title: A gene-expression signature as a predictor of survival in breast cancer.
abstract: BACKGROUND
  A more accurate means of prognostication in breast cancer will improve the selection of patients for adjuvant systemic therapy.
  
  
  METHODS
  Using microarray analysis to evaluate our previously established 70-gene prognosis profile, we classified a series of 295 consecutive patients with primary breast carcinomas as having a gene-expression signature associated with either a poor prognosis or a good prognosis. All patients had stage I or II breast cancer and were younger than 53 years old; 151 had lymph-node-negative disease, and 144 had lymph-node-positive disease. We evaluated the predictive power of the prognosis profile using univariable and multivariable statistical analyses.
  
  
  RESULTS
  Among the 295 patients, 180 had a poor-prognosis signature and 115 had a good-prognosis signature, and the mean (+/-SE) overall 10-year survival rates were 54.6+/-4.4 percent and 94.5+/-2.6 percent, respectively. At 10 years, the probability of remaining free of distant metastases was 50.6+/-4.5 percent in the group with a poor-prognosis signature and 85.2+/-4.3 percent in the group with a good-prognosis signature. The estimated hazard ratio for distant metastases in the group with a poor-prognosis signature, as compared with the group with the good-prognosis signature, was 5.1 (95 percent confidence interval, 2.9 to 9.0; P<0.001). This ratio remained significant when the groups were analyzed according to lymph-node status. Multivariable Cox regression analysis showed that the prognosis profile was a strong independent factor in predicting disease outcome.
  
  
  CONCLUSIONS
  The gene-expression profile we studied is a more powerful predictor of the outcome of disease in young patients with breast cancer than standard systems based on clinical and histologic criteria.
authors.affiliation: Division of Diagnostic Oncology, Netherlands Cancer Institute, Amsterdam, The Netherlands.
authors.forename: Marc J
authors.initials: MJ
authors.lastname: van de Vijver
authors.forename: Yudong D
authors.initials: YD
authors.lastname: He
authors.forename: Laura J
authors.initials: LJ
authors.lastname: van't Veer
authors.forename: Hongyue
authors.initials: H
authors.lastname: Dai
authors.forename: Augustinus A M
authors.initials: AA
authors.lastname: Hart
authors.forename: Dorien W
authors.initials: DW
authors.lastname: Voskuil
authors.forename: George J
authors.initials: GJ
authors.lastname: Schreiber
authors.forename: Johannes L
authors.initials: JL
authors.lastname: Peterse
authors.forename: Chris
authors.initials: C
authors.lastname: Roberts
authors.forename: Matthew J
authors.initials: MJ
authors.lastname: Marton
authors.forename: Mark
authors.initials: M
authors.lastname: Parrish
authors.forename: Douwe
authors.initials: D
authors.lastname: Atsma
authors.forename: Anke
authors.initials: A
authors.lastname: Witteveen
authors.forename: Annuska
authors.initials: A
authors.lastname: Glas
authors.forename: Leonie
authors.initials: L
authors.lastname: Delahaye
authors.forename: Tony
authors.initials: T
authors.lastname: van der Velde
authors.forename: Harry
authors.initials: H
authors.lastname: Bartelink
authors.forename: Sjoerd
authors.initials: S
authors.lastname: Rodenhuis
authors.forename: Emiel T
authors.initials: ET
authors.lastname: Rutgers
authors.forename: Stephen H
authors.initials: SH
authors.lastname: Friend
authors.forename: René
authors.initials: R
authors.lastname: Bernards
mesh_terms: D000328:Adult; D000367:Age Factors; D001943:Breast Neoplasms / Q000235:genetics* / Q000401:mortality / Q000473:pathology; D015331:Cohort Studies; D005260:Female; D020869:Gene Expression Profiling; D006801:Humans; D008207:Lymphatic Metastasis; D008875:Middle Aged; D015999:Multivariate Analysis; D009362:Neoplasm Metastasis; D020411:Oligonucleotide Array Sequence Analysis; D018579:Patient Selection; D011379:Prognosis; D016016:Proportional Hazards Models; D016019:Survival Analysis