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Relationship: 1978

Title

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

Increase, Mutations leads to Increase, Cell Proliferation

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

Key Event Relationship Overview

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

AOPs Referencing Relationship

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 Do not cite EAGMST Under Review

Taxonomic Applicability

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

Mutations are defined as changes in the DNA sequence, which could occur in the form of deletions, insertions, missense mutations, nonsense mutations or frameshift mutations (Bertram, 2001; Danesi et al., 2003; Lodish, 2000). Elevated mutation frequencies may impact cellular activities by activating or inhibiting essential processes that control the natural course of cell proliferation (Bertram, 2001; Vogelstein and Kinzler, 2004; Lodish, 2000). Increased rates of cellular proliferation may arise due to mutations that activate proto-oncogenes, which results in sustained signaling for cell growth (Bertram, 2001; Vogelstein and Kinzler, 2004; Larsen and Minna, 2011; Lodish, 2000) and due to mutations that inactivate tumour suppressor genes (TSGs), resulting in the removal of cell cycle inhibition and/or decreased cell death signaling (Bertram, 2001; Vogelstein and Kinzler, 2004; Lodish, 2000). Mutations altering gene expression or protein activity can enable cells to escape growth inhibition by increasing resistance to apoptosis, or other inhibitory signals, or by escape of cell cycle checkpoints. Alternatively, mutations can stimulate growth by activating proliferative pathways such as EGFR.

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

There is a strong biological plausibility for a relationship between increasing mutation frequencies and increasing cellular proliferation.  This relationship is especially evident when examining the molecular biology of carcinogenesis. It is well-known that exposure of cells to a DNA-damaging agent, such as ionizing radiation, may result in damage to the DNA that manifests as genomic instability, including mutations. If enough mutations accumulate in critical genes, cells may begin to proliferate uncontrollably. This, alongside other events, may eventually result in tumourigenesis and cancer (reviewed in Bertram, 2001; Vogelstein and Kinzler, 2004; Panov, 2005; Lodish, 2000). In fact, one of the hallmarks of cancer is sustained proliferative signalling, and one of the enabling characteristics of this increased proliferation is genomic instability/mutations (Hanahan and Weinberg, 2011).

For a mutation to occur, damaged DNA must be passed on to the next generation (Bertram, 2001).  To prevent the propagation of erroneous DNA, there are specific cell cycle checkpoints that must be passed before DNA replication and mitosis can proceed. One of the most important checkpoints for committing to cell proliferation occurs during late G1 (Bertram, 2001; Lodish, 2000). This checkpoint is managed by retinoblastoma protein (RB), transcription factor E2F, and transcription factor p53. In a resting cell, RB is tightly bound to E2F; when growth factor signals are present, proteins are activated that phosphorylate RB, resulting in a conformation change and the release of E2F. This transcription factor then initiates transcription of genes required for DNA synthesis and thus cell proliferation. If there is damage to the DNA, p53 is upregulated and binds to unphosphorylated RB, thereby preventing the dissociation of RB and E2F (Bertram, 2001). This gives the cell enough time to repair the damaged DNA prior to DNA replication, and thus minimizes the propagation of the DNA errors. Existing mutations in the checkpoint genes, however, may compromise this process. For example, if mutations in p53 render it non-functional, damaged DNA will not be stopped at the checkpoint and will continue to be synthesized, despite the damage. Accumulation of mutations in this manner may affect genes that impact cell proliferation rates (Bertram, 2001; Lodish, 2000). There are three categories of genes that, if mutated, may allow for uncontrolled cell proliferation: proto-oncogenes, TSGs, and caretaker/stability genes. 

Proto-oncogenes are defined as genes that, when activated, promote cellular proliferation (Bertram, 2001; Lodish, 2000); they have been likened to the gas pedal of the car (Vogelstein and Kinzler, 2004). These genes are particularly dangerous if they are rendered abnormally active by gain-of-function (GOF) mutations; this may result in cellular proliferation being aberrantly activated (Bertram, 2001; Vogelstein and Kinzler,, 2004; Larsen and Minna 2011; Lodish, 2000).  Two common examples of mutated proto-oncogenes that contribute to increased cell proliferation rates are EGFR and KRAS. The EGFR gene encodes the epidermal growth factor receptor (EGFR), a trans-membrane protein with tyrosine kinase activity. Binding of growth factors to EGFRs results in receptor dimerization, autophosphorylation, and propagation of pro-proliferative signals to the nucleus (Danesi et al., 2003; Santos et al., 2010; Larsen and Minna, 2011; NIH, 2018 EGFR). KRAS is responsible for making the KRAS protein, which is a G-protein with GTPase activity that is used in the RAS/MAPK signalling pathway. When a signal that promotes cellular growth is detected, KRAS binds to GTP and activates downstream signalling molecules, thus facilitating signal propagation to the nucleus (Adjei, 2001; Panov, 2005; Jancik et al., 2010; NIH, 2018 KRAS). Mutations that render these receptors constitutively active would thus result in increased rates of cellular proliferation (Sanders and Albitar, 2010).

