| 1. |
- Barrdahl, Myrto, et al.
(author)
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Post-G WAS gene-environment interplay in breast cancer : results from the Breast and Prostate Cancer Cohort Consortium and a meta-analysis on 79 000 women
- 2014
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In: Human Molecular Genetics. - : Oxford University Press (OUP). - 0964-6906 .- 1460-2083. ; 23:19, s. 5260-5270
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Journal article (peer-reviewed)abstract
- We studied the interplay between 39 breast cancer (BC) risk SNPs and established BC risk (body mass index, height, age at menarche, parity, age at menopause, smoking, alcohol and family history of BC) and prognostic factors (TNM stage, tumor grade, tumor size, age at diagnosis, estrogen receptor status and progesterone receptor status) as joint determinants of BC risk. We used a nested case-control design within the National Cancer Institute's Breast and Prostate Cancer Cohort Consortium (BPC3), with 16 285 BC cases and 19 376 controls. We performed stratified analyses for both the risk and prognostic factors, testing for heterogeneity for the risk factors, and case-case comparisons for differential associations of polymorphisms by subgroups of the prognostic factors. We analyzed multiplicative interactions between the SNPs and the risk factors. Finally, we also performed a meta-analysis of the interaction ORs from BPC3 and the Breast Cancer Association Consortium. After correction for multiple testing, no significant interaction between the SNPs and the established risk factors in the BPC3 study was found. The meta-analysis showed a suggestive interaction between smoking status and SLC4A7-rs4973768 (P-interaction = 8.84 x 10(-4)) which, although not significant after considering multiple comparison, has a plausible biological explanation. In conclusion, in this study of up to almost 79 000 women we can conclusively exclude any novel major interactions between genome-wide association studies hits and the epidemiologic risk factors taken into consideration, but we propose a suggestive interaction between smoking status and SLC4A7-rs4973768 that if further replicated could help our understanding in the etiology of BC.
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| 2. |
- Campa, Daniele, et al.
(author)
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A genome-wide "pleiotropy scan'' does not identify new susceptibility loci for estrogen receptor negative breast cancer
- 2014
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In: PLOS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 9:2, s. e85955-
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Journal article (peer-reviewed)abstract
- Approximately 15-30% of all breast cancer tumors are estrogen receptor negative (ER-). Compared with ER- positive (ER+) disease they have an earlier age at onset and worse prognosis. Despite the vast number of risk variants identified for numerous cancer types, only seven loci have been unambiguously identified for ER- negative breast cancer. With the aim of identifying new susceptibility SNPs for this disease we performed a pleiotropic genome-wide association study (GWAS). We selected 3079 SNPs associated with a human complex trait or disease at genome-wide significance level (P<5x10(-8)) to perform a secondary analysis of an ER- negative GWAS from the National Cancer Institute's Breast and Prostate Cancer Cohort Consortium (BPC3), including 1998 cases and 2305 controls from prospective studies. We then tested the top ten associations (i.e. with the lowest P-values) using three additional populations with a total sample size of 3509 ER+ cases, 2543 ER- cases and 7031 healthy controls. None of the 3079 selected variants in the BPC3 ER- GWAS were significant at the adjusted threshold. 186 variants were associated with ER- breast cancer risk at a conventional threshold of P<0.05, with P-values ranging from 0.049 to 2.3 x 10(-4). None of the variants reached statistical significance in the replication phase. In conclusion, this study did not identify any novel susceptibility loci for ER-breast cancer using a "pleiotropic approach''.
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| 3. |
- Campa, Daniele, et al.
(author)
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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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In: Journal of the National Cancer Institute. - : Oxford University Press (OUP). - 0027-8874 .- 1460-2105. ; 103:16, s. 1252-1263
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Journal article (peer-reviewed)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. |
- Huesing, Anika, et al.
(author)
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Prediction of breast cancer risk by genetic risk factors, overall and by hormone receptor status
- 2012
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In: Journal of Medical Genetics. - : BMJ. - 0022-2593 .- 1468-6244. ; 49:9, s. 601-608
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Journal article (peer-reviewed)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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| 5. |
- Lindstroem, Sara, et al.
(author)
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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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In: Cancer Epidemiology, Biomarkers and Prevention. - 1055-9965 .- 1538-7755. ; 21:3, s. 437-444
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Journal article (peer-reviewed)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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| 6. |
- Lindstroem, Sara, et al.
