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A deep learning approach reveals unexplored landscape of viral expression in cancer

Author

Listed:
  • Abdurrahman Elbasir

    (The Wistar Institute)

  • Ying Ye

    (The Wistar Institute)

  • Daniel E. Schäffer

    (The Wistar Institute
    Carnegie Mellon University)

  • Xue Hao

    (The Wistar Institute)

  • Jayamanna Wickramasinghe

    (The Wistar Institute)

  • Konstantinos Tsingas

    (The Wistar Institute
    University of Pennsylvania)

  • Paul M. Lieberman

    (The Wistar Institute)

  • Qi Long

    (University of Pennsylvania)

  • Quaid Morris

    (Sloan Kettering Institute)

  • Rugang Zhang

    (The Wistar Institute)

  • Alejandro A. Schäffer

    (National Cancer Institute, National Institutes of Health)

  • Noam Auslander

    (The Wistar Institute)

Abstract

About 15% of human cancer cases are attributed to viral infections. To date, virus expression in tumor tissues has been mostly studied by aligning tumor RNA sequencing reads to databases of known viruses. To allow identification of divergent viruses and rapid characterization of the tumor virome, we develop viRNAtrap, an alignment-free pipeline to identify viral reads and assemble viral contigs. We utilize viRNAtrap, which is based on a deep learning model trained to discriminate viral RNAseq reads, to explore viral expression in cancers and apply it to 14 cancer types from The Cancer Genome Atlas (TCGA). Using viRNAtrap, we uncover expression of unexpected and divergent viruses that have not previously been implicated in cancer and disclose human endogenous viruses whose expression is associated with poor overall survival. The viRNAtrap pipeline provides a way forward to study viral infections associated with different clinical conditions.

Suggested Citation

  • Abdurrahman Elbasir & Ying Ye & Daniel E. Schäffer & Xue Hao & Jayamanna Wickramasinghe & Konstantinos Tsingas & Paul M. Lieberman & Qi Long & Quaid Morris & Rugang Zhang & Alejandro A. Schäffer & Noa, 2023. "A deep learning approach reveals unexplored landscape of viral expression in cancer," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-36336-z
    DOI: 10.1038/s41467-023-36336-z
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    2. Jordi Barretina & Giordano Caponigro & Nicolas Stransky & Kavitha Venkatesan & Adam A. Margolin & Sungjoon Kim & Christopher J. Wilson & Joseph Lehár & Gregory V. Kryukov & Dmitriy Sonkin & Anupama Re, 2012. "The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity," Nature, Nature, vol. 483(7391), pages 603-607, March.
    3. Ardi Tampuu & Zurab Bzhalava & Joakim Dillner & Raul Vicente, 2019. "ViraMiner: Deep learning on raw DNA sequences for identifying viral genomes in human samples," PLOS ONE, Public Library of Science, vol. 14(9), pages 1-17, September.
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    Cited by:

    1. Z. L. Liu & X. Y. Meng & R. J. Bao & M. Y. Shen & J. J. Sun & W. D. Chen & F. Liu & Y. He, 2024. "Single cell deciphering of progression trajectories of the tumor ecosystem in head and neck cancer," Nature Communications, Nature, vol. 15(1), pages 1-18, December.

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