Analysis of CENP-B Boxes as Anchor of Kinetochores in Centromeres of Human Chromosomes.

The kinetochore is a multiprotein structure that attaches at one end to DNA in the centromere and at the other end to microtubules in the mitotic spindle. By connecting centromere and spindle, the kinetochore controls the migration of chromosomes during cell division. The exact position where the kinetochore assembles on each centromere was uncertain because large sections of centromeric DNA had not been sequenced due to highly repetitive alpha-satellite arrays. Embedded in the arrays is a 17 bp consensus sequence, the so-called CENP-B box, which binds the CENP-B protein, the only protein that binds directly to centromeric DNA. Recently, the Telomere-to-Telomere Consortium published the complete centromeric DNA sequences of all chromosomes including their epigenetic modifications in the T2T-CHM13 map. I used data from the T2T-CHM13 map to locate the CENP-B boxes in the centromeres as anchor of kinetochores. Most of the CENP-B boxes in centromeric DNA are methylated with the exception of the so-called centromere dip region (CDR), where CENP-B protein dimers bind to adjacent unmethylated CENP-B boxes and interact with CENP-A and CENP-C proteins to assemble the kinetochore. The centromeres of all chromosomes combined have a size of 407 Mb of which the kinetochores account for 5.0 Mb or 1.2%. There is no correlation between centromere and kinetochore size (P = .77). While the number of CENP-B boxes varies 4-fold between chromosomes, their density (number/Kb) varies less than 2-fold with a mean of 2.61 ± 0.33. The narrow range ensures a uniform pull of the spindle on the centromeres. I illustrate the findings in a model of the human kinetochore anchored at unmethylated CENP-B boxes in the CDR and present circos plots of chromosomes to show the location of kinetochores in their respective centromeres.

Mutation Burden Independently Predicts Survival in the Pan-Cancer Atlas.

PURPOSE: Long-standing clinical predictors of cancer survival have included histopathologic type, stage, and grade. We hypothesized that the principal categories of tumor somatic mutations might also portend survival. We investigated this hypothesis using the Pan-Cancer Atlas, encompassing clinical, genomic, and outcome data of 10,652 patients and 32 cancer types. METHODS: We evaluated the prognostic capability of cancer type, stage, grade and the burden of each major mutation category on overall and disease-specific survival. Mutation categories included short substitution and insertion-deletion mutations (SMs), copy number alterations (CNAs), and gene fusions. RESULTS: SM count and CNA fraction proved to be strong independent predictors of survival (joint P = 5.3e-95) that remained highly significant when adjusted for the traditional factors. Importantly, the relationship between mutation burden and survival proved to be nonlinear (P = 9.5e-56); survival improved at both low- and high-burden extremes. In clinically predictive modeling, SM count together with CNA fraction meaningfully distinguished survival even among patients sharing a given cancer type, stage, or grade. CONCLUSION: Burden of somatic mutation is a key index of survival of analogous clinical utility to these traditional factors.

Different Tumor Types Share a Common Nuclear Map of Chromosome Territories.

Different tumor types are characterized by unique histopathological patterns including distinctive nuclear architectures. I hypothesized that the difference in nuclear appearance is reflected in different nuclear maps of chromosome territories, the discrete regions occupied by individual chromosomes in the interphase nucleus. To test this hypothesis, I used interchromosomal translocations (ITLs) as an analytical tool to map chromosome territories in 11 different tumor types from the TCGA PanCancer database encompassing 6003 tumors with 5295 ITLs. For each chromosome I determined the number and percentage of all ITLs for any given tumor type. Chromosomes were ranked according to the frequency and percentage of ITLs per chromosome. The ranking showed similar patterns for all tumor types. Chromosomes 1, 8, 11, 17, and 19 were ranked in the top quarter, accounting for 35.2% of 5295 ITLs, whereas chromosomes 13, 15, 18, 21, and X were in the bottom quarter, accounting for only 10.5% ITLs. The correlation between the chromosome ranking in the total group of 6003 tumors and the ranking in individual tumor types was significant, ranging from P < .0001 to .0033. Thus, contrary to my hypothesis, different tumor types share a common nuclear map of chromosome territories. Based on the large number of ITLs in 11 different types of malignancy one can discern a shared pattern of chromosome territories in cancer and propose a probabilistic model of chromosomes 1, 8, 11, 17, 19 in the center of the nucleus and chromosomes 13, 15, 18, 21, X at the periphery.

