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Relationship: 1984

Title

The title of the KER should clearly define the two KEs being considered and the sequential relationship between them (i.e., which is upstream and which is downstream). Consequently all KER titles take the form “upstream KE leads to downstream KE”.  More help

Increase, Mutations leads to Increase, lung cancer

Upstream event
Upstream event in the Key Event Relationship. On the KER page, clicking on the Event name under Upstream Relationship will bring the user to that individual KE page. More help
Downstream event
Downstream event in the Key Event Relationship. On the KER page, clicking on the Event name under Upstream Relationship will bring the user to that individual KE page. More help

Key Event Relationship Overview

The utility of AOPs for regulatory application is defined, to a large extent, by the confidence and precision with which they facilitate extrapolation of data measured at low levels of biological organisation to predicted outcomes at higher levels of organisation and the extent to which they can link biological effect measurements to their specific causes. Within the AOP framework, the predictive relationships that facilitate extrapolation are represented by the KERs. Consequently, the overall WoE for an AOP is a reflection in part, of the level of confidence in the underlying series of KERs it encompasses. Therefore, describing the KERs in an AOP involves assembling and organising the types of information and evidence that defines the scientific basis for inferring the probable change in, or state of, a downstream KE from the known or measured state of an upstream KE. More help

AOPs Referencing Relationship

This table is automatically generated upon addition of a KER to an AOP. All of the AOPs that are linked to this KER will automatically be listed in this subsection. Clicking on the name of the AOP in the table will bring you to the individual page for that AOP. More help
AOP Name Adjacency Weight of Evidence Quantitative Understanding Point of Contact Author Status OECD Status
Deposition of energy leading to lung cancer non-adjacent High Low Vinita Chauhan (send email) Under development: Not open for comment. Do not cite EAGMST Under Review

Taxonomic Applicability

Select one or more structured terms that help to define the biological applicability domain of the KER. In general, this will be dictated by the more restrictive of the two KEs being linked together by the KER. Authors can indicate the relevant taxa for this KER in this subsection. The process is similar to what is described for KEs (see pages 30-31 and 37-38 of User Handbook) More help
Term Scientific Term Evidence Link
human Homo sapiens High NCBI
mouse Mus musculus High NCBI
rat Rattus norvegicus High NCBI

Sex Applicability

Authors can indicate the relevant sex for this KER in this subsection. The process is similar to what is described for KEs (see pages 31-32 of the User Handbook). More help
Sex Evidence
Male High

Life Stage Applicability

Authors can indicate the relevant life stage for this KER in this subsection. The process is similar to what is described for KEs (see pages 31-32 of User Handbook). More help
Term Evidence
All life stages High

Key Event Relationship Description

Provide a brief, descriptive summation of the KER. While the title itself is fairly descriptive, this section can provide details that aren’t inherent in the description of the KEs themselves (see page 39 of the User Handbook). This description section can be viewed as providing the increased specificity in the nature of upstream perturbation (KEupstream) that leads to a particular downstream perturbation (KEdownstream), while allowing the KE descriptions to remain generalised so they can be linked to different AOPs. The description is also intended to provide a concise overview for readers who may want a brief summation, without needing to read through the detailed support for the relationship (covered below). Careful attention should be taken to avoid reference to other KEs that are not part of this KER, other KERs or other AOPs. This will ensure that the KER is modular and can be used by other AOPs. More help

A mutation occurs when there is a change in the DNA sequence. In some cases, mutations are silent and do not cause any functional changes in the cell; in other cases, mutations may have catastrophic consequences.  If these errors occur in genes implicated in critical regulatory pathways such as DNA repair mechanisms, cell-cycle checkpoints, apoptosis, or telomere length genes, then the cells are generally more susceptible to carcinogenesis (Chen et al. 1990; Hei et al. 1994; Kronenberg et al. 1995; Zhu et al. 1996, NRC 1999). The result of disrupting these regulatory pathways is ultimately the abnormal accumulation of malignant cells that may lead to cancer. Lung cancer in particular may occur if catastrophic mutations occur in cells of the lung.

