ΔNp63γ/SRC/Slug Signaling Axis Promotes Epithelial-to-Mesenchymal Transition in Squamous Cancers
Bibliographic record
Abstract
Abstract Purpose: To investigate the regulation of epithelial-to-mesenchymal transition (EMT) in head and neck squamous cell carcinoma (HNSCC) and its importance in tumor invasion. Experimental Design: We use a three-dimensional invasive organotypic raft culture model of human foreskin keratinocytes expressing the E6/E7 genes of the human papilloma virus-16, coupled with bioinformatic and IHC analysis of patient samples to investigate the role played by EMT in invasion and identify effectors and upstream regulatory pathways. Results: We identify SNAI2 (Slug) as a critical effector of EMT-activated downstream of TP63 overexpression in HNSCC. Splice-form–specific depletion and rescue experiments further identify the ΔNp63γ isoform as both necessary and sufficient to activate the SRC signaling axis and SNAI2-mediated EMT and invasion. Moreover, elevated SRC levels are associated with poor outcome in patients with HNSCC in The Cancer Genome Atlas dataset. Importantly, the effects on EMT and invasions and SNAI2 expression can be reversed by genetic or pharmacologic inhibition of SRC. Conclusions: Overexpression of ΔNp63γ modulates cell invasion by inducing targetable SRC-Slug–evoked EMT in HNSCC, which can be reversed by inhibitors of the SRC signaling. Clin Cancer Res; 24(16); 3917–27. ©2018 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 | 0.000 |
| 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".