Mutations in cancer-causing genes induce changes in gene expression programs critical for malignant cell transformation. Publicly available gene expression profiles produced by modulating the expression of distinct cancer genes may therefore represent a rich resource for the identification of gene signatures common to seemingly unrelated cancer genes. We combined automatic retrieval with manual validation to obtain a data set of high-quality gene microarray profiles. This data set was used to create logical models of the signaling events underlying the observed expression changes produced by various cancer genes and allowed to uncover unknown and verifiable interactions. Data clustering revealed novel sets of gene expression profiles commonly regulated by distinct cancer genes. Our method allows retrieval of significant new information and testable hypotheses from a pool of deposited cancer gene expression experiments that are otherwise not apparent or appear insignificant from single measurements. The complete results are available through a web-application at http://biodata.ethz.ch/cgi-bin/geologic.
Keywords: cancer genes; clustering; gene expression signatures; gene microarray database analysis; meta-analysis; network interactions.