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A gene expression database for the molecular pharmacology of cancer

Abstract

We used cDNA microarrays to assess gene expression profiles in 60 human cancer cell lines used in a drug discovery screen by the National Cancer Institute. Using these data, we linked bioinformatics and chemoinformatics by correlating gene expression and drug activity patterns in the NCI60 lines. Clustering the cell lines on the basis of gene expression yielded relationships very different from those obtained by clustering the cell lines on the basis of their response to drugs. Gene-drug relationships for the clinical agents 5-fluorouracil and L-asparaginase exemplify how variations in the transcript levels of particular genes relate to mechanisms of drug sensitivity and resistance. This is the first study to integrate large databases on gene expression and molecular pharmacology.

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Figure 1: Simplified schematic overview of database generation in relation to the NCI drug discovery program.
Figure 2: Dendrograms showing average-linkage hierarchical clustering of human cancer cell lines.
Figure 3: Dendrograms showing average-linkage hierarchical clustering of 118 ‘mechanism of action’ drugs.
Figure 4: CIM relating activity patterns of 118 tested compounds to the expression patterns of 1,376 genes in the 60 cell lines.
Figure 5: Relationship between ASNS expression levels and chemosensitivity of the NCI cell lines to L-asparaginase.

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Acknowledgements

We thank the staff of the NCI DTP, particularly K.D. Paull, whose efforts over the years have resulted in the pharmacological databases used in this study. This study was supported in part by NCI grant CA77097 and by the Howard Hughes Medical Institute. D.T.R. is a Walter and Iden Berry Fellow. P.O.B. is an associate investigator of the Howard Hughes Medical Institute. The work of U.S. and J.N.W. was supported in part by a grant from the NCI intramural Breast Cancer Think Tank.

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Correspondence to Patrick O. Brown or John N. Weinstein.

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Scherf, U., Ross, D., Waltham, M. et al. A gene expression database for the molecular pharmacology of cancer. Nat Genet 24, 236–244 (2000). https://doi.org/10.1038/73439

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