2019), “Standard protocol for the total red blood cell Pig-a assay used in the interlaboratory trial organized by the Mammalian Mutagenicity Study Group of the Japanese Environmental Mutagen Society”,  Genes Environ.  27:41-5. Doi: 10.1186/s41021-019-0121-z.

Dobrovolsky, V.N. et al. (2015), "CD48-deficient T-lymphocytes from DMBA-treated rats have de novo mutations in the endogenous Pig-a gene. CD48-Deficient T-Lymphocytes from DMBA-Treated Rats Have De Novo Mutations in the Endogenous Pig-a Gene", Environ. Mol. Mutagen., (6): 674-683. Doi: 10.1002/em.21959.

Douglas, G.R. et al. (1995), "Temporal and molecular characteristics of mutations induced by ethylnitrosourea in germ cells isolated from seminiferous tubules and in spermatozoa of lacZ transgenic mice", Proceedings of the National Academy of Sciences of the United States of America, 92(16): 7485-7489. Doi: 10.1073/pnas.92.16.7485.

Kraytsberg,Y. &  Khrapko, K. (2005),  “Single-molecule PCR: an artifact-free PCR approach for the analysis of somatic mutations”,  Expert Rev Mol Diagn. 5(5):809-15. Doi: 10.1586/14737159.5.5.809.

Krüger, T. C., Hofmann, M., & Hartwig, A. (2015), “The in vitro PIG-A gene mutation assay: mutagenicity testing via flow cytometry based on the glycosylphosphatidylinositol (GPI) status of TK6 cells”, Arch Toxicol. 89(12), 2429-43. Doi: 10.1007/s00204-014-1413-5.

Lambert, I.B. et al. (2005), "Detailed review of transgenic rodent mutation assays", Mutat Res., 590(1-3):1-280. Doi: 10.1016/j.mrrev.2005.04.002.

Liber, L. H., & Thilly, G. W. (1982),  “Mutation assay at the thymidine kinase locus in diploid human lymphoblasts”,  Mutation Research. 94: 467-485. Doi:10.1016/0027-5107(82)90308-6.

Lloyd, M., & Kidd, D. (2012), “The Mouse Lymphoma Assay. In: Parry J., Parry E. (eds) Genetic Toxicology, Methods in Molecular Biology (Methods and Protocols), 817. Springer, New York, NY.

Myers, M. B. et al., (2014), “ACB-PCR Quantification of Somatic Oncomutation”,  Molecular Toxicology Protocols, Methods in Molecular Biology. DOI: 10.1007/978-1-62703-739-6_27

Nakamura, J. et al., (2012), “Detection of PIGO-deficient cells using proaerolysin: a valuable tool to investigate mechanisms of mutagenesis in the DT40 cell system”, PLoS One.7(3): e33563. Doi:10.1371/journal.pone.0033563.

Nicklas, J.A., E.W. Carter and R.J. Albertini (2015), "Both PIGA and PIGL mutations cause GPI-a deficient isolates in the Tk6 cell line", Environ. Mol. Mutagen., 6(8):663-73. Doi: 10.1002/em.21953.

Norris, M.B. and R.N. Winn (2010), "Isolated spermatozoa as indicators of mutations transmitted to progeny", Mutat Res., 688(1-2): 36–40. Doi: 10.1016/j.mrfmmm.2010.02.008.

O'Brien, J.M. et al.(2013), "No evidence for transgenerational genomic instability in the F1 or F2 descendants of Muta™Mouse males exposed to N-ethyl-N-nitrosourea", Mutat. Res., 741-742:11-7. Doi: 10.1016/j.mrfmmm.2013.02.004.

O'Brien, J.M. et al. (2014), "Transgenic rodent assay for quanitifying male germ cell mutation frequency", Journal of Visual Experimentation, Aug 6;(90). Doi: 10.3791/51576.

O’Brien, J.M. et al. (2015), "Sublinear response in lacZ mutant frequency of Muta™ Mouse spermatogonial stem cells after low dose subchronic exposure to N-ethyl-N-nitrosourea", Environ. Mol. Mutagen., 6(4): 347-355. Doi: 10.1002/em.21932.

OECD (2020), Test No. 471: Bacterial Reverse Mutation Test, OECD Guidelines for the Testing of Chemicals, Section 4, OECD Publishing, Paris.

OECD (2016), Test No. 476: In vitro Mammalian Cell Gene Mutation Test, OECD Guidelines for the Testing of Chemicals, Section 4, OECD Publishing, Paris.

OECD (2009), Detailed Review Paper on Transgenic Rodent Mutation Assays, Series on Testing and Assessment, N° 103, ENV/JM/MONO 7, OECD, Paris.

OECD (2020), Test No. 488: Transgenic Rodent Somatic and Germ Cell Gene Mutation Assays, OECD Guidelines for the Testing of Chemicals, Section 4, OECD Publishing, Paris.

