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