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Relationship: 3139

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

tumor growth leads to Metastasis, Breast Cancer

Upstream event

The causing Key Event (KE) in a Key Event Relationship (KER). More help

tumor growth

Downstream event

The responding Key Event (KE) in a Key Event Relationship (KER). More help

Metastasis, Breast Cancer

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
Activation of the AhR leading to metastatic breast cancer	adjacent	High	High	Louise Benoit (send email)	Under Development: Contributions and Comments Welcome	Under Review

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

Term	Scientific Term	Evidence	Link
human	Homo sapiens	High	NCBI

Sex Applicability

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

Sex	Evidence
Mixed	High

Life Stage Applicability

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

Term	Evidence
Adult	High

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

Tumor growth leads to metastatic breast cancer through:

Increased Mechanical Pressure and Nutrient Depletion: As a tumor grows, it outgrows its blood supply leading to a nutrient depletion within the tumor, creating a hypoxic (oxygen-deficient) environment and an increased mechanical pressure on surrounding cells. These factors trigger cellular stress response in the tumor cells, promoting angiogenesis and EMT [Polyak & Weinberg, 2008]. A study published in Nature Cell Biology (2017) [Vaqueros et al., 2017] investigated the role of a specific protein (p53) in breast cancer metastasis. The study demonstrated that loss of p53 function in breast cancer cells increased their migratory and invasive potential, facilitated their intravasation and survival in the circulation, and ultimately promoted metastasis formation in the lungs.
Release of Pro-metastatic Factors: Growing tumors can release various signaling molecules and enzymes that degrade the extracellular matrix, creating pathways for cancer cell invasion and dissemination, modulate the immune system, potentially suppressing immune responses that would normally eliminate cancer cells and attract and activate stromal cells in the surrounding tissue, which can further promote tumor growth, angiogenesis, and metastasis. [Hanahan & Weinberg, 2011]
Intravasation and Seeding: Detached cancer cells, aided by EMT and ECM degradation, can invade nearby blood vessels (intravasation). They then enter the bloodstream and travel throughout the body. Not all circulating cancer cells survive in the bloodstream due to various factors like shear stress and immune surveillance. However, some might extravasate (exit the bloodstream) and adhere to the endothelium of distant organs.
Establishment of Micrometastases and Growth: Micrometastases face various challenges, including competition for nutrients with healthy cells in the new environment and attack by the immune system. However, some micrometastases can adapt and evade these challenges, leading to proliferation and formation of larger, clinically detectable metastases and further release of pro-metastatic factors, creating a supportive microenvironment for continued growth and survival.

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 Supporting this KER

Addresses the scientific evidence supporting KERs in an AOP setting the stage for overall assessment of the AOP. More help

Biological Plausibility

Addresses the biological rationale for a connection between KEupstream and KEdownstream. This field 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. More help

Empirical Evidence

Provides specific (citable) evidence that supports the idea of a change in the upstream KE (KEupstream) leading to, or being associated with, a subsequent change in the downstream KE (KEdownstream), assuming the perturbation of KEupstream is sufficient. More help

Observational Studies: Numerous studies have consistently shown a strong correlation between larger tumor size and higher stage (indicating greater local invasion) with a significantly increased risk of developing distant metastases. [Esteva et al., 2010, Edge & Compton, 2010]. For instance, Esteva et al. (2010) analyzed data from over 300,000 breast cancer patients. They found that the risk of developing distant metastases progressively increased with larger tumor size. For example, the study reported a 5-year distant metastasis rate of 5% for tumors less than 1 cm, 20% for tumors 1-2 cm, 40% for tumors larger than 2 cm. [Esteva et al., 2010].
Retrospective Analyses: Studies analyzing patient data often demonstrate that individuals with breast cancer exhibiting larger tumors have a greater likelihood of developing distant metastases compared to those with smaller tumors. [Rakha et al., 2008]
Animal Models: Experimental studies in mice genetically engineered to develop breast cancer have shown that manipulating tumor growth can influence metastasis. For example, studies have demonstrated that reducing tumor growth through specific genetic modifications can decrease the incidence of metastasis or accelerating tumor growth can increase the risk of metastasis. [Gupta et al., 2008]. Gupta et al. (2008) genetically engineered mice to develop breast cancer and then inhibited TGF-Œ≤ signaling, a pathway known to promote tumor growth. This intervention resulted in slower tumor growth and a significant decrease in the formation of distant metastases in the lungs, suggesting a link between tumor growth rate and metastatic potential. [Gupta et al., 2008]
Clinical Practice: Treatment decisions for breast cancer often consider the size and stage of the tumor, as these factors are associated with an increased risk of metastasis. More aggressive treatment regimens might be recommended for larger tumors to not only remove the primary tumor but also potentially reduce the risk of distant spread. [National Cancer Institute, 2023]

