Selective stimulation of mast cells with a TLR2 agonist inhibits tumor growth <i>in vivo</i>
Bibliographic record
Abstract
Mast cells are potent effector cells abundant at the growing edge of solid tumors, where they can enhance angiogenesis. Mast cells stimulated with the Toll‐like receptor‐2 (TLR2) activator Pam 3 CSK 4 lipopeptide (LP) secrete angiostatic chemokines. We hypothesized that LP‐stimulated mast cells would have anti‐tumor potential. LP‐activated bone marrow derived mast cellls (BMMC) significantly inhibited the growth of B16.F10 melanoma (n=10, P=0.0195 ) and LLC1 Lewis lung carcinoma (n=9, P=0.0078 ), in Matrigel plugs in vivo . The presence of BMMC alone had no significant effect on tumour growth. Tumors containing LP‐activated BMMC had reduced blood vessel frequency (B16.F10, n=7, P<0.05; LLC1, n=7, P =0.08) and blood volume (LLC1, n=6, P<0.05 ), correlating with tumor weight (r=0.48, P =0.02). In vitro studies demonstrated this anti‐tumor effect was not attributable to BMMC‐mediated tumor cytotoxicity. Dependence on mast cell expressed TLR2 was confirmed by the lack of LLC1 or B16.F10 tumor inhibition in C57Bl/6 mice using LP and BMMC derived from TLR2 knock‐out mice. Protein array analyses identified several candidate mediators that may contribute to the anti‐tumor effect of LP‐activated BMMC. These findings suggest a novel role for mast cells as intermediaries for anti‐cancer immunotherapies Supported by National Cancer Institute of Canada. SAO is supported by the Cancer Research Training Program.
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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 | 0.001 |
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".