Sensitive detection of viral transcripts in human tumor transcriptomes.

Area of Science:

• Oncology
• Virology
• Bioinformatics

Background:

• Over 12% of human cancers involve a viral cofactor, yet many idiopathic cancers may have undiscovered viral etiologies.
• Neuroblastoma, a common infant cancer, has clinical and epidemiological features suggesting a potential viral cofactor, though this remains debated.

Purpose of the Study:

• To develop and apply a novel computational approach for identifying viral transcripts in human cancers, addressing challenges like low viral abundance and sequence divergence.
• To conduct the first systematic search for known and novel viruses in neuroblastoma using this advanced computational method.

Main Methods:

• Developed a sensitive computational approach to detect viral transcripts in human tumor transcriptomes, accounting for technical challenges.
• Mapped 14 neuroblastoma transcriptomes against human and viral genomes, including positive and negative controls, to identify viral sequences.
• Utilized de-novo assembly and chimeric transcript analysis to detect potential novel viral pathogens.

Main Results:

• The computational approach demonstrated high sensitivity in detecting viral transcripts.
• No evidence of known human viruses was found in the analyzed neuroblastoma samples.
• No expression signatures indicative of novel human viral pathogens were identified.

Conclusions:

• The study's sensitive methodology and analysis of multiple replicates suggest that frequent viral cofactors are unlikely in metastatic neuroblastoma.
• While confounding factors could potentially mask viral presence, the findings challenge the hypothesis of a common viral etiology for this cancer.