Chemosensitivity prediction by transcriptional profiling

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 c hemosensitivity based on gene expression profiles alone. Using oligonucleot ide microarrays, the expression levels of 6,817 genes were measured in a pa nel of 60 human cancer cell lines (the NCl-60) for which the chemosensitivi ty profiles of thousands of chemical compounds have been determined. We sou ght to determine whether the gene expression signatures of untreated cells were sufficient for the prediction of chemosensitivity. Gene expression-bas ed classifiers of sensitivity or resistance for 232 compounds were generate d and then evaluated on independent sets of data. The classifiers were desi gned to be independent of the cells' tissue of origin. The accuracy of chem osensitivity prediction was considerably better than would be expected by c hance. Eighty-eight of 232 expression-based classifiers performed accuratel y (with P < 0.05) on an independent test set, whereas only 12 of the 232 wo uld be expected to do so by chance. These results suggest that at least for a subset of compounds genomic approaches to chemosensitivity prediction ar e feasible.