OECD (2016), Test. No. 490: In vitro mammalian cell gene mutation mutation tests using the thymidine kinase gene, OECD Guidelines for the Testing of Chemicals, Section 4, OECD Publishing, Paris.

OECD Guidelines for the Testing of Chemicals, Section 4, OECD Publishing, Paris.

Parry MJ, & Parry ME. 2012. Genetic Toxicology Principles and Methods. Humana Press. Springer Protocols.

Parsons BL, McKim KL, Myers MB. 2017. Variation in organ-specific PIK3CA and KRAS mutant levels in normal human tissues correlates with mutation prevalence in corresponding carcinomas. Environ Mol Mutagen. 58(7):466-476. Doi: 10.1002/em.22110.

Parsons BL. Multiclonal tumor origin: Evidence and implications. Mutat Res. 2018. 777:1-18. doi: 10.1016/j.mrrev.2018.05.001.

Salk JJ, Schmitt MW, &Loeb LA. (2018), “Enhancing the accuracy of next-generation sequencing for detecting rare and subclonal mutations”, Nat Rev Genet. 19(5):269-285. Doi: 10.1038/nrg.2017.117.

Shen, T., S.H. Pajaro-Van de Stadt, N.C. Yeat and J.C. Lin (2015), "Clinical applications of next generation sequencing in cancer: from panels, to exomes, to genomes" Front. Genet., 6: 215. Doi: 10.3389/fgene.2015.00215.

Singer, T.M. and C.L. Yauk CL (2010), "Germ cell mutagens: risk assessment challenges in the 21st century", Environ. Mol. Mutagen., 51(8-9): 919-928. Doi: 10.1002/em.20613.

Tindall, R. K., & Stankowski Jr., F. L. (1989),  “Molecular analysis of spontaneous mutations at the GPT locus in Chinese hamster ovary (AS52) cells”, Mutation Research, 220, 241-53. Doi: 10.1016/0165-1110(89)90028-6.

Waters, M.D. et al. (1994), "The performance of short-term tests in identifying potential germ cell mutagens: a qualitative and quantitative analysis", Mutat. Res., 341(2): 109-31. Doi: 10.1016/0165-1218(94)90093-0.

Yamamoto, A. et al. (2017), “Radioprotective activity of blackcurrant extract evaluated by in vitro micronucleus and gene mutation assays in TK6 human lymphoblastoid cells”, Genes and Environment. 39: 22. Doi: 10.1186/s41021-017-0082-z.

Yauk, C.L. et al. (2002), "A novel single molecule analysis of spontaneous and radiation-induced mutation at a mouse tandem repeat locus", Mutat. Res., 500(1-2): 147-56. Doi: 10.1016/s0027-5107(02)00005-2.

Yauk, C.L. et al. (2015), "Approaches for Identifying Germ Cell Mutagens: Report of the 2013 IWGT Workshop on Germ Cell Assays", Mutat. Res. Genet. Toxicol. Environ. Mutagen., 783: 36-54. Doi: 10.1016/j.mrgentox.2015.01.008.

Yeat and J.C. Lin. 2015. Clinical applications of next generation sequencing in cancer: from panels, to exomes, to genomes. Front. Genet., 6: 215. Doi: 10.3389/fgene.2015.00215.

Event: 1636: Increase, Chromosomal aberrations

Short Name: Increase, Chromosomal aberrations

AOPs Including This Key Event

Stressors

Name
Ionizing Radiation

Biological Context

Level of Biological Organization
Cellular

Domain of Applicability

Taxonomic Applicability
Term Scientific Term Evidence Links
human Homo sapiens High NCBI
rat Rattus norvegicus High NCBI
mouse Mus musculus High NCBI
Life Stage Applicability
Life Stage Evidence
All life stages High
Sex Applicability
Sex Evidence
Unspecific High

Chromosomal aberrations indicating clastogenicity can occur in any eukaryotic or prokaryotic cell. However, dose-response curves can differ depending on the cell cycle stage when the DSB agent was introduced (Obe et al., 2002).

Key Event Description

Structural chromosomal aberrations describe the damage to chromosomes that results from breaks along the DNA and may lead to deletion, addition, or rearrangement of sections in the chromosome. Chromosomal aberrations can be divided in two major categories: chromatid-type or chromosome-type depending on whether one or both chromatids are involved, respectively. They can be further classified as rejoined or non-rejoined aberrations. Rejoined aberrations include translocations, insertions, dicentrics and rings, while unrejoined aberrations include acentric fragments and breaks (Savage, 1976). Some of these aberrations are stable (i.e., reciprocal translocations) and can persist for many years (Tucker and Preston, 1996). Others are unstable (i.e., dicentrics, acentric fragments) and decline at each cell division because of cell death (Boei et al., 1996). These events may be detectable after cell division and such damage to DNA is irreversible. Chromosomal aberrations are associated with cell death and carcinogenicity (Mitelman, 1982).

