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Development and validation of a method for using breast core needle biopsies for gene expression microarray analyses.

Matthew Ellis1, Natalie Davis, Andrew Coop

  • 1Department of Oncology, Georgetown University School of Medicine, Washington, DC 20007, USA.

Clinical Cancer Research : an Official Journal of the American Association for Cancer Research
|May 15, 2002
PubMed
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This study presents an optimized method for processing breast core needle biopsies for gene expression analysis. The developed protocol ensures high-quality RNA preservation and enables accurate prediction of cancer versus non-cancer tissues.

Area of Science:

  • Biotechnology
  • Molecular Biology
  • Genomics

Background:

  • Gene expression microarrays offer potential for defining molecular profiles in cancer diagnosis, prognosis, and treatment prediction.
  • Optimal tissue acquisition and processing are crucial for reliable gene expression studies.

Purpose of the Study:

  • To develop optimal procedures for tissue acquisition, processing, and analysis of breast core needle biopsies for gene expression profiling.
  • To explore gene expression profiles differentiating cancer from non-cancer breast tissues.

Main Methods:

  • Evaluated tissue processing methods using human breast cancer xenografts for RNA quality and quantity.
  • Applied an optimized protocol to breast core needle biopsies, comparing cancer and non-cancer tissues.
  • Utilized cDNA microarrays, hierarchical clustering, principal component analysis, and neural networks for data analysis.

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Main Results:

  • Processing with RNALater and washing preserves tissue architecture for histopathological analysis.
  • Adequate high-quality RNA was recovered from most biopsies (51/55).
  • Snap-freezing or RNALater did not impact RNA recovery or molecular profiles; neural networks accurately discriminated between cancer and non-cancer biopsies.

Conclusions:

  • Developed a simple, safe, and effective method for prospective acquisition and processing of breast core needle biopsies for gene expression studies.
  • Gene expression data can be used to build accurate predictive models for differentiating molecular profiles.
  • Established the utility of these approaches for future prospective research.