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      A metabolic function of FGFR3-TACC3 gene fusions in cancer

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          Abstract

          Chromosomal translocations that generate in-frame oncogenic gene fusions are powerful examples of success of targeted cancer therapies 13 . We discovered FGFR3-TACC3 (F3-T3) gene fusions in 3% of human glioblastoma 4 . Subsequent studies reported similar frequencies of F3-T3 in many other cancers, thus qualifying F3-T3 as one of the most recurrent fusions across all tumor types 5, 6 . F3-T3 fusions are potent oncogenes that confer sensitivity to FGFR inhibitors but the downstream oncogenic signaling remains largely unknown 2, 46 . Here, we report that tumors harboring F3-T3 cluster within transcriptional subgroups characterized by activation of mitochondrial functions. F3-T3 activates oxidative phosphorylation and mitochondrial biogenesis and induces sensitivity to inhibitors of oxidative metabolism. We show that phosphorylation of PIN4 is the signaling intermediate for the activation of mitochondrial metabolism. The F3-T3-PIN4 axis triggers peroxisome biogenesis and new protein synthesis. The anabolic response converges on PGC1α through intracellular ROS, enabling mitochondrial respiration and tumor growth. Our analyses uncover the oncogenic circuit engaged by F3-T3, expose reliance on mitochondrial respiration as unexpected therapeutic opportunity for F3-T3-positive tumors and provide a clue to the genetic alterations that initiate the chain of metabolic responses driving mitochondrial metabolism in cancer.

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          Most cited references37

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          Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search.

          We present a statistical model to estimate the accuracy of peptide assignments to tandem mass (MS/MS) spectra made by database search applications such as SEQUEST. Employing the expectation maximization algorithm, the analysis learns to distinguish correct from incorrect database search results, computing probabilities that peptide assignments to spectra are correct based upon database search scores and the number of tryptic termini of peptides. Using SEQUEST search results for spectra generated from a sample of known protein components, we demonstrate that the computed probabilities are accurate and have high power to discriminate between correctly and incorrectly assigned peptides. This analysis makes it possible to filter large volumes of MS/MS database search results with predictable false identification error rates and can serve as a common standard by which the results of different research groups are compared.
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            Empirical Statistical Model To Estimate the Accuracy of Peptide Identifications Made by MS/MS and Database Search

            We present a statistical model to estimate the accuracy of peptide assignments to tandem mass (MS/MS) spectra made by database search applications such as SEQUEST. Employing the expectation maximization algorithm, the analysis learns to distinguish correct from incorrect database search results, computing probabilities that peptide assignments to spectra are correct based upon database search scores and the number of tryptic termini of peptides. Using SEQUEST search results for spectra generated from a sample of known protein components, we demonstrate that the computed probabilities are accurate and have high power to discriminate between correctly and incorrectly assigned peptides. This analysis makes it possible to filter large volumes of MS/MS database search results with predictable false identification error rates and can serve as a common standard by which the results of different research groups are compared.
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              The transcriptional network for mesenchymal transformation of brain tumors

              Inference of transcriptional networks that regulate transitions into physiologic or pathologic cellular states remains a central challenge in systems biology. A mesenchymal phenotype is the hallmark of tumor aggressiveness in human malignant glioma but the regulatory programs responsible for implementing the associated molecular signature are largely unknown. Here, we show that reverse-engineering and unbiased interrogation of a glioma-specific regulatory network reveal the transcriptional module that activates expression of mesenchymal genes in malignant glioma. Two transcription factors (C/EBPβ and Stat3) emerge as synergistic initiators and master regulators of mesenchymal transformation. Ectopic co-expression of C/EBPβ and Stat3 reprograms neural stem cells along the aberrant mesenchymal lineage whereas elimination of the two factors in glioma cells leads to collapse of the mesenchymal signature and reduces tumor aggressiveness. In human glioma, expression of C/EBPβ and Stat3 correlates with mesenchymal differentiation and predicts poor clinical outcome. These results reveal that activation of a small regulatory module is necessary and sufficient to initiate and maintain an aberrant phenotypic state in cancer cells.
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                Author and article information

                Journal
                0410462
                6011
                Nature
                Nature
                Nature
                0028-0836
                1476-4687
                28 November 2017
                03 January 2018
                11 January 2018
                03 July 2018
                : 553
                : 7687
                : 222-227
                Affiliations
                [1 ]Institute for Cancer Genetics, Columbia University Medical Center, New York 10032, USA
                [2 ]Department of Science and Technology, Universita’ degli Studi del Sannio, Benevento, 82100, Italy
                [3 ]The Arthur and Sonia Labatt Brain Tumour Research Centre, Program in Developmental and Stem Cell Biology, The Hospital for Sick Children, Toronto, Ontario, M5G 1A4, Canada
                [4 ]Department of Molecular Genetics, University of Toronto, Toronto, Ontario, M5S 1A8, Canada
                [5 ]BIOGEM Istituto di Ricerche Genetiche “G. Salvatore”, Campo Reale, 83031 Ariano Irpino, Italy
                [6 ]Department of Pathology and Laboratory Medicine, Perelman School of Medicine at University of Pennsylvania, Philadelphia, PA 19104-6100, USA
                [7 ]Qatar Computing Research Institute (QCRI), Hamad Bin Khalifa University, Doha, Qatar
                [8 ]Sorbonne Universités UPMC Univ Paris 06, Inserm, CNRS, APHP, Institut du cerveau et de la moelle (ICM)- Hôpital Pitié-salpêtrière, Boulevard de l’hôpital, F-75013, Paris, France
                [9 ]AP-HP, Groupe Hospitalier Pitié Salpêtrière, Laboratoire de Neuropathologie R Escourolle, Paris, 75013, France
                [10 ]Onconeurotek, AP-HP, Paris, 75013, France
                [11 ]Department of Pathology and Cell Biology, Columbia University Medical Center, New York 10032, USA
                [12 ]Department of Pediatrics, Columbia University Medical Center, New York 10032, USA
                [13 ]Department of Neurology, Columbia University Medical Center, New York 10032, USA
                Author notes
                []To whom correspondence should be addressed. al2179@ 123456cumc.columbia.edu (A.L.), ai2102@ 123456cumc.columbia.edu (A.I.)
                [*]

                These authors contributed equally to this work.

                Article
                NIHMS922777
                10.1038/nature25171
                5771419
                29323298
                21cae3da-9766-4f17-b152-94aea50771f6

                Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use: http://www.nature.com/authors/editorial_policies/license.html#terms

                Reprints and permissions information is available at www.nature.com/reprints. A.I. and A.L. received research funds from AstraZeneca and Tahio Pharmaceutical CO., LDT. The remaining authors declare no competing financial interests. Readers are welcome to comment on the online version of the paper. Publisher’s note Correspondence and requests for materials should be addressed to A.L. ( al2179@ 123456cumc.columbia.edu ) or A.I. ( ai2102@ 123456cumc.columbia.edu ).

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