Evidence Supporting this KER

Assembly and description of the scientific evidence supporting KERs in an AOP is an important step in the AOP development process that sets the stage for overall assessment of the AOP (see pages 49-56 of the User Handbook). To do this, biological plausibility, empirical support, and the current quantitative understanding of the KER are evaluated with regard to the predictive relationships/associations between defined pairs of KEs as a basis for considering WoE (page 55 of User Handbook). In addition, uncertainties and inconsistencies are considered. More help
Biological Plausibility
Define, in free text, the biological rationale for a connection between KEupstream and KEdownstream. What are the structural or functional relationships between the KEs? For example, there is a functional relationship between an enzyme’s activity and the product of a reaction it catalyses. Supporting references should be included. However, it is recognised that there may be cases where the biological relationship between two KEs is very well established, to the extent that it is widely accepted and consistently supported by so much literature that it is unnecessary and impractical to cite the relevant primary literature. Citation of review articles or other secondary sources, like text books, may be reasonable in such cases. The primary intent is to provide scientifically credible support for the structural and/or functional relationship between the pair of KEs if one is known. The description of biological plausibility can also incorporate additional mechanistic details that help inform the relationship between KEs, this is useful when it is not practical/pragmatic to represent these details as separate KEs due to the difficulty or relative infrequency with which it is likely to be measured (see page 40 of the User Handbook for further information).   More help

The biological rationale for linking mutations to lung cancer is strongly supported by the literature. Numerous studies and reviews are available on this topic.

There is evidence that mutation patterns may be specific to cancer type. Results from large bioinformatics-based studies have suggested that each cancer may have a characteristic mutation fingerprint. Twenty-one mutation signatures were detected upon analysis of approximately 7000 samples with nearly 5 million mutations across 30 different cancer categories, with each cancer type displaying a different profile of mutation signatures (Alexandrov et al. 2013). Similarly, analysis of approximately 2100 genomes across 9 different cancers also identified numerous mutation signatures that, in combination, were able to differentiate between cancer types (Jia et al. 2014). Lung adenocarcinoma and lung squamous cell carcinoma, for example, shared two of the same mutational signatures, but were ultimately found to have different overall profiles; lung adenocarcinoma had four mutation signatures, and lung squamous cell carcinoma had three (Alexandrov et al. 2013; Jia et al. 2014). Likewise lung small cell carcinoma had only two signatures, one of which was associated with smoking and was shared with both lung adenocarcinoma and lung squamous cell carcinoma (Alexandrov et al. 2013). There were also 39 significant associations found between mutational signatures and driver mutations upon analysis of nearly 8000 cancer exomes across 26 types of cancer, suggesting that the mutation signatures may be informative as to biological processes occurring in cancer (Poulos et al. 2018).

Mutations are thought to be at the heart of many of the features associated with tumours.  In a report on the hallmarks of cancer by Hanahan and Weinburg (2011), the six original hallmarks were identified as sustained proliferative signalling, evading growth receptors, activating invasion and metastasis, enabling replicative memory, inducing angiogenesis, and resisting cell death; two new emerging hallmarks of cancer were identified as deregulating cellular energetics and avoiding immune destruction. One of the ‘enabling characteristics’ proposed to be underlying these key cancer hallmarks was genome instability/mutations (Hanahan and Weinberg 2011). This suggests that many of the processes involved in tumourigenesis are facilitated by accumulating mutations that confer a survival advantage to the cells, allowing for the development of cancer (Vogelstein and Kinzler 2004; Hanahan and Weinberg 2011). Cancer thus arises from a large accumulation of genetic abnormalities over time, rather than one single detrimental mutation (Vogelstein and Kinzler 2004); these abnormalities may occur at the level of the nucleotides, the chromosomes, or the transcriptome (Larsen and Minna 2011). Many of the cancer-enabling mutations are found in tumour suppressor genes (TSGs), proto-oncogenes, or caretaker/stability genes (Vogelstein and Kinzler 2004; Larsen and Minna 2011).

