Upstream eventFormation, Pro-mutagenic DNA Adducts
Clonal Expansion/Cell Proliferation, to form Altered Hepatic Foci (AHF)
Key Event Relationship Overview
AOPs Referencing Relationship
|AOP Name||Adjacency||Weight of Evidence||Quantitative Understanding|
|AFB1: Mutagenic Mode-of-Action leading to Hepatocellular Carcinoma (HCC)||non-adjacent||High||High|
Life Stage Applicability
Key Event Relationship Description
Formation of the pro-mutagenic DNA adduct, N7-AFB1-G (or its conversion product, N7-AFB1-FAPy) is the first step in the initiation of a process that may finish in development of altered
Evidence Supporting this KER
While there is no specific information for AFB1, it is widely recognized that pro-mutagenic adducts formed by AFB1 metabolites may be repaired/removed or may result in mutations. The fidelity of the repair processes and probability of mis-repair determine whether mutations arise in tumor-critical genes. The altered hepatic foci (AHF) are believed to result from mutations expressed in cells that demonstrate reduced apoptosis and increased proliferation, likely linked to the mutations. (Alekseyev et al., 2004; Zhang et al., 2003; Giri et al., 2002; Bailey et al., 1996; Lin et al., 2014).
The plethora of published chemoprevention studies support a relationship between AFB1-induced DNA adducts and altered hepatic foci (AHF). There are some evaluations that claim (Bechtel et al. 1989) linearity in the dose-response for adduct formation as a function of dose in both rats and trout; but linearity does not seem to apply, however, for adducts and AHF. Recent work of Johnson et al. (2014) showed that AHF were reduced to background levels by the chemoprotectant CDDO-Im, while around 30% of the original level of N7-AFB1-G adducts were still present. These authors note that there remained ~15,000 adducts per cell after application of the chemoprotectant, while AHF are at background levels, and no HCC tumors were induced.
Uncertainties and Inconsistencies
The direct KER relationships between adducts and mutations (MIE→KE#2) and from mutations to AHF (KE#2→KE#3) determine this indirect relationship. Unfortunately, there is a paucity of data to support quantification of a relationship between adducts and AHF; neither to address an AFB1 dose-response for both KEs.
Quantitative Understanding of the Linkage
It is clear that the dose-response relationship between adducts and AHF is disproportionate, and thus likely not linear, given that a ~30% reduction in adducts resulted in a 100% reduction in AHF (Johnson et al., 2014). However, a quantitative prediction from DNA adducts to AHF cannot be determined due to lack of data.
There is a study which includes a comparison of AFB1 adduct formation in animals co-administered chemopreventive agent (CDDO-Im) and AHF data (Johnson et al., 2014); these data were developed in rats following 28 d of exposure to AFB1 (young adult rats). Control data for AHF in rats from standard cancer bioassays—thus 18- to 24-month old rats—was analyzed and back-extrapolated in age to determine whether the background level of AHF in the young adult CDD)-Im treated rats was reasonable. Below is provided an analysis of background AHF formation for the placental form of glutathione-S-transferase (GSTP+) foci for use in comparisons of chemoprevention studies with background.
To compare the occurrence of GSTP+ AHF in rats treated with AFB1 and AFB1 + CDDO-Im with controls, a modelled fit of the growth of volume fraction of AHF was developed from data in Popp et al. . Most of the data on AHF in control rats were developed by histological identification of foci rather than enzymatic identification (Harada et al., 1990; Bannash et al., 1985).
One drawback to all these papers on AHF is that parameters for AHF were determined late in the lifetimes of the rats; by contrast, Johnson et al. (2014) examined AHF for up to 28 days of AFB1 administration, a much shorter treatment time and in young adult rats. In addition, many papers used strains and sexes other than F344 males. The use of the same strain and sex in Popp et al. (1985) was considered important for developing the comparison.
To extrapolate the data of Popp et al. (1985) to earlier times in the lives of the rats, the equation derived by Dragan et al. (1995) based on the Moogavkar-Venson-Knudsen (MVK) model was fit to the data of Popp et al. (1985). This equation is as follows:
where E(t) = expected numbers of transformed cells
μ1 = initiation frequency
α2 = rate of division of initiated cells
β2 = rate of differentiation, damage or death of initiated cells
Xs = number of susceptible untransformed cells in the liver
The figure below shows the fit to the data of Popp et al. (1985) for male F344 rats, the same strain and sex used by Johnson et al. (2014).
Figure 1. Plot of data on GSTP+ AHF in untreated F344 male rats and the fit to Eq. 1. The number of susceptible cells was assumed to be 3.6E+08 (Popp et al., 1985). The initiation rate (u1) was 1.35e-04 per day and the birth rate minus the death rate (a2 – b2) was 3.89E-03 per day. The data from Popp et al. (1985) are shown as the mean ± 95% CIs.
