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

Tutkimustuotos: Lehtiartikkelivertaisarvioitu



  • AstraZeneca
  • European Molecular Biology Laboratory
  • Helmholtz Zentrum München - German Research Center for Environmental Health
  • University of Sheffield
  • Sage Bionetworks
  • Semmelweis University
  • Hungarian Academy of Sciences
  • RWTH Aachen University
  • University of Michigan, Ann Arbor
  • Korea University
  • SAS Institute, Inc.
  • University of Nevada, Reno
  • Owkin, Inc.
  • Texas A and M University
  • Maastricht University
  • Netherlands Cancer Institute
  • Delft University of Technology
  • Virginia Polytechnic Institute and State University
  • University of Helsinki
  • North Carolina State University
  • Institute of Cancer Research
  • King Abdullah University of Science and Technology
  • University of Cambridge
  • Alexandria University
  • University of North Carolina
  • Igenomix SL
  • Stanford University
  • Leloir Institute Foundation
  • University of Toronto
  • Institut national de la santé et de la recherche médicale
  • Institut Paoli Calmettes
  • Aix-Marseille Université
  • University of Minho
  • Institut HyperCube
  • Agency for Science, Technology and Research
  • Indiana University - Purdue University Indianapolis
  • Cancer Genomics Netherlands
  • Koc University
  • Kyoto University
  • City University of Hong Kong
  • National Institutes of Health
  • Karolinska Institutet


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.


JulkaisuNature Communications
TilaJulkaistu - 17 kesäkuuta 2019
OKM-julkaisutyyppiA1 Julkaistu artikkeli, soviteltu

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