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Relationship: 1979


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, 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 Moderate Low Brendan Ferreri-Hanberry (send email) Under development: Not open for comment. Do not cite EAGMST Approved

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

CAs are defined as abnormalities in the chromosome structure, often due to losses or gains of chromosome sections or the entire chromosomes itself, or chromosomal rearrangements (van Gent et al., 2001). These aberrant structures can come in a multitude of different forms. Types of CAs include: inversions, insertions, deletions, translocations, dicentric chromosomes (chromosomes that contain two centromeres, often resulting from telomere end fusions (Fenech & Natarajan 2011; Rode et al., 2016), centric ring chromosomes, acentric chromosome fragments, micronuclei (MN; small nucleus-like structures containing entire chromosomes or chromosome fragments (Fenech & Natarajan, 2011; Doherty et al., 2016), nucleoplasmic bridges (NBPs; a corridor of nucleoplasmic material containing chromatin that is attached to both daughter cell nuclei), nuclear buds (NBUDs; small MN-type structures that are still connected to the main nucleus (Fenech & Natarajan, 2011), and copy number variants (CNVs; deletions or duplications of chromosome segments (Russo et al., 2015).

If these CAs affect genes involved in controlling the cell cycle, this may result in increased cellular proliferation. There are three types of genes that, if modified, may result in high rates of proliferation: proto-oncogenes, tumour suppressor genes (TSGs), and caretaker/stability genes (Vogelstein & Kinzler, 2004; Hanahan & Weinberg, 2011). Furthermore, gene fusions that result from CAs have also been implicated in augmenting cellular proliferation (Sanders & Albitar, 2010; Ghazavi et al., 2015; Kang et al., 2016).

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 CAs and rates of cellular proliferation. This is particularly emphasized in the context of carcinogenesis, as high cellular proliferation is a known hallmark of cancer, and an enabling characteristic of increased proliferation is genomic instability (Hanahan & Weinberg, 2011).Topical reviews are available documenting the contribution of CAs to cellular proliferation and/or cancer development  (Mes-Masson & Witte, 1987; Bertram, 2001; Vogelstein & Kinzler, 2004; Ghazavi et al. ,2015; Kang et al., 2016). The link between chromosomal instability (CIN), which describes the rate of chromosome gains and losses, and cancer development has also been well documented (Thompson et al., 2017; Gronroos, 2018; Targa & Rancati, 2018; Lepage et al., 2019).

Many CAs are thought to be formed through two main mechanisms: inadequate repair of DNA damage, and errors in mitosis. If there is damage to the DNA that the cell is unable to properly repair, the unrepaired lesion may translate into a CAs (Bignold, 2009; Danford, 2012; Schipler & Iliakis, 2013); the type of resulting CA is often influenced by the cell cycle stage when the damage occurred (Danford, 2012; Registre et al., 2016; Vodicka et al., 2018), and the type of erroneous repair (Ferguson & Alt, 2001; Povirk, 2006; Bignold, 2009; Danford, 2012; Schipler & Iliakis, 2013). Errors made during repair may be particularly detrimental if they interrupt or modify critical genes, or if chromosome structures are created that cannot undergo mitosis (Schipler & Iliakis, 2013). Similarly, errors in mitosis that prevent chromosomes from being properly segregated may also lead to CAs. These errors could be due to by improper timing of centrosome separation, the presence of extra centrosomes, inappropriate mitotic spindle assembly and attachment to kinetochores (found on the centromeres), and incorrect sister-chromatid cohesion (Levine & Holland, 2018).

The presence of CAs in cells may be particularly detrimental if they alter the rate of cellular proliferation by affecting  genes that control the cell cycle, namely proto-oncogenes, TSGs (Bertram, 2001; Vogelstein & Kinzler, 2004) or caretaker/stability genes (Vogelstein & Kinzler, 2004). Proto-oncogenes are genes that, when activated, promote cellular proliferation. CAs that increase activation of these genes may aberrantly boost cell cycling and therefore increase proliferation (Bertram, 2001; Vogelstein & Kinzler, 2004). Activation of proto-oncogenes have also been implicated in the cancer stem cell theory of carcinogenesis (Vicente-duen et al., 2013). Examples or proto-oncogenes include EGFR and KRAS (Sanders & Albitar, 2010). TSGs refer to genes that actively suppress cell proliferation and, in some cases, promote apoptosis (Bertram, 2001; Vogelstein & Kinzler, 2004; Sanders & Albitar, 2010). If these genes are silenced by CAs, this may remove cell cycle checkpoints, thus allowing for unhindered cellular proliferation and decreased apoptosis (Bertram, 2001; Vogelstein & Kinzler, 2004). Common TSGs are TP53 and RB (Hanahan & Weinberg, 2011). Lastly, caretaker/stability genes are those involved in the prevention and detection of DNA damage, and the instigation and completion of the required DNA repair (Vogelstein & Kinzler, 2004; Hanahan & Weinberg, 2011). If the function of these caretaker/stability genes is affected by CAs, this may result in genome-wide inadequate DNA repair, which in turn may result in genetic damage to TSGs or proto-oncogenes (Vogelstein & Kinzler, 2004). Genes involved in mismatch repair (MMR), nucleotide-excision repair (NER) and base-excision repair (BER) are all examples of caretaker/stability genes (Vogelstein & Kinzler, 2004). 

