Upstream eventIncrease, Mutations
Increase, lung cancer
Key Event Relationship Overview
AOPs Referencing Relationship
|AOP Name||Adjacency||Weight of Evidence||Quantitative Understanding|
|Direct deposition of ionizing energy leading to lung cancer||non-adjacent||High||Low|
Life Stage Applicability
|All life stages||High|
Key Event Relationship Description
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
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) (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). 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), 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).
There is moderate empirical evidence supporting the relationship between the frequency of mutations and the incidence of lung cancer. The evidence presented below is summarized in table 10, here (click link). There is little empirical evidence available supporting a dose and incidence concordance, some empirical evidence supporting a temporal concordance, and strong empirical evidence supporting essentiality. Several review papers provide summaries of the relationship between these two key events. Genetic abnormalities found in lung cancer that result in genomic instability are discussed by Massion (2003). Several radon-specific review papers are also available that discuss available evidence for the link between radon exposure, mutation induction and lung carcinogenesis across a variety of models (Jostes 1996; Robertson et al. 2013).
Dose and Incidence Concordance
There is a lack of empirical evidence to show dose and incidence concordance between mutations and lung cancer, particularly in the field of ionizing radiation. As described above, numerous studies are available that highlight mutation signatures in different tumours with strong evidence linking specific mutations to cancer incidence, radiation exposure to mutation frequency, and radiation exposure to cancer incidence. However, there is a lack of studies that assess whether increasing doses of a stressor, such as ionizing radiation, translate into dose-dependent changes in mutation frequencies and dose-dependent changes in cancer incidences.
Attempts were thus made to identify studies using similar radiological and biological conditions that assessed either mutation frequency or cancer incidence independently. Using this strategy, two studies were found that addressed the link between mutations and cancer with increasing doses of radiation. In these two complementary studies, a microbeam system was used to precisely and selectively expose the nuclei of cells to a specific number of alpha particles. Upon exposure to 1 - 8 individual alpha particles, there was a dose-dependent increase in the number of S1- mutations in hybrid hamster-human cells (AL) (Hei et al. 1997). This correlated well with a study conducted by Miller et al., that showed an increase in the frequency of oncogenic transformations in mouse fibroblasts (C3H10T1/2 ) within that same dose range (Miller et al. 1999).
Likewise, side-by-side comparisons of other studies using comparable radiation doses and biological systems also provide evidence of a dose-dependent relationship between mutation frequency and oncogenic potential. Exposure of two different cell lines, Chinese hamster embryonal lung fibroblasts and normal human bronchial epithelial cells, to gamma-ray radiation at doses between 0 and 6 Gy resulted in dose-dependent increases in mutations in both cell types (Suzuki and Hei 1996; Canova et al. 2002). Radiation in the range of 0 and 6 Gy can thus induce dose-dependent increases in mutation frequencies; in vitro treatment with similar radiation doses can also evoke cancer-like changes. For example, exposure of bronchial epithelial cells to 0.3 or 0.6 Gy of radiation from helium-4 ions resulted in cells with tumour-like characteristics (Hei et al. 1994)). Similarly, C3H10T1/2 fibroblasts exposed to several types of ions at varying LETs displayed dose-dependent increases in oncogenic transformations between 0 - 1 Gy for all radiation conditions tested (Miller et al. 1999).
Analyses of lung cancer incidences in radon-exposed rats and humans equally echo these results. There was a dose-dependent increase in lung cancer incidence in rats exposed to radon and radon progeny at levels of 25 - 3000 working level months (WLM) (Monchaux et al. 1994). (One WLM is calculated based on 170 hours of exposure to one working level (WL), and 1 WL refers to the equivalent of 1.3 x105 MeV of alpha particle energy in 1 L of air.) Damage from 1 WLM is thought to be equivalent to 0.8 - 10.0 mGy (Jostes 1996), which corresponds to 100 - 1250 WLM/Gy; thus 25 WLM is equivalent to 0.02 - 0.25 Gy, and 3000 WLM is equivalent to 2.4 - 30 Gy. In epidemiological studies of uranium miners exposed to radon radiation within this exposure range, there was a dose-dependent increase in the relative risk of lung cancer with increasing cumulative radon exposure (Lubin et al. 1995; Ramkissoon et al. 2018).
Further support for this relationship can be derived from a study using a known tobacco carcinogen, NNK. Exposure of Gprc5a knock-out mice to NNK increased both the somatic mutation burden and the rate of tumourigenesis in the lungs of these mice relative to saline-treated controls (Fujimoto et al. 2017).
