Eldon R. Jupe

MTHFR C677T and postmenopausal breast cancer risk by intakes of one-carbon metabolism nutrients: a nested case-control study

IntroductionThe C677T polymorphism of the methylenetetrahydrofolate reductase (MTHFR) gene has been hypothesized to increase breast cancer risk. However, results have been inconsistent, and few studies have reported the association by menopausal status or by intakes of nutrients participating in one-carbon metabolism. Our aims were to investigate whether MTHFR C677T was associated with postmenopausal breast cancer risk and whether this relation was modified by intakes of folate, methionine, vitamins B2, B6, and B12, and alcohol.MethodsWe studied 318 incident breast cancer cases and 647 age- and race-matched controls participating in a nested case-control study of postmenopausal women within the VITamins And Lifestyle (VITAL) cohort. Genotyping was conducted for MTHFR C677T and dietary and supplemental intakes were ascertained from a validated questionnaire. Adjusted odds ratios (OR) and 95% confidence intervals (CI) were calculated using unconditional logistic regression.ResultsWe observed a 62% increased risk of breast cancer among postmenopausal women with the TT genotype (OR = 1.62; 95% CI: 1.05 to 2.48). Women with a higher number of variant T alleles had higher risk of breast cancer (P for trend = 0.04). Evidence of effect-modification by intakes of some B vitamins was observed. The most pronounced MTHFR-breast cancer risks were observed among women with the lowest intakes of dietary folate (P for interaction = 0.02) and total (diet plus supplemental) vitamin B6 (P for interaction = 0.01), with no significant increased risks among women with higher intakes.ConclusionsThis study provides support that the MTHFR 677TT genotype is associated with a moderate increase in risk of postmenopausal breast cancer and that this risk may be attenuated with high intakes of some one-carbon associated nutrients.

Polymorphisms in genes involved in sex hormone metabolism, estrogen plus progestin hormone therapy use, and risk of postmenopausal breast cancer

Hormone therapy, estrogen plus progestin (E+P) particularly, is associated with increased risk of breast cancer. Functionally relevant polymorphisms in genes involved in sex hormone metabolism may alter exposure to exogenous sex hormones and affect risk of postmenopausal breast cancer. We evaluated associations of common polymorphisms in genes involved in estrogen and/or progesterone metabolism, E+P use, and their interactions with breast cancer risk in a case-control study of postmenopausal women (324 cases; 651 controls) nested within the VITAL cohort. None of the polymorphisms studied was, by itself, statistically significantly associated with breast cancer risk. E+P use was significantly associated with increased breast cancer risk (≥10 years versus never; odds ratio, 1.9; 95% confidence interval, 1.3-2.8; Ptrend = 0.0002). Statistically significant interactions between CYP1A1 Ile462Val (Pinteraction = 0.04), CYP1A1 MspI (Pinteraction = 0.003), CYP1B1 Val432Leu (Pinteraction = 0.007), CYP1B1 Asn453Ser (Pinteraction = 0.04) and PGR Val660Leu (Pinteraction = 0.01), and E+P use were observed. The increased risk of breast cancer associated with E+P use was greater among women with at least one rare allele of the CYP1A1 Ile462Val, CYP1A1 MspI, CYP1B1 Asn453Ser, and PGR Val660Leu polymorphisms than among women homozygous for the common allele of these polymorphisms. Risk of breast cancer increased little with increasing years of E+P use among women with at least one CYP1B1 Val432 allele; a large increase in risk was seen among women homozygous for CYP1B1 Leu432. Our results support the hypothesis that specific polymorphisms in genes involved in sex hormone metabolism may modify the effect of E+P use on breast cancer risk. (Cancer Epidemiol Biomarkers Prev 2008;17(7):1751–9)

Vitamin D Receptor Polymorphisms and Breast Cancer Risk in a High-Incidence Population: A Pilot Study

BACKGROUND Marin County, CA has very high incidence of breast cancer. Traditional risk factors, such as those included in the Gail model, do not effectively stratify breast cancer in this population. This retrospective case-control pilot study evaluates DNA from volunteers from a previous Marin County breast cancer epidemiology study. A polyfactorial risk model (OncoVue; InterGenetics Incorporated) that incorporates 22 polymorphisms in 19 genes and 5 clinical risk factors was used to stratify risk in Marin County women. STUDY DESIGN DNA genotyping was performed on 164 Caucasian women diagnosed with primary breast cancer in Marin County from 1997 to 1999 and 174 age- and ethnicity-matched control subjects. Individual lifetime risks were determined using the polyfactorial risk model and genotype frequencies in women at elevated risk were compared with the overall genotypes. RESULTS The vitamin D receptor VDR ApaI A2/A2 (rs7975232) homozygous polymorphism was present in high frequency in elevated-risk women. Sixty-four percent of elevated-risk women had the VDR Apa1 A2/A2 genotype compared with only 34% in the overall study, a statistically significant 1.9-fold difference (p = 0.0003). VDR Apa1 A2/a1 and a1/a1 genotypes were also present, but in lower frequencies. CONCLUSIONS The high frequency of the VDR Apa1 A2/A2 homozygous polymorphism in women designated as elevated risk for breast cancer by the polyfactorial risk model might be related to the high incidence rates of breast cancer in Marin County, CA. Vitamin D supplementation could modify risk of breast cancer in this population.

