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

Zhaleh Safikhani, Petr Smirnov, Kelsie L. Thu, Jennifer Silvester, Nehme El-Hachem, Rene Quevedo, Mathieu Lupien, Tak W. Mak, David Cescon and Benjamin Haibe-Kains ()
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Zhaleh Safikhani: University Health Network
Petr Smirnov: University Health Network
Kelsie L. Thu: University Health Network
Jennifer Silvester: University Health Network
Nehme El-Hachem: Institut de Recherches Cliniques de Montréal
Rene Quevedo: University Health Network
Mathieu Lupien: University Health Network
Tak W. Mak: University Health Network
David Cescon: University Health Network
Benjamin Haibe-Kains: University Health Network

Nature Communications, 2017, vol. 8, issue 1, 1-11

Abstract: Abstract 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.

Date: 2017
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DOI: 10.1038/s41467-017-01153-8

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