title: Combining gene signatures improves prediction of breast cancer survival.
abstract: BACKGROUND
  Several gene sets for prediction of breast cancer survival have been derived from whole-genome mRNA expression profiles. Here, we develop a statistical framework to explore whether combination of the information from such sets may improve prediction of recurrence and breast cancer specific death in early-stage breast cancers. Microarray data from two clinically similar cohorts of breast cancer patients are used as training (n = 123) and test set (n = 81), respectively. Gene sets from eleven previously published gene signatures are included in the study.
  
  
  RESULTS
  To investigate the relationship between breast cancer survival and gene expression on a particular gene set, a Cox proportional hazards model is applied using partial likelihood regression with an L2 penalty to avoid overfitting and using cross-validation to determine the penalty weight. The fitted models are applied to an independent test set to obtain a predicted risk for each individual and each gene set. Hierarchical clustering of the test individuals on the basis of the vector of predicted risks results in two clusters with distinct clinical characteristics in terms of the distribution of molecular subtypes, ER, PR status, TP53 mutation status and histological grade category, and associated with significantly different survival probabilities (recurrence: p = 0.005; breast cancer death: p = 0.014). Finally, principal components analysis of the gene signatures is used to derive combined predictors used to fit a new Cox model. This model classifies test individuals into two risk groups with distinct survival characteristics (recurrence: p = 0.003; breast cancer death: p = 0.001). The latter classifier outperforms all the individual gene signatures, as well as Cox models based on traditional clinical parameters and the Adjuvant! Online for survival prediction.
  
  
  CONCLUSIONS
  Combining the predictive strength of multiple gene signatures improves prediction of breast cancer survival. The presented methodology is broadly applicable to breast cancer risk assessment using any new identified gene set.
authors.affiliation: Department of Genetics, Institute for Cancer Research, Oslo University Hospital, Radiumhospitalet, Oslo, Norway. xi.zhao@medisin.uio.no
authors.forename: Xi
authors.initials: X
authors.lastname: Zhao
authors.forename: Einar Andreas
authors.initials: EA
authors.lastname: Rødland
authors.forename: Therese
authors.initials: T
authors.lastname: Sørlie
authors.forename: Bjørn
authors.initials: B
authors.lastname: Naume
authors.forename: Anita
authors.initials: A
authors.lastname: Langerød
authors.forename: Arnoldo
authors.initials: A
authors.lastname: Frigessi
authors.forename: Vessela N
authors.initials: VN
authors.lastname: Kristensen
authors.forename: Anne-Lise
authors.initials: AL
authors.lastname: Børresen-Dale
authors.forename: Ole Christian
authors.initials: OC
authors.lastname: Lingjærde
mesh_terms: D001943:Breast Neoplasms / Q000175:diagnosis / Q000235:genetics*; D002874:Chromosome Mapping; D016000:Cluster Analysis; D023381:Endpoint Determination; D005260:Female; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic; D052138:Genes, Neoplasm / Q000235:genetics; D006801:Humans; D053208:Kaplan-Meier Estimate; D008957:Models, Genetic; D015999:Multivariate Analysis; D025341:Principal Component Analysis; D011379:Prognosis; D016016:Proportional Hazards Models; D012307:Risk Factors