title: Can multiple SNP testing in BRCA2 and BRCA1 female carriers be used to improve risk prediction models in conjunction with clinical assessment?
abstract: BACKGROUND
  Several single nucleotide polymorphisms (SNPs) at different loci have been associated with breast cancer susceptibility, accounting for around 10% of the familial component. Recent studies have found direct associations between specific SNPs and breast cancer in BRCA1/2 mutation carriers. Our aim was to determine whether validated susceptibility SNP scores improve the predictive ability of risk models in comparison/conjunction to other clinical/demographic information.
  
  
  METHODS
  Female BRCA1/2 carriers were identified from the Manchester genetic database, and included in the study regardless of breast cancer status or age. DNA was extracted from blood samples provided by these women and used for gene and SNP profiling. Estimates of survival were examined with Kaplan-Meier curves. Multivariable Cox proportional hazards models were fit in the separate BRCA datasets and in menopausal stages screening different combinations of clinical/demographic/genetic variables. Nonlinear random survival forests were also fit to identify relevant interactions. Models were compared using Harrell's concordance index (1 - c-index).
  
  
  RESULTS
  548 female BRCA1 mutation carriers and 523 BRCA2 carriers were identified from the database. Median Kaplan-Meier estimate of survival was 46.0 years (44.9-48.1) for BRCA1 carriers and 48.9 (47.3-50.4) for BRCA2. By fitting Cox models and random survival forests, including both a genetic SNP score and clinical/demographic variables, average 1 - c-index values were 0.221 (st.dev. 0.019) for BRCA1 carriers and 0.215 (st.dev. 0.018) for BRCA2 carriers.
  
  
  CONCLUSIONS
  Random survival forests did not yield higher performance compared to Cox proportional hazards. We found improvement in prediction performance when coupling the genetic SNP score with clinical/demographic markers, which warrants further investigation.
authors.affiliation: Institute of Population Health, Centre for Health Informatics, University of Manchester, Manchester, UK. mattia.prosperi@manchester.ac.uk.
authors.forename: Mattia C F
authors.initials: MC
authors.lastname: Prosperi
authors.forename: Sarah L
authors.initials: SL
authors.lastname: Ingham
authors.forename: Anthony
authors.initials: A
authors.lastname: Howell
authors.forename: Fiona
authors.initials: F
authors.lastname: Lalloo
authors.forename: Iain E
authors.initials: IE
authors.lastname: Buchan
authors.forename: Dafydd Gareth
authors.initials: DG
authors.lastname: Evans
mesh_terms: D019313:BRCA1 Protein; D024682:BRCA2 Protein; D001943:Breast Neoplasms / Q000175:diagnosis* / Q000235:genetics*; D005260:Female; D005820:Genetic Testing / Q000706:statistics & numerical data*; D006579:Heterozygote; D006801:Humans; D008875:Middle Aged; D020641:Polymorphism, Single Nucleotide; D018570:Risk Assessment; D016019:Survival Analysis
chemical_list: D019313:BRCA1 Protein; D024682:BRCA2 Protein; C551750:BRCA2 protein, human