Chromosomal aberrations (CA) refer to a missing, extra or irregular portion of chromosomal DNA. These DNA changes in the chromosome structure may be produced by different double strand break (DSB) repair mechanisms (Obe et al., 2002).

There are 4 main types of CAs: deletions, duplications, translocations, and inversions. Deletions happen when a portion of the genetic material from a chromosome is lost. Terminal deletions occur when an end piece of the chromosome is cleaved. Interstitial deletions arise when a chromosome breaks in two separate locations and rejoins incorrectly, with the center piece being omitted. Duplications transpire when there is any addition or rearrangement of excess genetic material; types of duplications include transpositions, tandem duplications, reverse duplications, and displaced duplications (Griffiths et al., 2000). Translocations result from a section of one chromosome being transferred to a non-homologous chromosome (Bunting and Nussenzweig, 2013). When there is an exchange of segments on two non-homologous chromosomes, it is called a reciprocal translocation. Inversions occur in a single chromosome and involve both of the ends breaking and being ligated on the opposite ends, effectively inverting the DNA sequence.    
 

A fifth type of CA that can occur in the genome is the copy number variant (CNV). CNVs, which may comprise greater than 10% of the human genome (Shlien et al., 2009; Zhang et al., 2016; Hastings et al., 2009),  are deletions or duplications that can vary in size from 50 base pairs (Arlt et al., 2012; Arlt et al., 2014; Liu et al., 2013) up into the megabase pair range (Arlt et al., 2012; Wilson et al., 2015; Arlt et al., 2014; Zhang et al., 2016). CNV regions are especially enriched in large genes and large active transcription units (Wilson et al., 2015), and are of particular concern when they cause deletions in tumour suppressor genes or duplications in oncogenes (Liu et al., 2013; Curtis et al., 2012). There are two types of CNVs: recurrent and non-recurrent. Recurrent CNVs are thought to be produced through a recombination process during meiosis known as non-allelic homologous recombination (NAHR) (Arlt et al., 2012; Hastings et al., 2009). These recurrent CNVs, also called germline CNVs, could be inherited and are thus common across different individuals (Shlien et al., 2009; Liu et al., 2013). Non-recurrent CNVs are believed to be produced in mitotic cells during the process of replication. Although the mechanism is not well studied, it has been suggested that stress during replication, in particular stalling replication forks, prompt microhomology-mediated mechanisms to overcome the replication stall, which often results in duplications or deletions. Two models that have been proposed to explain this mechanism include the Fork Stalling and Template Switching (FoSTeS) model, and the Microhomology-Mediated Break-Induced Replication (MMBIR) model (Arlt et al., 2012; Wilson et al., 2015; Lee et al., 2007; Hastings et al., 2009).

 

CAs can be classified according to whether the chromosome or chromatid is affected by the aberration. Chromosome-type aberrations (CSAs) include chromosome-type breaks, ring chromosomes, marker chromosomes, and dicentric chromosomes; chromatid-type aberrations (CTAs) refer to chromatid breaks and chromatid exchanges (Bonassi et al., 2008; Hagmar et al., 2004). When cells are blocked at the cytokinesis step, CAs are evident in binucleated cells as micronuclei (MN; small nucleus-like structures that contain a chromosome or a piece of a chromosome that was lost during mitosis) and nucleoplasmic bridges (NPBs; physical connections that exist between the two nuclei) (El-Zein et al., 2014). Other CAs can be assessed by examining the DNA sequence, as is the case when detecting copy number variants (CNVs) (Liu et al., 2013).

OECD defines clastogens as ‘any substance that causes structural chromosomal aberrations in populations of cells or organisms’.

How it is Measured or Detected

Chromosome aberrations can be detected before and after cell division. Widely used assays are described in the table below.

 

Assay

References

Description

OECD-approved assay

Fluorescent In Situ  Hybridization (FISH)

Beaton et al., 2013; Pathak

et al., 2017

Fluorescent assay of metaphase chromosomes that can detect CAs through chromosome painting and microscopic analysis

N/A

Cytokinesis Block Micronucleus (CBMN)

Assay with Microscopy in vitro

Fenech, 2000; OECD, 2016a

Cells are cultured with cytokinesis blocking agent, fixed to slides, and undergo MN quantification using microscopy.

 

Yes (No.487)

Micronucleus (MN)

Assay by Microscopy in vivo

OECD, 2016b

Cells are fixed on slides and MN are scored using microscopy. Red blood cells can also be scored for MN using flow cytometry (see below)

Yes

(No. 474)

CBMN with Imaging Flow Cytometry

Rodrigues et al., 2015

Cells are cultured with cytokinesis blocking agent, fixed in solution, and imaged with flow cytometry to quantify MN

N/A

Flow cytometry detection of MN

Dertinger et al., 2004; Bryce et al., 2007; OECD 2016a, 2016b

In vivo and in vitro flow cytometry-based, automated micronuclei measurements are also done without cytokinesis block. MN analysis in vivo is performed in peripheral blood cells to detect MN in erythrocytes and reticulocytes.

