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Related Experiment Videos

Systematic variation in gene expression patterns in human cancer cell lines.

D T Ross1, U Scherf, M B Eisen

  • 1Department of Biochemistry, Stanford University School of Medicine, Stanford, California, USA.

Nature Genetics
|March 4, 2000
PubMed
Summary

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Gene expression patterns in cancer cell lines accurately reflect tumor origins and physiological traits. This molecular profiling offers new insights into cancer cell line classification and their relationship to tumors in vivo.

Area of Science:

  • Molecular biology
  • Genomics
  • Cancer research

Background:

  • The National Cancer Institute (NCI) utilizes a panel of 60 human cancer cell lines for anti-cancer drug screening.
  • Understanding the molecular characteristics of these cell lines is crucial for interpreting drug response data.

Purpose of the Study:

  • To analyze gene expression variation across 60 NCI cancer cell lines.
  • To correlate gene expression patterns with cell line origins and physiological properties.
  • To compare cancer cell line gene expression to that of normal and tumor tissues.

Main Methods:

  • Utilized cDNA microarrays to profile the expression of approximately 8,000 unique genes.
  • Classified cell lines based on gene expression patterns.
  • Compared gene expression profiles between cell lines and human tissues.

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Main Results:

  • Gene expression patterns successfully classified cell lines according to their tissue of origin.
  • Identified outlier cell lines with misclassified origins based on gene expression.
  • Observed correlations between gene expression patterns and cell line physiological properties (e.g., doubling time, drug metabolism).
  • Found recognizable counterparts in cell lines for gene expression features in tumor, stromal, and inflammatory components of breast tumor tissues.

Conclusions:

  • Gene expression profiling provides a novel molecular characterization of NCI cancer cell lines.
  • This approach enhances the understanding of cell line relationships to their tumors of origin.
  • The findings support the utility of gene expression data for refining cancer cell line classification and research.