title: Connectivity mapping using a combined gene signature from multiple colorectal cancer datasets identified candidate drugs including existing chemotherapies.
abstract: 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.
authors.forename: Qing
authors.initials: Q
authors.lastname: Wen
authors.forename: Paul
authors.initials: P
authors.lastname: O'Reilly
authors.forename: Philip D
authors.initials: PD
authors.lastname: Dunne
authors.forename: Mark
authors.initials: M
authors.lastname: Lawler
authors.forename: Sandra
authors.initials: S
authors.lastname: Van Schaeybroeck
authors.forename: Manuel
authors.initials: M
authors.lastname: Salto-Tellez
authors.forename: Peter
authors.initials: P
authors.lastname: Hamilton
authors.forename: Shu-Dong
authors.initials: SD
authors.lastname: Zhang
mesh_terms: D000970:Antineoplastic Agents / Q000032:analysis / Q000627:therapeutic use*; D002874:Chromosome Mapping; D015179:Colorectal Neoplasms / Q000188:drug therapy* / Q000235:genetics; D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D055808:Drug Discovery / Q000379:methods*; D006801:Humans
chemical_list: D000970:Antineoplastic Agents