 

 

Yes (No.487; No. 474)

High-throughput biomarker assays (indirect measures to confirm clastogenicity)

Bryce et al. 2014, 2016, 2018

 

Khoury et al., 2013, Khoury et al., 2016)

 

 

Hendriks et al., 2012, 2016; Wink et al., 2014

Multiplexed biomarkers can be measured by flow cytometry are used to discern clastogenic and aneugenic mechanisms for MN induction. Flow cytometry-based quantification of γH2AX foci and p53 protein expression (Bryce et al., 2016).

 

Prediscreen Assay– In-Cell Western -based quantification of γH2AX

 

Green fluorescent protein reporter assay to detect the activation of stress signaling pathways, including DNA damage signaling including a reporter porter that is associated with DNA double strand breaks.

N/A

Dicentric Chromosome Assay (DCA)

Abe et al., 2018

Cells are fixed on microscope slides, chromosomes are stained, and the number of dicentric chromosomes are quantified

N/A

High content imaging

Shahane et al., 2016

DNA can be stained using fluorescent dyes and micronuclei can be scored high-throughput microscopy image analysis.

 

N/A

Chromosomal aberration test

 

OECD, 2016c; 2016d; 20l16e

In vitro, the cell cycle is arrested at metaphase after 1.5 cell cycle following 3-6 hour exposure

 

In vivo, the test chemical is administered as a single treatment and bone marrow is collected 18-24 hrs later (TG 475), while testis is collected 24-48 hrs later (TG 483). The cell cycle is arrested with a metaphase-arresting chemical (e.g., colchicine) 2-5 hours before cell collection. Once cells are fixed and stained on microscope slides, chromosomal aberrations are scored

Yes.

In vitro (No. 473)

In vivo (No. 475 and No. 483)

Array Comparative Genomic Hybridization (aCGH) or SNP

Microarray

Adewoye et al., 2015; Wilson et al., 2015; Arlt et al., 2014; Redon et al., 2006; Keren, 2014; Mukherjee, 2017

CNVs are most commonly detected using global DNA microarray technologies; This method, however, is unable to detect balanced CAs, such as inversions

 

N/A

Next Generation Sequencing (NGS): Whole Genome Sequencing (WGS) or

Whole Exome Sequencing (WES)

Liu, 2013; Shen, 2016; Mukherjee, 2017

CNVs are detected by fragmenting the genome and using NGS to sequence either the entire genome (WGS), or only the exome (WES); Challenges with this methodology include only being able to detect CNVs in exon-rich areas if using WES, the computational investment required for the storage and analysis of these large datasets, and the lack of computational algorithms available for effectively detecting somatic CNVs

N/A

 

References

Abe, Y et al. (2018), “Dose-response curves for analyzing of dicentric chromosomes and chromosome translocations following doses of 1000 mGy or less, based on irradiated peripheral blood samples from five healthy individuals”,  J Radiat Res. 59(1), 35-42. doi:10.1093/jrr/rrx052

Adewoye, A.B.et al. (2015), “The genome-wide effects of ionizing radiation on mutation induction in the mammalian germline”, Nat. Commun. 6:66-84. doi: 10.1038/ncomms7684.

Arlt MF, Wilson TE, Glover TW. (2012), “Replication stress and mechanisms of CNV formation”, Curr Opin Genet Dev. 22(3):204-10. doi: 10.1016/j.gde.2012.01.009.

Arlt, MF. Et al. (2014), “Copy number variants are produced in response to low-dose ionizing radiation in cultured cells”, Environ Mol Mutagen. 55(2):103-13. doi: 10.1002/em.21840.

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Hagmar, L. et al. (2004), “Impact of types of lymphocyte chromosomal aberrations on human cancer risk: results from Nordic and Italian cohorts”, Cancer Res. 64(6):2258-63.

Hastings PJ, Ira G & Lupski JR. (2009), “A microhomology-mediated break-induced replication model for the origin of human copy number variation”. PLoS Genet. 2009 Jan;5(1): e1000327. doi: 10.1371/journal.pgen.1000327.

Hendriks, G. et al. (2012), “The ToxTracker assay: novel GFP reporter systems that provide mechanistic insight into the genotoxic properties of chemicals”, Toxicol Sci, 125:285-298. Doi: 10.1093/toxsci/kfr281.

Hendriks, G. et al. (2016), “The Extended ToxTracker Assay Discriminates Between Induction of DNA Damage, Oxidative Stress, and Protein Misfolding”, Toxicol Sci, 150:190-203. Doi: 10.1093/toxsci/kfv323.

