One of the greatest challenges facing human geneticists is the identification and characterization of susceptibility genes for common complex multifactorial human diseases. This challenge is partly due to the limitations of parametric-statistical methods for detection of gene effects that are dependent solely or partially on interactions with other genes and with environmental exposures. We introduce multifactor-dimensionality reduction (MDR) as a method for reducing the dimensionality of multilocus information, to improve the identification of polymorphism combinations associated with disease risk. The MDR method is nonparametric (i.e., no hypothesis about the value of a statistical parameter is made), is model-free (i.e., it assumes no particular inheritance model), and is directly applicable to case-control and discordant-sib-pair studies. Using simulated case-control data, we demonstrate that MDR has reasonable power to identify interactions among two or more loci in relatively small samples. When it was applied to a sporadic breast cancer case-control data set, in the absence of any statistically significant independent main effects, MDR identified a statistically significant high-order interaction among four polymorphisms from three different estrogen-metabolism genes. To our knowledge, this is the first report of a four-locus interaction associated with a common complex multifactorial disease.

The correlation reported previously, as well as our current findings, suggest that further investigations are warranted to understand the possible linkage of the ER gene locus to hereditary breast cancer.

The oxidative metabolism of estrogens has been implicated in the development of breast cancer; yet, relatively little is known about the mechanism by which estrogens cause DNA damage and thereby initiate mammary carcinogenesis. To determine how the metabolism of the parent hormone 17B-estradiol (E 2 ) leads to the formation of DNA adducts, we used the recombinant, purified phase I enzyme, cytochrome P450 1B1 (CYP1B1), which is expressed in breast tissue, to oxidize E 2 in the presence of 2 ¶-deoxyguanosine or 2 ¶-deoxyadenosine. We used both gas and liquid chromatography with tandem mass spectrometry to measure E 2 , the 2-and 4-catechol estrogens (2-OHE 2 , 4-OHE 2 ), and the depurinating adducts 4-OHE 2 -1(A,B)-N7-guanine (4-OHE 2 -N7-Gua) and 4-OHE 2 -1(A,B)-N3-adenine (4-OHE 2 -N3-Ade). CYP1B1 oxidized E 2 to the catechol 4-OHE 2 and the labile quinone 4-hydroxyestradiol-quinone to produce 4-OHE 2 -N7-Gua and 4-OHE 2 -N3-Ade in a time-and concentration-dependent manner. Because the reactive quinones were produced as part of the CYP1B1-mediated oxidation reaction, the adduct formation followed MichaelisMenten kinetics. Under the conditions of the assay, the 4-OHE 2 -N7-Gua adduct (K m , 4.6 F 0.7 Mmol/L; k cat , 45 F

Oxidative metabolites of estrogens have been implicated in the development of breast cancer, yet relatively little is known about the metabolism of estrogens in the normal breast. We developed a mathematical model of mammary estrogen metabolism based on the conversion of 17B-estradiol (E 2 ) by the enzymes cytochrome P450 (CYP) 1A1 and CYP1B1, catechol-O-methyltransferase (COMT), and glutathione S-transferase P1 into eight metabolites [i.e., two catechol estrogens, 2-hydroxyestradiol (2-OHE 2 ) and 4-hydroxyestradiol (4-OHE 2 ); three methoxyestrogens, 2-methoxyestradiol, 2-hydroxy-3-methoxyestradiol, and 4-methoxyestradiol; and three glutathione (SG)-estrogen conjugates, 2-OHE 2 -1-SG, 2-OHE 2 -4-SG, and 4-OHE 2 -2-SG]. When used with experimentally determined rate constants with purified enzymes, the model provides for a kinetic analysis of the entire metabolic pathway. The predicted concentration of each metabolite during a 30-minute reaction agreed well with the experimentally derived results. The model also enables simulation for the transient quinones, E 2 -2,3-quinone (E 2 -2,3-Q) and E 2 -3,4-quinone (E 2 -3,4-Q), which are not amenable to direct quantitation. Using experimentally derived rate constants for genetic variants of CYP1A1, CYP1B1, and COMT, we used the model to simulate the kinetic effect of enzyme polymorphisms on the pathway and identified those haplotypes generating the largest amounts of catechols and quinones. Application of the model to a breast cancer case-control population identified a subset of women with an increased risk of breast cancer based on their enzyme haplotypes and consequent E 2 -3,4-Q production. This in silico model integrates both kinetic and genomic data to yield a comprehensive view of estrogen metabolomics in the breast. The model offers the opportunity to combine metabolic, genetic, and lifetime exposure data in assessing estrogens as a breast cancer risk factor. (Cancer Epidemiol Biomarkers Prev 2006;15(9):1620 -9)

