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Chemosensitivity Prediction by Transcriptional Profiling
Staunton JE, Slonim DK, Coller HA, Tamayo P, Angelo MJ, Park J,
Scherf U, Lee JK, Reinhold WO, Weinstein JN, Mesirov JP, Lander ES, Golub TR.
Proc Natl Acad Sci U S A 2001 Sep 11;98(19):10787-92
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Gene expression data, tab delimited (7129 genes x 60 cells, 1,540 Kb)
Description(Download compressed: gz format) |
Abstract: In an effort to develop a genomics-based approach to the prediction of
drug response, we have developed an algorithm for classification of cell
line chemosensitivity based on gene expression profiles alone. Using
oligonucleotide microarrays, the expression levels of 6,817 genes were
measured in a panel of 60 human cancer cell lines (the NCI-60) for which
the chemosensitivity profiles of thousands of chemical compounds have been
determined. We sought to determine whether the gene expression signatures
of untreated cells were sufficient for the prediction of chemosensitivity.
Gene expression-based classifiers of sensitivity or resistance for 232
compounds were generated and then evaluated on independent sets of data.
The classifiers were designed to be independent of the cells' tissue of
origin. The accuracy of chemosensitivity prediction was considerably
better than would be expected by chance. Eighty-eight of 232
expression-based classifiers performed accurately (with P < 0.05) on an
independent test set, whereas only 12 of the 232 would be expected to do
so by chance. These results suggest that at least for a subset of
compounds genomic approaches to chemosensitivity prediction are feasible.
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