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.

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.

Since 1990, the National Cancer Institute (NCI) has screened more than 60,000 compounds against a panel of 60 human cancer cell lines. The 50-percent growth-inhibitory concentration (GI50) for any single cell line is simply an index of cytotoxicity or cytostasis, but the patterns of 60 such GI50 values encode unexpectedly rich, detailed information on mechanisms of drug action and drug resistance. Each compound's pattern is like a fingerprint, essentially unique among the many billions of distinguishable possibilities. These activity patterns are being used in conjunction with molecular structural features of the tested agents to explore the NCI's database of more than 460,000 compounds, and they are providing insight into potential target molecules and modulators of activity in the 60 cell lines. For example, the information is being used to search for candidate anticancer drugs that are not dependent on intact p53 suppressor gene function for their activity. It remains to be seen how effective this information-intensive strategy will be at generating new clinically active agents.

The differential transcriptional response of Mycobacterium tuberculosis to drugs and growth-inhibitory conditions was monitored to generate a data set of 430 microarray profiles. Unbiased grouping of these profiles independently clustered agents of known mechanism of action accurately and was successful at predicting the mechanism of action of several unknown agents. These predictions were validated biochemically for two agents of previously uncategorized mechanism, pyridoacridones and phenothiazines. Analysis of this data set further revealed 150 underlying clusters of coordinately regulated genes offering the first glimpse at the full metabolic potential of this organism. A signature subset of these gene clusters was sufficient to classify all known agents as to mechanism of action. Transcriptional profiling of both crude and purified natural products can provide critical information on both mechanism and detoxification prior to purification that can be used to guide the drug discovery process. Thus, the transcriptional profile generated by a crude marine natural product recapitulated the mechanistic prediction from the pure active component. The underlying gene clusters further provide fundamental insights into the metabolic response of bacteria to drug-induced stress and provide a rational basis for the selection of critical metabolic targets for screening for new agents with improved activity against this important human pathogen.