Therapeutic Advances and Challenges for the Management of HPV-Associated Oropharyngeal Cancer
Bibliographic record
Abstract
The use of conventional chemotherapy in conjunction with targeted and immunotherapy drugs has emerged as an option to limit the severity of side effects in patients diagnosed with head and neck cancer (HNC), particularly oropharyngeal cancer (OPC). OPC prevalence has increased exponentially in the past 30 years due to the prevalence of human papillomavirus (HPV) infection. This study reports a comprehensive review of clinical trials registered in public databases and reported in the literature (PubMed/Medline, Scopus, and ISI web of science databases). Of the 55 clinical trials identified, the majority (83.3%) were conducted after 2015, of which 77.7% were performed in the United States alone. Eight drugs have been approved by the FDA for HNC, including both generic and commercial forms: bleomycin sulfate, cetuximab (Erbitux), docetaxel (Taxotere), hydroxyurea (Hydrea), pembrolizumab (Keytruda), loqtorzi (Toripalimab-tpzi), methotrexate sodium (Trexall), and nivolumab (Opdivo). The most common drugs to treat HPV-associated OPC under these clinical trials and implemented as well for HPV-negative HNC include cisplatin, nivolumab, cetuximab, paclitaxel, pembrolizumab, 5-fluorouracil, and docetaxel. Few studies have highlighted the necessity for new drugs specifically tailored to patients with HPV-associated OPC, where molecular mechanisms and clinical prognosis are distinct from HPV-negative tumors. In this context, we identified most mutated genes found in HPV-associated OPC that can represent potential targets for drug development. These include TP53, PIK3CA, PTEN, NOTCH1, RB1, FAT1, FBXW7, HRAS, KRAS, and CDKN2A.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".