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      Transcriptomic landscape of breast cancers through mRNA sequencing

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          Abstract

          Breast cancer is a heterogeneous disease with a poorly defined genetic landscape, which poses a major challenge in diagnosis and treatment. By massively parallel mRNA sequencing, we obtained 1.2 billion reads from 17 individual human tissues belonging to TNBC, Non-TNBC, and HER2-positive breast cancers and defined their comprehensive digital transcriptome for the first time. Surprisingly, we identified a high number of novel and unannotated transcripts, revealing the global breast cancer transcriptomic adaptations. Comparative transcriptomic analyses elucidated differentially expressed transcripts between the three breast cancer groups, identifying several new modulators of breast cancer. Our study also identified common transcriptional regulatory elements, such as highly abundant primary transcripts, including osteonectin, RACK1, calnexin, calreticulin, FTL, and B2M, and “genomic hotspots” enriched in primary transcripts between the three groups. Thus, our study opens previously unexplored niches that could enable a better understanding of the disease and the development of potential intervention strategies.

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          Most cited references38

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          Computational methods for transcriptome annotation and quantification using RNA-seq.

          High-throughput RNA sequencing (RNA-seq) promises a comprehensive picture of the transcriptome, allowing for the complete annotation and quantification of all genes and their isoforms across samples. Realizing this promise requires increasingly complex computational methods. These computational challenges fall into three main categories: (i) read mapping, (ii) transcriptome reconstruction and (iii) expression quantification. Here we explain the major conceptual and practical challenges, and the general classes of solutions for each category. Finally, we highlight the interdependence between these categories and discuss the benefits for different biological applications.
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            Molecular classification and molecular forecasting of breast cancer: ready for clinical application?

            Profiling breast cancer with expression arrays has become common, and it has been suggested that the results from early studies will lead to understanding of the molecular differences between clinical cases and allow individualization of care. We critically review two main applications of expression profiling; studies unraveling novel breast cancer classifications and those that aim to identify novel markers for prediction of clinical outcome. Breast cancer may now be subclassified into luminal, basal, and HER2 subtypes with distinct differences in prognosis and response to therapy. However, profiling studies to identify predictive markers have suffered from methodologic problems that prevent general application of their results. Future work will need to reanalyze existing microarray data sets to identify more representative sets of candidate genes for use as prognostic signatures and will need to take into account the new knowledge of molecular subtypes of breast cancer when assessing predictive effects.
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              Basal-like and triple-negative breast cancers: a critical review with an emphasis on the implications for pathologists and oncologists.

              Breast cancer is a heterogeneous disease encompassing a variety of entities with distinct morphological features and clinical behaviors. Although morphology is often associated with the pattern of molecular aberrations in breast cancers, it is also clear that tumors of the same histological type show remarkably different clinical behavior. This is particularly true for 'basal-like cancer', which is an entity defined using gene expression analysis. The purpose of this article was to review the current state of knowledge of basal-like breast cancers, to discuss the relationship between basal-like and triple-negative breast cancers, and to clarify practical implications of these diagnoses for pathologists and oncologists.
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                Author and article information

                Journal
                Sci Rep
                Scientific Reports
                Nature Publishing Group
                2045-2322
                14 February 2012
                2012
                : 2
                : 264
                Affiliations
                [1 ]simpleMcCormick Genomic and Proteomics Center, The George Washington University , Washington, DC 20037, USA
                [2 ]simpleGlobal Cancer Genomic Consortium, The George Washington University , Washington, DC 20037, USA
                [3 ]simpleDepartment of Biochemistry and Molecular Biology, The George Washington University , Washington, DC 20037, USA
                [4 ]simpleMcKusick-Nathans Institute of Genetic Medicine, School of Medicine, Johns Hopkins University , Baltimore 21205
                [5 ]simpleBreast Center, Baylor College of Medicine , One Baylor Plaza, 1220 Alkek, Houston, Texas 77030, USA
                Author notes
                Article
                srep00264
                10.1038/srep00264
                3278922
                22355776
                1c5121bb-6ce8-4559-b317-d65f05a4e24c
                Copyright © 2012, Macmillan Publishers Limited. All rights reserved

                This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/3.0/

                History
                : 06 September 2011
                : 17 January 2012
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