Discovering long noncoding RNA predictors of anticancer drug sensitivity beyond protein-coding genes.

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ID: 52534
2019
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Ranked #55 of 292 articles by views in Proceedings of the National Academy of Sciences of the United States of America

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Abstract
Large-scale cancer cell line screens have identified thousands of protein-coding genes (PCGs) as biomarkers of anticancer drug response. However, systematic evaluation of long noncoding RNAs (lncRNAs) as pharmacogenomic biomarkers has so far proven challenging. Here, we study the contribution of lncRNAs as drug response predictors beyond spurious associations driven by correlations with proximal PCGs, tissue lineage, or established biomarkers. We show that, as a whole, the lncRNA transcriptome is equally potent as the PCG transcriptome at predicting response to hundreds of anticancer drugs. Analysis of individual lncRNAs transcripts associated with drug response reveals nearly half of the significant associations are in fact attributable to proximal -PCGs. However, adjusting for effects of PCGs revealed significant lncRNAs that augment drug response predictions for most drugs, including those with well-established clinical biomarkers. In addition, we identify lncRNA-specific somatic alterations associated with drug response by adopting a statistical approach to determine lncRNAs carrying somatic mutations that undergo positive selection in cancer cells. Lastly, we experimentally demonstrate that 2 lncRNAs, and are functionally relevant predictors of anti-epidermal growth factor receptor (EGFR) drug response.
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nath2019discoveringproceedings Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Nath, Aritro;Lau, Eunice Y T;Lee, Adam M;Geeleher, Paul;Cho, William C S;Huang, R Stephanie;
Journal Proceedings of the National Academy of Sciences of the United States of America
Year 2019
DOI
201909998
URL
Keywords
Machine learning pharmacogenomics drug response prediction long noncoding rna

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