New insight for pharmacogenomics studies from the transcriptional analysis of two large-scale cancer cell line panels

• Published in: Scientific reports 2017-11, Vol.7 (1), p.15126-12, Article 15126
• Main Authors: Sadacca, Benjamin, Hamy, Anne-Sophie, Laurent, Cécile, Gestraud, Pierre, Bonsang-Kitzis, Hélène, Pinheiro, Alice, Abecassis, Judith, Neuvial, Pierre, Reyal, Fabien
• Format: Article
• Language: English
• Subjects: 631/114/2164; 631/154/1435/2163; 631/67/2322; Antineoplastic drugs; Antitumor agents; Bioinformatics; Cancer; Classification; Computer Science; Data processing; Datasets; Drug development; Humanities and Social Sciences; Mathematics; multidisciplinary; Pharmacogenomics; Science; Science (multidisciplinary); Statistics; Transcription; Tumor cell lines
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-017-14770-6

One of the most challenging problems in the development of new anticancer drugs is the very high attrition rate. The so-called “drug repositioning process” propose to find new therapeutic indications to already approved drugs. For this, new analytic methods are required to optimize the information present in large-scale pharmacogenomics datasets. We analyzed data from the Genomics of Drug Sensitivity in Cancer and Cancer Cell Line Encyclopedia studies. We focused on common cell lines (n = 471), considering the molecular information, and the drug sensitivity for common drugs screened (n = 15). We propose a novel classification based on transcriptomic profiles of cell lines, according to a biological network-driven gene selection process. Our robust molecular classification displays greater homogeneity of drug sensitivity than cancer cell line grouped based on tissue of origin. We then identified significant associations between cell line cluster and drug response robustly found between both datasets. We further demonstrate the relevance of our method using two additional external datasets and distinct sensitivity metrics. Some associations were still found robust, despite cell lines and drug responses’ variations. This study defines a robust molecular classification of cancer cell lines that could be used to find new therapeutic indications to known compounds.