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

Aritro Nath, Eunice Y.T. Lau, Adam M. Lee, Paul Geeleher, William C.S. Cho, R. Stephanie Huang

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


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 cis-PCGs. However, adjusting for effects of cis-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, EGFR-AS1 and MIR205HG, are functionally relevant predictors of anti-epidermal growth factor receptor (EGFR) drug response.

Original languageEnglish (US)
Pages (from-to)22020-22029
Number of pages10
JournalProceedings of the National Academy of Sciences of the United States of America
Issue number44
StatePublished - Oct 29 2019

Bibliographical note

Funding Information:
ACKNOWLEDGMENTS. This study was supported by an NIH/National Cancer Institute (NCI) Grant 1R01CA204856-01A1. R.S.H. also receives support from a research grant from the Avon Foundation for Women.


  • Drug response prediction
  • Long noncoding RNA
  • Machine learning
  • Pharmacogenomics

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