Keren, B. (2014),”The advantages of SNP arrays over CGH arrays”, Molecular Cytogenetics.7( 1):I31. Doi: 10.1186/1755-8166-7-S1-I31.

Khoury, L., Zalko, D., Audebert, M. (2016), “Evaluation of four human cell lines with distinct biotransformation properties for genotoxic screening”, Mutagenesis. 31:83-96. Doi: 10.1093/mutage/gev058.

Khoury, L., Zalko, D., Audebert, M. (2013), “Validation of high-throughput genotoxicity assay screening using cH2AX in-cell Western assay on HepG2 cells”, Environ Mol Mutagen, 54:737-746. Doi: 10.1002/em.21817.

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Event: 870: Increase, Cell Proliferation

Short Name: Increase, Cell Proliferation

Key Event Component

Process Object Action
cell proliferation increased

AOPs Including This Key Event

Stressors

Name
Ionizing Radiation

Biological Context

Level of Biological Organization
Cellular

Domain of Applicability

Taxonomic Applicability
Term Scientific Term Evidence Links
rat Rattus norvegicus High NCBI
mouse Mus musculus High NCBI
human Homo sapiens High NCBI
Life Stage Applicability
Life Stage Evidence
All life stages High
Sex Applicability
Sex Evidence
Unspecific High

Cell proliferation is a central process supporting development, tissue homeostasis and carcinogenesis, each of which occur in all vertebrates. This key event has been observed nasal tissues of rats exposed to the chemical initiator vinyl acetate. In general, cell proliferation is necessary in the biological development and reproduction of most organisms. This KE is thus relevant and applicable to all multicellular cell types, tissue types, and taxa.

Key Event Description

In the context of cancer, one hallmark is the sustained and uncontrolled cell proliferation (Hanahan et al., 2011, Portt et al., 2011). When cells in the lung epithelium  obtain a growth advantage due to mutations in critical genes that regulate cell cycle progression, they may begin to proliferate excessively, resulting in hyperplasia and potentially leading to the development of a tumour (Hanahan et al., 2011). It has been hypothesized that stressors such as radiation can result in cell inactivation and the replacement of these cells can initiate clonal expansion (Heidenreich adn Paretzke et al., 2008).

Sustained atrophy/degeneration olfactory epithelium under the influence of a cytotoxic agent leads to adaptive tissue remodeling. Cell types unique to olfactory epithelium, e.g. olfactory neurons, sustentacular cells and Bowmans glands, are replaced by cell types comprising respiratory epithelium or squamous epithelium.

How it is Measured or Detected

Two common methods of measuring cell proliferation in vivo are the use of Bromodeoxyuridine (5-bromo-2'-deoxyuridine, BrdU) labeling (Pera, 1977), and Ki67 immunostaining (Grogan, 1988). BrdU is a synthetic analogue of the nucleoside Thymidine. BrDu is incorporated into DNA synthesized during the S1 phase of cell replication and is stable for long periods. Labeling of dividing cells by BrdU is accomplished by infusion, bolus injection, or implantation of osmotic pumps containing BrdU for a period of time sufficient to generate measureable numbers of labeled cells. Tissue sections are stained immunhistochemically with antibodies for BrdU and labeled cells are counted as dividing cells. Ki67 is a cellular marker of replication not found in quiescent cells (Roche, 2015). Direct immunohistochemical staining of cells for protein Ki67 using antibodies is an alternative to the use of BrdU, with the benefit of not requiring a separate treatment (injection for pulse-labeling). Cells positive for Ki67 are counted as replicating cells. Replicating cell number is reported per unit tissue area or per cell nuclei (Bogdanffy, 1997).

Assay Name References Description OECD Approved Assay
CyQuant Cell Proliferation Assay Jones et al., 2001 DNA-binding dye is added to cell cultures, and the dye signal is measured directly to provide a cell count and thus an indication of cellular proliferation N/A
Nucleotide Analog Incorporation Assays (e.g. BrdU, EdU) Romar et al., 2016, Roche; 2013 Nucleoside analogs are added to cells in culture or injected into animals and become incorporated into the DNA at different rates, depending on the level of cellular proliferation; Antibodies conjugated to a peroxidase or fluorescent tag are used for quantification of the incorporated nucleoside analogs using techniques such as ELISA, flow cytometry, or microscopy Yes (No. 442B)
Cytoplasmic Proliferation Dye Assays Quah & Parish, 2012 Cells are incubated with a cytoplasmic dye of a certain fluorescent intensity; Cell divisions decrease the intensity in such a way that the number of divisions can be calculated using flow cytometry measurements N/A
Colourimetric Dye Assays Vega-Avila & Pugsley, 2011; American Type Culture Collection Cells are incubated with a dye that changes colour following metabolism; Colour change can be measured and extrapolated to cell number and thus provide an indication of cellular proliferation rates N/A

 

References

Bogdanffy. et al. (1997). “FOUR-WEEK INHALATION CELL PROLIFERATION STUDY OF THE EFFECTS OF VINYL ACETATE ON RAT NASAL EPITHELIUM”, Inhalation Toxicology, Taylor & Francis. 9: 331-350.

