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Relationship: 1896

Title

A descriptive phrase which clearly defines the two KEs being considered and the sequential relationship between them (i.e., which is upstream, and which is downstream). More help

Increase, Mutations leads to Increase, Cell Proliferation (Epithelial Cells)

Upstream event
The causing Key Event (KE) in a Key Event Relationship (KER). More help
Downstream event
The responding Key Event (KE) in a Key Event Relationship (KER). 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

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 Evgeniia Kazymova (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 Do not cite Under Development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 Allie Always (send email) Under development: Not open for comment. Do not cite Under Development

Taxonomic Applicability

Latin or common names of a species or broader taxonomic grouping (e.g., class, order, family) 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.  More help

Sex Applicability

An indication of the the relevant sex for this KER. More help

Life Stage Applicability

An indication of the the relevant life stage(s) for this KER.  More help

Key Event Relationship Description

Provides a concise overview of the information given below as well as addressing details that aren’t inherent in the description of the KEs themselves. 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 Collection Strategy

Include a description of the approach for identification and assembly of the evidence base for the KER. For evidence identification, include, for example, a description of the sources and dates of information consulted including expert knowledge, databases searched and associated search terms/strings.  Include also a description of study screening criteria and methodology, study quality assessment considerations, the data extraction strategy and links to any repositories/databases of relevant references.Tabular summaries and links to relevant supporting documentation are encouraged, wherever possible. More help

Evidence Map 2.0

ID Experimental Design Species Upstream Observation Downstream Observation Citation (first author, year) Notes

Evidence Map

Addresses the scientific evidence supporting KERs in an AOP setting the stage for overall assessment of the AOP. More help
Title First Author
Biological Plausibility
Dose Concordance
Temporal Concordance
Incidence Concordance
Biological Plausibility
Dose Concordance Evidence
Temporal Concordance Evidence
Incidence Concordance Evidence
Uncertainties and Inconsistencies
Addresses inconsistencies or uncertainties in the relationship including the identification of experimental details that may explain apparent deviations from the expected patterns of concordance. 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.

Known modulating factors

This table captures specific information on the MF, its properties, how it affects the KER and respective references.1.) What is the modulating factor? Name the factor for which solid evidence exists that it influences this KER. Examples: age, sex, genotype, diet 2.) Details of this modulating factor. Specify which features of this MF are relevant for this KER. Examples: a specific age range or a specific biological age (defined by...); a specific gene mutation or variant, a specific nutrient (deficit or surplus); a sex-specific homone; a certain threshold value (e.g. serum levels of a chemical above...) 3.) Description of how this modulating factor affects this KER. Describe the provable modification of the KER (also quantitatively, if known). Examples: increase or decrease of the magnitude of effect (by a factor of...); change of the time-course of the effect (onset delay by...); alteration of the probability of the effect; increase or decrease of the sensitivity of the downstream effect (by a factor of...) 4.) Provision of supporting scientific evidence for an effect of this MF on this KER. Give a list of references.  More help

Domain of Applicability

A free-text section of the KER description that the developers can use to explain their rationale for the taxonomic, life stage, or sex applicability structured terms. More help