There are also several studies showing that successive addition of mutations in vitro or in vivo result in increased oncogenic potential. The sequential accrual of mutations in TP53, KRAS, and EGFR to immortalized human bronchial epithelial cells resulted in cells that were increasingly more oncogenic (Sato et al. 2006). In a similar study using small airway epithelial cells, the accumulation of hTERT, CDK4, p53 and KRAS manipulations plus the addition of manipulations to either PIK3KA, CYCLIN-D1, or LKB2 was successful in producing fully malignant cells (Sasai et al. 2011). Furthermore, in vivo mouse models that required Cre to induce mutations selectively in the lungs also found a relationship between the induction of mutations and lung tumours. In bitransgenic mice engineered such that ingestion of doxycycline (a tetracycline analog) induced expression of mutant K-Ras4b in type II pneumocytes of the lung, addition of a second mutation (specifically a constitutive deletion in either TP53 or Ink4A/Arf) resulted in a faster rate of lung tumourigenesis (Fisher et al. 2001). Similarly, a faster rate of lung tumourigenesis was also achieved when higher intratracheal doses of Cre-carrying adenoviruses were delivered to the lungs of transgenic mice with Cre-inducible mutations in KRAS and TP53 (Kasinski and Slack 2012).
There is some empirical evidence of temporal concordance between mutations and lung cancer incidence. Mutations have been shown to occur prior to lung tumourigenesis, but the exact period of time between mutation incidence and cancer development is difficult to pinpoint and appears to be affected by a variety of different factors. Results from a number of different studies, however, are in general agreement that the accumulation of oncogenic traits in vitro occurs weeks after mutations are induced (Miller et al. 1995; Hei et al. 1997; Miller et al. 1999), while in vivo tumourigenesis is not evident for weeks, months or even years after mutations are induced/accumulated (Hei et al. 1994; Monchaux et al. 1994; Lubin et al. 1995; Fisher et al. 2001; Kasinski and Slack 2012; Fujimoto et al. 2017).
In contrast to sparse studies indicating dose and incidence concordance between mutations and cancer, there are many different studies showing essentiality of mutations for the induction of lung cancer, especially for mutations in TP53, KRAS, and EGFR. 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.
In general, there are strong links between mutations in TP53 and tumourigenesis. A review of results from experiments involving in vivo p53 mouse models found that mice with dysfunctional/null p53 resulted in more tumourigenesis than mice with functional or semi-functional p53. The results of these studies demonstrate that mutant or absent p53 has a key role in promoting tumour growth (Iwakuma and Lozano 2007). Restoration of p53 function may cause tumour regression, as evidenced in a study using a conditional TP53 knock-out mouse model. All of the mice in the study had confirmed tumour growth, and restoring p53 function by tamoxifen injection decreased tumour size in 7 of 10 tumours by 46 - 100% (Ventura et al. 2007).
Similar results were found in a Phase 1 clinical trial specifically examining lung cancer. Seven NSCLC patients with metastatic or recurring lung tumours that had been unresponsive to previous treatments and that harboured mutations in TP53 were included in the study. The clinical trial was designed to examine the effect of delivering functional, wild-type p53 to the tumours by direct injection of a retroviral vector carrying p53 into the tumour, with the goal of restoring p53 function. All patients showed evidence of gene transfer. Four weeks post-treatment, tumours in six of these patients showed increased apoptosis; furthermore, the tumour had also regressed in three patients and stabilized in three patients (Roth et al. 1996).
Mutations in TP53 have also been examined alongside KRAS mutations. In one particular study, transgenic mice were engineered such that ingestion of doxycycline (an analogue of tetracycline) induced mutant K-Ras4b expression specifically in type II pneumocytes of the lung. Activation of this mutant K-Ras4b, in turn, resulted in lung tumourigenesis two months later. Addition of a constitutive deletion in TP53 or Ink4A/Arf resulted in a faster rate of tumourigenesis, such that tumours were present one month after the activation of mutant K-Ras4b. Analogous to the above studies where restoration of normal p53 resulted in tumour regression, the tumour growth in this study was reversed when doxycycline was withdrawn and the expression of mutant K-Ras4b was effectively stopped; the same regression was observed both with and without the constitutive deletion (Fisher et al. 2001).
