Abstract
BackgroundNumerous 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.MethodsWe 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-κB. 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.ResultsOur 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®.ConclusionTaken 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.
Highlights
Numerous studies have used microarrays to identify gene signatures for predicting cancer patient clinical outcome and responses to chemotherapy
The results presented here indicate that the pattern of gene expression in the cell cycle pathway can serve as a powerful biomarker for breast cancer prognosis
One notable example is the development of gene expression signatures based on microarray data to predict prognosis and responses to chemotherapy in cancers [5]
Summary
Numerous studies have used microarrays to identify gene signatures for predicting cancer patient clinical outcome and responses to chemotherapy. Motivated by the lack of accurate outcome prediction with the best clinical predictors of metastasis including lymph-node status and histological grade, numerous studies sought to utilize microarray technology in order to identify gene expression patterns that could be used to distinguish between patients who had the same stage of disease but different responses to treatment and different overall clinical outcomes. The 70gene signature was further validated in a follow-up study of 295 breast cancer patients [3]. These studies showed that gene-expression-based biomarkers were more powerful predictors of outcome than traditional clinical criteria. Microarray-based gene expression signatures have been developed to predict patient responses to therapeutic agents [4,5]
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