title: Population-based molecular prognosis of breast cancer by transcriptional profiling.
abstract: OBJECTIVE
  The purpose of this study is to predict breast cancer recurrence and metastases and to identify gene signatures indicative of clinicopathologic characteristics using gene expression patterns derived from cDNA microarray.
  
  
  METHODS
  Expression profiles of 7,650 genes were investigated on an unselected group of 99 node-negative and node-positive breast cancer patients to identify prognostic gene signature of recurrence and metastases. The identified gene signature was validated on independent 78 patients with primary invasive carcinoma (T(1)/T(2) and N(0)) and on 58 patients with locally advanced breast cancer (T(3)/T(4) and/or N(2)). The gene predictors were identified using a combination of random forests and linear discriminant analysis function.
  
  
  RESULTS
  This study identified a new 28-gene signature that achieved highly accurate disease-free survival and overall survival (both at P < 0.001, time-dependent receiver operating characteristic analysis) in individual breast cancer patients. Patients categorized into high-risk, intermediate-risk, and low-risk groups had distinct disease-free survival (P < 0.005, Kaplan-Meier analysis, log-rank test) in three patient cohorts. A strong association (P < 0.05) was identified between risk groups and tumor size, tumor grade, estrogen receptor and progesterone receptor status, and HER2/neu overexpression in the studied cohorts. We also identified 14-gene predictors of nodal status and 9-gene predictors of tumor grade.
  
  
  CONCLUSIONS
  This study has established a population-based approach to predicting breast cancer outcomes at the individual level exclusively based on gene expression patterns. The 28-gene recurrence signature has been validated as quantifying the probability of recurrence and metastases in patients with heterogeneous histology and disease stage.
authors.affiliation: Mary Babb Randolph Cancer Center, Department of Statistics, Division of Hematology, West Virginia University, Morgantown, WV 26506-9300, USA.
authors.forename: Yan
authors.initials: Y
authors.lastname: Ma
authors.forename: Yong
authors.initials: Y
authors.lastname: Qian
authors.forename: Liang
authors.initials: L
authors.lastname: Wei
authors.forename: Jame
authors.initials: J
authors.lastname: Abraham
authors.forename: Xianglin
authors.initials: X
authors.lastname: Shi
authors.forename: Vincent
authors.initials: V
authors.lastname: Castranova
authors.forename: E James
authors.initials: EJ
authors.lastname: Harner
authors.forename: Daniel C
authors.initials: DC
authors.lastname: Flynn
authors.forename: Lan
authors.initials: L
authors.lastname: Guo
mesh_terms: D000328:Adult; D001943:Breast Neoplasms / Q000235:genetics* / Q000401:mortality / Q000473:pathology; D018270:Carcinoma, Ductal, Breast / Q000235:genetics*; D005260:Female; D015870:Gene Expression; D020869:Gene Expression Profiling; D006801:Humans; D008875:Middle Aged; D009362:Neoplasm Metastasis / Q000235:genetics*; D009364:Neoplasm Recurrence, Local / Q000235:genetics*; D020411:Oligonucleotide Array Sequence Analysis; D011379:Prognosis; D012372:ROC Curve