Oxidative metabolites of estrogens have been implicated in the development of breast cancer, yet relatively little is known about the metabolism of estrogens in the normal breast. We developed an experimental in vitro model of mammary estrogen metabolism in which we combined purified, recombinant phase I enzymes CYP1A1 and CYP1B1 with the phase II enzymes COMT and GSTP1 to determine how 17β-estradiol (E2) is metabolized. We employed both gas and liquid chromatography with mass spectrometry to measure the parent hormone E2 as well as eight metabolites, that is, the catechol estrogens, methoxyestrogens, and estrogen–GSH conjugates. We used these experimental data to develop an in silico model, which allowed the kinetic simulation of converting E2 into eight metabolites. The simulations showed excellent agreement with experimental results and provided a quantitative assessment of the metabolic interactions. Using rate constants of genetic variants of CYP1A1, CYP1B1, and COMT, the model further allowed examination of the kinetic impact of enzyme polymorphisms on the entire metabolic pathway, including the identification of those haplotypes producing the largest amounts of catechols and quinones. Application of the model to a breast cancer case-control population defined the estrogen quinone E2-3,4-Q as a potential risk factor and identified a subset of women with an increased risk of breast cancer based on their enzyme haplotypes and consequent E2-3,4-Q production. Our in silico model integrates diverse types of data and offers the exciting opportunity for researchers to combine metabolic and genetic data in assessing estrogenic exposure in relation to breast cancer risk.

More than 500 studies have examined the association of the glutathione S-transferase M1 (GSTM1) genotype with various malignancies yielding inconsistent results. The genotyping was based on a PCR assay that identified the GSTM1 null (؊/؊) genotype but did not distinguish homozygous wild-type (؉/؉) and heterozygous (؉/؊) individuals. We developed an assay that allowed the definition of ؉/؉, ؉/؊, and ؊/؊ genotypes by separate identification of wild-type and null alleles, which were found with frequencies of 0.225 and 0.775, respectively, in Caucasian women. We applied the new assay to a breast cancer case-control study and identified the ؉/؉ genotype in 14 (6.9%) of 202 control subjects compared with 37 (18.2%) of 203 patients. Compared with women with the ؊/؊ genotype, the relative risk of breast cancer for the ؉/؉ genotype was 2.83 (95% confidence interval, 1.45-5.59; P ‫؍‬ 0.002), suggesting a protective effect of the GSTM1 deletion.

Estrogens and their oxidative metabolites, the catechol estrogens, have been implicated in the development of breast cancer; yet, relatively little is known about estrogen metabolism in the breast. To determine how the parent hormone, 17 beta-estradiol (E(2)), is metabolized, we used recombinant, purified phase I enzymes, cytochrome P450 (CYP) 1A1 and 1B1, with the phase II enzymes catechol-O-methyltransferase (COMT) and glutathione S-transferase P1 (GSTP1), all of which are expressed in breast tissue. We employed both gas and liquid chromatography with mass spectrometry to measure E(2), the catechol estrogens 2-hydroxyestradiol (2-OHE(2)) and 4-hydroxyestradiol (4-OHE(2)), as well as methoxyestrogens and estrogen-GSH conjugates. The oxidation of E(2) to 2-OHE(2) and 4-OHE(2) was exclusively regulated by CYP1A1 and 1B1, regardless of the presence or concentration of COMT and GSTP1. COMT generated two products, 2-methoxyestradiol and 2-hydroxy-3-methoxyestradiol, from 2-OHE(2) but only one product, 4-methoxyestradiol, from 4-OHE(2). Similarly, GSTP1 yielded two conjugates, 2-OHE(2)-1-SG and 2-OHE(2)-4-SG, from the corresponding quinone 2-hydroxyestradiol-quinone and one conjugate, 4-OHE(2)-2-SG, from 4-hydroxyestradiol-quinone. Using the experimental data, we developed a multicompartment kinetic model for the oxidative metabolism of the parent hormone E(2), which revealed significant differences in rate constants for its C-2 and C-4 metabolites. The results demonstrated a tightly regulated interaction of phase I and phase II enzymes, in which the latter decreased the concentration of catechol estrogens and estrogen quinones, thereby reducing the potential of these oxidative estrogen metabolites to induce DNA damage.

Little is known about early carcinogen-induced protein alterations in mammary epithelium. Detection of early alterations would enhance our understanding of early-stage carcinogenesis. Here, normal human mammary epithelial cells (HMECs) were exposed to dietary and environmental carcinogens [2-amino-1-methyl-6-phenylimidazo[4,5b]pyridine (PhIP), 4-aminobiphenyl (ABP), benzo[a]pyrene, 2,3,7,8-tetrachlorodibenzo-p-dioxin] individually or in combination. A phage display library of single-chain variable fragment antibodies was used to screen protein targets altered by the treatment. In combination with matrix-assisted laser desorption time of flight, we identified histone H3 as a target antigen. Although histone H3 total protein remained unchanged in control and treated HMEC, the methylation of lysine 4 was altered. A reduction in mono-methyl histone H3 (Lys 4) was observed in treated HMEC compared with control HMEC. This alteration was shown to be dependent on carcinogen concentration and specific for PhIP and ABP. To characterize potential histone demethylation mechanisms, localization and protein expression patterns of lysine-specific demethylase 1 (LSD1) were analyzed. In control HMEC, LSD1 was present at the nuclear periphery. However, following 72 h carcinogen treatment, LSD1 localized within the nucleus. Within 48 h after treatment, mono-methyl histone H3 (Lys 4) was restored and LSD1 localization was reversed. Protein expression levels of LSD1 were also increased in treated HMEC compared with control HMEC. Our data suggest that the induction of a single enzyme, LSD1, represents an early response to carcinogen exposure, which leads to the demethylation of histone H3 (Lys 4), which, in turn, may influence the expression of multiple genes critical in early-stage mammary carcinogenesis.