Pancreatic Tumor Microenvironment Modulation by EphB4-ephrinB2 Inhibition and Radiation Combination
Bibliographic record
Abstract
Abstract Purpose: A driving factor in pancreatic ductal adenocarcinoma (PDAC) treatment resistance is the tumor microenvironment, which is highly immunosuppressive. One potent immunologic adjuvant is radiotherapy. Radiation, however, has also been shown to induce immunosuppressive factors, which can contribute to tumor progression and formation of fibrotic tumor stroma. To capitalize on the immunogenic effects of radiation and obtain a durable tumor response, radiation must be rationally combined with targeted therapies to mitigate the influx of immunosuppressive cells and fibrosis. One such target is ephrinB2, which is overexpressed in PDAC and correlates negatively with prognosis. Experimental Design: On the basis of previous studies of ephrinB2 ligand-EphB4 receptor signaling, we hypothesized that inhibition of ephrinB2-EphB4 combined with radiation can regulate the microenvironment response postradiation, leading to increased tumor control in PDAC. This hypothesis was explored using both cell lines and in vivo human and mouse tumor models. Results: Our data show this treatment regimen significantly reduces regulatory T-cell, macrophage, and neutrophil infiltration and stromal fibrosis, enhances effector T-cell activation, and decreases tumor growth. Furthermore, our data show that depletion of regulatory T cells in combination with radiation reduces tumor growth and fibrosis. Conclusions: These are the first findings to suggest that in PDAC, ephrinB2–EphB4 interaction has a profibrotic, protumorigenic role, presenting a novel and promising therapeutic target.
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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) | 0.002 | 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".