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| Verfasst von: | Triantafyllidis, Charalampos P. [VerfasserIn]  |
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| | Barberis, Alessandro [VerfasserIn] |
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| | Hartley, Fiona [VerfasserIn] |
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| | Cuervo, Ana Miar [VerfasserIn] |
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| | Gjerga, Enio [VerfasserIn] |
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| | Charlton, Philip [VerfasserIn] |
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| | van Bijsterveldt, Linda [VerfasserIn] |
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| | Sáez Rodríguez, Julio [VerfasserIn] |
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| | Buffa, Francesca M. [VerfasserIn] |
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| Titel: | A machine learning and directed network optimization approach to uncover TP53 regulatory patterns |
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| Verf.angabe: | Charalampos P. Triantafyllidis, Alessandro Barberis, Fiona Hartley, Ana Miar Cuervo, Enio Gjerga, Philip Charlton, Linda van Bijsterveldt, Julio Saez Rodriguez, Francesca M. Buffa |
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| E-Jahr: | 2023 |
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| Jahr: | 15 December 2023 |
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| Umfang: | 19 S. |
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| Illustrationen: | Illustrationen, Diagramme |
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| Fussnoten: | Online verfügbar: 26. Oktober 2022, Artikelversion: 17. November 2023 ; Gesehen am 22.11.2024 |
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| Titel Quelle: | Enthalten in: iScience |
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| Ort Quelle: | Amsterdam : Elsevier, 2018 |
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| Jahr Quelle: | 2023 |
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| Band/Heft Quelle: | 26(2023), 12, Artikel-ID 108291, Seite 1-19 |
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| ISSN Quelle: | 2589-0042 |
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| Abstract: | TP53, the Guardian of the Genome, is the most frequently mutated gene in human cancers and the functional characterization of its regulation is fundamental. To address this we employ two strategies: machine learning to predict the mutation status of TP53 from transcriptomic data, and directed regulatory networks to reconstruct the effect of mutations on the transcipt levels of TP53 targets. Using data from established databases (Cancer Cell Line Encyclopedia, The Cancer Genome Atlas), machine learning could predict the mutation status, but not resolve different mutations. On the contrary, directed network optimization allowed to infer the TP53 regulatory profile across: (1) mutations, (2) irradiation in lung cancer, and (3) hypoxia in breast cancer, and we could observe differential regulatory profiles dictated by (1) mutation type, (2) deleterious consequences of the mutation, (3) known hotspots, (4) protein changes, (5) stress condition (irradiation/hypoxia). This is an important first step toward using regulatory networks for the characterization of the functional consequences of mutations, and could be extended to other perturbations, with implications for drug design and precision medicine. |
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| DOI: | doi:10.1016/j.isci.2023.108291 |
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| URL: | Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.
kostenfrei: Volltext: https://doi.org/10.1016/j.isci.2023.108291 |
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| | kostenfrei: Volltext: https://www.sciencedirect.com/science/article/pii/S2589004223023684 |
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| | DOI: https://doi.org/10.1016/j.isci.2023.108291 |
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| Datenträger: | Online-Ressource |
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| Sprache: | eng |
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| Sach-SW: | cancer systems biology |
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| | causal inference |
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| | directed networks |
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| | machine learning |
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| | mutations |
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| | Regulatory networks |
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| | regulon |
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| | TP53 |
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| | trascriptomics |
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| K10plus-PPN: | 1909382698 |
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| Verknüpfungen: | → Zeitschrift |
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¬A¬ machine learning and directed network optimization approach to uncover TP53 regulatory patterns / Triantafyllidis, Charalampos P. [VerfasserIn]; 15 December 2023 (Online-Ressource)
