Connectivity mapping using a combined gene signature from multiple colorectal cancer datasets identified candidate drugs including existing chemotherapies

BACKGROUND: While the discovery of new drugs is a complex, lengthy and costly process, identifying new uses for existing drugs is a cost-effective approach to therapeutic discovery. Connectivity mapping integrates gene expression profiling with advanced algorithms to connect genes, diseases and small molecule compounds and has been applied in a large number of studies to identify potential drugs, particularly to facilitate drug repurposing. Colorectal cancer (CRC) is a commonly diagnosed cancer with high mortality rates, presenting a worldwide health problem. With the advancement of high throughput omics technologies, a number of large scale gene expression profiling studies have been conducted on CRCs, providing multiple datasets in gene expression data repositories. In this work, we systematically apply gene expression connectivity mapping to multiple CRC datasets to identify candidate therapeutics to this disease.

RESULTS: We developed a robust method to compile a combined gene signature for colorectal cancer across multiple datasets. Connectivity mapping analysis with this signature of 148 genes identified 10 candidate compounds, including irinotecan and etoposide, which are chemotherapy drugs currently used to treat CRCs. These results indicate that we have discovered high quality connections between the CRC disease state and the candidate compounds, and that the gene signature we created may be used as a potential therapeutic target in treating the disease. The method we proposed is highly effective in generating quality gene signature through multiple datasets; the publication of the combined CRC gene signature and the list of candidate compounds from this work will benefit both cancer and systems biology research communities for further development and investigations.

Development of TMTP-1 targeted designer biopolymers for gene delivery to prostate cancer

Designer biopolymers (DBPs) represent state of the art genetically engineered biomacromolecules designed to condense plasmid DNA, and overcome intra- and extra- cellular barriers to gene delivery. Three DBPs were synthesized, each with the tumor molecular targeting peptide-1 (TMTP-1) motif to specifically target metastases. Each DBP was complexed with a pEGFP-N1 reporter plasmid to permit physiochemical and biological assay analysis. Results indicated that two of the biopolymers (RMHT and RM3GT) effectively condensed pEGFP-N1 into cationic nanoparticles< 100nm and were capable of transfecting PC-3 metastatic prostate cancer cells. Conversely the anionic RMGT DBP nanoparticles could not transfect PC-3 cells. RMHT and RM3GT nanoparticles were stable in the presence of serum and protected the cargo from degradation. Additionally it was concluded that cell viability could recover post-transfection with these DBPs, which were less toxic than the commercially available transfection reagent Lipofectamine® 2000. With both DBPs, a higher transfection efficacy was observed in PC-3 cells than in the moderately metastatic, DU145, and normal, PNT2-C2, cell lines. Blocking of the TMTP-1 receptors inhibited gene transfer indicating internalization via this receptor. In conclusion RMHT and RM3GT are fully functional DBPs that address major obstacles to gene delivery and target metastatic cells expressing the TMTP-1 receptor.

A gene-signature progression approach to identifying candidate small-molecule cancer therapeutics with connectivity mapping

Gene expression connectivity mapping has gained much popularity recently with a number of successful applications in biomedical research testifying its utility and promise. Previously methodological research in connectivity mapping mainly focused on two of the key components in the framework, namely, the reference gene expression profiles and the connectivity mapping algorithms. The other key component in this framework, the query gene signature, has been left to users to construct without much consensus on how this should be done, albeit it has been an issue most relevant to end users. As a key input to the connectivity mapping process, gene signature is crucially important in returning biologically meaningful and relevant results. This paper intends to formulate a standardized procedure for constructing high quality gene signatures from a user’s perspective.