Gene isoforms as expression-based biomarkers predictive of drug response in vitro

• Published in: Nature communications Vol. 8; no. 1; pp. 1126 - 11
• Main Authors: Safikhani, Zhaleh, Smirnov, Petr, Thu, Kelsie L., Silvester, Jennifer, El-Hachem, Nehme, Quevedo, Rene, Lupien, Mathieu, Mak, Tak W., Cescon, David, Haibe-Kains, Benjamin
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 24-10-2017; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114/2413; 631/67; 631/67/1857; Alternative Splicing; Antineoplastic Agents - pharmacology; Benzimidazoles - pharmacology; Biomarkers; Biomarkers - metabolism; Breast cancer; Breast Neoplasms - genetics; Cancer; Carrier Proteins - genetics; Cell Adhesion Molecules - genetics; Chemistry, Pharmaceutical; Data processing; Datasets; Drug screening; Drug Screening Assays, Antitumor; Erlotinib Hydrochloride - pharmacology; Gene expression; Gene sequencing; Genome, Human; Humanities and Social Sciences; Humans; Integrin beta Chains - genetics; Isoforms; Lapatinib; multidisciplinary; Neoplasms - drug therapy; Neoplasms - genetics; Paclitaxel; Paclitaxel - pharmacology; Pharmacogenetics; Pharmacogenomics; Pharmacology; Protein Isoforms - genetics; Quinazolines - pharmacology; Ribonucleic acid; RNA; RNA, Messenger - metabolism; RNA-Binding Proteins - genetics; Science; Science (multidisciplinary); Sequence Analysis, RNA; Splicing; Transcriptome; Tumor cell lines
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-017-01153-8

Next-generation sequencing technologies have recently been used in pharmacogenomic studies to characterize large panels of cancer cell lines at the genomic and transcriptomic levels. Among these technologies, RNA-sequencing enable profiling of alternatively spliced transcripts. Given the high frequency of mRNA splicing in cancers, linking this feature to drug response will open new avenues of research in biomarker discovery. To identify robust transcriptomic biomarkers for drug response across studies, we develop a meta-analytical framework combining the pharmacological data from two large-scale drug screening datasets. We use an independent pan-cancer pharmacogenomic dataset to test the robustness of our candidate biomarkers across multiple cancer types. We further analyze two independent breast cancer datasets and find that specific isoforms of IGF2BP2, NECTIN4, ITGB6, and KLHDC9 are significantly associated with AZD6244, lapatinib, erlotinib, and paclitaxel, respectively. Our results support isoform expressions as a rich resource for biomarkers predictive of drug response.
Altered mRNA splicing features in many cancers, but it has not been linked to drug response. Here, with their meta-analytic framework, the authors analyse pharmacogenomic data to identify isoform-based biomarkers predictive of in vitro drug response, and show them to frequently be strong predictors.