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      Identification of Key Processes Underlying Cancer Phenotypes Using Biologic Pathway Analysis

      research-article
      1 , 1 , 1 , 2 , *
      PLoS ONE
      Public Library of Science

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          Abstract

          Cancer is recognized to be a family of gene-based diseases whose causes are to be found in disruptions of basic biologic processes. An increasingly deep catalogue of canonical networks details the specific molecular interaction of genes and their products. However, mapping of disease phenotypes to alterations of these networks of interactions is accomplished indirectly and non-systematically. Here we objectively identify pathways associated with malignancy, staging, and outcome in cancer through application of an analytic approach that systematically evaluates differences in the activity and consistency of interactions within canonical biologic processes. Using large collections of publicly accessible genome-wide gene expression, we identify small, common sets of pathways – Trka Receptor, Apoptosis response to DNA Damage, Ceramide, Telomerase, CD40L and Calcineurin – whose differences robustly distinguish diverse tumor types from corresponding normal samples, predict tumor grade, and distinguish phenotypes such as estrogen receptor status and p53 mutation state. Pathways identified through this analysis perform as well or better than phenotypes used in the original studies in predicting cancer outcome. This approach provides a means to use genome-wide characterizations to map key biological processes to important clinical features in disease.

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          Most cited references38

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          Cancer genes and the pathways they control.

          The revolution in cancer research can be summed up in a single sentence: cancer is, in essence, a genetic disease. In the last decade, many important genes responsible for the genesis of various cancers have been discovered, their mutations precisely identified, and the pathways through which they act characterized. The purposes of this review are to highlight examples of progress in these areas, indicate where knowledge is scarce and point out fertile grounds for future investigation.
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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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              Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses.

              We have generated a molecular taxonomy of lung carcinoma, the leading cause of cancer death in the United States and worldwide. Using oligonucleotide microarrays, we analyzed mRNA expression levels corresponding to 12,600 transcript sequences in 186 lung tumor samples, including 139 adenocarcinomas resected from the lung. Hierarchical and probabilistic clustering of expression data defined distinct subclasses of lung adenocarcinoma. Among these were tumors with high relative expression of neuroendocrine genes and of type II pneumocyte genes, respectively. Retrospective analysis revealed a less favorable outcome for the adenocarcinomas with neuroendocrine gene expression. The diagnostic potential of expression profiling is emphasized by its ability to discriminate primary lung adenocarcinomas from metastases of extra-pulmonary origin. These results suggest that integration of expression profile data with clinical parameters could aid in diagnosis of lung cancer patients.
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                Author and article information

                Contributors
                Role: Academic Editor
                Journal
                PLoS ONE
                plos
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2007
                9 May 2007
                : 2
                : 5
                : e425
                Affiliations
                [1 ]National Cancer Institute Center for Bioinformatics, Rockville, Maryland, United States of America
                [2 ]Laboratory of Population Genetics, National Cancer Institute, Bethesda, Maryland, United States of America
                University of Sheffield, United Kingdom
                Author notes
                * To whom correspondence should be addressed. E-mail: buetowk@ 123456mail.nih.gov

                Conceived and designed the experiments: KB SE CS. Performed the experiments: SE. Analyzed the data: SE. Wrote the paper: KB SE CS.

                Article
                07-PONE-RA-00567R1
                10.1371/journal.pone.0000425
                1855990
                17487280
                890bb4fa-5b7f-4a68-965a-625de0bdb5c2
                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
                : 5 January 2007
                : 29 March 2007
                Page count
                Pages: 10
                Categories
                Research Article
                Oncology
                Cell Biology/Cell Signaling
                Cell Biology/Gene Expression
                Computational Biology/Signaling Networks
                Computational Biology/Systems Biology
                Genetics and Genomics/Bioinformatics
                Genetics and Genomics/Gene Expression
                Oncology/Breast Cancer
                Oncology/Lung Cancer

                Uncategorized
                Uncategorized

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