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Relationship: 2584


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

Increased, circulating estrogen levels leads to Hyperplasia, ovarian stromal 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
Hypothalamic estrogen receptors inhibition leading to ovarian cancer non-adjacent High Not Specified Cataia Ives (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 Do not cite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 High NCBI
rat Rattus norvegicus High NCBI
mice Mus sp. 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
Female 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
Adult, reproductively mature 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

Ovarian tumor termed as hyperplasia is characterized as enlarged ovary with increased numbers of corpora lutea and tertiary follicles. In some cases cystic/incompletely lutenised corpora lutea may also be observed. Ovarian tumors may contain Leydig cells and originate within the specific ovarian stroma cells (Sternberg and Roth, 1973). Studies on the female rats have shown increased hormonal levels (e.g. estradiol 17-β, progesterone, prolactin) in the plasma are causing the tumor formation in the ovarian granulosa cell (Long et al., 2001).

High levels of circulating estrogen in the plasma can produce tumors in the ovarian granulosa cells. Magnetic resonance (MR) imaging was used for the detection of the ovarian tumors directly (Tanaka et al., 2004). Eriksson et al., had shown the estrogen levels (1 pg/mL ±0.048) in men samples using gas chromatography - mass spectrometry (GC-MS) or liquid chromatography tandem mass spectrometry(Eriksson et al., 2018). In another study the serum estradiol concentration ranges was determined (~20 - 80 pg/mL) in females during the early to mid-follicular phases of the menstrual cycle and before puberty (~ 20 pg/mL) (Carmina et al., 2019). Barr Fritcher et al., had found that the expression of estrogen receptor (ER) is proportional with age and diagnosed with atypical hyperplasia (Barr Fritcher et al., 2011).

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

Barr Fritcher et al., had studied the expression of estrogen receptors (ER) over 246 women with atypical hyperplasia and found that  87 (35%) had atypical ductal hyperplasia (ADH), 141 (57%) had atypical lobular hyperplasia (ALH), and 18 (7%) had both type of hyperplasia and also found the increasing ER expression in atypical hyperplasia with increasing age(Barr Fritcher et al., 2011).

In a diiferent study Shaaban et al., had shown the positive correlation between ER-α and cellular proliferation causing hyperplasia  with an increased risk of subsequent breast cancer development (Shaaban et al., 2002).

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

Estradiol is the most biologically active estrogen, primarily secreted by ovarian granulosa cells and the conversion of  estradiol to estrone occur with the action of 17β-hydroxysteroid dehydrogenase enzyme(Melmed et al., 2015).

Samavat, H. and M.S. Kurzer, found that in postmenopausal women endogenous estrogens are associated with breast cancer. But for premenopausal women this relationship has not been firmly established but it may possible during the menstrual cycle due to the large variations in hormone levels (Samavat and Kurzer, 2015).

Hankinson, S.E. and A.H. Eliassen, found that a positive association has been observed to the women with high levels of estrogen consistently with approximate two fold increases in invasive breast cancer risk(Hankinson and Eliassen, 2007).

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

Zhao et al., had shown serum estrogen concentration decreased to naormal level after three days of the removal of ovarian tumor (Zhiyi Zhao et al., 2019).

Montgomery et al., had reviewed the works on endometrial and mentioned that unopposed estrogen in woman taking the hormone replace therapy increase the risk of endometrial hyperplasia (Montgomery et al., 2004).

Travis et al., had suggested that circulating oestrogens have strong corelation with the increased risk of breast cancer in postmenopausal women (Travis and Key, 2003).

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 specified

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

Observed in months

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

Regulation of gonadotropin secretion, Dysregulation of ovarian function,  Insulin-resistant hyperinsulinism, Modulation of androgen action (Rosenfield and Ehrmann, 2016).

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

Wood, et al., had found that circulating estrogen level positively correlated with uterine width and stromal cell proliferation and negatively correlated with glandular epithelial proliferation and stromal compartments in the rodents (Wood et al., 2007).

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

Increase in circulating estrogen level causing  increase in the ovarian stromal cells observed in adult female (human) also in rodents.


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

Barr Fritcher, E. G., Degnim, A. C., Hartmann, L. C., Radisky, D. C., Boughey, J. C., Anderson, S. S., et al. (2011). Estrogen receptor expression in atypical hyperplasia: lack of association with breast cancer. Cancer prevention research, 4(3), 435-444.