TSGs, which are analogous to the brakes in a car (Vogelstein and Kinzler, 2004; Lodish, 2000), are genes that negatively regulate cellular growth by preventing proliferation and in some cases, promoting apoptosis (Bertram, 2001; Vogelstein and Kinzler, 2004; Panov, 2005; Sanders and Albitar, 2010; Lodish, 2000). Many of the cell cycle checkpoint proteins and proteins controlling cell death are TSGs (Bertram, 2001; Lodish, 2000). Loss-of function (LOF) mutations that result in the inactivation of these TSGs may thus promote cellular proliferation (Bertram, 2001; Vogelstein and Kinzler, 2004; Lodish, 2000). A common example of a mutated TSG is TP53, which encodes the p53 protein. As mentioned above, p53 is a cell checkpoint protein that delays replication when damaged DNA is present; if damage is severe enough, p53 may also activate an apoptotic pathway (Bertram, 2001; Danesi et al., 2003; Panov, 2005; Larsen and Minna, 2011; Lodish, 2000, NIH 2018c). Inactivating mutations in p53 thus allow for unhindered progression through the cell cycle, resulting in higher cell proliferation rates (Danesi et al., 2003; Fernandez-Antoran et al., 2019).

Finally, caretaker/stability genes encode for proteins involved in the detection, repair and prevention of DNA damage (Vogelstein and Kinzler 2004; Hanahan and Weinberg 2011).  Genes involved in mismatch repair (MMR), nucleotide excision repair (NER) and base-excision repair (BER) pathways are examples of caretaker/stability genes (Vogelstein and Kinzler, 2004). Mutations in these genes may compromise aspects of DNA repair—the detection of damage, the initiation of repair, the repair process itself, or the removal of mutagens that could possibly damage DNA—thus allowing for more mutations to accumulate in the genome than usual (Hanahan and Weinberg, 2011). Although all genes may suffer from increased mutation rates when caretaker/stability genes are improperly functioning, mutations in TSGs and proto-oncogenes are the main contributors to increased cellular proliferation (Vogelstein and Kinzler, 2004). Caretaker/stability genes are similar to TSGs in that disruption of both alleles must occur for the gene function to be compromised (Vogelstein and Kinzler, 2004; Hanahan and Weinberg, 2011).

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 in this KER are as follows:

  1. The location of the mutation will be critical in determining the downstream effects. This can also be modulated by an individual’s susceptibility (Loewe and Hill 2010).
  2. Although activating mutations in oncogenes such as RAS and MYC may induce abnormally high rates of cellular proliferation, extremely high levels of these proteins may actually lead to the opposite—cells may enter into a state of senescence and cease proliferation (Hanahan and Weinberg 2011).\
  3. Cellular proliferation may be impacted by circadian cycles, such that disruptions to this natural circadian rhythm may also affect the cell cycle (Shostak 2017).
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

Data establishing a response-response relationship between mutation frequency and cellular proliferation was not identified. More research is required to establish the response-response relationship between these two events.

Time-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

Although the time scale is not well-established for this KER, there are a few studies that have examined how cellular proliferation changes overtime in the presence of mutations. In Cul9-/- mouse embryonic fibroblasts, a higher proliferation rate relative to Cul9+/+ cells was evident by 3 days in culture (Li and Xiong 2017). A similar relationship was observed in mouse embryonic fibroblasts with p53 manipulations. Increased proliferation in p53-/-, p53 515A/+ and p53 515A/515A relative to p53+/- and p53+/+ cells was present by the fourth day in culture (Lang et al. 2004).  Examination of population doublings in various cell lines found that Cul9-/- and Cul9 mutant cells had higher population doublings than wild-type cells by approximately passage 7; Arf-/-, p53-/-, and Cul9-/-p53-/- cells, however, displayed even higher rates of population doublings by passage 6  (Li and Xiong 2017). Additionally, tumour growth in mice inoculated with lung epithelial cells engineered with LT (suppresses p53 and pRB) and an activated oncogene (either EGFR or KRAS) was monitored over 40 days post-injection. Relative to mice inoculated with either LT-lung epithelial cells or activated oncogene-lung epithelial cells, mice inoculated cells containing both mutations had detectable tumours by approximately day 10 - 12 post-injection; the volumes of these tumours continued increasing until the end of the experiment (Sato et al. 2017).

