SNAPSHOT
Created at: 2019-10-30 18:08
AOP ID and Title:
Graphical Representation

Status
Author status | OECD status | OECD project | SAAOP status |
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Under development: Not open for comment. Do not cite |
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 (eg. asbestos, air pollution and arsenic) and radiation stressors (eg. 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 interaraction with the cell is identified as the direct 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 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. By developing this AOP, we have supported the necessary efforts highlighted by national and international radiation protection agencies to consolidate and enhance the knowledge in understanding the mechanisms of low dose radiation exposures.
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). 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 blamed for 1.5 million deaths annually. 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.
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 (eg. X-rays, gamma rays, alpha particles, beta particles, heavy ions, neutrons) and well supported thorough empirical evidence. 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.
This AOP is also a case example of how existing evidence from radiation stressors can fortify 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 |
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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 |
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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. 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 (Egawa et al., 2012). 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 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 KE 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 as well 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 an 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 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. 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, 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 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 ApplicabilityLife Stage | Evidence |
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All life stages | High |
Term | Scientific Term | Evidence | Links |
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human | Homo sapiens | High | NCBI |
rat | Rattus norvegicus | High | NCBI |
mouse | Mus musculus | High | NCBI |
Sex | Evidence |
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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), 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; 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 |
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MIE: Direct Deposition of Energy |
Evidence for Essentiality of KE: Weak 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). |
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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, 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. |
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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). 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). Moreoever, 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 Off1+/+ mice). |
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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). |
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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). |
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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).
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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; Okayasu 2012; Lomax et al. 2013; Moore et al. 2014; Desouky et al. 2015; Sage and Shikazono 2017,Asaithamby and Chen, 2011). 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). |
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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). 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). |
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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 (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).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). |
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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; 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). |
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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). |
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Inadequate DNA Repair, Increase (KE2) --> Chromosomal Aberrations, Increase (KE4) |
Evidence for Biological Plausibility of KER: Strong DSBs are repaired by NHEJ and 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). |
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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, inactivating mutations in tumour suppressor genes (Bertram 2001; Vogelstein and Kinzler 2004,Lee and Muller 2010) 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. |
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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). |
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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).
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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).
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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). |
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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). 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). |
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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). |
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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 |
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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). 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). |
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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; 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 (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. |
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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, 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). 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). |
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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). |
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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). |
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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. |
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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.
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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.
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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).
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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. |
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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). |
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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 more 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 indication 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). |
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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). |
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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 (Darby et al. 2005; Krewski et al. 2005) (Krewski et al. 2006; Torres-Durán et al. 2014; Sheen et al. 2016; Lubin et al. 1995; Hazelton et al. 2001; Al-Zoughool and Krewski 2009; Rodríguez-Martínez et al. 2018; Ramkissoon et al. 2018). 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) and with longer periods of exposure (Lubin et al. 1995).
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Quantitative Consideration
There is strong biological plausibility and empirical evidence to suggest a qualitative link between 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 |
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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) . |
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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. |
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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). |
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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). |
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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). |
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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. |
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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). |
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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). |
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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). |
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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. |
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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. |
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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). |
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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). |
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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). |
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 - Direct deposition of ionizing energy onto DNA 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
Life Stage | Evidence |
---|---|
All life stages | High |
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 phenomena 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 conjustion 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
AOP ID and Name | Event Type |
---|---|
Aop:296 - Oxidative DNA damage leading to chromosomal aberrations and mutations | KeyEvent |
Aop:272 - Direct deposition of ionizing energy onto DNA leading to lung cancer | KeyEvent |
Stressors
Name |
---|
Ionizing Radiation |
Topoisomerase inhibitors |
Radiomimetic compounds |
Biological Context
Level of Biological Organization |
---|
Molecular |
Domain of Applicability
Term | Scientific Term | Evidence | Links |
---|---|---|---|
human and other cells in culture | human and other cells in culture | NCBI |
Life Stage | Evidence |
---|---|
All life stages | High |
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.
DSBs 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
- Comet Assay (Single cell gel electrophoresis)
- There are two variations of the comet assay for measuring DNA strand breaks
- Alkaline comet assay (pH >13) (Platel et al., 2011; Nikolova et al., 2017)
- OECD test guideline for in vivo mammalian alkaline comet assay (#489) is available (OECD, 2014)
- Detects SSB and DSB resulting from direct-acting genotoxicants, alkali labile sites, or strand breaks that are intermediates of DNA excision repair (OECD, 2014)
- Neutral comet assay (Anderson and Laubenthal, 2013; Nikolova et al., 2017)
- Electrophoresis is performed in neutral pH and DNA is not denatured – mostly detects DSB
- γH2AX foci detection (Detects DSB)
Phosphorylation of histone H2AX (γH2AX) at serine 139 is an early response to DSB; it causes chromatin decondensation and plays a critical role in recruiting repair machineries to the site of damage (Rogakou et al., 1998). γH2AX foci can be detected by immunostaining on several platforms:
- Flow cytometry (Bryce et al., 2016); γH2AX foci counting can be high-throughput and automated using flow cytometry-based immunodetection.
- Fluorescent microscopy (Garcia-Canton et al., 2013; Khoury et al., 2013); γH2AX foci can be counted in fluorescent microscope images. Image acquisition and foci count can be automated to increase the assay throughput
- In-Cell Western technique (Khoury et al., 2013; Khoury et al., 2016) combines the principles of Western blotting (e.g., "blocking" to prevent non-specific antibody binding) and fluorescent microscopy for immunodetection of γH2AX foci.
- Western blotting (Revet et al., 2011); this method does not provide a quantitative measurement of γH2AX foci and is no longer commonly applied in screening for γH2AX induction.
- Pulsed field gel electrophoresis (detects DSB) (Kawashima et al., 2017)
- Cells are embedded and lysed in agarose and fractionated by electrophoresis
- The length of fragments can be determined by running a DNA ladder in the adjacent lane
- The TUNEL (Terminal deoxynucleotidyl transferase dUTP nick end labeling) assay
- Terminal deoxynucleotidyl transferase (TdT) is a DNA polymerase that adds deoxynucleotides to the 3’OH end of DNA strand breaks without the need for a template strand. The dUTPs incorporated at the sites of strand breaks are tagged with a fluorescent dye or a reporter enzyme to allow visualization (Loo, 2011).
- We note that this method is typically used to measure apoptosis.
When measuring these events, it is important to distinguish between breaks that may lead to mutation or chromosomal aberrations, and those that are associated with cell death processes.
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 Quantification - ELISA | Ji et al., 2017 | Measurement of γ-H2AX in cells by ELISA, normalized to total levels of H2AX | 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 | 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.
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.
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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
Life Stage | Evidence |
---|---|
All life stages | High |
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:
- 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.
- 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 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, and 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. 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). A third form of excision repair is mismatch repair (MMR), 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)).
- 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 measures 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 2008 | 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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Olive, L. P., J. P. Bnath & E. R. Durand, (1990), “Heterogeneity in Radiation-Induced DNA Damage and Repairing Tumor and Normal Cells Measured Using the "Comet" Assay”, Radiation Research. 122: 86-94. Doi: 10.1667/rrav04.1.
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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
Life Stage | Evidence |
---|---|
All life stages | High |
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 (TG 471). 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).
A variety of in vitro mammalian cell gene mutation tests are described in OECD’s Test Guidelines 476 and 490. TG 476 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 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) 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). 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) or in vitro (OECD Test No. 476: In vitro Mammalian Cell Gene Mutation Test), or in bacterial cells (i.e., OECD Test No. 471) 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 (1997), Test No. 471: Bacterial Reverse Mutation Test, OECD Guidelines for the Testing of Chemicals, Section 4, OECD Publishing, Paris.
OECD (1997), 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 (2011), 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 (2015), 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. (2013), “Transgenic Rodent Somatic and Germ Cell Gene Mutation Assays.”
OECD Guidelines for the Testing of Chemicals, Section 4, OECD Publishing, Paris.
OECD. 2015. 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.
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Event: 1636: Increase, Chromosomal aberrations
Short Name: Increase, Chromosomal aberrations
AOPs Including This Key Event
AOP ID and Name | Event Type |
---|---|
Aop:296 - Oxidative DNA damage leading to chromosomal aberrations and mutations | AdverseOutcome |
Aop:272 - Direct deposition of ionizing energy onto DNA leading to lung cancer | KeyEvent |
Stressors
Name |
---|
Ionizing Radiation |
Biological Context
Level of Biological Organization |
---|
Cellular |
Domain of Applicability
Life Stage | Evidence |
---|---|
All life stages | High |
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
Chromosomal aberrations describe the structural damage to chromosomes that result 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 are typically measured after cell division.
- Micronucleus detection:
- Micronuclei are DNA fragments that are not incorporated in the nucleus during cell division. Micronucleus induction indicates chromosomal breakage and irreversible damage.
- Traditional (microscopy-based) micronucleus assay; OECD guidelines for both in vivo (#474) and in vitro (#487) testing are available (OECD, 2014; OECD, 2016b)
- In vivo and in vitro flow cytometry-based, automated micronuclei measurements (Dertinger et al., 2004; Bryce et al., 2014)
- High content imaging (Shahane et al., 2016)
- DNA can be stained using fluorescent dyes and micronuclei can be scored in microscope images.
- Chromosomal aberration test
- OECD guidelines exist for both in vitro (#473) and in vivo (#475 and #483) testing (OECD, 2015; OECD, 2016a; OECD, 2016c)
- In vitro, the cell cycle is arrested at metaphase after 1.5 cell cycle following 3-6 hour exposure
- In vivo, the test chemically is administered as a single treatment and bone marrow is collected 18-24 hrs later (#475) while testis is collected 24-48 hrs later (#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
- Indirect measurement of clastogenicity via protein expression:
- Flow cytometry-based quatification of γH2AX foci and p53 protein expression (Bryce et al., 2016).
- Prediscreen Assay– In-Cell Western -based quantification of γH2AX (Khoury et al., 2013, Khoury et al., 2016)
- 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 (Hendriks et al., 2012; Hendriks et al., 2016; Wink et al., 2014).
Assay Name | References | Description | OECD Approved Assay |
Fluorescent In Situ Hybridization (FISH) |
Beaton et al., 2013; Pathak et al., 2017 |
Fluorescent assay of condensed chromosomes that can detect CAs through chromosome painting and microscopic analysis | N/A |
Cytokinesis Block Micronucleus (CBMN) Assay with Microscopy | Fenech, 2000 | Cells are cultured with cytokinesis blocked, fixed to slides, and undergo MN quantification using microscopy | Yes (No.487) |
CBMN with Imaging Flow Cytometry | Rodrigues et al., 2015 | Cells are cultured with cytokinesis blocked, fixed in solution, and imaged with flow cytometry to quantify MN | 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 |
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 detected in single-stranded and fluorescently-tagged DNA using a microarray plate with fixed, known DNA (or SNP) probes; 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.
Beaton, L. A. et al. (2013), “Investigating chromosome damage using fluorescent in situ hybridization to identify biomarkers of radiosensitivity in prostate cancer patients”, Int J Radiat Biol. 89(12): 1087-1093. doi:10.3109/09553002.2013.825060
Boei, J.J., Vermeulen, S., Natarajan, A.T. (1996), “Detection of chromosomal aberrations by fluorescence in situ hybridization in the first three postirradiation divisions of human lymphocytes”, Mutat Res, 349:127-135. Doi: 10.1016/0027-5107(95)00171-9.
Bonassi, S. (2008),”Chromosomal aberration frequency in lymphocytes predicts the risk of cancer: results from a pooled cohort study of 22 358 subjects in 11 countries”, Carcinogenesis. 29(6):1178-83. doi: 10.1093/carcin/bgn075.
Bryce, S. et al. (2014), “Interpreting In VitroMicronucleus Positive Results: Simple Biomarker Matrix Discriminates Clastogens, Aneugens, and Misleading Positive Agents”, Environ Mol Mutagen, 55:542-555. Doi:10.1002/em.21868.
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.
Bunting, S. F., & Nussenzweig, A. (2013), “End-joining, translocations and cancer”, Nature Reviews Cancer.13 (7): 443-454. doi:10.1038/nrc3537
Curtis, C. et al. (2012), “The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups”, Nature. 486(7403):346-52. doi: 10.1038/nature10983.
Dertinger, S.D. et al.(2004), “Three-color labeling method for flow cytometric measurement of cytogenetic damage in rodent and human blood”, Environ Mol Mutagen, 44:427-435. Doi: 10.1002/em.20075.
El-Zein, RA. Et al. (2014), “The cytokinesis-blocked micronucleus assay as a strong predictor of lung cancer: extension of a lung cancer risk prediction model”, Cancer Epidemiol Biomarkers Prev. 23(11):2462-70. doi: 10.1158/1055-9965.EPI-14-0462.
Fenech, M. (2000), “The in vitro micronucleus technique”, Mutation Research. 455(1-2), 81-95. Doi: 10.1016/s0027-5107(00)00065-8
Griffiths, A. J. F., Miller, J. H., & Suzuki, D. T. (2000), “An Introduction to Genetic Analysis”, 7th edition. New York: W. H. Freeman. Available from: https://www.ncbi.nlm.nih.gov/books/NBK21766/
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.
Lee JA, Carvalho CM, Lupski JR. (2007). “Replication mechanism for generating nonrecurrent rearrangements associated with genomic disorders”, Cell. 131(7):1235-47. Doi: 10.1016/j.cell.2007.11.037.
Liu B. et al. (2013). “Computational methods for detecting copy number variations in cancer genome using next generation sequencing: principles and challenges”, Oncotarget. 4(11):1868-81. Doi: 10.18632/oncotarget.1537.
Mitelman, F. (1982), “Application of cytogenetic methods to analysis of etiologic factors in carcinogenesis”, IARC Sci Publ, 39:481-496.
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Obe, G. et al. (2002), “Chromosomal Aberrations: formation, Identification, and Distribution”, Mutation Research/Fundamental and Molecular Mechanisms of Mutagenesis. 504(1-2), 17-36. Doi: 10.1016/s0027-5107(02)00076-3.
Savage, J.R. (1976), “Classification and relationships of induced chromosomal structual changes”, J Med Genet, 13:103-122. Doi: 10.1136/jmg.13.2.103.
OECD. (2016a), “In Vitro Mammalian Chromosomal Aberration Test 473.”
OECD. (2016b), “Test No. 474: Mammalian Erythrocyte Micronucleus Test. OECD Guideline for the Testing of Chemicals, Section 4.”Paris: OECD Publishing.
OECD. (2016c). Test No. 475: Mammalian Bone Marrow Chromosomal Aberration Test. OECD Guideline for the Testing of Chemicals, Section 4. Paris: OECD Publishing.
OECD. (2015). Test No. 483: Mammalian Spermatogonial Chromosomal Aberration Test. Paris: OECD Publishing.
OECD. (2014). Test No. 487: In Vitro Mammalian Cell Micronucleus Test. Paris: OECD Publishing.
Pathak, R., Koturbash, I., & Hauer-Jensen, M. (2017), “Detection of Inter-chromosomal Stable Aberrations by Multiple Fluorescence In Situ Hybridization (mFISH) and Spectral Karyotyping (SKY) in Irradiated Mice”, J Vis Exp(119). doi:10.3791/55162.
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Rodrigues, M. A., Beaton-Green, L. A., & Wilkins, R. C. (2016), “Validation of the Cytokinesis-block Micronucleus Assay Using Imaging Flow Cytometry for High Throughput Radiation Biodosimetry”, Health Phys. 110(1): 29-36. doi:10.1097/HP.0000000000000371
Shahane S, Nishihara K, Xia M. (2016), “High-Throughput and High-Content Micronucleus Assay in CHO-K1 Cells”, In: Zhu H, Xia M, editors. High-Throughput Screening Assays in Toxicology. New York, NY: Humana Press. p 77-85.
Shen.TW, (2016),”Concurrent detection of targeted copy number variants and mutations using a myeloid malignancy next generation sequencing panel allows comprehensive genetic analysis using a single testing strategy”, Br J Haematol. 173(1):49-58. doi: 10.1111/bjh.13921.
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Wink, S. et al. (2014), “Quantitative high content imaging of cellular adaptive stress response pathways in toxicity for chemical safety assessment”, Chem Res Toxicol, 27:338-355.
Zhang N, Wang M, Zhang P, Huang T. 2016. Classification of cancers based on copy number variation landscapes. Biochim Biophys Acta. 1860(11 Pt B):2750-5. doi: 10.1016/j.bbagen.2016.06.003.
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
Life Stage | Evidence |
---|---|
All life stages | High |
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).
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.
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 - Direct deposition of ionizing energy onto DNA leading to lung cancer | AdverseOutcome |
Stressors
Name |
---|
Ionizing Radiation |
Biological Context
Level of Biological Organization |
---|
Organ |
Domain of Applicability
Life Stage | Evidence |
---|---|
All life stages | High |
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
List of Adjacent Key Event Relationships
Relationship: 1977: Energy Deposition leads to Increase, DNA strand breaks
AOPs Referencing Relationship
AOP Name | Adjacency | Weight of Evidence | Quantitative Understanding |
---|---|---|---|
Direct deposition of ionizing energy onto DNA leading to lung cancer | adjacent | High | High |
Evidence Supporting Applicability of this Relationship
Life Stage | Evidence |
---|---|
All life stages | High |
Sex | Evidence |
---|---|
Unspecific | High |
The domain of applicability relates to all eukaryotic species that contain genetic information in the form of a double strand helix of DNA (Parris et al., 2015; Cannan & Pederson, 2016).
Key Event Relationship Description
Direct deposition of ionizing energy refers to imparted energy interacting directly with the DNA double helix and producing randomized damage in the form of strand breaks. Among the different types of damage, the most detrimental type of DNA damage to a cell is the double-strand break (DSB). DSBs are caused by the breaking of the sugar-phosphate backbone on both strands of the DNA double helix molecule, either directly across from each other or several nucleotides apart (Ward, 1988; Iliakis et al., 2015). The number of DSBs produced and the complexity of the breaks is highly dependent on the amount of energy deposited on and absorbed by the cell. This can vary as a function of the dose-rate (Brooks et al., 2016) and the radiation quality which is a function of its linear energy transfer (LET) (Sutherland et al., 2000; Nikjoo et al., 2001; Jorge et al., 2012). LET describes the amount of energy that an ionizing particle transfers to media per unit distance (Smith et al., 2003; Okayasu, 2012a; Christensen et al., 2014). High LET radiation, such as alpha particle radiation, can deposit larger quantities of energy within a single track than low LET radiation, such as gamma-ray radiation (Kadhim et al., 2006). As such, radiation with higher LETs tends to produce more complex, dense structural damage, particularly in the form of clustered damage, in comparison to lower LET radiation (Nikjoo et al., 2001; Terato and Ide, 2005; Hada and Georgakilas, 2008; Okayasu, 2012a; Lorat et al., 2015; Nikitaki et al., 2016). Thus, the complexity and yield of clustered DNA damage increases with ionizing density (Ward, 1988; Goodhead, 2006). However, clustered damage can also be induced even by a single radiation track through a cell.
Evidence Supporting this KER
Biological PlausibilityThe biological rationale linking the direct deposition of energy on DNA with an increase in DSB formation is strongly supported by numerous literature reviews that are available on this topic (J .F. Ward, 1988; Terato & Ide, 2005; Goodhead, 2006; Asaithamby et al., 2008; Hada & Georgakilas, 2008; Okayasu, 2012b; M. E. Lomax et al., 2013; Moore et al., 2014; Desouky et al., 2015; Sage & Shikazono, 2017; Jeggo, 2009). Ionizing radiation can be in the form of high energy particles (such as alpha particles, beta particles, or charged ions) or high energy waves (such as gamma-rays or X-rays). Ionizing radiation can break the DNA within chromosomes both directly and indirectly, as shown through using velocity sedimentation of DNA through neutral and alkaline sucrose gradients. The most direct path entails a collision between a high-energy particle or photon and a strand of DNA. The high-energy subatomic particles can interact with the orbital electrons of the DNA causing ionization (where electrons are ejected from atoms) and excitation (where electrons are raised to higher energy levels) (Joiner, 2009). These processes ultimately break the phosphodiester backbone.
Additionally, excitation of secondary electrons in the DNA allows for a cascade of ionization events to occur, which can lead to the formation of multiple damage sites (Joiner, 2009). As an example, high-speed electrons will traverse a DNA molecule in a mammalian cell within 10-18 s and 10-14 s, resulting in 100,000 ionizing events per 1 Gy dose in a 10 μm cell (Joiner, 2009). The amount of damage can be influenced by factors such as the cell cycle stage and chromatin structure. It has been shown that in more condensed, packed chromatin structures such as those present in intact cells and heterochromatin, it is more difficult for the DNA to be damaged (Radulescu et al., 2006; Agrawala et al., 2008; Falk et al., 2008; Venkatesh et al., 2016). In contrast, DNA damage is more easily induced in lightly-packed chromatin such as euchromatin, nucleoids, and naked genome DNA (Radulescu et al., 2006; Falk et al., 2008; Venkatesh et al., 2016).
