Molecular-targeted therapies in the treatment of squamous cell carcinomas of the head and neck
Bibliographic record
Abstract
PURPOSE OF REVIEW: The present study reviews recent developments of molecular-targeted therapies in the treatment of recurrent and/or metastatic head and neck squamous cell carcinoma. It also highlights ongoing research regarding predictive markers of sensitivity or resistance to anti-epidermal growth factor receptor agents and discusses some promising novel targets in head and neck squamous cell carcinoma, as well as clinical trial design challenges. RECENT FINDINGS: Phase III randomized studies have brought the proof that cetuximab, an anti-epidermal growth factor receptor agent, is able to improve survival, either in combination with radiation therapy or in first-line treatment for recurrent and/or metastatic head and neck squamous cell carcinoma. In addition, promising results have been obtained with antiangiogenic therapies in phase II trials. Some clinical and molecular markers of resistance to anti-epidermal growth factor receptor agents have been identified, but they have not yet been validated for clinical practice. Other interesting targets, such as insulin-like growth factor 1R or the PI3K/AKT/mTOR pathway, have been shown in vitro to play key roles in head and neck squamous cell carcinoma, and their inhibition warrants further evaluations. SUMMARY: Proof of the concept that molecular-targeted therapy is a valid therapeutic approach for head and neck squamous cell carcinoma has emerged with anti-epidermal growth factor receptor agents. Nevertheless, identification of predictive biomarkers of resistance or sensitivity to these therapies remains the main challenge in the optimal selection of patients most likely to benefit from them.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".