Grogan. et al. (1988). “Independent prognostic significance of a nuclear proliferation antigen in diffuse large cell lymphomas as determined by the monoclonal antibody Ki-67”, Blood. 71: 1157-1160.

Hanahan, D. & R. A. Weinberg, (2011),” Hallmarks of cancer: the next generation”, Cell. 144(5):646-74. doi: 10.1016/j.cell.2011.02.013.

Heidenreich WF, Paretzke HG. (2008) Promotion of initiated cells by radiation-induced cell inactivation. Radiat Res. Nov;170(5):613-7. doi: 10.1667/RR0957.1. PMID: 18959457.

Jones, J. L. et al. (2001), Sensitive determination of cell number using the CyQUANT cell proliferation assay. Journal of Immunological Methods. 254(1-2), 85-98. Doi:10.1016/s0022-1759(01)00404-5.

Pera, Mattias and Detzer (1977). “Methods for determining the proliferation kinetics of cells by means of 5-bromodeoxyuridine”, Cell Tissue Kinet.10: 255-264. Doi: 10.1111/j.1365-2184.1977.tb00293.x.

Portt, L. et al. (2011), “Anti-apoptosis and cell survival: a review”, Biochim Biophys Acta. 21813(1):238-59. doi: 10.1016/j.bbamcr.2010.10.010.

Quah, J. C. B. & R. C. Parish (2012), “New and improved methods for measuring lymphocyte proliferation in vitro and in vivo using CFSE-like fluorescent dyes”, Journal of Immunological Methods. 379(1-2), 1-14. doi: 10.1016/j.jim.2012.02.012.

Roche Applied Science, (2013), “Cell Proliferation Elisa, BrdU (Colourmetric) ». Version 16

Romar, A. G., S. T. Kupper & J. S. Divito (2015), “Research Techniques Made Simple: Techniques to Assess Cell Proliferation”,  Journal of Investigative Dermatology. 136(1), e1-7. doi: 10.1016/j.jid.2015.11.020.

Vega-Avila, E. & K. M. Pugsley (2011), “An Overview of Colorimetric Assay Methods Used to Assess Survival or Proliferation of Mammalian Cells”, Proc. West. Pharmacol. Soc. 54, 10-4.

List of Adverse Outcomes in this AOP

Event: 1556: Increase, lung cancer

Short Name: Increase, lung cancer

AOPs Including This Key Event

AOP ID and Name Event Type
Aop:272 - Deposition of energy leading to lung cancer AdverseOutcome

Stressors

Name
Ionizing Radiation

Biological Context

Level of Biological Organization
Organ

Domain of Applicability

Taxonomic Applicability
Term Scientific Term Evidence Links
human Homo sapiens High NCBI
rat Rattus norvegicus High NCBI
mouse Mus musculus High NCBI
Life Stage Applicability
Life Stage Evidence
All life stages High
Sex Applicability
Sex Evidence
Unspecific High

Lung cancer and subsequent metastasis occurs in multicellular eukaryotic vertebrate organisms that have lungs.

Key Event Description

Abnormally high levels of cell proliferation in the lungs may eventually culminate in the formation of malignant tumours and thus lung cancer. The term lung cancer refers to all malignant neoplasms arising from the bronchial, bronchiolar, and alveolar epithelium (Keshamouni et al., 2009). The cellular origin(s) of lung cancer remains largely unknown. It has been speculated that different tumour histopathological subtypes arise from distinct cells of origin localized in defined microenvironments. Histological characteristics of lung cancers, as defined by light microscopy, have led to the categorization of lung cancers into four main subtypes: small cell carcinoma, adenocarcinoma, squamous cell carcinoma, and large cell carcinoma (Beasly et al., 2005). These histological subtypes are grouped under one of the two umbrella terms used to describe lung cancers:  small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC). The term SCLC refers to small cell carcinoma. The term NSCLC, which represents approximately 85% of all lung cancers (Molina et al., 2008), encompasses squamous cell carcinoma, adenocarcinoma, and large cell carcinoma. These three tumour types are grouped together due to similarities in their prognosis and management (Keshamouni et al., 2009); patients with NSCLC often have poor prognoses and low 5-year survival rates due to the high metastatic potential of the tumours (Spira and Ettinger, 2004; Herbst et al., 2008). Some of the most common sites for lung cancer metastasis are the other lobe of the lungs, skeleton, adrenal glands, liver, and brain (Simon et al., 2015).

