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Prediction of breast cancer risk by genetic risk factors, overall and by hormone receptor status.

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.

SUBMITTER: Husing A

PROVIDER: S-EPMC3793888 | biostudies-literature | 2012 Sep

REPOSITORIES: biostudies-literature

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Prediction of breast cancer risk by genetic risk factors, overall and by hormone receptor status.

Hüsing Anika A Canzian Federico F Beckmann Lars L Garcia-Closas Montserrat M Diver W Ryan WR Thun Michael J MJ Berg Christine D CD Hoover Robert N RN Ziegler Regina G RG Figueroa Jonine D JD Isaacs Claudine C Olsen Anja A Viallon Vivian V Boeing Heiner H Masala Giovanna G Trichopoulos Dimitrios D Peeters Petra H M PH Lund Eiliv E Ardanaz Eva E Khaw Kay-Tee KT Lenner Per P Kolonel Laurence N LN Stram Daniel O DO Le Marchand Loïc L McCarty Catherine A CA Buring Julie E JE Lee I-Min IM Zhang Shumin S Lindström Sara S Hankinson Susan E SE Riboli Elio E Hunter David J DJ Henderson Brian E BE Chanock Stephen J SJ Haiman Christopher A CA Kraft Peter P Kaaks Rudolf R

Journal of medical genetics 20120901 9

<h4>Objective</h4>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 ...[more]

PMID: 22972951

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Project description:We examined whether differences in tumor DNA methylation were associated with more aggressive hormone receptor-negative breast cancer in an ethnically diverse group of patients in the Breast Cancer Care in Chicago (BCCC) study and using data from The Cancer Genome Atlas (TCGA).DNA was extracted from formalin-fixed, paraffin-embedded samples on 75 patients (21 White, 31 African-American, and 23 Hispanic) (training dataset) enrolled in the BCCC. Hormone receptor status was defined as negative if tumors were negative for both estrogen and progesterone (ER/PR) receptors (N?=?22/75). DNA methylation was analyzed at 1505 CpG sites within 807 gene promoters using the Illumina GoldenGate assay. Differential DNA methylation as a predictor of hormone receptor status was tested while controlling for false discovery rate and assigned to the gene closest to the respective CpG site. Next, those genes that predicted ER/PR status were validated using TCGA data with respect to DNA methylation (validation dataset), and correlations between CpG methylation and gene expression were examined. In the training dataset, 5.7 % of promoter mean methylation values (46/807) were associated with receptor status at P?<?0.05; for 88 % of these (38/46), hypermethylation was associated with receptor-positive disease. Hypermethylation for FZD9, MME, BCAP31, HDAC9, PAX6, SCGB3A1, PDGFRA, IGFBP3, and PTGS2 genes most strongly predicted receptor-positive disease. Twenty-one of 24 predictor genes from the training dataset were confirmed in the validation dataset. The level of DNA methylation at 19 out 22 genes, for which gene expression data were available, was associated with gene activity.Higher levels of promoter methylation strongly correlate with hormone receptor positive status of breast tumors. For most of the genes identified in our training dataset as ER/PR receptor status predictors, DNA methylation correlated with stable gene expression level. The predictors performed well when evaluated on independent set of samples, with different racioethnic distribution, thus providing evidence that this set of DNA methylation biomarkers will likely generalize to prospective patient samples.

Dataset Information

Prediction of breast cancer risk by genetic risk factors, overall and by hormone receptor status.

Publications

Prediction of breast cancer risk by genetic risk factors, overall and by hormone receptor status.

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