A cancer drug atlas enables synergistic targeting of independent drug vulnerabilities

Ravi S. Narayan, Piet Molenaar, Jian Teng, Fleur M. G. Cornelissen, Irene Roelofs, Renee Menezes, Rogier Dik, Tonny Lagerweij, Yoran Broersma, Naomi Petersen, Jhon Alexander Marin Soto, Eelke Brands, Philip van Kuiken, Maria C. Lecca, Kristiaan J. Lenos, Sjors G. J. G. in ‘t Veld, Wessel van Wieringen, Frederick F. Lang, Erik Sulman, Roel VerhaakBrigitta G. Baumert, Lucas J. A. Stalpers, Louis Vermeulen, Colin Watts, David Bailey, Ben J. Slotman, Rogier Versteeg, David Noske, Peter Sminia, Bakhos A. Tannous, Tom Wurdinger, Jan Koster, Bart A. Westerman

Research output: Contribution to journalArticleAcademicpeer-review

43 Citations (Scopus)


Personalized cancer treatments using combinations of drugs with a synergistic effect is attractive but proves to be highly challenging. Here we present an approach to uncover the efficacy of drug combinations based on the analysis of mono-drug effects. For this we used dose-response data from pharmacogenomic encyclopedias and represent these as a drug atlas. The drug atlas represents the relations between drug effects and allows to identify independent processes for which the tumor might be particularly vulnerable when attacked by two drugs. Our approach enables the prediction of combination-therapy which can be linked to tumor-driving mutations. By using this strategy, we can uncover potential effective drug combinations on a pan-cancer scale. Predicted synergies are provided and have been validated in glioblastoma, breast cancer, melanoma and leukemia mouse-models, resulting in therapeutic synergy in 75% of the tested models. This indicates that we can accurately predict effective drug combinations with translational value.
Original languageEnglish
Article number2935
JournalNature communications
Issue number1
Publication statusPublished - 1 Dec 2020

Cite this