How it is Measured or Detected

Assay Name Reference Description OECD Approved Assay
Computed Tomography (CT) Scans: CT, High-Resolution CT (HRCT), and   Positron Emission Tomography-CT (PET-CT) Bach et al., 2012; Ollier et al., 2014 CT scans are described as a 3D X-ray; They provide cross-sections of organs/tissues/bones, and can thus be used to detect tumours N/A
Magnetic Resonance Imaging (MRI) Khalil et al., 2016; Wu et al., 2011 This technique uses magnetic fields and radio waves (NOT ionizing radiation) to generate a picture of organs, and can thus be used to detect tumours N/A
Sputum Analysis Hubers et al., 2013 Sputum is collected and analyzed for a variety of markers, including mutations in KRAS and TP53, specific RNA/protein biomarkers, and chromosomal aberrations N/A
Bronchoscopy: Conventional White Light Bronchoscopy, Autofluorescence Bronchoscopy (AFB), and Endobronchial Ultrasonography (EBUS) Ikeda et al., 2007 Bronchoscope (usually with a camera) is passed down through the throat to the lungs to provide a visual of the respiratory tract; Traditionally, visualization has been performed using conventional white light, but new technologies have also allowed for visualization using fluorescence and ultrasound technologies N/A
Transbronchial Needle Aspiration Navani et al., 2015; Aziz, 2012 A needle is used to aspirate a tissue sample from a lesion of suspected lung cancer  for analysis N/A
Analysis of Volatile Organic Compounds in the Breath Zhou et al., 2017 Volatile organic compounds, which may act as lung cancer biomarkers, are  collected from the breath and quantified (mostly using mass spectrometry) N/A
Cell Transformation Assays Redpath et al., 1987 Measurement of the tumourigenicity of a tumour/biopsy sample by analyzing changes in cell physiology and morphology in response to tumour-inducing radiation or chemicals Yes (No. 231)

Rodent Two-Year Cancer Bioassays

 

(Carcinogenicity Studies)

Matsumo, 2012; Nambiar, 2014; Maronpot, 2015 Animals are exposed to a possible carcinogen for a long period of time (often two years), allowing for long-term cancer-related studies Yes (No. 451)
Window Chamber Models Moeller, 2004; Schafer, 2014; Chen, 2016 Window chambers are implanted into the animal to observe tumour progression in living animals using imaging techniques such as in vivo microscopy, MRI or nuclear imaging N/A
Xenograft Assays Wang, 2018; Shi, 2017; Jin, 2018; Wang, 2017; Zhou, 2012 Tumour cells (usually human) are grown in vitro and injected into animals to induce tumour growth and/or to test the tumourigenicity of the injected cells N/A

 

Regulatory Significance of the AO

At present the AOP framework is not readily used to support regulatory decision-making in radiation protection practices.The goal of developing this AOP is to bring attention to the framework as an effective means to organize knowledge and identify gaps associated with the mechanistic understanding of low dose radiation exposures. We have used lung cancer as the case example due to its relevance to both radiation and chemical risk assessment. This AOP will help build the concept of an “all hazards” approach to risk assessment, as it will be the first  with a molecular initiating event that is specific to a radiation insult. This in turn could serve to identify networks that are critical to both radiation and chemical exposure scenarios and contribute to prioritizing co-exposures of relevance to risk assessment. By developing this AOP, we will support the necessary efforts highlighted by the international and national radiation protection agencies such as, the United Nations Scientific Committee on the Effects of Atomic Radiation, International Commission of Radiological Protection, International Dose Effect Alliance and the Electric Power Research Institute Radiation Program to consolidate and enhance the knowledge in understanding the mechanisms of low dose radiation exposures from the cellular to organelle levels within the system.

References

Aziz, F. (2012), “Endobronchial ultrasound-guided transbronchial needle aspiration for staging of lung cancer: a concise review”, Transl Lung Cancer Res, 1(3), 208-213. doi:10.3978/j.issn.2218-6751.2012.09.08.

Bach, P. B. et al. (2012), “Benefits and harms of CT screening for lung cancer: a systematic review”, JAMA, 307(22), 2418-2429. doi:10.1001/jama.2012.5521

Beasley, M. B., Brambilla, E., & Travis, W. D. (2005), “The 2004 World Health Organization classification of lung tumors”, Seminars in Roentgenology, 40(2), 90-97. doi:10.1053/j.ro.2005.01.001

Chen Y, Maeda A, Bu J, DaCosta R. (2016), “Femur Window Chamber Model for In Vivo Cell Tracking in the Murine Bone Marrow”, J Vis Exp. (113). doi: 10.3791/54205.

Herbst, R. S., Heymach, J. V., & Lippman, S. M. (2008), “Lung cancer”, N Engl J Med. 359, 1367– 80.

