| 1. |
- Ahn, Jiyoung, et al.
(författare)
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Quantitative trait loci predicting circulating sex steroid hormones in men from the NCI-Breast and Prostate Cancer Cohort Consortium (BPC3).
- 2009
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Ingår i: Human molecular genetics. - : Oxford University Press (OUP). - 1460-2083 .- 0964-6906. ; 18:19, s. 3749-57
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Tidskriftsartikel (refereegranskat)abstract
- Twin studies suggest a heritable component to circulating sex steroid hormones and sex hormone-binding globulin (SHBG). In the NCI-Breast and Prostate Cancer Cohort Consortium, 874 SNPs in 37 candidate genes in the sex steroid hormone pathway were examined in relation to circulating levels of SHBG (N = 4720), testosterone (N = 4678), 3 alpha-androstanediol-glucuronide (N = 4767) and 17beta-estradiol (N = 2014) in Caucasian men. rs1799941 in SHBG is highly significantly associated with circulating levels of SHBG (P = 4.52 x 10(-21)), consistent with previous studies, and testosterone (P = 7.54 x 10(-15)), with mean difference of 26.9 and 14.3%, respectively, comparing wild-type to homozygous variant carriers. Further noteworthy novel findings were observed between SNPs in ESR1 with testosterone levels (rs722208, mean difference = 8.8%, P = 7.37 x 10(-6)) and SRD5A2 with 3 alpha-androstanediol-glucuronide (rs2208532, mean difference = 11.8%, P = 1.82 x 10(-6)). Genetic variation in genes in the sex steroid hormone pathway is associated with differences in circulating SHBG and sex steroid hormones.
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| 2. |
- Berndt, Sonja I, et al.
(författare)
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Large-scale fine mapping of the HNF1B locus and prostate cancer risk
- 2011
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Ingår i: Human Molecular Genetics. - : Oxford University Press (OUP). - 0964-6906 .- 1460-2083. ; 20:16, s. 3322-3329
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Tidskriftsartikel (refereegranskat)abstract
- Previous genome-wide association studies have identified two independent variants in HNF1B as susceptibility loci for prostate cancer risk. To fine-map common genetic variation in this region, we genotyped 79 single nucleotide polymorphisms (SNPs) in the 17q12 region harboring HNF1B in 10 272 prostate cancer cases and 9123 controls of European ancestry from 10 case-control studies as part of the Cancer Genetic Markers of Susceptibility (CGEMS) initiative. Ten SNPs were significantly related to prostate cancer risk at a genome-wide significance level of P < 5 × 10(-8) with the most significant association with rs4430796 (P = 1.62 × 10(-24)). However, risk within this first locus was not entirely explained by rs4430796. Although modestly correlated (r(2)= 0.64), rs7405696 was also associated with risk (P = 9.35 × 10(-23)) even after adjustment for rs4430769 (P = 0.007). As expected, rs11649743 was related to prostate cancer risk (P = 3.54 × 10(-8)); however, the association within this second locus was stronger for rs4794758 (P = 4.95 × 10(-10)), which explained all of the risk observed with rs11649743 when both SNPs were included in the same model (P = 0.32 for rs11649743; P = 0.002 for rs4794758). Sequential conditional analyses indicated that five SNPs (rs4430796, rs7405696, rs4794758, rs1016990 and rs3094509) together comprise the best model for risk in this region. This study demonstrates a complex relationship between variants in the HNF1B region and prostate cancer risk. Further studies are needed to investigate the biological basis of the association of variants in 17q12 with prostate cancer.
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| 3. |
- Campa, Daniele, et al.
