JUNB suppresses distant metastasis by influencing the initial metastatic stage
Bibliographic record
Abstract
The complex interactions between cells of the tumor microenvironment and cancer cells are considered a major determinant of cancer progression and metastasis. Yet, our understanding of the mechanisms of metastatic disease is not sufficient to successfully treat patients with advanced-stage cancer. JUNB is a member of the AP-1 transcription factor family shown to be frequently deregulated in human cancer and associated with invasion and metastasis. A strikingly high stromal JUNB expression in human breast cancer samples prompted us to functionally investigate the consequences of JUNB loss in cells of the tumor microenvironment on cancer progression and metastasis in mice. To adequately mimic the clinical situation, we applied a syngeneic spontaneous breast cancer metastasis model followed by primary tumor resection and identified stromal JUNB as a potent suppressor of distant metastasis. Comprehensive characterization of the JUNB-deficient tumor microenvironment revealed a strong influx of myeloid cells into primary breast tumors and lungs at early metastatic stage. In these infiltrating neutrophils, BV8 and MMP9, proteins promoting angiogenesis and tissue remodeling, were specifically upregulated in a JUNB-dependent manner. Taken together, we established stromal JUNB as a strong suppressor of distant metastasis. Consequently, therapeutic strategies targeting AP-1 should be carefully designed not to interfere with stromal JUNB expression as this may be detrimental for cancer patients.
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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".