Cetuximab-Containing Combinations in Locally Advanced and Recurrent or Metastatic Head and Neck Squamous Cell Carcinoma
Bibliographic record
Abstract
Cetuximab remains to date the only targeted therapy approved for the treatment of head and neck squamous cell carcinoma (HNSCC). The EGFR pathway plays a key role in the tumorigenesis and progression of this disease as well as in the resistance to radiotherapy (RT). While several anti-EGFR agents have been tested in HNSCC, cetuximab, an IgG1 subclass monoclonal antibody against EGFR, is the only drug with proven efficacy for the treatment of both locoregionally-advanced (LA) and recurrent/metastatic (R/M) disease. The addition of cetuximab to radiotherapy is a validated treatment option in LA-HNSCC. However, its use has been limited to patients who are considered unfit for standard of care chemoradiotherapy (CRT) with single agent cisplatin given the lack of direct comparison of these two regimens in randomized phase III trials and the inferiority suggested by metanalysis and phase II studies. The current use of cetuximab in HNSCC is about to change given the recent results from randomized prospective clinical trials in both the LA and R/M setting. Two phase III studies evaluating RT-cetuximab vs. CRT in Human Papillomavirus (HPV)-positive LA oropharyngeal squamous cell carcinoma (De-ESCALaTE and RTOG 1016) showed inferior overall survival and progression-free survival for RT-cetuximab combination, and therefore CRT with cisplatin remains the standard of care in this disease. In the R/M HNSCC, the EXTREME regimen has been the standard of care as first-line treatment for the past 10 years. However, the results from the KEYNOTE-048 study will likely position the anti-PD-1 agent pembrolizumab as the new first line treatment either alone or in combination with chemotherapy in this setting based on PD-L1 status. Interestingly, cetuximab-mediated immunogenicity through antibody dependent cell cytotoxicity (ADCC) has encouraged the evaluation of combined approaches with immune-checkpoint inhibitors in both LA and R/M-HNSCC settings. This article reviews the accumulated evidence on the role of cetuximab in HNSCC in the past decade, offering an overview of its current impact in the treatment of LA and R/M-HNSCC disease and its potential use in the era of immunotherapy.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".