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.
Highlights
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
Cell lines are the most widely used cancer models to study response of tumor cells to anticancer drugs. Have these cell lines recently been comprehensively profiled at the molecular level, but they have been used in highthroughput drug screening studies, such as the Genomics of Drug Sensitivity in Cancer (GDSC)[1] and the Cancer Cell Line Encyclopedia[2]
We identified a wide range of statistically significant biomarkers for each drug (10 to 1984 biomarkers with false discovery rate (FDR) 0.55; Fig. 2a; Supplementary Data 1), with a significantly larger proportion of isoform-based biomarkers are predictive of drug response (Wilcoxon-signed rank test p-value
Summary
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. The GDSC and CCLE investigators were able to confirm a number of established gene–drug associations, including association between ERBB2 amplification and sensitivity to lapatinib, and BCR/ABL fusion expression and nilotinib They found new associations such as SLFN11 expression and response to topoisomerase inhibitors, thereby supporting the relevance of cell-based high-throughput drug screening for biomarker discovery. Two other initiatives used RNA-seq to profile panels of 70 (GRAY3) and 84 (UHN21) breast cancer cell lines The availability of these valuable datasets offers unprecedented opportunities to further explore the transcriptomic features of cancer cells and study their association with drug response
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