TSGs have been compared to the brakes in a car (Vogelstein and Kinzler 2004), as they are genes that typically prevent proliferation and, in some cases, promote apoptosis. They thus play an important role in negatively regulating cellular growth. This preventative function is especially important in situations where DNA is damaged, as the products of TSGs will stop the cell from undergoing mitosis and may even initiate apoptotic pathways in order to avoid the propagation of damaged DNA. Mutations that reduce the activity of or completely inactivate TSGs may thus promote tumourigenesis by removing cell proliferation checkpoints and blocking apoptotic pathways (Vogelstein and Kinzler 2004; Panov 2005; Sanders and Albitar 2010). For TSGs to contribute to cancer development, however, generally both copies of the allele must be disrupted (Vogelstein and Kinzler 2004; Larsen and Minna 2011); this typically occurs through the loss of an entire chromosomal segment containing one allele and an inactivating or activity-reducing mutation that occurs in the second allele (such as missense mutations in a critical residue, mutations that produce a truncated protein, or deletions/insertions) (Vogelstein and Kinzler 2004). In lung cancer, some of the commonly inactivated TSGs include TP53, RB1, STK11, CDKN2A, FHIT, RASSF1A and PTEN (Larsen and Minna 2011).

If TSGs are the brakes for cellular proliferation, proto-oncogenes have been described as the gas pedal (Vogelstein and Kinzler 2004). Mutations in proto-oncogenes that render these genes constitutively or abnormally active may result in high rates of cellular proliferation,, thus supporting tumourigenesis (Vogelstein and Kinzler 2004; Larsen and Minna 2011). These mutations could be in the form of chromosomal translocations, gene amplifications, or mutations that affect critical segments for activity regulation. In contrast to TSGs, an activating mutation in one allele is often adequate to increase proliferation rates in the cell (Vogelstein and Kinzler 2004). Thus mutations in proto-oncogenes are frequently found in cancers, particularly in solid tumours such as non-small cell lung carcinoma (NSCLC) (Danesi et al. 2003). Some commonly activated proto-oncogenes in lung cancer include EGFR, ERBB2, MYC, KRAS, MET, CCND1, CSK4, MET, and BCL2 (Larsen and Minna 2011) .

Overall, TSGs and proto-oncogenes are similar in that they both increase the number of tumour cells through increasing proliferation, decreasing cell death, or by increasing angiogenesis in the area (thus enabling nutrient delivery) (Vogelstein and Kinzler 2004). In addition, mutations to caretaker/stability genes may also play a role in promoting cancer. These genes function differently from TSGs and proto-oncogenes in tumourigenesis in that they facilitate the accumulation of mutations. In normal situations, caretaker/stability genes are 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 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 than usual (Hanahan and Weinberg 2011). All genes across the genome are equally susceptible to gaining increased mutations when caretaker/stability genes are not functioning properly; however, only mutations that affect TSGs and proto-oncogenes contribute to tumourigenesis (Vogelstein and Kinzler 2004). Similar to TSGs, generally both alleles of the caretaker/stability genes must be disrupted for the gene function to be lost (Vogelstein and Kinzler 2004; Hanahan and Weinberg 2011). 

According to the COSMIC database (https://cancer.sanger.ac.uk/cosmic)  the TSG TP53 and the proto-oncogenes KRAS and EGFR are identified as the top three mutations found in lung cancer. Numerous epidemiological reports and analyses of lung tumours have confirmed this finding, as have many studies involving in vitro and in vivo manipulation of these genes.

