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Relationship: 1896

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 (Epithelial Cells)

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
Increased reactive oxygen and nitrogen species (RONS) leading to increased risk of breast cancer adjacent Moderate Not Specified Jessica Helm (send email) Under development: Not open for comment. Do not cite Under Development
Increased DNA damage leading to increased risk of breast cancer adjacent Moderate Not Specified Jessica Helm (send email) Under development: Not open for comment. Do not cite Under Development

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

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

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

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 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 is High. Multiple mechanisms limit the proliferation of cells in healthy biological systems. Mutations in many of the genes controlling these mechanisms promote proliferation.

Empirical support is Moderate. Mutations that promote proliferation are frequently found in cancers, and both mutation and proliferation occur in response to tumorigenic stressors like ionizing radiation. Mutations appear over the same time frame or prior to the appearance of proliferation. Multiple uncertainties and conflicting evidence weaken this key event relationship. The two key events differ in their dose response- mutation but not proliferation increases with ionizing radiation dose. Furthermore, a single mutation is not necessarily sufficient to increase proliferation- proliferation typically requires multiple mutations or a change in the surrounding environment. In mammary tissue, stromal state – which is modified by hormones - strongly influences the proliferative nature of epithelial cells, and mutated epithelial cells alone appear to be insufficient for tumor growth.

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

High. Multiple mechanisms limit the proliferation of cells in healthy biological systems. Mutations in many of the genes controlling these mechanisms promote proliferation. Biological mechanisms such as contact inhibition, apoptosis, cell cycle checkpoints, and growth factor availability act to restrain proliferation (Sonnenschein and Soto 1999). Under conditions of proliferation such as ductal branching during development of the mammary gland, selected mechanisms are engaged to permit controlled or directed proliferation. In the case of ductal branching, stromal cells respond to estrogen and growth hormone by releasing IGF1, which activates IGF-1R in epithelial cells to promote survival and proliferation (Hinck and Silberstein 2005; Sternlicht, Sunnarborg et al. 2005; Sternlicht 2006). At puberty, epithelial cells respond to estrogen by signaling to the stroma via EGFR to which the stroma replies with proliferative signals via FGFR (Sternlicht, Sunnarborg et al. 2005; Sternlicht 2006). Multiple additional mechanisms of control include proliferation inhibition by TGF-β, which can both directly inhibit proliferation (Francis, Bergsied et al. 2009) and act through stromal cells to stabilize an inhibitory extracellular matrix (Hinck and Silberstein 2005). When mechanisms controlling proliferation are altered, proliferation can occur outside of the normal biological context (Radice, Ferreira-Cornwell et al. 1997; Davies, Platt-Higgins et al. 1999; Ewan, Shyamala et al. 2002; Lanigan, O'Connor et al. 2007; Croce 2008; de Ostrovich, Lambertz et al. 2008).

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

Mutations are clearly not the only events driving proliferation in mammary gland, particularly in female mammary glands after exposure to a stressor like ionizing radiation where proliferation varies with age and microenvironment (Tang, Fernandez-Garcia et al. 2014). In mammary tissue, stromal state strongly influences the proliferative and metastatic nature of epithelial cells, and mutated epithelial cells alone appear to be insufficient for tumor growth.  Stroma exposed to carcinogens can make transplanted unexposed epithelial cells tumorigenic in rats (Maffini, Soto et al. 2004) and transplanted p53 mutant epithelial cells tumorigenic in BALB/c mice (Barcellos-Hoff and Ravani 2000), while neither epithelia exposed to carcinogens nor p53 mutant cells are tumorigenic when transplanted into unexposed animals (Barcellos-Hoff and Ravani 2000; Maffini, Soto et al. 2004). Similarly, post-lactational stroma can make tumor cells more invasive and metastatic than nulliparous stroma (McDaniel, Rumer et al. 2006), and younger and nulliparous stroma makes tumor cells proliferate more than older and multiparous stroma (Maffini, Calabro et al. 2005). Even proliferating tissue and tumors can regress (Haslam and Bern 1977; Purnell 1980), suggesting that proliferation is insufficient for carcinogenesis in some cases.

