Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen
Makaleler > Tam Makale · 2019
Yazarlar: 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, Jordi Abante, Barbara Schmitz Abecassis, Nanne Aben, Delasa Aghamirzaie, Tero Aittokallio, Farida S. Akhtari, Bissan Al‐Lazikani, Tanvir Alam, Amin Allam, C. F. H. Allen, Mariana Pelicano de Almeida, Doaa Altarawy, Vinícius M. Alves, Alicia Amadoz, Benedict Anchang, Albert A. Antolín, Jeremy R. Ash, V. Aznar, Wail Ba-Alawi, Mohammad Bagheri, Vladimir B. Bajic, G. Ball, Pablo Ballester, Delora Baptista, Christopher Bare, Mathilde Bateson, Andreas Bender, Denis Bertrand, Bhagya K. Wijayawardena, Keith A. Boroevich, Evert Bosdriesz, Salim Bougouffa, Gergana Bounova, Thomas Brouwer, Barbara M. Bryant, Manuel Calaza, Alberto Calderone, Stefano Calza, Stephen J. Capuzzi, Jose Carbonell-Caballero, Daniel E. Carlin, Hannah Carter, Luisa Castagnoli, Remzi Çelebi, Gianni Cesareni, Hyeokyoon Chang, Guocai Chen, Haoran Chen, Huiyuan Chen, Long Cheng, Ariel Chernomoretz, Davide Chicco, Kwang‐Hyun Cho, Sung-Hwan Cho, Daeseon Choi, Ji Il Choi, Kwanghun Choi, Mack Joong Choi, Martine De Cock, Elizabeth A. Coker, Isidro Cortés‐Ciriano, Miklós Cserző, Cankut Çubuk, Charles Curtis, Dries Van Daele, Cuong Cao Dang, Tjeerd M. H. Dijkstra, Joaquín Dopazo, Sorin Drăghici, Anastasios Drosou, Michel Dumontier, Friederike Ehrhart, Fatma-Elzahraa Eid, Mahmoud ElHefnawi, Haitham Elmarakeby, Bo van Engelen, H. Billur Engin, Iwan de Esch, Chris T. Evelo, André O. Falcão, Sherif Farag, Carlos Fernández-Lozano, Kathleen Fisch, Åsmund Flobak, Chiara Fornari, Amir Foroushani, Donatien Chedom Fotso, Denis Fourches
Greater South Information System, Uluslararası, 2019
DOI: 10.60692/3f1af-xf873
Özet
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.