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

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 and Noam Auslander ()
Additional contact information
Abdurrahman Elbasir: The Wistar Institute
Ying Ye: The Wistar Institute
Daniel E. Schäffer: The Wistar Institute
Xue Hao: The Wistar Institute
Jayamanna Wickramasinghe: The Wistar Institute
Konstantinos Tsingas: The Wistar Institute
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

Nature Communications, 2023, vol. 14, issue 1, 1-12

Abstract: 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.

Date: 2023
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DOI: 10.1038/s41467-023-36336-z

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