The transcription factor TP53 is amongst the most commonly mutated TSGs in not only lung cancer (Varella-garcia 2009; Sanders and Albitar 2010; George et al., 2015) (COSMIC database, https://cancer.sanger.ac.uk/cosmic), but also human cancers in general (Iwakuma 2007, Kim 2018, Hollstein 1991). TP53, which produces the protein p53, plays a role in controlling cell cycling and promoting apoptosis in times of cellular or genotoxic stress (Danesi et al. 2003; Vogelstein and Kinzler 2004; Panov 2005; Iwakuma and Lozano 2007; Varella-garcia 2009; Larsen and Minna 2011; Cortot et al. 2014; Kim and Lozano 2018). It acts as a checkpoint for passing into the G2 phase of the cell cycle in order to prevent cells with damaged DNA from undergoing mitosis (Danesi et al. 2003; Panov 2005). If DNA damage is detected by p53 at this checkpoint, p53 is responsible for arresting the cell and either activating genes responsible for DNA repair or activating apoptotic pathways (Panov 2005; Larsen and Minna 2011).  Mutations in TP53 that disrupt the function of p53 thus allow for unrestricted cellular proliferation and promotion of tumourigenesis (Iwakuma and Lozano 2007; Kim and Lozano 2018), as cells are no longer stopped at the G2 checkpoint. Loss of this checkpoint also supports tumourigenesis by allowing potentially damaged DNA to be used in mitosis, thus increasing the likelihood of mutation accumulation (Danesi et al. 2003). The function of p53 may be disrupted by complete deletion of TP53, but it is more often affected by mutations, especially missense rather than null mutations. In the case of mutant p53 production, the altered protein may be able to bind to different partners and alter the expression of different genes, thus displaying a gain-of-function phenotype (Kim and Lozano 2018).

Mutations in TP53 are very common in lung cancer (Varella-garcia 2009), occurring in more than two-thirds of patients (Massion and Carbone 2003). In one study that looked at the genomes of small cell lung cancer (SCLC) samples, 100% of those without chromotrhipsis were found to have mutations in the TP53 gene (George et al., 2015). Mutant TP53 is especially common in smokers and in aggressive tumours (Varella-garcia 2009). It is thought that loss of p53 function is an early occurrence in lung cancer, and may be associated with deregulation of telomerase activity (Danesi et al. 2003). Low levels of p14arf, which is the product of CDKN2A (Cortot et al. 2014), another commonly mutated TSG in NSCLC (Sanders and Albitar 2010), and SCLC (George et al., 2015), may further exacerbate the cellular consequences of a mutated p53. Normally, p14arf plays a role in stabilizing and activating p53; tumourigenesis is thus particularly encouraged when mutations are present that cause not only the downregulation of p14arf and/or p53, but also the upregulation of proto-oncogenes (Cortot et al. 2014).

KRAS is one of the most commonly mutated members of the RAS family in lung cancer (Varella-garcia 2009; Sanders and Albitar 2010). Mutations in this gene have been reported in at least 20% of NSCLC cases (Massion and Carbone 2003; Sanders and Albitar 2010; Cortot et al. 2014; Wang et al. 2018), and are most frequently found in lung adenocarcinomas (Massion and Carbone 2003). KRAS is classified as a proto-oncogene and encodes a G-protein that plays an important role in signal transduction, especially in differentiation, proliferation and survival pathways (Varella-garcia 2009). When a signal that promotes cellular growth is detected, KRAS, which is attached to the inner side of the cellular membrane, is activated and binds to GTP. Using its inherent GTPase activity to hydrolyze GTP to GDP, KRAS interacts with its downstream partner, Raf 1, before returning to an inactive state.  The signal, meanwhile, is propagated all the way to the nucleus by downstream kinases, eventually leading to the activation and translocation of MAPK to the nucleus to stimulate pro-proliferation activities.  Mutations in KRAS may result in GTPase errors such that GTP remains bound to KRAS (Panov 2005) and the protein remains constitutively active, thus extending pro-proliferative signalling indefinitely (Panov 2005; Varella-garcia 2009). Mutated KRAS may also play a role in mediating cell invasion through epithelial mesenchymal transition (EMT), as seen in cases of NSCLC (Wang et al. 2018). This is supported by a study in which KRAS was identified as a cancer driver in cell invasion, as well as pathways related to hypoxia, inducing angiogenesis, and blocking apoptosis (Cava et al. 2018).

