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dc.contributor.authorStupnikov, Aen_US
dc.contributor.authorO'Reilly, PGen_US
dc.contributor.authorMcInerney, CEen_US
dc.contributor.authorRoddy, ACen_US
dc.contributor.authorDunne, PDen_US
dc.contributor.authorGilmore, Aen_US
dc.contributor.authorEllis, HPen_US
dc.contributor.authorFlannery, Ten_US
dc.contributor.authorHealy, Een_US
dc.contributor.authorMcIntosh, SAen_US
dc.contributor.authorSavage, Ken_US
dc.contributor.authorKurian, KMen_US
dc.contributor.authorEmmert-Streib, Fen_US
dc.contributor.authorPrise, KMen_US
dc.contributor.authorSalto-Tellez, Men_US
dc.contributor.authorMcArt, DGen_US
dc.identifier.citationJCO precision oncology, 2018, 2en_US
dc.description.abstractPurpose:Gene expression profiling can uncover biologic mechanisms underlying disease and is important in drug development. RNA sequencing (RNA-seq) is routinely used to assess gene expression, but costs remain high. Sample multiplexing reduces RNAseq costs; however, multiplexed samples have lower cDNA sequencing depth, which can hinder accurate differential gene expression detection. The impact of sequencing depth alteration on RNA-seq-based downstream analyses such as gene expression connectivity mapping is not known, where this method is used to identify potential therapeutic compounds for repurposing. Methods:In this study, published RNA-seq profiles from patients with brain tumor (glioma) were assembled into two disease progression gene signature contrasts for astrocytoma. Available treatments for glioma have limited effectiveness, rendering this a disease of poor clinical outcome. Gene signatures were subsampled to simulate sequencing alterations and analyzed in connectivity mapping to investigate target compound robustness. Results:Data loss to gene signatures led to the loss, gain, and consistent identification of significant connections. The most accurate gene signature contrast with consistent patient gene expression profiles was more resilient to data loss and identified robust target compounds. Target compounds lost included candidate compounds of potential clinical utility in glioma (eg, suramin, dasatinib). Lost connections may have been linked to low-abundance genes in the gene signature that closely characterized the disease phenotype. Consistently identified connections may have been related to highly expressed abundant genes that were ever-present in gene signatures, despite data reductions. Potential noise surrounding findings included false-positive connections that were gained as a result of gene signature modification with data loss. Conclusion:Findings highlight the necessity for gene signature accuracy for connectivity mapping, which should improve the clinical utility of future target compound discoveries.en_US
dc.titleImpact of Variable RNA-Sequencing Depth on Gene Expression Signatures and Target Compound Robustness: Case Study Examining Brain Tumor (Glioma) Disease Progression.en_US
dc.typeJournal Article
rioxxterms.typeJournal Article/Reviewen_US
dc.relation.isPartOfJCO precision oncologyen_US
pubs.notesNot knownen_US
pubs.organisational-group/ICR/Primary Group
pubs.organisational-group/ICR/Primary Group/ICR Divisions
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Molecular Pathology
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Molecular Pathology/Integrated Pathology
pubs.embargo.termsNot knownen_US
icr.researchteamIntegrated Pathologyen_US
dc.contributor.icrauthorSalto-Tellez, Manuelen_US

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