To the extent possible under law, AOP-Wiki has waived all copyright and related or neighboring rights to KER:343

Relationship: 343


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, Proliferation/Clonal Expansion of Mutant Cells (Pre-Neoplastic Lesions/Altered H leads to Tumorigenesis, Hepatocellular carcinoma

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

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

While there is no direct evidence addressing how AFB1 exposure affects cellular proliferation and the clonal expansion of mutant cells to ultimately form HCC, there are multiple biological processes that are generally involved in tumor development. These are discussed in a previous section and include effects on apoptosis, inflammation, the development of a tumor microenvironment, interference with the anti-oxidant response, and likely others.

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
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

Chemoprevention studies, reviewed in another section of this AOP, suggest a strong relationship between altered hepatic foci (AHF) and HCC tumor formation (Olden and Vulimiri, 2014; Liby et al., 2008; Yates et al., 2007; Kensler et al., 2004). For example, Johnson et al. (2014) observed background levels of AHF along with a complete absence of tumors in rats treated with a triterpenoid chemoprotectant CDDO-Im, despite maintaining a significant burden of AFB1-induced adducts. Cell proliferation appears to be six- to seven-fold greater in AHF than in surrounding liver parenchyma (Dragan et al., 1994). In tree shrews, the apoptosis-related genes p53, bcl-2, bax and survivin were expressed to a much greater extent at 30 and 60 weeks in rats treated with AFB1 than in control rats (Duan et al., 2005). However, the measurements were made from liver biopsies, and whether the increased expression was associated with foci is not known. The Nrf2-Keap1 pathway activated by chemoprotectants appears to be a large factor in preventing hepatocellular carcinoma (HCC). Hence, the oxidative environment and resulting cellular stress that are the targets of this pathway likely contribute to tumor development from AHF.

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

A seemingly strong relationship exists between AHF and tumors; AHF have been considered as pre-neoplastic lesions for a number of years (Bannasch et al., 1986; Ikeda et al., 2004; Ribback et al., 2013).

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
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

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

AHF have been observed essentially universally in AFB1-treated mammals, birds, and fish examined (Pottenger et al., 2014; Kensler et al., 2011; Kimura et al., 2004; Cullen et al., 1900; Kirby et al., 1990).


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

Bannasch P, Benner U, Enzmann H, Hacker HJ. 1985. Tigroid cell foci and neoplastic nodules in the liver of rats treated with a single dose of aflatoxin B1. Carcinogenesis 6: 1641-1648

Cullen JM, Marion PL, Sherman GJ, Hong X, Newbold JE. 1990. Hepatic neoplasms in aflatoxin B1-treated, congenital duck hepatitis B virus-infected, and virus-free pekin ducks. Cancer Res 50: 4072-4080.

Dragan YP, Hully J, Crow R, Mass M, Pitot HC. 1994. Incorporation of bromodeoxyuridine in glutathione S-transferase-positive hepatocytes during rat multistage hepatocarcinogenesis. Carcinogenesis 15: 1939-1947.

Dragan Y, Teeguarden J, Campbell H, Hsia S, Pitot H. 1995. The quantitation of altered hepatic foci during multistage hepatocarcinogenesis in the rat: transforming growth factor alpha expression as a marker for the stage of progression. Cancer Lett 93: 73-83.

Duan XX, Ou JS, Li Y, Su JJ, Ou C, et al. 2005. Dynamic expression of apoptosis-related genes during development of laboratory hepatocellular carcinoma and its relation to apoptosis. World J Gastroenterol 11: 4740-4744.

Grasl-Kraupp B, Ruttkay-Nedecky B, Müllauer L, Taper H, Huber W, et al. 1997. Inherent increase of apoptosis in liver tumors: implications for carcinogenesis and tumor regression. Hepatology 25: 906-912.

Ikeda H, Nishi S, Sakai M. 2004. Transcription factor Nrf2/MafK regulates rat placental glutathione S-transferase gene during hepatocarcinogenesis. Biochem J 380: 515-521.

