title: Development of a microarray platform for FFPET profiling: application to the classification of human tumors.
abstract: BACKGROUND
  mRNA profiling has become an important tool for developing and validating prognostic assays predictive of disease treatment response and outcome. Archives of annotated formalin-fixed paraffin-embedded tissues (FFPET) are available as a potential source for retrospective studies. Methods are needed to profile these FFPET samples that are linked to clinical outcomes to generate hypotheses that could lead to classifiers for clinical applications.
  
  
  METHODS
  We developed a two-color microarray-based profiling platform by optimizing target amplification, experimental design, quality control, and microarray content and applied it to the profiling of FFPET samples. We profiled a set of 50 fresh frozen (FF) breast cancer samples and assigned class labels according to the signature and method by van 't Veer et al 1 and then profiled 50 matched FFPET samples to test how well the FFPET data predicted the class labels. We also compared the sorting power of classifiers derived from FFPET sample data with classifiers derived from data from matched FF samples.
  
  
  RESULTS
  When a classifier developed with matched FF samples was applied to FFPET data to assign samples to either "good" or "poor" outcome class labels, the classifier was able to assign the FFPET samples to the correct class label with an average error rate = 12% to 16%, respectively, with an Odds Ratio = 36.4 to 60.4, respectively. A classifier derived from FFPET data was able to predict the class label in FFPET samples (leave-one-out cross validation) with an error rate of approximately 14% (p-value = 3.7 x 10(-7)). When applied to the matched FF samples, the FFPET-derived classifier was able to assign FF samples to the correct class labels with 96% accuracy. The single misclassification was attributed to poor sample quality, as measured by qPCR on total RNA, which emphasizes the need for sample quality control before profiling.
  
  
  CONCLUSIONS
  We have optimized a platform for expression analyses and have shown that our profiling platform is able to accurately sort FFPET samples into class labels derived from FF classifiers. Furthermore, using this platform, a classifier derived from FFPET samples can reliably provide the same sorting power as a classifier derived from matched FF samples. We anticipate that these techniques could be used to generate hypotheses from archives of FFPET samples, and thus may lead to prognostic and predictive classifiers that could be used, for example, to segregate patients for clinical trial enrollment or to guide patient treatment.
authors.affiliation: Translational Sciences, Department of Molecular Profiling, Merck Research Laboratories, Seattle, WA 98109, USA. sven_duenwald@merck.com
authors.forename: Sven
authors.initials: S
authors.lastname: Duenwald
authors.forename: Mingjie
authors.initials: M
authors.lastname: Zhou
authors.forename: Yanqun
authors.initials: Y
authors.lastname: Wang
authors.forename: Serguei
authors.initials: S
authors.lastname: Lejnine
authors.forename: Amit
authors.initials: A
authors.lastname: Kulkarni
authors.forename: Jaime
authors.initials: J
authors.lastname: Graves
authors.forename: Ryan
authors.initials: R
authors.lastname: Smith
authors.forename: John
authors.initials: J
authors.lastname: Castle
authors.forename: George
authors.initials: G
authors.lastname: Tokiwa
authors.forename: Bernard
authors.initials: B
authors.lastname: Fine
authors.forename: Hongyue
authors.initials: H
authors.lastname: Dai
authors.forename: Thomas
authors.initials: T
authors.lastname: Fare
authors.forename: Matthew
authors.initials: M
authors.lastname: Marton
mesh_terms: D014408:Biomarkers, Tumor / Q000235:genetics; D005557:Formaldehyde; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D009369:Neoplasms / Q000145:classification / Q000235:genetics / Q000473:pathology; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D016612:Paraffin Embedding; D016133:Polymerase Chain Reaction; D012372:ROC Curve; D016707:Tissue Fixation / Q000379:methods
chemical_list: D014408:Biomarkers, Tumor; D005557:Formaldehyde