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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.
Abstract:
We used cDNA microarrays to explore the variation in expression of approximately 8,000 unique genes among the 60 cell lines used in the National Cancer Institute's screen for anti-cancer drugs. Classification of the cell lines based solely on the observed patterns of gene expression revealed a correspondence to the ostensible origins of the tumours from which the cell lines were derived. The consistent relationship between the gene expression patterns and the tissue of origin allowed us to recognize outliers whose previous classification appeared incorrect. Specific features of the gene expression patterns appeared to be related to physiological properties of the cell lines, such as their doubling time in culture, drug metabolism or the interferon response. Comparison of gene expression patterns in the cell lines to those observed in normal breast tissue or in breast tumour specimens revealed features of the expression patterns in the tumours that had recognizable counterparts in specific cell lines, reflecting the tumour, stromal and inflammatory components of the tumour tissue. These results provided a novel molecular characterization of this important group of human cell lines and their relationships to tumours in vivo.
Insights
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.
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.