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      Multiple Oncogenic Pathway Signatures Show Coordinate Expression Patterns in Human Prostate Tumors

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      PLoS ONE
      Public Library of Science

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          Abstract

          Background

          Gene transcription patterns associated with activation of oncogenes Myc, c-Src, beta-catenin, E2F3, H-Ras, HER2, EGFR, MEK, Raf, MAPK, Akt, and cyclin D1, as well as of the cell cycle and of androgen signaling have been generated in previous studies using experimental models. It was not clear whether genes in these “oncogenic signatures” would show coordinate expression patterns in human prostate tumors, particularly as most of the signatures were derived from cell types other than prostate.

          Principal Findings

          The above oncogenic pathway signatures were examined in four different gene expression profile datasets of human prostate tumors (representing ∼250 patients in all), using both Q1-Q2 and one-sided Fisher's exact enrichment analysis methods. A significant fraction (∼5%) of genes up-regulated experimentally by Myc, c-Src, HER2, Akt, or androgen were co-expressed in human tumors with the oncogene or biomarker corresponding to the pathway signature. Genes down-regulated experimentally, however, did not show anticipated patterns of anti-enrichment in the human tumors.

          Conclusions

          Significant subsets of the genes in these experimentally-derived oncogenic signatures are relevant to the study of human prostate cancer. Both molecular biologists and clinical researchers could focus attention on the relatively small number of genes identified here as having coordinate patterns that arise from both the experimental system and the human disease system.

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          Most cited references24

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          Cluster analysis and display of genome-wide expression patterns.

          A system of cluster analysis for genome-wide expression data from DNA microarray hybridization is described that uses standard statistical algorithms to arrange genes according to similarity in pattern of gene expression. The output is displayed graphically, conveying the clustering and the underlying expression data simultaneously in a form intuitive for biologists. We have found in the budding yeast Saccharomyces cerevisiae that clustering gene expression data groups together efficiently genes of known similar function, and we find a similar tendency in human data. Thus patterns seen in genome-wide expression experiments can be interpreted as indications of the status of cellular processes. Also, coexpression of genes of known function with poorly characterized or novel genes may provide a simple means of gaining leads to the functions of many genes for which information is not available currently.
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            Oncogenic pathway signatures in human cancers as a guide to targeted therapies.

            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. 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 studies. Various studies have also demonstrated the potential for using gene expression profiles for the analysis of oncogenic pathways. 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.
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              Molecular determinants of resistance to antiandrogen therapy.

              Using microarray-based profiling of isogenic prostate cancer xenograft models, we found that a modest increase in androgen receptor mRNA was the only change consistently associated with the development of resistance to antiandrogen therapy. This increase in androgen receptor mRNA and protein was both necessary and sufficient to convert prostate cancer growth from a hormone-sensitive to a hormone-refractory stage, and was dependent on a functional ligand-binding domain. Androgen receptor antagonists showed agonistic activity in cells with increased androgen receptor levels; this antagonist-agonist conversion was associated with alterations in the recruitment of coactivators and corepressors to the promoters of androgen receptor target genes. Increased levels of androgen receptor confer resistance to antiandrogens by amplifying signal output from low levels of residual ligand, and by altering the normal response to antagonists. These findings provide insight toward the design of new antiandrogens.
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                Author and article information

                Contributors
                Role: Academic Editor
                Journal
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2008
                19 March 2008
                : 3
                : 3
                : e1816
                Affiliations
                [1]Division of Biostatistics, Department of Medicine, Dan L. Duncan Cancer Center, Baylor College of Medicine, Houston, Texas, United States of America
                Deutsches Krebsforschungszentrum, Germany
                Author notes

                Conceived and designed the experiments: CC. Performed the experiments: CC. Analyzed the data: CC. Contributed reagents/materials/analysis tools: CC. Wrote the paper: CC.

                Article
                07-PONE-RA-03005R1
                10.1371/journal.pone.0001816
                2263127
                18350153
                81f06d6b-08fd-469e-860b-5f38e4191a6d
                Chad Creighton. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
                History
                : 11 December 2007
                : 20 February 2008
                Page count
                Pages: 8
                Categories
                Research Article
                Genetics and Genomics/Bioinformatics
                Genetics and Genomics/Gene Expression
                Oncology/Prostate Cancer

                Uncategorized
                Uncategorized

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