EGFR is classified as a receptor tyrosine kinase and a proto-oncogene. When activated by phosphorylation, EGFR plays an important role in stimulating cellular proliferation and survival using the RAS-REF-MEK and PI3K-AKT-mTOR pathways (Danesi et al. 2003; Varella-garcia 2009; Sanders and Albitar 2010). When inactive, these receptors exist in monomeric form; upon binding of a ligand, receptors will homo- or hetero- dimerize to active the tyrosine kinase domain. This leads to autophosphorylation, and a downstream signalling cascade that eventually results in pro-proliferative activities in the nucleus (Danesi et al. 2003). Mutations affecting this pathway may support tumourigenesis (Danesi et al. 2003; Sanders and Albitar 2010) by increasing cellular proliferation, inducing angiogenesis, stimulating metastasis and inhibiting apoptosis (Danesi et al. 2003). In lung cancer specifically, EGFR mutations have been found in approximately one third of adenocarcinoma patients (Cai et al. 2013; Cortot et al. 2014). In a study composed only of non-smoker NSCLC patients, EGFR mutations were likewise present in nearly half of the patients (Kim et al. 2012). Most EGFR mutations result in overexpression of EGFR (Varella-garcia 2009). In general, lung cancer patients with mutations resulting in the amplification of EGFR have a more negative prognosis (Varella-garcia 2009; Sanders and Albitar 2010).

Cancers are also known to obtain specific driver mutations that play a major role in tumourigenesis and help to drive carcinogenic pathways. Driver mutations allow for the continued aberrant signalling by mutated proteins, and as such, they sustain tumour growth. In NSCLC, important driver mutations include rearrangements in ALK, RET, and ROS1; mutations in AKT1, BRAF, DDR2, EGFR, HER2, KRAS, MEK1, NRAS, PIK3CA, and PTEN; and amplifications in FGFR1 and MET. In general, the majority of NSCLC tumours harbour only one of these driver mutations (Larsen and Minna 2011).

The presence of these mutations may affect several signalling pathways associated with cancer development. Examples include the PI3K-AKT-mTOR pathway and RAS-REF-MEK pathway. Mutations affecting factors involved in the PI3K-AKT-mTOR pathway tend to result in increased cell proliferation, growth and survival; thus mutations that cause constitutive or uncontrolled activation of this pathway may result in tumour growth (Varella-garcia 2009; Sanders and Albitar 2010; Larsen and Minna 2011). For example, activating PIK3CA mutations and inactivating PTEN mutations are associated with increased activity of the PI3K-AKT-mTOR pathway(Sanders and Albitar 2010; Larsen and Minna 2011); the opposite effects are shown when PIK3CA is inhibited (Kang et al., 2005; Cheng et al., 2014). Activity of this signalling pathway can also be stimulated by interactions involving IGF1R, PDGF, EGFR, EGF, TNF-alpha, PI3Ks, PDK-1 and Akt/PKB (Varella-garcia 2009). Specifically in lung cancer, this pathway is thought to be activated relatively early in the pathogenesis process (Larsen 2011). Similarly, activity of the RAS-REF-MEK pathway helps to direct cell growth, differentiation, and prevent apoptosis (McCubrey et al., 2006). This pathway functions through activated receptor tyrosine kinases, which allow RAS GTPases to bind GTP and ultimately activate MEK and ERK signalling cascades. Alterations to this pathway, such as the presence of activating KRAS mutations that cause irreversible binding of GTP and thus increased signalling activity, may result in tumour formation (Sanders and Albitar 2010)(; McCubrey et al., 2006).  This pathway is often found to be activated in lung cancer, especially when KRAS obtains activating mutations (Larsen and Minna 2011).

