ABSTRACT
BACKGROUND: Viruses and other infectious agents cause more than 15% of human cancer cases. High-throughput sequencing-based studies of virus-cancer associations have mainly focused on cancer transcriptome data. METHODS: In this study, we applied a diverse selection of presequencing enrichment methods targeting all major viral groups, to characterize the viruses present in 197 samples from 18 sample types of cancerous origin. Using high-throughput sequencing, we generated 710 datasets constituting 57 billion sequencing reads. RESULTS: Detailed in silico investigation of the viral content, including exclusion of viral artefacts, from de novo assembled contigs and individual sequencing reads yielded a map of the viruses detected. Our data reveal a virome dominated by papillomaviruses, anelloviruses, herpesviruses, and parvoviruses. More than half of the included samples contained 1 or more viruses; however, no link between specific viruses and cancer types were found. CONCLUSIONS: Our study sheds light on viral presence in cancers and provides highly relevant virome data for future reference.
Subject(s)
High-Throughput Nucleotide Sequencing/methods , Metagenome/genetics , Neoplasms/virology , Anelloviridae/genetics , Anelloviridae/isolation & purification , Biopsy , Datasets as Topic , Female , Herpesviridae/genetics , Herpesviridae/isolation & purification , Humans , Male , Neoplasms/pathology , Papillomaviridae/genetics , Papillomaviridae/isolation & purification , Parvovirus/genetics , Parvovirus/isolation & purificationABSTRACT
Virus discovery from high throughput sequencing data often follows a bottom-up approach where taxonomic annotation takes place prior to association to disease. Albeit effective in some cases, the approach fails to detect novel pathogens and remote variants not present in reference databases. We have developed a species independent pipeline that utilises sequence clustering for the identification of nucleotide sequences that co-occur across multiple sequencing data instances. We applied the workflow to 686 sequencing libraries from 252 cancer samples of different cancer and tissue types, 32 non-template controls, and 24 test samples. Recurrent sequences were statistically associated to biological, methodological or technical features with the aim to identify novel pathogens or plausible contaminants that may associate to a particular kit or method. We provide examples of identified inhabitants of the healthy tissue flora as well as experimental contaminants. Unmapped sequences that co-occur with high statistical significance potentially represent the unknown sequence space where novel pathogens can be identified.