(författare)
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Interactions Between Genetic Variants and Breast Cancer Risk Factors in the Breast and Prostate Cancer Cohort Consortium
- 2011
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Ingår i: Journal of the National Cancer Institute. - : Oxford University Press (OUP). - 0027-8874 .- 1460-2105. ; 103:16, s. 1252-1263
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Tidskriftsartikel (refereegranskat)abstract
- Background Recently, several genome-wide association studies have identified various genetic susceptibility loci for breast cancer. Relatively little is known about the possible interactions between these loci and the established risk factors for breast cancer. Methods To assess interactions between single-nucleotide polymorphisms (SNPs) and established risk factors, we prospectively collected DNA samples and questionnaire data from 8576 breast cancer case subjects and 11 892 control subjects nested within the National Cancer Institute's Breast and Prostate Cancer Cohort Consortium (BPC3). We genotyped 17 germline SNPs (FGFR2-rs2981582, FGFR2-rs3750817, TNRC9-rs3803662, 2q35-rs13387042, MAP3K1-rs889312, 8q24-rs13281615, CASP8-rs1045485, LSP1-rs3817198, COL1A1-rs2075555, COX11-rs6504950, RNF146-rs2180341, 6q25-rs2046210, SLC4A7-rs4973768, NOTCH2-rs11249433, 5p12-rs4415084, 5p12-rs10941679, RAD51L1-rs999737), and odds ratios were estimated by logistic regression to confirm previously reported associations with breast cancer risk. We performed likelihood ratio test to assess interactions between 17 SNPs and nine established risk factors (age at menarche, parity, age at menopause, use of hormone replacement therapy, family history, height, body mass index, smoking status, and alcohol consumption), and a correction for multiple testing of 153 tests (adjusted P value threshold = .05/153 = 3 x 10(-4)) was done. Casecase comparisons were performed for possible differential associations of polymorphisms by subgroups of tumor stage, estrogen and progesterone receptor status, and age at diagnosis. All statistical tests were two-sided. Results We confirmed the association of 14 SNPs with breast cancer risk (P(trend) = 2.57 x 10(-3) -3.96 x 10(-19)). Three SNPs (LSP1-rs3817198, COL1A1-rs2075555, and RNF146-rs2180341) did not show association with breast cancer risk. After accounting for multiple testing, no statistically significant interactions were detected between the 17 SNPs and the nine risk factors. We also confirmed that SNPs in FGFR2 and TNRC9 were associated with greater risk of estrogen receptor-positive than estrogen receptor-negative breast cancer (P(heterogeneity) = .0016 for FGFR2-rs2981582 and P(heterogeneity) = .0053 for TNRC9-rs3803662). SNP 5p12-rs10941679 was statistically significantly associated with greater risk of progesterone receptor-positive than progesterone receptor-negative breast cancer (P(heterogeneity) = .0028). Conclusion This study does not support the hypothesis that known common breast cancer susceptibility loci strongly modify the associations between established risk factors and breast cancer.
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| 4. |
- Canzian, Federico, et al.
(författare)
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Comprehensive analysis of common genetic variation in 61 genes related to steroid hormone and insulin-like growth factor-I metabolism and breast cancer risk in the NCI breast and prostate cancer cohort consortium.
- 2010
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Ingår i: Human Molecular Genetics. - : Oxford University Press (OUP). - 0964-6906 .- 1460-2083. ; 19:19, s. 3873-84
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Tidskriftsartikel (refereegranskat)abstract
- There is extensive evidence that increases in blood and tissue concentrations of steroid hormones and of insulin-like growth factor I (IGF-I) are associated with breast cancer risk. However, studies of common variation in genes involved in steroid hormone and IGF-I metabolism have yet to provide convincing evidence that such variants predict breast cancer risk. The Breast and Prostate Cancer Cohort Consortium (BPC3) is a collaboration of large US and European cohorts. We genotyped 1416 tagging single nucleotide polymorphisms (SNPs) in 37 steroid hormone metabolism genes and 24 IGF-I pathway genes in 6292 cases of breast cancer and 8135 controls, mostly Caucasian, postmenopausal women from the BPC3. We also imputed 3921 additional SNPs in the regions of interest. None of the SNPs tested was significantly associated with breast cancer risk, after correction for multiple comparisons. The results remained null when cases and controls were stratified by age at diagnosis/recruitment, advanced or nonadvanced disease, body mass index, with or without in situ cases; or restricted to Caucasians. Among 770 estrogen receptor-negative cases, an SNP located 3' of growth hormone receptor (GHR) was marginally associated with increased risk after correction for multiple testing (P(trend) = 1.5 × 10(-4)). We found no significant overall associations between breast cancer and common germline variation in 61 genes involved in steroid hormone and IGF-I metabolism in this large, comprehensive study. Although previous studies have shown that variations in these genes can influence endogenous hormone levels, the magnitude of the effect of single SNPs does not appear to be sufficient to alter breast cancer risk.