There were also differences in the rate of DNA synthesis over time, which could possibly indicate higher rates of cell division. In all cell types examined (p53-/-, p53+/- and p53+/+, p53 515A/+, and p53 515A/515A), DNA synthesis declined over the first 6 days in culture, though the mutant p53 lines always had higher synthesis rates than p53-/-, p53+/- and p53+/+ cells. During culture days 6 - 10, DNA synthesis in the mutant p53 lines drastically increased, while the other p53 lines remained at the same relatively low level of synthesis (Lang et al. 2004).  

Known modulating 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
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

Proliferation increases the likelihood that existing DNA damage will result in mutation and creates new mutations through errors in replication.

It is generally accepted that proliferation increases the risk of mutation and cancer (Preston-Martin, Pike et al. 1990). DNA damage that has not been completely or correctly repaired when a cell undergoes mitosis can be fixed in the genome permanently as a mutation, to be propagated to future daughter cells. Incomplete DNA repair can also cause additional DNA damage when encountered by replicative forks. Therefore, in the presence of any DNA damage (and there is a background rate of damage in addition to any other genotoxic stimuli) mutations will increase with cell division (Kiraly, Gong et al. 2015). Mutation-prone double strand breaks can also arise from replicative stress in hyperplastic cells including hyperplasia arising from excess growth factor stimulation (Gorgoulis, Vassiliou et al. 2005). This relationship between proliferation and mutation is thought to drive a significant portion of the risk of cancer from estrogen exposure since breast cells proliferate in response to estrogen or estrogen plus progesterone and risk increases with cumulative estrogen exposure (Preston-Martin, Pike et al. 1990).

Not all proliferating tissue shows replicative stress and DSBs - tissue with a naturally high proliferative index like colon cells don’t show any sign of damage (Halazonetis, Gorgoulis et al. 2008). Additional factors are therefore required beyond replication for damage and mutation from replicative stress, but replication is essential for the expression of these factors.

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 pertains to all multicellular organisms, as cell proliferation and death regulate tissue homeostasis (Pucci et al. 2000).

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

Adjei, A.A. (2001), "Blocking Oncogenic Ras Signaling for Cancer Therapy.", Journal of the National Cancer Institute, 93(14):1062–1074. doi:10.1093/jnci/94.13.1032

Bertram, J.S. (2001), "The molecular biology of cancer.", Mol. Aspects. Med. 21:166–223. doi:10.1016/S0098-2997(00)00007-8.

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.

Duan, W. et al. (2008), "Lung specific expression of a human mutant p53 affects cell proliferation in transgenic mice.", Transgenic Res. 17(3):355–366. doi:10.1007/s11248-007-9154-3.

Fernandez-Antoran, D. et al., (2019), Outcompeting p53-mutant cells in the normal esophagus by redox manipulation., Cell Stem Cell. 25(3):329-341. doi: 10.1016/j.stem.2019.06.011.

Forbes, S.A. et al. (2011), "COSMIC: Mining Complete Cancer Genomes in the Catalogue of Somatic Mutations in Cancer.", Nucleic Acids Res., 39(Database issue):945–950. doi:10.1093/nar/gkq929.

Geng, C. et al. (2017), "SPOP regulates prostate epithelial cell proliferation and promotes ubiquitination and turnover of c-MYC oncoprotein.", Oncogene. 36(33):4767–4777. doi:10.1038/onc.2017.80.

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.

Hundley, J.E. et al. (1997), "Increased Tumor Proliferation and Genomic Instability without Decreased Apoptosis in MMTV- ras Mice Deficient in p53.", Mol. Cell. Biol. 17(2):723–731. doi:10.1128/MCB.17.2.723.

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.

Jancik, S. et al. (2010), "Clinical Relevance of KRAS in Human Cancers", J. BioMed. & BioTech. 2010. doi:10.1155/2010/150960.