DNA damage can be in the form of DSBs, single-strand breaks, base damage, or the crosslinking of DNA to other molecules (Smith et al., 2003; Joiner, 2009; Christensen, 2014; Sage and Shikazono, 2017). Of the possible radiation-induced DNA damage types, DSB is considered to be the most harmful to the cell, as there may be severe consequences if this damage is not adequately repaired (Khanna & Jackson, 2001; Smith et al., 2003; Okayasu, 2012a; M. E. Lomax et al., 2013; Rothkamm et al., 2015).
A considerable fraction of DSBs can also be formed in cells through indirect mechanisms. In this case, deposited energy can split water molecules near DNA, which can generate a significant quantity of reactive oxygen species in the form of hydroxyl free radicals (Ward, 1988; Desouky et al., 2015; Maier et al., 2016). Estimates using models and experimental results suggest that hydroxyl radicals may be present within nanoseconds of energy deposition by radiation (Yamaguchi et al., 2005). These short-lived but highly reactive hydroxyl radicals may react with nearby DNA. This will produce DNA damage, including single-strand breaks and DSBs (Ward, 1988; Desouky et al., 2015; Maier et al., 2016). DNA breaks are especially likely to be produced if the sugar moiety is damaged, and DSBs occur when two single-strand breaks are in close proximity to each other (Ward, 1988).
Empirical EvidenceEmpirical data strongly supports this KER. The evidence presented below is summarized in table 1, here (click link). The types of DNA damage produced by ionizing radiation and the associated mechanisms, including the induction of DSBs, are reviewed by Lomax et al. (2013) and documents produced by international radiation governing frameworks (Valentin, 1998; UNSCEAR, 2000). Other reviews also highlight the relationship between DSB induction and the deposition of energy by radiation, and discuss the various methods available to detect these DSBs (Terato & Ide, 2005; Rothkamm et al., 2015; Sage & Shikazono, 2017). A visual respresentation of the time frames and dose ranges probed by the dedicated studies discussed here is shown in Figures 1 & 2 below.
Figure 1: Plot of studies (y-axis) against equivalent dose (Sv) used to determine the empircal link between direct deposition of energy and DSBs. The z-axis denotes the equivalent dose rate used in each study. The y-axis is ordered from low LET to high LET from top to bottom.
Figure 2: Plot of studies (y-axis) against time scales used to determine the empircal link between direct deposition of energy and DSBs. The z-axis denotes the equivalent dose rate used in each study. The y-axis is ordered from low LET to high LET from top to bottom.
Dose and Incidence Concordance
There is evidence in the literature suggesting a dose/incidence concordance between the direct deposition of energy by ionizing radiation and the incidence (Grudzenski et al., 2010) of DNA DSBs. Results from in vitro (Rogakou et al., 1999; Sutherland et al., 2000; Lara et al., 2001; Rothkamm and Lo, 2003; Kuhne et al., 2005; Sudprasert et al., 2006; Beels et al., 2009; Grudzenski et al., 2010; Shelke & Das, 2015; Antonelli et al., 2015), in vivo (Sutherland et al., 2000; Rube et al., 2008; Beels et al., 2009; Grudzenski et al., 2010), ex vivo (Rube et al., 2008; Flegal et al., 2015) and simulation studies (Charlton et al., 1989) suggest that there is a dose-dependent increase in DSBs with increasing deposition of energy across a wide range of radiation types (iron ions, X-rays, ultrasoft X-rays, gamma-rays, photons, and alpha particles) and radiation doses (1 mGy - 100 Gy). DSBs have been predicted to occur at energy deposition levels as low as 75 eV (Charlton et al., 1989). Although all the radiation types studied were able to induce DSBs, some types were found to be more damaging in terms of the number of DSBs induced per dose (Lara et al., 2001; Kuhne et al., 2005; Antonelli et al., 2015).
Temporal Concordance
There is evidence suggesting a time concordance between the direct deposition of energy and the incidence of DSBs. A number of different models and experiments have provided evidence of DSBs seconds (Mosconi et al., 2011) or minutes after radiation exposure (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).
Essentiality
Results from a number of antioxidant studies found that pre-treatment of in vitro and in vivo lymphocytes with various antioxidants resulted in reduced DNA damage in response to radiation exposure (results summarized in a review by (Kuefner et al., 2015)). Similar results were also found in numerous in vitro and in vivo studies using various cell types, rodents, and humans exposed to antioxidants prior to radiation (reviewed by (Smith et al., 2017)). This suggests that deposition of energy on DNA by ionizing radiation is required to produce DNA DSBs.
Uncertainties and InconsistenciesUncertainties and inconsistencies in this KER are as follows:
- Studies have shown that dose-rates (Brooks et al., 2016) and radiation quality (Sutherland et al., 2000; Nikjoo et al., 2001; Jorge et al., 2012) are factors that can influence the dose-response relationship.
- Low-dose radiation has been observed to have beneficial effects and may even invoke protection against spontaneous genomic damage (Feinendegen, 2005; Day et al., 2007; Feinendegen et al., 2007; Shah et al., 2012; Nenoi et al., 2015). This protective effect has been documented in in vivo and in vitro, as reviewed by ICRP (2007) and UNSCEAR (2008) and can vary depending on the cell type, the tissue, the organ, or the entire organism (Brooks et al., 2016).
- Depositing ionizing energy is a stochastic event; as such this can influence the location, degree and type of DNA damage imparted on a cell. As an example, studies have shown that mitochondrial DNA may also be an important target for genotoxic effects of ionizing radiation (Wu et al., 1999).
Quantitative Understanding of the Linkage
Quantitative understanding of this linkage suggests that DSBs can be predicted upon exposure to ionizing radiation. This is dependent on the biological model, the type of radiation and the radiation dose. In general, 1 Gy of radiation is thought to result in 3000 damaged bases (Maier et al., 2016), 1000 single-strand breaks, and 40 DSBs (Ward, 1988; Maier et al., 2016) . The table below provides representative examples of the calculated DNA damage rates across different model systems, most of which are examining DNA DSBs.
Reference | Summary |
Ward, 1988 |
Under the assumption of 6 pg of DNA per cell. 60 eV of energy deposited per event over a total of 1 Gy. Deoxyribose (2.3 pg/cell): 14,000 eV deposited, 235 events. Bases (2.4 pg/cell): 14,700 eV deposited, 245 events. Phosphate (1.2 pg/cell): 7,300 eV deposited, 120 events. Bound water (3.1 pg/cell): 19,000 eV deposited, 315 events. Inner hydration shell (4.2 pg/cell): 25,000 eV deposited 415 events. |
Charlton, 1989 | Simulated dose-concordance prediction of increase in number of DSBs/54 nucleotide pairs as direct deposition of energy increases in the range 75-400 eV. In the range 100 - 150 eV: 0.38 DSBs/54 nucleotide pairs and at 400 eV: ~0.80 DSBs per 64 nucleotide pairs. |
Sutherland, 2000 | Using isolated bacteriophage T7 DNA and 0-100 Gy of gamma radiations, observed a response of 2.4 DSBs per megabase pair per Gy. |
Rogakou et al., 1999 | Radiation doses of 0.6 Gy & 2 Gy to normal human fibroblasts (IMR90) and MCF7 cells resulted in 10.1 & 12.2 DSBs per nucleus on average (0.6 Gy), respectively; increasing to 24 & 27.1 DSBs per nucleus (2 Gy). DSBs present at 3 min, persisted from 15 - 60 min, and then were decreased to almost baseline levels by 180 min. |
Kuhne et al., 2005 | Gamma-ray and X-ray irradiation of primary human skin gibroblasts (HSF2) at 0 - 70 Gy. Gamma-rays: (6.1 ± 0.2) x 10-9 DSBs per base pair per Gy, X-rays: (7.0 ± 0.2) x 10-9 DSBs per base pair per Gy. C_k X-rays: (12.1 ± 1.9) x 10-9 DSBs per base pair per Gy. |
Rothkamm, 2003 | X-ray irradiation of primary human fibroblasts (MRC-5) in the range 1 mGy - 100 Gy, 35 DSBs per cell per Gy. |
Grudzenski et al, 2010 | X-rays irradiating primary human fibroblasts (HSF1) in the range 2.5 - 100 mGy yielded a response of 21 foci per gy. When irradiating adult C57BL/6NCrl mice with photons a response of 0.07 foci per cell at 10 mGy was found. At 100 mGy the response was 0.6 foci per cell and finally, at 1 Gy; 8 foci per cell. |
de Lara, 2001 | V79-4 cells irradiated with gamma-rays and ultrasoft x-rays (carbon K-shell, copper L-shell, aluminium K-shell and titanum K-shell) in the range 0 - 20 Gy. Response (DSBs per Gy per cell): Gamma-rays: 41, carbon K-shell: 112, copper L-shell: 94, aluminum K-shell: 77, titanium K-shell: 56. |
Rube et al., 2008 | Linear dose-dependent increase in DSBs in the brain, small intestine, lung and heart of C57BL/6CNrl mice after whole-body irradiation with 0.1 - 1.0 Gy of radiation. 0.8 foci per cell (0.1 Gy) and 8 foci per cell (1 Gy) |
Antonelli et al., 2015 | Linear dose-dependent increase in the number of DSBs from 0 - 1 Gy for gamma-rays and alpha particles as follows: Gamma-rays: 24.1 foci per Gy per cell nucleus, alpha particles: 8.8 foci per Gy per cell nucleus. |
Dubrova & Plumb, 2002 | At 1 Gy observe 70 DSBs, 1000 single-strange breaks and 2000 damaged DNA bases per cell per Gy. |
Response-response relationship
There is evidence of a response-response relationship between the deposition of energy and the frequency of DSBs. In studies encompassing a variety of biological models, radiation types and radiation doses, a positive, linear relationship was found between the radiation dose and the number of DSBs (Sutherland et al., 2000; de Lara et al., 2001; Rothkamm & Lo, 2003; Kuhne et al., 2005; Rube et al., 2008; Grudzenski et al., 2010; Shelke & Das, 2015; Antonelli et al., 2015). There were, however, two exceptions reported. When human blood lymphocytes were irradiated with X-rays in vitro, a linear relationship was only found for doses ranging from 6 - 500 mGy; at low doses from 0 - 6 mGy, there was a quadratic relationship reported (Beels et al., 2009). Secondly, simulation studies predicted that there would be a non-linear increase in DSBs as energy deposition increased, with a saturation point at higher LETs (Charlton et al., 1989).
Time-scaleData from temporal response studies suggests that DSBs likely occur within seconds to minutes of energy deposition by ionizing radiation. In a variety of biological models, the presence of DSBs has been well documented within 10 - 30 minutes of radiation exposure (Rogakou et al., 1999; Rube et al., 2008; Beels et al., 2009; Kuefner et al., 2009; Grudzenski et al., 2010; Antonelli et al., 2015); there is also evidence that DSBs may actually be present within 3 - 5 minutes of irradiation (Rogakou et al., 1999; Rothkamm & Lo, 2003; Rube et al., 2008; Grudzenski et al., 2010). Interestingly, one study that focussed on monitoring the cells before, during and after irradiation by taking photos every 5, 10 or 15 seconds found that foci indicative of DSBs were present 25 and 40 seconds after collision of the alpha particles and protons with the cell, respectively. The number of foci were found to increase over time until plateauing at approximately 200 seconds after alpha particle exposure and 800 seconds after proton exposure (Mosconi et al., 2011).
After the 30 minute mark, DSBs have been shown to rapidly decline in number. By 24 hours post-irradiation, DSB numbers had declined substantially in systems exposed to radiation doses between 40 mGy and 80 Gy (Rothkamm & Lo, 2003; Rube et al., 2008; Grudzenski et al., 2010; Russo et al., 2015; Antonelli et al., 2015), with the sharpest decrease documented within the first 5 hours (Rogakou et al., 1999; Rube et al., 2008; Kuefner et al., 2009; Grudzenski et al., 2010; Shelke and Das, 2015). Interestingly, DSBs were found to be more persistent when they were induced by higher LET radiation (Antonelli et al., 2015).
Some common clinical radiation modifiers include cisplatin, 5-fluorouracil, thiols, and nitroxides (reviewed in (Citrin and Mitchel, 2014)). Clinical approaches have identified many modulating radiation factors, which are often categorized as either sensitizers or protectors. Sensitizers enhance radiation-induced tumour cell killing, and protectors protect normal tissues from the deleterious effects of ionizing radiation (Citrin & Mitchel, 2014).
Known Feedforward/Feedback loops influencing this KERNot identified.
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Relationship: 1911: Increase, DNA strand breaks leads to N/A, Inadequate DNA repair
AOPs Referencing Relationship
AOP Name | Adjacency | Weight of Evidence | Quantitative Understanding |
---|---|---|---|
Oxidative DNA damage leading to chromosomal aberrations and mutations | adjacent | High | Low |
Direct deposition of ionizing energy onto DNA leading to lung cancer | adjacent | Moderate | Moderate |
Evidence Supporting Applicability of this Relationship
Life Stage | Evidence |
---|---|
All life stages | High |
Sex | Evidence |
---|---|
Unspecific | High |
The domain of applicability is multicellular eukaryotes (Lieber, 2008; Hartlerode & Scully, 2009) , plants (Gorbunova, 1997; Puchta, 2005), certain strains of bacteria such as Mycobacteria, Pseudomonas, Bacillus and Agrobacterium (Shuman & Glickman, 2007), and yeast (Wilson & Lieber, 1999).
Key Event Relationship Description
The maintenance of DNA integrity is essential for genomic stability; for this reason cells have multiple response mechanisms that enable the repair of damaged DNA. Thus when DNA double strand breaks (DSBs) occur, the most detrimental type of lesion, the cell will initiate repair machinery. These mechanisms are not foolproof, and emerging evidence suggests that closely spaced lesions can compromise the repair machinery. The two most common DSB repair mechanisms are non-homologous end joining (NHEJ) and homologous recombination (HR). NHEJ is initiated in G1 and early S phases of the cell cycle (Lieber et al., 2003) and is preferentially used to repair DSB damage (Godwint et al., 1994), as it is rapid and more efficient than HR (Lliakis, 1991; Jeggo, 1998; Mao et al., 2008). In higher-order eukaryotes such as humans, NHEJ is the favoured DNA repair mechanism because of the large non-coding regions within the genome. NHEJ can occur through one of two subtypes: canonical NHEJ (C‐NHEJ) or alternative non-homologous end joining (alt-NHEJ). C-NHEJ, as the name suggests, simply ligates the broken ends back together. In contrast, alt‐NHEJ occurs when one strand of the DNA on either side of the break is resected to repair the lesion (Bétermier et al., 2014). Both repair mechanisms are error‐prone, meaning insertions and deletions are sometimes formed due to the DSBs being repaired imperfectly (Thurtle-Schmidt and Lo, 2018). However, alt-NHEJ is considered more error-prone than C-NHEJ, as studies have shown that it more often leads to chromosomal aberrations (Zhu et al., 2002; Guirouilh-Barbat et al., 2007; Simsek & Jasin, 2010).
Evidence Supporting this KER
Biological PlausibilityThe biological rationale linking increased DNA DSB formation with inadequate DSB repair is supported strongly by literature. This is evident from the number of review articles that have been published on the subject. Of particular relevance is a recent review which focussed particularly on DSBs induced by ionizing radiation and extensively detailed the processes involved in repairing DSBs, including discussions of entire pathways and individual proteins involved in DNA repair (Thompson, 2012). Multiple other shorter reviews are also available on the subject, which cover such topics as: the mechanisms of DSB formation and repair, how to quantify these two events, and the biological consequences of unrepaired or misrepaired DNA damage (van Gent et al., 2001; Khanna & Jackson, 2001; Vignard et al., 2013; Moore et al., 2014; Rothkamm et al., 2015; Chang et al., 2017; Sage and Shikazono, 2017). A brief overview of the biological plausibility of this KER is given below; for more detail, please consult the above-cited reviews.
NHEJ is commonly used in repairing DSBs in multicellular eukaryotic organisms, especially in humans (Feldmann et al., 2000). Due to being inherently error-prone, this repair process is used to generate genetic variation within antigen receptor axons through VDJ recombination, a process that leads to the careful breakage and repair of DNA (Murakami & Keeney, 2008; Malu et al., 2012). Genetic variation is also often generated during the repair of highly toxic DSB lesions. Repair to these DSB sites normally triggers cell cycle delay. NHEJ is most active in the following order of the cell cycle: G1 > S > G2/M (Mao et al., 2008). Since most somatic mammalian cells are in the G1 pre-replicative phase, DSBs also usually appear in this phase and thus are often repaired using the error-prone NHEJ (Jeggo et al., 1995).
The two broken ends of DNA DSBs are bridged by overlapping single-strand microhomology termini (Anderson, 1993; Getts & Stamato, 1994; Rathmell & Chu, 1994; Jeggo et al., 1995; Miller et al., 1995; Kirchgessner et al., 1995). The microhomology termini are ligated only when complementary base pairs are overlapped and, depending on where this match is found on the termini, it can lead to deletions and other rearrangements. With increasing DSBs, the probability of insufficient or incorrect repair of these breaks increases proportionately. It has been suggested that clustered DNA damage is less easily repairable than any other form of DNA damage (United Nations, 2000). With multiple lesions in close proximity within a damaged cluster, the probability of misrepair is high. This leads to an increased number of misrepaired termini (Goodhead et al., 1994; Goodhead, 1980), as the presence of multiple damage sites interferes with the ability of the repair enzymes to recognize and bind to the DNA accurately (Harrison et al., 1999).
Empirical EvidenceEmpirical data obtained for this KER strongly supports the idea that an increase in DNA DSBs will increase the frequency of inadequate DSB repair. The evidence presented below is summarized in table 4, here (click link). Much of the evidence comes from work with radiation stressors, which directly cause DNA DSBs in the genome (Pinto & Prise, 2005; Dong et al., 2017) in a dose-dependent fashion (Dikomey & Brammer, 2000; Kuhne et al., 2000; Lobrich et al., 2000; Rothkamm & Lo, 2003; Kuhne et al., 2005; Asaithamby & Chen, 2009; Bracalente et al., 2013).
The formation of DSBs by ionizing radiation, the repair process, the various methods used to analyze this repair process, and the biological consequences of unrepaired or misrepaired DNA damage are reviewed in Sage & Shikazono (2017).
Dose and Incidence Concordance
There is evidence in the literature suggesting a dose/incidence concordance between the occurrence of DSBs and the incidence of inadequate DNA repair upon exposure to radiation. Inadequate DNA repair appears to occur at the same radiation dose as DSBs. Visually, immunofluorescence has demonstrated a colocalization of DNA repair proteins with DSB foci in response to a radiation stressor (Paull et al., 2000; Asaithamby & Chen, 2009; Dong et al., 2017). In studies examining cellular responses to increasing doses of radiation, which is known to evoke a dose-dependent increase in DNA DSBs (Dikomey & Brammer, 2000; Kuhne et al., 2000; Lobrich et al., 2000; Rothkamm & Lo, 2003; Kuhne et al., 2005; Asaithamby & Chen, 2009; Bracalente et al., 2013), there were resulting dose-dependent increases in non-repaired DSBs (Dikomey & Brammer, 2000), DSB misrepair rates (Mcmahon et al., 2016), and misrejoined DSBs (Kuhne et al., 2000; Kuhne et al., 2005; Rydberg et al., 2005), as well as a dose-dependent decrease in the total DSB rejoining (Lobrich et al., 2000). Furthermore, only 50% of the rejoined DSBs were found to be correctly repaired (Kuhne et al., 2000; Lobrich et al., 2000); 24 hours after being irradiated with an 80 Gy dose of alpha particles, this frequency of misrejoining increased to and remained constant at 80% (Kuhne et al., 2000). Furthermore, delivering radiation doses in fractionated increments also showed a dose-dependent change in the percentage of misrejoinings, such that larger fractionated doses (for example, 2 x 40 Gy) had a higher rate of DSB misrejoining than smaller fractionated doses (for example, 4 x 10 Gy) (Kuhne et al., 2000).
Temporal Concordance
There is evidence suggesting a time concordance between DSBs and DNA repair. DSBs and DNA repair have both been observed within minutes to hours of radiation exposure (Paull et al., 2000; Rothkamm & Lo, 2003; Pinto & Prise, 2005; Asaithamby & Chen, 2009).