Breast Cancer Risk Estimation Using the OncoVue® Model Compared to Combined GWAS Single Nucleotide Polymorphisms.

Background: We have developed a new breast cancer risk assessment model (OncoVue) that integrates information from 22 single nucleotide polymorphisms (SNPs) and 5 personal risk factors. It has been shown to effectively stratify risk in three independent validation populations. Genome wide association studies (GWAS) have identified seven candidate SNPs independently associated with breast cancer risk. Using estimates of relative risks and allele frequencies derived from these studies, a theoretical examination of these SNPs has concluded that they have the potential to modestly improve clinical risk estimation. We have examined both the OncoVue model and these GWAS SNPs in the same case-control population to determine their utility in risk estimation.Materials and Methods: A randomly selected subset of participants (376 cases and 982 controls) that had enrolled in a larger case-control study conducted in six distinct geographic regions of the United States were examined. DNAs were genotyped for the 22 SNP variants in OncoVue and combined with personal factors to calculate the risk scores for the individual participants. DNAs were also genotyped for the following GWAS SNPs: rs2981582 (FGFR2), rs3817198 (LSP1), rs889312 (MAP3K), rs4415084 (MRPS30), rs13281615 (POU5F1P1), rs13387042 (TNP1), and rs3803662 (TOX3, formerly TNRCR9). Association of individual GWAS SNP genotypes with breast cancer risk was evaluated by calculating odds ratios (ORs). Assuming independent contribution of each SNP to risk, a combined GWAS risk score was calculated using a multiplicative model. Positive likelihood ratios (PLRs) were calculated using a risk threshold of 1.5-fold the control population mean risk to evaluate the proportion of individuals placed at elevated risk by OncoVue compared to the combined GWAS risk scores.Results: OncoVue exhibited significant ability to stratify risk at the 1.5-fold mean with a PLR of 2.2 and at a risk level of 2.5-fold mean the PLR increased to 5.0. In analyses of individual GWAS SNPs, statistically significant associations with breast cancer were identified for homozygous carriers of the rare allele for the FGFR2, MAP3K and TOX3 genes. However, the PLR for the combined GWAS risk scores at 1.5-fold mean was 1.0 indicating no ability to discriminate risk in this study population.Conclusions: In this case-control population OncoVue effectively stratified risk by accurately assigning elevated risk to breast cancer cases. In the same population, a combined risk score produced from seven GWAS SNPs did not effectively stratify risk. These results indicate that the OncoVue model has clinical utility for identifying elevated risk women who might benefit from additional screening and prevention. Citation Information: Cancer Res 2009;69(24 Suppl):Abstract nr 3177.

Expression of prohibitin 3? untranslated region suppressor RNA alters morphology and inhibits motility of breast cancer cells

The prohibitin 3′ untranslated region (3′UTR) belongs to a novel class of non-coding regulatory RNAs. It arrests cell cycle progression by blocking G1-S transition in breast and other cancers. Our previous studies comparing MCF7 derived clones constitutively expressing a common allelic form of prohibitin RNA (UTR/C) to various controls demonstrated that it functions as a tumor suppressor. Here, we further characterized the morphology and motility of these transgenic breast cancer cells when grown in cell culture and on nude mice. In contrast to empty vector (EV) cells, UTR/C cells were observed to grow in an organized manner with more cell-cell contact and differentiate into structures with a duct-like appearance. Computer assisted cytometry to evaluate differences in nuclear morphology was performed on UTR/C and EV tissues from nude mice. Receiver operator curve areas generated using a logistic regression model were 0.8, indicating the ability to quantitatively distinguish UTR/C from EV tissues. Keratinocyte growth factor-induced motility experiments showed that migration of UTR/C cells was significantly reduced (80–90%) compared to EV cells. Together, these data indicate that this novel 3′UTR influences not only the tumorigenic phenotype but also may play a role in differentiation and migration of breast cancer cells.

Validation of OncoVue®, a new individualized breast cancer risk estimator in the Marin County, California adolescent risk study.

