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Relationship: 1984


A descriptive phrase which clearly defines the two KEs being considered and the sequential relationship between them (i.e., which is upstream, and which is downstream). More help

Increase, Mutations leads to Increase, lung cancer

Upstream event
The causing Key Event (KE) in a Key Event Relationship (KER). More help
Downstream event
The responding Key Event (KE) in a Key Event Relationship (KER). 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

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) Open for citation & comment WPHA/WNT Endorsed

Taxonomic Applicability

Latin or common names of a species or broader taxonomic grouping (e.g., class, order, family) 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.  More help
Term Scientific Term Evidence Link
human Homo sapiens High NCBI
mouse Mus musculus High NCBI
rat Rattus norvegicus High NCBI

Sex Applicability

An indication of the the relevant sex for this KER. More help
Sex Evidence
Male High

Life Stage Applicability

An indication of the the relevant life stage(s) for this KER.  More help
Term Evidence
All life stages High

Key Event Relationship Description

Provides a concise overview of the information given below as well as addressing details that aren’t inherent in the description of the KEs themselves. 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 Collection Strategy

Include a description of the approach for identification and assembly of the evidence base for the KER. For evidence identification, include, for example, a description of the sources and dates of information consulted including expert knowledge, databases searched and associated search terms/strings.  Include also a description of study screening criteria and methodology, study quality assessment considerations, the data extraction strategy and links to any repositories/databases of relevant references.Tabular summaries and links to relevant supporting documentation are encouraged, wherever possible. More help

Evidence Supporting this KER

Addresses the scientific evidence supporting KERs in an AOP setting the stage for overall assessment of the AOP. More help
Biological Plausibility
Addresses the biological rationale for a connection between KEupstream and KEdownstream.  This field 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.   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 (  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,, 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
Addresses inconsistencies or uncertainties in the relationship including the identification of experimental details that may explain apparent deviations from the expected patterns of concordance. 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).  

Known modulating factors

This table captures specific information on the MF, its properties, how it affects the KER and respective references.1.) What is the modulating factor? Name the factor for which solid evidence exists that it influences this KER. Examples: age, sex, genotype, diet 2.) Details of this modulating factor. Specify which features of this MF are relevant for this KER. Examples: a specific age range or a specific biological age (defined by...); a specific gene mutation or variant, a specific nutrient (deficit or surplus); a sex-specific homone; a certain threshold value (e.g. serum levels of a chemical above...) 3.) Description of how this modulating factor affects this KER. Describe the provable modification of the KER (also quantitatively, if known). Examples: increase or decrease of the magnitude of effect (by a factor of...); change of the time-course of the effect (onset delay by...); alteration of the probability of the effect; increase or decrease of the sensitivity of the downstream effect (by a factor of...) 4.) Provision of supporting scientific evidence for an effect of this MF on this KER. Give a list of references.  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).

Response-response Relationship
Provides sources of data that define the response-response relationships between the KEs.  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).

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?). 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 Feedforward/Feedback loops influencing this KER
Define whether there are known positive or negative feedback mechanisms involved and what is understood about their time-course and homeostatic limits. More help

Not identified.

Domain of Applicability

A free-text section of the KER description that the developers can use to explain their rationale for the taxonomic, life stage, or sex applicability structured terms. More help

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


List of the literature that was cited for this KER description. 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.",

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.

Canova, S. et al. (2002), "Minisatellite and HPRT Mutations in V79 And Human Cells Irradiated with Gamma Rays.", Radiat Prot. Dosimetry, 99:207–209. doi: 10.1093/oxfordjournals.rpd.a006763

Cava, C. et al. (2018), "Integration of multiple networks and pathways identifies cancer driver genes in pan-cancer analysis.", BMC Genomics, 19(1):25, doi:10.1186/s12864-017-4423-x.

Chen, D.J. et al. (1992), "Mutagenic effects of alpha particles in normal human skin fibroblasts.", United States: Battelle Press., 26(18):569-580.

Cheng, H. et al. (2014), "Targeting the PI3K/AKT/mTOR pathway: potential for lung cancer treatment. Lung cancer management.", Lung Cancer Manag., 3(1):67-75, doi: 10.2217/lmt.13.72.

Cortot, A.B. et al. (2014), "Mutation of TP53 and Alteration of p14 arf Expression in EGFR- and KRAS -Mutated Lung Adenocarcinomas.", Clinical Lung Cancer, 15(2):124–130, doi:10.1016/j.cllc.2013.08.003.

Danesi, R. et al. (2003), "Pharmacogenetics of Anticancer Drug Sensitivity in Non-Small Cell Lung Cancer." 55(1):57-103. doi:10.1124/pr.

Fisher, G.H. et al. (2001), "Induction and apoptotic regression of lung adenocarcinomas by regulation of a K-Ras transgene in the presence and absence of tumor suppressor genes.", Genes Dev., 15(24):3249–3262, doi:10.1101/gad.947701.NSCLCs.

Fujimoto, J. et al. (2017), "Development of Kras mutant lung adenocarcinoma in mice with knockout of the airway lineage-specific gene Gprc5a.", Int. J. Cancer, 141(8):1589–1599, doi:10.1002/ijc.30851.

George, J. et al., (2015), Comprehensive genomic profiles of small cell lung cancer., Nature. 524(7563):47-53. doi:10.1038/nature14664.

Hanahan, D. & R.A. Weinberg (2011), "Review Hallmarks of Cancer: The Next Generation.", Cell. 144(5):646–674. doi:10.1016/j.cell.2011.02.013.

Hei, T.K et al. (1994), "Malignant transformatin of human bronchial epithelial cells by radon-simulated α-particles.", Carcinogenesis 15(3):431-437, doi: 10.1093/carcin/15.3.431.

Hei, T.K. et al. (1997), "Mutagenic effects of a single and an exact number of particles in mammalian cells.", Proceedings of the National Academy of Sciences, 94(8):3765-3770. doi:10.1073/pnas.94.8.3765.

Hei, T.K., L.X. Zhu & C.A. Waldren (1994), "Molecular mechanisms of mutagenesis by radiation of different qualities.", Pp 171-176 in Molecular Mechanisms in Radiation Mutagenesis and Carcinogenesis,Chadwick KH, Cox R, Leehouts HP, Thacker J, eds. Brussels: European Commission.

Hussain, S.P. et al. (1997), "Radon and lung carcinogenesis: mutability of p53 codons 249 and 250 to 238 Pu α-particles in human bronchial epithelial cells.", Carcinogenesis, 18(1):121–125.

Iwakuma, T. & G. Lozano (2007), "Crippling p53 activities via knock-in mutations in mouse models.", Oncogene, 26(15):2177–2184. doi:10.1038/sj.onc.1210278.

Jia, P., W. Pao & Z. Zhao (2014), "Patterns and processes of somatic mutations in nine major cancers.", BMC Med. Genomics, 7(1):1–11, doi:10.1186/1755-8794-7-11.

Jia, Y. et al. (2016), "EGF816 Exerts Anticancer Effects in Non – Small Cell Lung Cancer by Irreversibly and Selectively Targeting Primary and Acquired Activating Mutations in the EGF Receptor.", Cancer Res., 76(6):1591–1602. doi:10.1158/0008-5472.CAN-15-2581.

Jostes, R.F. (1996), "Genetic, cytogenetic, and carcinogenic effects of radon: a review.", Mutat. Res. / Rev. in Genet. Toxicol. 340(2-3):125–139. doi: 10.1016/s0165-1110(96)90044-5.

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