While mutations increase linearly in response to ionizing radiation or carcinogens, proliferation (or proliferation of stem cell populations) apparently does not (Beuving, Bern et al. 1967; Mukhopadhyay, Costes et al. 2010; Nguyen, Oketch-Rabah et al. 2011; Tang, Fernandez-Garcia et al. 2014). Because we expect only a subset of mutations to affect cell-cycle or proliferation-related genes and because most cells require multiple mutations for proliferation to commence, only a very small number of cells would be expected to proliferate in response to mutation. It is therefore possible that the proliferation typically observed is actually due to a separate mechanism such as the self-renewal of stem-like or senescent-resistant cells and that a delayed mutation-based proliferation is not being measured.

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
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
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.

Gorgoulis, V. G., L. V. Vassiliou, et al. (2005). "Activation of the DNA damage checkpoint and genomic instability in human precancerous lesions." Nature 434(7035): 907-913.

Halazonetis, T. D., V. G. Gorgoulis, et al. (2008). "An oncogene-induced DNA damage model for cancer development." Science 319(5868): 1352-1355.

Kiraly, O., G. Gong, et al. (2015). "Inflammation-induced cell proliferation potentiates DNA damage-induced mutations in vivo." PLoS Genet 11(2): e1004901.

Preston-Martin, S., M. C. Pike, et al. (1990). "Increased cell division as a cause of human cancer." Cancer Res 50(23): 7415-7421.

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

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

Alexandrov, L. B., S. Nik-Zainal, et al. (2013). "Signatures of mutational processes in human cancer." Nature 500(7463): 415-421.

Ameziane-El-Hassani, R., M. Boufraqech, et al. (2010). "Role of H2O2 in RET/PTC1 chromosomal rearrangement produced by ionizing radiation in human thyroid cells." Cancer Res 70(10): 4123-4132.

Barcellos-Hoff, M. H. and S. A. Ravani (2000). "Irradiated mammary gland stroma promotes the expression of tumorigenic potential by unirradiated epithelial cells." Cancer Res 60(5): 1254-1260.

Beuving, L. J., H. A. Bern, et al. (1967). "Occurrence and Transplantation of Carcinogen-Induced Hyperplastic Nodules in Fischer Rats2." JNCI: Journal of the National Cancer Institute 39(3): 431-447.

Beuving, L. J., J. L. J. Faulkin, et al. (1967). "Hyperplastic Lesions in the Mammary Glands of Sprague-Dawley Rats After 7,12-Dimethylbenz[a]anthracene Treatment2." JNCI: Journal of the National Cancer Institute 39(3): 423-429.

CGAN (Cancer Genome Atlas Network) (2012). "Comprehensive molecular portraits of human breast tumours." Nature 490(7418): 61-70.

Cho, S. J., H. Kang, et al. (2016). "Site-Specific Phosphorylation of Ikaros Induced by Low-Dose Ionizing Radiation Regulates Cell Cycle Progression of B Lymphoblast Through CK2 and AKT Activation." International journal of radiation oncology, biology, physics 94(5): 1207-1218.

Croce, C. M. (2008). "Oncogenes and cancer." The New England journal of medicine 358(5): 502-511.

Datta, K., D. R. Hyduke, et al. (2012). "Exposure to ionizing radiation induced persistent gene expression changes in mouse mammary gland." Radiat Oncol 7: 205.

Davies, B. R., A. M. Platt-Higgins, et al. (1999). "Development of hyperplasias, preneoplasias, and mammary tumors in MMTV-c-erbB-2 and MMTV-TGFalpha transgenic rats." The American journal of pathology 155(1): 303-314.

Davies, H., D. Glodzik, et al. (2017). "HRDetect is a predictor of BRCA1 and BRCA2 deficiency based on mutational signatures." Nat Med 23(4): 517-525.

de Ostrovich, K. K., I. Lambertz, et al. (2008). "Paracrine overexpression of insulin-like growth factor-1 enhances mammary tumorigenesis in vivo." The American journal of pathology 173(3): 824-834.

Ewan, K. B., G. Shyamala, et al. (2002). "Latent transforming growth factor-beta activation in mammary gland: regulation by ovarian hormones affects ductal and alveolar proliferation." Am J Pathol 160(6): 2081-2093.

Faulkin, J. L. J., C. J. Shellabarger, et al. (1967). "Hyperplastic Lesions of Sprague-Dawley Rat Mammary Glands After X Irradiation2." JNCI: Journal of the National Cancer Institute 39(3): 449-459.

Fibach, E. and E. A. Rachmilewitz (2015). "The Effect of Fermented Papaya Preparation on Radioactive Exposure." Radiation research 184(3): 304-313.

