The Drivers, Mechanisms, and Consequences of Genome Instability in HPV-Driven Cancers
Bibliographic record
Abstract
Human papillomavirus (HPV) is the causative driver of cervical cancer and a contributing risk factor of head and neck cancer and several anogenital cancers. HPV's ability to induce genome instability contributes to its oncogenicity. HPV genes can induce genome instability in several ways, including modulating the cell cycle to favour proliferation, interacting with DNA damage repair pathways to bring high-fidelity repair pathways to viral episomes and away from the host genome, inducing DNA-damaging oxidative stress, and altering the length of telomeres. In addition, the presence of a chronic viral infection can lead to immune responses that also cause genome instability of the infected tissue. The HPV genome can become integrated into the host genome during HPV-induced tumorigenesis. Viral integration requires double-stranded breaks on the DNA; therefore, regions around the integration event are prone to structural alterations and themselves are targets of genome instability. In this review, we present the mechanisms by which HPV-dependent and -independent genome instability is initiated and maintained in HPV-driven cancers, both across the genome and at regions of HPV integration.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".