Systematic identification of non-coding pharmacogenomic landscape in cancer

Yue Wang, Zehua Wang, Jieni Xu, Jiang Li, Song Li, Min Zhang () and Da Yang ()
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Yue Wang: University of Pittsburgh
Zehua Wang: University of Pittsburgh
Jieni Xu: University of Pittsburgh
Jiang Li: University of Pittsburgh
Song Li: University of Pittsburgh
Min Zhang: University of Pittsburgh
Da Yang: University of Pittsburgh

Nature Communications, 2018, vol. 9, issue 1, 1-15

Abstract: Abstract 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.

Date: 2018
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DOI: 10.1038/s41467-018-05495-9

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