Pre-Clinical Characterization of Dacomitinib (PF-00299804), an Irreversible Pan-ErbB Inhibitor, Combined with Ionizing Radiation for Head and Neck Squamous Cell Carcinoma
Bibliographic record
Abstract
Epidermal growth factor receptor (EGFR) is over-expressed in nearly all cases of squamous cell carcinoma of the head and neck (SCCHN), and is an important driver of disease progression. EGFR targeted therapies have demonstrated clinical benefit for SCCHN treatment. In this report, we investigated the pre-clinical efficacy of Dacomitinib (PF-00299804), an irreversible pan-ErbB inhibitor, both alone and in combination with ionizing radiation (IR), a primary curative modality for SCCHN. One normal oral epithelial (NOE) and three SCCHN (FaDu, UT-SCC-8, UT-SCC-42a) cell lines were used to conduct cell viability, clonogenic survival, cell cycle, and immunoblotting assays in vitro, using increasing doses of Dacomitinib (10-500 nM), both with and without IR (2-4 Gy). The FaDu xenograft model was utilized for tumor growth delay assays in vivo, and immunohistochemical analyses were conducted on extracted tumors. A dose-dependent reduction in cell viability and clonogenic survival after Dacomitinib treatment was observed in all three SCCHN models. Treatment led to a significant reduction in EGFR signalling, with a subsequent decrease in phosphorylation of downstream targets such as ERK, AKT, and mTOR. In vivo, Dacomitinib treatment delayed tumor growth, while decreasing phospho-EGFR and Ki-67 immunoexpression. These effects were further enhanced when combined with IR, both in vitro and in vivo. The preclinical data support the further evaluations of Dacomitinib combined with IR for the future management of patients with SCCHN.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".