title: Prognostic breast cancer signature identified from 3D culture model accurately predicts clinical outcome across independent datasets.
abstract: BACKGROUND
  One of the major tenets in breast cancer research is that early detection is vital for patient survival by increasing treatment options. To that end, we have previously used a novel unsupervised approach to identify a set of genes whose expression predicts prognosis of breast cancer patients. The predictive genes were selected in a well-defined three dimensional (3D) cell culture model of non-malignant human mammary epithelial cell morphogenesis as down-regulated during breast epithelial cell acinar formation and cell cycle arrest. Here we examine the ability of this gene signature (3D-signature) to predict prognosis in three independent breast cancer microarray datasets having 295, 286, and 118 samples, respectively.
  
  
  RESULTS
  Our results show that the 3D-signature accurately predicts prognosis in three unrelated patient datasets. At 10 years, the probability of positive outcome was 52, 51, and 47 percent in the group with a poor-prognosis signature and 91, 75, and 71 percent in the group with a good-prognosis signature for the three datasets, respectively (Kaplan-Meier survival analysis, p<0.05). Hazard ratios for poor outcome were 5.5 (95% CI 3.0 to 12.2, p<0.0001), 2.4 (95% CI 1.6 to 3.6, p<0.0001) and 1.9 (95% CI 1.1 to 3.2, p = 0.016) and remained significant for the two larger datasets when corrected for estrogen receptor (ER) status. Hence the 3D-signature accurately predicts breast cancer outcome in both ER-positive and ER-negative tumors, though individual genes differed in their prognostic ability in the two subtypes. Genes that were prognostic in ER+ patients are AURKA, CEP55, RRM2, EPHA2, FGFBP1, and VRK1, while genes prognostic in ER- patients include ACTB, FOXM1 and SERPINE2 (Kaplan-Meier p<0.05). Multivariable Cox regression analysis in the largest dataset showed that the 3D-signature was a strong independent factor in predicting breast cancer outcome.
  
  
  CONCLUSIONS
  The 3D-signature accurately predicts breast cancer outcome across multiple datasets and holds prognostic value for both ER-positive and ER-negative breast cancer. The signature was selected using a novel biological approach and hence holds promise to represent the key biological processes of breast cancer.
authors: Bioarray Consulting, Belmont, Massachusetts, United States of America.
authors: Katherine J
authors: KJ
authors: Martin
authors: Denis R
authors: DR
authors: Patrick
authors: Mina J
authors: MJ
authors: Bissell
authors: Marcia V
authors: MV
authors: Fournier
mesh_terms: D001943:Breast Neoplasms / Q000145:classification / Q000235:genetics / Q000401:mortality / Q000473:pathology* / Q000628:therapy*; D018929:Cell Culture Techniques / Q000379:methods; D002453:Cell Cycle; D016208:Databases, Factual; D005260:Female; D015870:Gene Expression; D006801:Humans; D008954:Models, Biological; D009367:Neoplasm Staging; D020411:Oligonucleotide Array Sequence Analysis; D011379:Prognosis; D011960:Receptors, Estrogen / Q000032:analysis / Q000172:deficiency; D016019:Survival Analysis; D016896:Treatment Outcome
chemical_list: D011960:Receptors, Estrogen