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| Content Provider | World Health Organization (WHO)-Global Index Medicus |
|---|---|
| Author | Katzir, Rotem Ruppin, Eytan Notebaart, Richard A. Megchelenbrink, Wout Lu, Xiaowen |
| Description | Author Affiliation: Megchelenbrink W ( Institute for Computing and Information Science, Radboud University, Nijmegen, 6525 EC Nijmegen, The Netherlands); Katzir R ( Center for Bioinformatics and Computational Biology, Department of Computer Science, University of Maryland, College Park, MD 20742); Lu X ( Centre for Molecular and Biomolecular Informatics, Radboud University Medical Center, 6525 GA, Nijmegen, The Netherlands); Ruppin E ( Center for Bioinformatics and Computational Biology, Department of Computer Science, University of Maryland, College Park, MD 20742); Notebaart RA ( Department of Internal Medicine, Radboud University Medical Center, 6525 GA, Nijmegen, The Netherlands ruppin@post.tau.ac.il richard.notebaart@radboudumc.nl.); |
| Abstract | Synthetic dosage lethality (SDL) denotes a genetic interaction between two genes whereby the underexpression of gene A combined with the overexpression of gene B is lethal. SDLs offer a promising way to kill cancer cells by inhibiting the activity of SDL partners of activated oncogenes in tumors, which are often difficult to target directly. As experimental genome-wide SDL screens are still scarce, here we introduce a network-level computational modeling framework that quantitatively predicts human SDLs in metabolism. For each enzyme pair (A, B) we systematically knock out the flux through A combined with a stepwise flux increase through B and search for pairs that reduce cellular growth more than when either enzyme is perturbed individually. The predictive signal of the emerging network of 12,000 SDLs is demonstrated in five different ways. (i) It can be successfully used to predict gene essentiality in shRNA cancer cell line screens. Moving to clinical tumors, we show that (ii) SDLs are significantly underrepresented in tumors. Furthermore, breast cancer tumors with SDLs active (iii) have smaller sizes and (iv) result in increased patient survival, indicating that activation of SDLs increases cancer vulnerability. Finally, (v) patient survival improves when multiple SDLs are present, pointing to a cumulative effect. This study lays the basis for quantitative identification of cancer SDLs in a model-based mechanistic manner. The approach presented can be used to identify SDLs in species and cell types in which 'omics' data necessary for data-driven identification are missing. |
| ISSN | 00278424 |
| e-ISSN | 10916490 |
| Journal | Proceedings of the National Academy of Sciences of the United States of America |
| Issue Number | 39 |
| Volume Number | 112 |
| Language | English |
| Publisher | National Academy of Sciences |
| Publisher Date | 2015-09-01 |
| Publisher Place | United States |
| Access Restriction | Open |
| Subject Keyword | Gene Dosage Physiology Gene Expression Regulation, Neoplastic Genes, Lethal Genetics Metabolic Networks And Pathways Models, Genetic Neoplasms Systems Biology Computer Simulation Genes, Tumor Suppressor Metabolism Oncogenes Research Support, Non-U.S. Gov't Multidisciplinary |
| Content Type | Text |
| Resource Type | Article |
| Subject | Multidisciplinary |
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