There are also other CAs commonly associated with cancer. In prostate cancer, truncated TSGs such as TP53, PTEN, BRCA1, and BRCA2 are a result of chromosomal rearrangements (Mao et al., 2011). Similarly, chromosomal inversions were found to be responsible for just over half of the RET gene fusions associated with lung adenocarcinoma samples (Mizukami et al., 2014).

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. A study using peripheral blood lymphocytes isolated from head and neck cancer patients found significantly increased CAs (including chromosome-type aberrations, chromatid-type aberrations, dicentric chromosomes, aneuploidy, MN, NPBs and NBUDs) relative to healthy controls. In the lymphocytes from these same cancer patients, however, the cell proliferation rates were significantly decreased (George et al., 2014).
  2. Characterization of 20 different ameloblastomas, which are benign tumours associated with the jaw, found low CAs frequencies and low rates of cellular proliferation (Jääskeläinen et al., 2002).
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

Not established.

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

Studies that directly assessed the time scale between CAs and cellular proliferation were not identified. However, differences in cellular proliferation rates for cells with different CA-related manipulations or treatments were evident within the first 3 days of culture (Stopper et al., 2003; Li et al., 2007; Soda et al., 2007; Irwin et al., 2013; Guarnerio et al., 2016). More studies are required, however, to formulate a detailed time scale relating these two events.

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

Not established.

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 established.

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).


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

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

Bignold, L.P. (2009), "Mechanisms of clastogen-induced chromosomal aberrations: A critical review and description of a model based on failures of tethering of DNA strand ends to strand-breaking enzymes.", Mutat. Res., 681(2-3):271–298. doi:10.1016/j.mrrev.2008.11.004.

Danford, N. (2012), "The Interpretation and Analysis of Cytogenetic Data.", Methods Mol. Biol., 817:93-120. doi:10.1007/978-1-61779-421-6.

Doherty, A., S.M. Bryce & J.C. Bemis (2016), "The In Vitro Micronucleus Assay. Methods in molecular biology", (Clifton, N.J.). 817:121-41. doi: 10.1007/978-1-61779-421-6_7.

Fenech, M. & A.T. Natarajan (2011), "Molecular mechanisms of micronucleus, nucleoplasmic bridge and nuclear bud formation in mammalian and human cells.", Mutagenesis 26(1):125–132. doi:10.1093/mutage/geq052.

Ferguson, D.O. & F.W. Alt (2001), "DNA double strand break repair and chromosomal translocation: Lessons from animal models.", Oncogene 20(40):5572–5579.

Fowlis, D.J. & A. Balmain (1993), "Oncogenes and Tumour Suppressor Genes in Transgenic Mouse Models of Neoplasia.", Eur. J. of Cancer 29A(4):638-45. doi: 10.1016/S0959-8049(05)80170-4.

van Gent D.C., J.H.J. Hoeijmakers & R. Kanaar (2001), "Chromosomal stability and the DNA double-stranded break connection.", Nat. Rev. Genet. 2(3):196–206. doi:10.1038/35056049.

George, A., R. Dey & V.B. Dqhumhh (2014), "Nuclear Anomalies, Chromosomal Aberrations and Proliferation Rates in Cultured Lymphocytes of Head and Neck Cancer Patients.", Asian Pacific journal of cancer prevention. 15(3):1119-1123. doi:10.7314/APJCP.2014.15.3.1119.

Ghazavi, F. et al. (2015), "Molecular basis and clinical significance of genetic aberrations in B-cell precursor acute lymphoblastic leukemia.", Exp Hematol. 43(8):640–653. doi:10.1016/j.exphem.2015.05.015.

Gronroos, E. (2018), "Tolerance of Chromosomal Instability in Cancer: Mechanisms and Therapeutic Opportunities.", Cancer Res. 78(23):6529-6535, doi:10.1158/0008-5472.CAN-18-1958.

Guarnerio, J. et al. (2016), "Oncogenic Role of Fusion-circRNAs Derived from Article Oncogenic Role of Fusion-circRNAs Derived from Cancer-Associated Chromosomal Translocations.", Cell. 165(2):289–302. doi:10.1016/j.cell.2016.03.020.

