Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Jupe ER

PROVIDER: S-EPMC4633888 | biostudies-literature | 2014 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Jupe Eldon R ER Dalessandri Kathie M KM Mulvihill John J JJ Miike Rei R Knowlton Nicholas S NS Pugh Thomas W TW Zhao Lue Ping LP DeFreese Daniele C DC Manjeshwar Sharmila S Gramling Bobby A BA Wiencke John K JK Benz Christopher C CC

BBA clinical 20141108

<h4>Background</h4>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.<h4>Methods</h4>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), ...[more]

PMID: 26673457

Similar Datasets

Project description:PURPOSE:To update and expand the Rosner-Colditz breast cancer incidence model by evaluating the contributions of more recently identified risk factors as well as predicted percent mammographic density (MD) to breast cancer risk. METHODS:Using data from the Nurses' Health Study (NHS) and NHSII, we added adolescent somatotype (9 unit scale), vegetable intake (servings/day), breastfeeding (months), physical activity (MET-h/week), and predicted percent MD to the Rosner-Colditz model to determine whether these variables improved model discrimination. We evaluated all invasive as well as ER+/PR+, ER+/PR-, and ER-/PR- breast cancer. RESULTS:In the NHS/NHSII, we accrued over 5200 cases of invasive breast cancer over more than 20 years of follow-up with complete data on the risk factors. Adolescent somatotype and predicted percent MD significantly improved the original Rosner-Colditz model for all invasive breast cancer (change in age-adjusted AUC = 0.020, p < 0.001). The relative risk (RR) of invasive breast cancer for a 4-unit increase in adolescent somatotype was 0.62 (95% CI 0.56, 0.70), whereas the RR for a 20-unit increase in predicted percent MD was 1.32 (95% CI 1.28, 1.36). Adolescent somatotype and predicted percent MD also significantly improved the ER+/PR+model (change in age-adjusted AUC = 0.020, p < 0.001) as well as the ER+/PR- model (change in age-adjusted AUC = 0.012, p = 0.007). Adolescent somatotype, predicted percent MD, breastfeeding, and vegetable intake improved the ER-/PR- model (change in AUC = 0.031, p < 0.0001). The RR of ER-/PR- disease for 5 vegetable servings/day increase was 0.83 (95% CI 0.70, 0.99), while the RR for every 12 months of breastfeeding was 0.88 (95% CI 0.77, 1.01). Physical activity did not improve risk classification in any model. CONCLUSION:Adolescent somatotype and predicted percent MD significantly improved breast cancer risk classification using the Rosner-Colditz model. Further, risk factors specific to ER- disease, such as breastfeeding and vegetable intake, may also help improve risk prediction of this aggressive subtype.

Project description:BackgroundPopulation-based risk prediction tools exist for individual chronic diseases. From a population health perspective, studying chronic diseases together provides a comprehensive view of the burden of disease in the population. Thus, public health officials and health policymakers would benefit from a prediction tool that measures the incidence of chronic diseases compositely. This study protocol proposes the development and validation of the Chronic Disease Population Risk Tool (CDPoRT) that will predict the incidence of six chronic diseases in the population setting using multivariable modeling techniques.MethodsCDPoRT will be built using population-based responses to the first six cycles of the Canadian Community Health Survey linked to health administrative data in Ontario and Manitoba from 2000 to 2014. Predictors including modifiable lifestyle risk factors (i.e., alcohol consumption, cigarette smoking, diet, and physical activity) will be used to predict time-to-chronic disease incidence (i.e., congestive heart failure, chronic obstructive pulmonary disease, diabetes, lung cancer, myocardial infarction, and stroke including transient ischemic heart attack). Sex-specific Royston-Parmar models will be used for model development and validation with death free of chronic disease as a competing risk. CDPoRT will be developed using an Ontario derivation cohort consisting of 47,960 females and 38,267 males with 7035 and 6220 chronic disease events, respectively. The model will be validated using split-sample validation using an Ontario validation cohort consisting of 20,325 females and 16,627 males with 2972 and 2658 chronic disease events, respectively. The model will be externally validated in the Manitoba validation cohort (i.e., geographic validation) expected to consist of 11,800 females and 9700 males with 1650 and 1550 chronic disease events, respectively. Measures of overall predictive accuracy (e.g., Nagelkerke's R 2), discrimination (e.g., Harrell's concordance statistic), and calibration (e.g., calibration plots) will be used to assess predictive performance.DiscussionTo the extent of our knowledge, CDPoRT will be the first population-based regression prediction model that will predict the incidence of multiple chronic diseases simultaneously at the population level.

Dataset Information

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

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets