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Relationship: 2613


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

Antagonism, Estrogen receptor leads to EMT

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
DNA damage and mutations leading to Metastatic Breast Cancer adjacent High High Usha Adiga (send email) Under development: Not open for comment. Do not cite

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 Moderate 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
Mixed 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
Not Otherwise Specified Not Specified

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

Upstream event: Decreased, Estrogen receptor activity

Downstream event: EMT, Increased

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

Estrogen/ERa signaling maintains an epithelial phenotype and suppresses EMT.ERa signaling promotes proliferation and epithelial differentiation and opposes EMT. ERa activated by E2 inhibits TGF-b signaling and cytokine signaling through Smad and NF-kB, respectively, both of which promote EMT. EMT-related transcription factors and microRNAs are likewise suppressed by ERa signalling. This anti-EMT stance is thought to be a major component in luminal A breast cancer's low spreading potential and excellent prognosis. ERa signalling, on the other hand, promotes the proliferation and survival of ERa-positive breast cancer cells by increasing cell cycle and anti-apoptotic gene expression. Furthermore, because GATA3 is a marker for luminal progenitor cell development and both GATA3 and FOXA1 are cofactors that affect ERa signalling and activity, ERa signalling interacts with luminal-related transcription factors GATA3 and FOXA1 to promote an epithelial phenotype. These elements work together to enhance cell–cell adhesion, basolateral polarity, and low motility in epithelial tissues.

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

E2/ERa signalling, in part through transcriptional activation of luminal/epithelial-related transcription factors, promotes the development of mammary epithelia along a luminal/epithelial lineage. GATA3 and ERa both promote each other (Eeckhoute et al.,2007). In normal breast epithelia, GATA3 is needed for luminal differentiation(Kouros-Mehr et al.,2008) and GATA3 and ERa control many of the same genes (Wilson et al.,2008).  In mice, forcing GATA3 expression in mesenchymal breast cancer cells produces mesenchymal–epithelial transition (MET), a reversible mechanism analogous to EMT, and prevents tumour metastasis (Yan et al.,2010). Another ERa-interacting transcription factor, FOXA1, is essential for luminal lineage in mammary epithelia and stimulates ductal development in mice (Bernardo et al.,2010). FOXA1 enhances ERa gene expression by increasing the accessibility of estrogen-response regions for ERa binding (Nakshatri et al., 2009). In breast cancer cells, on the other hand, E2 appears to increase FOXA1 expression. Importantly, ERa, FOXA1, and GATA3 are all positive breast cancer prognostic factors(Nakshatri et al.,2009).

ERa signalling enhances primary lesion formation (and therefore is mitogenic), but it can control the EMT process (and thus is anti-metastatic) up to a point. Signaling pathways that lead to EMT are antagonised by E2/ERa signalling. TGF-b, for example, has been demonstrated to generate EMT in human mammary epithelial cells, and overexpression of the EMT-inducing protein Snail boosted TGF-b signalling and invasiveness while decreasing adhesion and ERa expression in MCF-7 cells (Taylor et al.,2010). TGF-b has an anti-estrogen impact on MCF-7 cells. Smad2/3 and the Smad-selective E3 ubiquitin ligase Smurf create a ternary complex with ERa, which enhances the proteosomal degradation of Smad proteins, according to Ito et al (Ito et al.,2010).

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

No specific Uncertainties and Inconsistencies noted to the best of our knowledge.

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

- Endogenous ER silencing causes EMT in ER-positive breast cancer cells.

ER-positive MCF-7 cells were infected with ER shRNA lentiviral particles and stable clones were selected with puromycin (optimal dose of 0.8 g/mL) to knockdown ER gene expression (Zheng et al.,2014).

-When the number of cell passages was increased following infection, the expression of ER was gradually knocked down.

-ER gene expression was decreased by roughly 25% four passages after infection compared to control lentiviral particles transfected cells (MCF-7/c cells). The ER gene expression was lowered even more in the following passage (passage 5 post-infection) (by around 50% compared to MCF-7/c cells). In passage 7, a significant reduction in ER gene expression (about 75–80%) was seen, along with a distinct transition of cells from an epithelial to a mesenchymal phenotype.

