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Development and Validation of a Method for Using Breast Core Needle Biopsies for Gene Expression Microarray Analyses¹

Departments of Oncology [M. E., N. D., A. C., M. L., L. S., R. Y. L., V. S., A. G., A. Z., D. F. H., M. E. L., R. C.], Pathology [B. S.], Surgery [M. P., T. T.], Physiology and Biophysics [R. Y. L., R. C.], and Biostatistics and Biomathematics [A. L.], Georgetown University School of Medicine, Washington, DC 20007; Department of Electrical Engineering and Computer Science, The Catholic University of America, Washington, DC 20064 [R. S., Y. W.]; ResGen, part of the Invitrogen Corporation, Huntsville, Alabama 35801 [C. G. R.]; and Department of Oncology, University of Edinburgh, Western General Hospital, Edinburgh, Scotland, EH4 2XU United Kingdom [W. R. M.]

Purpose: Gene expression microarray technologies have the potential to definemolecular profiles that may identify specific phenotypes(diagnosis), establish a patient’s expected clinical outcome (prognosis), and indicate the likelihood of a beneficial effect of a specific therapy (prediction). We wished to develop optimal tissue acquisition, processing, and analysis procedures for exploring the gene expression profiles of breast core needle biopsies representing cancer and noncancer tissues.

Experimental Design: Human breast cancer xenografts were used to evaluate several processing methods for prospectively collecting adequate amounts of high-quality RNA for gene expression microarray studies. Samples were assessed for the preservation of tissue architecture and the quality and quantity of RNA recovered. An optimized protocol was applied to a small study of core needle breast biopsies from patients, in which we compared the molecular profiles from cancer with those from noncancer biopsies. Gene expression data were obtained using Research Genetics, Inc. NamedGenes cDNA microarrays. Data were visualized using simple hierarchical clustering and a novel principal component analysis-based multidimensional scaling. Data dimensionality was reduced by simple statistical approaches. Predictive neural networks were built using a multilayer perceptron and evaluated in an independent data set from snap-frozen mastectomy specimens.

Results: Processing tissue through RNALater preserves tissue architecture when biopsies are washed for 5 min on ice with ice-cold PBS before histopathological analysis. Cell margins are clear, tissue folding and fragmentation are not observed, and integrity of the cores is maintained, allowing optimal pathological interpretation and preservation of important diagnostic information. Adequate concentrations of high-quality RNA are recovered; 51 of 55 biopsies produced a median of 1.34 µg of total RNA (range, 100 ng to 12.60 µg). Snap-freezing or the use of RNALater does not affect RNA recovery or the molecular profiles obtained from biopsies. The neural network predictors accurately discriminate between predominantly cancer and noncancer breast biopsies.

Conclusions: The approaches generated in these studies provide a simple, safe, and effective method for prospectively acquiring and processing breast core needle biopsies for gene expression studies. Gene expression data from these studies can be used to build accurate predictive models that separate different molecular profiles. The data establish the use and effectiveness of these approaches for future prospective studies.

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