(author)
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Replication of five prostate cancer loci identified in an Asian population-results from the NCI breast and prostate cancer cohort consortium (BPC3)
- 2012
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In: Cancer Epidemiology, Biomarkers and Prevention. - Philadelphia : American Association for Cancer Research. - 1055-9965 .- 1538-7755. ; 21:1, s. 212-216
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Journal article (peer-reviewed)abstract
- Background: A recent genome-wide association study (GWAS) of prostate cancer in a Japanese population identified five novel regions not previously discovered in other ethnicities. In this study, we attempt to replicate these five loci in a series of nested prostate cancer case-control studies of European ancestry. Methods: We genotyped five single-nucleotide polymorphism (SNP): rs13385191 (chromosome 2p24), rs12653946 (5p15), rs1983891 (6p21), rs339331 (6p22), and rs9600079 (13q22), in 7,956 prostate cancer cases and 8,148 controls from a series of nested case-control studies within the National cancer Institute Breast and Prostate Cancer Cohort Consortium (BPC3). We tested each SNP for association with prostate cancer risk and assessed whether associations differed with respect to disease severity and age of onset. Results: Four SNPs (rs13385191, rs12653946, rs1983891, and rs339331) were significantly associated with prostate cancer risk (P values ranging from 0.01 to 1.1 x 10(-5)). Allele frequencies and ORs were overall lower in our population of European descent than in the discovery Asian population. SNP rs13385191 (C2orf43) was only associated with low-stage disease (P = 0.009, case-only test). No other SNP showed association with disease severity or age of onset. We did not replicate the 13q22 SNP, rs9600079 (P = 0.62). Conclusions: Four SNPs associated with prostate cancer risk in an Asian population are also associated with prostate cancer risk in men of European descent. Impact: This study illustrates the importance of evaluation of prostate cancer risk markers across ethnic groups. Cancer Epidemiol Biomarkers Prev; 21(1); 212-16. (C) 2011 AACR.
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| 7. |
- Shui, Irene M., et al.
(author)
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Prostate Cancer (PCa) Risk Variants and Risk of Fatal PCa in the National Cancer Institute Breast and Prostate Cancer Cohort Consortium
- 2014
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In: European Urology. - : Elsevier BV. - 0302-2838 .- 1873-7560. ; 65:6, s. 1069-1075
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Journal article (peer-reviewed)abstract
- Background: Screening and diagnosis of prostate cancer (PCa) is hampered by an inability to predict who has the potential to develop fatal disease and who has indolent cancer. Studies have identified multiple genetic risk loci for PCa incidence, but it is unknown whether they could be used as biomarkers for PCa-specific mortality (PCSM). Objective: To examine the association of 47 established PCa risk single-nucleotide polymorphisms (SNPs) with PCSM. Design, setting, and participants: We included 10 487 men who had PCa and 11 024 controls, with a median follow-up of 8.3 yr, during which 1053 PCa deaths occurred. Outcome measurements and statistical analysis: The main outcome was PCSM. The risk allele was defined as the allele associated with an increased risk for PCa in the literature. We used Cox proportional hazards regression to calculate the hazard ratios of each SNP with time to progression to PCSM after diagnosis. We also used logistic regression to calculate odds ratios for each risk SNP, comparing fatal PCa cases to controls. Results and limitations: Among the cases, we found that 8 of the 47 SNPs were significantly associated (p < 0.05) with time to PCSM. The risk allele of rs11672691 (intergenic) was associated with an increased risk for PCSM, while 7 SNPs had risk alleles inversely associated (rs13385191 [C2orf43], rs17021918 [PDLIM5], rs10486567 [JAZF1], rs6465657 [LMTK2], rs7127900 (intergenic), rs2735839 [KLK3], rs10993994 [MSMB], rs13385191 [C2orf43]). In the case-control analysis, 22 SNPs were associated (p < 0.05) with the risk of fatal PCa, but most did not differentiate between fatal and nonfatal PCa. Rs11672691 and rs10993994 were associated with both fatal and nonfatal PCa, while rs6465657, rs7127900, rs2735839, and rs13385191 were associated with nonfatal PCa only. Conclusions: Eight established risk loci were associated with progression to PCSM after diagnosis. Twenty-two SNPs were associated with fatal PCa incidence, but most did not differentiate between fatal and nonfatal PCa. The relatively small magnitudes of the associations do not translate well into risk prediction, but these findings merit further follow-up, because they may yield important clues about the complex biology of fatal PCa. Patient summary: In this report, we assessed whether established PCa risk variants could predict PCSM. We found eight risk variants associated with PCSM: One predicted an increased risk of PCSM, while seven were associated with decreased risk. Larger studies that focus on fatal PCa are needed to identify more markers that could aid prediction. (C) 2013 European Association of Urology. Published by Elsevier B.V. All rights reserved.