Jarvis, E.M., J.A. Kirk & C.L. Clarke (1998), "Loss of Nuclear BRCA1 Expression in Breast Cancers Is Associated with a Highly Proliferative Tumor Phenotype.", Cancer Genet Cytogenet. 101(97):101–115. doi:10.1016/S0165-4608(97)00267-7.

Kim, M.P. & G. Lozano (2018), "Mutant p53 partners in crime.", Nat Publ Gr. 25(1):161–168. doi:10.1038/cdd.2017.185.

Lang, G.A. et al. (2004), "Gain of Function of a p53 Hot Spot Mutation in a Mouse Model of Li-Fraumeni Syndrome.", Cell. 119(6):861–872. doi:10.1016/j.cell.2004.11.006.

Larsen, J.E. & J. Minna (2011), "Molecular Biology of Lung Cancer: Clinical Implications.", Clin. Chest Med., 32(4):703–740. doi:10.1016/j.ccm.2011.08.003.

Li, Z. & Y. Xiong (2017), "Cytoplasmic E3 ubiquitin ligase CUL9 controls cell proliferation, senescence, apoptosis and genome integrity through p53.", Oncogene, 36(36):5212–5218. doi:10.1038/onc.2017.141.

Lodish, H. et al. "Molecular Cell Biology.", 4th edition. New York: W. H. Freeman; 2000. Available from: https://www.ncbi.nlm.nih.gov/books/NBK21475.

Loewe, L. & W.G. Hill (2010), "The population genetics of mutations: Good, bad and indifferent." Philos Trans R Soc B Biol Sci. 365(1544):1153–1167. doi:10.1098/rstb.2009.0317.

López-lázaro M. (2018), "The stem cell division theory of cancer.", Crit. Rev. Oncol. Hematol. 123:95–113. doi:10.1016/j.critrevonc.2018.01.010.

Muller, P.A., K.H. Vousden & J.C. Norman (2011), "p53 and its mutants in tumor cell migration and invasion.", J. Cell. Biol. 192(2):209–218. doi:10.1083/jcb.201009059.

NIHa,  EGFR gene epidermal growth factor receptor Normal. 2018. Genetics Home Reference EGFR gene.

NIHb, KRAS proto-oncogene, GTPase Normal. 2018. Genetics Home Reference KRAS gene.

NIHc, TP53 gene tumor protein p53 Normal. 2018. Genetics Home Reference TP53 gene.

Beir V. 1999. Health Effects of Exposure to Radon. National Academies Press.

Panov, S.Z. (2005), "Molecular biology of the lung cancer.", Radiology and Oncology 39(3):197–210.

Pucci, B., M. Kasten & A. Giordano (2000), "Cell Cycle and Apoptosis 1", Neoplasia, 2(4):291–299.doi: 10.1038/sj.neo.7900101

Sanders, H.R. & M. Albitar (2010), "Somatic mutations of signaling genes in non-small-cell lung cancer.", Cancer Genet Cytogenet. 203(1):7–15. doi:10.1016/j.cancergencyto.2010.07.134.

da Cunha Santos, G., F.A. Shepherd & M.S. Tsao (2010), "EGFR Mutations and Lung Cancer.", Annu Rev. Pathol., doi:10.1146/annurev-pathol-011110-130206.

Sato, T. et al. (2017), "Ex vivo model of non-small cell lung cancer using mouse lung epithelial cells.", Oncol. Lett. :6863–6868. doi:10.3892/ol.2017.7098.

Schabath, M.B. et al. (2016), "Differential association of STK11 and TP53 with KRAS.", Oncogene, 35(24):3209–3216. doi:10.1038/onc.2015.375.

Shostak, A. (2017), "Circadian Clock, Cell Division, and Cancer: From Molecules to Organism.", Int. J. Mol. Sci., doi:10.3390/ijms18040873.

Ventura, A. et al. (2007), "Restoration of p53 function leads to tumour regression in vivo.", Nature 445(7128):661-5. doi:10.1038/nature05541.

Vogelstein, B. & K.W. Kinzler (2004), "Cancer genes and the pathways they control.", Nat. Med, 10(8):789–799. doi:10.1038/nm1087.

Welcker, M. & B.E. Clurman (2008), "FBW7 ubiquitin ligase: a tumour suppressor at the crossroads of cell division, growth and differentiation.", Nature Publishing Group. doi:10.1038/nrc2290.