Methodology

Development of a microarray platform for FFPET profiling: application to the classification of human tumors

Sven Duenwald^1,2 , Mingjie Zhou¹ , Yanqun Wang^3,4 , Serguei Lejnine⁵ , Amit Kulkarni⁶ , Jaime Graves^1,7 , Ryan Smith¹ , John Castle⁶ , George Tokiwa¹ , Bernard Fine^8,9 , Hongyue Dai³ , Thomas Fare^10,11 and Matthew Marton¹

¹  Translational Sciences, Department of Molecular Profiling, Merck Research Laboratories, 401 Terry Ave N, Seattle, WA 98109, USA

²  Sirna Therapeutics, LLC, Department of Lead Discovery, 1700 Owens Street, San Francisco, CA 94158

³  Custom Analysis, Department of Molecular Profiling, Merck Research Laboratories, 401 Terry Ave N, Seattle, WA 98109, USA

⁴  Custom Analysis Informatics, 33 Avenue Louis Pasteur, Boston, MA 02115

⁵  Gene Expression Laboratory, Department of Molecular Profiling, Merck Research Laboratories, 401 Terry Ave N, Seattle, WA 98109, USA

⁶  Molecular Informatics, Department of Molecular Profiling, Merck Research Laboratories, 401 Terry Ave N, Seattle, WA 98109, USA

⁷  Epigenomics, Inc., 1000 Seneca Street, Seattle, WA 98101

⁸  Clinical Molecular Profiling, Department of Molecular Profiling, Merck Research Laboratories, 401 Terry Ave N, Seattle, WA 98109, USA

⁹  Genentech, Inc., One DNA way, South San Francisco, CA 94080

¹⁰  Advanced Technology Solutions, Department of Molecular Profiling, Merck Research Laboratories, 401 Terry Ave N, Seattle, WA 98109, USA

¹¹  External Scientific Affairs, Sumneytown Pike, PO BOX 4, West Point, PA 19486

author email corresponding author email

Journal of Translational Medicine 2009, 7:65doi:10.1186/1479-5876-7-65

Published:	28 July 2009

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 ~14% (p-value = 3.7 × 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.

Conclusion

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.