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Multi-variant pathway association analysis reveals the importance of genetic determinants of estrogen metabolism in breast and endometrial cancer susceptibility.

ABSTRACT: Despite the central role of estrogen exposure in breast and endometrial cancer development and numerous studies of genes in the estrogen metabolic pathway, polymorphisms within the pathway have not been consistently associated with these cancers. We posit that this is due to the complexity of multiple weak genetic effects within the metabolic pathway that can only be effectively detected through multi-variant analysis. We conducted a comprehensive association analysis of the estrogen metabolic pathway by interrogating 239 tagSNPs within 35 genes of the pathway in three tumor samples. The discovery sample consisted of 1,596 breast cancer cases, 719 endometrial cancer cases, and 1,730 controls from Sweden; and the validation sample included 2,245 breast cancer cases and 1,287 controls from Finland. We performed admixture maximum likelihood (AML)-based global tests to evaluate the cumulative effect from multiple SNPs within the whole metabolic pathway and three sub-pathways for androgen synthesis, androgen-to-estrogen conversion, and estrogen removal. In the discovery sample, although no single polymorphism was significant after correction for multiple testing, the pathway-based AML global test suggested association with both breast (p(global) = 0.034) and endometrial (p(global) = 0.052) cancers. Further testing revealed the association to be focused on polymorphisms within the androgen-to-estrogen conversion sub-pathway, for both breast (p(global) = 0.008) and endometrial cancer (p(global) = 0.014). The sub-pathway association was validated in the Finnish sample of breast cancer (p(global) = 0.015). Further tumor subtype analysis demonstrated that the association of the androgen-to-estrogen conversion sub-pathway was confined to postmenopausal women with sporadic estrogen receptor positive tumors (p(global) = 0.0003). Gene-based AML analysis suggested CYP19A1 and UGT2B4 to be the major players within the sub-pathway. Our study indicates that the composite genetic determinants related to the androgen-estrogen conversion are important for the induction of two hormone-associated cancers, particularly for the hormone-driven breast tumour subtypes.

SUBMITTER: Low YL

PROVIDER: S-EPMC2895650 | biostudies-literature | 2010 Jul

REPOSITORIES: biostudies-literature

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Multi-variant pathway association analysis reveals the importance of genetic determinants of estrogen metabolism in breast and endometrial cancer susceptibility.

Low Yen Ling YL Li Yuqing Y Humphreys Keith K Thalamuthu Anbupalam A Li Yi Y Darabi Hatef H Wedrén Sara S Bonnard Carine C Czene Kamila K Iles Mark M MM Heikkinen Tuomas T Aittomäki Kristiina K Blomqvist Carl C Nevanlinna Heli H Hall Per P Liu Edison T ET Liu Jianjun J

PLoS genetics 20100701 7

Despite the central role of estrogen exposure in breast and endometrial cancer development and numerous studies of genes in the estrogen metabolic pathway, polymorphisms within the pathway have not been consistently associated with these cancers. We posit that this is due to the complexity of multiple weak genetic effects within the metabolic pathway that can only be effectively detected through multi-variant analysis. We conducted a comprehensive association analysis of the estrogen metabolic p ...[more]

PMID: 20617168

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Project description:BackgroundEndometrial cancer (EC) is one of the most common gynecological cancers. The traditional diagnosis of EC relies on histopathology, which, however, is invasive and may arouse tumor spread. There have been many studies aiming to find the metabolomic biomarkers of EC to improve the early diagnosis of cancer in a non-invasive or minimally invasive way, which can also provide valuable information for understanding the disease. However, most of these studies only analyze a single type of sample by metabolomics, and cannot provide a comprehensive view of the altered metabolism in EC patients. Our study tries to gain a pathway-based view of multiple types of samples for understanding metabolomic disorders in EC by combining metabolomics and proteomics.MethodsForty-four EC patients and forty-three controls were recruited for the research. We collected endometrial tissue, urine, and intrauterine brushing samples. Untargeted metabolomics and untargeted proteomics were both performed on the endometrial tissue samples, while only untargeted metabolomics was performed on the urine and intrauterine brushing samples.ResultsBy integrating the differential metabolites and proteins between EC patients and controls detected in the endometrial tissue samples, we identified several EC-related significant pathways, such as amino acid metabolism and nucleotide metabolism. The significance of these pathways and the potential of metabolite biomarker-based diagnosis were then further verified by using urine and intrauterine brushing samples. It was found that the regulation of metabolites involved in the significant pathways showed similar trends in the intrauterine brushings and the endometrial tissue samples, while opposite trends in the urine and the endometrial tissue samples.ConclusionsWith multi-omics characterization of multi-biosamples, the metabolomic changes related to EC are illustrated in a pathway-based way. The network of altered metabolites and related proteins provides a comprehensive view of altered metabolism in the endometrial tissue samples. The verification of these critical pathways by using urine and intrauterine brushing samples provides evidence for the possible non-invasive or minimally invasive biopsy for EC diagnosis in the future.

Project description:Estrogen metabolites may have different genotoxic and mitogenic properties yet their relationship with endometrial and ovarian cancer risk remains unclear. Within the Breast and Bone Follow-up to the Fracture Intervention Trial (B ∼ FIT, n = 15,595), we conducted a case-cohort study to evaluate 15 pre-diagnostic serum estrogens and estrogen metabolites with risk of incident endometrial and ovarian cancer among postmenopausal women not on hormone therapy. Participants included 66 endometrial and 67 ovarian cancer cases diagnosed during follow-up (∼ 10 years) and subcohorts of 346 and 416 women, respectively, after relevant exclusions. Serum concentrations were measured by liquid chromatography-tandem mass spectrometry. Hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated using Cox proportional hazard regression. Exposures were categorized in tertiles (T) and analyzed individually, as metabolic pathways (C-2, -4, or -16) and as ratios to parent estrogens (estradiol, estrone). Estradiol was significantly associated with increased endometrial cancer risk (BMI-adjusted HRT3vsT1 = 4.09, 95% CI 1.70, 9.85; p trend = 0.003). 2-Hydroxyestrone and 16α-hydroxyestrone were not associated with endometrial risk after estradiol adjustment (2-OHE1:HRT3vsT1 = 1.97, 95% CI 0.78, 4.94; 16-OHE1:HRT3vsT1 = 1.50, 95% CI 0.65, 3.46; p trend = 0.16 and 0.36, respectively). Ratios of 2- and 4-pathway catechol-to-methylated estrogens remained positively associated with endometrial cancer after BMI or estradiol adjustment (2-pathway catechols-to-methylated: HRT3vsT1 = 4.02, 95% CI 1.60, 10.1; 4-pathway catechols-to-methylated: HRT3vsT1 = 4.59, 95% CI 1.64, 12.9; p trend = 0.002 for both). Estrogens and estrogen metabolites were not associated with ovarian cancer risk; however, larger studies are needed to better evaluate these relationships. Estrogen metabolism may be important in endometrial carcinogenesis, particularly with less extensive methylation of 2- or 4-pathway catechols associated with elevated endometrial cancer risk.

Dataset Information

Multi-variant pathway association analysis reveals the importance of genetic determinants of estrogen metabolism in breast and endometrial cancer susceptibility.

Publications

Multi-variant pathway association analysis reveals the importance of genetic determinants of estrogen metabolism in breast and endometrial cancer susceptibility.

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