Prognostic breast cancer signature identified from 3D culture model accurately predicts clinical outcome across independent datasets.
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.
Martin
Katherine J
KJ
Bioarray Consulting, Belmont, Massachusetts, United States of America.
Patrick
Denis R
DR
Bissell
Mina J
MJ
Fournier
Marcia V
MV
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
D011960:Receptors, Estrogen