title: An optimized workflow for improved gene expression profiling for formalin-fixed, paraffin-embedded tumor samples.
abstract: BACKGROUND
  Whole genome microarray gene expression profiling is the 'gold standard' for the discovery of prognostic and predictive genetic markers for human cancers. However, suitable research material is lacking as most diagnostic samples are preserved as formalin-fixed, paraffin-embedded tissue (FFPET). We tested a new workflow and data analysis method optimized for use with FFPET samples.
  
  
  METHODS
  Sixteen breast tumor samples were split into matched pairs and preserved as FFPET or fresh-frozen (FF). Total RNA was extracted and tested for yield and purity. RNA from FFPET samples was amplified using three different commercially available kits in parallel, and hybridized to Affymetrix GeneChip® Human Genome U133 Plus 2.0 Arrays. The array probe set was optimized in silico to exclude misdesigned and misannotated probes.
  
  
  RESULTS
  FFPET samples processed using the WT-Ovation™ FFPE System V2 (NuGEN) provided 80% specificity and 97% sensitivity compared with FF samples (assuming values of 100%). In addition, in silico probe set redesign improved sequence detection sensitivity and, thus, may rescue potentially significant small-magnitude gene expression changes that could otherwise be diluted by the overall probe set background.
  
  
  CONCLUSIONS
  In conclusion, our FFPET-optimized workflow enables the detection of more genes than previous, nonoptimized approaches, opening new possibilities for the discovery, validation, and clinical application of mRNA biomarkers in human diseases.
authors: Pharma Research and Early Development (pRED), Roche Diagnostics GmbH, TR-H, Bldg 231/206a, Nonnenwald 2, 82377 Penzberg, Germany. marlene.thomas@roche.com.
authors: Marlene
authors: M
authors: Thomas
authors: Manuela
authors: M
authors: Poignée-Heger
authors: Martin
authors: M
authors: Weisser
authors: Stephanie
authors: S
authors: Wessner
authors: Anton
authors: A
authors: Belousov
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