Interchromosomal Translocations as a Means to Map Chromosome Territories in Breast Cancer.

The genome-wide identification of mutated genes is an important advance in our understanding of tumor biology, but several fundamental questions remain open. How do these genes act together to promote cancer development and, a related question, how are they spatially arranged in the nucleus to allow coordinated expression? We examined the nuclear topography of mutated genes in breast cancer and their relation to chromosome territories (CTs). We performed a literature review and analyzed 1 type of mutation, interchromosomal translocations, in 1546 primary breast cancers to infer the spatial arrangement of chromosomes. The cosegregation of all observed fusion genes was used to create a matrix of genome-wide CT contacts and develop a tentative CT map of breast cancer. Regression analysis was performed to determine the association between CTs and all types of mutations. Chromosomes 17, 11, 8, and 1 had the majority of interchromosomal fusions and are presumably clustered in the nuclear center, whereas chromosomes 22, 21, X, and 18 had the lowest number of contacts, likely reflecting a more peripheral position. Regression analysis revealed that there was no significant association between chromosome length indicated by the number of base pairs per chromosome and the number of total (inter- and intrachromosomal) translocations, point mutations, or copy number aberrations (CNAs). The gene density of chromosomes (genes/Mb) was significantly correlated with total translocations (P = .02), but not with point mutations P = .19 and CNAs P = .62. Finally, the association of the 3 genetic alterations with the CT map deduced from the interchromosomal fusions was significant, ie, total translocations P = 7 × 10-11, point mutations P = .01, CNAs P = .002. In conclusion, we developed a tentative CT map and observed a spatial association with genetic alterations in breast cancer.

Genomic-Epidemiologic Evidence That Estrogens Promote Breast Cancer Development.

Background: Estrogens are a prime risk factor for breast cancer, yet their causal relation to tumor formation remains uncertain. A recent study of 560 breast cancers identified 82 genes with 916 point mutations as drivers in the genesis of this malignancy. Because estrogens play a major role in breast cancer development and are also known to regulate the expression of numerous genes, we hypothesize that the 82 driver genes are likely to be influenced by estrogens, such as 17ß-estradiol (E2), and the estrogen receptor ESR1 (ERα). Because different types of tumors are characterized by unique sets of cancer driver genes, we also argue that the fraction of driver genes regulated by E2-ESR1 is lower in malignancies not associated with estrogens, e.g., acute myeloid leukemia (AML).Methods: We performed a literature search of each driver gene to determine its E2-ESR1 regulation.Results: Fifty-three of the 82 driver genes (64.6%) identified in breast cancers showed evidence of E2-ESR1 regulation. In contrast, only 19 of 54 mutated driver genes (35.2%) identified in AML were linked to E2-ESR1. Among the 916 driver mutations found in breast cancers, 813 (88.8%) were linked to E2-ESR1 compared with 2,046 of 3,833 in AML (53.4%).Conclusions: Risk assessment revealed that mutations in estrogen-regulated genes are much more likely to be associated with elevated breast cancer risk, while mutations in unregulated genes are more likely to be associated with AML.Impact: These results increase the plausibility that estrogens promote breast cancer development. Cancer Epidemiol Biomarkers Prev; 27(8); 899-907. ©2018 AACR.

Targeted multiplex imaging mass spectrometry with single chain fragment variable (scfv) recombinant antibodies.

Recombinant scfv antibodies specific for CYP1A1 and CYP1B1 P450 enzymes were combined with targeted imaging mass spectrometry to simultaneously detect the P450 enzymes present in archived, paraffin-embedded, human breast cancer tissue sections. By using CYP1A1 and CYP1B1 specific scfv, each coupled to a unique reporter molecule (i.e., a mass tag) it was possible to simultaneously detect multiple antigens within a single tissue sample with high sensitivity and specificity using mass spectrometry. The capability of imaging multiple antigens at the same time is a significant advance that overcomes technical barriers encountered when using present day approaches to develop assays that can simultaneously detect more than a single antigen in the same tissue sample.

A multistage genetic association study identifies breast cancer risk loci at 10q25 and 16q24.

