Drug combination sensitivity scoring facilitates the discovery of synergistic and efficacious drug combinations in cancer

Show full item record



Permalink

http://hdl.handle.net/10138/304567

Citation

Malyutina , A , Majumder , M M , Wang , W , Pessia , A , Heckman , C A & Tang , J 2019 , ' Drug combination sensitivity scoring facilitates the discovery of synergistic and efficacious drug combinations in cancer ' , PLoS Computational Biology , vol. 15 , no. 5 , 1006752 . https://doi.org/10.1371/journal.pcbi.1006752

Title: Drug combination sensitivity scoring facilitates the discovery of synergistic and efficacious drug combinations in cancer
Author: Malyutina, Alina; Majumder, Muntasir Mamun; Wang, Wenyu; Pessia, Alberto; Heckman, Caroline A.; Tang, Jing
Contributor: University of Helsinki, Institute for Molecular Medicine Finland
University of Helsinki, Institute for Molecular Medicine Finland
University of Helsinki, Institute for Molecular Medicine Finland
University of Helsinki, Institute for Molecular Medicine Finland
University of Helsinki, Institute for Molecular Medicine Finland
University of Helsinki, Medicum
Date: 2019-05-20
Language: eng
Number of pages: 19
Belongs to series: PLoS Computational Biology
ISSN: 1553-734X
URI: http://hdl.handle.net/10138/304567
Abstract: High-throughput drug screening has facilitated the discovery of drug combinations in cancer. Many existing studies adopted a full matrix design, aiming for the characterization of drug pair effects for cancer cells. However, the full matrix design may be suboptimal as it requires a drug pair to be combined at multiple concentrations in a full factorial manner. Furthermore, many of the computational tools assess only the synergy but not the sensitivity of drug combinations, which might lead to false positive discoveries. We proposed a novel cross design to enable a more cost-effective and simultaneous testing of drug combination sensitivity and synergy. We developed a drug combination sensitivity score (CSS) to determine the sensitivity of a drug pair, and showed that the CSS is highly reproducible between the replicates and thus supported its usage as a robust metric. We further showed that CSS can be predicted using machine learning approaches which determined the top pharmaco-features to cluster cancer cell lines based on their drug combination sensitivity profiles. To assess the degree of drug interactions using the cross design, we developed an S synergy score based on the difference between the drug combination and the single drug dose-response curves. We showed that the S score is able to detect true synergistic and antagonistic drug combinations at an accuracy level comparable to that using the full matrix design. Taken together, we showed that the cross design coupled with the CSS sensitivity and S synergy scoring methods may provide a robust and accurate characterization of both drug combination sensitivity and synergy levels, with minimal experimental materials required. Our experimental-computational approach could be utilized as an efficient pipeline for improving the discovery rate in high-throughput drug combination screening, particularly for primary patient samples which are difficult to obtain.
Subject: ANTICANCER
CELL
IDENTIFY
MODELS
SCREEN
THERAPY
1182 Biochemistry, cell and molecular biology
113 Computer and information sciences
3122 Cancers
Rights:


Files in this item

Total number of downloads: Loading...

Files Size Format View
journal.pcbi.1006752.pdf 2.281Mb PDF View/Open

This item appears in the following Collection(s)

Show full item record