CTRC-AACR San Antonio Breast Cancer Symposium: 2008 Abstracts Abstract #502 Background: Improved models for estimating individual breast cancer risk are urgently needed for guiding clinical decisions. The OncoVue model was developed by analysis of a large case-control study genotyped for 117 common, functional single nucleotide polymorphisms (SNPs) in candidate genes likely to influence breast carcinogenesis. Multivariate logistic regression analyses were used to develop a model employing 22 SNPs in 19 genes together with the Gail Model risk factors. In the original studies of the model building set and two independent validation sets, OncoVue exhibited improved individualized risk estimation, compared to the Gail Model alone, by correctly placing more cases and fewer controls at elevated risk. Here, we sought to examine the performance of OncoVue in women from the Marin County, California breast cancer adolescent risk factor study. Materials and Methods: Buccal cell DNA was isolated from 177 controls and 169 age-matched women diagnosed with breast cancer between 1997 and 1999. All samples were anonymously coded to remove case-control status and provided to InterGenetics along with all other relevant personal history information. DNAs were genotyped for the 22 SNP variants in OncoVue and combined with personal factors to calculate the risk scores for the individual participants. OncoVue scores were then returned to the Marin County study investigators who conducted the analyses using case-control status. Results: Positive likelihood ratios (PLR) were calculated as the proportion of patients with breast cancer with an elevated risk estimate (12%) divided by the proportion of disease-free individuals with an elevated risk estimate. For OncoVue the PLR was 2.2, whereas for the Gail Model the PLR was 0.9, demonstrating a 2.4-fold statistically significant (p=0.036) improvement. In additional comparisons using normalized control scores, OncoVue exhibited a 51% improvement compared to the Gail Model in assigning elevated risk to cases. Conclusion: In this blinded validation study OncoVue exhibited significantly improved performance, compared to the Gail model alone, in estimating individual risk among Marin County, California women. The improved performance of OncoVue was similar to that observed in two previous independent validation sets, thus, supporting the clinical utility of OncoVue for more accurate individualized breast cancer risk estimation. Citation Information: Cancer Res 2009;69(2 Suppl):Abstract nr 502.

A steroid metabolizing gene variant in a polyfactorial model improves risk prediction in a high incidence breast cancer population

Background We have combined functional gene polymorphisms with clinical factors to improve prediction and understanding of sporadic breast cancer risk, particularly within a high incidence Caucasian population. Methods A polyfactorial risk model (PFRM) was built from both clinical data and functional single nucleotide polymorphism (SNP) gene candidates using multivariate logistic regression analysis on data from 5022 US Caucasian females (1671 breast cancer cases, 3351 controls), validated in an independent set of 1193 women (400 cases, 793 controls), and reassessed in a unique high incidence breast cancer population (165 cases, 173 controls) from Marin County, CA. Results The optimized PFRM consisted of 22 SNPs (19 genes, 6 regulating steroid metabolism) and 5 clinical risk factors, and its 5-year and lifetime risk prediction performance proved significantly superior (~ 2-fold) over the Gail model (Breast Cancer Risk Assessment Tool, BCRAT), whether assessed by odds (OR) or positive likelihood (PLR) ratios over increasing model risk levels. Improved performance of the PFRM in high risk Marin women was due in part to genotype enrichment by a CYP11B2 (-344T/C) variant. Conclusions and general significance Since the optimized PFRM consistently outperformed BCRAT in all Caucasian study populations, it represents an improved personalized risk assessment tool. The finding of higher Marin County risk linked to a CYP11B2 aldosterone synthase SNP associated with essential hypertension offers a new genetic clue to sporadic breast cancer predisposition.

Abstract P2-14-05: Sporadic breast cancer risk assessment in women without family history and biopsy using the OncoVue® polyfactorial model