Uncertainties and Inconsistencies
In addition to outlining the evidence supporting a particular linkage, it is also important to identify inconsistencies or uncertainties in the relationship. Additionally, while there are expected patterns of concordance that support a causal linkage between the KEs in the pair, it is also helpful to identify experimental details that may explain apparent deviations from the expected patterns of concordance. Identification of uncertainties and inconsistencies contribute to evaluation of the overall WoE supporting the AOPs that contain a given KER and to the identification of research gaps that warrant investigation (seep pages 41-42 of the User Handbook).Given that AOPs are intended to support regulatory applications, AOP developers should focus on those inconsistencies or gaps that would have a direct bearing or impact on the confidence in the KER and its use as a basis for inference or extrapolation in a regulatory setting. Uncertainties that may be of academic interest but would have little impact on regulatory application don’t need to be described. In general, this section details evidence that may raise questions regarding the overall validity and predictive utility of the KER (including consideration of both biological plausibility and empirical support). It also contributes along with several other elements to the overall evaluation of the WoE for the KER (see Section 4 of the User Handbook).  More help

Uncertainties and inconsistencies in this KER are as follows:

  1. Tumours often have many different mutations present, some at such low levels that they are very difficult to detect. This is an issue, as these low-level mutants may still play a significant role in tumour growth, relapse and resistance to therapy. There has been some improvement in detecting these mutations with new technologies such as consensus sequencing-based error-correction approaches (Salk et al. 2018).
  2. Opposing results were found for two studies examining TP53 mutations in lung tumours from New Mexico uranium miners. In an earlier study by Vahakangas (1992), lung tumours were examined from 19 underground miners exposed to an average of 111 WLM of radon. Seven of the tumours harboured a TP53 mutation, but none of the mutations were found to be G to T transversions in the coding strand of TP53. In contrast, a study by Taylor (1994) that examined TP53 mutations in lung tumours of 29 New Mexico uranium miners exposed to an average of 1,382 WLM of radiation found that 16 of the TP53 mutations were G to T transversions at codon 249. An in vitro study using normal human bronchial epithelial cells irradiated with alpha particles equivalent to 1,460 WLM (4 Gy) was also performed, mimicking the above studies. The resulting irradiated cells exhibited malignant characteristics such as distinct morphology, a high rate of mitosis, and an extended lifespan. The mutational analysis, however, was in agreement with the results from Vahakangas(1992) , as there were no G to T transversions found at codon 249 and codon 250 of TP53 (Hussain et al. 1997).  
Response-response Relationship
This subsection should be used to define sources of data that define the response-response relationships between the KEs. In particular, information regarding the general form of the relationship (e.g., linear, exponential, sigmoidal, threshold, etc.) should be captured if possible. If there are specific mathematical functions or computational models relevant to the KER in question that have been defined, those should also be cited and/or described where possible, along with information concerning the approximate range of certainty with which the state of the KEdownstream can be predicted based on the measured state of the KEupstream (i.e., can it be predicted within a factor of two, or within three orders of magnitude?). For example, a regression equation may reasonably describe the response-response relationship between the two KERs, but that relationship may have only been validated/tested in a single species under steady state exposure conditions. Those types of details would be useful to capture.  More help

Studies assessing the nature of the relationship between mutation frequencies and cancer incidence directly are difficult to locate. There are, however, separate studies that assess the relationship between radiation exposure and mutation frequencies, and the relationship between radiation exposure and lung cancer incidence. More research is required to directly assess the response-response relationship between mutations and lung cancer.

Mutation frequencies were found to increase in a positive, dose-dependent manner with increasing gamma-ray radiation doses between 0 Gy and 6 Gy in Chinese hamster embryonal lung fibroblasts (Canova et al. 2002) and normal human bronchial epithelial cells (Suzuki and Hei 1996). Similarly, fibroblasts exposed to a number of different ions of varying LETs were found to have a positive, dose-dependent relationship between oncogenic transformations and radiation doses ranging from 0 - 1 Gy (Miller et al. 1995). This positive, dose-dependent relationship was also found between the incidence of lung cancer in rats and their cumulative radon exposure between 25 and 3000 WLM (Monchaux et al. 1994). (According to a conversion factor from Jostes (Jostes 1996), 25 WLM is equivalent to 0.02 - 0.25 Gy, and 3000 WLM is equivalent to 2.4 - 30 Gy.) Furthermore, two epidemiological studies examining lung cancer in radon-exposed uranium miners found a positive, linear relationship between the relative risk of lung cancer and the cumulative radon exposure (Lubin et al. 1995; Ramkissoon et al. 2018).