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| 8. |
- Tsilidis, Konstantinos K., et al.
(author)
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Insulin-like growth factor pathway genes and blood concentrations, dietary protein and risk of prostate cancer in the NCI Breast and Prostate Cancer Cohort Consortium (BPC3)
- 2013
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In: International Journal of Cancer. - Hoboken, NJ, USA : Wiley-Blackwell. - 0020-7136 .- 1097-0215. ; 133:2, s. 495-504
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Journal article (peer-reviewed)abstract
- It has been hypothesized that a high intake of dairy protein may increase prostate cancer risk by increasing the production of insulin-like growth factor 1 (IGF-1). Several single nucleotide polymorphisms (SNPs) have been weakly associated with circulating concentrations of IGF-1 and IGF binding protein 3 (IGFBP-3), but none of these SNPs was associated with risk of prostate cancer. We examined whether an association between 16 SNPs associated with circulating IGF-1 or IGFBP-3 concentrations and prostate cancer exists within subgroups defined by dietary protein intake in 5,253 cases and 4,963 controls of European ancestry within the NCI Breast and Prostate Cancer Cohort Consortium (BPC3). The BPC3 includes nested casecontrol studies within large North-American and European cohorts. Per-allele odds ratios for prostate cancer for the SNPs were compared across tertiles of protein intake, which was expressed as the percentage of energy derived from total, animal, dairy or plant protein sources, using conditional logistic regression models. Total, animal, dairy and plant protein intakes were significantly positively associated with blood IGF-1 (p<0.01), but not with IGFBP-3 concentrations (p>0.10) or with risk of prostate cancer (p>0.20). After adjusting for multiple testing, the SNP-prostate cancer associations did not differ by intakes of protein, although two interactions by intake of plant protein were of marginal statistical significance [SSTR5 (somatostatin receptor 5)-rs197056 (uncorrected p for interaction, 0.001); SSTR5-rs197057 (uncorrected p for interaction, 0.002)]. We found no strong evidence that the associations between 16 IGF pathway SNPs and prostate cancer differed by intakes of dietary protein.
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| 9. |
- Zaitlen, Noah, et al.
(author)
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Informed Conditioning on Clinical Covariates Increases Power in Case-Control Association Studies
- 2012
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In: PLoS Genetics. - : Public Library of Science (PLoS). - 1553-7404. ; 8:11
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Journal article (peer-reviewed)abstract
- Genetic case-control association studies often include data on clinical covariates, such as body mass index (BMI), smoking status, or age, that may modify the underlying genetic risk of case or control samples. For example, in type 2 diabetes, odds ratios for established variants estimated from low-BMI cases are larger than those estimated from high-BMI cases. An unanswered question is how to use this information to maximize statistical power in case-control studies that ascertain individuals on the basis of phenotype (case-control ascertainment) or phenotype and clinical covariates (case-controlcovariate ascertainment). While current approaches improve power in studies with random ascertainment, they often lose power under case-control ascertainment and fail to capture available power increases under case-control-covariate ascertainment. We show that an informed conditioning approach, based on the liability threshold model with parameters informed by external epidemiological information, fully accounts for disease prevalence and non-random ascertainment of phenotype as well as covariates and provides a substantial increase in power while maintaining a properly controlled falsepositive rate. Our method outperforms standard case-control association tests with or without covariates, tests of gene x covariate interaction, and previously proposed tests for dealing with covariates in ascertained data, with especially large improvements in the case of case-control-covariate ascertainment. We investigate empirical case-control studies of type 2 diabetes, prostate cancer, lung cancer, breast cancer, rheumatoid arthritis, age-related macular degeneration, and end-stage kidney disease over a total of 89,726 samples. In these datasets, informed conditioning outperforms logistic regression for 115 of the 157 known associated variants investigated (P-value = 1x10(-9)). The improvement varied across diseases with a 16% median increase in chi(2) test statistics and a commensurate increase in power. This suggests that applying our method to existing and future association studies of these diseases may identify novel disease loci.
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