FES Kinase Promotes Mast Cell Recruitment to Mammary Tumors via the Stem Cell Factor/KIT Receptor Signaling Axis
Bibliographic record
Abstract
KIT receptor is required for mast cell development, survival, and migration toward its ligand stem cell factor (SCF). Many solid tumors express SCF and this leads to mast cell recruitment to tumors and release of mediators linked to tumor angiogenesis, growth, and metastasis. Here, we investigate whether FES protein-tyrosine kinase, a downstream effector of KIT signaling in mast cells, is required for migration of mast cells toward SCF-expressing mammary tumors. Using a novel agarose drop assay for chemotaxis of bone marrow-derived mast cells (BMMC) toward SCF, we found that defects in chemotaxis of fes-null BMMCs correlated with disorganized microtubule networks in polarized cells. FES displayed partial colocalization with microtubules in polarized BMMCs and has at least two direct microtubule binding sites within its N-terminal F-BAR and SH2 domains. An oligomerization-disrupting mutation within the Fer/CIP4 homology-Bin/Amphiphysin/Rvs (F-BAR) domain had no effect on microtubule binding, whereas microtubule binding to the SH2 domain was dependent on the phosphotyrosine-binding pocket. FES involvement in mast cell recruitment to tumors was tested using the AC2M2 mouse mammary carcinoma model. These tumor cells expressed SCF and promoted BMMC recruitment in a KIT- and FES-dependent manner. Engraftment of AC2M2 orthotopic and subcutaneous tumors in control or fes-null mice, revealed a key role for FES in recruitment of mast cells to the tumor periphery. This may contribute to the reduced tumor growth and metastases observed in fes-null mice compared with control mice. Taken together, FES is a potential therapeutic target to limit the progression of tumors with stromal mast cell involvement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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; both teacher heads agree on what is shown here.
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".