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Relationship: 1985

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, Chromosomal aberrations 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
Direct deposition of ionizing energy leading to lung cancer non-adjacent Moderate Moderate Brendan Ferreri-Hanberry (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 Do not cite EAGMST Under Review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
rat Rattus norvegicus High NCBI
mouse Mus musculus 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
Unspecific 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

Chromosomal aberrations (CAs) are described as irregularities in chromosome structure due to segments of the chromosome that have been lost, gained, or rearranged. This can lead to two categories of chromosomal exchanges: balanced, which do not impact the overall frame of chromosome structure, and unbalanced, which refers to CAs that do alter the frame of chromosome structure (Genetic Alliance 2010) . Specific categories of CAs include chromosome-type aberrations (CSAs) such as chromosome-type breaks, ring chromosomes, marker chromosomes, and dicentric aberrations; chromatid-type aberrations (CTAs) such as chromatid breaks and chromatid exchanges (Hagmar et al. 2004; Bonassi et al. 2008); micronuclei (MN); nucleoplasmic bridges (NPBs); and copy number variants (CNVs). When CAs affect genes related to tumourigenesis or their regulatory regions (Shlien and Malkin 2009; Liu et al. 2013), this may lead to an abnormal accumulation of malignant cells and ultimately may result in cancer. Lung cancer in particular may occur if these tumourigenesis-related CAs (which are more often unbalanced than balanced in lung cancer (Mitelman et al. 1997) 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

Biological Plausibility

The biological rationale linking CAs with lung cancer is strongly supported. There are many epidemiological studies that provide evidence of a link between increasing CAs and cancer incidence. Several published reports spanning over 22,000 study subjects across multiple European countries have examined the association between the presence of CAs in cultured blood lymphocytes and the incidence of cancer. In every cohort examined, the presence of CAs was predictive of cancer risk (Bonassi et al. 2000; Hagmar et al. 2004; Norppa et al. 2006; Boffetta et al. 2007; Bonassi et al. 2008). Although CSAs and CTAs both had predictive value, CSAs were considered to be slightly more indicative of cancer risk (Norppa et al. 2006).  Similarly, studies examining chromosomes in lymphocytes from lung cancer patients found significant increases in CTAs, CSAs, and overall CAs relative to lymphocytes from healthy controls. Furthermore, the CAs were shown to be significant predictors of lung cancer risk (Vodenkova et al. 2015). Analysis of MN and NPB levels within binucleated cells also found that these CAs were significantly increased in lung cancer patients relative to healthy controls (Lloyd et al. 2013; El-zein et al. 2014; El-zein et al. 2017), with very similar results for geographically-separated test and validation cohorts (El-zein et al. 2014).

Exposure to radiation has also been epidemiologically linked to the relationship between CAs and cancer. Studies of radon-exposed uranium miners have revealed evidence of an association between exposure to radon gas and an increased incidence of lung cancer (Roscoe et al. 1989; Tirmarchel et al. 1993; Smerhovsky et al. 2001; Smerhovsky et al. 2002; Vacquier et al. 2008; Walsh et al. 2010). Analysis of CAs in the blood lymphocytes of miners from the Czech Republic found that miners with higher levels of CAs had a significantly elevated risk of cancer (Smerhovsky et al. 2001; Smerhovsky et al. 2002). The results from these studies were likely not due to smoking status of the miners, as a study examining a cohort of 516 white, never-smoker American uranium miners found that the mortality rates from lung cancer were higher in the miners than in the general non-smoking population (Roscoe et al. 1989).

Beyond epidemiology, there are also many genetic and molecular studies that provide strong evidence of a relationship between CAs and cancer. A subset of these studies have investigated copy number variants (CNVs). Examination of CNVs and known cancer genes in a large population revealed that CNVs often overlap with cancer genes and thus have the potential to amplify carcinogenesis (Shlien and Malkin 2009; Ohshima et al. 2017)Moreover, using only CNV genetic information from a database, Zhang et al (2016) were able to categorize 3,480 samples into their respective cancer type based solely on the CNVs of the samples. This was accomplished by developing a panel of 19 discriminating genes that could predict cancer type with a high level of accuracy using only the CNV number. Interestingly, many of these discriminating genes have known associations with cancer or processes known to be important in cancer development (Zhang et al. 2016). Furthermore, cancer-prone individuals tend to have more CNV instability, which has been attributed to inherently less efficient DNA repair mechanisms (Shlien and Malkin 2009). In their 2013 review, Liu et al provided lists of cancer-related genes typically amplified by CNVs (ERBB2, EGFR, MYC, PIK3CA, IGF1R, FGFR1/2, KRAS, CDK4, CCDN1, MDM2, MET, and CDK6) and deleted by CNVs (RB1, PTEN, CDKN2A/B, ARID1A, MAPSK4, NF1, SMAD4, BRCA1/2, MSH2/6, DCC, and CDH1). There is also evidence associating CNVs to lung cancer specifically. Analysis of primary NSCLC samples revealed 27 chromosomal regions where CNVs were present in at least one third of the samples (Wrage et al. 2009). Furthermore, medically-relevant CNVs were found in 60% of lung cancer patients, encompassing genes such as TP53, BAP1, STK11, BRCA2, CDKN2A, and RB1 (Mukherjee et al. 2016). 