Carmina, E., Stanczyk, F. Z., & Lobo, R. A. (2019). Evaluation of hormonal status. Yen and Jaffe's Reproductive Endocrinology (pp. 887-915. e4). Elsevier.

Eriksson, A. L., Perry, J. R., Coviello, A. D., Delgado, G. E., Ferrucci, L., Hoffman, A. R., et al. (2018). Genetic determinants of circulating estrogen levels and evidence of a causal effect of estradiol on bone density in men. The Journal of Clinical Endocrinology & Metabolism, 103(3), 991-1004.

Hankinson, S. E., & Eliassen, A. H. (2007). Endogenous estrogen, testosterone and progesterone levels in relation to breast cancer risk. The Journal of steroid biochemistry and molecular biology, 106(1-5), 24-30.

Long, G. G., Cohen, I. R., Gries, C. L., Young, J. K., Francis, P. C., & Capen, C. C. (2001). Proliferative lesions of ovarian granulosa cells and reversible hormonal changes induced in rats by a selective estrogen receptor modulator. Toxicol Pathol, 29(6), 719-26. doi:10.1080/019262301753386031.

Melmed, S., Polonsky, K. S., Larsen, P. R., & Kronenberg, H. M. (2015). Williams Textbook of Endocrinology E-Book. Elsevier Health Sciences.

Montgomery, B. E., Daum, G. S., & Dunton, C. J. (2004). Endometrial hyperplasia: a review. Obstetrical & gynecological survey, 59(5), 368-378.

Nilsson, M. E., Vandenput, L., Tivesten, Å., Norlén, A.-K., Lagerquist, M. K., Windahl, S. H., et al. (2015). Measurement of a comprehensive sex steroid profile in rodent serum by high-sensitive gas chromatography-tandem mass spectrometry. Endocrinology, 156(7), 2492-2502.

Rosenfield, R. L., & Ehrmann, D. A. (2016). The Pathogenesis of Polycystic Ovary Syndrome (PCOS): The Hypothesis of PCOS as Functional Ovarian Hyperandrogenism Revisited. Endocrine reviews, 37(5), 467-520. doi:10.1210/er.2015-1104.

Samavat, H., & Kurzer, M. S. (2015). Estrogen metabolism and breast cancer. Cancer letters, 356(2), 231-243.

Schrader, E. A., Paterniti, T. A., & Ahmad, S. (2021). Lifestyle, nutrition, and risk of gynecologic cancers. Overcoming Drug Resistance in Gynecologic Cancers (pp. 23-48). Elsevier.

Schweikert, H. (2003). Estrogen in the male: nature, sources and biological effects. Encyclopedia of Hormones. Elsevier Inc. San Diego, California, S, 584, 587-589.

Shaaban, A. M., Sloane, J. P., West, C. R., & Foster, C. S. (2002). Breast cancer risk in usual ductal hyperplasia is defined by estrogen receptor-α and Ki-67 expression. The American journal of pathology, 160(2), 597-604.

Sternberg, W. H., & Roth, L. M. (1973). Ovarian stromal tumors containing Leydig cells. I. Stromal-Leydig cell tumor and non-neoplastic transformation of ovarian stroma to Leydig cells. Cancer, 32(4), 940-51. doi:10.1002/1097-0142(197310)32:4<940::aid-cncr2820320428>;2-5.

Tanaka, Y. O., Tsunoda, H., Kitagawa, Y., Ueno, T., Yoshikawa, H., & Saida, Y. (2004). Functioning Ovarian Tumors: Direct and Indirect Findings at MR Imaging. Radiographics, 24, 147-166.

Travis, R. C., & Key, T. J. (2003). Oestrogen exposure and breast cancer risk. Breast Cancer Research, 5(5), 1-9.

Wood, G. A., Fata, J. E., Watson, K. L., & Khokha, R. (2007). Circulating hormones and estrous stage predict cellular and stromal remodeling in murine uterus. Reproduction, 133(5), 1035-1044.

Zhao, H., Zhou, L., Shangguan, A. J., & Bulun, S. E. (2016). Aromatase expression and regulation in breast and endometrial cancer. Journal of molecular endocrinology, 57(1), R19.

Zhao, Z., Yan, L., Lv, H., Liu, H., & Rong, F. (2019). Sclerosing stromal tumor of the ovary in a postmenopausal woman with estrogen excess: A case report. Medicine, 98(47).