STAT3 as a Chemoprevention Target in Carcinogen-Induced Head and Neck Squamous Cell Carcinoma
Bibliographic record
Abstract
Head and neck squamous cell carcinoma (HNSCC) is a frequently fatal disease due, in large part, to a high rate of second primary tumor (SPT) formation. The 4-nitroquinoline 1-oxide (4-NQO) mouse model of oral carcinogenesis provides a robust system in which to study chemopreventive agents in the context of chemically induced HNSCC tumors. STAT3 is a potent oncogene that is hyperactivated by tyrosine phosphorylation early in HNSCC carcinogenesis and is a rational therapeutic target. We recently reported that loss-of-function of the STAT3 phosphatase PTPRT promotes STAT3 activation in HNSCC tumors and preclinical models and may serve as a predictive biomarker of response to STAT3 inhibitors, including the small-molecule Stattic. We therefore investigated the hypothesis that Ptprt-knockout (KO) mice would be more susceptible to 4-NQO-induced oral carcinogenesis and more sensitive to Stattic-mediated chemoprevention compared with wild-type (WT) mice. Herein, we demonstrate that Ptprt WT and KO mice develop similar spectra of HNSCC disease severity upon 12 weeks of 4-NQO administration, with no apparent effect of Ptprt genotype on carcinogenesis or treatment outcome. Targeting of STAT3 with Stattic resulted in a chemopreventive effect against 4-NQO-induced oral cancer (P = 0.0402). While these results do not support a central role for PTPRT in 4-NQO-induced HNSCC carcinogenesis, further investigation of STAT3 as a chemoprevention target in this cancer is warranted. Cancer Prev Res; 9(8); 657-63. ©2016 AACR.
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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) | 0.001 | 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".