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| 5. |
- Dossus, Laure, et al.
(författare)
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PTGS2 and IL6 genetic variation and risk of breast and prostate cancer : results from the Breast and Prostate Cancer Cohort Consortium (BPC3)
- 2010
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Ingår i: Carcinogenesis. - : Oxford University Press (OUP). - 0143-3334 .- 1460-2180. ; 31:3, s. 455-461
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Tidskriftsartikel (refereegranskat)abstract
- Genes involved in the inflammation pathway have been associated with cancer risk. Genetic variants in the interleukin-6 (IL6) and prostaglandin-endoperoxide synthase-2 (PTGS2, encoding for the COX-2 enzyme) genes, in particular, have been related to several cancer types, including breast and prostate cancers. We conducted a study within the Breast and Prostate Cancer Cohort Consortium to examine the association between IL6 and PTGS2 polymorphisms and breast and prostate cancer risk. Twenty-seven polymorphisms, selected by pairwise tagging, were genotyped on 6292 breast cancer cases and 8135 matched controls and 8008 prostate cancer cases and 8604 matched controls. The large sample sizes and comprehensive single nucleotide polymorphism tagging in this study gave us excellent power to detect modest effects for common variants. After adjustment for multiple testing, none of the associations examined remained statistically significant at P = 0.01. In analyses not adjusted for multiple testing, one IL6 polymorphism (rs6949149) was marginally associated with breast cancer risk (TT versus GG, odds ratios (OR): 1.32; 99% confidence intervals (CI): 1.00-1.74, P(trend) = 0.003) and two were marginally associated with prostate cancer risk (rs6969502-AA versus rs6969502-GG, OR: 0.87, 99% CI: 0.75-1.02; P(trend) = 0.002 and rs7805828-AA versus rs7805828-GG, OR: 1.11, 99% CI: 0.99-1.26; P(trend) = 0.007). An increase in breast cancer risk was observed for the PTGS2 polymorphism rs7550380 (TT versus GG, OR: 1.38, 99% CI: 1.04-1.83). No association was observed between PTGS2 polymorphisms and prostate cancer risk. In conclusion, common genetic variation in these two genes might play at best a limited role in breast and prostate cancers.
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| 6. |
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| 7. |
- Huesing, Anika, et al.
(författare)
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Prediction of breast cancer risk by genetic risk factors, overall and by hormone receptor status
- 2012
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Ingår i: Journal of Medical Genetics. - : BMJ. - 0022-2593 .- 1468-6244. ; 49:9, s. 601-608
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Tidskriftsartikel (refereegranskat)abstract
- Objective There is increasing interest in adding common genetic variants identified through genome wide association studies (GWAS) to breast cancer risk prediction models. First results from such models showed modest benefits in terms of risk discrimination. Heterogeneity of breast cancer as defined by hormone-receptor status has not been considered in this context. In this study we investigated the predictive capacity of 32 GWAS-detected common variants for breast cancer risk, alone and in combination with classical risk factors, and for tumours with different hormone receptor status. Material and methods Within the Breast and Prostate Cancer Cohort Consortium, we analysed 6009 invasive breast cancer cases and 7827 matched controls of European ancestry, with data on classical breast cancer risk factors and 32 common gene variants identified through GWAS. Discriminatory ability with respect to breast cancer of specific hormone receptor-status was assessed with the age adjusted and cohort-adjusted concordance statistic (AUROC(a)). Absolute risk scores were calculated with external reference data. Integrated discrimination improvement was used to measure improvements in risk prediction. Results We found a small but steady increase in discriminatory ability with increasing numbers of genetic variants included in the model (difference in AUROC(a) going from 2.7% to 4%). Discriminatory ability for all models varied strongly by hormone receptor status. Discussion and conclusions Adding information on common polymorphisms provides small but statistically significant improvements in the quality of breast cancer risk prediction models. We consistently observed better performance for receptor-positive cases, but the gain in discriminatory quality is not sufficient for clinical application.
