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Cancer Res. 2016 Jul 15;76(14):4058-67. doi: 10.1158/0008-5472.CAN-15-2350. Epub 2016 May 23.

Copy Number Alterations in Enzyme-Coding and Cancer-Causing Genes Reprogram Tumor Metabolism.

Author information

1
Network Modeling, Leibniz Institute for Natural Products Research and Infection Biology, Hans-Knöll-Institute, Jena, Germany. Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany. a.sharma@dkfz.de rainer.koenig@uni-jena.de.
2
Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany. Institute for Pharmacy and Molecular Biotechnology (IPMB) and BioQuant, Heidelberg University, Heidelberg, Germany.
3
Network Modeling, Leibniz Institute for Natural Products Research and Infection Biology, Hans-Knöll-Institute, Jena, Germany. Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany. Integrated Research and Treatment Center, Center for Sepsis Control and Care (CSCC), Jena University Hospital, Jena, Germany. a.sharma@dkfz.de rainer.koenig@uni-jena.de.

Abstract

Somatic copy number alterations frequently occur in the cancer genome affecting not only oncogenic or tumor suppressive genes, but also passenger and potential codriver genes. An intrinsic feature resulting from such genomic perturbations is the deregulation in the metabolism of tumor cells. In this study, we have shown that metabolic and cancer-causing genes are unexpectedly often proximally positioned in the chromosome and share loci with coaltered copy numbers across multiple cancers (19 cancer types from The Cancer Genome Atlas). We have developed an analysis pipeline, Identification of Metabolic Cancer Genes (iMetCG), to infer the functional impact on metabolic remodeling from such coamplifications and codeletions and delineate genes driving cancer metabolism from those that are neutral. Using our identified metabolic genes, we were able to classify tumors based on their tissue and developmental origins. These metabolic genes were similar to known cancer genes in terms of their network connectivity, isoform frequency, and evolutionary features. We further validated these identified metabolic genes by (i) using gene essentiality data from several tumor cell lines, (ii) showing that these identified metabolic genes are strong indicators for patient survival, and (iii) observing a significant overlap between our identified metabolic genes and known cancer-metabolic genes. Our analyses revealed a hitherto unknown generic mechanism for large-scale metabolic reprogramming in cancer cells based on linear gene proximities between cancer-causing and -metabolic genes. We have identified 119 new metabolic cancer genes likely to be involved in rewiring cancer cell metabolism. Cancer Res; 76(14); 4058-67.

PMID:
27216182
DOI:
10.1158/0008-5472.CAN-15-2350
[Indexed for MEDLINE]
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