The development of an oncogenic state is a complex process involving the accumulation of multiple independent mutations that lead to deregulation of cell signalling pathways central to the control of cell growth and cell fate^{1, 2, 3}. The ability to define cancer subtypes, recurrence of disease and response to specific therapies using DNA microarray-based gene expression signatures has been demonstrated in multiple studies⁴. Various studies have also demonstrated the potential for using gene expression profiles for the analysis of oncogenic pathways^{5, 6, 7, 8, 9, 10, 11}. Here we show that gene expression signatures can be identified that reflect the activation status of several oncogenic pathways. When evaluated in several large collections of human cancers, these gene expression signatures identify patterns of pathway deregulation in tumours and clinically relevant associations with disease outcomes. Combining signature-based predictions across several pathways identifies coordinated patterns of pathway deregulation that distinguish between specific cancers and tumour subtypes. Clustering tumours based on pathway signatures further defines prognosis in respective patient subsets, demonstrating that patterns of oncogenic pathway deregulation underlie the development of the oncogenic phenotype and reflect the biology and outcome of specific cancers. Predictions of pathway deregulation in cancer cell lines are also shown to predict the sensitivity to therapeutic agents that target components of the pathway. Linking pathway deregulation with sensitivity to therapeutics that target components of the pathway provides an opportunity to make use of these oncogenic pathway signatures to guide the use of targeted therapeutics.

1. Institute for Genome Sciences and Policy, Duke University, Durham, North Carolina 27708, USA
2. Department of Molecular Genetics and Microbiology,
3. Department of Surgery,
4. Department of Medicine,
5. Department of Obstetrics & Gynecology, Duke University Medical Center, Durham, North Carolina 27710, USA
6. Institute of Statistics and Decision Sciences, Duke University, Durham, North Carolina 27708, USA
7. H. Lee Moffitt Cancer Center & Research Institute, University of South Florida, Tampa, Florida 33612, USA

Correspondence to: Joseph R. Nevins^1,2 Correspondence and requests for materials should be addressed to J.R.N. (Email: j.nevins@duke.edu). The GEO accession numbers for the datasets are: GSE3156, breast cancer cell lines; GSE3158, mouse tumour data set; GSE3151, oncogene signature data set; GSE3141, lung cancer data set; GSE3143, breast cancer data set; GSE3149, ovarian cancer data set.

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