Neutrophil TLR4 and PKR are targets of breast cancer cell glycosaminoglycans and effectors of glycosaminoglycan-induced APRIL secretion
Bibliographic record
Abstract
A proliferation-inducing ligand (APRIL), which induces survival and migration signals and tumor growth, is commonly observed in breast cancer tissues but is not often expressed in breast cancer cells themselves. Here, we examined whether breast cancer cells induce APRIL secretion from neutrophils, which are frequently recruited into the breast tumor microenvironment. We found that breast cancer cells do stimulate neutrophils to secrete APRIL through their glycosaminoglycans. Breast cancer cells depleted of heparan sulfate or chondroitin sulfate glycosaminoglycans lose their ability to induce APRIL secretion from neutrophils, and heparan sulfate and chondroitin sulfate can induce secretion that is comparable to that of breast cancer cell-induced secretion. While stimulation of the RNA-activated protein kinase (PKR) is sufficient to induce neutrophil APRIL secretion, both PKR and the toll-like receptor 4 (TLR4) are required for breast cancer cell glycosaminoglycan-induced secretion as separate and specific inhibition of TLR4 or PKR completely prevents the process, suggesting that breast cancer cell glycosaminoglycans target neutrophil TLR4 and PKR to trigger APRIL secretion. Thus, apart from the putative role of cell surface heparan sulfate in binding APRIL that leads to cell growth, we demonstrate that heparan sulfate, as well as chondroitin sulfate plays a novel role in promoting neutrophil secretion of APRIL that could lead to further cell growth. We propose that breast cancer cells take advantage of the neutrophil recruitment to the tumor microenvironment through the dual role of heparan sulfate as cell surface receptor or docking molecule for APRIL and as a ligand that induces neutrophil APRIL secretion to promote their own growth.
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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) | 0.002 | 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".