Connectivity Mapping for Candidate Therapeutics Identification Using Next Generation Sequencing RNA-Seq Data

• Published in: PloS one Vol. 8; no. 6; p. e66902
• Main Authors: McArt, Darragh G, Dunne, Philip D, Blayney, Jaine K, Salto-Tellez, Manuel, Van Schaeybroeck, Sandra, Hamilton, Peter W, Zhang, Shu-Dong
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 26.06.2013; Public Library of Science (PLoS)
• Subjects: Acids; Bioinformatics; Biology; Cell Line, Tumor; Cell proliferation; Cotinine; DNA microarrays; Drugs; Feasibility studies; Gene expression; Gene Expression Profiling; Gene mapping; Gene sequencing; Genomes; Genomics; Genomics - methods; Growth factors; High-Throughput Nucleotide Sequencing; Humans; Kinases; Male; Mapping; Medical research; Medicine; Nicotine; Prostate cancer; Prostatic Neoplasms - genetics; Prostatic Neoplasms - pathology; Ribonucleic acid; RNA; Sequence Analysis, RNA; Technology; United Kingdom > UK
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0066902

The advent of next generation sequencing technologies (NGS) has expanded the area of genomic research, offering high coverage and increased sensitivity over older microarray platforms. Although the current cost of next generation sequencing is still exceeding that of microarray approaches, the rapid advances in NGS will likely make it the platform of choice for future research in differential gene expression. Connectivity mapping is a procedure for examining the connections among diseases, genes and drugs by differential gene expression initially based on microarray technology, with which a large collection of compound-induced reference gene expression profiles have been accumulated. In this work, we aim to test the feasibility of incorporating NGS RNA-Seq data into the current connectivity mapping framework by utilizing the microarray based reference profiles and the construction of a differentially expressed gene signature from a NGS dataset. This would allow for the establishment of connections between the NGS gene signature and those microarray reference profiles, alleviating the associated incurring cost of re-creating drug profiles with NGS technology. We examined the connectivity mapping approach on a publicly available NGS dataset with androgen stimulation of LNCaP cells in order to extract candidate compounds that could inhibit the proliferative phenotype of LNCaP cells and to elucidate their potential in a laboratory setting. In addition, we also analyzed an independent microarray dataset of similar experimental settings. We found a high level of concordance between the top compounds identified using the gene signatures from the two datasets. The nicotine derivative cotinine was returned as the top candidate among the overlapping compounds with potential to suppress this proliferative phenotype. Subsequent lab experiments validated this connectivity mapping hit, showing that cotinine inhibits cell proliferation in an androgen dependent manner. Thus the results in this study suggest a promising prospect of integrating NGS data with connectivity mapping.