Likewise, lung tumours resulting from mutations in TP53 and/or KRAS may be prevented or reversed using microRNAs (miRNAs) that are linked with p53 and KRAS regulation. Two miRNAs were examined: miR-223-3p and miR-34a. The connection between miR-223-3p, p53 and tumourigenesis was explored in a study using lung squamous cell carcinoma tumours, NSCLC cell lines with mutant p53, and mouse xenograft models using nude mice inoculated with primary human lung squamous cell carcinoma tumour fragments. In general, miR-223-3p expression was found to be significantly decreased in tumours of human origin and in successful mouse xenografts. When miR-223-3p expression was examined in relation to TP53 mutational status, miR-223-3p expression was significantly lower in the tumours with mutant p53 relative to those with wild-type p53. Confirming this reciprocity, silencing the mutant TP53 in NSCLC cell lines using short interfering RNA (siRNA) significantly increased miR-223-3p expression. Similarly, transfection of NSCLC cells with a vector to overexpress the mutant p53 resulted in decreased miR-223-3p expression. This led to experiments involving the in vivo treatment of the xenograft tumours with a miR-223-3p agonist. In comparison to non-agonist treated tumours, treatment with the miR-223-3p agonist resulted in not only increased expression of miR-223-3p, but also significant decreases in tumour weight, tumour volume and p53 expression (Luo et al. 2019).
Comparable results were found in a study using a mouse model with Cre-inducible heterozygous mutations in both KRAS and TP53. Intratracheal delivery of Cre via an adenovirus directly to the lungs resulted in significant tumour growth in the lungs after several weeks. When the mice were treated with a lentivirus carrying miR-34a at the same time as the Cre-adenovirus, there were significantly fewer tumours found in the treated animals, and the lungs of the treated animals were significantly smaller than the tumour-burdened, inflamed lungs of the untreated group. Furthermore, treatment with miR-34a 10 weeks after delivery of the Cre-adenovirus resulted in tumour regression by 4 weeks post-treatment, with tumour numbers and sizes decreasing significantly to near baseline levels in treated mice relative to the untreated controls (Kasinski and Slack 2012).
Tumour regression has also been achieved using the EGFR inhibitor EGF816. Multiple rodent xenograft models, with tumours derived from several cell lines with different EGFR mutations, were examined after administration of EGF816. Relative to rodents treated with vehicle, those treated with the inhibitor showed a reduction in tumour growth over 14, 18 or 21 days of treatment. In most cases, there was a dose-dependent increase in tumour regression, such that tumours were smaller in animals given higher doses of the inhibitor (Jia et al. 2016).
In contrast, the process of tumourigenesis may be expediated by addition of a carcinogen. Exposure of Gprc5a knock-out mice to a known carcinogen, NNK, resulted in a faster rate of lung tumourigenesis and more somatic mutations. Mice treated with NNK for 2 months showed increased tissue abnormalities within 1 month of treatment and detectable tumours by 3 months. At 6 months post-treatment, all NNK-exposed mice were presenting with lung adenocarcinomas, and the tumour burden significantly increased from 6 to 7 months post-treatment. In comparison, saline-treated controls had very few tissue abnormalities present at 7 months post-treatment, and did not develop adenocarcinomas until 16 months post-treatment. The NNK-treated animals also showed an increased somatic mutation burden at 5 - 7 months post-treatment relative to saline-treated controls at 16+ months post-treatment (Fujimoto et al. 2017).
Uncertainties and Inconsistencies
Uncertainties and inconsistencies in this KER are as follows:
- 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).
- 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).
Quantitative Understanding of the Linkage
Quantitative understanding of the relationship between mutation frequency and lung cancer incidence is not well-defined. Although it is well known that mutations are linked with cancer incidence and that some mutations are more common or specific to certain types of cancer, it is difficult to precisely predict cancer incidence from the somatic mutation frequency. A review paper by Saini (2018) discusses mutation loads in healthy and cancerous cells and methods of measuring these mutations. Interestingly, pre-cancerous, healthy cells are thought to be responsible for generating the majority of somatic mutations found in tumours (Tomasetti et al. 2013).
Mutation frequencies for healthy and cancerous cells are summarized in the table below.
|Milholland et al., 2017||Observation of somatic mutation rates in healthy human & mouse cells observed: human cells: 2.8x10-7 mutations per base pair and 2.66x10-9 mutations per base pair per mitosis. Mouse cells: 4.4 x 10-7 mutations per base pair and 8.1 x 10-9 mutations per base pair per mitosis.|
|Vogelstein, 2004||Tumor mutation rates are thought to be similar to mutation rates in healthy human cells of a similar number of generations. Observation of 1 mutation per megabase pair.|
|Saini, 2018||Somatic mutations in cancerous cells, 100 to 106 mutations per genome.|
|Alexandrov, 2013||Somatic mutations in cancerous cells, 0.001 to > 400 mutations per megabase pair. Higher mutation frequencies in cancers that are linked to environmental causes (e.g. lung cancer).|
|Danesi, 2003||Clinical detection of lung cancer observed 10-20 genetic events.|
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).
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
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
Domain of Applicability
The domain of applicability applies to mammals, including rodents and humans.
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