BACKGROUND: Heritable risk for breast cancer includes an increasing number of common, low effect risk variants. We conducted a multistage genetic association study in a series of independent epidemiologic breast cancer study populations to identify novel breast cancer risk variants. METHODS: We tested 1,162 SNPs of greatest nominal significance from stage I of the Cancer Genetic Markers of Susceptibility breast cancer study (CGEMS; 1,145 cases, 1,142 controls) for evidence of replicated association with breast cancer in the Nashville Breast Cohort (NBC; 599 cases, 1,161 controls), the Collaborative Breast Cancer Study (CBCS; 1,552 cases, 1,185 controls), and BioVU Breast Cancer Study (BioVU; 1,172 cases, 1,172 controls). RESULTS: Among these SNPs, a series of validated breast cancer risk variants yielded expected associations in the study populations. In addition, we observed two previously unreported loci that were significantly associated with breast cancer risk in the CGEMS, NBC, and CBCS study populations and had a consistent, although not statistically significant, risk effect in the BioVU study population. These were rs1626678 at 10q25.3 near ENO4 and KIAA1598 (meta-analysis age-adjusted OR = 1.13 [1.07-1.20], P = 5.6 × 10(-5)), and rs8046508 at 16q23.1 in the eighth intron of WWOX (meta-analysis age-adjusted OR = 1.20 [1.10-1.31], P = 3.5 × 10(-5)). CONCLUSIONS: Our data supports the association of two novel loci, at 10q25.3 and 16q23.1, with risk of breast cancer. IMPACT: The expanding compendium of known breast cancer genetic risk variants holds increasing power for clinical risk prediction models of breast cancer, improving upon the Gail model.

A multistage association study identifies a breast cancer genetic locus at NCOA7.

Estrogen metabolism and growth factor signaling pathway genes play key roles in breast cancer development. We evaluated associations between breast cancer and tagging single-nucleotide polymorphisms (SNP) of 107 candidate genes of these pathways using single allele- and haplotype-based tests. We first sought concordance of associations between two study populations: the Nashville Breast Cohort (NBC; 510 cases, 988 controls), and the Cancer Genetic Markers of Susceptibility (CGEMS) breast cancer study (1,145 cases, 1,142 controls). Findings across the two study populations were concordant at tagging SNPs of six genes, and at previously published SNPs of FGFR2. We sought further replication of results for EGFR, NCOA7, and FGFR2 in the independent Collaborative Breast Cancer Study (CBCS; 1,552 cases, 1,185 controls). Associations at NCOA7 and FGFR2 replicated across all three studies. The association at NCOA7 on 6q22.32, detected by a haplotype spanning the initial protein-coding exon (5'-rs9375411, rs11967627, rs549438, rs529858, rs490361, rs17708107-3'), has not been previously reported. The haplotype had a significant inverse association with breast cancer in each study [OR(Het): 0.69 (NBC), 0.76 (CGEMS), 0.79 (CBCS)], and a meta-analysis OR(Het) of 0.75 (95% CI, 0.65-0.87, P = 1.4 × 10(-4)) in the combined study populations. The haplotype frequency was 0.07 among cases, and 0.09 among controls; homozygotes were infrequent and each OR(Hom) was not significant. NCOA7 encodes a nuclear receptor coactivator that interacts with estrogen receptor α to modulate its activity. These observations provide consistent evidence that genetic variants at the NCOA7 locus may confer a reduced risk of breast cancer.

Estrogen metabolism and exposure in a genotypic-phenotypic model for breast cancer risk prediction.

