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Background gene expression networks significantly enhance drug response prediction by transcriptional profiling

Academic Article
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Overview

authors

  • Torkamani, Ali
  • Schork, Nicholas

publication date

  • October 2012

journal

  • Pharmacogenomics Journal  Journal

abstract

  • A central goal of gene expression studies coupled with drug response screens is to identify predictive profiles that can be exploited to stratify patients. Numerous methods have been proposed towards this end, most of them focusing on novel statistical methods and model selection techniques that attempt to uncover groups of genes, whose expression profiles are directly and robustly correlated with drug response. However, biological systems process information through the crosstalk of multiple signaling networks, whose ultimate phenotypic consequences may only be determined by the combined input of relevant interacting systems. By restricting predictive signatures to direct gene-drug correlations, biologically meaningful interactions that may serve as superior predictors are ignored. Here we demonstrate that predictive signatures, which incorporate the interaction between background gene expression patterns and individual predictive probes, can provide superior models than those that directly relate gene expression levels to pharmacological response, and thus should be more widely utilized in pharmacogenetic studies.

subject areas

  • Antineoplastic Agents
  • Biomarkers, Pharmacological
  • Cell Line, Tumor
  • Gene Expression Profiling
  • Gene Expression Regulation, Neoplastic
  • Humans
  • Neoplasms
  • Oligonucleotide Array Sequence Analysis
  • Signal Transduction
  • Transcription, Genetic
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Research

keywords

  • cancer
  • gene expression
  • interaction
  • nci60
  • prediction
  • signatures
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Identity

PubMed Central ID

  • PMC4846279

International Standard Serial Number (ISSN)

  • 1470-269X

Digital Object Identifier (DOI)

  • 10.1038/tpj.2011.35

PubMed ID

  • 21826086
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Additional Document Info

start page

  • 446

end page

  • 452

volume

  • 12

issue

  • 5

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