- When MCF-7 cells reach confluency, they develop as closely packed colonies that produce sheet-like monolayer structures. Stable clones from stage 7 post-infection, on the other hand, grew as more elongated individual cells rather than tight clusters, with a spindle-like shape. For stable clones with a distinct mesenchymal character, a very substantial down-regulation of ER gene expression (above 99 percent) was found from passage 10 and beyond. MCF-7/SP10+ was given to these cells to emphasise the stable transfection (S) and passage 10 or more (P10+). The substantial down-regulation of ERa was confirmed by immunofluorescence and Western blot analysis of the same stable clones (MCF-7/ SP10 + cells).

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

Downstream key event occurs within hours of the occurrence of the upstream key event.

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

Tumour characteristics and heterogeneity, biological changes of tumour progression and interacting molecules, all of which can influence the degree of hormone responsiveness in a particular individual with hormone receptor-positive breast cancer.

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

EMT is inhibited by ERa, and microRNAs either promote or inhibit EMT . These findings raise the question of whether microRNAs have a role in the control of EMT by targeting ERa mRNA. The large (>4000 nt) 30 untranslated region (30-UTR) of human ERa mRNA, as well as results that particular microRNAs are differentially expressed between ERa-positive and ERa-negative breast tumours , suggest the possibility of microRNA-mediated control of human ERa mRNA(Adams et al.,2008).

Pro-metastatic/anti-proliferative (miR-206), pro-metastatic/pro-proliferative (miR-221/222), and anti-proliferative/anti-metastatic (miR-221/223) ERa-targeting microRNAs (miR-130a, miR-145). MiR-17/92 appears to be prometastatic, although it is implicated in several feedback loops, which could make miR-17/92's expression and effects on proliferation extremely reliant on the microenvironment as well as the genetic and epigenetic background.

Accurate identification of micro-RNAs that contribute significantly to a particular pathway (such as EMT) within breast cancers in situ is one hurdle. MicroRNAs have hundreds of potential targets, and in vivo studies will be needed to identify physiologically important targets in the context of breast cancer, as well as to develop effective treatments for breast cancer that involve manipulating microRNA expression levels and identifying off-target effects. (Adams et al.,2007;Zhao et al.,2008;Leva et al.,2010;Stinson et al.,2011;Acunzo et al.,2011;Castellano et al.,2009).

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

Humans and animals with no specific gender or life stage specificity.


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

Acunzo, M., Visone, R., Romano, G., Veronese, A., Lovat, F., Palmieri, D., ... & Croce, C. M. (2012). miR-130a targets MET and induces TRAIL-sensitivity in NSCLC by downregulating miR-221 and 222. Oncogene31(5), 634-642.

Adams, B. D., Guttilla, I. K., & White, B. A. (2008, November). Involvement of microRNAs in breast cancer. In Seminars in reproductive medicine (Vol. 26, No. 06, pp. 522-536). © Thieme Medical Publishers.

Adams, B. D., Furneaux, H., & White, B. A. (2007). The micro-ribonucleic acid (miRNA) miR-206 targets the human estrogen receptor-α (ERα) and represses ERα messenger RNA and protein expression in breast cancer cell lines. Molecular endocrinology21(5), 1132-1147.

Al Saleh, S., Al Mulla, F., & Luqmani, Y. A. (2011). Estrogen receptor silencing induces epithelial to mesenchymal transition in human breast cancer cells. PloS one6(6), e20610.

Bernardo, G. M., Lozada, K. L., Miedler, J. D., Harburg, G., Hewitt, S. C., Mosley, J. D., ... & Keri, R. A. (2010). FOXA1 is an essential determinant of ERα expression and mammary ductal morphogenesis. Development137(12), 2045-2054.

Bouris, P., Skandalis, S. S., Piperigkou, Z., Afratis, N., Karamanou, K., Aletras, A. J., ... & Karamanos, N. K. (2015). Estrogen receptor alpha mediates epithelial to mesenchymal transition, expression of specific matrix effectors and functional properties of breast cancer cells. Matrix Biology43, 42-60.

Castellano, L., Giamas, G., Jacob, J., Coombes, R. C., Lucchesi, W., Thiruchelvam, P., ... & Stebbing, J. (2009). The estrogen receptor-α-induced microRNA signature regulates itself and its transcriptional response. Proceedings of the National Academy of Sciences106(37), 15732-15737.