BACKGROUND: Current models of breast cancer risk prediction do not directly reflect mammary estrogen metabolism or genetic variability in exposure to carcinogenic estrogen metabolites. METHODS: We developed a model that simulates the kinetic effect of genetic variants of the enzymes CYP1A1, CYP1B1, and COMT on the production of the main carcinogenic estrogen metabolite, 4-hydroxyestradiol (4-OHE(2)), expressed as area under the curve metric (4-OHE(2)-AUC). The model also incorporates phenotypic factors (age, body mass index, hormone replacement therapy, oral contraceptives, and family history), which plausibly influence estrogen metabolism and the production of 4-OHE(2). We applied the model to two independent, population-based breast cancer case-control groups, the German GENICA study (967 cases, 971 controls) and the Nashville Breast Cohort (NBC; 465 cases, 885 controls). RESULTS: In the GENICA study, premenopausal women at the 90th percentile of 4-OHE(2)-AUC among control subjects had a risk of breast cancer that was 2.30 times that of women at the 10th control 4-OHE(2)-AUC percentile (95% CI: 1.7-3.2, P = 2.9 × 10(-7)). This relative risk was 1.89 (95% CI: 1.5-2.4, P = 2.2 × 10(-8)) in postmenopausal women. In the NBC, this relative risk in postmenopausal women was 1.81 (95% CI: 1.3-2.6, P = 7.6 × 10(-4)), which increased to 1.83 (95% CI: 1.4-2.3, P = 9.5 × 10(-7)) when a history of proliferative breast disease was included in the model. CONCLUSIONS: The model combines genotypic and phenotypic factors involved in carcinogenic estrogen metabolite production and cumulative estrogen exposure to predict breast cancer risk. IMPACT: The estrogen carcinogenesis-based model has the potential to provide personalized risk estimates.

A mathematical model for DNA damage and repair.

In cells, DNA repair has to keep up with DNA damage to maintain the integrity of the genome and prevent mutagenesis and carcinogenesis. While the importance of both DNA damage and repair is clear, the impact of imbalances between both processes has not been studied. In this paper, we created a combined mathematical model for the formation of DNA adducts from oxidative estrogen metabolism followed by base excision repair (BER) of these adducts. The model encompasses a set of differential equations representing the sequence of enzymatic reactions in both damage and repair pathways. By combining both pathways, we can simulate the overall process by starting from a given time-dependent concentration of 17beta-estradiol (E(2)) and 2'-deoxyguanosine, determine the extent of adduct formation and the correction by BER required to preserve the integrity of DNA. The model allows us to examine the effect of phenotypic and genotypic factors such as different concentrations of estrogen and variant enzyme haplotypes on the formation and repair of DNA adducts.

Implementation of a closed-loop reporting system for critical values and clinical communication in compliance with goals of the joint commission.

BACKGROUND: Current practices of reporting critical laboratory values make it challenging to measure and assess the timeliness of receipt by the treating physician as required by The Joint Commission's 2008 National Patient Safety Goals. METHODS: A multidisciplinary team of laboratorians, clinicians, and information technology experts developed an electronic ALERTS system that reports critical values via the laboratory and hospital information systems to alphanumeric pagers of clinicians and ensures failsafe notification, instant documentation, automatic tracking, escalation, and reporting of critical value alerts. A method for automated acknowledgment of message receipt was incorporated into the system design. RESULTS: The ALERTS system has been applied to inpatients and eliminated approximately 9000 phone calls a year made by medical technologists. Although a small number of phone calls were still made as a result of pages not acknowledged by clinicians within 10 min, they were made by telephone operators, who either contacted the same physician who was initially paged by the automated system or identified and contacted alternate physicians or the patient's nurse. Overall, documentation of physician acknowledgment of receipt in the electronic medical record increased to 95% of critical values over 9 months, while the median time decreased to <3 min. CONCLUSIONS: We improved laboratory efficiency and physician communication by developing an electronic system for reporting of critical values that is in compliance with The Joint Commission's goals.

Estrogen exposure, metabolism, and enzyme variants in a model for breast cancer risk prediction.

Estrogen is a well-known risk factor for breast cancer. Current models of breast cancer risk prediction are based on cumulative estrogen exposure but do not directly reflect mammary estrogen metabolism or address genetic variability between women in exposure to carcinogenic estrogen metabolites. We are proposing a mathematical model that forecasts breast cancer risk for a woman based on three factors: (1) estimated estrogen exposure, (2) kinetic analysis of the oxidative estrogen metabolism pathway in the breast, and (3) enzyme genotypes responsible for inherited differences in the production of carcinogenic metabolites. The model incorporates the main components of mammary estrogen metabolism, i.e. the conversion of 17beta-estradiol (E(2)) by the phase I and II enzymes cytochrome P450 (CYP) 1A1 and 1B1, catechol-O-methyltransferase (COMT), and glutathione S-transferase P1 (GSTP1) into reactive metabolites, including catechol estrogens and estrogen quinones, such as E(2)-3,4-Q which can damage DNA. Each of the four genes is genotyped and the SNP data used to derive the haplotype configuration for each subject. The model then utilizes the kinetic and genotypic data to calculate the amount of E(2)-3,4-Q carcinogen as ultimate risk factor for each woman. The proposed model extends existing models by combining the traditional "phenotypic" measures of estrogen exposure with genotypic data associated with the metabolic fate of E(2) as determined by critical phase I and II enzymes. Instead of providing a general risk estimate our model would predict the risk for each individual woman based on her age, reproductive experiences as well as her genotypic profile.

