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Menden, M.P. ; Wang, D.* ; Mason, M.J.* ; Szalai, B.* ; Bulusu, K.C.* ; Guan, Y.* ; Yu, T.* ; Kang, J.* ; Jeon, M.* ; Wolfinger, R.* ; Nguyen, T.* ; Zaslavskiy, M.* ; Jang, I.S.* ; Ghazoui, Z.* ; Ahsen, M.E.* ; Vogel, R.* ; Neto, E.C.* ; Norman, T.* ; Tang, E.K.Y.* ; Garnett, M.J.* ; Veroli, G.Y.D.* ; Fawell, S.* ; Stolovitzky, G.* ; Guinney, J.* ; Dry, J.R.* ; Saez-Rodriguez, J.* ; AstraZeneca-Sanger Drug Combination DREAM Consortium (Kurz, C.F.)

Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen.

Nat. Commun. 10:2674 (2019)
Verlagsversion Forschungsdaten DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
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.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Androgen Receptor; Breast-cancer; Gene; Cell; Inhibition; Resistance; Pathway; Mutations; Landscape; Resource
Sprache englisch
Veröffentlichungsjahr 2019
HGF-Berichtsjahr 2019
ISSN (print) / ISBN 2041-1723
e-ISSN 2041-1723
Zeitschrift Nature Communications
Quellenangaben Band: 10, Heft: 1, Seiten: , Artikelnummer: 2674 Supplement: ,
Verlag Nature Publishing Group
Verlagsort London
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Computational Biology (ICB)
POF Topic(s) 30205 - Bioengineering and Digital Health
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-554700-001
DOI 10.1038/s41467-019-09799-2
WOS ID WOS:000471758500010
Scopus ID 85067453487
PubMed ID 31209238
Erfassungsdatum 2019-06-26
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