A systems biology approach to prediction of oncogenes and molecular perturbation targets in B‐cell lymphomas
Abstract The computational identification of oncogenic lesions is still a key open problem in cancer biology. Although several methods have been proposed, they fail to model how such events are mediated by the network of molecular interactions in the cell. In this paper, we introduce a systems biolo...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Springer Nature
2008-02-01
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| Series: | Molecular Systems Biology |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/msb.2008.2 |
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| _version_ | 1849225760013811712 |
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| author | Kartik M Mani Celine Lefebvre Kai Wang Wei Keat Lim Katia Basso Riccardo Dalla‐Favera Andrea Califano |
| author_facet | Kartik M Mani Celine Lefebvre Kai Wang Wei Keat Lim Katia Basso Riccardo Dalla‐Favera Andrea Califano |
| author_sort | Kartik M Mani |
| collection | DOAJ |
| description | Abstract The computational identification of oncogenic lesions is still a key open problem in cancer biology. Although several methods have been proposed, they fail to model how such events are mediated by the network of molecular interactions in the cell. In this paper, we introduce a systems biology approach, based on the analysis of molecular interactions that become dysregulated in specific tumor phenotypes. Such a strategy provides important insights into tumorigenesis, effectively extending and complementing existing methods. Furthermore, we show that the same approach is highly effective in identifying the targets of molecular perturbations in a human cellular context, a task virtually unaddressed by existing computational methods. To identify interactions that are dysregulated in three distinct non‐Hodgkin's lymphomas and in samples perturbed with CD40 ligand, we use the B‐cell interactome (BCI), a genome‐wide compendium of human B‐cell molecular interactions, in combination with a large set of microarray expression profiles. The method consistently ranked the known gene in the top 20 (0.3%), outperforming conventional approaches in 3 of 4 cases. |
| format | Article |
| id | doaj-art-4c438249fef14a0ca2d3a770c125aaca |
| institution | Kabale University |
| issn | 1744-4292 |
| language | English |
| publishDate | 2008-02-01 |
| publisher | Springer Nature |
| record_format | Article |
| series | Molecular Systems Biology |
| spelling | doaj-art-4c438249fef14a0ca2d3a770c125aaca2025-08-24T12:01:37ZengSpringer NatureMolecular Systems Biology1744-42922008-02-01411910.1038/msb.2008.2A systems biology approach to prediction of oncogenes and molecular perturbation targets in B‐cell lymphomasKartik M Mani0Celine Lefebvre1Kai Wang2Wei Keat Lim3Katia Basso4Riccardo Dalla‐Favera5Andrea Califano6Department of Biomedical Informatics (DBMI), Columbia UniversityCenter for Computational Biology and Bioinformatics (C2B2), Columbia UniversityDepartment of Biomedical Informatics (DBMI), Columbia UniversityDepartment of Biomedical Informatics (DBMI), Columbia UniversityInstitute for Cancer Genetics and Herbert Irving Comprehensive Cancer Center, Columbia UniversityInstitute for Cancer Genetics and Herbert Irving Comprehensive Cancer Center, Columbia UniversityDepartment of Biomedical Informatics (DBMI), Columbia UniversityAbstract The computational identification of oncogenic lesions is still a key open problem in cancer biology. Although several methods have been proposed, they fail to model how such events are mediated by the network of molecular interactions in the cell. In this paper, we introduce a systems biology approach, based on the analysis of molecular interactions that become dysregulated in specific tumor phenotypes. Such a strategy provides important insights into tumorigenesis, effectively extending and complementing existing methods. Furthermore, we show that the same approach is highly effective in identifying the targets of molecular perturbations in a human cellular context, a task virtually unaddressed by existing computational methods. To identify interactions that are dysregulated in three distinct non‐Hodgkin's lymphomas and in samples perturbed with CD40 ligand, we use the B‐cell interactome (BCI), a genome‐wide compendium of human B‐cell molecular interactions, in combination with a large set of microarray expression profiles. The method consistently ranked the known gene in the top 20 (0.3%), outperforming conventional approaches in 3 of 4 cases.https://doi.org/10.1038/msb.2008.2B‐cell lymphomadrug mechanism‐of‐action (MOA)gene networkinteractomeoncogene |
| spellingShingle | Kartik M Mani Celine Lefebvre Kai Wang Wei Keat Lim Katia Basso Riccardo Dalla‐Favera Andrea Califano A systems biology approach to prediction of oncogenes and molecular perturbation targets in B‐cell lymphomas Molecular Systems Biology B‐cell lymphoma drug mechanism‐of‐action (MOA) gene network interactome oncogene |
| title | A systems biology approach to prediction of oncogenes and molecular perturbation targets in B‐cell lymphomas |
| title_full | A systems biology approach to prediction of oncogenes and molecular perturbation targets in B‐cell lymphomas |
| title_fullStr | A systems biology approach to prediction of oncogenes and molecular perturbation targets in B‐cell lymphomas |
| title_full_unstemmed | A systems biology approach to prediction of oncogenes and molecular perturbation targets in B‐cell lymphomas |
| title_short | A systems biology approach to prediction of oncogenes and molecular perturbation targets in B‐cell lymphomas |
| title_sort | systems biology approach to prediction of oncogenes and molecular perturbation targets in b cell lymphomas |
| topic | B‐cell lymphoma drug mechanism‐of‐action (MOA) gene network interactome oncogene |
| url | https://doi.org/10.1038/msb.2008.2 |
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