Hubers, A. J. et al. (2013), “Molecular sputum analysis for the diagnosis of lung cancer”, Br J Cancer. 109(3), 530-537. doi:10.1038/bjc.2013.393

Ikeda, N. et al. (2007), “Comprehensive diagnostic bronchoscopy of central type early stage lung cancer”, Lung Cancer, 56(3), 295-302. doi:10.1016/j.lungcan.2007.01.009

Jin, Y. et al. (2018), “Simvastatin inhibits the development of radioresistant esophageal cancer cells by increasing the radiosensitivity and reversing EMT process via the PTEN-PI3K/AKT pathway”, Exp Cell Res.362(2):362-369. Doi: 10.1016/j.yexcr.2017.11.037.

Keshamouni, V., Arenberg, D., & Kalemkerian, G. (2009), “Lung Cancer Metastasis: Novel Biological Mechanisms and Impact on Clinical Practice”, Springer Science + Business Media. Doi: 10.1007/978-1-4419-0772-1.

Khalil, A.et al. (2016), “Contribution of magnetic resonance imaging in lung cancer imaging”, Diagnostic and Interventional Imaging, 97(10), 991-1002. doi:10.1016/j.diii.2016.08.015

Maronpot RR, Thoolen RJ, Hansen B. (2015), “Two-year carcinogenicity study of acrylamide in Wistar Han rats with in utero exposure”,Exp Toxicol Pathol. 67(2):189-95. doi: 10.1016/j.etp.2014.11.009.

Matsumoto, M. et al. (2012), “Carcinogenicity of ortho-phenylenediamine dihydrochloride in rats and mice by two-year drinking water treatment”,  Arch Toxicol. 86(5):791-804. doi: 10.1007/s00204-012-0800-z.

Moeller, BJ. et al.(2004), “Radiation activates HIF-1 to regulate vascular radiosensitivity in tumors: role of reoxygenation, free radicals, and stress granules”, Cancer Cell. 5(5):429-41.

Molina JR. et al. (2008), “Non-small cell lung cancer: epidemiology, risk factors, treatment, and survivorship”, Mayo Clin Proc. 83(5):584-94. doi: 10.4065/83.5.584.

Nambiar PR. et al. (2015), “Two-year carcinogenicity study in rats with a nonnucleoside reverse transcriptase inhibitor”, Toxicol Pathol. 43(3):354-65. doi: 10.1177/0192623314544381.

Navani, N. et al. (2015), “Lung cancer diagnosis and staging with endobronchial ultrasound-guided transbronchial needle aspiration compared with conventional approaches: an open-label, pragmatic, randomised controlled trial”, Lancet Respir Med. 3(4), 282-9. doi: 10.1016/S2213-2600(15)00029-6

Ollier, M. et al. (2014), “Chest CT scan screening for lung cancer in asbestos occupational exposure: a systematic review and meta-analysis”, Chest. 145(6), 1339-1346. doi:10.1378/chest.13-2181

Redpath, J. L. et al. (1987), “Neoplastic Transformation of Human Hybrid Cells by y Radiation: A Quantitative Assay”, Radiat.Res. 110, 468-472.

Schafer R, Leung HM, Gmitro AF. (2014), “Multi-modality imaging of a murine mammary window chamber for breast cancer research”, Biotechniques. 57(1):45-50. Doi: 10.2144/000114191.

Sher, T., Dy, G. K., & Adjei, A. A. (2008), “Small cell lung cancer”, MayoClin Proc. 83(3), 335-367. doi: 10.4065/83.3.355

Shi ZM. Et al.(2017), “Downregulation of miR-218 contributes to epithelial-mesenchymal transition and tumor metastasis in lung cancer by targeting Slug/ZEB2 signaling”, Oncogene. 36(18):2577-2588. Doi: 0.1038/onc.2016.414.

Simon,  G.R., & Brustugun, O.T. (2015), “Metastatic Patterns of Lung Cancer”, Oncolex Oncology Encyclopedia. http://oncolex.org/Lung-cancer/Background/MetastaticPatterns.

Spira, A., & Ettinger, D. S. (2004), “Multidisciplinary management of lung cancer”,Engl J Med. 350(4), 379–92. doi: 10.1056/NEJMra035536

Wang T. et al. (2017), “Role of Nrf2 signaling pathway in the radiation tolerance of patients with head and neck squamous cell carcinoma: an in vivo and in vitro study”, Onco Targets Ther. 2017 Mar 23;10:1809-1819.

Wang L. et al. (2018), “K-ras mutation promotes ionizing radiation-induced invasion and migration of lung cancer in part via the Cathepsin L/CUX1 pathway”, Exp Cell Res. 362(2):424-435. Doi: 10.1016/j.yexcr.2017.12.006.

Wu, N. Y. et al. (2011), “Magnetic resonance imaging for lung cancer detection: experience in a population of more than 10,000 healthy individuals”, BMC Cancer, 11, 242. doi:10.1186/1471-2407-11-242.

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Appendix 2

List of Key Event Relationships in the AOP