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| Verfasst von: | Menden, Michael [VerfasserIn]  |
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| | Wang, Dennis [VerfasserIn] |
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| | Mason, Mike J. [VerfasserIn] |
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| | Szalai, Bence [VerfasserIn] |
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| | Bulusu, Krishna C. [VerfasserIn] |
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| | Guan, Yuanfang [VerfasserIn] |
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| | Yu, Thomas [VerfasserIn] |
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| | Kang, Jaewoo [VerfasserIn] |
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| | Jeon, Minji [VerfasserIn] |
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| | Wolfinger, Russ [VerfasserIn] |
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| | Nguyen, Tin [VerfasserIn] |
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| | Zaslavskiy, Mikhail [VerfasserIn] |
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| | Jang, In Sock [VerfasserIn] |
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| | Ghazoui, Zara [VerfasserIn] |
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| | Ahsen, Mehmet Eren [VerfasserIn] |
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| | Vogel, Robert [VerfasserIn] |
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| | Neto, Elias Chaibub [VerfasserIn] |
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| | Norman, Thea [VerfasserIn] |
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| | Tang, Eric K. Y. [VerfasserIn] |
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| | Garnett, Mathew J. [VerfasserIn] |
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| | Veroli, Giovanni Y. Di [VerfasserIn] |
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| | Fawell, Stephen [VerfasserIn] |
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| | Stolovitzky, Gustavo [VerfasserIn] |
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| | Guinney, Justin [VerfasserIn] |
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| | Dry, Jonathan R. [VerfasserIn] |
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| | Sáez Rodríguez, Julio [VerfasserIn] |
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| Titel: | Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen |
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| Verf.angabe: | Michael P. Menden, Dennis Wang, Mike J. Mason, Bence Szalai, Krishna C. Bulusu, Yuanfang Guan, Thomas Yu, Jaewoo Kang, Minji Jeon, Russ Wolfinger, Tin Nguyen, Mikhail Zaslavskiy, In Sock Jang, Zara Ghazoui, Mehmet Eren Ahsen, Robert Vogel, Elias Chaibub Neto, Thea Norman, Eric K. Y. Tang, Mathew J. Garnett, Giovanni Y. Di Veroli, Stephen Fawell, Gustavo Stolovitzky, Justin Guinney, Jonathan R. Dry, Julio Saez-Rodriguez |
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| E-Jahr: | 2019 |
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| Jahr: | Jun 17 2019 |
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| Umfang: | 17 S. |
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| Fussnoten: | Published online 2019 Jun 17 ; Gesehen am 21.01.2020 |
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| Titel Quelle: | Enthalten in: Nature Communications |
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| Ort Quelle: | [London] : Springer Nature, 2010 |
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| Jahr Quelle: | 2019 |
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| Band/Heft Quelle: | 10(2019) Artikel-Nummer 2674, 17 Seiten |
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| ISSN Quelle: | 2041-1723 |
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| Abstract: | The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca’s large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells., Resistance to first line treatment is a major hurdle in cancer treatment, that can be overcome with drug combinations. Here, the authors provide a large drug combination screen across cancer cell lines to benchmark crowdsourced methods and to computationally predict drug synergies. |
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| DOI: | doi:10.1038/s41467-019-09799-2 |
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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.
Volltext: https://doi.org/10.1038/s41467-019-09799-2 |
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| | Verlag: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6572829/ |
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| | DOI: https://doi.org/10.1038/s41467-019-09799-2 |
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| Datenträger: | Online-Ressource |
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| Sprache: | eng |
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| K10plus-PPN: | 1687989044 |
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| Verknüpfungen: | → Zeitschrift |
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Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen / Menden, Michael [VerfasserIn]; Jun 17 2019 (Online-Ressource)
