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

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dc.contributor.author Malyutina, Alina
dc.contributor.author Majumder, Muntasir Mamun
dc.contributor.author Wang, Wenyu
dc.contributor.author Pessia, Alberto
dc.contributor.author Heckman, Caroline A.
dc.contributor.author Tang, Jing
dc.date.accessioned 2019-08-14T07:41:01Z
dc.date.available 2019-08-14T07:41:01Z
dc.date.issued 2019-05-20
dc.identifier.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
dc.identifier.other PURE: 124955232
dc.identifier.other PURE UUID: fc955933-fae2-4fcd-b48f-54fa0102b1d3
dc.identifier.other RIS: urn:C56BD8C49BA1D5D60267F326BB76B726
dc.identifier.other WOS: 000471040500013
dc.identifier.other Scopus: 85066969918
dc.identifier.other ORCID: /0000-0002-5550-2951/work/60611738
dc.identifier.other ORCID: /0000-0001-7480-7710/work/60612665
dc.identifier.uri http://hdl.handle.net/10138/304567
dc.description.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. en
dc.format.extent 19
dc.language.iso eng
dc.relation.ispartof PLoS Computational Biology
dc.rights cc_by
dc.rights.uri info:eu-repo/semantics/openAccess
dc.subject ANTICANCER
dc.subject CELL
dc.subject IDENTIFY
dc.subject MODELS
dc.subject SCREEN
dc.subject THERAPY
dc.subject 1182 Biochemistry, cell and molecular biology
dc.subject 113 Computer and information sciences
dc.subject 3122 Cancers
dc.title Drug combination sensitivity scoring facilitates the discovery of synergistic and efficacious drug combinations in cancer en
dc.type Article
dc.contributor.organization Institute for Molecular Medicine Finland
dc.contributor.organization Helsinki Institute of Life Science HiLIFE
dc.contributor.organization Research Programs Unit
dc.contributor.organization Medicum
dc.contributor.organization Research Program in Systems Oncology
dc.description.reviewstatus Peer reviewed
dc.relation.doi https://doi.org/10.1371/journal.pcbi.1006752
dc.relation.issn 1553-734X
dc.rights.accesslevel openAccess
dc.type.version publishedVersion

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