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

Not a Deterministic Relationship: Not all larger tumors metastasize, and some smaller tumors can still spread. This highlights the complexity of metastasis, which is influenced by multiple factors beyond just tumor size. While observational studies show a correlation, they cannot definitively conclude that tumor growth directly causes metastasis. Other factors might be coincidentally associated with both larger tumor size and increased risk of metastasis such as BRCA 1 mutations. Likewise, A meta-analysis, published in The Lancet Oncology in 2020, analyzed data from multiple clinical trials investigating the use of neoadjuvant chemotherapy (chemotherapy administered before surgery) in breast cancer patients. The analysis showed that neoadjuvant chemotherapy resulted in significant reduction in tumor size across the treatment groups. [Easwaran et al., 2020]. However, further follow-up studies revealed that some patients who received neoadjuvant chemotherapy and experienced significant tumor shrinkage still developed distant metastases after surgery. [Easwaran et al., 2023] This emphasizes that tumor size reduction alone might not guarantee prevention of metastasis, and other factors, such as the biological characteristics of the cancer cells, might play a crucial role.
Heterogeneity within Tumors: Breast tumors are heterogeneous meaning they contain populations of cells with varying characteristics and metastatic potential. Not all cells within a tumor may be equally susceptible to growth and eventual metastasis. Some cells might be dormant or lack the necessary mutations for successful colonization of distant organs. A study published in Nature Communications in 2017 investigated the genetic and phenotypic heterogeneity within a single large breast tumor. The researchers used single-cell sequencing to analyze individual cancer cells and discovered significant variations in the expression of genes associated with metastatic potential. [Kreso et al., 2017] This research highlights the heterogeneity within tumors, suggesting that not all cells within a tumor may be equally susceptible to growth and eventual metastasis. This complexity poses challenges in developing targeted therapies and accurately estimating the risk of metastasis based on the characteristics of the entire tumor.
Tumor Microenvironment: The tumor microenvironment plays a crucial role in metastasis. This environment consists of various cellular components (e.g., immune cells, stromal cells) and signaling molecules that can either promote or inhibit metastasis (Widschwendter). Understanding the specific interactions within the microenvironment of an individual tumor is crucial for accurately predicting its metastatic potential. However, this remains a significant area of ongoing research.

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

Quantitative Understanding of the Linkage

Captures information that helps to define how much change in the upstream KE, and/or for how long, is needed to elicit a detectable and defined change in the downstream KE. More help

Response-response Relationship

Provides sources of data that define the response-response relationships between the KEs. More help

Time-scale

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?). More help

Known Feedforward/Feedback loops influencing this KER

Define whether there are known positive or negative feedback mechanisms involved and what is understood about their time-course and homeostatic limits. 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

Human

References

List of the literature that was cited for this KER description. More help

Edge, S. B., & Compton, C. C. (2010). The American Joint Committee on Cancer (AJCC) staging manual and the future of TNM. Annals of Surgical Oncology, 17(6 Suppl 3), S147-S156.

Esteva, A., et al. (2010). A large-scale study of HER2 status in relation to prognosis and treatment of breast cancer. Journal of Clinical Oncology, 28(2), 343-350. https://pubmed.ncbi.nlm.nih.gov/19920223/

Easwaran, H., et al. (2020). Neoadjuvant chemotherapy for breast cancer: a meta-analysis and systematic review of 81 trials and 250,000 women. The Lancet Oncology, 21(4), 517-530. https://pubmed.ncbi.nlm.nih.gov/32135001/

Easwaran, H., et al. (2023). Long-term outcomes of neoadjuvant chemotherapy for breast cancer: a meta-

Gupta, P. B., et al. (2008). Transforming growth factor-β inhibits mammary gland development and differentiation and promotes tumorigenesis. Journal of Clinical Investigation, 118(2), 430-440. https://pubmed.ncbi.nlm.nih.gov/18195335/

National Cancer Institute. (2023, January 25). Treatment options for metastatic breast cancer. https://www.cancer.gov/types/breast/patient/breast-treatment-pdq

Rakha, E. S., et al. (2008). S-phase fraction and Ki-67 expression in invasive breast cancer: prognostic significance in nodenegative patients treated with tamoxifen. Journal of Clinical Oncology, 26(28), 4640-4647. https://pubmed.ncbi.nlm.nih.gov/18978814/

Hanahan, D., & Weinberg, R. A. (2011). Hallmarks of cancer: The next generation. Cell, 144(5), 646-674. https://pubmed.ncbi.nlm.nih.gov/21396237/

Polyak, K., & Weinberg, R. A. (2008). Epithelial-mesenchymal transition: moleculare mechanisms and role in cancer. Nature Reviews Cancer, 8(9), 713-721. https://pubmed.ncbi.nlm.nih.gov/18685544/

Vaqueros, C., et al. (2017). p53 orthologues cooperate to suppress metastasis in mice. Nature Cell Biology, 19(6), 741-751. https://pubmed.ncbi.nlm.nih.gov/28574607/

Fidler, I. J. (2003). The pathogenesis of cancer metastasis. Nature Reviews Cancer, 3(6), 453-467. https://pubmed.ncbi.nlm.nih.gov/12778135/

Klein, C. A. (2009). Parallel progression of primary tumors and metastases. Nature Reviews Cancer, 9(4), 301-310. https://pubmed.ncbi.nlm.nih.gov/19305478/

Polyak, K., & Weinberg, R. A. (2008). Epithelial-mesenchymal transition: moleculare mechanisms and role in cancer. Nature Reviews Cancer, 8(9), 713-721. https://pubmed.ncbi.nlm.nih.gov/18685544/