Time-scale
This sub-section should be used to provide information regarding the approximate time-scale of the changes in KEdownstream relative to changes in KEupstream (i.e., do effects on KEdownstream lag those on KEupstream by seconds, minutes, hours, or days?). This can be useful information both in terms of modelling the KER, as well as for analyzing the critical or dominant paths through an AOP network (e.g., identification of an AO that could kill an organism in a matter of hours will generally be of higher priority than other potential AOs that take weeks or months to develop). Identification of time-scale can also aid the assessment of temporal concordance. For example, for a KER that operates on a time-scale of days, measurement of both KEs after just hours of exposure in a short-term experiment could lead to incorrect conclusions regarding dose-response or temporal concordance if the time-scale of the upstream to downstream transition was not considered. More help

It is difficult to pinpoint exact time scales in terms of how long it takes for lung cancer to develop after mutations are accumulated. Differing experimental or biological conditions may modify the time scale between these events, making it challenging to predict exactly when tumours will develop. Another level to this challenge is the difficulty in pinpointing exactly when mutations occur after exposure to a stressor. Synthesis of results from various studies highlights this variety in time scales between stressor exposure, mutation induction and tumourigenesis.

Studies examining the time scale between mutations and lung cancer generally agree that tumourigenesis occurs at least weeks or months after the induction of mutations.   In cells whose nuclei were precisely irradiated with 1 - 8 alpha particles, mutations were evident 2 weeks after irradiation (Hei et al. 1997). Oncogenic transformations, however, were not evident until 7 weeks after irradiation (Miller et al. 1999). Likewise, xenografts using human bronchial epithelial cells that were transformed into tumour cells by irradiation resulted in detectable tumours in Nu/Nu mice within 13 weeks of injection; the tumours grew to diameters of 0.6 - 0.7 cm by 6 months post-injection (Hei et al. 1994).  In Gprc5a knock-out mice, there were tissue abnormalities present in approximately 10% of mice at 10-11 months of age, but spontaneous tumours did not develop until at least 20 months of age. Exposure of these mice to known tobacco carcinogen NNK  from 2 - 4 months of age resulted in a faster rate of tumourigenesis, with tissue abnormalities present in roughly 65% of the population by 1 month post-exposure (5 months of age), and adenocarcinomas in approximately 15% of the population by 3 months post-exposure (7 months of age). At 6 months post-exposure (10 months of age), 100% of the population presented with adenocarcinomas; one month later, there was a significant increase in the tumour burden. Furthermore, somatic mutation burdens in NNK-treated mice between the ages of 9 and 11 months were higher relative to untreated mice of at least 20 months of age (Fujimoto et al. 2017). Moreover, epidemiological analysis of radon-exposed uranium miners found that the relative risk of lung cancer was amplified with increasing years of radon exposure (Lubin et al. 1995).

Cre-inducible transgenic mouse models of lung cancer are likewise useful for highlighting that mutations precede lung tumourigenesis. In the presence of Cre-induced mutant K-Ras4b expression, focal hyperplasia lesions were present within 7 - 14 days of expression induction, and tumours were present by 2 months post-induction. In animals with an additional constitutive mutation, tumours were present within 1 month of mutant K-Ras4b expression (Fisher et al. 2001). Likewise, clinically detectable lung cancer was present in the lungs of transgenic mice with Cre-inducible KRAS and TP53 mutations within 15 to 37 weeks of the mutations being expressed, depending on the dose of Cre-carrying adenovirus used (Kasinski and Slack 2012).

