CD271 Confers an Invasive and Metastatic Phenotype of Head and Neck Squamous Cell Carcinoma through the Upregulation of Slug
Bibliographic record
Abstract
Abstract Purpose: Head and neck squamous cell carcinoma (HNSCC) is comprised of heterogeneous populations of cells, and CD271 (NGFR; p75NTR) has been associated with a tumor-initiating cell subpopulation. This study assessed the role of CD271 in modulating metastatic behavior in HNSCC. Experimental Design: CD271 was overexpressed in murine and human oral squamous cell carcinoma cells to assess the impact of CD271 activation on the invasive and metastatic phenotype of these cells, using in vitro and orthotopic in vivo modeling. Treatment with human nerve growth factor (NGF) to activate CD271, as well as shRNA knockdown of the CD271-upregulated Snai2 expression, was used to assess the mechanism of the CD271-induced invasive phenotype. Relevance of CD271 expression in human HNSCC was evaluated in patient-derived xenografts (PDX) and primary human oral cancers, annotated with clinical behavior characteristics and survival data. Results: Forced expression of CD271 resulted in a more invasive and metastatic phenotype. Slug, an epithelial-to-mesenchymal transition (EMT)-related transcription factor, encoded by Snai2, was highly expressed in MOC2-CD271 and HSC3-CD271, compared with respective parental cells. CD271 activation by NGF conferred enhanced invasiveness in CD271-overexpressing cells, which was abrogated by Snai2 knockdown. In PDXs and primary human HNSCC, CD271 expression correlated with higher Snai2 expression, greater nodal metastasis, and shorter disease-free survival. Conclusions: Activation of CD271 results in upregulation of Snai2/Slug, which, in turn, results in a more invasive phenotype and an enhanced capacity for metastasis to regional lymph nodes. These findings point to CD271 as a promising, therapeutic target for oral cancer metastasis. Clin Cancer Res; 24(3); 674–83. ©2017 AACR.
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 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".