Human Papillomavirus-Associated Tumor Extracellular Vesicles in HPV+ Tumor Microenvironments
Bibliographic record
Abstract
Most infections with human papillomaviruses (HPVs) are self-resolving and asymptomatic. However, some infections can lead to the development of cancer at different mucosal sites, such as the cervix and the head and neck. Head and neck cancers (HNCs) are dichotomized into HPV-positive (HPV+) or HPV-negative (HPV−) based on their respective etiologies. Notably, the tumor microenvironment (TME) of the HPV+ subtype has an immune landscape characterized with increased immune infiltration, higher levels of T cell activation, and higher levels of immunoregulatory stimuli compared to their HPV− counterparts. Both enveloped and nonenveloped viruses hijack the extracellular vesicle (EV) biogenesis pathway to deploy a “trojan horse” strategy with a pseudoviral envelope to enhance infectivity and evade inflammation. EVs derived from HPV-infected tumor cells could allow for the stealth transport of viral cargo to neighboring nonmalignant cellular populations or infiltrating immune cells within the TME. Furthermore, viral cargo or altered cellular cargo from HPV-associated tumor EVs (HPV-TEVs) could alter the functional state or biological responses of the recipient cellular populations, which could shape the distinctive HPV+ TME. This review will cover the impact of EVs released from HPV-infected cells on HPV-induced carcinogenesis, their role in shaping the distinctive HPV+ tumor microenvironment, and current efforts to develop a painless EV-based liquid biopsy for HPV+ cancers.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".