Essentiality
There is evidence from inhibition studies and knock-out/knock down studies suggesting that there is a strong relationship between DSBs and DNA repair. When an inhibitor of a DNA repair protein was added to cells prior to exposure to a radiation stressor, DNA repair foci were not formed post-irradiation (Paull et al., 2000), and there were significant increases in DSBs at 6 hours and 12 hours after the radiation treatment (Dong et al., 2017). Similarly, there have been several knock-out/knock-down studies in which cells lacking a DNA repair protein have been exposed to a radiation stressor. As a result, DSBs were found to persist in these cells longer than in the wild-type cells (Rothkamm and Lo, 2003; Bracalente et al., 2013; Mcmahon et al., 2016; Dong et al., 2017), and there was an increase in incorrectly rejoined DSBs (Lobrich et al., 2000). In one striking example, a human cell line lacking DNA ligase IV had DSBs that were still present approximately 240 - 340 hours post-irradiation (Mcmahon et al., 2016). Interestingly, there were also increased levels of DSBs in these cells prior to being exposed to a radiation stressor (Paull et al., 2000) . Similarly, a study examining DSB repair kinetics after irradiation found that DSBs persisted for a longer time period in two repair-deficient mouse strains relative to a repair-proficient mouse strain; this pattern was found in lymphocytes, as well as tissues from the brains, lungs, hearts and intestines of these mice (Rube et al., 2008). The roles of various DNA repair proteins in the context of DSBs are highlighted in reviews by Chang et al. (2001) and Van Gent et al. (2001) with discussions focussing on the consequences of losing some of these proteins in cells, mice and humans (Van Gent et al., 2001)
Uncertainties and InconsistenciesUncertainties and inconsistencies in this KER are as follows:
- There is controversy surrounding how error-prone NHEJ truly is. Recent studies suggest that the process may be quite accurate (reviewed in (Bétermier et al. 2014)). The accuracy of NHEJ may actually be dependent on the structure of the termini. Thus, the termini processing rather than the NHEJ mechanism itself is argued to be the error-prone process (Bétermier et al. 2014).
- There may be different cellular responses associated with low-dose radiation exposure and high-dose radiation exposure; these differences may also be dependent on a DSB threshold being exceeded prior to initiation repair. It has been suggested that DNA repair may not be activated at low doses of radiation exposure in order to prevent the risk of mutations from error-prone repair mechanisms (Marples 2004).
- DSB repair fidelity varies in terms of confounding factors and the genetic characteristics of individuals (Scott 2006). For example, individuals who smoke have a 50% reduction in the mean level of DSB repair capacity relative to the non-smokers; this is due to an increased methylation index in smokers. A higher methylation index indicates more inactivation of gene expression. It is thus possible that expression of DNA repair proteins in smokers is decreased due to increased methylation of the genes encoding for repair proteins. In terms of individual genetics, single nucleotide polymorphisms (SNPs) within the MRE11A, CHEK2, XRCC3, DNA-PKcs, and NBN repair genes have been highly associated with the methylation index (Leng et al. 2008). SNPs can critically affect the function of these core proteins, varying the fidelity of DNA repair from person to person.
- Cells containing DNA damaged may be eliminated by apoptotic pathways, therefore not undergo repair, alternatively evidence has also shown that damaged cells can propagate due to lack of detection by repair machinery (Valentin 2005).
Quantitative Understanding of the Linkage
Quantitative understanding of this linkage suggests that DSB repair can be predicted from the presence of DSBs. In terms of DNA repair in response to radiation-induced DSBs, one study suggests that complete DNA DSB repair occurs starting at a threshold dose of 5 mGy (0.005 Gy), as measured by phosphorylation of gamma-H2AX (Lobrich et al., 2005) and presence of 53BPI foci (Asaithamby & Chen, 2009). After a 10 Gy dose of radiation, approximately 10 - 15% of DSBs were found to be misrepaired (Mcmahon et al., 2016); at a dose of 80 Gy, the relative percentage of DSBs incorrectly repaired was estimated at 50 - 60% (Kuhne et al., 2000; Lobrich et al., 2000; Mcmahon et al., 2016). Twenty-four hours post-irradiation, this rate increased to approximately 80% for alpha particle irradiation at 80 Gy, and remained constant until the end of the assay (10 days) (Kuhne et al., 2000).
Response-response relationshipThere is evidence of a response-response relationship for DNA repair of radiation-induced DSBs. The frequency of DSBs has been shown to increase linearly with radiation dose (Lobrich et al., 2000; Rothkamm & Lo, 2003; Kuhne et al., 2005; Asaithamby & Chen, 2009). For DNA repair, increasing doses of a radiation stressor were found to cause a linear-quadratic relationship between the radiation dose and the number of misrejoined DSBs per cell (Kuhne et al., 2005). Interestingly, the relationships between radiation and DNA repair were found to vary depending on the type of radiation. There was a more linear response between radiation dose and the number of misrejoined DSBs for high LET particles relative to a more curvilinear relationship for lower LET particles (Rydberg et al., 2005). Additionally, a linear relationship was defined for low dose-rate radiation and the number of non-repaired DNA DSBs, but a linear-quadratic equation was described for high dose-rate radiation (Dikomey & Brammer, 2000).
Time-scaleData from temporal response studies suggests that DSB repair may occur within 15 - 30 minutes of a DSB-inducing radiation stressor (Paull et al., 2000; Rothkamm & Lo, 2003; Pinto & Prise, 2005; Dong et al., 2017), with foci documented as early as 3-5 minutes post-irradiation (Asaithamby & Chen, 2009). The majority of DSB repair has been reported to occur within the first 3 - 6 hours following DSB induction (Rothkamm & Lo, 2003; Pinto & Prise, 2005; Asaithamby & Chen, 2009; Dong et al., 2017), with complete or near-complete DSB repair within 24 hours of the radiation stressor (Dikomey & Brammer, 2000; Lobrich et al., 2000; Rothkamm & Lo, 2003; Asaithamby & Chen, 2009; Mcmahon et al., 2016). In one 48-hour time-course experiment for DSB repair using two different types of radiation, the following repair progression was found at 30 minutes, 1 hour, 3 hours, 24 hours and 48 hours, respectively: 40 - 55%, 55 - 70%, 85%, 97 - 98% and 98% repair for X-rays and 30%, 45 - 50%, 65 - 70%, 85 - 90% and 90 - 96% repair for alpha particles (Pinto & Prise, 2005). Twenty-four hours post-irradiation, the frequency of DSB misrejoining was found to remain constant at approximately 80% for the 10 days that the DSB repair was monitored (Kuhne et al., 2000).
Known modulating factorsNot identified.
Known Feedforward/Feedback loops influencing this KERNot identified.
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Relationship: 164: N/A, Inadequate DNA repair leads to Increase, Mutations
AOPs Referencing Relationship
AOP Name | Adjacency | Weight of Evidence | Quantitative Understanding |
---|---|---|---|
Alkylation of DNA in male pre-meiotic germ cells leading to heritable mutations | adjacent | High | Moderate |
Alkylation of DNA leading to cancer 2 | adjacent | High | Moderate |
Alkylation of DNA leading to cancer 1 | non-adjacent | High | Moderate |
Oxidative DNA damage leading to chromosomal aberrations and mutations | adjacent | High | Low |
Direct deposition of ionizing energy onto DNA leading to lung cancer | adjacent | Moderate | Moderate |
Evidence Supporting Applicability of this Relationship
Life Stage | Evidence |
---|---|
All life stages | High |
Sex | Evidence |
---|---|
Unspecific | High |
The domain of applicability is multicellular eukaryotes (Lieber, 2008; Hartlerode & Scully, 2009), plants (Gorbunova, 1997; Puchta, 2005), certain strains of bacteria such as Mycobacteria, Pseudomonas, Bacillus and Agrobacterium (Shuman & Glickman, 2007), and yeast (Wilson & Lieber, 1999).
All organisms, from prokaryotes to eukaryotes, have DNA repair systems. Indeed, much of the empirical evidence on the fundamental principles described in this KER are derived from prokaryotic models. DNA adducts can occur in any cell type, and may or may not be repaired, leading to mutation. While there are differences among DNA repair systems across eukaryotic taxa, all species develop mutations following excessive burdens of DNA lesions like DNA adducts. Theoretically, any sexually reproducing organism (i.e., producing gametes) can also acquire DNA lesions that may or may not be repaired, leading to mutations in gametes.
Key Event Relationship Description
Insufficient repair results in the retention of damaged DNA that is then used as a template during DNA replication. During replication of damaged DNA, incorrect nucleotides may be inserted, and upon replication these become ‘fixed’ in the cell. Further replication propagates the mutation to additional cells.
For example, it is well established that replication of alkylated DNA can cause insertion of an incorrect base in the DNA duplex (i.e., mutation). Replication of non-repaired O4 thymine alkylation leads primarily to A:T→G:C transitions. Retained O6 guanine alkylation causes primarily G:C→A:T transitions.
For repairing DNA double strand breaks (DSBs), non-homologous end joining (NHEJ) is one of the repair mechanisms used in human somatic cells (Petrini et al., 1997; Mao et al., 2008). However, this mechanism is error-prone and may create mutations during the process of DNA repair (Little, 2000). NHEJ is considered error-prone because it does not use a homologous template to repair the DSB. The NHEJ mechanism involves many proteins that work together to bridge the DSB gap by overlapping single-strand termini that are usually less than 10 nucleotides long (Anderson, 1993; Getts & Stamato, 1994; Rathmell & Chu, 1994). Inherent in this process is the introduction of errors that may result in mutations such as insertions, deletions, inversions, or translocations.
Evidence Supporting this KER
Biological PlausibilityIf DNA repair is able to correctly and efficiently repair DNA lesions introduced by a genotoxic stressor, then no increase in mutation frequency will occur.
For example, for alkylated DNA, efficient removal by AGT will result in no increases in mutation frequency. However, above a certain dose AGT becomes saturated and is no longer able to efficiently remove the alkyl adducts. Replication of O-alkyl adducts leads to mutation. The evidence demonstrating that replication of unrepaired O-alkylated DNA causes mutations is extensive in somatic cells and has been reviewed (Basu and Essigmann 1990; Shrivastav et al. 2010); specific examples are given below.
It is important to note that not all DNA lesions will cause mutations. It is well documented that many are bypassed error-free. For example, N-alkyl adducts can quite readily be bypassed error-free with no increase in mutations (Philippin et al., 2014).
Inadequate repair of DSB
Collective data from tumors and tumor cell lines has emerged that suggests that DNA repair mechanisms may be error-prone (reviewed in Sishc et al., 2017) (Sishc & Davis, 2017). NHEJ, the most common pathway used to repair DSBs, has been described as error-prone. The error-prone nature of NHEJ, however, is thought to be dependent on the structure of the DSB ends being repaired, and not necessarily dependent on the NHEJ mechanism itself (Bétermier et al., 2014). Usually when perfectly cohesive ends are formed as a result of a DSB event, ligase 4 (LIG4) will have limited end processing to perform, thereby keeping ligation errors to a minimum (Waters et al., 2014). When the ends are difficult to ligate, however, the resulting repair may not be completed properly; this often leads to point mutations and other chromosomal rearrangements. It has been shown that approximately 25 - 50% of DSBs are misrejoined after exposure to ionizing radiation (Löbrich et al., 1998; Kuhne et al., 2000; Lobrich et al., 2000). Defective repair mechanisms can increase sensitivity to agents that induce DSBs and lead eventually to genomic instability (reviewed in Sishc et al., (2017)).
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).
Empirical EvidenceINSUFFICIENT REPAIR OF ALKYLATED DNA
Evidence in somatic cells
Empirical evidence to support this KER is primarily from studies in which synthetic oligonucleotides containing well-characterized DNA lesions were genetically engineered in viral or plasmid genomes and subsequently introduced into bacterial or mammalian cells. Mutagenicity of each lesion is ascertained by sequencing, confirming that replication of alkylated DNA (i.e., unrepaired DNA) causes mutations in addition to revealing the important DNA repair pathways and polymerases involved in the process. For example, plasmids containing O6-methyl or O6-ethylguanine were introduced into AGT deficient or normal Chinese hamster ovary cells (Ellison et al. 1989). Following replication, an increase in mutant fraction to 19% for O6-methylguanine and 11% for O6-ethylguanine adducts was observed in AGT deficient cells versus undetectable levels for control plasmids. The relationship between input of alkylated DNA versus recovered mutant fractions revealed that a large proportion of alkyl adducts were converted to mutations in the AGT deficient cells (relationship slightly sublinear, with more adducts than mutations). The primary mutation occurring was G:C-A:T transitions. The results indicate that replication of the adducted DNA caused mutations and that this was more prevalent with reduced repair capacity. The number of mutations measured is less than the unrepaired alkyl adducts transfected into cells, supporting that insufficient repair occurs prior to mutation. Moreover, the alkyl adducts occur prior to mutation formation, demonstrating temporal concordance.
Various studies in cultured cells and microorganisms have shown that the expression of AGT/MGMT (repair machinery – i.e., decrease in KE1) greatly reduces the incidence of mutations caused by exposure to methylating agents such as MNU and MNNG (reviewed in Kaina et al. 2007; Pegg 2011). Thomas et al. (2013) used O6-benzylguanine to specifically inhibit MGMT activity in AHH-1 cells. Inhibition was carried out for one hour prior to exposure to MNU, a potent alkylating agent. Inactivation of MGMT resulted in increased MNU-induced HPRT (hypoxanthine-guanine phosphoribosyltransferase) mutagenesis and shifted the concentrations at which induced mutations occurred to the left on the dose axis (10 fold reduction of the lowest observed genotoxic effect level from 0.01 to 0.001 µg/ml). The ratio of mutants recovered in DNA repair deficient cells was 3-5 fold higher than repair competent cells at concentrations below 0.01 µg/ml, but was approximately equal at higher concentrations, indicating that repair operated effectively to a certain concentration. Only at this concentration (above 0.01 µg/ml when repair machinery is overwhelmed and repair becomes deficient) do the induced mutations in the repair competent cells approach those of repair deficient. Thus, induced mutation frequencies in wild type cells are suppressed until repair is overwhelmed for this alkylating agent. The mutations prevented by MGMT are predominantly G:C-A:T transitions caused by O6-methylguanine.
Evidence in germ cells
That saturation of repair leads to mutation in spermatogonial cells is supported by work using the OECD TG488 rodent mutation reporter assay in sperm. A sub-linear dose-response was found using the lacZ MutaMouse assay in sperm exposed as spermatogonial stem cells, though the number of doses was limited (van Delft and Baan 1995). This is indirect evidence that repair occurs efficiently at low doses and that saturation of repair causes mutations at high doses. Lack of additional data motivated a dose-response study using the MutaMouse model following both acute and sub-chronic ENU exposure by oral gavage (O’Brien et al. 2015). The results indicate a linear dose-response for single acute exposures, but a sub-linear dose-response occurs for lower dose sub-chronic (28 day) exposures, during which mutation was only observed to occur at the highest dose. This is consistent with the expected pattern for dose-response based on the hypothesized AOP. Thus, this sub-linear curve for mutation at low doses following sub-chronic ENU exposure suggests that DNA repair in spermatogonia is effective in preventing mutations until the process becomes overwhelmed at higher doses.
Mutation spectrum: Following exposure to alkylating agents, the most mutagenic adducts to DNA in pre-meiotic male germ cells include O6-ethylguanine, O4-ethylthymine and O2-ethylthymine (Beranek 1990; Shelby and Tindall 1997). Studies on sperm samples collected post-ENU exposure in transgenic rodents have shown that 70% of the observed mutations are at A:T sites (Douglas et al. 1995). The mutations observed at G:C base pairs are almost exclusively G:C-A:T transitions, presumably resulting from O6-ethylguanine. It is proposed that the prevalence of mutations at A:T basepairs is the result of efficient removal of O6-alkylguanine by AGT in spermatogonia, which is consistent with observation in human somatic cells (Bronstein et al. 1991; Bronstein et al. 1992). This results in the majority of O6-ethylguanine adducts being removed, leaving O4- and O2-ethylthymine lesions to mispair during replication. Thus, lack of repair predominantly at thymines and guanines at increasing doses leads to mutations in these nucleotides, consistent with the concordance expected between diminished repair capabilities at these adducts and mutation induction (i.e., concordance relates to seeing these patterns across multiple studies, species and across the data in germ cells and offspring).
Inadequate repair of oxidative DNA lesions: In vitro studies
- AS52 Chinese hamster ovary cells (wild type and OGG1-overexpressing) were exposed to kJ/m2 UVA radiation (Dahle et al., 2008).
- Mutations in the gpt gene were quantified in both wild type and OGG1+ cells by sequencing after 13-15 days following 400 kJ/m2 UVA irradiation
- G:C-A:T mutations in UVA-irradiated OGG1+ cells were completely eliminated
- G:C-A:T mutation frequency in wild type cells increased from 1.8 mutants/million cells to 3.8 mutants/million cells following irradiation – indicating incorrect repair or lack of repair of accumulated 8-oxo-dG
- Elevated levels of OGG1 was able to prevent G:C-A:T mutations, while the OGG1 levels in wild type cells was insufficient, leading to an increase in mutants (demonstrates inadequate repair leading to mutations)
- Mutations in the gpt gene were quantified in both wild type and OGG1+ cells by sequencing after 13-15 days following 400 kJ/m2 UVA irradiation
- Xeroderma pigmentosum complementation group A (XPA) knockout (KO) and wild type TSCER122 human lymphoblastoid cells were transfected with TK gene-containing vectors with no adduct, a single 8-oxo-dG, or two 8-oxo-dG adducts in tandem (Sassa et al., 2015).
- XPA is a key protein in nucleotide excision repair (NER) that acts as a scaffold in the assembly the repair complex.
- Mutation frequency was determined by the number of TK-revertant colonies
- Control vector induced a mutation frequency of 1.3% in both WT and XPA KO
- Two 8-oxo-dG in tandem on the transcribed strand were most mutagenic in XPA KO, inducing 12% mutant frequency compared to 7% in WT
- For both XPA KO and WT, G:C-A:T transversion due to 8-oxo-dG was the most predominant point mutation in the mutants
- The lack of a key factor in NER leading to increased 8-oxo-dG-induced transversions demonstrates insufficient repair leading to increase in mutations
Inadequate repair of oxidative DNA lesions: In vivo studies in mice
- Spontaneous mutation frequencies in the liver of Ogg1-deficient (-/-) Big Blue mice was measured at 10 weeks of age (Klungland et al., 1999).
- Mutation frequencies were 2- to 3-fold higher in the Ogg1-/- mice than in wild type
- Of the 16 base substitutions detected in Ogg1 -/- mutant plaques analyzed by sequencing, 10 indicated G:C-A:T transversions consistent with the known spectrum of mutation
- The results support that insufficient repair of oxidized bases leads to mutation.
- Ogg1 knockout (Ogg1-/-) in C57BL/6J mice resulted in 4.2-fold and 12-fold increases in the amount of 8-oxo-dG in the liver compared to wild type at 9 and 14 weeks of age, respectively (Minowa et al., 2000).
- In these mice, there was an average of 2.3-fold increase in mutation frequencies in the liver (measured between 16-20 weeks)
- 57% of the observed base substitutions were G:C-A:T transversions, while 35% in wild type mice corresponded to this transversion.
- Approximately 70% of the increase in mutation frequency was due to G to T transversions.
- Concordantly, KBrO3 treatment resulted in a 2.9-fold increase in mutation frequency in the kidney of Ogg1 -/- mice compared to KBrO3-treated wild type (Arai et al., 2002).
- G:C-A:T transversions made up 50% of the base substitutions in the Ogg1-/- mice.
- Heterozygous Ogg1 mutants (Ogg1+/-) retained the original repair capacity, where no increase in 8-oxo-dG lesions was observed in the liver at 9 and 14 weeks (Minowa et al., 2000).
- This observation was consistent even after KBrO3 treatment of the mice (Arai et al., 2002).
- From these results, we can infer that OGG1 proteins are present in excess and that one functional copy of the gene is sufficient in addressing endogenous and, to a certain degree, chemical-induced oxidative DNA lesions.
- In these mice, there was an average of 2.3-fold increase in mutation frequencies in the liver (measured between 16-20 weeks)
Inadequate Repair of DSB
Empirical data obtained for this KER moderately supports the idea that inadequate DNA repair increases the frequency of mutations. The evidence presented below related to the inadequate repair of DSBs is summarized in table 5, here (click link). The review article by Sishc & Davis (2017) provides an overview of NHEJ mechanisms with a focus on the inherently error-prone nature of DSB repair mechanisms, particularly when core proteins of NHEJ are knocked-out. Another review also provides an overview of DSB induction, the repair process and how mutations may result, as well as the biological relevance of misrepaired or non-repaired DNA damage (Sage & Shikazono, 2017).
