VEGF-C expression in TAMs rewires the metastatic fate of breast cancer cells
Bibliographic record
Abstract
Summary Expression of pro-lymphangiogenic vascular endothelial growth factor C (VEGF-C) in primary tumors correlates with the occurrence of proximal lymph node metastasis in most solid cancer types. However, the role of VEGF-C in regulating tumor cell dissemination to distant organs is currently unclear. Perivascular tumor-associated macrophages (TAMs) are key regulators of hematogenous cancer cell spreading, forming tumor microenvironment of metastasis (TMEM) doorways for breast cancer cells to intravasate tumor blood vessels and fuel distant metastases. Using an experimental breast cancer (BC) model, we show here that TAMs expressing VEGF-C decrease cancer cell dissemination to the lung while enhancing lymph node metastasis. These TAMs express podoplanin and associate with normalized tumor blood vessels expressing VEGFR3. Further clinical data reveal that VEGF-C + TAMs correlate inversely with malignant grade and with the occurrence of TMEM complexes in a cohort of BC patients. Thus, our study displays an apparently paradoxical role of VEGF-C expressing TAMs in redirecting cancer cells to preferentially disseminate to the lymph nodes, at least in part, by normalizing tumor blood vessels and promoting lymphangiogenesis.
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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".