Implications and Emerging Therapeutic Avenues of Inflammatory Response in HPV+ Head and Neck Squamous Cell Carcinoma
Bibliographic record
Abstract
Head and neck squamous cell carcinomas (HNSCC) are a heterogeneous group of malignancies which have shown exponential incidence in the last two decades especially due to human papillomavirus (HPV) infection. The HPV family comprises more than 100 types of viruses with HPV16 and HPV18 being the most prevalent strains in HNSCC. Literature data reveal that the mutation profile as well as the response to chemotherapy and radiotherapy are distinct among HPV+ versus HPV-negative tumors. Furthermore, the presence of the virus induces activation of an immune response, in particular the recruitment of specific antiviral T lymphocytes to tumor sites. These T cells when activated produce soluble factors including cytokines and chemokines capable of modifying the local immune tumor microenvironment and impact on tumor response to the treatment. In this comprehensive review we investigated current knowledge on how the presence of an HPV can modify the inflammatory response systemically and within the tumor microenvironment's immunological responses, thereby impacting on disease prognosis and survival. We highlighted the research gaps and emerging approaches necessary to discover novel immunotherapeutic targets for HPV-associated HNSCC.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".