Influence of breast cancer risk factors on proliferation and DNA damage in human breast glandular tissues: role of intracellular estrogen levels, oxidative stress and estrogen biotransformation

Abstract

Breast cancer etiology is associated with both proliferation and DNA damage induced by estrogens. Breast cancer risk factors (BCRF) such as body mass index (BMI), smoking, and intake of estrogen-active drugs were recently shown to influence intratissue estrogen levels. Thus, the aim of the present study was to investigate the influence of BCRF on estrogen-induced proliferation and DNA damage in 41 well-characterized breast glandular tissues derived from women without breast cancer. Influence of intramammary estrogen levels and BCRF on estrogen receptor (ESR) activation, ESR-related proliferation (indicated by levels of marker transcripts), oxidative stress (indicated by levels of GCLC transcript and oxidative derivatives of cholesterol), and levels of transcripts encoding enzymes involved in estrogen biotransformation was identified by multiple linear regression models. Metabolic fluxes to adducts of estrogens with DNA (E-DNA) were assessed by a metabolic network model (MNM) which was validated by comparison of calculated fluxes with data on methoxylated and glucuronidated estrogens determined by GC- and UHPLC-MS/MS. Intratissue estrogen levels significantly influenced ESR activation and fluxes to E-DNA within the MNM. Likewise, all BCRF directly and/or indirectly influenced ESR activation, proliferation, and key flux constraints influencing E-DNA (i.e., levels of estrogens, CYP1B1, SULT1A1, SULT1A2, and GSTP1). However, no unambiguous total effect of BCRF on proliferation became apparent. Furthermore, BMI was the only BCRF to indeed influence fluxes to E-DNA (via congruent adverse influence on levels of estrogens, CYP1B1 and SULT1A2).

Keywords: Estrogens; Human breast; Metabolic network model; Multiple linear regression.

Fig. 1

Possible ways of interaction of breast cancer risk factors (BCRF) with cell proliferation and estrogen biotransformation resulting in formation of adducts of estrogens with DNA (E-DNA) in human breast glandular tissue. Variables in boxes were investigated as dependent variables in “Multiple linear regression models”. EBioT enzymes involved in biotransformation of estrogens

Fig. 2

Influence of various exVARs on levels of transcripts of marker for ESR1 activation and proliferation identified by multiple linear regression models using stepwise forward selection as detailed in Online Resource 7. For each model, the number (n) of observations (O) contributing to the final model, the adjusted coefficient of determination (R²), and the observations (i.e., biospecimens) to exVAR ratio (O/exVAR) after forward selection of variables is given. EAD estrogen-active drug, EE ethinyl-E2, ERD E2-releasing drugs, IPE intake of dietary supplements containing phytoestrogens, Lob 1np lobule type 1 of nulliparous women, Lob 1p lobule type 1 of parous women, periMP perimenopausal, postMP postmenopausal

Fig. 3

Influence of various exVARs on levels of transcripts encoding enzymes in biotransformation of E2 and E1, markers for (oxidative) cellular stress identified by multiple linear regression models using stepwise forward selection as detailed in Online Resource 7. For each model, the number (n) of observations (O) contributing to the final model, the adjusted coefficient of determination (R²), and the observations (i.e., biospecimens) to exVAR ratio (O/exVAR) after forward selection of variables is given. EAD estrogen-active drug, EE ethinyl-E2, ERD E2-releasing drugs, IPE intake of dietary supplements containing phytoestrogens, Lob 1np lobule type 1 of nulliparous women, Lob 1p lobule type 1 of parous women, periMP perimenopausal, postMP postmenopausal

Fig. 4

Validation of the metabolic network model. A Median modeled metabolic fluxes (MFs) to 2-MeO-E1 and E1-G were compared between samples with E1-G and 2-MeO-E1 levels below and above LOD by unpaired Wilcoxon test. B For samples exhibiting levels of 2-MeO-E1 above LOD, correlation between MFs to 2-MeO-E1 and levels of 2-MeO-E1 was analyzed by Spearman’s rank correlation analysis. Furthermore, individual differences in the MFs to the methoxylated metabolite detected at levels above LOD (2-MeO-E1) and of the methoxylated estrogens detected below LOD (MeO-E, i.e., 4-MeO-E1, 2-MeO-E2 and 4-MeO-E2) were analyzed by Friedman test (P = 0.001). Differences from “0” were identified by Dunn’s post hoc test (C). One difference between the MFs to 2-MeO-E1 and 2-MeO-E2 and one difference between the MFs to 2-MeO-E1 and 4-MeO-E2, respectively (both 11 × 0.001), are not shown

Fig. 5

Influence of various exVARs on levels of calculated fluxes to adducts of E2 and E1 with DNA adducts in the network model considering transcripts encoding enzymes in biotransformation of E2/E1 identified by multiple linear regression models using stepwise forward selection as detailed in Online Resource 7. For each model, the number (n) of observations (O) contributing to the final model, the adjusted coefficient of determination (R²), and the observations (i.e., biospecimens) to exVAR ratio (O/exVAR) after forward selection of variables is given

Fig. 6

Influence of various exVARs on levels of calculated fluxes to adducts of E2 and E1 with DNA adducts in the network model considering breast cancer risk factors and markers for (oxidative) cellular stress identified by multiple linear regression models using stepwise forward selection as detailed in Online Resource 7. For each model, the number (n) of observations (O) contributing to the final model, the adjusted coefficient of determination (R²), and the observations (i.e., biospecimens) to exVAR ratio (O/exVAR) after forward selection of variables is given. EAD estrogen-active drug, EE ethinyl-E2, ERD E2-releasing drugs, IPE intake of dietary supplements containing phytoestrogens, Lob 1np lobule type 1 of nulliparous women, Lob 1p lobule type 1 of parous women, periMP perimenopausal, postMP postmenopausal

Fig. 7

Influence of breast cancer risk factors (bold font) on cell proliferation and (de)activation of (pre)genotoxic estrogens identified by multiple linear regression. *Influence identified in Pemp et al. (2020). Estrogen levels as well as levels of AREG, PGR, TFF1, and of WNT4 were tested as their principal components PC_E1 as well as PC_EA1 and PC_EA2, respectively (Online Resource 7). Red: flux constraints significantly influencing metabolic fluxes to adducts of estrogens with DNA (E-DNA). For details which exVARs influence metabolic fluxes to adducts of E2 or E1 with DNA, see Figs. 5 and 6. EE ethinyl-E2, ERD E2-releasing drugs