Likewise, lung cancer-specific studies have been performed to identify chromosomes most often affected by CNV gains and losses. Analysis of DNA from primary human lung tumours and early-passage primary cell lines established from human tumours revealed that gains were most frequently found at chromosomes 3q, 5p, 7p, and 8q, while losses were most frequent at chromosome 3p (Balsara et al. 1997). Separation of CNV analyses into squamous cell carcinoma and lung adenocarcinoma groups demonstrated differences between the two lung cancers (Petersen et al. 1997; Bjorkqvist et al. 1998; Feder et al. 1998; Massion et al. 2002). In general, CNV changes were present in 84% of squamous cell carcinoma samples, but only in 68% of adenocarcinomas (Bjorkqvist et al. 1998). In squamous cell carcinoma, the most frequent gain was found at chromosome 3q (Bjorkqvist et al. 1998), specifically at 3q26 (Massion et al. 2002); other common CNV gains were found at chromosomes 8q, 5p and 7p (Bjorkqvist et al. 1998). Losses in 3p were also more common in squamous cell carcinoma than adenocarcinoma (Feder et al. 1998). In adenocarcinoma, the most common documented CNV was a gain at chromosome 7p (Feder et al. 1998).  Gains in squamous cell carcinoma were often found in genes GLUT2, THRB, PIK3CA and BCL6, and losses in FHIT, EG9F2 and CACNAID. Interestingly, CNV gains affecting PIK3CA were correlated with increased activity of PKB in squamous cell carcinoma (Massion et al. 2002). A review by Knuutila et al (1999) summarizes DNA copy number losses found in 73 human tumour types, with results separated by chromosome number.

Loss of heterozygosity is also a common occurrence in cancer. Preneoplastic lesions from seven NSCLC tumours were histologically categorized and genetically analyzed. Consistent with above studies that revealed CNV losses at chromosome 3p, loss of heterozygosity was also common at the 3p locus. Percentages of 3p loss of heterozygosity increased from hyperplastic lesions (76%) to dysplastic lesions (86%) to carcinoma in situ (100%). Overall, cumulative loss of heterozygosity was nearly doubled in carcinoma in situ and invasive carcinoma lesions relative to preneoplastic hyperplasia and dysplasia lesions (Hung et al. 1995).

Other studies have revealed a link between gene rearrangements and cancer. Truncated tumour suppressor genes TP53, BRCA1 and BRAC2 have been reported in prostate cancer (Mao et al. 2011). In lung cancer, the gene ALK has been observed to undergo rearrangements, often in the form of gene fusions with EML4 (Sanders and Albitar 2010; Sasaki et al. 2010).  These ALK rearrangements often result in increased activity of ALK, higher activation of PI3K-AKT pathways, and ultimately an increased risk of tumourigenesis (Sanders and Albitar 2010). Another common example of a gene fusion is the Philadelphia chromosome, which is formed by a translocation between chromosome 9 and 22 and results in the fusion of BCR and ABL genes. The resulting BCR/ABL gene fusion product was found to be the cause of chronic myelogenous leukemia (reviewed by (Trask 2002). This fusion may be induced by stressors such as ionizing radiation; exposure of human leukemic promyelocytic cells (HL-60) to 5 Gy of gamma-ray radiation resulted in homologous BCR and ABL genes in closer proximity to each other and to the centre of the nucleus (Bartova et al. 2000).

