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Systematic identification of non-coding pharmacogenomic landscape in cancer

Author

Listed:
  • 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
    University of Pittsburgh
    University of Pittsburgh)

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.

Suggested Citation

  • Yue Wang & Zehua Wang & Jieni Xu & Jiang Li & Song Li & Min Zhang & Da Yang, 2018. "Systematic identification of non-coding pharmacogenomic landscape in cancer," Nature Communications, Nature, vol. 9(1), pages 1-15, December.
    • Handle: RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-018-05495-9
    DOI: 10.1038/s41467-018-05495-9
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