Dose and Incidence Concordance
There is evidence in the literature suggesting a dose/incidence concordance between inadequate DNA repair and increases in mutation frequencies. In response to increasing doses from a radiation stressor, dose-dependent increases in both measures of inadequate DNA repair and mutation frequency have been found. In an analysis that amalgamated results from several different studies conducted using in vitro cell-lines, the rate of DSB misrepair was revealed to increase in a dose-dependent fashion from 0 - 80 Gy, with the mutation rate also similarly increasing from 0 - 6 Gy (Mcmahon et al., 2016). Additionally, using a plant model, it was shown that increasing radiation dose from 0 - 10 Gy resulted in increased DNA damage as a consequence of inadequate repair. Mutations were observed 2 - 3 weeks post-irradiation (Ptácek et al., 2001). Moreover, increases in mutation densities were found in specific genomic regions of cancer samples (namely promoter DNAse I-hypersensitive sites (DHS) and 100 bp upstream of transcription start sites (TSS)) that were also found to have decreased DNA repair rates attributable to inadequate nucleotide excision repair (NER) (Perera et al., 2016).
Interestingly, mutation rates have been shown to increase as the required DNA repair becomes more complex. Upon completion of DSB repair in response to radiation and treatment with restriction enzymes, more mutations were found in cases where the ends were non-complementary and thus required more complex DNA repair (1 - 4% error-free) relative to cases where ends were complementary (34 - 38% error-free) (Smith et al., 2001).
Temporal Concordance
There is evidence in the literature suggesting a time concordance between the initiation of DNA repair and the occurrence of mutations. For simple ligation events, mutations were not evident until 12 - 24 hours, whereas DSB repair was evident at 6 -12 hours. For complex ligation events, however, mutations and DSB repair were both evident at 12 - 24 hours. As the relative percent of DNA repair increased over time, the corresponding percent of error-free rejoining decreased over time in both ligation cases, suggesting that overall DNA repair fidelity decreases with time ((Smith et al., 2001).
Essentiality
There is evidence from knock-out/knock-down studies suggesting that there is a strong relationship between the adequacy of DNA repair and mutation frequency. In all examined cases, deficiencies in proteins involved in DNA repair resulted in altered mutation frequencies relative to wild-type cases. There were significant decreases in the frequency and accuracy of DNA repair in cell lines deficient in LIG4 (Smith et al., 2003) and Ku80 (Feldmann et al., 2000); rescue experiments performed with these two cell lines further confirmed that inadequate DNA repair was the cause of the observed decreases in repair frequency and accuracy (Feldmann et al., 2000; Smith et al., 2003). In primary Nibrin-deficient mouse fibroblasts, there was increased spontaneous DNA damage relative to wild-type controls, suggestive of inadequate DNA repair. Using the corresponding Nibrin-deficient and wild-type mice, in vivo mutation frequencies were also found to be elevated in the Nibrin-deficient animals (Wessendorf et al., 2014). Furthermore, mutation densities were differentially affected in specific genomic regions in cancer patients depending on their XPC status. Specifically, mutation frequencies were increased in XPC-wild-type patients at DHS promoters and 100 bp upstream of TSS relative to cancer patients lacking functional XPC (Perera et al., 2016) . Lastly, in a study using WKT1 cells with less repair capacity, radiation exposure induced four times more mutations in these cells than in TK6 cell, which had a normal repair capacity (Amundson and Chen, 1996).
Uncertainties and InconsistenciesRepair of alkylated DNA
There were no inconsistencies in the empirical data reviewed or in the literature relating to biological plausibility. Much of the support for this KER comes predominantly from data in somatic cells and in prokaryotic organisms. We note that all of the data in germ cells used in this KER are produced exclusively from ENU exposure. Data on other chemicals are required. We consider the overall weight of evidence of this KER to be strong because of the obvious biological plausibility of the KER, and documented temporal association and incidence concordance based on studies over-expressing and repressing DNA repair in somatic cells.
Repair of oxidative lesions
- Thresholded concentration-response curve of mutation frequency was observed in AHH-1 human lymphoblastoid cells after treatment with pro-oxidants (H2O2 and KBrO2) known to cause oxidative DNA damage (Seager et al., 2012), suggesting that cells are able to tolerate low levels of DNA damage using basal repair. However, increase in 8-oxo-dG lesions and up-regulation of DNA repair proteins were not observed under the same experimental condition.
- Mutagenicity of oxidative DNA lesions other than 8-oxo-dG, such as FaPydG and thymidine glycol, has not been as extensively studied and there are mixed results regarding the mutagenic outcome of these lesions.
Overall
- Mutation induction is stochastic, spontaneous, and dependent on the cell type as well as the individual’s capability to repair efficiently (NRC, 1990; Pouget & Mather, 2001).
Quantitative Understanding of the Linkage
Thresholds for mutagenicity indicate that the response at low doses is modulated by the DNA repair machinery, which is effectively able to remove alkylated DNA at low doses [Gocke and Muller 2009; Lutz and Lutz 2009; Pozniak et al. 2009]. Kinetics of DNA repair saturation in somatic cells is described in Muller et al. [Muller et al. 2009].
For O-methyl adducts, once the primary repair process is saturated, in vitro data suggest that misreplication occurs almost every time a polymerase encounters a methylated guanine [Ellison et al. 1989; Singer et al. 1989]; however, it should be noted that this process can be modulated by flanking sequence. This conversion of adducts to mutations also appears to be reduced substantially in vivo [Ellison et al. 1989]. The probability of mutation will also depend on the type of adduct (e.g., O-alkyl adducts are more mutagenic than N-alkyl adducts; larger alkyl groups are generally more mutagenic, etc.). Overall, a substantive number of factors must be considered in developing a quantitative model.
Inadequate repair of oxidative lesions
The relationship between the quantity/activity of repair enzymes such as OGG1 in the cell and the quantity of oxidative lesions need to be better understood to define a threshold on the quantity of oxidative lesions exceeding basal repair capacity. Moreover, the proportion of oxidative lesions formed that lead to mutation versus strand breaks is not clearly understood.
Mutations resulting from oxidative DNA damage can occur via replicative polymerases and translesion synthesis (TLS) polymerases during replication, and during attempted repair. However, an in vitro study on TLS in yeast has shown that bypass of 8-oxo-dG by TLS polymerases during replication is approximately 94-95% accurate. Therefore, the mutagenicity of 8-oxo-dG and other oxidative lesions may depend on their abundance, not on a single lesion (Rodriguez et al., 2013). Applicability of this observation in mammalian cells needs further investigation. Information on the accuracy of 8-oxo-dG bypass in mammalian cells is limited.
The most notable example of mutation arising from inadequate repair of DNA oxidation is G to T transversion due to 8-oxo-dG lesions. Previous studies have demonstrated higher mutation frequency of this lesion compared to other oxidative lesions; for example, Tan et al. (1999) compared the mutation rate of 8-oxo-dG and 8-oxo-dA in COS-7 monkey kidney cells and reported that under similar conditions, 8-oxo-dG was observed to be four times more likely to cause base substitution (Tan et al., 1999).
Inadequate Repair of DSB
Quantitative understanding of this linkage is derived from the studies that examined DSB misrepair rates or mutation rates in response to a radiation stressor. In general, combining results from these studies suggests that increased mutations can be predicted when DNA repair is inadequate. 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). For mutation rates in response to radiation across a variety of models and radiation doses, please refer to the table below.
Reference | Summary |
Matuo et al., 2018 | Yeast cells (saccharomyces cerevisiae) exposed to high LET cardbon ions (25 keV/um) and low LET carbon ions (13 keV/um) between 0-200 Gy induces a 24-fold increase overbaseline of mutations (high LET) and 11-fold increase over baseline mutations (low LET). |
Nagashima et al., 2018 | Hamster cells (GM06318-10) exposed to x-rays in the 0-1 Gy. Response of 19.0 ± 6.1 mutants per 109 survivors. |
Albertini et al., 1997 | T-lymphcytes isolated from human peripheral blood exposed to low LET gamma-rays (0.5-5 Gy) and high LET radon gas (0-1 Gy). Response of 7.0x10-6 mutants/Gy (Gamma-rays 0-2 Gy), 54x10-6 mutants/Gy (Gamma-rays 2-4 Gy) and 63x10-6 mutants/Gy (0-1 Gy). |
Dubrova et al., 2002 | Observation of paternal ESTR mutation rates in CBAH mice following exposure to acute low LET X-rays (0-1 Gy), chronic low LET gamma-rays (0-1 Gy) and chronic high LET neutrons (0-0.5 Gy). Modelled response of y = mx + C, values of (m,C): X-rays: (0.338, 0.111), Gamma-rays: (0.373±0.082, 0.110), Neutrons: (1.135±0.202, 0.136). |
McMahon et al., 2016 | Study of HPRT gene in Chinese hamster cells following exposure to radiation of 1-6 Gy. Observation of 0.2 mutations in HPRT gene per 104 cells and 0.1 point mutations per 104 cells (1 Gy). At 6 Gy, observation of 1.5 mutations in the HPRT gene per 104 cells and 0.4 point mutations per 104 cells. |
Response-response relationship
Inadequate Repair of DSB
There is evidence of a response-response relationship between inadequate DNA repair and increased frequency of mutations. When exposed to a radiation stressor, there was a positive relationship between the radiation dose and the DSB misrepair rate, and between the mutation rate and the radiation dose (Mcmahon et al., 2016). Similarly, there was a negative correlation found between NER and the mutation densities at specific genomic regions in cancer patients. Specifically, inadequate NER resulted in more mutations in the promoter DHS and the TSS, but normal NER at DHS flanking regions resulted in fewer mutations (Perera et al., 2016).
Time-scaleInadequate Repair of DSB
Two studies were used to provide data regarding the time scale of DNA repair and the appearance of mutations. In a study using plants, DNA damage was evident immediately following radiation with 30 Gy of radiation; 50% of repairs were complete by 51.7 minutes, 80% by 4 hours, and repair was completed by 24 hours post-irradiation. Although no mutational analysis was performed during the period of repair, irradiated plants were found to have increased mutations when they were examined 2 - 3 weeks later (Ptácek et al., 2001). Both DNA repair and mutation frequency were examined at the same time in a study comparing simple and complex ligation of linearized plasmids. In this study, repaired plasmids were first detected between 6 - 12 hours for simple ligation events and between 12 - 24 hours for more complex ligation events; this first period was when the most error-free rejoining occurred in both cases. After this initial period of repair until its completion at 48 hr, repair became increasingly more erroneous such that mutations were found in more than half of the repaired plasmids at 48 hr regardless of the type of required ligation (Smith et al., 2001).
Known modulating factorsNot identified.
Known Feedforward/Feedback loops influencing this KERNot identified.
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Relationship: 1912: N/A, Inadequate DNA repair leads to Increase, Chromosomal aberrations
AOPs Referencing Relationship
AOP Name | Adjacency | Weight of Evidence | Quantitative Understanding |
---|---|---|---|
Oxidative DNA damage leading to chromosomal aberrations and mutations | adjacent | High | Low |
Direct deposition of ionizing energy onto DNA leading to lung cancer | adjacent | High | Low |
Evidence Supporting Applicability of this Relationship
Life Stage | Evidence |
---|---|
All life stages | High |
Sex | Evidence |
---|---|
Unspecific | High |
The domain of applicability for this KER is multicellular eukaryotes at any stage of development, including plants (Varga & Aplan 2005; Schipler & Iliakis 2013; Manova & Gruszka 2015).
Key Event Relationship Description
Cells are exposed to many insults, both endogenous and exogenous, that may cause damage to their DNA. In response to this constant threat, cells have accordingly evolved many different pathways for repairing DNA damage (Pfeiffer & Goedecke, 2000; Hoeijmakers, 2001; Jeggo & Markus, 2015; Rode et al., 2016). When confronted with double strand breaks (DSBs), there are two common repair pathways employed by the cell: homologous recombination (HR) and non-homologous end-joining (NHEJ). In HR, a homologous sequence on a sister chromatid is used as a template, ensuring that no sequence information is lost over the course of repair (Ferguson & Alt, 2001; van Gent et al., 2001; Hoeijmakers, 2001; Jeggo & Markus, 2015; Schipler & Iliakis, 2013; Venkitaraman, 2002). However, this method of DNA repair may result in a loss of an allele leading to heterozygosity. This may occur if a non-homologous chromosome with a erronous sequence is used as the template instead of the homologous chromosome, thus leading to a loss of genetic information (Ferguson & Alt, 2001). Despite this possible error, HR is generally considered to be one of the more accurate methods of DNA repair because it does make use of a template (van Gent et al., 2001; Schipler & Iliakis, 2013; Venkitaraman, 2002) . NHEJ, however, does not use a template and is generally described as being error-prone. This repair process allows for the direct religation of broken DNA ends without using template DNA as a guide (van Gent et al., 2001; Ferguson & Alt, 2001; Hoeijmakers, 2001; Venkitaraman, 2002; Schipler & Iliakis, 2013; Jeggo & Markus, 2015; Rode et al., 2016). In lieu of a template, NHEJ utilizes rapid repair kinetics to religate the broken ends before they have time to diffuse away from each other (Schipler & Iliakis, 2013), thus fitting two ‘sticky’ DNA ends back together (Danford, 2012). There is not, however, an inherent quality control check; as such, sections of DNA may be gained or lost, or the wrong ends may be rejoined (Schipler & Iliakis, 2013). There are two versions of this error-prone DNA repair: classical or canonical NHEJ (c-NHEJ), and alternative NHEJ (alt-NHEJ) (Schipler & Iliakis, 2013). It is not well understood when or why one pathway is selected over another (Venkitaraman, 2002; Schipler & Iliakis, 2013). It has been proposed that the phase of the cell cycle may influence repair pathway choice (Ferguson & Alt, 2001; Vodicka et al., 2018); for instance, HR is generally more common than NHEJ when sister chromatids are available in S and G2 phases of the cell cycle (Hoeijmakers, 2001; Venkitaraman, 2002). If both HR and c-NHEJ are compromised, alt-NHEJ, which is slower and more error-prone than c-NHEJ, is thought to be the stand-by repair mechanism (Schipler & Iliakis, 2013).
If these repair processes are not able to properly and adequately repair the DNA, this may lead to the formation of chromosomal aberrations (CAs). CAs are defined as abnormalities in the chromosome structure, often due to losses or gains of chromosome sections or the entire chromosomes itself (van Gent et al., 2001). These abnormalities can take many different forms and can be classified according to several different schemes. CAs can be defined as breaks, which occur when DSBs are not rejoined, or as exchanges, where the presence of multiple DSBs results in misrejoining of the DNA ends (Danford, 2012; Registre et al., 2016). CA classes can be further subdivided into chromosome-type aberrations (CTAs) that affect both sister chromatids, and chromatid-type aberrations (CSAs), affecting only one chromatid (Danford, 2012) . Examples of CTAs include chromosome-type breaks, centric ring chromosomes, and dicentric chromosomes (which have two centromeres), while CSAs refer to chromatid-type breaks and chromatid exchanges (Hagmar et al., 2004; Bonassi et al., 2008). Other types of CAs that may occur include micronuclei (MN; small nucleus-like structures containing chromosome fragments enclosed by a nuclear membrane (Fenech & Natarajan, 2011; Doherty et al., 2016)), nucleoplasmic bridges (NPBs; a stretch of chromatin enclosed by a nuclear membrane that is attached to two centromeres (Fenech & Natarajan, 2011; Russo et al., 2015)), nuclear buds (NBUDs; a MN that is still connected to the nucleus by nucleoplasmic material (Fenech & Natarajan, 2011)), and copy number variants (CNVs; base pair to megabase pair deletions or duplications of chromosomal segments (Russo et al., 2015)). CAs may also be classified as stable aberrations (translocations, inversions, insertions and deletions) and unstable aberrations (dicentric chromosomes, acentric fragments, centric rings and MN) (Hunter & Muirhead, 2009; Qian et al., 2016).
Evidence Supporting this KER
Biological PlausibilityThere is strong biological plausibility for a relationship between inadequate repair of DNA damage and a corresponding increase in CAs. This is evident in a variety of reviews on the topic (van Gent et al., 2001; Hoeijmakers, 2001; Povirk, 2006; Weinstock et al., 2006; Lieber et al., 2010; Rode et al., 2016).
The two most common methods used to repair DSBs, which are one of the most dangerous types of DNA lesions, are HR and NHEJ. Mechanisms for these two methods of DNA repair are well-established and have been thoroughly reviewed (Van Gent et al. 2001; Hoeijmakers 2001; Lieber et al. 2010; Jeggo and Markus 2015; Sishc and Davis 2017). Briefly, HR requires a template DNA strand to repair damage and thus facilitates the invasion of the damaged strand with matching sequences on homologous chromosomes or sister chromatids (Ferguson and Alt 2001; van Gent et al. 2001; Hoeijmakers 2001; Jeggo and Markus 2015; Schipler and Iliakis 2013; Venkitaraman 2002). Proteins involved in the HR pathway include the RAD50 proteins, MRE11, BRCA1, and BRCA2 (Ferguson and Alt 2001; van Gent et al. 2001; Hoeijmakers 2001; Jeggo and Markus 2015; Venkitaraman 2002). In contrast to this relatively accurate form of DNA repair ( van Gent et al. 2001; Schipler and Iliakis 2013; Venkitaraman 2002), NHEJ is more error-prone. It does not require a template to guide repair, but simply re-ligates broken DNA ends back together (Van Gent et al. 2001; Ferguson and Alt 2001; Hoeijmakers 2001; Lieber et al. 2010; Schipler and Iliakis 2013; Jeggo and Markus 2015; Rode et al. 2016; Sishc and Davis 2017) Proteins used during NHEJ include the DNA-PK complex (encompassing Ku70, Ku80 and DNA-PKcs), and the XRCC4-DNA ligase IV complex (Ferguson & Alt, 2001; van Gent et al., 2001; Hoeijmakers, 2001; Jeggo & Markus, 2015; Sishc & Davis, 2017).Interestingly, NHEJ is used in the biological V(D)J recombination process because its error-prone mechanism allows immune cells to develop a wide range of unique receptors for antigen detection (Ferguson & Alt, 2001; van Gent et al., 2001; Lieber, 2010).
Damaged DNA in the form of DSBs can follow three possible outcomes: the DSB is rejoined accurately, with no changes made to the genome; the DSB is left unrepaired and the ends diffuse away from each other; or the DSB is repaired incorrectly such that the repaired version is different from the original version (Danford, 2012). These latter two errors in repair (the complete absence of repair or inaccurate repair) could arise due to interruptions to the repair process that allow time for the broken ends to move away from each other before they can be rejoined, mis-rejoining of the wrong DNA ends, or post-repair alterations that modify the junction point and lead to nucleotide losses (Schipler and Iliakis 2013). Errors occurring during repair may be particularly detrimental if they interrupt or modify key genes, or if chromosome structures are created that cannot undergo proper mitosis (Schipler and Iliakis 2013).
The classic model of CA formation has centred around misrepair of DSBs. Exposing DNA to an endogenous or exogenous DSB-inducing agent directly results in DSBs, which may either persist or be misrepaired by inadequate repair mechanisms; in the event of this erroneous repair, CAs often eventually result (Bignold, 2009; Danford, 2012; Schipler & Iliakis, 2013) . Another model has been proposed that suggests CAs may actually be due to failure of enzymes that tether the DNA strands during the repair of enzyme-induced breaks in the DNA; the various pathways in the cell would likely employ assorted tethering enzymes. The numerous types of CAs would thus result from different kinds of tethering errors (Bignold 2009).
The type of CA that results may be dependent on the timing of inadequate repair. For example, DSBs may result in CSAs or CTAs depending on when during the cell cycle the DSB was incurred. DSBs that are not repaired before DNA duplication in the S-phase will be replicated and result in CTAs. If DSBs are incurred after the S-phase and are improperly repaired, CSAs will result (Danford, 2012; Registre et al., 2016; Vodicka et al., 2018). Similarly, CNVs are thought to be induced during the DNA replication phase. 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 (Lee et al. 2007; Hastings et al. 2009; Arlt et al. 2012; Arlt et al. 2014; Wilson et al. 2015).