Di Leva, G., Gasparini, P., Piovan, C., Ngankeu, A., Garofalo, M., Taccioli, C., ... & Croce, C. M. (2010). MicroRNA cluster 221-222 and estrogen receptor α interactions in breast cancer. JNCI: Journal of the National Cancer Institute102(10), 706-721.

Eeckhoute, J., Keeton, E. K., Lupien, M., Krum, S. A., Carroll, J. S., & Brown, M. (2007). Positive cross-regulatory loop ties GATA-3 to estrogen receptor α expression in breast cancer. Cancer research67(13), 6477-6483.

Ito, I., Hanyu, A., Wayama, M., Goto, N., Katsuno, Y., Kawasaki, S., ... & Yanagisawa, J. (2010). Estrogen inhibits transforming growth factor β signaling by promoting Smad2/3 degradation. Journal of biological chemistry285(19), 14747-14755.

Kouros-Mehr, H., Kim, J. W., Bechis, S. K., & Werb, Z. (2008). GATA-3 and the regulation of the mammary luminal cell fate. Current opinion in cell biology20(2), 164-170.

Lin, H. Y., Liang, Y. K., Dou, X. W., Chen, C. F., Wei, X. L., Zeng, D., ... & Zhang, G. J. (2018). Notch3 inhibits epithelial–mesenchymal transition in breast cancer via a novel mechanism, upregulation of GATA-3 expression. Oncogenesis7(8), 1-15.

Liu, Y., Liu, R., Fu, P., Du, F., Hong, Y., Yao, M., ... & Zheng, S. (2015). N1-Guanyl-1, 7-diaminoheptane sensitizes estrogen receptor negative breast cancer cells to doxorubicin by preventing epithelial-mesenchymal transition through inhibition of eukaryotic translation initiation factor 5A2 activation. Cellular Physiology and Biochemistry36(6), 2494-2503.

Nakshatri, H., & Badve, S. (2009). FOXA1 in breast cancer. Expert reviews in molecular medicine11.

Stinson, S., Lackner, M. R., Adai, A. T., Yu, N., Kim, H. J., O’Brien, C., ... & Dornan, D. (2011). TRPS1 targeting by miR-221/222 promotes the epithelial-to-mesenchymal transition in breast cancer. Science signaling4(177), ra41-ra41.

Taylor, M. A., Parvani, J. G., & Schiemann, W. P. (2010). The pathophysiology of epithelial-mesenchymal transition induced by transforming growth factor-β in normal and malignant mammary epithelial cells. Journal of mammary gland biology and neoplasia15(2), 169-190.

Wilson, B. J., & Giguère, V. (2008). Meta-analysis of human cancer microarrays reveals GATA3 is integral to the estrogen receptor alpha pathway. Molecular cancer7(1), 1-8.

Wik, E., Ræder, M. B., Krakstad, C., Trovik, J., Birkeland, E., Hoivik, E. A., ... & Salvesen, H. B. (2013). Lack of estrogen receptor-α is associated with epithelial–mesenchymal transition and PI3K alterations in endometrial carcinoma. Clinical Cancer Research19(5), 1094-1105.

Yan, W., Cao, Q. J., Arenas, R. B., Bentley, B., & Shao, R. (2010). GATA3 inhibits breast cancer metastasis through the reversal of epithelial-mesenchymal transition. Journal of Biological Chemistry285(18), 14042-14051.

Ye, Y., Xiao, Y., Wang, W., Yearsley, K., Gao, J. X., Shetuni, B., & Barsky, S. H. (2010). ERα signaling through slug regulates E-cadherin and EMT. Oncogene29(10), 1451-1462.

Zeng, Q., Zhang, P., Wu, Z., Xue, P., Lu, D., Ye, Z., ... & Yan, X. (2014). Quantitative proteomics reveals ER-α involvement in CD146-induced epithelial-mesenchymal transition in breast cancer cells. Journal of proteomics103, 153-169.

Zhao, J. J., Lin, J., Yang, H., Kong, W., He, L., Ma, X., ... & Cheng, J. Q. (2008). MicroRNA-221/222 negatively regulates estrogen receptorα and is associated with tamoxifen resistance in breast cancer. Journal of Biological Chemistry283(45), 31079-31087.