Plasminogen Receptor S100A10 Is Essential for the Migration of Tumor-Promoting Macrophages into Tumor Sites
Bibliographic record
Abstract
Macrophages are critical drivers of tumor growth, invasion, and metastasis. Movement of macrophages into tumors requires the activity of cell surface proteases such as plasmin. In this study, we offer genetic evidence that plasminogen receptor S100A10 is essential for recruitment of macrophages to the tumor site. Growth of murine Lewis lung carcinomas or T241 fibrosarcomas was dramatically reduced in S100A10-deficient mice compared with wild-type mice. The tumor growth deficit corresponded with a decrease in macrophage density that could be rescued by intraperitoneal injection of wild-type but not S100A10-deficient macrophages. Notably, macrophages of either genotype could rescue tumor growth if they were injected into the tumor itself, establishing that S100A10 was required specifically for the migratory capability needed for tumor homing. Conversely, selective depletion of macrophages from wild-type mice phenocopied the tumor growth deficit seen in S100A10-deficient mice. Together, our findings show that S100A10 is essential and sufficient for macrophage migration to tumor sites, and they define a novel rate-limiting step in tumor progression.
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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 | 0.001 |
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".