The type of CA may also be dependent on the type of erroneous repair that occurs. Deletions or chromosome breaks may occur when DSBs are left unrepaired (Danford 2012). Deletions may also occur when nucleotides are removed at the junctions (Schipler and Iliakis 2013) or when the wrong DNA ends are religated (Venkitaraman 2002). Ligation of the incorrect ends of DNA DSBs may also lead to translocations (Ferguson & Alt, 2001; Lieber, 2010; Povirk, 2006; Venkitaraman, 2002). This type of error may occur when there are two or more DSBs in close proximity to each other that are misrejoined, thus resulting in the exchange of genetic material and a translocated chromosome (Ferguson and Alt 2001; Povirk 2006). NHEJ has been shown to play a significant role in the generation of translocations ( Lieber 2010; Povirk 2006; Weinstock et al. 2006). Evidence for this comes from analysis of breakpoint junctions, which typically have little to no chromosomal homology when NHEJ repair is used (Povirk 2006; Weinstock et al. 2006); this was demonstrated in studies using translocation reporters (reviewed in Weinstock et al., 2006). There are, however, two types of NHEJ. c-NHEJ has been shown to suppress translocations (Simsek and Jasin 2010) , which may be due to its relatively rapid repair kinetics (Schipler and Iliakis 2013). Translocations are thus suggested to originate more often from alt-NHEJ (Simsek and Jasin 2010; Zhang and Jasin 2011; Schipler and Iliakis 2013) .
NHEJ is also thought to mediate the formation of other types of CAs. Based on analysis of breakpoint junctions in lung adenocarcinoma samples where reciprocal inversions were found between genes RET and KIF5B/CCDC6, the majority of the inversions were thought to be induced by NHEJ (Mizukami et al. 2014). Chromothripsis, which refers to a single event that results in a massive number of CAs localized to a single or very few chromosomes (Russo et al. 2015; Leibowitz et al. 2015; Rode et al. 2016), may also be linked to NHEJ. The single catastrophic event sparking chromothripsis likely induces a large quantity of DSBs, essentially shattering the chromosome(s). These DSBs are then processed mainly by the error-prone NHEJ, which results in a large number of CAs, including chromosomal rearrangements, CNVs, and loss of heterozygosity (Leibowitz et al. 2015; Rode et al. 2016).
Fusing two broken chromosomes may lead to the formation of dicentric chromosomes, which are characterized by the presence of two centromeres. Dicentrics may also be formed by telomere-to-telomere end fusions (Fenech and Natarajan 2011; Rode et al. 2016). Telomeres, composed of TTAGGG repeats, are important structures that protect the ends of chromosomes and ensure accurate replication (Ferguson and Alt 2001; Hoeijmakers 2001; Vodicka et al. 2018); these nucleoprotein structures are shortened (Vodicka et al. 2018) by approximately 100 base pairs after each division, and are only replenished in cell types expressing the enzyme telomerase (Hoeijmakers 2001). If the telomeres become critically short, they can be mistaken for broken DNA ends by DNA repair machinery, and thus may be ‘repaired’ by fusing the ends of two chromosomes together (Ferguson and Alt 2001; Vodicka et al. 2018).
Dicentrics can also contribute to other types of CAs. During mitosis, dicentric chromosomes may be pulled to opposite ends of the cell by mitotic spindle (Ferguson and Alt 2001; Fenech and Natarajan 2011; Leibowitz et al. 2015; Rode et al. 2016). Because the ends of the chromosomes are fused, this can lead to the formation of an anaphase chromatin bridge between the daughter cells (Russo et al. 2015; Leibowitz et al. 2015; Rode et al. 2016). If this bridge persists beyond anaphase, it may become enclosed in a nucleoplasmic membrane along with the nucleus, thus generating a NPB (Fenech and Natarajan 2011). Eventually, however, these bridges do break (Ferguson and Alt 2001; Fenech and Natarajan 2011; Russo et al. 2015; Leibowitz et al. 2015; Rode et al. 2016); the break is nearly always uneven, meaning that one daughter cell will be missing genetic material and one will have extra genetic material (Fenech and Natarajan 2011). These fragments, with their ‘sticky’ ends from the break, may further propagate the formation of CAs by being ligated inappropriately to another chromosome. Thus the cycle, known as the breakage-fusion-bridge (BFB) cycle, is propagated and further contributes to chromosomal instability (Ferguson and Alt 2001; Fenech and Natarajan 2011; Russo et al. 2015; Leibowitz et al. 2015; Rode et al. 2016) .
MN may also be formed during this BFB cycle. When the anaphase bridges break, the remaining chromosome fragments may be packaged by a nuclear membrane into its own mini nucleus, thus forming an MN. MN may also enclose acentric chromosome fragments, chromatid fragments, or even entire chromosomes that were not properly segregated during mitosis (Fenech and Natarajan 2011; Doherty et al. 2016). Similar to MN in structure are NBUDs; the only difference between these two structures is that NBUDs are still attached to the nucleus by nucleoplasmic material. A NBUD is formed if there is amplified DNA that needs to be removed; this amplified material is often segregated from the other DNA at the periphery of the nuclear membrane and excluded from the nucleus by budding, resulting in a NBUD. Additionally, NBUDs may also result from NPB breakages (Fenech and Natarajan 2011).
Empirical EvidenceThere is moderate empirical evidence supporting the relationship between inadequate DNA repair and the frequency of CAs. The evidence presented below is summarized in table 6, here (click link). Several reviews discuss evidence that associates these two events (Ferguson and Alt 2001; van Gent et al. 2001; Sishc and Davis 2017; Venkitaraman 2002). Overall, however, there is weak empirical evidence available supporting a dose and incidence concordance, little empirical evidence supporting a temporal concordance, and strong empirical evidence supporting essentiality for this KER.
Dose and Incidence Concordance
There is weak empirical evidence available that directly examines the dose and incidence concordance between DNA repair and CAs within the same study. There are, however, studies that use an ionizing radiation stressor to examine dose concordance of either inadequate DNA repair in response to radiation exposure, or CA frequencies in response to irradiation. In an analysis that amalgamated results from several different studies conducted using in vitro experiments, the rate of DSB misrepair was revealed to increase in a dose-dependent fashion from 0 - 80 Gy (Mcmahon et al. 2016). Similarly, there was a clear correlation between radiation dose (i.e., increasing amounts of energy deposition) between 0 - 10 Gy and different clastogenic endpoints (Thomas et al. 2003; Tucker et al. 2005A; George et al. 2009; Arlt et al. 2014; Balajee et al. 2014; Lin et al. 2014; Suto et al. 2015; Mcmahon et al. 2016) . Overall, this suggests that exposure to radiation may increase both inadequate repair of DNA damage and the frequency of CAs in a dose-dependent fashion. More studies, however, are required to better assess the dose and incidence concordance of this KER.
Temporal Concordance
Temporal concordance between inadequate DNA repair and CA frequency is not well established. One study using cells pretreated with a DNA-PK inhibitor and irradiated with gamma rays found that DNA repair and MN were evident when they were assessed at 3 hours post-irradiation and 24 hours post-irradiation, respectively (Chernikova et al. 1999). This study does therefore suggest that there may be temporal concordance between these two events. Other radiation-based studies examining these two events separately, however, do not provide clear evidence of temporal concordance between DNA repair and CA frequency.
Essentiality
There is strong evidence for essentiality. Numerous studies demonstrate that simply knocking-out one gene involved in DNA repair, without any other added stressor, is enough to increase the frequency of CAs in several types of cells (Karanjawala et al. 1999; Patel et al. 1998; Wilhelm et al. 2014). Further fortifying this relationship, addition of a DSB-inducing stressor to these DNA repair knock-out cells also significantly increases CA levels relative to wild-type cells receiving the same treatment (Cornforth and Bedford 1985; Simsek and Jasin 2010; Lin et al. 2014; Mcmahon et al. 2016) .
Inhibitor studies have also found similar results. Two strains of wild-type cells that were treated with hydroxyurea, which is known to inhibit DNA repair, both had increased CAs relative to untreated wild-type cells (Wilhelm et al. 2014). Similarly, immortalized myeloid cell lines, cells from patients with myeloid leukemia, and cells from healthy donors were all found to have dose-dependent decreases in ligation efficiency after being treated with increasing doses of antibodies against various NHEJ proteins (Heterodimer et al. 2002). Lastly, cells that were pretreated with DNA-PK inhibitor wortmannin prior to being irradiated were found to have not only increased levels of MN, but also decreased rates of DNA rejoining (Chernikova et al. 1999).
A rescue experiment provided further evidence of the essential role DNA repair plays in relation to CA frequencies. Inhibition of NHEJ through knocking out either Ku70 or Xrcc4 resulted in higher CA frequencies in the form of translocations; when Xrcc4 was transiently expressed in Xrcc4-/- cells, translocations were significantly decreased by 5-fold(Simsek and Jasin 2010) . This provides strong evidence that the NHEJ repair pathway plays an important role in the formation of CAs, specifically translocations.
Uncertainties and InconsistenciesUncertainties in this KER are as follows:
- In an experiment using both wild-type and Ku70-/- cells, knock-down of alt-NHEJ protein CtIP resulted in significantly decreased translocations in both cell types. When CtIP expression was rescued, translocation frequencies in these cells also returned to normal levels. This however, is opposite to results obtained in a similar study, where knock-out of Ku70 or Xrcc4 led to increased translocation frequency, and Xrcc4 rescue experiments resulted in decreased translocations (Simsek and Jasin 2010). It should be noted that alt-NHEJ is thought to be the major repair pathway responsible for generating translocations (Simsek and Jasin 2010; Zhang and Jasin 2011; Schipler and Iliakis 2013).
- There is currently discussion regarding the accuracy of HR relative to NHEJ. Traditionally HR has been considered the more accurate type of DNA repair, while NHEJ is classically described as error-prone. There is emerging evidence, however, suggesting that HR may in fact be a mutagenic process. Evidence supporting HR as an error-prone repair pathway has been reviewed (Guirouilh-barbat et al. 2014).
Quantitative Understanding of the Linkage
Quantitative understanding of this linkage is lacking. Most data is derived from the studies that examined DSB misrepair rates or CA rates in response to a radiation stressor. In terms of inadequate DNA repair, the rate of DSB misrepair was found to be approximately 10 - 15% at 10 Gy of radiation (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 inadequate repair directly relates to CA frequency. Overall, more studies are required that directly assess this relationship.
Response-response relationshipStudies directly examining the response-response relationship between inadequate repair and CA frequency are lacking. One study examined both DNA repair and CA frequency in cells exposed to DNA-PK inhibitor wortmannin. There was a negative, approximately linear relationship between DNA repair and increasing wortmannin dose, and a positive, approximately linear relationship between MN frequency and increasing wortmannin dose; this suggests that as adequate DNA repair declines, CA frequency increases (Chernikova et al. 1999). More studies are required, however, that directly assess the quantitative response-response relationship between inadequate DNA repair and CAs.
Time-scaleThe time scale between inadequate DNA repair and the increased frequency of CAs has not been well-established. Most data comes from studies that assess only one of these events in relation to a radiation stressor rather than assessing the timing of the events relative to each other. More studies are thus required that directly assess this relationship.
Known modulating factorsNot identified.
Known Feedforward/Feedback loops influencing this KERNot identified.
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Relationship: 1978: Increase, Mutations leads to Increase, Cell Proliferation
AOPs Referencing Relationship
AOP Name | Adjacency | Weight of Evidence | Quantitative Understanding |
---|---|---|---|
Direct deposition of ionizing energy onto DNA leading to lung cancer | adjacent | High | Low |
Evidence Supporting Applicability of this Relationship
Life Stage | Evidence |
---|---|
All life stages | High |
Sex | Evidence |
---|---|
Unspecific | High |
The domain of applicability pertains to all multicellular organisms, as cell proliferation and death regulate tissue homeostasis (Pucci et al. 2000).
Key Event Relationship Description
Mutations are defined as changes in the DNA sequence, which could occur in the form of deletions, insertions, missense mutations, nonsense mutations or frameshift mutations (Bertram, 2001; Danesi et al., 2003; Lodish, 2000). Elevated mutation frequencies may impact cellular activities by activating or inhibiting essential processes that control the natural course of cell proliferation (Bertram, 2001; Vogelstein and Kinzler, 2004; Lodish, 2000). Increased rates of cellular proliferation may arise due to mutations that activate proto-oncogenes, which results in sustained signaling for cell growth (Bertram, 2001; Vogelstein and Kinzler, 2004; Larsen and Minna, 2011; Lodish, 2000) and due to mutations that inactivate tumour suppressor genes (TSGs), resulting in the removal of cell cycle inhibition and/or decreased cell death signaling (Bertram, 2001; Vogelstein and Kinzler, 2004; Lodish, 2000).
Evidence Supporting this KER
Biological PlausibilityThere is a strong biological plausibility for a relationship between increasing mutation frequencies and increasing cellular proliferation. This relationship is especially evident when examining the molecular biology of carcinogenesis. It is well-known that exposure of cells to a DNA-damaging agent, such as ionizing radiation, may result in damage to the DNA that manifests as genomic instability, including mutations. If enough mutations accumulate in critical genes, cells may begin to proliferate uncontrollably. This, alongside other events, may eventually result in tumourigenesis and cancer (reviewed in Bertram, 2001; Vogelstein and Kinzler, 2004; Panov, 2005; Lodish, 2000). 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).
For a mutation to occur, damaged DNA must be passed on to the next generation (Bertram, 2001). To prevent the propagation of erroneous DNA, there are specific cell cycle checkpoints that must be passed before DNA replication and mitosis can proceed. One of the most important checkpoints for committing to cell proliferation occurs during late G1 (Bertram, 2001; Lodish, 2000). This checkpoint is managed by retinoblastoma protein (RB), transcription factor E2F, and transcription factor p53. In a resting cell, RB is tightly bound to E2F; when growth factor signals are present, proteins are activated that phosphorylate RB, resulting in a conformation change and the release of E2F. This transcription factor then initiates transcription of genes required for DNA synthesis and thus cell proliferation. If there is damage to the DNA, p53 is upregulated and binds to unphosphorylated RB, thereby preventing the dissociation of RB and E2F (Bertram, 2001). This gives the cell enough time to repair the damaged DNA prior to DNA replication, and thus minimizes the propagation of the DNA errors. Existing mutations in the checkpoint genes, however, may compromise this process. For example, if mutations in p53 render it non-functional, damaged DNA will not be stopped at the checkpoint and will continue to be synthesized, despite the damage. Accumulation of mutations in this manner may affect genes that impact cell proliferation rates (Bertram, 2001; Lodish, 2000). There are three categories of genes that, if mutated, may allow for uncontrolled cell proliferation: proto-oncogenes, TSGs, and caretaker/stability genes.
Proto-oncogenes are defined as genes that, when activated, promote cellular proliferation (Bertram, 2001; Lodish, 2000); they have been likened to the gas pedal of the car (Vogelstein and Kinzler, 2004). These genes are particularly dangerous if they are rendered abnormally active by gain-of-function (GOF) mutations; this may result in cellular proliferation being aberrantly activated (Bertram, 2001; Vogelstein and Kinzler,, 2004; Larsen and Minna 2011; Lodish, 2000). Two common examples of mutated proto-oncogenes that contribute to increased cell proliferation rates are EGFR and KRAS. The EGFR gene encodes the epidermal growth factor receptor (EGFR), a trans-membrane protein with tyrosine kinase activity. Binding of growth factors to EGFRs results in receptor dimerization, autophosphorylation, and propagation of pro-proliferative signals to the nucleus (Danesi et al., 2003; Santos et al., 2010; Larsen and Minna, 2011; NIH, 2018 EGFR). KRAS is responsible for making the KRAS protein, which is a G-protein with GTPase activity that is used in the RAS/MAPK signalling pathway. When a signal that promotes cellular growth is detected, KRAS binds to GTP and activates downstream signalling molecules, thus facilitating signal propagation to the nucleus (Adjei, 2001; Panov, 2005; Jancik et al., 2010; NIH, 2018 KRAS). Mutations that render these receptors constitutively active would thus result in increased rates of cellular proliferation (Sanders and Albitar, 2010).
TSGs, which are analogous to the brakes in a car (Vogelstein and Kinzler, 2004; Lodish, 2000), are genes that negatively regulate cellular growth by preventing proliferation and in some cases, promoting apoptosis (Bertram, 2001; Vogelstein and Kinzler, 2004; Panov, 2005; Sanders and Albitar, 2010; Lodish, 2000). Many of the cell cycle checkpoint proteins and proteins controlling cell death are TSGs (Bertram, 2001; Lodish, 2000). Loss-of function (LOF) mutations that result in the inactivation of these TSGs may thus promote cellular proliferation (Bertram, 2001; Vogelstein and Kinzler, 2004; Lodish, 2000). A common example of a mutated TSG is TP53, which encodes the p53 protein. As mentioned above, p53 is a cell checkpoint protein that delays replication when damaged DNA is present; if damage is severe enough, p53 may also activate an apoptotic pathway (Bertram, 2001; Danesi et al., 2003; Panov, 2005; Larsen and Minna, 2011; Lodish, 2000, NIH 2018c). Inactivating mutations in p53 thus allow for unhindered progression through the cell cycle, resulting in higher cell proliferation rates (Danesi et al., 2003).
Finally, caretaker/stability genes encode for proteins involved in the detection, repair and prevention of DNA damage (Vogelstein and Kinzler 2004; Hanahan and Weinberg 2011). Genes involved in mismatch repair (MMR), nucleotide excision repair (NER) and base-excision repair (BER) pathways are examples of caretaker/stability genes (Vogelstein and Kinzler, 2004). Mutations in these genes may compromise aspects of DNA repair—the detection of damage, the initiation of repair, the repair process itself, or the removal of mutagens that could possibly damage DNA—thus allowing for more mutations to accumulate in the genome than usual (Hanahan and Weinberg, 2011). Although all genes may suffer from increased mutation rates when caretaker/stability genes are improperly functioning, mutations in TSGs and proto-oncogenes are the main contributors to increased cellular proliferation (Vogelstein and Kinzler, 2004). Caretaker/stability genes are similar to TSGs in that disruption of both alleles must occur for the gene function to be compromised (Vogelstein and Kinzler, 2004; Hanahan and Weinberg, 2011).
Empirical EvidenceThere is moderate empirical evidence supporting the relationship between mutations and the cellular proliferation. The evidence presented below is summarized in table 7, here (click link). There are some available reviews that provide evidence for this relationship in the context of carcinogenesis (Welcker 2008, Kim 2018, Iwakuma 2007, Muller 2011), as one of the hallmarks of this disease is high levels of cellular proliferation (Hanahan and Weinberg 2011). Another review article explores the relationship between mutation accumulation and cellular proliferation through discussion of the stem cell division theory of cancer, and how it compares to the somatic mutation theory of cancer (López-lázaro 2018). Overall, however, there is little empirical evidence available supporting dose and incidence concordance, little empirical evidence supporting temporal concordance, and strong empirical evidence supporting essentiality for this KER. Some evidence from human epidemiology association and genetic studies also provides support for this KER.
Dose and Incidence Concordance
There are few studies available that assess the dose and incidence concordance between mutations and cell proliferation. One study providing dose information on this particular relationship analyzed the effect of sequentially adding mutations to mouse lung epithelial cells. Addition of mutations in the form of LT (suppression of p53 and pRB) or Kras(G12V) (an activated oncogene) on their own to lung epithelial cells did not increase tumour volume, but a combination of these genetic manipulations resulted in increasing tumour volume (suggestive of increased cell proliferation) over 40 days. The same results for LT and EGFR(ex19del) genetic manipulations were also achieved. This suggests that addition of multiple mutations increases cell proliferation (Sato et al. 2017). More studies, however, are required to directly assess this particular aspect of the relationship between mutations and cellular proliferation.
Time Concordance
Few studies are available that study the time concordance between mutations and cell proliferation. The timing between these two events is explored in a review that discusses theories of carcinogenesis. The somatic mutation theory of cancer states that accumulation of mutations results in higher rates of cellular proliferation, which eventually leads to cancer. A component of the stem cell division theory of cancer also states that an increased mutation burden may elevate rates of stem cell divisions in late carcinogenesis; however, a high frequency of stem cell division in the initial stages of cancer development is thought to be a key factor that contributes to mutation accumulation (López-lázaro 2018). More research is thus required to definitively determine whether mutations occur prior to increased rates of cellular proliferation.
