Show simple item record

dc.contributor.authorFridley, Brooke L.
dc.contributor.authorTan, Xiang-Lin
dc.contributor.authorJenkins, Gregory D.
dc.contributor.authorBatzler, Anthony
dc.contributor.authorMoyer, Ann M.
dc.contributor.authorBiernacka, Joanna M.
dc.contributor.authorWang, Liewei
dc.contributor.authorAbo, Ryan
dc.date.accessioned2017-07-26T18:06:06Z
dc.date.available2017-07-26T18:06:06Z
dc.date.issued2014-01
dc.identifier.issn1536-2310
dc.identifier.issn1557-8100
dc.identifier.urihttp://hdl.handle.net/1721.1/110858
dc.description.abstractIntegrative genomics has the potential to uncover relevant loci, as clinical outcome and response to chemotherapies are most likely not due to a single gene (or data type) but rather a complex relationship involving genetic variation, mRNA, DNA methylation, and copy number variation. In addition to this complexity, many complex phenotypes are thought to be controlled by the interplay of multiple genes within the same molecular pathway or gene set (GS). To address these two challenges, we propose an integrative gene set analysis approach and apply this strategy to a cisplatin (CDDP) pharmacogenomics study involving lymphoblastoid cell lines for which genome-wide SNP and mRNA expression data was collected. Application of the integrative GS analysis implicated the role of the RNA binding and cytoskeletal part GSs. The genes LMNB1 and CENPF, within the cytoskeletal part GS, were functionally validated with siRNA knockdown experiments, where the knockdown of LMNB1 and CENPF resulted in CDDP resistance in multiple cancer cell lines. This study demonstrates the utility of an integrative GS analysis strategy for detecting novel genes associated with response to cancer therapies, moving closer to tailored therapy decisions for cancer patients.en_US
dc.description.sponsorshipNational Institutes of Health (U.S.) (NIH/NCI GM61388)en_US
dc.description.sponsorshipNational Institutes of Health (U.S.) (NIH/NCI CA140879)en_US
dc.description.sponsorshipNational Institutes of Health (U.S.) (NIH/NCI GM86689)en_US
dc.description.sponsorshipNational Institutes of Health (U.S.) (NIH/NCI CA130828)en_US
dc.description.sponsorshipNational Institutes of Health (U.S.) (NIH/NCI CA138461)en_US
dc.description.sponsorshipNational Institutes of Health (U.S.) (NIH/NCI CA102701)en_US
dc.description.sponsorshipMayo Foundation for Medical Education and Researchen_US
dc.language.isoen_US
dc.publisherMary Ann Liebert, Inc.en_US
dc.relation.isversionofhttp://dx.doi.org/10.1089/omi.2013.0099en_US
dc.rightsArticle is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.en_US
dc.sourceMary Ann Leiberten_US
dc.titleIntegrative Gene Set Analysis: Application to Platinum Pharmacogenomicsen_US
dc.typeArticleen_US
dc.identifier.citationFridley, Brooke L., Ryan Abo, Xiang-Lin Tan, Gregory D. Jenkins, Anthony Batzler, Ann M. Moyer, Joanna M. Biernacka, and Liewei Wang. “Integrative Gene Set Analysis: Application to Platinum Pharmacogenomics.” OMICS: A Journal of Integrative Biology 18, no. 1 (January 2014): 34–41.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Biologyen_US
dc.contributor.mitauthorAbo, Ryan
dc.relation.journalOMICS: A Journal of Integrative Biologyen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dspace.orderedauthorsFridley, Brooke L.; Abo, Ryan; Tan, Xiang-Lin; Jenkins, Gregory D.; Batzler, Anthony; Moyer, Ann M.; Biernacka, Joanna M.; Wang, Lieweien_US
dspace.embargo.termsNen_US
mit.licensePUBLISHER_POLICYen_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record