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      An integrated genomics approach identifies drivers of proliferation in luminal subtype human breast cancer

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          Abstract

          Elucidating the molecular drivers of human breast cancers requires a strategy capable of integrating multiple forms of data and an ability to interpret the functional consequences of a given genetic aberration. Here we present an integrated genomic strategy based on the use of gene expression signatures of oncogenic pathway activity (n=52) as a framework to analyze DNA copy number alterations in combination with data from a genome-wide RNAi screen. We identify specific DNA amplifications, and importantly, essential genes within these amplicons representing key genetic drivers, including known and novel regulators of oncogenesis. The genes identified include eight that are essential for cell proliferation ( FGD5, METTL6, CPT1A, DTX3, MRPS23, EIF2S2, EIF6 and SLC2A10) and are uniquely amplified in patients with highly proliferative luminal breast tumors, a clinical subset of patients for which few therapeutic options are effective. Our results demonstrate that this general strategy has the potential to identify putative therapeutic targets within amplicons through an integrated use of genetic, genomic, and genome-wide RNAi data sets.

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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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            The genomic landscapes of human breast and colorectal cancers.

            Human cancer is caused by the accumulation of mutations in oncogenes and tumor suppressor genes. To catalog the genetic changes that occur during tumorigenesis, we isolated DNA from 11 breast and 11 colorectal tumors and determined the sequences of the genes in the Reference Sequence database in these samples. Based on analysis of exons representing 20,857 transcripts from 18,191 genes, we conclude that the genomic landscapes of breast and colorectal cancers are composed of a handful of commonly mutated gene "mountains" and a much larger number of gene "hills" that are mutated at low frequency. We describe statistical and bioinformatic tools that may help identify mutations with a role in tumorigenesis. These results have implications for understanding the nature and heterogeneity of human cancers and for using personal genomics for tumor diagnosis and therapy.
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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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                Author and article information

                Journal
                9216904
                2419
                Nat Genet
                Nat. Genet.
                Nature genetics
                1061-4036
                1546-1718
                20 August 2014
                24 August 2014
                October 2014
                01 April 2015
                : 46
                : 10
                : 1051-1059
                Affiliations
                [1 ]Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC
                [2 ]Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC
                [3 ]Curriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC
                [4 ]Department of Pathology and Laboratory Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC
                Author notes
                [* ]Correspondence: Charles M. Perou, PhD., Lineberger Comprehensive Cancer Center, 450 West Drive, CB7295, University of North Carolina, Chapel Hill, NC, 27599, USA, Tel. 919-843-5740, cperou@ 123456med.unc.edu
                Article
                NIHMS617453
                10.1038/ng.3073
                4300117
                25151356
                fb3c0dc2-3224-4350-b24a-879bc29a65da
                History
                Categories
                Article

                Genetics
                Genetics

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