Known modulating factors
This sub-section presents information regarding modulating factors/variables known to alter the shape of the response-response function that describes the quantitative relationship between the two KEs (for example, an iodine deficient diet causes a significant increase in the slope of the relationship; a particular genotype doubles the sensitivity of KEdownstream to changes in KEupstream). Information on these known modulating factors should be listed in this subsection, along with relevant information regarding the manner in which the modulating factor can be expected to alter the relationship (if known). Note, this section should focus on those modulating factors for which solid evidence supported by relevant data and literature is available. It should NOT list all possible/plausible modulating factors. In this regard, it is useful to bear in mind that many risk assessments conducted through conventional apical guideline testing-based approaches generally consider few if any modulating factors. More help

There are known modulating factors that affect the relationship between mutations and lung cancer. Overall, increasing age is correlated with more mutations (Tomasetti et al. 2013), and a higher incidence of cancer has been documented in those exposed to radiation at a younger age (Bijwaard et al. 2001). A direct relationship has also been established between the degree of tissue damage in the respiratory structures and the consumption of cigarettes (Auerbach et al. 1957). Furthermore, mutations linked to lung cancer are more common in specific groups of people. EGFR mutations have been found more frequently in non-smokers (Lim et al. 2009; Sanders and Albitar 2010; Paik et al. 2012; Cortot et al. 2014), adenocarcinoma patients (Lim et al. 2009; Sanders and Albitar 2010), and females (Lim et al. 2009; Cortot et al. 2014). In general, KRAS mutations are more common in smokers (Paik et al. 2012; Cortot et al. 2014); however, the KRAS G12D transition variant is more common in non-smokers, while the G12V transversion variant is more common in smokers (Paik et al. 2012). Patients with stage I NSCLC also tend to have more frequent mutations in KRAS compared to patients at a higher stage (Cortot et al. 2014). Although TP53 mutations are not associated with smoking status overall, G to T transversions were found to be more common in smokers (Cortot et al. 2014).

Known Feedforward/Feedback loops influencing this KER
This subsection should define whether there are known positive or negative feedback mechanisms involved and what is understood about their time-course and homeostatic limits? In some cases where feedback processes are measurable and causally linked to the outcome, they should be represented as KEs. However, in most cases these features are expected to predominantly influence the shape of the response-response, time-course, behaviours between selected KEs. For example, if a feedback loop acts as compensatory mechanism that aims to restore homeostasis following initial perturbation of a KE, the feedback loop will directly shape the response-response relationship between the KERs. Given interest in formally identifying these positive or negative feedback, it is recommended that a graphical annotation (page 44) indicating a positive or negative feedback loop is involved in a particular upstream to downstream KE transition (KER) be added to the graphical representation, and that details be provided in this subsection of the KER description (see pages 44-45 of the User Handbook).  More help

Not identified.

Domain of Applicability

As for the KEs, there is also a free-text section of the KER description that the developer can use to explain his/her rationale for the structured terms selected with regard to taxonomic, life stage, or sex applicability, or provide a more generalizable or nuanced description of the applicability domain than may be feasible using standardized terms. More help

The domain of applicability applies to mammals, including rodents and humans.

References

List of the literature that was cited for this KER description using the appropriate format. Ideally, the list of references should conform, to the extent possible, with the OECD Style Guide (OECD, 2015). More help

Alexandrov, L.B. et al. (2013), "Signatures of mutational processes in human cancer.", Nature 500:415-421, doi:10.1038/nature12477.

Auerbach, O. et al. (1957), "Changes in Relation in the Bronchial Epithelium to Smoking and Cancer of the Lung.", N. Engl. J. Med., 256:97-104

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

Bijwaard, H., P. Brugmans & P. Leenhouts (2001), "A consistent two-mutation model of lung cancer for different data sets of radon-exposed rats.", Biophysik, 40(4):269-77, doi:10.1007/s00411-001-0118-3.

Cai, G. et al. (2013), "Identification of EGFR Mutation, KRAS Mutation, and ALK Gene Rearrangement in Cytological Specimens of Primary and Metastatic Lung Adenocarcinoma.", Cancer Cytopathol., 121(9):500-507, doi:10.1002/cncy.21288.

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