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

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

Menden, MP; Wang, D; Mason, MJ; Szalai, Bence; Bulusu, KC; Guan, Y; Yu, T; Kang, J; Jeon, M; Wolfinger, R; Nguyen, T; Zaslavskiy, M; Abante, J; Abecassis, BS; Aben, N; Aghamirzaie, D; Aittokallio, T; Akhtari, FS; Al-lazikani, B; Alam, T; Allam, A; Allen, C; de Almeida, MP; Altarawy, D; Alves, V; Amadoz, A; Anchang, B; Antolin, AA; Ash, JR; Aznar, VR; Ba-alawi, W; Bagheri, M; Bajic, V; Ball, G; Ballester, PJ; Baptista, D; Bare, C; Bateson, M; Bender, A; Bertrand, D; Wijayawardena, B; Boroevich, KA; Bosdriesz, E; Bougouffa, S; Bounova, G; Brouwer, T; Bryant, B; Calaza, M; Calderone, A; Calza, S; Capuzzi, S; Carbonell-Caballero, J; Carlin, D; Carter, H; Castagnoli, L; Celebi, R; Cesareni, G; Chang, H; Chen, G; Chen, H; Chen, H; Cheng, L; Chernomoretz, A; Chicco, D; Cho, K-H; Cho, S; Choi, D; Choi, J; Choi, K; Choi, M; Cock, MD; Coker, E; Cortes-Ciriano, I; Cserzö, Miklós; Cubuk, C; Curtis, C; Daele, DV; Dang, CC; Dijkstra, T; Dopazo, J; Draghici, S; Drosou, A; Dumontier, M; Ehrhart, F; Eid, F-E; ElHefnawi, M; Elmarakeby, H; van Engelen, B; Engin, HB; de Esch, I; Evelo, C; Falcao, AO; Farag, S; Fernandez-Lozano, C; Fisch, K; Flobak, A; Fornari, C; Foroushani, ABK; Fotso, DC; Fourches, D; Friend, S; Frigessi, A; Gao, F; Gao, X; Gerold, JM; Gestraud, P; Ghosh, S; Gillberg, J; Godoy-Lorite, A; Godynyuk, L; Godzik, A; Goldenberg, A; Gomez-Cabrero, D; Gonen, M; de Graaf, C; Gray, H; Grechkin, M; Guimera, R; Guney, E; Haibe-Kains, B; Han, Y; Hase, T; He, D; He, L; Heath, LS; Hellton, KH; Helmer-Citterich, M; Hidalgo, MR; Hidru, D; Hill, SM; Hochreiter, S; Hong, S; Hovig, E; Hsueh, Y-C; Hu, Z; Huang, JK; Huang, RS; Hunyady, László; Hwang, J; Hwang, TH; Hwang, W; Hwang, Y; Isayev, O; Don’t Walk, OB IV; Jack, J; Jahandideh, S; Ji, J; Jo, Y; Kamola, PJ; Kanev, GK; Karacosta, L; Karimi, M; Kaski, S; Kazanov, M; Khamis, AM; Khan, SA; Kiani, NA; Kim, A; Kim, J; Kim, J; Kim, K; Kim, K; Kim, S; Kim, Y; Kim, Y; Kirk, PDW; Kitano, H; Klambauer, G; Knowles, D; Ko, M; Kohn-Luque, A; Kooistra, AJ; Kuenemann, MA; Kuiper, M; Kurz, C; Kwon, M; van Laarhoven, T; Laegreid, A; Lederer, S; Lee, H; Lee, J; Lee, YW; Lepp_aho, E; Lewis, R; Li, J; Li, L; Liley, J; Lim, WK; Lin, C; Liu, Y; Lopez, Y; Low, J; Lysenko, A; Machado, D; Madhukar, N; Maeyer, DD; Malpartida, AB; Mamitsuka, H; Marabita, F; Marchal, K; Marttinen, P; Mason, D; Mazaheri, A; Mehmood, A; Mehreen, A; Michaut, M; Miller, RA; Mitsopoulos, C; Modos, D; Moerbeke, MV; Moo, K; Motsinger-Reif, A; Movva, R; Muraru, S; Muratov, E; Mushthofa, M; Nagarajan, N; Nakken, S; Nath, A; Neuvial, P; Newton, R; Ning, Z; Niz, CD; Oliva, B; Olsen, C; Palmeri, A; Panesar, B; Papadopoulos, S; Park, J; Park, S; Park, S; Pawitan, Y; Peluso, D; Pendyala, S; Peng, J; Perfetto, L; Pirro, S; Plevritis, S; Politi, R; Poon, H; Porta, E; Prellner, I; Preuer, K; Pujana, MA; Ramnarine, R; Reid, JE; Reyal, F; Richardson, S; Ricketts, C; Rieswijk, L; Rocha, M; Rodriguez-Gonzalvez, C; Roell, K; Rotroff, D; de Ruiter, JR; Rukawa, P; Sadacca, B; Safikhani, Z; Safitri, F; Sales-Pardo, M; Sauer, S; Schlichting, M; Seoane, JA; Serra, J; Shang, M-M; Sharma, A; Sharma, H; Shen, Y; Shiga, M; Shin, M; Shkedy, Z; Shopsowitz, K; Sinai, S; Skola, D; Smirnov, P; Soerensen, IF; Soerensen, P; Song, J-H; Song, SO; Soufan, O; Spitzmueller, A; Steipe, B; Suphavilai, C; Tamayo, SP; Tamborero, D; Tang, J; Tanoli, Z-U-R; Tarres-Deulofeu, M; Tegner, J; Thommesen, L; Tonekaboni, SAM; Tran, H; Troyer, ED; Truong, A; Tsunoda, T; Turu, Gábor; Tzeng, G-Y; Verbeke, L; Videla, S; Vis, D; Voronkov, A; Votis, K; Wang, A; Wang, H-QH; Wang, P-W; Wang, S; Wang, W; Wang, X; Wang, X; Wennerberg, K; Wernisch, L; Wessels, L; van Westen, GJP; Westerman, BA; White, SR; Willighagen, E; Wurdinger, T; Xie, L; Xie, S; Xu, H; Yadav, B; Yau, C; Yeerna, H; Yin, JW; Yu, M; Yu, MH; Yun, SJ; Zakharov, A; Zamichos, A; Zanin, M; Zeng, L; Zenil, H; Zhang, F; Zhang, P; Zhang, W; Zhao, H; Zhao, L; Zheng, W; Zoufir, A; Zucknick, M; AstraZeneca-Sanger, Drug Combination DREAM Consortium; Jang, IS; Ghazoui, Z; Ahsen, ME; Vogel, R; Neto, EC; Norman, T; Tang, EKY; Garnett, MJ; Veroli, GYD; Fawell, S; Stolovitzky, G; Guinney, J; Dry, JR; Saez-Rodriguez, J

Folyóiratcikk

NATURE COMMUNICATIONS vol.:10, issue.:1, p.:2674, pages: 17.

ISSN: 2041-1723

URI: http://repo.lib.semmelweis.hu//handle/123456789/8007
http://dx.doi.org/10.1038/s41467-019-09799-2

MTMT azonosító: 30791492

WoS ID: 000471758500010

PubMed ID: 31209238

Megjelenés éve: 2019

Kivonat:

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. © 2019, The Author(s).

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