TY - JOUR
T1 - Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen
AU - 354 authors, including
AU - Menden, Michael P.
AU - Wang, Dennis
AU - Mason, Mike J.
AU - Szalai, Bence
AU - Bulusu, Krishna C.
AU - Guan, Yuanfang
AU - Yu, Thomas
AU - Kang, Jaewoo
AU - Jeon, Minji
AU - Wolfinger, Russ
AU - Nguyen, Tin
AU - Zaslavskiy, Mikhail
AU - Abante, Jordi
AU - Abecassis, Barbara Schmitz
AU - Aben, Nanne
AU - Aghamirzaie, Delasa
AU - Aittokallio, Tero
AU - Akhtari, Farida S.
AU - Al-lazikani, Bissan
AU - Alam, Tanvir
AU - Allam, Amin
AU - Allen, Chad
AU - de Almeida, Mariana Pelicano
AU - Altarawy, Doaa
AU - Alves, Vinicius
AU - Amadoz, Alicia
AU - Anchang, Benedict
AU - Antolin, Albert A.
AU - Ash, Jeremy R.
AU - Aznar, Victoria Romeo
AU - Ba-alawi, Wail
AU - Bagheri, Moeen
AU - Bajic, Vladimir
AU - Ball, Gordon
AU - Ballester, Pedro J.
AU - Baptista, Delora
AU - Bare, Christopher
AU - Bateson, Mathilde
AU - Bender, Andreas
AU - Bertrand, Denis
AU - Wijayawardena, Bhagya
AU - Boroevich, Keith A.
AU - Bosdriesz, Evert
AU - Bougouffa, Salim
AU - Bounova, Gergana
AU - Brouwer, Thomas
AU - Bryant, Barbara
AU - Gao, Feng
AU - Wang, Wei
AU - Wang, Xin
AU - Zhao, Lan
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/85067453487#tab=impact
UR - https://www.scopus.com/pages/publications/85067453487
U2 - 10.1038/s41467-019-09799-2
DO - 10.1038/s41467-019-09799-2
M3 - RGC 21 - Publication in refereed journal
C2 - 31209238
SN - 2041-1723
VL - 10
JO - Nature Communications
JF - Nature Communications
M1 - 2674
ER -
SDG 3 Good Health and Well-being