Several rearrangements have also been significantly associated with lung cancer. A balanced translocation at chromosome 19 that results in overexpression of Notch3 in lung epithelial cells has been identified in a number of NSCLC lung cancer cell lines and tumours. This is significant, as Notch3 is not normally expressed in the cells of the lungs (Dang et al. 2000). In fact, transgenic mice engineered to overexpress Notch3 in the lung epithelium died at birth. Analysis of these embryos at embryonic day 18.5 revealed tissue abnormalities in locations where Notch3 mRNA was found, which suggests that overexpression of Notch3 in the lungs may play a role in lung tumourigenesis (Dang et al. 2003). Significant associations have been found between rearrangements in chromosome Xp and higher NSCLC tumour stage, as well as rearrangements in 17p and lower NSCLC tumour stage; 3p and 6q rearrangements were linked with better NSCLC survival (Feder et al. 1998).

CAs that affect pathways controlling cellular growth and apoptosis may promote the development of cancer. In some cases, CAs may alter the activity of proto-oncogenes or tumour suppressor genes (Mitelman et al. 1997; Albertson et al. 2003). Proto-oncogene regulation may be modified such that its gene product is overexpressed; alternatively, the product of the proto-oncogene itself may be affected, producing an abnormally-functioning protein. CAs that affect tumour suppressor genes may inactivate its expression; deletion of the chromosome housing the tumour suppressor gene(s) or unbalanced structural rearrangements may also prevent the expression of tumour suppressor genes (Mitelman et al. 1997). If these alterations enhance cell growth and/or inhibit apoptosis, the cell may become excessively proliferative and unresponsive to external environmental signals (Albertson et al. 2003). There are several pathways that could conceivably be pushed towards malignant transformation by the formation of CAs, including signalling pathways AKT-PI3K-mTOR and RAS-REF-MEK. If a CA occurs within gene(s) related to either of these pathways such that the activity is augmented, this may contribute to the development of a tumour (Sanders and Albitar 2010).

Other factors that may also contribute to increasing the CAs in a tumour include aberrant centromeres and telomerase deficiencies. In some cases, centromeres may become abnormally large due to aberrant amplification. These large centromeres may no longer separate the chromosomes appropriately during cell division, increasing the CA burden in the resulting daughter cells. In telomerase-deficient tumour cells that are proliferating but not being monitored closely, the telomeres may become abnormally short. This becomes an issue for cells that continue dividing because the chromosomes may become damaged during cell division, resulting in chromosomal fusions and breakages. Ultimately, this would also increase the CAs in the daughter cells (Albertson et al. 2003).

Ionizing radiation may also play a role in carcinogenesis. A series of studies focussed on irradiating human papillomavirus (HPV18)-immortalized human bronchial epithelial cells and transplanting the cells into nude mice. The transplantation of these irradiated cells resulted in tumour induction, an effect that was not found when unirradiated cells were transplanted into the mice (Hei et al. 1994). From these tumours, 6 different tumour cell lines were established and analyzed for cytogenetics. All of the lines were found to have CAs, and all harboured losses in genetic information (Weaver et al. 1997). Establishment of further tumour cell lines and their subsequent genetic analysis confirmed that there were CAs, especially in the form of deletions, that were common among the different tumour cell lines (Weaver et al. 2000).

Whether the CA is spontaneous or inherited may also be an important factor in the development of cancer. Non-clonal CAs, which are acquired spontaneously, promote genetic instability and are thought to confer a growth advantage. Ionizing radiation and carcinogens are two stressors that are thought to push the cell towards production of non-clonal CAs, which dominate during the pre-crisis stage of tumour development. After the tumour cells have passed the crisis stage and become immortal, clonal CAs (which are stable, inherited and recurrent in the cell population) dominate the CA landscape of the tumour. Clonal CAs are thought to confer a survival advantage to the cells. Overall, it is suggested that the shifting of equilibrium between non-clonal and clonal CAs is key in the initiation and progression of cancers (Heng, Stevens, et al. 2006; Heng, Bremer, et al. 2006). Interestingly, non-clonal CAs are affected by genotype. In both mouse embryonic stem cells and cultured lymphocytes that were lacking ATM, the spontaneous frequency of non-clonal CAs were significantly increased relative to wild-type cells; the same pattern was also observed in p53-/- cells from a human lung cancer cell line and an ovarian carcinoma cell line (Heng, Stevens, et al. 2006).