Background: The majority of breast cancer cases are considered to be sporadic and occur in women without a strong family history. Accurate estimation of a woman9s risk of developing breast cancer is essential for guiding decisions regarding surveillance and prevention. The Breast Cancer Risk Assessment Tool (BCRAT) uses only clinical factors and has been shown to be of marginal value for individual risk counseling. Even though the BCRAT risks for women without a biopsy and no first degree relative with breast cancer are low, a significant fraction of cases occur in these women. OncoVue® is a logistic regression model developed from individual genetic and personal factor data from a large case-control study. In three independent validation populations, including a blinded validation, OncoVue has been shown to significantly outperform BCRAT. Here we compare OncoVue to the BCRAT in stratifying risk in women without a first degree relative with breast cancer that have never had a breast biopsy. Materials and Methods: Absolute risks for Caucasian participants ranging in age from 35 to 89 were analyzed for a case-control study conducted in six distinct geographic regions of the United States. This analysis focused on participants that had never had a biopsy and reported no first degree relatives with breast cancer (cases prior to diagnosis/controls at time of enrollment). DNAs from a total of 2729 women (842 cases and 1887 controls) were genotyped for 22 SNP variants and genotype information was combined with clinical risk information to calculate 5-year and lifetime risks. Clinical factor information alone was used to calculate BCRAT risk scores for comparison to OncoVue. Results: The 5-year risks for OncoVue ranged from 0.10 to 5.0% (50-fold) compared to 0.20 to 3.0% (15-fold) for BCRAT risks. OncoVue lifetime risks ranged from 2.4 to 25.0% (11-fold) compared to 4 to 16% (4-fold) for BCRAT risks. The OncoVue model 5-year risk placed 11% of this sub-population of controls at ≥ 2.0% risk compared to the BCRAT which placed only 3.5% at this risk. The BCRAT did not place anyone at a lifetime risk over 16% while OncoVue placed 10% of this sub-population of controls above 16%. The Odds Ratio (OR) was determined at increasing model risk output levels. Over comparable ranges, OncoVue 5-year risks exhibited statistically significant ORs while BCRAT ORs did not reach significance. OncoVue associations for lifetime risk reach high levels. For example, an OR of 5.0 (p = 0.03) was observed at a risk level ≥ 23%. Conclusion: The OncoVue model stratifies risk over a much wider range than the BCRAT in this group of women at low risk by traditional methods. In the 5-year risk frame OncoVue exhibited improved performance in estimating individual risk compared to the BCRAT. Due to the lack of any elevated lifetime risks generated by the BCRAT, a direct comparison of lifetime risks for OncoVue to the BCRAT was not possible. The OncoVue model which uses both genetics and clinical factors significantly improves individualized breast cancer risk estimation. These results demonstrate the importance of the genetic component of the OncoVue model in accurately estimating individual risk of sporadic breast cancer. Citation Information: Cancer Res 2013;73(24 Suppl): Abstract nr P2-14-05.

P4-10-05: Improved Breast Cancer Risk Assessment in Biopsied Women Using the Polyfactorial Model OncoVue®.

Background Regardless of the result, women that have undergone a breast biopsy are considered at increased risk due to the clinical circumstances leading to the need for the procedure. Both the number and outcome of biopsy are considered in the widely used Breast Cancer Risk Assessment Tool (BCRAT) also known as the Gail model. Accurately estimating individualized risk of developing breast cancer is useful for early detection and cancer prevention. Although the BCRAT has been demonstrated to accurately estimate the number of breast cancers likely to emerge in a population of women seeking regular mammography screening, it does not produce accurate individualized risk estimates for patient counseling. We have simultaneously analyzed gene polymorphism and clinical factor data in breast cancer cases and matched controls to develop a polyfactorial risk model (OncoVue) to improve estimation of individual risk. In three independent patient populations, OncoVue has been shown to significantly outperform both the BCRAT and composite risk scores produced by combining GWAS SNP risks with the BCRAT. Here we have characterized the performance of OncoVue in stratifying risk in women that have had one or more breast biopsies. Materials and Methods : Risk scores were analyzed for participants ranging in age from 35 to 89 for a subset of participants that had enrolled in a larger case-control study conducted in six distinct geographic regions of the United States. The current study focused on the analysis of participants that had reported one or more biopsies (cases prior to diagnosis/controls at time of enrollment) amounting to 1265 Caucasian women (537 cases and 728 controls) in a model building set and 303 women in an independent validation set (134 cases and 169 controls). DNAs were genotyped for 22 SNP variants and genotype information was combined with clinical risk factor information to calculate the risk scores for the individual participants. Clinical factor information was also used to calculate BCRAT risk scores. The performance of OncoVue was examined in comparison to the BCRAT alone. Results : For both models, positive likelihood ratios (PLR) were calculated as the proportion of patients with breast cancer with an elevated lifetime risk estimate (≥20%) divided by the proportion of disease-free individuals with an elevated risk estimate. In both the model building and validation sets, OncoVue exhibited approximately a 2.0-fold improvement compared to the BCRAT in more accurately assigning elevated risk estimates to breast cancer cases. In these women that are already considered at increased risk because of a history of biopsy, the observed level of improved performance of OncoVue was similar to that in our previous overall studies. Conclusions : The OncoVue polyfactorial risk model incorporating both genetics and clinical factors improves on individualized breast cancer risk estimation compared to the BCRAT which uses only clinical factors. The performance in biopsied women further supports the potential utility of OncoVue for directing prevention and screening decisions. Citation Information: Cancer Res 2011;71(24 Suppl):Abstract nr P4-10-05.