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.

Irwin, M.E. et al. (2013), "Small Molecule ErbB Inhibitors Decrease Proliferative Signaling and Promote Apoptosis in Philadelphia Chromosome – Positive Acute Lymphoblastic Leukemia.", PLoS One, 8(8):1–10. doi:10.1371/journal.pone.0070608.

Jääskeläinen, K. et al. (2002), "Cell proliferation and chromosomal changes in human ameloblastoma.", Cancer Genetics and Cytogenetics. 136(1):31-7. doi: 10.1016/S0165-4608(02)00512-5.

Kang, Z.J. et al. (2016), "The Philadelphia chromosome in leukemogenesis.", Chin J Cancer.:1–15. doi:10.1186/s40880-016-0108-0.

Lepage, C.C. et al. (2019), "Detecting Chromosome Instability in Cancer: Approaches to Resolve Cell-to-Cell Heterogeneity.", Cancers (Basel), 11(2): pii: E226. doi:10.3390/cancers11020226.

Levine, M.S. & A.J. Holland (2018), "The impact of mitotic errors on cell proliferation and tumorigenesis.", Genes Dev., 32(9-10):620–638. doi:10.1101/gad.314351.118.620.

Li, H. et al. (2007), "Effects of rearrangement and allelic exclusion of JJAZ1 / SUZ12 on cell proliferation and survival.", PNAS, 104(50):20001–20006.

Mao, X. et al. (2011), "Chromosome rearrangement associated inactivation of tumour suppressor genes in prostate cancer.", American Journal of Cancer Research. 1(5):604-17.

Mes-Masson, A.-M. & O.N. Witte (1987), "Role of The abl Oncogene in Chronic Myelogenous Leukemia.", Advances in Cancer Research. 49:53-74. doi: 10.1016/S0065-230X(08)60794-0.

Mitelman, F. (2005), "Deep Insight Section: Cancer cytogenetics update", Atlas of Genetic and Cytogenetics in Oncology and Haematology,  9(2):188–190. doi:10.4267/2042/38202.

Mizukami, T. et al. (2014), "Molecular Mechanisms Underlying Oncogenic RET Fusion in lung adenocarcinoma.", J Thorac Oncol. 9(5):622–630. doi:10.1097/JTO.0000000000000135.

Povirk, L.F. (2006), "Biochemical mechanisms of chromosomal translocations resulting from DNA double-strand breaks.", DNA Repair (Amst). 5(9-10):1199–1212. doi:10.1016/j.dnarep.2006.05.016.

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

Registre, M., R. Proudlock & N. Carolina (2016), "The In Vitro Chromosome Aberration Test.", Genetic Toxicology Testing. 207-267. doi: 10.1016/B978-0-12-800764-8.00007-0.

Rode, A. et al. (2016), "Chromothripsis in cancer cells: An update.", Int. J. Cancer, 138(10):2322–2333. doi:10.1002/ijc.29888.

Russo, A. et al. (2015), "Review Article Genomic Instability: Crossing Pathways at the Origin of Structural and Numerical Chromosome Changes.", Envrion. Mol. Mutagen. 56(7):563-580. doi:10.1002/em.

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.

Schipler, A. & G. Iliakis (2013), "DNA double-strand – break complexity levels and their possible contributions to the probability for error-prone processing and repair pathway choice.", Nucleic Acids Res., 41(16):7589–7605. doi:10.1093/nar/gkt556.

Soda, M. et al. (2007), "Identification of the transforming EML4 – ALK fusion gene in non-small-cell lung cancer.", Nature, 448(7153):561-566. doi:10.1038/nature05945.

Stopper, H. et al. (2003), "Increased cell proliferation is associated with genomic instability: elevated micronuclei frequencies in estradiol-treated human ovarian cancer cells.", Mutagenesis 18(3):243-247. doi:10.1093/mutage/18.3.243.

Targa, A. & G. Rancati (2018), "Cancer: a CINful evolution.", Curr Opin Cell Biol. 2018 Jun;52:136-144., doi:10.1016/

Thompson, L.L. et al. (2017), "Evolving Therapeutic Strategies to Exploit Chromosome Instability in Cancer.", Cancers (Basel), 9(11): pii: E151 doi:10.3390/cancers9110151.

Vicente-Duenas, C. et al. (2013), "Function of oncogenes in cancer development: a changing paradigm", EMBO J., 32(11):1502–1513. doi:10.1038/emboj.2013.97.

Vodicka, P. et al. (2018), "Genetic variation of acquired structural chromosomal aberrations.", Mutat Res Gen Tox En, 836(May):13–21. doi:10.1016/j.mrgentox.2018.05.014.

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