Candidate cancer-targeting agents identified by expression-profiling arrays

One particularly promising component of personalized medicine in cancer treatment is targeted therapy, which aims to maximize therapeutic efficacy while minimizing toxicity. However, the number of approved targeted agents remains limited. Expression microarray data for different types of cancer are resources to identify genes that were upregulated. The genes are candidate targets for cancer-targeting agents for future anticancer research and targeted treatments.

The gene expression profiles of 48 types of cancer from 2,141 microarrays reported in the Gene Expression Omnibus were analyzed. These data were organized into 78 experimental groups, on which we performed comprehensive analyses using two-tailed Student's t-tests with significance set at P < 0.01 to identify genes that were upregulated compared with normal cells in each cancer type. The resulting list of significantly upregulated genes was cross-referenced with three categories of protein inhibitor targets, categorized by inhibitor type ('Targets of US Food and Drug Administration (FDA)-approved anticancer drugs', 'Targets of FDA-approved nonantineoplastic drugs', or 'Targets of non-FDA-approved chemical agents'). Of the 78 experimental groups studied, 57 (73%) represent cancers that are currently treated with FDA-approved targeted treatment agents. However, the target genes for the indicated therapies are upregulated in only 33 of these groups (57%). Nevertheless, the mRNA expression of the genes targeted by FDA-approved treatment agents is increased in every experimental group, including all of the cancers without FDA-approved targeted treatments. Moreover, many targets of protein inhibitors that have been approved by the FDA as therapies for nonneoplastic diseases, such as 3-hydroxy-3-methylglutaryl-CoA reductase and cyclooxygenase-2 and the targets of many non-FDA-approved chemical agents, such as cyclin-dependent kinase 1 and DNA-dependent protein kinase, are also overexpressed in many types of cancer.

This research demonstrates a clinical correlation between bioinformatics data and currently approved treatments and suggests novel uses for known protein inhibitors in future antineoplastic research and targeted therapies.