Essentiality
There is strong evidence for the essentiality component of this KER. Numerous studies indicate that cellular proliferation is increased in biological systems with genetically manipulated TSGs and/or proto-oncogenes. It is important to note that uncontrolled cellular proliferation is a hallmark of human cancers (Hanahan and Weinberg 2011); the Catalogue of Somatic Mutations in Cancer (COSMIC) includes over 136,000 coding mutations in over 500,000 tumour samples (83 major cancer genes and 49 fusion gene pairs) and this number is continually increasing (Forbes et al. 2011). The managers of COSMIC note that key amongst all of these genes is TP53. Several review articles that focussed on genetic manipulations of TP53 demonstrated that mutant or knocked-out p53 increased carcinogenesis across a variety of biological systems (Iwakuma and Lozano 2007; Muller et al. 2011; Kim and Lozano 2018). Furthermore, a number of studies that measured cellular proliferation directly found that both cells and mice lacking p53 had increased rates of cell proliferation (Hundley et al. 1997; Lang et al. 2004; Ventura et al. 2007; Duan et al. 2008; Li and Xiong 2017), in addition to modifications to the cell cycle such that more cells were found in the S- and G2/M phases and less in the G1 phase (Hundley et al. 1997). Some p53 mutations, including 515A, may also result in increased cellular proliferation (Lang et al. 2004). Further underlining the importance of p53 in controlling cellular proliferation, restoration of p53 in a p53-/- mouse model resulted in a significant size reduction in 7 out of 10 tumours, with some tumours disappearing altogether (Ventura et al. 2007).
Manipulations to other genes have also been shown to affect cellular proliferation. A review article centred on the tumour suppressor FBW7, which is a ubiquitin ligase that plays a role in degrading proto-oncogene products and thus controlling cellular proliferation, demonstrated that mutations to FBW7 may contribute to carcinogenesis (Welcker and Clurman 2008). Knock-out of prostate SPOP (an E3 ubiquitin ligase adaptor commonly mutated in primary prostate adenocarcinoma) in Spopfl/fl;PBCre(+) mice resulted in prostates with significantly higher masses, significantly more cellular proliferation, and increased expression of c-MYC protein relative to prostates from Spopfl/fl;PBCre(-) controls with normal prostate SPOP expression. Furthermore, there was a strong inverse correlation between c-MYC activity and SPOP mRNA levels in two independent prostate cancer patient cohorts, suggesting that c-MYC upregulation in the absence of SPOP may be responsible for the increased cellular proliferation (Geng et al. 2017). Similarly, mouse embryonic fibroblasts lacking Cul9, a scaffold protein for assembly of E3 ubiquitin ligases, had an increased cellular proliferation rate and an increased number of cells in the S-phase of the cell cycle relative to wild-type controls. Cul9 mutant cells also showed similar cellular proliferation rates to Cul9-/- cells. In contrast, Arf-/- cells, p53-/- cells, and Cul9-/-p53-/- double knock-out cells had significantly higher cellular proliferation rates relative to the Cul9-/- and Cul9 mutant cells; all of these mutant cells, however, showed increased proliferation relative to wild-type cells (Li and Xiong 2017).
Inhibitor studies further highlight the role of mutations in increasing cellular proliferation. Mouse lung epithelial cells transformed with both Large T-antigen (LT; suppresses TSGs p53 and pRB) and activated oncogene Kras(G12V) or EGFR(ex19del) resulted in increased tumour volumes, which is suggestive of cell proliferation. Increasing concentrations of MEK inhibitor, which blocks the signalling pathway downstream of both Kras and EGFR, caused declines in cell number in the two transformed cell lines and in the parental lung epithelial cells. An EGFR inhibitor, which blocks signalling downstream of EGFR but upstream of Kras, had no effect on the transformed cells with activated Kras, but caused rapid declines in cell proliferation of transformed cells with activated EGFR. Altogether, these inhibitor studies suggest that the activated oncogene has an important role in promoting high rates of cell proliferation (Sato et al. 2017).
Human epidemiology association and genetic studies
Association studies in humans clearly show the correlation between mutations in specific genes and the proliferative status of human tumours. Human lung adenocarcinoma tumours were assessed for mutational status of KRAS, TP53 and STK11, and cellular proliferation levels were measured in the mutant tumours relative to the wild-type tumours. Overall, mutations in TP53 were associated with significantly increased proliferation levels regardless of the mutational status of KRAS. In contrast, mutations in STK11, either alone or in combination with KRAS mutations, were not associated with increased proliferation (Schabath et al. 2016). Assessment of breast cancer tumours demonstrated that those with low BRCA1 expression displayed increased cellular proliferation relative to those with high BRCA1 expression, as measured by nuclear Ki-67 levels (Jarvis et al. 1998).
Uncertainties and InconsistenciesUncertainties in this KER are as follows:
- The location of the mutation will be critical in determining the downstream effects. This can also be modulated by an individual’s susceptibility (Loewe and Hill 2010).
- Although activating mutations in oncogenes such as RAS and MYC may induce abnormally high rates of cellular proliferation, extremely high levels of these proteins may actually lead to the opposite—cells may enter into a state of senescence and cease proliferation (Hanahan and Weinberg 2011).\
- Cellular proliferation may be impacted by circadian cycles, such that disruptions to this natural circadian rhythm may also affect the cell cycle (Shostak 2017).
Quantitative Understanding of the Linkage
Data establishing a quantitative understanding between mutation frequency and cellular proliferation was not identified. More research is required to establish the quantitative relationship between these two events.
Response-response relationshipData establishing a response-response relationship between mutation frequency and cellular proliferation was not identified. More research is required to establish the response-response relationship between these two events.
Time-scaleAlthough the time scale is not well-established for this KER, there are a few studies that have examined how cellular proliferation changes overtime in the presence of mutations. In Cul9-/- mouse embryonic fibroblasts, a higher proliferation rate relative to Cul9+/+ cells was evident by 3 days in culture (Li and Xiong 2017). A similar relationship was observed in mouse embryonic fibroblasts with p53 manipulations. Increased proliferation in p53-/-, p53 515A/+ and p53 515A/515A relative to p53+/- and p53+/+ cells was present by the fourth day in culture (Lang et al. 2004). Examination of population doublings in various cell lines found that Cul9-/- and Cul9 mutant cells had higher population doublings than wild-type cells by approximately passage 7; Arf-/-, p53-/-, and Cul9-/-p53-/- cells, however, displayed even higher rates of population doublings by passage 6 (Li and Xiong 2017). Additionally, tumour growth in mice inoculated with lung epithelial cells engineered with LT (suppresses p53 and pRB) and an activated oncogene (either EGFR or KRAS) was monitored over 40 days post-injection. Relative to mice inoculated with either LT-lung epithelial cells or activated oncogene-lung epithelial cells, mice inoculated cells containing both mutations had detectable tumours by approximately day 10 - 12 post-injection; the volumes of these tumours continued increasing until the end of the experiment (Sato et al. 2017).
There were also differences in the rate of DNA synthesis over time, which could possibly indicate higher rates of cell division. In all cell types examined (p53-/-, p53+/- and p53+/+, p53 515A/+, and p53 515A/515A), DNA synthesis declined over the first 6 days in culture, though the mutant p53 lines always had higher synthesis rates than p53-/-, p53+/- and p53+/+ cells. During culture days 6 - 10, DNA synthesis in the mutant p53 lines drastically increased, while the other p53 lines remained at the same relatively low level of synthesis (Lang et al. 2004).
Known modulating factorsNot identified.
Known Feedforward/Feedback loops influencing this KERNot identified.
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Relationship: 1979: Increase, Chromosomal aberrations leads to Increase, Cell Proliferation
AOPs Referencing Relationship
AOP Name | Adjacency | Weight of Evidence | Quantitative Understanding |
---|---|---|---|
Direct deposition of ionizing energy onto DNA leading to lung cancer | adjacent | Moderate | Low |
Evidence Supporting Applicability of this Relationship
Life Stage | Evidence |
---|---|
All life stages | High |
Sex | Evidence |
---|---|
Unspecific | High |
The domain of applicability pertains to all multicellular organisms, as cell proliferation and death regulate tissue homeostasis (Pucci et al., 2000).
Key Event Relationship Description
CAs are defined as abnormalities in the chromosome structure, often due to losses or gains of chromosome sections or the entire chromosomes itself, or chromosomal rearrangements (van Gent et al., 2001). These aberrant structures can come in a multitude of different forms. Types of CAs include: inversions, insertions, deletions, translocations, dicentric chromosomes (chromosomes that contain two centromeres, often resulting from telomere end fusions (Fenech & Natarajan 2011; Rode et al., 2016), centric ring chromosomes, acentric chromosome fragments, micronuclei (MN; small nucleus-like structures containing entire chromosomes or chromosome fragments (Fenech & Natarajan, 2011; Doherty et al., 2016), nucleoplasmic bridges (NBPs; a corridor of nucleoplasmic material containing chromatin that is attached to both daughter cell nuclei), nuclear buds (NBUDs; small MN-type structures that are still connected to the main nucleus (Fenech & Natarajan, 2011), and copy number variants (CNVs; deletions or duplications of chromosome segments (Russo et al., 2015).
If these CAs affect genes involved in controlling the cell cycle, this may result in increased cellular proliferation. There are three types of genes that, if modified, may result in high rates of proliferation: proto-oncogenes, tumour suppressor genes (TSGs), and caretaker/stability genes (Vogelstein & Kinzler, 2004; Hanahan & Weinberg, 2011). Furthermore, gene fusions that result from CAs have also been implicated in augmenting cellular proliferation (Sanders & Albitar, 2010; Ghazavi et al., 2015; Kang et al., 2016).
Evidence Supporting this KER
Biological PlausibilityThere is a strong biological plausibility for a relationship between CAs and rates of cellular proliferation. This is particularly emphasized in the context of carcinogenesis, as high cellular proliferation is a known hallmark of cancer, and an enabling characteristic of increased proliferation is genomic instability (Hanahan & Weinberg, 2011).Topical reviews are available documenting the contribution of CAs to cellular proliferation and/or cancer development (Mes-Masson & Witte, 1987; Bertram, 2001; Vogelstein & 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 well documented (Thompson et al., 2017; Gronroos, 2018; Targa & Rancati, 2018; Lepage et al., 2019).
Many CAs are thought to be formed through two main mechanisms: inadequate repair of DNA damage, and errors in mitosis. If there is damage to the DNA that the cell is unable to properly repair, the unrepaired lesion may translate into a CAs (Bignold, 2009; Danford, 2012; Schipler & Iliakis, 2013); the type of resulting CA is often influenced by the cell cycle stage when the damage occurred (Danford, 2012; Registre et al., 2016; Vodicka et al., 2018), and the type of erroneous repair (Ferguson & Alt, 2001; Povirk, 2006; Bignold, 2009; Danford, 2012; Schipler & Iliakis, 2013). Errors made during repair may be particularly detrimental if they interrupt or modify critical genes, or if chromosome structures are created that cannot undergo mitosis (Schipler & Iliakis, 2013). Similarly, errors in mitosis that prevent chromosomes from being properly segregated may also lead to CAs. These errors could be due to by improper timing of centrosome separation, the presence of extra centrosomes, inappropriate mitotic spindle assembly and attachment to kinetochores (found on the centromeres), and incorrect sister-chromatid cohesion (Levine & Holland, 2018).
The presence of CAs in cells may be particularly detrimental if they alter the rate of cellular proliferation by affecting genes that control the cell cycle, namely proto-oncogenes, TSGs (Bertram, 2001; Vogelstein & Kinzler, 2004) or caretaker/stability genes (Vogelstein & Kinzler, 2004). Proto-oncogenes are genes that, when activated, promote cellular proliferation. CAs that increase activation of these genes may aberrantly boost cell cycling and therefore increase proliferation (Bertram, 2001; Vogelstein & Kinzler, 2004). Activation of proto-oncogenes have also been implicated in the cancer stem cell theory of carcinogenesis (Vicente-duen et al., 2013). Examples or proto-oncogenes include EGFR and KRAS (Sanders & Albitar, 2010). TSGs refer to genes that actively suppress cell proliferation and, in some cases, promote apoptosis (Bertram, 2001; Vogelstein & Kinzler, 2004; Sanders & Albitar, 2010). If these genes are silenced by CAs, this may remove cell cycle checkpoints, thus allowing for unhindered cellular proliferation and decreased apoptosis (Bertram, 2001; Vogelstein & Kinzler, 2004). Common TSGs are TP53 and RB (Hanahan & Weinberg, 2011). Lastly, caretaker/stability genes are those involved in the prevention and detection of DNA damage, and the instigation and completion of the required DNA repair (Vogelstein & Kinzler, 2004; Hanahan & Weinberg, 2011). If the function of these caretaker/stability genes is affected by CAs, this may result in genome-wide inadequate DNA repair, which in turn may result in genetic damage to TSGs or proto-oncogenes (Vogelstein & Kinzler, 2004). Genes involved in mismatch repair (MMR), nucleotide-excision repair (NER) and base-excision repair (BER) are all examples of caretaker/stability genes (Vogelstein & Kinzler, 2004).
There are also other CAs commonly associated with cancer. In prostate cancer, truncated TSGs such as TP53, PTEN, BRCA1, and BRCA2 are a result of chromosomal rearrangements (Mao et al., 2011). Similarly, chromosomal inversions were found to be responsible for just over half of the RET gene fusions associated with lung adenocarcinoma samples (Mizukami et al., 2014).
Empirical EvidenceThere is moderate empirical evidence supporting the relationship between CAs and the cellular proliferation. The evidence presented below is summarized in table 8, here (click link). There are some available reviews that provide evidence for this relationship in the context of carcinogenesis, as high levels of cellular proliferation is one of the hallmarks of cancer (Hanahan & Weinberg, 2011). Many of these reviews focus especially on the structure and function of specific cancer-associated CAs (Mes-Masson & Witte, 1987; Ghazavi et al., 2015; Kang et al., 2016). Another interesting review discusses transgenic mouse models that have contributed to our understanding of how oncogenes and TSGs promote carcinogenesis in a variety of tissues (Fowlis & Balmain, 1992). Overall, however, there is a lack of empirical evidence available supporting dose and incidence concordance, little empirical evidence supporting temporal concordance, but strong empirical evidence supporting essentiality for this KER.
Dose and Incidence Concordance
Not identified.
Temporal Concordance
There were no studies identified that directly assessed the temporal concordance between CA and increasing rates of cellular proliferation. In a study examining MN frequency and cell proliferation in estrogen-responsive cancer cells treated with estradiol, both MN levels and proliferation rates were higher in estradiol-treated cells relative to controls at 140 and 216 hours post-treatment (Stopper et al., 2003). This suggests that both events are increased at the same time points in response to the estradiol. More work is required, however, to directly assess the temporal concordance between CA frequency and cell proliferation rates.
Essentiality
Much of the evidence for essentiality stems from studies of gene fusions produced by chromosomal translocations and the corresponding impact on cellular proliferation rates. One such gene fusion, JAFZ1-JJAZ1, has been identified in endometrial stromal sarcomas. The role of this relatively unknown translocation was evaluated using knock-down and knock-in experiments. When wild-type JJAZ1 was disabled by siRNA, HEK 293 cells expressing the JAFZ1-JJAZ1 fusion were found to have an increased rate of cellular proliferation (Li et al., 2007). Similarly, the role of the EML4-ALK fusion gene was examined in IL-3 dependent BA/F3 cells. These cells were transfected with a plasmid carrying only CD8, or CD8 in combination with ALK, EML4-ALK, or mutant EML4-ALK (which contained a lysine to methionine mutation in the kinase domain). In all cases, cell proliferation was found to increase linearly over 7 days in the presence of IL-3; in the absence of IL-3, all cells died by day 3 of culture, with the exception, however, of cells carrying EML4-ALK. Only cells with EML4-ALK were able to maintain a positive, linear growth in both the presence and absence of IL-3. Addition of a JAK2 inhibitor to these EML4-ALK cells resulted in a dose-dependent decline in cellular proliferation, such that at a dose of 10 um of inhibitor, cells numbers declined steadily until death at day 5. This is in contrast to the CD8-expressing cells exposed to the same inhibitor doses, in which there was only a very slight decline in cellular proliferation rates (Soda et al. 2007). Both of these studies provide evidence that translocations increase proliferation rates in cells.
In addition to causing gene fusions, translocations may also lead to the production of circular RNA fusion products (f-CircRNA), which can be studied to further understand the link between CAs and cellular proliferation. For example, f-CircPR has been associated with the PML-RARα translocation, f-CircM9 has been associated with the MLL-AF9 translocation, and expressions of f-CircPR or f-CircM9 were both found to increase cell proliferation rates in mouse embryonic fibroblasts. Inhibition of these f-CircRNAs, either through addition of silencing shRNA or by using a mutant non-circularizing f-CircRNA, resulted in decreased rates of cell proliferation (Guarnerio et al., 2016). These results again indicate that there is a relationship between CAs and increased cellular proliferation.
Other experiments provide evidence that CAs can increase cellular proliferation using cancer cells. Using two human Philadelphia chromosome-positive acute lymphoblastic leukemia (Ph+ALL) cell lines (both of which contain the BCR-ABL translocation), cellular proliferation was studied by cell counting and by analyzing levels of phosphorylated ErbB2. ErbB2 is a member of the ERB receptor tyrosine kinase family that is involved in pro-proliferative signalling, and it is known to be expressed in cells from ALL patients. Cell proliferation rates were found to decline in a dose-dependent fashion when treated with either an ErbB family tyrosine kinase inhibitor, or a more specific ErbB1/ErbB2 tyrosine kinase inhibitor. Furthermore, treatment with the ErbB family inhibitor also resulted in significant decreases in phosphorylated ErbB2 (Irwin et al., 2013). In another set of experiments using estrogen receptor-positive human ovarian cancer cells, treatment of cells with estrogen were found to have significantly increased levels of MN and significantly increased proliferation rates relative to vehicle-treated control cells; furthermore, there were more cells in S-phase and fewer in the G2/M phases of the cell cycle relative to controls. These results were specific to estrogen-response cells, as treatment of estrogen receptor-negative human ovarian cancer cells with estrogen did not result in any changes to MN or cell proliferation. Furthermore, addition of an estrogen antagonist to estrogen-responsive cells maintained MN frequencies and cell proliferation at control levels (Stopper et al., 2003).
Human Epidemiology Association and Genetic Studies
Very often, CAs result in gene fusions. A gene fusion occurs when two genes not normally in close proximity to each other are juxtaposed; this may result in altered expression of one or both genes, or an altered gene product (Mitelman, 2005). There are several well-known gene fusions implicated in carcinogenesis that are associated with increased cellular proliferation. One well-characterized gene fusion is the Philadelphia chromosome, also known as the BCR-ABL1 fusion. This gene fusion is formed by a translocation between chromosome 9 and 22, and is commonly found in chronic myelogenous leukemia (CML) (Mes-Masson and Witte 1987; Kang et al. 2016), as well as acute lymphoblastic leukemia (ALL) (Ghazavi et al., 2015). The protein created from BCR-ABL1 has elevated tyrosine kinase activity, and was shown to increase activation of cellular proliferation pathways (Ghazavi et al., 2015; Kang et al., 2016) including JAK2/STAT, PI3K-AKT, and MAPK/ERK (Kang et al., 2016). Another common gene fusion partner is ALK, which is a receptor tyrosine kinase involved in the PI3K-AKT signalling pathway. Very often, ALK gene fusions result in upregulated ALK expression, and a resulting increase in pro-proliferative signalling in the PI3K-AKT pathway. In non-small cell lung cancer, the ALK-EML4 gene fusion is particularly common (Sanders & Albitar, 2010). Similarly, ETV6-RUNX1 is the most common fusion gene in B-cell precursor acute lymphoblastic leukemia (BCP-ALL), and is thought to initiate leukemogenesis (Ghazavi et al., 2015).
Uncertainties and InconsistenciesUncertainties in this KER are as follows:
- A study using peripheral blood lymphocytes isolated from head and neck cancer patients found significantly increased CAs (including chromosome-type aberrations, chromatid-type aberrations, dicentric chromosomes, aneuploidy, MN, NPBs and NBUDs) relative to healthy controls. In the lymphocytes from these same cancer patients, however, the cell proliferation rates were significantly decreased (George et al., 2014).
- Characterization of 20 different ameloblastomas, which are benign tumours associated with the jaw, found low CAs frequencies and low rates of cellular proliferation (Jääskeläinen et al., 2002).
Quantitative Understanding of the Linkage
Quantitative understanding has not been well-established for this KER. There were no studies identified that documented a response-response relationship between CA frequency and cell proliferation rates, and a severe lack of time scale-oriented studies. Overall, more research is required to establish a quantitative understanding of this KER.
Response-response relationshipNot established.
Time-scaleStudies that directly assessed the time scale between CAs and cellular proliferation were not identified. However, 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). More studies are required, however, to formulate a detailed time scale relating these two events.
Known modulating factorsNot established.
Known Feedforward/Feedback loops influencing this KERNot established.
