EphB4 and ephrinB2 act in opposition in the head and neck tumor microenvironment
Bibliographic record
Abstract
Differential outcomes of EphB4-ephrinB2 signaling offers formidable challenge for the development of cancer therapeutics. Here, we interrogate the effects of targeting EphB4 and ephrinB2 in head and neck squamous cell carcinoma (HNSCC) and within its microenvironment using genetically engineered mice, recombinant constructs, pharmacologic agonists and antagonists. We observe that manipulating the EphB4 intracellular domain on cancer cells accelerates tumor growth and angiogenesis. EphB4 cancer cell loss also triggers compensatory upregulation of EphA4 and T regulatory cells (Tregs) influx and their targeting results in reversal of accelerated tumor growth mediated by EphB4 knockdown. EphrinB2 knockout on cancer cells and vasculature, on the other hand, results in maximal tumor reduction and vascular normalization. We report that EphB4 agonism provides no additional anti-tumoral benefit in the absence of ephrinB2. These results identify ephrinB2 as a tumor promoter and its receptor, EphB4, as a tumor suppressor in HNSCC, presenting opportunities for rational drug design.
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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) | 0.001 | 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".