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      Metabolic enzyme expression highlights a key role for MTHFD2 and the mitochondrial folate pathway in cancer

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          Abstract

          Metabolic remodeling is now widely regarded as a hallmark of cancer, but it is not clear whether individual metabolic strategies are frequently exploited by many tumours. Here we compare messenger RNA profiles of 1,454 metabolic enzymes across 1,981 tumours spanning 19 cancer types to identify enzymes that are consistently differentially expressed. Our meta-analysis recovers established targets of some of the most widely used chemotherapeutics, including dihydrofolate reductase, thymidylate synthase and ribonucleotide reductase, while also spotlighting new enzymes, such as the mitochondrial proline biosynthetic enzyme PYCR1. The highest scoring pathway is mitochondrial one-carbon metabolism and is centred on MTHFD2. MTHFD2 RNA and protein are markedly elevated in many cancers and correlated with poor survival in breast cancer. MTHFD2 is expressed in the developing embryo, but is absent in most healthy adult tissues, even those that are proliferating. Our study highlights the importance of mitochondrial compartmentalization of one-carbon metabolism in cancer and raises important therapeutic hypotheses.

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          Most cited references25

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            Strong Time Dependence of the 76-Gene Prognostic Signature for Node-Negative Breast Cancer Patients in the TRANSBIG Multicenter Independent Validation Series

            Recently, a 76-gene prognostic signature able to predict distant metastases in lymph node-negative (N(-)) breast cancer patients was reported. The aims of this study conducted by TRANSBIG were to independently validate these results and to compare the outcome with clinical risk assessment. Gene expression profiling of frozen samples from 198 N(-) systemically untreated patients was done at the Bordet Institute, blinded to clinical data and independent of Veridex. Genomic risk was defined by Veridex, blinded to clinical data. Survival analyses, done by an independent statistician, were done with the genomic risk and adjusted for the clinical risk, defined by Adjuvant! Online. The actual 5- and 10-year time to distant metastasis were 98% (88-100%) and 94% (83-98%), respectively, for the good profile group and 76% (68-82%) and 73% (65-79%), respectively, for the poor profile group. The actual 5- and 10-year overall survival were 98% (88-100%) and 87% (73-94%), respectively, for the good profile group and 84% (77-89%) and 72% (63-78%), respectively, for the poor profile group. We observed a strong time dependence of this signature, leading to an adjusted hazard ratio of 13.58 (1.85-99.63) and 8.20 (1.10-60.90) at 5 years and 5.11 (1.57-16.67) and 2.55 (1.07-6.10) at 10 years for time to distant metastasis and overall survival, respectively. This independent validation confirmed the performance of the 76-gene signature and adds to the growing evidence that gene expression signatures are of clinical relevance, especially for identifying patients at high risk of early distant metastases.
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              Large-scale meta-analysis of cancer microarray data identifies common transcriptional profiles of neoplastic transformation and progression.

              Many studies have used DNA microarrays to identify the gene expression signatures of human cancer, yet the critical features of these often unmanageably large signatures remain elusive. To address this, we developed a statistical method, comparative metaprofiling, which identifies and assesses the intersection of multiple gene expression signatures from a diverse collection of microarray data sets. We collected and analyzed 40 published cancer microarray data sets, comprising 38 million gene expression measurements from >3,700 cancer samples. From this, we characterized a common transcriptional profile that is universally activated in most cancer types relative to the normal tissues from which they arose, likely reflecting essential transcriptional features of neoplastic transformation. In addition, we characterized a transcriptional profile that is commonly activated in various types of undifferentiated cancer, suggesting common molecular mechanisms by which cancer cells progress and avoid differentiation. Finally, we validated these transcriptional profiles on independent data sets.
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                Author and article information

                Journal
                101528555
                37539
                Nat Commun
                Nat Commun
                Nature communications
                2041-1723
                1 May 2014
                2014
                22 July 2014
                : 5
                : 3128
                Affiliations
                [1 ]Unit of Computational Medicine, Department of Medicine, Karolinska Institutet, 17176 Stockholm, Sweden
                [2 ]Center for Molecular Medicine, Karolinska Institutet, 17176 Stockholm
                [3 ]Broad Institute, Cambridge, Massachusetts 02142, USA
                [4 ]Department of Systems Biology, Harvard Medical School, Boston, Massachusetts 02115, USA
                [5 ]Department of Molecular Biology and the Center for Human Genetic Research, Massachusetts General Hospital, Boston, Massachusetts 02114, USA
                [6 ]Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts 02115, USA
                [7 ]Department of Immunology, Genetics & Pathology and Science for Life Laboratory, Uppsala University, 75185 Uppsala, Sweden
                [8 ]La Jolla Institute for Allergy and Immunology, San Diego, CA 92037. USA
                Author notes
                Please address correspondence to: Roland Nilsson, Assistant Professor, Dept. of Medicine, Karolinska Institutet, L8:01 Karolinska University Hospital, 17176 Stockholm, Sweden, +46 722 334 580, roland.nilsson@ 123456ki.se . Vamsi K. Mootha, Professor of Systems Biology, Medicine, Harvard Medical School, 185 Cambridge Street, CPZN 7250, Boston, MA 02114, USA, +1 617 643 9710, vamsi@ 123456hms.harvard.edu
                [*]

                These authors contributed equally to this work

                Article
                NIHMS550439
                10.1038/ncomms4128
                4106362
                24451681
                2d8332b4-1a36-4764-8dac-f009cc03b357

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