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Relationship: 1980: Increase, Cell Proliferation leads to Increase, lung cancer
AOPs Referencing Relationship
AOP Name | Adjacency | Weight of Evidence | Quantitative Understanding |
---|---|---|---|
Direct deposition of ionizing energy onto DNA leading to lung cancer | adjacent | High | Low |
Evidence Supporting Applicability of this Relationship
Life Stage | Evidence |
---|---|
All life stages | High |
Sex | Evidence |
---|---|
Unspecific | High |
The domain of applicability for this KER is mammals.
Key Event Relationship Description
Cell proliferation is a process that occurs in normal healthy cells, allowing for tissue growth and repair. It is controlled by the cell cycle, which contains specific and highly controlled checkpoints that must be passed before the cell can undergo DNA synthesis and mitosis (Pucci et al., 2000; Bertram, 2001; Eymin & Gazzeri, 2009). In cases where there are cells that contain severely damaged DNA or that are unneeded, regulatory mechanisms may arrest pro-proliferative signals and instead direct the cell cycle towards apoptosis (programmed cell death) (Portt et al., 2011). Proliferation may also be halted if the protective telomeres capping the ends of chromosomes become too short to support DNA replication; this causes cells to either enter into a state of replicative senescence (Bertram, 2001; Panov, 2005; Hanahan & Weinberg, 2011) or to undergo apoptosis (Hanahan & Weinberg, 2011). The cell cycle thus plays an important role in balancing cell proliferation with cell death to maintain homeostasis (Pucci et al., 2000; Bertram, 2001; Panov, 2005; Portt et al., 2011).
Dysregulation of the cell cycle may lead to abnormally high rates of cellular proliferation. This may occur through upregulation of pro-proliferative signalling, downregulation of anti-proliferative signaling (including alterations to proteins controlling cell cycle checkpoints), increasing resistance to pro-apoptotic signalling, and evasion of replicative senescence (Bertram, 2001; Panov, 2005; Hanahan & Weinberg, 2011). As these pro-proliferative events accumulate and cellular proliferation rates increase, cells may become increasingly tumourigenic. High rates of cellular proliferation may thus lead to the development of cancer; if these processes occur in the lung specifically, the end result may be lung cancer (Panov, 2005; Eymin & Gazzeri, 2009; Sanders & Albitar, 2010; Larsen & Minna, 2011).
Evidence Supporting this KER
Biological PlausibilityThere is a strong biological plausibility for the relationship between cell proliferation and lung cancer. This is heavily supported by the multitude of research examining the general mechanistic control of cell proliferation, and the ways in which dysregulation of cell proliferation promotes the transformation of normal cells to carcinogenic ones (Pucci et al. 2000; Bertram 2001; Panov 2005; Eymin and Gazzeri 2009; Hanahan and Weinberg 2011; Larsen and Minna 2011). In this section, an overview cell proliferation processes will be provided, followed by a discussion of how these control mechanisms are modified to increase cell proliferation rates in carcinogenesis.
Cell proliferation rates are controlled by the cell cycle. The cell cycle consists of five phases: G0, G1, S, G2, and M. G0 is described as the quiescent stage, where cells are inactive in terms of cellular proliferation. The cell exits G0 and enters G1, when growth signals are initiated. G1 is known as a gap phase, where the cell begins to prepare for DNA synthesis. In the S-phase, DNA is replicated and identical sister chromatids are formed in preparation for cell division. Another gap phase, known as G2, follows DNA synthesis; during G2, cell organelles are duplicated as the cell prepares to divide. Mitosis occurs during the M-phase, which culminates in cytokinesis and the production of two genetically-identical daughter cells (Pucci et al. 2000; Eymin and Gazzeri 2009).
Progression through the cell cycle is highly regulated and very tightly controlled, as there is a very specific and time-sensitive order of events that must occur to ensure proper cell division (Pucci et al. 2000; Bertram 2001; Eymin and Gazzeri 2009; Hanahan and Weinberg 2011). As such, there are several key check-points that must be passed before the cell can proceed into the next phase of the cell cycle. One of the most important checkpoints is between G1 and S, known as the restriction point; it is the ‘point of no return’ in terms of DNA synthesis. This check point is controlled by RB (Pucci et al. 2000; Bertram 2001; Eymin and Gazzeri 2009), a protein that decides whether the cell cycle progresses by integrating intra- and extra-cellular signals (Hanahan and Weinberg 2011). In its unphosphorylated state, RB binds tightly to the transcription factor E2F and thus prevents transcription of genes required for DNA synthesis. When growth signals are received by the cell, this activates the transcription of cyclin-D and cyclin-dependent kinase (CDK) 4 and CDK6. Binding of cyclin-D with CDK4 or CDK6 allows activation of the kinase function, which results in the phosphorylation of RB. Phosphorylated RB releases E2F, allowing for the transcription of genes required not only for DNA synthesis, but also for maintaining the phosphorylated state of RB throughout the DNA synthesis process (Pucci et al. 2000; Bertram 2001; Panov 2005; Eymin and Gazzeri 2009).
The protein product of TP53, p53, also plays an important role in controlling the cell cycle. This tumour suppressor protein is responsible for DNA quality control and for monitoring stresses within the cell. If DNA damage is detected (Bertram 2001; Panov 2005; Hanahan and Weinberg 2011; Larsen and Minna 2011) or if cellular supplies (such as nucleotides, oxygen or glucose) are inadequate (Bertram 2001; Hanahan and Weinberg 2011), p53 is upregulated. Even in the presence of growth signals, p53 inhibits RB phosphorylation and prevents activation of E2F (Bertram 2001), thereby halting the cell cycle. This cell cycle arrest provides the DNA repair machinery time to repair the damaged DNA before the process of cell division is resumed. If the damage is too severe, p53 can trigger cell death through the process of apoptosis (Bertram 2001; Hanahan and Weinberg 2011; Larsen and Minna 2011).
Apoptosis is a non-inflammatory process of programmed cell death that is used to remove heavily damaged, defective, or unneeded cells. This process is homeostatically balanced with cell proliferation, thus allowing the organism to adapt to and change with its environment as required (Pucci et al. 2000; Bertram 2001; Panov 2005; Portt et al. 2011). A higher proportion of pro-apoptotic compared to anti-apoptotic factors will trigger a cell to undergo apoptosis (Hanahan and Weinberg 2011; Portt et al. 2011). This programmed cell death can be initiated by an intrinsic pathway mediated by cytochrome C release from the mitochondria, or by an extrinsic pathway mediated by death receptors on the plasma membrane. After initiation of apoptosis, a sequential cascade of caspase activations eventually leads to the characteristic hallmarks of apoptosis, including DNA and nuclear fragmentation, and break-down of cellular components (Panov 2005; Hanahan and Weinberg 2011; Portt et al. 2011). Key regulators of apoptosis include p53 and Bcl-2, while the main executors are the caspases (Panov 2005; Hanahan and Weinberg 2011).
In addition to cell cycle checkpoints and apoptosis, cell proliferation is also limited by telomere length. Telomeres are six-nucleotide repeats found on the ends of chromosomes that protect coding DNA from damage (Bertram 2001; Ferguson and Alt 2001; Panov 2005; Vodicka et al. 2018). After each round of replication, however, telomeres become progressively shorter due to the unidirectionality (5’-3’) of the replication machinery (Bertram 2001; Panov 2005). Eventually, the telomeres become too short to support cellular proliferation (Bertram 2001; Ferguson and Alt 2001; Hanahan and Weinberg 2011; Vodicka et al. 2018). In this case, DNA repair machinery may fuse the short telomeres (mistaken for damaged DNA) to form dicentric chromosomes (Ferguson and Alt 2001; Vodicka et al. 2018). The short telomeres may also trigger the cell to enter into a state of replicative senescence in which cell division is no longer supported (Bertram 2001; Hanahan and Weinberg 2011), or to undergo apoptosis (Hanahan and Weinberg 2011). In contrast, germ cells and stem cells are able to infinitely divide; this is due to their expression of the enzyme telomerase, which maintains telomere length (Bertram 2001). Most somatic cells, however, do not express telomerase and are thus limited in their replicative potential (Bertram 2001; Panov 2005; Hanahan and Weinberg 2011).
All of these processes play a role in controlling the rate of cellular proliferation in cells. In general, cellular proliferation is balanced with cell death to maintain homeostasis within an organism. If any of the above processes become aberrantly regulated such that cells begin to proliferate at excessively high rates, this may result in cancer. High rates of proliferation are considered one of the most dominant characteristics of cancer cells (Bertram 2001; Eymin and Gazzeri 2009; Hanahan and Weinberg 2011). In fact, several of the identified hallmarks of cancer are processes that relate to increases in proliferation. These hallmarks, as stated by Hanahan 2011, include: sustained proliferative signalling, evading growth suppressors, resisting cell death, and enabling replicative immortality (Hanahan and Weinberg 2011).
Sustained proliferative signalling allows cancer cells to carry out pro-proliferative activities even in the absence of external growth signals (Eymin and Gazzeri 2009; Hanahan and Weinberg 2011). This may be achieved by abnormally activated proto-oncogenes which stimulate cell proliferation and thus are able to increase the level of pro-proliferative signalling within the cell (Bertram 2001; Vogelstein and Kinzler 2004; Hanahan and Weinberg 2011; Larsen and Minna 2011). The mechanisms by which proto-oncogenes enhance proliferative signaling include: increased expression of growth factor receptors on the cell surface, increased production of ligands for growth factor receptors, constitutive activation of downstream pro-proliferative signalling molecules (Bertram 2001; Hanahan and Weinberg 2011), or structurally modified growth factor receptors that activate downstream pathways even in the absence of ligand binding (Hanahan and Weinberg 2011). In lung cancer specifically, several commonly activated proto-oncogenes include EGFR, ERBB2, MYC, KRAS, MET, CCND1, CDK4 and BCL2 (Larsen and Minna 2011).
As cells transition from normal to tumourigenic, cellular proliferation can be further enhanced by evading growth suppressors and resisting cell death (Eymin and Gazzeri 2009; Hanahan and Weinberg 2011). This is often achieved by genetic alterations that inactivate tumour suppressor genes (TSGs). TSGs encode proteins, often involved in cell cycle checkpoints, which limit cell proliferation and promote apoptosis (Harris 1996; Bertram 2001; Vogelstein and Kinzler 2004). Two of the most common TSGs inactivated in cancer include RB1 (Vogelstein and Kinzler 2004; Hanahan and Weinberg 2011) and TP53 (Harris 1996; Vogelstein and Kinzler 2004; Hanahan and Weinberg 2011). Inactivation of RB1 (and therefore decreased levels of RB) allows for uncontrolled proliferation by removing the restriction checkpoint in the cell cycle, thus allowing cells to easily pass from G1 to S (Bertram 2001; Hanahan and Weinberg 2011; Larsen and Minna 2011). In a similar fashion, inactivation of TP53 (and therefore decreased p53) removes DNA quality control, meaning that cells with damaged DNA are able to continue with cell proliferation unhindered (Bertram 2001; Panov 2005; Hanahan and Weinberg 2011; Larsen and Minna 2011). Loss of the pro-apoptotic p53 as well as downregulation of other pro-apoptotic factors, coupled with the upregulation of anti-apoptotic factors such as Bcl-2, further promotes cell proliferation by increasing the cell’s resistance to apoptotic pathways (Hanahan and Weinberg 2011; Portt et al. 2011). In terms of lung cancer, TSGs that are commonly inactivated include not only TP53 and RB1, but also STK11, CDKN2A, FHIT, RASSF1A, and PTEN (Larsen and Minna 2011).
Lastly, cancer cells often accumulate genetic abnormalities that allow them to overcome replicative senescence. These immortalized cancer cells are thus capable of dividing an infinite number of times. Immortalization is most often achieved in tumour cells through activation of telomerase. Expression of telomerase allows telomeres to be regenerated upon DNA replication, which prevents cells from undergoing replicative senescence or apoptosis from critically shortened telomeres (Bertram 2001; Panov 2005; Hanahan and Weinberg 2011; Larsen and Minna 2011). In lung cancer specifically, telomerase has been found to be 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).
Empirical EvidenceThere is moderate empirical evidence supporting the relationship between increased cellular proliferation and lung cancer. The evidence presented below is summarized in table 9, here (click link). There are several lung cancer-specific reviews available that discuss the various molecular mechanisms by which abnormal cell proliferation occurs in cells, and how this leads to carcinogenesis of the lungs (Panov 2005; Eymin and Gazzeri 2009; Sanders and Albitar 2010; Larsen and Minna 2011). Furthermore, one of the hallmarks of cancer is high levels of cellular proliferation (Hanahan and Weinberg 2011), thus aberrant cell proliferation and lung tumourigenesis will inevitably be linked. Overall, however, there is a weak empirical evidence available supporting dose, incidence and temporal concordance, and strong empirical evidence supporting essentiality for this KER.
Dose and Incidence Concordance
There are not limited studies available that assess the dose/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). More studies, however, are required to further explore the dose/incidence concordance between these two events.
Temporal Concordance
Studies examining temporal concordance between increased cellular proliferation rates and lung carcinogenesis are also lacking. 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 (Pal et al. 2013; Warin et al. 2014; Sun et al. 2016; Tu et al. 2018). This tumour growth necessarily suggests a high rate of cell proliferation. Accordingly, 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). Although these studies do suggest that increased rates of proliferation occur prior to and during tumour development, more research is required to more firmly establish temporal concordance between these two events.
Essentiality
Much of the evidence for essentiality is derived from studies where anti-tumourigenic compounds were applied to in vitro and in vivo NSCLC models. Application of suspected anti-cancer compound cleistanthoside A tetraacetate (CAT) to lung cancer cells resulted in changes to the cell cycle such that there were fewer cells involved in proliferative cell cycle phases; there were also corresponding declines in levels of the G1/S checkpoint proteins cyclin-D1, CDK4 and CDK6 (Wanitchakool et al. 2012). Likewise, treatment of two NSCLC cell lines with histone demethylase inhibitor pargyline resulted in significant decreases in cell proliferation rates (Lv et al. 2012). In a similar fashion, treatment of EGFR- and VEGFR2-over expressing NSCLC cells with EGFR/VEGFR2 inhibitor delphinidin resulted in significant decreases in cell proliferation markers in vitro. In vivo delphinidin treatment of xenograft nude mice inoculated with these NSCLC cells accordingly led to decreased cell proliferation and dose-dependent decreases in tumour volume (Pal et al. 2013). Corresponding in vitro and in vivo results were found in NSCLC models treated with taurine, an amino acid thought to be protective against tumourigenesis. Not only were in vitro cell proliferation rates decreased in taurine-treated NSCLC cells, but anti-apoptotic Bcl-2 levels were decreased and pro-apoptotic PUMA and Bax levels were increased. When xenograft nude mice inoculated with tumour-promoting NSCLC cells were treated with either taurine, exogenous PUMA, or a combination of taurine and PUMA, there were significant in vivo declines in cell proliferation, tumour volume and tumour weight; the largest declines, however, were found in mice treated with both taurine and exogenous PUMA (Tu et al. 2018). In another experiment involving NSCLC xenograft nude mice, treatment of mice with 6-shogaol (6S; a component of dry ginger) or its metabolite cysteine-conjugated 6S (M2) resulted in decreases in cell proliferation, tumour volumes and tumour weights (Warin et al. 2014). Other experiments were performed using healthy mice that ingested carcinogens NNK and BaP over 4 weeks, and were then treated orally with suggested tumour suppressor indole-3-carbinol (I3C). Regardless of whether I3C treatment started halfway through the carcinogenic treatment period (10 - 112 µmol/g diet) or after completion of the 4 week carcinogenic paradigm (112 µmol/g diet), there were significant decreases in cell proliferation and in the number of tumours per mouse (Kassie et al. 2008).
Other evidence for the association between cell proliferation and carcinogenesis comes from studies involving genetic manipulations. NSCLC cells transfected with a vector to silence abnormally expressed histone demethylase LSD1 resulted in decreased cell proliferation in vitro. In contrast, transfection of these cells with a vector to overexpress LSD1 led to increased in vitro proliferation rates (Lv et al. 2012). NSCLC cells and tumours have also been shown to have increased levels of ZIC5, which belongs to a family of transcription factors thought to play a role in regulation of the cell cycle during periods of high proliferation. Knock-down of ZIC5 by transfecting NSCLC cells with ZIC5-silencing RNA resulted in decreased cell proliferation and decreased clone formation in vitro. In xenograft nude mice inoculated with NSCLC cells carrying the ZIC5-silencing RNA, there were also in vivo declines in tumour growth and in tumour cell proliferation relative to mice inoculated with non-manipulated NSCLC cells (Sun et al. 2016).
Uncertainties and InconsistenciesUncertainties in this KER are as follows:
- Inconsistencies in results were observed in studies using radiation as a stressor.The dose threshold for the onset of proliferation and lung cancer induction varies with radiation quality, individual cell sensitivity, and confounding factors (Taylor 2013). The latter two are also be true for chemical carcinogens (Malhotra et al., 2016).
Quantitative Understanding of the Linkage
Quantitative understanding has not been well-established for this KER. In terms of human non-carcinogenic cells, 50 - 70 cell divisions are thought to be possible before telomeres become too short to support further cell division (Panov 2005); this cell division number would presumably increase in carcinogenic cells. There were no studies, however, that documented a response-response relationship between cell proliferation rates and lung carcinogenesis, and a severe lack of time scale-oriented studies. Overall, more research is required to establish a quantitative understanding of this KER.
Response-response relationshipNot identified.
Time-scaleStudies that directly assessed the time scale between increased cellular proliferation and lung carcinogenesis are lacking. There are some studies, however, that provide 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. Delphinidin caused changes in the expression levels of EGFR, pEGFR, VEGFR2 and pVEGFR2 within the first 3 hours (Pal et al. 2013), and pargyline decreased LSD1 levels within 6 hours of treatment (Lv et al. 2012). Delphinidin-induced changes to the expression of PI3K/p110, PI3K/p85, pAKT, pERK1/2, pJNK1/2, pp38, PCNA and cyclin-D1 were documented within 48 hours of treatment (Pal et al. 2013). Similarly, CAT application led to significant declines in cell cycle checkpoint proteins cyclin-D1, CDK4 and CDK6 by 36 hours post-treatment (Wanitchakool et al. 2012). Additionally, changes to the cell cycle were evident within 24 - 48 hours of CAT treatment (Wanitchakool et al. 2012), and within 48 hours of ZIC5 knockdown with silencing RNA (Sun et al. 2016). ZIC5 knockdown also caused declines in cell proliferation by 96 hours post-transfection, and declines in clone formation after 2 weeks (Sun et al. 2016). Overall, these in vitro studies demonstrate that modifications to both cell cycle regulation and cell proliferation rates in cancer cells can be affected within hours to days of a perturbance.
In vivo studies also provide information regarding the timescale between cell proliferation and tumourigenesis. Tumours in xenograft nude mice were detected within two weeks of NSCLC-cell inoculation (Pal et al. 2013; Warin et al. 2014; Sun et al. 2016; Tu et al. 2018), with one study showing tumour detection as early as 1 week post-inoculation (Warin et al. 2014).Tumours continued to grow over the experimental period until time of harvest (Pal et al. 2013; Warin et al. 2014; Sun et al. 2016; Tu et al. 2018). Differences in tumour growth rates between treated and untreated mice were evident within 13 -16 days of delphinidin treatment (Pal et al. 2013), 3 weeks of ZIC5 knock-down (Sun et al. 2016), and by 27 days of either taurine, PUMA or taurine and PUMA treatment (Tu et al. 2018). At the time of xenograft nude mouse tumour harvest (which varied between 22 days and 27 weeks), there were significant differences in markers of cell proliferation and tumour size or number in mice exposed to anti-cancer compounds and their respective controls (Kassie et al. 2008; Pal et al. 2013; Warin et al. 2014; Sun et al. 2016; Tu et al. 2018). In non-xenograft mice exposed to a high levels of tobacco smoke, increased markers of cell proliferation and the incidence of airway squamous metaplasia was evident upon sacrifice after 14 weeks of constant tobacco smoke exposure (Zhong et al. 2005).
Known modulating factorsIngestible materials, such as wine and vitamin E, may be capable of modulating cell proliferation and thus tumourigenesis. Treatment of NSCLC cells with wine at low doses was found to inhibit proliferation of the cells, suggesting that wine may have an anti-tumourigenic effect (Barron et al. 2014). Vitamin E exposure has also been associated with anti-tumourigenesis by inducing apoptosis in proliferating endothelial cells and thus decreasing angiogenesis. This is significant, as angiogenesis is required to support tumour development (Dong et al. 2007).
