A cancer drug atlas enables synergistic targeting of independent drug vulnerabilities
Abstrak
Abstract 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.
Topik & Kata Kunci
Penulis (33)
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 Verhaak
Brigitta 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
Akses Cepat
- Tahun Terbit
- 2020
- Sumber Database
- DOAJ
- DOI
- 10.1038/s41467-020-16735-2
- Akses
- Open Access ✓