Human papillomavirus type 16 sub-lineages and integration in cancer
Bibliographic record
Abstract
ABSTRACT Our lab has been intrigued by the fact that viral genomes often take on the role of mobile elements to perpetuate their existence in a complex organism’s genome. Multiple DNA viruses such as Epstein-Barr virus, hepatitis B virus, and human papillomavirus (HPV) can invade their host genome, as “genomic parasites”. We have been investigating HPV type 16 (HPV16), which is a prominent human tumour virus. In our recent in vitro work using 3D organoids, a common variant of HPV16’s coding region elicited early integration into the host genome compared to the HPV16 prototype sequence. Next-generation sequencing (NGS) data confirmed a transcriptomic profile of increased proliferation and chromosomal instability—both hallmarks of cancer. Epidemiologically, this variant is associated with a high cervical cancer incidence. To take inquiries a step further, we investigated variant-specific integration across HPV16-related cancers using NGS data from population-derived clinical samples in The Cancer Genome Atlas (TCGA)-curated database. Data were analyzed for HPV16 positivity, sub-lineage, and viral-host integration using a bioinformatic pipeline of open-source tools, including HPVDetector. Here, we report the analysis of 120 cervical cancer cases comprising HPV16 positive and negative samples as well as their different sub-lineage and integration status. The integration signature between variant and prototype did not differ quantitatively but qualitatively: that of the variant being related to hypoxia/energetics (Warburg effect) and that of the prototype being much more varied to include host immune abrogation and cancer pathways activation. We conclude by discussing challenges and future directions for expanding these analyses.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".