Known Feedforward/Feedback loops influencing this KERUsually, non-cancerous cells are stimulated by growth factors originating from other cell types. For cancer cell lines, cell proliferation rates can be increased by autocrine signalling. Some cancer cells acquire the ability to produce both the growth factors and the required receptors, thus allowing the cell to respond to its own growth signals, and further stimulate more cell proliferation (Hanahan and Weinberg 2011).
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List of Non Adjacent Key Event Relationships
Relationship: 1981: Energy Deposition leads to Increase, Mutations
AOPs Referencing Relationship
AOP Name | Adjacency | Weight of Evidence | Quantitative Understanding |
---|---|---|---|
Direct deposition of ionizing energy onto DNA leading to lung cancer | non-adjacent | High | High |
Evidence Supporting Applicability of this Relationship
Life Stage | Evidence |
---|---|
All life stages | High |
Sex | Evidence |
---|---|
Unspecific | High |
The domain of applicability applies to single-celled organisms such as bacteria and yeast, eukaryotic cells, and multi-cellular organisms such as fish, mice and humans.
Key Event Relationship Description
Energy can be deposited on biomolecules from various forms of radiation. Radiation with high linear energy transfer (LET) tends to produce more complex, dense structural damage than low LET radiation; both, however, can lead to detrimental damage within a cell (Hada & Georgakilas, 2008; Okayasu, 2012; Lorat et al., 2015; Nikitaki et al., 2016). The DNA is particularly susceptible to damage which can be in the form of mutations. Mutations may occur in germ cells or somatic cells; mutations in germ stem and progenitor cells are often of the greatest concern, as they may persist and be propagated to offspring. Regardless of the cell type, there are several different categories of mutations including: missense, nonsense, insertion, deletion, duplication, and frame-shift mutations. These mutations can present with different downstream effects which are not predictable but can potentially initiate a path to carcinogenesis.
Evidence Supporting this KER
Biological PlausibilityThe biological rationale for linking direct deposition of energy by ionizing radiation to mutation induction is strong. The structural and functional relationships in this KER contribute sufficiently to the overall biological plausibility.
There are numerous studies that demonstrate, using various model systems, an increase in mutation frequency in response to radiation exposure (Russell et al., 1957; Winegar et al., 1994; Gossen et al., 1995; Suzuki & Hei 1996; Albertini et al., 1997; Dubrova et al., 1998; Dubrova, Plumb, et al., 2000; Canova et al., 2002; Dubrova et al., 2002; Dubrova & Plumb, 2002; Masumura et al., 2002; Somers et al., 2004; Burr et al., 2007; Ali et al., 2012; Bolsunovsky et al., 2016; Mcmahon et al., 2016; Matuo et al., 2018; Nagashima et al., 2018). The process of mutation induction by radiation is initiated when cells are exposed to ionizing radiation. These high-energy waves or particles interact with the genetic material in the nucleus, damaging the DNA and triggering a cascade of signalling events and activities aimed at repairing the damage. This process, however, may result in not only the repair of the DNA, but also the formation of mutations (Sankaranarayanan & Nikjoo, 2015). Of note, radiation is not likely to impact only one gene; more often than not, the random nature of energy deposition by radiation results in mutations to many genes and genomic sites clustered in the same area (Sankaranarayanan & Nikjoo, 2015; Adewoye et al., 2015). Many of the radiation-induced mutations have been documented as deletions (Gossen et al., 1995; Behjati et al., 2016), often of differing sizes in a number of different genes (Sankaranarayanan & Nikjoo, 2015). The mechanism for radiation-induced mutations is thought to be similar to the process for spontaneously-occurring mutations, as the structure of radiation-induced mutations examined at expanded simple tandem repeat (ESTR) loci was not found to differ from the structure of spontaneous mutations (Dubrova, 2005). Moreover, exposure to radiation may produce specific mutational signatures. Two ionizing radiation-specific mutational signatures were found when 12 radiation-induced secondary tumours across 4 different tumour types underwent whole-genome sequencing and bioinformatics processing. In particular, these radiation-exposed tumours were significantly enriched in small deletions and balanced inversions. These results were validated when the same mutational signatures were observed in radiation-exposed but not radiation-naïve prostate tumours from a previously-published dataset (Behjati et al., 2016). Similarly, another study examining mutations present in radiation-induced tumours of Nf1 heterozygous and wild-type mice revealed three distinctive mutational signatures. Interestingly, these signatures were found in all of the tumours regardless of its histology or of the animal’s genotype. Moreover, these signatures were still present after removal of the 33 most mutated samples from the analysis, after analysis of only the non-synonymous substitutions, and after analysis of only the synonymous substitutions (though the third mutational signature could not be extracted in this last analysis group) (Sherborne et al. 2015). There were also common cellular pathways that were found to be frequently mutated in the tumours of these mice. In sarcomas from mice of both genetic backgrounds (Nf1 heterozygous and wild-type), the top two pathways harbouring mutations were those influencing cellular assembly and organization, and those involved in cellular function and maintenance. Additionally, Ras pathways were commonly mutated in tumours from both genetic backgrounds. Specific to wild-type sarcomas, mutations were also found in cell cycle and cell signalling pathways (Sherborne et al., 2015). Supporting the finding that different genetic backgrounds in mice do not affect mutational signatures in tumours (Sherborne et al., 2015), there also does not appear to be strain-specific differences in ESTR mutational frequencies in response to radiation. One study examined five different strains of male mice that were irradiated and mated to unirradiated females at least 4 weeks post-irradiation. Although there was a difference in doubling doses between strains, the ESTR mutations themselves were not significantly different. Furthermore, there were no significant differences found between strains in terms of germline mutation induction (Dubrova, 2005).
Germline mutations have been further interrogated in studies examining the effects of radiation exposure on germ cells. There is evidence from mouse studies suggesting that the germ cells of radiation-exposed males have elevated ESTR mutations and that the offspring of these irradiated males inherit more ESTR mutations as a result of the germline mutations (Dubrova et al., 1998; Dubrova, Bersimbaev, et al., 2000; Dubrova & Plumb, 2002; Somers et al., 2004; Barber et al., 2009; Ali et al., 2012; T.E. Wilson et al., 2015). This was reviewed by Somers et al. (2006). Interestingly, in utero irradiation of embryos at day 12 resulted in increased ESTR mutations across several tissue types in males and females; however, only the offspring of the irradiated males showed an elevated ESTR mutation rate (Barber et al., 2009). On a genome-wide scale, the offspring of irradiated males were found to have significantly more clustered single nucleotide variants (SNVs) and insertion/deletion events compared to offspring from unirradiated fathers (Adewoye et al., 2015).
Human studies have also shown correlations in radiation exposure and increased germline mutations. This relationship was assessed in families exposed accidently to high doses of ionizing radiation after the Chernobyl accident in Ukraine, and in families living in close proximity to the Semipalatinsk nuclear test site in Kazakhstan. In both cases, germline mutations were evaluated using eight hypervariable minisatellite probes. In the Chernobyl study, the paternal mutation rate in the exposed group was significantly increased by 1.6-fold relative to an unexposed control group; there was, however, no significant difference in the maternal germline mutation rates between the exposed group and the unexposed control group (Dubrova et al., 2002C). In the Semipalatinsk study, analysis of families living in the affected region over three generations found that germline mutations in the first and second generation were significantly increased relative to unexposed families living in a low-radiation area. Overall, the germline mutation rate in the families exposed to radiation from this test site was doubled (Dubrova, Bersimbaev, et al., 2000).
Empirical EvidenceOverall, there is strong supporting evidence that direct deposition of energy increases the frequency of mutations. The evidence presented below is summarized in table 2, here (click link). In general, exposure to ionizing radiation has been documented to elevate mutation frequencies in a number of different studies spanning different models and cell types (Russell et al., 1957; Winegar et al., 1994; Gossen et al., 1995; Suzuki & Hei, 1996; Albertini et al., 1997; Canova et al., 2002; Dubrova & Plumb, 2002; Masumura et al., 2002; Bolsunovsky et al., 2016; Mcmahon et al., 2016; Matuo et al., 2018; Nagashima et al., 2018) . Furthermore, several reviews outline evidence of the relationship specifically between radon gas exposure and mutation frequency (Jostes, 1996; Robertson et al., 2013; ICRP, 2005).
Figure 1: Plot of studies (y-axis) against equivalent dose (Sv) used to determine the empircal link between direct deposition of energy and increased cell mutation rates. The z-axis denotes the equivalent dose rate used in each study. The y-axis is ordered from low LET to high LET from top to bottom.
Figure 2: Plot of studies (y-axis) against time scales used to determine the empircal link between direct deposition of energy and and increased cell mutation rates. The z-axis denotes the equivalent dose rate used in each study. The y-axis is ordered from low LET to high LET from top to bottom.
Dose and Incidence Concordance
It is clear that increasing doses of ionizing radiation is concordant with increased incidence of mutations (see table under Quantitative Understanding of the Linkage). Extensive evidence from in vitro studies using human cells (Suzuki & Hei 1996; Canova et al., 2002), animal cells (Canova et al., 2002; Mcmahon et al., 2016; Nagashima et al., 2018), yeast cells (Matuo et al., 2018), and bacteria (Bolsunovsky et al., 2016) demonstrates this concordance. In vivo studies using mice have also found a dose-dependent increase in mutations across several different types of radiation (Russell et al., 1957; Dubrova & Plumb 2002).
This relationship between radiation exposure and mutation incidence is impacted by several different factors. Higher LET radiation, such as high LET carbon ions and neutrons, were found to induce more mutations in comparison to radiation of a lower LET, including low LET carbon ions, gamma-rays and X-rays (Dubrova & Plumb, 2002; Matuo et al., 2018). Similarly, more mutations were present in the gametes of mice exposed to acute X-rays compared to those exposed to chronic gamma-rays (Russell et al., 1957). The tissue being irradiated may also have a role in determining mutant frequency, as whole body irradiation of mice led to a significant increase in mutations (mostly deletions) of the spleen, liver, lung and kidneys (Gossen et al., 1995; Masumura et al., 2002), but not the testis (Masumura et al., 2002). Furthermore, the specific kind of mutation may be dependent on the type of radiation. In one study, irradiation of the liver with carbon ions resulted in a significant increase in deletion mutations, while irradiation with X-rays or gamma-rays resulted in a significant increase in point mutations (Masumura et al., 2002).
Temporal Concordance
Temporal concordance is well established. As described above, energy deposition happens immediately upon radiation exposure, with an increased incidence of mutations documented days or weeks after irradiation (Russell et al., 1957; Winegar et al., 1994; Gossen et al., 1995; Albertini et al., 1997; Canova et al., 2002; Dubrova & Plumb, 2002; Masumura et al., 2002; Matuo et al., 2018; Nagashima et al., 2018).
Essentiality
Not identified.
Uncertainties and InconsistenciesUncertainties and inconsistencies in this KER are as follows:
- In a review paper describing the role ionizing radiation plays in elevating mutation frequency in the germline and therefore genetic risk, Sankaranarayanan & Nikjoo (2015) stated that most radiation-induced mutations tended to be deletions. In contrast, an examination of ESTR loci mutations in offspring and their irradiated fathers found that the ESTR mutations tended to be gains more often than losses (Dubrova ,2005). This may, however, highlight a characteristic specific to ESTR mutations rather than mutations in general.
- In a study examining the long-term of effects of in utero radiation exposure, males irradiated at embryonic day 12 showed significant increases in both somatic and germline ESTR mutations as adults, and produced offspring with significantly elevated ESTR mutations in their sperm (Barber et al., 2009). In contrast, male mice exposed to radiation during their neonatal days (6 - 8 days old) or pubertal stage (18 - 25 days) did not have increased mutations in adult spermatozoa, as mutant frequencies that were present in spermatogenesis stages immediately after radiation returned to normal levels later in the spermatogenesis process (Xu et al., 2008).
- Factors such as dose, dose-rate, tissue type and radiation quality can influence mutation rate induction (Hooker et al., 2004; Rydberg et al., 2005; Day et al., 2007; Okudaira et al., 2010; Brooks et al., 2016).
Quantitative Understanding of the Linkage
Below are representative examples of the mutation frequency rates across different studies. Overall, a quantitative understanding of this linkage suggests that mutation rates can be predicted and are dependent on the type and dose of radiation exposure.
Reference | Summary |
Matuo et al., 2018 | Study of impact of high and low LET radiation (high LET: carbon ions, 25 keV/um, low LET: carbon ions, 13 keV/um) in the dose range of 0 - 200 Gy incident on Saccharomyces cerevisiae (yeast cells). Found a 24-fold increase over baseline of mutations from high LET radiation and an 11-fold increase for low LET radiation. |
Nagashima et al, 2018 | Study of X-rays incident on GM06318-10 hamster cells in the dose range of 0-1 Gy. Found a calculated mutation rate of 19.0 ± 6.1 mutants per 104 survivors per Gy. |
Albertini et al., 1997 | Study of T-lyphocytes from human peripheral blood exposed to low LET gamma-rays and high LET radon gas. Doses in the range 0.5 - 5 Gy (gamma-rays) and 0 - 1 Gy (radon gas). The calculated mutation rate was as follows: gamma-rays (0-2 Gy): 7.0x10-6 mutants / Gy, gamma-rays (2-4 Gy): 54.0x10-6 mutants / Gy, radon gas (0-1 Gy): 63.0x10-6 mutants / Gy. |
Dubrova and Plumb 2002 | Study of paternal ESTR mutation rates in CBA/H mice. Mice exposed to acute low LET X-rays, chronic low LET gamma-rays and chronic high LET neutrons. X-rays in the 0 - 1 Gy dose (D) range, gamma-rays: 0 - 1 Gy & neutrons: 0 - 0.5 Gy. Calculated mutation rate (y) (of the form y = a + bD) as follows: X-rays (a, b := 0.111, 0.338), gamma-rays (a,b := 0.110, 0.373 ± 0.082), neutrons (a, b := 0.136, 1.135 ± 0.202). |
McMahon et al., 2016 | Study across various studies of the HPRT gene in chinese hamster cells exposed to doses in the range of 1 - 6 Gy. Found 0.2 mutations in HPRT gene per 104 cells and 0.1 point mutations per 104 cells (1 Gy). At higher doses (6 Gy) observed 1.5 mutations per 104 cells and 0.4 point mutations per 104 cells. |
Response-response relationship
There is evidence of a positive response-response relationship between the radiation dose and the frequency of mutations (Russell et al., 1957; Suzuki & Hei, 1996; Albertini et al., 1997; Canova et al., 2002; Dubrova & Plumb, 2002; J.W. Wilson et al., 2015; Bolsunovsky et al., 2016; Mcmahon et al., 2016; Nagashima et al., 2018) . Most studies found that the response-response relationship was linear (Russell et al., 1957; Albertini et al., 1997; Canova et al., 2002; Dubrova et al., 2002; Nagashima et al., 2018). There were however, two exceptions. In a study using normal human bronchial epithelial cells irradiated with 1 - 6 Gy of gamma-rays, the relationship between the number of induced HPRT mutants and the radiation dose was described as non-linear (Suzuki & Hei, 1996) Similarly, in a study examining HPRT mutations in isolated peripheral blood T-lymphocytes irradiated with low LET gamma-rays, the slope of the line from 0 - 2 Gy differed from the slope at the 2 - 4 Gy interval; thus this was described as two different linear relationships or an overall linear-quadratic relationship (Albertini et al., 1997).
Time-scaleThe time scale relationship between radiation exposure and the frequency of mutations is not well defined. Most studies look for manifestation of mutations days or weeks after irradiation, making it particularly difficult to pinpoint exactly when the mutations first occur. Analysis of various organs from mice after in vivo radiation found that mutations were present at 2 days (Winegar et al., 1994; Masumura et al., 2002) and 3 days (Gossen et al., 1995)(Gossen, 1995) post-exposure. Mutations were still present at 7 days and 14 days (Winegar et al., 1994), and 10 days and 21 days (Gossen, 1995) following irradiation. One study documented a doubling in the number of mutations from 7 to 14 days (Winegar et al., 1994) while the other reported a two-fold decrease from 3 to 21 days (Gossen et al., 1995).
An attempt to better define this time scale relationship was made in a study using Salmonella typhimurium bacteria. This study was designed to determine how mutation frequency was affected by constant cesium-137 gamma-ray radiation exposure at defined dose rates of 67.8 uGy/hour, 3.2 uGy/hour, and 0.6 uGy/hour; these mutation frequencies were compared to a control group exposed to background radiation levels (0.09 uGy/hour). Mutation frequencies were evaluated after 24, 48, 72 and 96 hours of constant exposure. At 24 hours, the 67.8 uGy/hour, 3.2 uGy/hour and 0.6 uGy/hour mutant frequencies were significantly higher than background exposure controls. Interestingly, however, these levels were decreased at 48 hours and continued to decline gradually towards control frequencies over time. This decline was proposed to be due to an elimination of the highly mutated cells, leaving behind an increasing number of cells that had adapted to the radiation and were thus more equipped for survival (Bolsunovsky et al., 2016). Other studies are required to build a more complete understanding of this timeline.
Known modulating factorsThere are several factors that have been documented to affect the relationship between direct deposition of energy and increased mutation frequency. The sex, age, and use of adaptive dosing have been demonstrated to affect the radiation-induced mutations present in offspring. In contrast to male mice, female mice that were irradiated in utero (Barber et al., 2009) or as adults (Ali et al., 2012)(Ali, 2012) did not produce offspring with increased ESTR mutations. This suggests that radiation-induced mutations are only heritable through the paternal line. As such, the age of the father may affect the mutant frequency in the offspring, as increased mutations were present in spermatogenic cells of older male mice relative to younger males both at baseline levels and post-irradiation (Xu et al., 2012). Lastly, the use of ‘adaptive’ radiation dosing, or giving a very small dose 24 hours prior to the full radiation dose, may also affect offspring’s mutational frequency. In male mice who received adaptive dosing relative to males who received only the full radiation dose, there were significant decreases in germline mutation frequencies and in the rate of paternal mutations in their offspring (Somers et al., 2004) .
The radiation-mutation relationship may also be impacted by the genetics of the organism, as the genotype appears to play an important role in determining how the biological system responds to radiation. In yeast with inactivated rad50 or rad52, the radiation-induced mutation frequency was significantly increased relative to wild-type yeast (Matuo et al., 2018). Msh2 knock-out mice (Burr et al., 2007) and medaka fish (Otozai et al., 2014) both had significantly increased baseline mutation frequencies relative to wild-type animals. Irradiation, however, did not change this mutation rate from baseline for these Msh2 knock-out animals (Burr et al., 2007; Otozai et al., 2014). Similarly, BRCA2 knock-out embryos had significantly elevated baseline mutation rates relative to wild-type littermates; however, in utero radiation was found to increase the mutation rate of all genotypes. Thus irradiated BRCA2 knock-out embryos also had a significantly increased mutation frequency relative to wild-type embryos by approximately three-fold (Tutt et al., 2002). Finally, baseline mutation levels in p53 knock-out medaka fish did not differ from wild-types; however, p53 knock-out fish exposed to radiation were found to have a 24-fold increase in mutation frequency relative to unirradiated p53 knock-out fish (Otozai et al., 2014). Construction of a dose response curve found the following mutation rates for wild-type, Msh2 knock-out, p53 knockout, and Msh2/p53 double knock-out medaka fish, respectively: 1.1x10-4 mutations/allele/Gy, 1.1x10-4 mutations/allele/Gy, 4.3x10-4 mutations/allele/Gy, and 5.6x10-4 mutations/allele/Gy (Otozai et al., 2014).
Finally, factors such as dose, dose-rate, tissue type and radiation quality can influence mutation rate induction (Suzuki & Hei ,1996; Hooker et al., 2004; Rydberg et al., 2005; Day et al., 2007; Okudaira et al., 2010; Brooks et al., 2016).
Known Feedforward/Feedback loops influencing this KERNot identified.
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Relationship: 1982: Energy Deposition leads to Increase, Chromosomal aberrations
AOPs Referencing Relationship
AOP Name | Adjacency | Weight of Evidence | Quantitative Understanding |
---|---|---|---|
Direct deposition of ionizing energy onto DNA leading to lung cancer | non-adjacent | High | High |
Evidence Supporting Applicability of this Relationship
Life Stage | Evidence |
---|---|
All life stages | High |
Sex | Evidence |
---|---|
Unspecific | High |
The domain of applicability applies to eukaryotic cells and multi-cellular organisms such as mice and humans.
Key Event Relationship Description
Energy can be deposited on biomolecules from various forms of radiation in a randomized manner. Radiation with high linear energy transfer (LET) tends to produce more complex, dense structural damage than low LET radiation; both, however, can lead to detrimental damage within a cell