DeSigN: connecting gene expression with therapeutics for drug repurposing and development

• Published in: BMC genomics 2017-01, Vol.18 (Suppl 1), p.934-934, Article 934
• Main Authors: Lee, Bernard Kok Bang, Tiong, Kai Hung, Chang, Jit Kang, Liew, Chee Sun, Abdul Rahman, Zainal Ariff, Tan, Aik Choon, Khang, Tsung Fei, Cheong, Sok Ching
• Format: Article
• Language: English
• Subjects: Algorithms; Antineoplastic Agents - pharmacology; Apoptosis - drug effects; Apoptosis - genetics; Bioinformatics; Biotechnology; Cancer; Cancer therapies; Cell Line, Tumor; Cell Proliferation - drug effects; Computational Biology - methods; Databases, Genetic; Design; Drug Design; Drug development; Drug efficacy; Drug metabolism; Drug Repositioning; Drugs; Enzyme inhibitors; Gene expression; Gene Expression Profiling; Gene Expression Regulation - drug effects; Genes; Genetic aspects; Genomics; Genotype; Genotype & phenotype; Health aspects; Humans; Inhibitors; Inhibitory Concentration 50; Kinases; Oral squamous cell carcinoma; Phenotypes; Precision medicine; Protein Kinase Inhibitors - pharmacology; Proteins; Reproducibility of Results; Software; Squamous cell carcinoma; Standard deviation; Transcriptome; Tumor cell lines; Tumors; Web Browser; Workflow
• ISSN: 1471-2164; 1471-2164
• DOI: 10.1186/s12864-016-3260-7

The drug discovery and development pipeline is a long and arduous process that inevitably hampers rapid drug development. Therefore, strategies to improve the efficiency of drug development are urgently needed to enable effective drugs to enter the clinic. Precision medicine has demonstrated that genetic features of cancer cells can be used for predicting drug response, and emerging evidence suggest that gene-drug connections could be predicted more accurately by exploring the cumulative effects of many genes simultaneously. We developed DeSigN, a web-based tool for predicting drug efficacy against cancer cell lines using gene expression patterns. The algorithm correlates phenotype-specific gene signatures derived from differentially expressed genes with pre-defined gene expression profiles associated with drug response data (IC ) from 140 drugs. DeSigN successfully predicted the right drug sensitivity outcome in four published GEO studies. Additionally, it predicted bosutinib, a Src/Abl kinase inhibitor, as a sensitive inhibitor for oral squamous cell carcinoma (OSCC) cell lines. In vitro validation of bosutinib in OSCC cell lines demonstrated that indeed, these cell lines were sensitive to bosutinib with IC of 0.8-1.2 μM. As further confirmation, we demonstrated experimentally that bosutinib has anti-proliferative activity in OSCC cell lines, demonstrating that DeSigN was able to robustly predict drug that could be beneficial for tumour control. DeSigN is a robust method that is useful for the identification of candidate drugs using an input gene signature obtained from gene expression analysis. This user-friendly platform could be used to identify drugs with unanticipated efficacy against cancer cell lines of interest, and therefore could be used for the repurposing of drugs, thus improving the efficiency of drug development.