Systematic identification of non-coding pharmacogenomic landscape in cancer

By Yue Wang, Zehua Wang, Jieni Xu, Jiang Li, Song Li, Da Yang. 2018

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Emerging evidence has shown long non-coding RNAs (lncRNAs) play important roles in cancer drug response. Here we report a lncRNA pharmacogenomic landscape by integrating multi-dimensional genomic data of 1005 cancer cell lines and drug response data of 265 anti-cancer compounds. Using Elastic Net (EN) regression, our analysis identifies 27,341 lncRNA-drug predictive pairs. We validate the robustness of the lncRNA EN-models using two independent cancer pharmacogenomic datasets. By applying lncRNA EN-models of 49 FDA approved drugs to the 5605 tumor samples from 21 cancer types, we show that cancer cell line based lncRNA EN-models can predict therapeutic outcome in cancer patients. Further lncRNA-pathway co-expression analysis suggests lncRNAs may regulate drug response through drug-metabolism or drug-target pathways. Finally, we experimentally validate that EPIC1, the top predictive lncRNA for the Bromodomain and Extra-Terminal motif (BET) inhibitors, strongly promotes iBET762 and JQ-1 resistance through activating MYC transcriptional activity. In this study the authors build lncRNA-drug response models for 265 anti-cancer agents across 27 cancer types. They report their cancer cell line based lncRNA EN-models are able to effectively predict therapeutic outcome for breast cancer, ovarian cancer, endometrial cancer and stomach cancer patients.

Subfields: Cancer Research, Molecular Biology