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      Inhibition of fatty acid oxidation as a therapy for MYC-overexpressing triple-negative breast cancer

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          Abstract

          Expression of the oncogenic transcription factor MYC is disproportionately elevated in triple-negative breast cancer (TNBC) compared to estrogen, progesterone and human epidermal growth factor 2 receptor-positive (RP) breast tumors 1, 2 . We and others have shown that MYC alters metabolism during tumorigenesis 3, 4 . However, the role of MYC in TNBC metabolism remains largely unexplored. We hypothesized that MYC-dependent metabolic dysregulation is essential for MYC-overexpressing (MO) TNBC and may thus identify novel therapeutic targets for this clinically challenging subset of breast cancer. Using a targeted metabolomics approach, we identified fatty acid oxidation (FAO) intermediates as being dramatically upregulated in a MYC-driven model of TNBC. A lipid metabolism gene signature was identified in patients with TNBC from The Cancer Genome Atlas (TCGA) database and multiple other clinical datasets, implicating FAO as a dysregulated pathway critical for TNBC metabolism. We find that MO-TNBC displays increased bioenergetic reliance upon fatty acid oxidation (FAO), and that pharmacologic inhibition of FAO catastrophically decreases energy metabolism of MO-TNBC, blocks growth of a MYC-driven transgenic TNBC model and that of MO-TNBC patient-derived xenografts. Our results demonstrate that inhibition of FAO is a novel therapeutic strategy against MO-TNBC.

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          Most cited references31

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          Is Open Access

          WGCNA: an R package for weighted correlation network analysis

          Background Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial. Results The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis. The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software. Along with the R package we also present R software tutorials. While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings. Conclusion The WGCNA package provides R functions for weighted correlation network analysis, e.g. co-expression network analysis of gene expression data. The R package along with its source code and additional material are freely available at .
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            Comprehensive molecular portraits of human breast tumors

            Summary We analyzed primary breast cancers by genomic DNA copy number arrays, DNA methylation, exome sequencing, mRNA arrays, microRNA sequencing and reverse phase protein arrays. Our ability to integrate information across platforms provided key insights into previously-defined gene expression subtypes and demonstrated the existence of four main breast cancer classes when combining data from five platforms, each of which shows significant molecular heterogeneity. Somatic mutations in only three genes (TP53, PIK3CA and GATA3) occurred at > 10% incidence across all breast cancers; however, there were numerous subtype-associated and novel gene mutations including the enrichment of specific mutations in GATA3, PIK3CA and MAP3K1 with the Luminal A subtype. We identified two novel protein expression-defined subgroups, possibly contributed by stromal/microenvironmental elements, and integrated analyses identified specific signaling pathways dominant in each molecular subtype including a HER2/p-HER2/HER1/p-HER1 signature within the HER2-Enriched expression subtype. Comparison of Basal-like breast tumors with high-grade Serous Ovarian tumors showed many molecular commonalities, suggesting a related etiology and similar therapeutic opportunities. The biologic finding of the four main breast cancer subtypes caused by different subsets of genetic and epigenetic abnormalities raises the hypothesis that much of the clinically observable plasticity and heterogeneity occurs within, and not across, these major biologic subtypes of breast cancer.
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              GEOquery: a bridge between the Gene Expression Omnibus (GEO) and BioConductor.

              Microarray technology has become a standard molecular biology tool. Experimental data have been generated on a huge number of organisms, tissue types, treatment conditions and disease states. The Gene Expression Omnibus (Barrett et al., 2005), developed by the National Center for Bioinformatics (NCBI) at the National Institutes of Health is a repository of nearly 140,000 gene expression experiments. The BioConductor project (Gentleman et al., 2004) is an open-source and open-development software project built in the R statistical programming environment (R Development core Team, 2005) for the analysis and comprehension of genomic data. The tools contained in the BioConductor project represent many state-of-the-art methods for the analysis of microarray and genomics data. We have developed a software tool that allows access to the wealth of information within GEO directly from BioConductor, eliminating many the formatting and parsing problems that have made such analyses labor-intensive in the past. The software, called GEOquery, effectively establishes a bridge between GEO and BioConductor. Easy access to GEO data from BioConductor will likely lead to new analyses of GEO data using novel and rigorous statistical and bioinformatic tools. Facilitating analyses and meta-analyses of microarray data will increase the efficiency with which biologically important conclusions can be drawn from published genomic data. GEOquery is available as part of the BioConductor project.
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                Author and article information

                Journal
                9502015
                8791
                Nat Med
                Nat. Med.
                Nature medicine
                1078-8956
                1546-170X
                18 May 2016
                07 March 2016
                April 2016
                07 September 2016
                : 22
                : 4
                : 427-432
                Affiliations
                [1 ]Department of Cell & Tissue Biology, University of California, San Francisco, San Francisco, California, USA
                [2 ]Biomedical Sciences Graduate Program, University of California, San Francisco, San Francisco, California, USA
                [3 ]Program in Metabolic Biology, University of California, Berkeley, Berkeley, California, USA
                [4 ]Diabetes Center, University of California, San Francisco, San Francisco, California, USA
                [5 ]Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California, San Francisco, San Francisco, California, USA
                [6 ]Department of Otolaryngology, University of California, San Francisco, San Francisco, California, USA
                [7 ]Department of Pathology, University of California, San Francisco, San Francisco, California, USA
                [8 ]Department of Medicine, University of California, San Francisco, San Francisco, California, USA
                [9 ]Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, California, USA
                Author notes
                [10 ]Correspondence should be addressed to A.G. ( andrei.goga@ 123456ucsf.edu )
                Article
                NIHMS757568
                10.1038/nm.4055
                4892846
                26950360
                420d8956-b7b8-4ded-8385-716597d36c2a

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