Abstract 562: Oncovirus detection and integration analysis from human tumor samples using targeted massively parallel sequencing
Authors: Burns Robert T. , Hunter Samuel S. , Ducar Matthew D. , Thorner Aaron R. , Decaprio James A. , Hummelen Paul Van , Frieden Alexander , Nag Anwesha , Coleman Haley A. , Slevin Michael K. , Clapp Andrea , Drinan Samantha D. , McShane Suzanne R. , Thai Edwin , Shivdasani Priyanka , Bohannon Joshua , Hoeftberger Johann , Jacobs Reuben , Wollison Bruce M. , Patel Neil A. , Manam Monica D. , Davineni Phani , Meyerson Matthew , MacConaill Laura E. 
Published: July 2017

Viruses are a major contributor to oncogenesis, causing 10-15% of human cancers. Molecular pathways involved in malignant transformation are frequently activated by genetic alterations, including but not limited to, somatic mutations, copy number aberrations, structural variants, and oncoviruses. Precision cancer medicine aims to classify tumors by site, histology, and molecular tests to determine an “individualized” profile of cancer alterations. However, clinical tests for these various alterations are sequential, time consuming, and use a lot of material, which is often quite limited (e.g., biopsies). Moreover, tests for the presence of viral sequence are generally performed separately to tests (such as massively parallel sequencing) to detect human genomic alterations. Here we present a hybrid capture and massively parallel sequencing approach to detect viral infection concurrently with targeted genomic analysis, which may decrease assay costs, increase sensitivity and scalability, and detect many types of alterations, thereby providing a more complete tumor genetic profile all from a single sample.

We have created a custom hybrid capture probeset for targeted Illumina sequencing to determine whether oncoviruses are present in tissue samples and also determine if the virus has integrated into the host’s genome. We have created both ‘detection’ and ‘integration’ baits for several oncoviruses, including polyomaviruses, human papilloma viruses, Epstein-Barr virus, human cytomegalovirus, Kaposi sarcoma herpesvirus, human T-lymphotropic virus, and hepatitis B virus. To distinguish between different strains of a single virus, strain-specific detection baits were created to bind to variable regions of viral genomes. The integration bait was designed to bind to regions of the viral genomes that are commonly integrated into the human genome. This baitset can also be combined with other capture panels targeting oncogenes to simultaneously determine infection and integration statuses, as well as somatic mutations, copy number and structural variants.

To detect virus presence, reads were aligned to a hybrid reference of both the human, and targeted virus genomes. Viral integration status and integration loci were determined by leveraging discordant read pairs that aligned to both the human genome and a viral genome. We have tested our techniques on tissue samples that were infected with either Merkel Cell Polyomavirus or Epstein-Barr virus, as determined using quantitative polymerase chain reaction (qPCR) or immunohistochemistry (IHC) techniques, and have successfully detected these viruses and identified viral integration loci. Overall, this viral hybrid capture probeset provides the ability to simultaneously determine a tissue sample’s infection and viral integration status alongside other somatic genomic analyses, saving both time and sample material.

Citation Format: Robert T. Burns, Samuel S. Hunter, Matthew D. Ducar, Aaron R. Thorner, James A. Decaprio, Paul Van Hummelen, Alexander Frieden, Anwesha Nag, Haley A. Coleman, Michael K. Slevin, Andrea Clapp, Samantha D. Drinan, Suzanne R. McShane, Edwin Thai, Priyanka Shivdasani, Joshua Bohannon, Johann Hoeftberger, Reuben Jacobs, Bruce M. Wollison, Neil A. Patel, Monica D. Manam, Phani Davineni, Matthew Meyerson, Laura E. MacConaill. Oncovirus detection and integration analysis from human tumor samples using targeted massively parallel sequencing [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 562. doi:10.1158/1538-7445.AM2017-562