Data from Johnson et al. (2014) at three time points following the onset of treatment (either AFB1 or AFB1 + CDDO-Im) are shown in comparison to the early extrapolation of occurrence of GSTP+ AHF in control rats.
Figure 2. Plot of the occurrence of GSTP+ AHF in male F344 rats from Johnson et al. (2014) (labeled points with error bars, AFB1 and AFB1 + CDDO-Im) and the early time extrapolated fit (solid line) of the data from Popp et al. (1985). The AFB1 data were so much greater than the other data that a split y-axis was needed.
As can be seen from Fig. 2, the % volume of fraction GSTP+ foci in rats treated with CDDO-Im is zero at both 8 and 14 days, and indistinguishable from the fit to the extrapolated control data at 21 and 28 days. The data of AFB1 administration without CDDO-Im are indistinguishable from the fit to the control data at 8 days but considerably greater than the fit to the control data at later times.
The extrapolation of the data in Popp et al. (1985) from long exposure times to short exposure times represented an uncertainty. Hence, data from Harada et al., (1989)  were also examined. These data did not include GSTP+ foci but rather histologically identified foci (e.g., basophilic, eosinophilic, mixed). Data were obtained at 6, 9, 12, 15, 18, and 24 months. These data were also fit to the MVK equation from Dragan et al., (1995) (not shown). When extrapolated to times less than 28 days, these data from Harada et al. (1989) were very similar to those from Popp et al. (1985).
Known modulating factors
Known Feedforward/Feedback loops influencing this KER
Domain of Applicability
The universality of both DNA adducts and AHF in the pathogenesis of liver cancer suggests that the wide taxonomic applicability noted elsewhere in this AOP is likely true for this KER.
1. Alekseyev YO, Hamm ML, Essigmann JM (2004) Aflatoxin B1 formamidopyrimidine adducts are preferentially repaired by the nucleotide excision repair pathway in vivo. Carcinogenesis 25: 1045-1051.
2. Zhang YJ, Chen Y, Ahsan H, Lunn RM, Lee PH, et al (2003) Inactivation of the DNA repair gene O6-methylguanine-DNA methyltransferase by promoter hypermethylation and its relationship to aflatoxin B1-DNA adducts and p53 mutation in hepatocellular carcinoma. Int J Cancer 103: 440-444.
3. Giri I, Johnston DS, Stone MP (2002) Mispairing of the 8,9-dihydro-8-(N7-guanyl)-9-hydroxy-aflatoxin B1 adduct with deoxyadenosine results in extrusion of the mismatched dA toward the major groove. Biochemistry 41: 5462-5472.
4. Bailey EA, Iyer RS, Stone MP, Harris TM, Essigmann JM (1996) Mutational properties of the primary aflatoxin B1-DNA adduct. Proc Natl Acad Sci U S A 93: 1535-1539.
5. Gursoy-Yuzugullu O, Yuzugullu H, Yilmaz M, Ozturk M (2011) Aflatoxin genotoxicity is associated with a defective DNA damage response bypassing p53 activation. Liver Int 31: 561-571.
6. Lin YC, Li L, Makarova AV, Burgers PM, Stone MP, Lloyd RS (2014) Molecular basis of aflatoxin-induced mutagenesis--role of the aflatoxin B1-formamidopyrimidine adduct. Carcinogenesis .
7. Bechtel DH (1989) Molecular dosimetry of hepatic aflatoxin B1-DNA adducts: linear correlation with hepatic cancer risk. Regul Toxicol Pharmacol 10: 74-81.
8. 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) .
9. Popp JA, Scortichini BH, Garvey LK (1985) Quantitative evaluation of hepatic foci of cellular alteration occurring spontaneously in Fischer-344 rats. Fundam Appl Toxicol 5: 314-319.
10. Harada T, Maronpot RR, Morris RW, Boorman GA (1990) Effects of mononuclear cell leukemia on altered hepatocellular foci in Fischer 344 rats. Vet Pathol 27: 110-116.
11. 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.
12. Dragan YP, Hully J, Baker K, Crow R, Mass MJ, Pitot HC (1995) Comparison of experimental and theoretical parameters of the Moolgavkar-Venzon-Knudson incidence function for the stages of initiation and promotion in rat hepatocarcinogenesis. Toxicology 102: 161-175.
13. Harada T, Maronpot RR, Morris RW, Stitzel KA, Boorman GA (1989) Morphological and stereological characterization of hepatic foci of cellular alteration in control Fischer 344 rats. Toxicol Pathol 17: 579-593.