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. CNVs are often difficult to detect in cancer cells, even with current advances in next generation sequencing. This is due to the sheer number of CNVs that could possibly be present within one tumour; the unknown ratio of cancer cells and healthy cells within a tumour sample; the unknown ploidy of tumours; and the possible presence of multiple clones in one tumour, including possible low-number subclones that may be difficult to detect (Liu et al. 2013).  
  2. In some studies, smoking does not affect the CA-cancer relationship (Bonassi et al. 2000; Bonassi et al. 2008; El-zein et al. 2014; Vodenkova et al. 2015; El-zein et al. 2017), but it does have a significant effect in other studies (Paik et al. 2012; Lloyd et al. 2013; Minina et al. 2017).
  3. In a study examining MN in lung fibroblasts isolated from Wistar rats and Syrian hamsters exposed to radon, Syrian hamsters were found to have a significantly increased rate of MN per 1000 bincleated cells per Gy relative to rats. According to the literature however, Wistar rats have a higher documented sensitivity to radon-induced lung cancer than Syrian hamsters (Khan et al. 1995).
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

There is evidence of a response-response relationship between radiation exposure and CAs in cells of the lung, and between radiation exposure and the risk of lung cancer in radon-exposed miners. In two different studies using lung fibroblasts isolated from irradiated rodents, there was a positive, linear, dose-dependent relationship found between the radiation dose and the number of MN (Brooks et al. 1995; Khan et al. 1995). A number of in vitro studies also confirmed the presence of a positive, linear dose-dependent relationship between the number of radiation-induced CAs and the radiation dose (Nagasawa et al. 1990; Yamada et al. 2002; Stevens et al. 2014). In studies examining mortality from lung cancer in radon-exposed uranium miners from France and Germany, there was a positive linear relationship between the radon exposure and risk of lung cancer mortality (Tirmarchel et al. 1993; Walsh et al. 2010). This relationship was found to be exponentially modified by the age at median exposure, the time since median exposure, and the radon exposure rate (Walsh et al. 2010). Furthermore, oncogenic transformations in C3H10T1/2 cells irradiated with alpha particles were found to increase in a positive, linear dose-dependent fashion (Miller et al. 1996).

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

There is evidence suggesting that time-related predictions can be made for CA incidence and the development of lung cancer after exposure to ionizing radiation. CAs have been demonstrated to occur within hours of irradiation and persist for days afterwards.  In mouse bronchial epithelial cells, 1 Gy of X-ray radiation induced a significant increase in the percentage of binucleated cells with MN by 24 hours post-irradiation. These levels remained significantly elevated at 48 hours and 72 hours post-irradiation, though there was a time-dependent decrease in the percentage of cells with CAs. By 7 days post-irradiation, these levels were no longer significantly different from controls (Werner et al. 2017). In a similar study, lung fibroblasts were isolated and cultured from Wistar rats, Syrian hamsters and Chinese hamsters after exposure to 323, 278 and 496 WLM of radon, respectively, at 0.2, 15, and 30 days post-exposure. In all species, MN levels were highest at 0.2 days post-irradiation, and decreased over 30 days. The MN levels in the irradiated fibroblasts, however, remained significantly elevated at all time points relative to unirradiated control cells (Khan et al. 1995). Other in vitro studies have shown the presence of CAs within 13 - 82 hours post-irradiation (Nagasawa et al. 1990; Deshpande et al. 1996; Yamada et al. 2002; Stevens et al. 2014). It was noted in one study that the number or sister chromatid exchanges per cell were significantly higher than non-irradiated control cells at 72 hr post-irradiation, but these levels did not change appreciably at 74, 76, 78 or 82 hours post-irradiation (Deshpande et al. 1996).

In comparison to the time between radiation exposure and CA detection, there is a much longer gap between radiation exposure and the incidence of lung cancer. Oncogenic transformations in fibroblasts irradiated with alpha particles or X-rays were present 4 - 8 weeks after radiation exposure (Robertson et al. 1983; Miller et al. 1996). In vivo irradiation of 1 week-, 5 week- and 15 week-old rats by 1 Gy of thoracic X-rays was found to induce lung tumours months to years after the radiation treatment, with the highest risk for lung tumours found in rats that died between 600 and 900 days of age (Yamada et al. 2017). Similarly, French uranium miners exposed to radon and radon progeny for a minimum of two years were diagnosed at least 10 years after the first radon exposure (Tirmarchel et al. 1993).

Furthermore, direct injection of a CA into mice has also been shown to result in cancer several weeks after the CA administration. Injection of tumourigenic A549 cells that harbour a loss of heterozygosity at chromosome 11 resulted in tumour growth 3 weeks after injection (Kuramochi et al. 2001). Similarly, administration of the BCR/ABL translocation resulted in the mouse equivalent of chronic myelogenous leukemia by 21 - 31 days post-injection (Pear et al. 1998).

