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      Rrp1b, a New Candidate Susceptibility Gene for Breast Cancer Progression and Metastasis

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          Abstract

          A novel candidate metastasis modifier, ribosomal RNA processing 1 homolog B ( Rrp1b), was identified through two independent approaches. First, yeast two-hybrid, immunoprecipitation, and functional assays demonstrated a physical and functional interaction between Rrp1b and the previous identified metastasis modifier Sipa1. In parallel, using mouse and human metastasis gene expression data it was observed that extracellular matrix (ECM) genes are common components of metastasis predictive signatures, suggesting that ECM genes are either important markers or causal factors in metastasis. To investigate the relationship between ECM genes and poor prognosis in breast cancer, expression quantitative trait locus analysis of polyoma middle-T transgene-induced mammary tumor was performed. ECM gene expression was found to be consistently associated with Rrp1b expression. In vitro expression of Rrp1b significantly altered ECM gene expression, tumor growth, and dissemination in metastasis assays. Furthermore, a gene signature induced by ectopic expression of Rrp1b in tumor cells predicted survival in a human breast cancer gene expression dataset. Finally, constitutional polymorphism within RRP1B was found to be significantly associated with tumor progression in two independent breast cancer cohorts. These data suggest that RRP1B may be a novel susceptibility gene for breast cancer progression and metastasis.

          Author Summary

          Metastasis, which is defined as the spread of malignant tumor cells from their original site to other parts of the body, accounts for the vast majority of solid cancer-related mortality. Our laboratory has previously shown that host germline-encoded variation modifies primary tumor metastatic capacity. Here, we detail how germline-encoded Rrp1b variation likely modulates metastasis. In mice, constitutional Rrp1b variation correlates with ECM gene expression, which are genes commonly differentially regulated in metastasis prone tumors. Furthermore, we demonstrate that Rrp1b expression levels are modulated by germline variation in mice with differing metastatic propensities, and that variation of Rrp1b expression in a highly metastatic mouse mammary tumor cell line modifies progression. Differential RRP1B functionality also appears to play an important role in human breast cancer progression. Specifically, we demonstrate that a microarray gene expression signature indicative of differential RRP1B expression predicts breast cancer-specific survival. Furthermore, we show that germline-encoded RRP1B variation is associated with markers of outcome in two breast cancer populations. In summary, these data suggest that Rrp1b may be a germline-encoded metastasis modifier in both mice and humans, which leads to the possibility that knowledge of RRP1B functionality and variation in breast cancer might facilitate improved assessment of prognosis.

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          Most cited references34

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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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            Systematic variation in gene expression patterns in human cancer cell lines.

            We used cDNA microarrays to explore the variation in expression of approximately 8,000 unique genes among the 60 cell lines used in the National Cancer Institute's screen for anti-cancer drugs. Classification of the cell lines based solely on the observed patterns of gene expression revealed a correspondence to the ostensible origins of the tumours from which the cell lines were derived. The consistent relationship between the gene expression patterns and the tissue of origin allowed us to recognize outliers whose previous classification appeared incorrect. Specific features of the gene expression patterns appeared to be related to physiological properties of the cell lines, such as their doubling time in culture, drug metabolism or the interferon response. Comparison of gene expression patterns in the cell lines to those observed in normal breast tissue or in breast tumour specimens revealed features of the expression patterns in the tumours that had recognizable counterparts in specific cell lines, reflecting the tumour, stromal and inflammatory components of the tumour tissue. These results provided a novel molecular characterization of this important group of human cell lines and their relationships to tumours in vivo.
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              An integrative genomics approach to infer causal associations between gene expression and disease.

              A key goal of biomedical research is to elucidate the complex network of gene interactions underlying complex traits such as common human diseases. Here we detail a multistep procedure for identifying potential key drivers of complex traits that integrates DNA-variation and gene-expression data with other complex trait data in segregating mouse populations. Ordering gene expression traits relative to one another and relative to other complex traits is achieved by systematically testing whether variations in DNA that lead to variations in relative transcript abundances statistically support an independent, causative or reactive function relative to the complex traits under consideration. We show that this approach can predict transcriptional responses to single gene-perturbation experiments using gene-expression data in the context of a segregating mouse population. We also demonstrate the utility of this approach by identifying and experimentally validating the involvement of three new genes in susceptibility to obesity.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Genet
                pgen
                plge
                plosgen
                PLoS Genetics
                Public Library of Science (San Francisco, USA )
                1553-7390
                1553-7404
                November 2007
                30 November 2007
                15 October 2007
                : 3
                : 11
                : e214
                Affiliations
                [1 ] Laboratory of Population Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America
                [2 ] Laboratory of Cellular Oncology, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America
                [3 ] Laboratory of Human Carcinogenesis, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America
                [4 ] Epidemiology Division, Department of Medicine, University of California Irvine, Irvine, California, United States of America
                Princeton University, United States of America
                Author notes
                * To whom correspondence should be addressed. E-mail: hunterk@ 123456mail.nih.gov
                Article
                07-PLGE-RA-0051R3 plge-03-11-23
                10.1371/journal.pgen.0030214
                2098807
                18081427
                a7d01e32-54cd-432d-8f62-36c40e88efbd
                This is an open-access article distributed under the terms of the Creative Commons Public Domain declaration which stipulates that, once placed in the public domain, this work may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose.
                History
                : 30 January 2007
                : 12 October 2007
                Page count
                Pages: 16
                Categories
                Research Article
                Genetics and Genomics
                Eukaryotes
                Vertebrates
                Mammals
                Mus (Mouse)
                Homo (Human)
                Custom metadata
                Crawford NPS, Qian X, Ziogas A, Papageorge AG, Boersma BJ, et al. (2007) Rrp1b, a new candidate susceptibility gene for breast cancer progression and metastasis. PLoS Genet 3(11): e214. doi: 10.1371/journal.pgen.0030214

                Genetics
                Genetics

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