Molecular targeted therapies in all histologies of head and neck cancers: an update
Bibliographic record
Abstract
PURPOSE OF REVIEW: This article reviewed the recent developments in molecular targeted therapy in head and neck cancers. A brief summary of other pathways of interest is also enclosed. RECENT FINDINGS: The use of cetuximab in squamous cell head and neck cancer is associated with clinical benefit and, in some cases, survival. However, the use of targeted agents beyond cetuximab in this disease remains investigational. Combination therapy of molecular targeted agents with chemoradiation in the locally advanced setting of head and neck squamous cell carcinomas and nasopharyngeal cancer shows early promising results, but at the expense of increased toxicity. In malignant salivary gland tumors, the evaluation of targeted therapy has been disappointing. New therapeutic targets warrant further evaluation in these cancers. SUMMARY: Despite the encouraging results achieved with antiepidermal growth factor receptor therapy, particularly with cetuximab, targeted therapy trials conducted in head and neck cancers to date have largely lacked efficacy or are associated with significant toxicity. Further research into modulation of other aberrant pathways is needed. The recent identification of improved prognosis among head and neck squamous cell carcinoma patients whose tumors harbor the human papilloma virus may allow better treatment selection for these patients, while the identification of a hallmark gene fusion transcript in adenocystic carcinoma may herald new treatment promise.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".