BACKGROUND: Numerous studies have used microarrays to identify gene signatures 
for predicting cancer patient clinical outcome and responses to chemotherapy. 
However, the potential impact of gene expression profiling in cancer diagnosis, 
prognosis and development of personalized treatment may not be fully exploited 
due to the lack of consensus gene signatures and poor understanding of the 
underlying molecular mechanisms.
METHODS: We developed a novel approach to derive gene signatures for breast 
cancer prognosis in the context of known biological pathways. Using unsupervised 
methods, cancer patients were separated into distinct groups based on gene 
expression patterns in one of the following pathways: apoptosis, cell cycle, 
angiogenesis, metastasis, p53, DNA repair, and several receptor-mediated 
signaling pathways including chemokines, EGF, FGF, HIF, MAP kinase, JAK and 
NF-kappaB. The survival probabilities were then compared between the patient 
groups to determine if differential gene expression in a specific pathway is 
correlated with differential survival.
RESULTS: Our results revealed expression of cell cycle genes is strongly 
predictive of breast cancer outcomes. We further confirmed this observation by 
building a cell cycle gene signature model using supervised methods. Validated 
in multiple independent datasets, the cell cycle gene signature is a more 
accurate predictor for breast cancer clinical outcome than the previously 
identified Amsterdam 70-gene signature that has been developed into a FDA 
approved clinical test MammaPrint.
CONCLUSION: Taken together, the gene expression signature model we developed 
from well defined pathways is not only a consistently powerful prognosticator 
but also mechanistically linked to cancer biology. Our approach provides an 
alternative to the current methodology of identifying gene expression markers 
for cancer prognosis and drug responses using the whole genome gene expression 
data.