Loss of p53 Expression Correlates with Metastatic Phenotype and Transcriptional Profile in a New Mouse Model of Head and Neck Cancer
Bibliographic record
Abstract
Squamous cell carcinoma of the head and neck (HNSCC) is the sixth most frequent cancer worldwide. Because HNSCC is largely acquired by environmental carcinogen exposure rather than through germ line mutations, there are no known familial forms of the disease in humans nor are there inbred rodent strains prone to spontaneous head and neck tumors. Transgenic animals with inactivation of tumor suppressor genes commonly mutated in human cases of HNSCC provide attractive models for studying the pathogenesis of head and neck cancer. p53 is the most frequently inactivated tumor suppressor gene in HNSCC. We used a chemical induction protocol in mice heterozygous for the p53 gene to evaluate how p53 inactivation contributed to head and neck carcinogenesis the mouse model. Metastatic squamous cell carcinomas developed in 100% of animals. Histopathologically, the tumors ranged from well to poorly differentiated and showed many molecular features of human HNSCC. Mice carrying only one p53 allele developed tumors with significantly reduced latency compared with wild-type controls (average, 18 versus 22 weeks). Metastatic cancer cells showed complete loss of p53 expression when compared with primary tumors. Transcriptional profiling showed not only distinct genetic differences between primary and metastatic tumors, but also when cancers from heterozygous null and wild-type animals were compared. Our results provide novel insights into the molecular genetics of tumor progression in head and neck cancer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".