Estrogen metabolism and breast cancer: a risk model.

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 17beta-estradiol (E(2)) is metabolized. We employed both gas and liquid chromatography with mass spectrometry to measure the parent hormone E(2) 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 E(2) 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 E(2)-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 E(2)-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.

Carcinogen-induced histone alteration in normal human mammary epithelial cells.

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.

Recombinant antibody piezoimmunosensors for the detection of cytochrome P450 1B1.

The phase I enzyme known as cytochrome P450 1B1 (CYP1B1) is involved in the metabolism of many endogenous and exogenous compounds, including carcinogens. CYP1B1 is overexpressed in a wide variety of human diseases ranging from diabetes to malignancies, such as invasive breast cancer. Because of its microsomal location in the cell, CYP1B1 could not be measured directly by existing methods but only assessed indirectly via the determination of the catalytic products. We report here a rapid, sensitive piezoimmunosensor for detection of CYP1B1 using single-chain fragment variable antibodies (scFv) as recognition elements and a quartz crystal microbalance (QCM) as the transducer. Three anti-CYP1B1 scFvs (designated B-66, D-23, and L-21) were biotinylated and used to capture and specifically detect CYP1B1 from samples in solution. ScFvs are smaller than most commonly used antibodies and can be coated onto QCM surfaces at much higher density to improve sensor sensitivity and specificity. The scFv-QCM biosensors showed excellent sensitivity (detection limit, 2.2 +/- 0.9 nM) and specificity with a dissociation constant K(d) = (1.54 +/- 0.59) x 10(-7) M. CYP1B1 were quantitatively detected in normal and malignant cell lysates (e.g., human T47D breast cancer cell microsomes). Results demonstrate that an anti-CYP1B1 scFv-QCM immunosensor could be used to detect P450 enzymes in biological samples.

Cytochrome P450 1B1-mediated estrogen metabolism results in estrogen-deoxyribonucleoside adduct formation.

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 17beta-estradiol (E2) 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 E2 in the presence of 2'-deoxyguanosine or 2'-deoxyadenosine. We used both gas and liquid chromatography with tandem mass spectrometry to measure E2, the 2- and 4-catechol estrogens (2-OHE2, 4-OHE2), and the depurinating adducts 4-OHE(2)-1(alpha,beta)-N7-guanine (4-OHE2-N7-Gua) and 4-OHE(2)-1(alpha,beta)-N3-adenine (4-OHE2-N3-Ade). CYP1B1 oxidized E2 to the catechol 4-OHE2 and the labile quinone 4-hydroxyestradiol-quinone to produce 4-OHE2-N7-Gua and 4-OHE2-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 Michaelis-Menten kinetics. Under the conditions of the assay, the 4-OHE2-N7-Gua adduct (Km, 4.6+/-0.7 micromol/L; kcat, 45+/-1.6/h) was produced 1.5 times more efficiently than the 4-OHE2-N3-Ade adduct (Km, 4.6+/-1.0 micromol/L; kcat, 30+/-1.5/h). The production of adducts was two to three orders of magnitude lower than the 4-OHE2 production. The results present direct proof of CYP1B1-mediated, E2-induced adduct formation and provide the experimental basis for future studies of estrogen carcinogenesis.

Meta- and pooled analyses of the cytochrome P-450 1B1 Val432Leu polymorphism and breast cancer: a HuGE-GSEC review.