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| 8. |
- Joshi, Amit D., et al.
(författare)
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Additive interactions between susceptibility single-nucleotide polymorphisms identified in genome-wide association studies and breast cancer risk factors in the Breast and Prostate Cancer Cohort Consortium
- 2014
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Ingår i: American Journal of Epidemiology. - : Oxford University Press. - 0002-9262 .- 1476-6256. ; 180:10, s. 1018-1027
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Tidskriftsartikel (refereegranskat)abstract
- Additive interactions can have public health and etiological implications but are infrequently reported. We assessed departures from additivity on the absolute risk scale between 9 established breast cancer risk factors and 23 susceptibility single-nucleotide polymorphisms (SNPs) identified from genome-wide association studies among 10,146 non-Hispanic white breast cancer cases and 12,760 controls within the National Cancer Institute's Breast and Prostate Cancer Cohort Consortium. We estimated the relative excess risk due to interaction and its 95% confidence interval for each pairwise combination of SNPs and nongenetic risk factors using age- and cohort-adjusted logistic regression models. After correction for multiple comparisons, we identified a statistically significant relative excess risk due to interaction (uncorrected P = 4.51 x 10(-5)) between a SNP in the DNA repair protein RAD51 homolog 2 gene (RAD51L1; rs10483813) and body mass index (weight (kg)/height (m)(2)). We also compared additive and multiplicative polygenic risk prediction models using per-allele odds ratio estimates from previous studies for breast-cancer susceptibility SNPs and observed that the multiplicative model had a substantially better goodness of fit than the additive model.
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| 9. |
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| 10. |
- Lindstroem, Sara, et al.
(författare)
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Common genetic variants in prostate cancer risk prediction-results from the NCI breast and prostate cancer cohort consortium (BPC3)
- 2012
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Ingår i: Cancer Epidemiology, Biomarkers and Prevention. - 1055-9965 .- 1538-7755. ; 21:3, s. 437-444
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Tidskriftsartikel (refereegranskat)abstract
- Background: One of the goals of personalized medicine is to generate individual risk profiles that could identify individuals in the population that exhibit high risk. The discovery of more than two-dozen independent single-nucleotide polymorphism markers in prostate cancer has raised the possibility for such risk stratification. In this study, we evaluated the discriminative and predictive ability for prostate cancer risk models incorporating 25 common prostate cancer genetic markers, family history of prostate cancer, and age.Methods: We fit a series of risk models and estimated their performance in 7,509 prostate cancer cases and 7,652 controls within the National Cancer Institute Breast and Prostate Cancer Cohort Consortium (BPC3). We also calculated absolute risks based on SEER incidence data.Results: The best risk model (C-statistic = 0.642) included individual genetic markers and family history of prostate cancer. We observed a decreasing trend in discriminative ability with advancing age (P = 0.009), with highest accuracy in men younger than 60 years (C-statistic = 0.679). The absolute ten-year risk for 50-year-old men with a family history ranged from 1.6% (10th percentile of genetic risk) to 6.7% (90th percentile of genetic risk). For men without family history, the risk ranged from 0.8% (10th percentile) to 3.4% (90th percentile).Conclusions: Our results indicate that incorporating genetic information and family history in prostate cancer risk models can be particularly useful for identifying younger men that might benefit from prostate-specific antigen screening.Impact: Although adding genetic risk markers improves model performance, the clinical utility of these genetic risk models is limited.
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