Targeted therapy is a rational and promising strategy for the treatment of advanced cancer. For the development of clinical agents targeting oncogenic signaling pathways, it is important to define the specificity of compounds to the target molecular pathway. Genome-wide transcriptomic analysis is an unbiased approach to evaluate the compound mode of action, but it is still unknown whether the analysis could be widely applicable to classify molecularly targeted anticancer agents. We comprehensively obtained and analyzed 129 transcriptomic datasets of cancer cells treated with 83 anticancer drugs or related agents, covering most clinically used, molecularly targeted drugs alongside promising inhibitors of molecular cancer targets. Hierarchical clustering and principal component analysis revealed that compounds targeting similar target molecules or pathways were clustered together. These results confirmed that the gene signatures of these drugs reflected their modes of action. Of note, inhibitors of oncogenic kinase pathways formed a large unique cluster, showing that these agents affect a shared molecular pathway distinct from classical antitumor agents and other classes of agents. The gene signature analysis further classified kinome-targeting agents depending on their target signaling pathways, and we identified target pathway-selective signature gene sets. The gene expression analysis was also valuable in uncovering unexpected target pathways of some anticancer agents. These results indicate that comprehensive transcriptomic analysis with our database (http://scads.jfcr.or.jp/db/cs/) is a powerful strategy to validate and re-evaluate the target pathways of anticancer compounds.
ASJC Scopus subject areas
- Cancer Research