Revealing Protein Networks and Gene-Drug Connectivity in Cancer from Direct Information

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dc.contributor.ORCID0000-0003-1697-8575 (Jiang, X-L)en_US
dc.contributor.authorJiang, Xian-Lien_US
dc.contributor.authorMartinez-Ledesma, Emmanuelen_US
dc.contributor.authorMorcos, Farucken_US
dc.contributor.utdAuthorJiang, Xian-Lien_US
dc.contributor.utdAuthorMorcos, Farucken_US
dc.date.accessioned2018-08-20T16:07:03Z
dc.date.available2018-08-20T16:07:03Z
dc.date.created2017-06-16en_US
dc.date.issued2018-08-20
dc.descriptionIncludes supplementary materialen_US
dc.description.abstractThe connection between genetic variation and drug response has long been explored to facilitate the optimization and personalization of cancer therapy. Crucial to the identification of drug response related genetic features is the ability to separate indirect correlations from direct correlations across abundant datasets with large number of variables. Here we analyzed proteomic and pharmacogenomic data in cancer tissues and cell lines using a global statistical model connecting protein pairs, genes and anti-cancer drugs. We estimated this model using direct coupling analysis (DCA), a powerful statistical inference method that has been successfully applied to protein sequence data to extract evolutionary signals that provide insights on protein structure, folding and interactions. We used Direct Information (DI) as a metric of connectivity between proteins as well as gene-drug pairs. We were able to infer important interactions observed in cancer-related pathways from proteomic data and predict potential connectivities in cancer networks. We also identified known and potential connections for anti-cancer drugs and gene mutations using DI in pharmacogenomic data. Our findings suggest that gene-drug connections predicted with direct couplings can be used as a reliable guide to cancer therapy and expand our understanding of the effects of gene alterations on drug efficacies.en_US
dc.description.departmentSchool of Natural Sciences and Mathematicsen_US
dc.description.departmentCenter for Systems Biologyen_US
dc.identifier.bibliographicCitationJiang, Xian-Li, Emmanuel Martinez-Ledesma, and Faruck Morcos. 2017. "Revealing protein networks and gene-drug connectivity in cancer from direct information." Scientific Reports 7: 3739-3739.en_US
dc.identifier.issn2045-2322en_US
dc.identifier.urihttp://hdl.handle.net/10735.1/5971
dc.identifier.volume7en_US
dc.language.isoenen_US
dc.publisherNature Publishing Groupen_US
dc.relation.urihttp://dx.doi.org/10.1038/s41598-017-04001-3
dc.rightsCC BY 4.0 (Attribution)en_US
dc.rights©2017 The Authorsen_US
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en_US
dc.sourceScientific Reports
dc.subjectCell Cycle Checkpointsen_US
dc.subjectSignal Transductionen_US
dc.subjectMEK inhibitoren_US
dc.subjectBreast—Canceren_US
dc.subjectLung—Canceren_US
dc.subjectTGF-beta Superfamily Proteinsen_US
dc.subjectGenomicsen_US
dc.subjectCancer—Chemotherapyen_US
dc.subjectInformation theoryen_US
dc.subjectProteomicsen_US
dc.subjectStatisticsen_US
dc.titleRevealing Protein Networks and Gene-Drug Connectivity in Cancer from Direct Informationen_US
dc.type.genrearticleen_US

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