Francis, S. M., J. Bergsied, et al. (2009). "A functional connection between pRB and transforming growth factor beta in growth inhibition and mammary gland development." Molecular and cellular biology 29(16): 4455-4466.

Francis, S. M., S. Chakrabarti, et al. (2011). "A context-specific role for retinoblastoma protein-dependent negative growth control in suppressing mammary tumorigenesis." PLoS One 6(2): e16434.

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Gustin, J. P., B. Karakas, et al. (2009). "Knockin of mutant PIK3CA activates multiple oncogenic pathways." Proceedings of the National Academy of Sciences of the United States of America 106(8): 2835-2840.

Han, W., S. Chen, et al. (2010). "Nitric oxide mediated DNA double strand breaks induced in proliferating bystander cells after alpha-particle irradiation." Mutation research 684(1-2): 81-89.

Haslam, S. Z. and H. A. Bern (1977). "Histopathogenesis of 7,12-diemthylbenz(a)anthracene-induced rat mammary tumors." Proceedings of the National Academy of Sciences of the United States of America 74(9): 4020-4024.

Higashiguchi, M., I. Nagatomo, et al. (2016). "Clarifying the biological significance of the CHK2 K373E somatic mutation discovered in The Cancer Genome Atlas database." FEBS letters 590(23): 4275-4286.

Hinck, L. and G. B. Silberstein (2005). "Key stages in mammary gland development: the mammary end bud as a motile organ." Breast cancer research : BCR 7(6): 245-251.

Imaoka, T., M. Nishimura, et al. (2006). "Persistent cell proliferation of terminal end buds precedes radiation-induced rat mammary carcinogenesis." In Vivo 20(3): 353-358.

Kaufmann, W. K., K. R. Nevis, et al. (2008). "Defective cell cycle checkpoint functions in melanoma are associated with altered patterns of gene expression." J Invest Dermatol 128(1): 175-187.

Kouros-Mehr, H., E. M. Slorach, et al. (2006). "GATA-3 maintains the differentiation of the luminal cell fate in the mammary gland." Cell 127(5): 1041-1055.

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Liang, L., L. Deng, et al. (2007). "X-rays induce distinct patterns of somatic mutation in fetal versus adult hematopoietic cells." DNA repair 6(9): 1380-1385.

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Mukhopadhyay, R., S. V. Costes, et al. (2010). "Promotion of variant human mammary epithelial cell outgrowth by ionizing radiation: an agent-based model supported by in vitro studies." Breast cancer research : BCR 12(1): R11.

Nguyen, D. H., H. A. Oketch-Rabah, et al. (2011). "Radiation acts on the microenvironment to affect breast carcinogenesis by distinct mechanisms that decrease cancer latency and affect tumor type." Cancer Cell 19(5): 640-651.

Nik-Zainal, S., H. Davies, et al. (2016). "Landscape of somatic mutations in 560 breast cancer whole-genome sequences." Nature 534(7605): 47-54.

Podsypanina, K., K. Politi, et al. (2008). "Oncogene cooperation in tumor maintenance and tumor recurrence in mouse mammary tumors induced by Myc and mutant Kras." Proceedings of the National Academy of Sciences of the United States of America 105(13): 5242-5247.

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Sternlicht, M. D., S. W. Sunnarborg, et al. (2005). "Mammary ductal morphogenesis requires paracrine activation of stromal EGFR via ADAM17-dependent shedding of epithelial amphiregulin." Development 132(17): 3923-3933.

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Tang, J., I. Fernandez-Garcia, et al. (2014). "Irradiation of juvenile, but not adult, mammary gland increases stem cell self-renewal and estrogen receptor negative tumors." Stem Cells 32(3): 649-661.

Tao, L., A. L. Roberts, et al. (2011). "Repression of mammary stem/progenitor cells by p53 is mediated by Notch and separable from apoptotic activity." Stem Cells 29(1): 119-127.

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Zhou, H., V. N. Ivanov, et al. (2005). "Mechanism of radiation-induced bystander effect: role of the cyclooxygenase-2 signaling pathway." Proceedings of the National Academy of Sciences of the United States of America 102(41): 14641-14646.

Zhou, X., X. Ma, et al. (2015). "Radiation-induced hyperproliferation of intestinal crypts results in elevated genome instability with inactive p53-related genomic surveillance." Life Sci 143: 80-88.