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

Some studies have documented modulating factors that affect CAs in lung cancer, including age, ethnicity (Lloyd et al. 2013), smoking (Feder et al. 1998; Paik et al. 2012; Lloyd et al. 2013; Minina et al. 2017), sex (Feder et al. 1998), and genotype (Kim et al. 2012; Minina et al. 2017). In NSCLC patients, ALK and EML4 rearrangements have reportedly been influenced by confounding variables such as age (Shaw et al. 2009; Wong et al. 2009; Sasaki et al. 2010), sex (Shaw et al. 2009), and smoking history (Koivunen et al. 2008; Shaw et al. 2009; Wong et al. 2009; Sasaki et al. 2010).

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 such as mice, rats, hamsters 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

Al-Zoughool, M. & D. Krewski (2009), "Health effects of radon: A review of the literature.", Int. J. Radiat. Biol., 85(1):57–69. doi:10.1080/09553000802635054.

Albertson, D.G. et al. (2003), "Chromosome aberrations in solid tumors.", Nature Genetics. 34(4):369-76. doi:10.1038/ng1215.

Balsara, B.R., J.R. Testa & J.M. Siegfried (1997), "Comparative genomic hybridization analysis detects frequent, often high-level overrepresentation of DNA sequences at 3q, 5p, 7p, and 8q in human non-small cell lung carcinomas", Cancer Research, 57(11):2116-20.

Bartova, E. et al. (2000), "The influence of the cell cycle, differentiation and irradiation on the nuclear location of the abl, bcr and c-myc genes in human leukemic cells.", Leukemia Research, 24(3):233-41, doi: 10.1016/S0145-2126(99)00174-5.

Bjorkqvist, A. et al. (1998), "DNA Gains in 3q Occur Frequently in Squamous Cell Carcinoma of the Lung, But Not in Adenocarcinoma.", Genes Chromosomes and Cancer, 22(1):79-82, doi: 10.1002/(SICI)1098-2264(199805)22:13.3.CO;2-6.

Boffetta, P. et al. (2007), "Original Contribution Chromosomal Aberrations and Cancer Risk: Results of a Cohort Study from Central Europe.", American Journal of Epidemiology, 165(1):36–43, doi:10.1093/aje/kwj367.

Bonassi, S. et al. (2000), "Chromosomal Aberrations in Lymphocytes Predict Human Cancer Independently of Exposure to Carcinogens. European study group on Cytogenetic Biomarkers and Health", Cancer Research, 60(6):1619–1625.

Bonassi, S. et al. (2008), "Chromosomal aberration frequency in lymphocytes predicts the risk of cancer: results from a pooled cohort study of 22,358 subjects in 11 countries.", Carcinogenesis, 29(6):1178–1183. doi:10.1093/carcin/bgn075.

Brooks, A.L. et al. (1995), "The Role of Dose Rate in the Induction of Micronuclei in Deep-Lung Fibroblasts In Vivo after Exposure to Cobalt-60 Gamma Rays The Role of Dose Rate in the Induction of Micronuclei in Deep-L Fibroblasts In Vivo after Exposure to Cobalt-60 Gamma Ray.", Radiat. Res, 144(1):114-8, doi:10.2307/3579244.

Danesi, R. et al. (2003), "Pharmacogenetics of Anticancer Drug Sensitivity in Non-Small Cell Lung Cancer." 55(1):57-103. doi:10.1124/pr.55.1.4.57.

Dang, T.P. et al. (2003), "Constitutive activation of Notch3 inhibits terminal epithelial differentiation in lungs of transgenic mice.", Oncogene, 22(13):1988–1997, doi:10.1038/sj.onc.1206230.

Dang, T.P. et al. (2000), "Chromosome 19 Translocation, Overexpression of Notch3, and Human Lung Cancer.", JNCI Journal of the National Cancer Institute, 92(16):1355–1357, doi: 10.1093/jnci/92.16.1355.

Deshpande, A.A. et al. (1996), "Alpha-Particle-Induced Sister Chromatid Exchange in Normal Human Lung Fibroblasts: Evidence for an Extranuclear Target.", Radiation Research, 145(3):260-267, doi: 10.2307/3578980.

El-Zein, R.A. et al. (2017), "Identification of Small and Non-Small Cell Lung Cancer Markers in Peripheral Blood Using Cytokinesis-Blocked Micronucleus and Spectral Karyotyping Assays.", Cytogenet. Genome Res., 152(3):122–131, doi:10.1159/000479809.

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