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.

Keywords: Aldosterone synthase variant (CYP11B2, -344T/C); Breast cancer; Polyfactorial risk model (PFRM); Single nucleotide polymorphisms (SNPs).

Fig. 1

Illustration of the  polyfactorial risk model. In each panel, the left ellipse shows the individual terms in the model and the right ellipse shows the terms interacting with age. The overlapping region in the middle shows terms included both individually and interacting with age. Panel A is for all ages, Panel B is for ages 30–49 without a first degree relative and Panel C is 30–49 with a first degree relative.

Fig. 2

Odds ratio of breast cancer at increasing model 5-year and lifetime risk scores. Odd ratios (ORs) were calculated at increasing model absolute risk outputs for both the PFRM and BCRAT. The relationships between the OR (y-axis) and risk score (x-axis) are shown for both model building and validation sample sets, and for both 5-year and lifetime risk scores, as indicated. The PFRM is represented by the solid line with circles and the BCRAT is represented by the dotted lines with squares. The solid line at OR = 1 illustrates the line that would be obtained for a model with random assignment of risk scores. The plots are initiated at the mean control population risk obtained from the PFRM.