Johnson NM, Egner PA, Baxter VK, Sporn MB, Wible RS, et al. 2014. Complete protection against aflatoxin B1-induced liver cancer with triterpenoid: DNA adduct dosimetry, molecular signature and genotoxicity threshold. Cancer Prev Res (Phila) .

Kensler TW, Egner PA, Wang JB, Zhu YR, Zhang BC, et al. 2004. Chemoprevention of hepatocellular carcinoma in aflatoxin endemic areas. Gastroenterology 127: S310-S318.

Kensler TW, Roebuck BD, Wogan GN, Groopman JD. 2011. Aflatoxin: a 50-year odyssey of mechanistic and translational toxicology. Toxicol Sci 120 Suppl 1: S28-S48.

Kimura M, Lehmann K, Gopalan-Kriczky P, Lotlikar PD. 2004. Effect of diet on aflatoxin B1-DNA binding and aflatoxin B1-induced glutathione S-transferase placental form positive hepatic foci in the rat. Exp Mol Med 36: 351-357.

Kirby GM, Stalker M, Metcalfe C, Kocal T, Ferguson H, Hayes MA. 1990. Expression of immunoreactive glutathione S-transferases in hepatic neoplasms induced by aflatoxin B1 or 1,2-dimethylbenzanthracene in rainbow trout (Oncorhynchus mykiss). Carcinogenesis 11: 2255-2257.

Liby K, Yore MM, Roebuck BD, Baumgartner KJ, Honda T, et al. 2008. A novel acetylenic tricyclic bis-(cyano enone) potently induces phase 2 cytoprotective pathways and blocks liver carcinogenesis induced by aflatoxin. Cancer Res 68: 6727-6733.

Olden K, Vulimiri SV. 2014. Laboratory to community: chemoprevention is the answer. Cancer Prev Res (Phila) 7: 648-652.

Pitot HC, Dragan Y, Sargent L, Xu YH. 1991. Biochemical markers associated with the stages of promotion and progression during hepatocarcinogenesis in the rat. Environ Health Perspect 93: 181-189.

Pitot HC, Dragan Y, Xu YH, Pyron M, Laufer C, Rizvi T. 1990. Role of altered hepatic foci in the stages of carcinogenesis. Prog Clin Biol Res 340D: 81-95.

Pitot HC. 1990. Altered hepatic foci: their role in murine hepatocarcinogenesis. Annu Rev Pharmacol Toxicol 30: 465-500.

Pottenger LH, Andrews LS, Bachman AN, Boogaard PJ, Cadet J, et al. 2014. An organizational approach for the assessment of DNA adduct data in risk assessment: case studies for aflatoxin B1, tamoxifen and vinyl chloride. Crit Rev Toxicol 44: 348-391.

Ribback S, Calvisi DF, Cigliano A, Sailer V, Peters M, et al. 2013 Molecular and metabolic changes in human liver clear cell foci resemble the alterations occurring in rat hepatocarcinogenesis. J Hepatol 58: 1147-1156. Xu YH, Campbell HA, Sattler GL, Hendrich S, Maronpot R, et al. 1990. Quantitative stereological analysis of the effects of age and sex on multistage hepatocarcinogenesis in the rat by use of four cytochemical markers. Cancer Res 50: 472-479.

Xu YH, Maronpot R, Pitot HC. 1990. Quantitative stereologic study of the effects of varying the time between initiation and promotion on four histochemical markers in rat liver during hepatocarcinogenesis. Carcinogenesis 11: 267-272.

Yates MS, Tauchi M, Katsuoka F, Flanders KC, Liby KT, et al. 2007. Pharmacodynamic characterization of chemopreventive triterpenoids as exceptionally potent inducers of Nrf2-regulated genes. Mol Cancer Ther 6: 154-162.

Yates MS, Kensler TW. 2007. Keap1 eye on the target: chemoprevention of liver cancer. Acta Pharmacol Sin 28: 1331-1342.