title: Using SNP genotypes to improve the discrimination of a simple breast cancer risk prediction model.
abstract: It has been shown that, for women aged 50 years or older, the discriminatory accuracy of the Breast Cancer Risk Prediction Tool (BCRAT) can be modestly improved by the inclusion of information on common single nucleotide polymorphisms (SNPs) that are associated with increased breast cancer risk. We aimed to determine whether a similar improvement is seen for earlier onset disease. We used the Australian Breast Cancer Family Registry to study a population-based sample of 962 cases aged 35-59 years, and 463 controls frequency matched for age and for whom genotyping data was available. Overall, the inclusion of data on seven SNPs improved the area under the receiver operating characteristic curve (AUC) from 0.58 (95 % confidence interval [CI] 0.55-0.61) for BCRAT alone to 0.61 (95 % CI 0.58-0.64) for BCRAT and SNP data combined (p < 0.001). For women aged 35-39 years at interview, the corresponding improvement in AUC was from 0.61 (95 % CI 0.56-0.66) to 0.65 (95 % CI 0.60-0.70; p = 0.03), while for women aged 40-49 years at diagnosis, the AUC improved from 0.61 (95 % CI 0.55-0.66) to 0.63 (95 % CI 0.57-0.69; p = 0.04). Using previously used classifications of low, intermediate and high risk, 2.1 % of cases and none of the controls aged 35-39 years, and 10.9 % of cases and 4.0 % of controls aged 40-49 years were classified into a higher risk group. Including information on seven SNPs associated with breast cancer risk, improves the discriminatory accuracy of BCRAT for women aged 35-39 years and 40-49 years. Given, the low absolute risk for women in these age groups, only a small proportion are reclassified into a higher category for predicted 5-year risk of breast cancer.
authors.affiliation: Centre for Molecular, Environmental, Genetic and Analytic Epidemiology, The University of Melbourne, Level 3/207 Bouverie Street, Carlton, VIC 3053, Australia.
authors.forename: Gillian S
authors.initials: GS
authors.lastname: Dite
authors.forename: Maryam
authors.initials: M
authors.lastname: Mahmoodi
authors.forename: Adrian
authors.initials: A
authors.lastname: Bickerstaffe
authors.forename: Fleur
authors.initials: F
authors.lastname: Hammet
authors.forename: Robert J
authors.initials: RJ
authors.lastname: Macinnis
authors.forename: Helen
authors.initials: H
authors.lastname: Tsimiklis
authors.forename: James G
authors.initials: JG
authors.lastname: Dowty
authors.forename: Carmel
authors.initials: C
authors.lastname: Apicella
authors.forename: Kelly-Anne
authors.initials: KA
authors.lastname: Phillips
authors.forename: Graham G
authors.initials: GG
authors.lastname: Giles
authors.forename: Melissa C
authors.initials: MC
authors.lastname: Southey
authors.forename: John L
authors.initials: JL
authors.lastname: Hopper
mesh_terms: D000328:Adult; D019540:Area Under Curve; D001315:Australia / Q000453:epidemiology; D001943:Breast Neoplasms / Q000453:epidemiology / Q000235:genetics* / Q000378:metabolism; D016022:Case-Control Studies; D016001:Confidence Intervals; D005260:Female; D020022:Genetic Predisposition to Disease; D006801:Humans; D008875:Middle Aged; D008957:Models, Genetic; D020641:Polymorphism, Single Nucleotide; D011960:Receptors, Estrogen / Q000378:metabolism; D011980:Receptors, Progesterone / Q000378:metabolism; D018570:Risk Assessment
chemical_list: D011960:Receptors, Estrogen; D011980:Receptors, Progesterone