title: 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
  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.
authors.affiliation: Research and Development, InterGenetics Incorporated, Oklahoma City, OK, USA.
authors.forename: Eldon R
authors.initials: ER
authors.lastname: Jupe
authors.affiliation: Surgeon-Scientist, Point Reyes Station, CA, USA.
authors.forename: Kathie M
authors.initials: KM
authors.lastname: Dalessandri
authors.affiliation: Department of Pediatrics, Section of Genetics, University of Oklahoma, Oklahoma City, OK, USA.
authors.forename: John J
authors.initials: JJ
authors.lastname: Mulvihill
authors.affiliation: Department of Neurological Surgery, University of California, San Francisco, CA, USA.
authors.forename: Rei
authors.initials: R
authors.lastname: Miike
authors.affiliation: NSK Statistical Solutions LLC, Choctaw, OK, USA.
authors.forename: Nicholas S
authors.initials: NS
authors.lastname: Knowlton
authors.affiliation: Research and Development, InterGenetics Incorporated, Oklahoma City, OK, USA.
authors.forename: Thomas W
authors.initials: TW
authors.lastname: Pugh
authors.affiliation: Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
authors.forename: Lue Ping
authors.initials: LP
authors.lastname: Zhao
authors.affiliation: Research and Development, InterGenetics Incorporated, Oklahoma City, OK, USA.
authors.forename: Daniele C
authors.initials: DC
authors.lastname: DeFreese
authors.affiliation: Research and Development, InterGenetics Incorporated, Oklahoma City, OK, USA.
authors.forename: Sharmila
authors.initials: S
authors.lastname: Manjeshwar
authors.affiliation: Research and Development, InterGenetics Incorporated, Oklahoma City, OK, USA.
authors.forename: Bobby A
authors.initials: BA
authors.lastname: Gramling
authors.affiliation: Department of Neurological Surgery, University of California, San Francisco, CA, USA.
authors.forename: John K
authors.initials: JK
authors.lastname: Wiencke
authors.affiliation: Division of Hematology-Oncology, University of California, San Francisco, CA, USA ; Buck Institute for Research on Aging, Novato, CA, USA.
authors.forename: Christopher C
authors.initials: CC
authors.lastname: Benz
keywords: Aldosterone synthase variant (CYP11B2, -344T/C); Breast cancer; Polyfactorial risk model (PFRM); Single nucleotide polymorphisms (SNPs)