The association between the cytochrome P-450 1B1 (CYP1B1) Val432Leu polymorphism and breast cancer was assessed through a meta-analysis of all published case-control studies and a pooled analysis of both published and unpublished case-control studies from the Genetic Susceptibility to Environmental Carcinogens (GSEC) database ( www.upci.upmc.edu/research/ccps/ccontrol/g_intro.html ). GSEC is a collaborative project that gathers information on studies of metabolic gene polymorphisms and cancer. Thirteen articles were included in the meta-analysis (14,331 subjects; 7,514 cases, 6,817 controls); nine data sets were included in the pooled analysis (6,842 subjects; 3,391 cases, 3,451 controls). A summary meta- or pooled estimate of the association between the CYP1B1 Val432Leu polymorphism and breast cancer could not be calculated because of statistically significant heterogeneity in the point estimates among studies. No association between the CYP1B1 Val432Leu polymorphism and breast cancer was observed in Asians (for Val/Val and Val/Leu combined, odds ratio (OR) = 1.0, 95% confidence interval (CI): 0.8, 1.2). An inverse association was observed in populations of mixed/African origin (OR = 0.8, 95% CI: 0.7, 0.9). The pooled analysis suggested a possible association in Caucasians (for Val/Val and Val/Leu combined, OR = 1.5, 95% CI: 1.1, 2.1), with effect modification across age categories. The observed effect of age on the association in Caucasians indicates that further studies are needed on the role of CYP1B1 Val432Leu in estrogen metabolism according to age, ethnicity, and menopausal status.

Calcium-phosphorus product and troponin-T values in renal failure.

PURPOSE: The purpose of this study was to see if there is a significant association between calcium-phosphorus product (CPP) and initial troponin-T values in patients with renal insufficiency. BASIC PROCEDURES: A retrospective study over 4 months from the laboratory database of all patients with serum creatinine values greater than 3 mg/dL who had concomitant troponin T and creatine kinase isoenzymes measured was conducted. The most recent calcium and phosphorus values were also abstracted. RESULTS: There were 87 patients with a mean age of 59 years, a median creatinine value of 51 mg/dL, a mean CPP of 47, and a median initial troponin-T value of 0.18 ng/mL. The troponin level was elevated (>0.05 ng/mL) in 74% of the patients. The CPP was higher than 55 in 28% of the patients. When comparing the troponin values with the CPP, there was no association noted (P = .72). CONCLUSIONS: Although both elevated CPP and troponin values are associated with increased cardiovascular mortality in the intermediate term, we could not demonstrate a useful relationship between the 2 values that would indicate causality or help with the interpretation of the initial troponin level when patients with renal insufficiency present with suspected acute myocardial infarction.

Estrogens, enzyme variants, and breast cancer: a risk model.

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 17beta-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.

Mitochondrial DNA G10398A polymorphism and invasive breast cancer in African-American women.

Mitochondria generate oxygen-derived free radicals that damage mitochondrial DNA (mtDNA) as well as nuclear DNA and in turn promote carcinogenesis. The mtDNA G10398A polymorphism alters the structure of Complex I in the mitochondrial electron transport chain, an important site of free radical production. This polymorphism is associated with several neurodegenerative disorders. We hypothesized that the 10398A allele is also associated with breast cancer susceptibility. African mitochondria harbor the 10398A allele less frequently than Caucasian mitochondria, which predominantly carry this allele. Mitochondrial genotypes at this locus were therefore determined in two separate populations of African-American women with invasive breast cancer and in controls. A preliminary study at Vanderbilt University (48 cases, 54 controls) uncovered an association between the 10398A allele and invasive breast cancer in African-American women, [odds ratio (OR), 2.90; 95% confidence interval (95% CI), 0.61-18.3; P = 0.11]. We subsequently validated this finding in a large, population-based, case-control study of breast cancer, the Carolina Breast Cancer Study at the University of North Carolina (654 cases, 605 controls). African-American women in this study with the 10398A allele had a significantly increased risk of invasive breast cancer (OR, 1.60; 95% CI, 1.10-2.31; P = 0.013). The 10398A allele remained an independent risk factor after adjustment for other well-accepted breast cancer risk factors. No association was detectable in white women (879 cases, 760 controls; OR, 1.03; 95% CI, 0.81-1.31; P = 0.81). This study provides novel epidemiologic evidence that the mtDNA 10398A allele influences breast cancer susceptibility in African-American women. mtDNA polymorphisms may be underappreciated factors in breast carcinogenesis.