Tissue Factor-Expressing Tumor Cells Can Bind to Immobilized Recombinant Tissue Factor Pathway Inhibitor under Static and Shear Conditions In Vitro
Bibliographic record
Abstract
Mammary tumors and malignant breast cancer cell lines over-express the coagulation factor, tissue factor (TF). High expression of TF is associated with a poor prognosis in breast cancer. Tissue factor pathway inhibitor (TFPI), the endogenous inhibitor of TF, is constitutively expressed on the endothelium. We hypothesized that TF-expressing tumor cells can bind to immobilized recombinant TFPI, leading to arrest of the tumor cells under shear in vitro. We evaluated the adhesion of breast cancer cells to immobilized TFPI under static and shear conditions (0.35 - 1.3 dyn/cm2). We found that high-TF-expressing breast cancer cells, MDA-MB-231 (with a TF density of 460,000/cell), but not low TF-expressing MCF-7 (with a TF density of 1,400/cell), adhered to recombinant TFPI, under static and shear conditions. Adhesion of MDA-MB-231 cells to TFPI required activated factor VII (FVIIa), but not FX, and was inhibited by a factor VIIa-blocking anti-TF antibody. Under shear, adhesion to TFPI was dependent on the TFPI-coating concentration, FVIIa concentration and shear stress, with no observed adhesion at shear stresses greater than 1.0 dyn/cm2. This is the first study showing that TF-expressing tumor cells can be captured by immobilized TFPI, a ligand constitutively expressed on the endothelium, under low shear in vitro. Based on our results, we hypothesize that TFPI could be a novel ligand mediating the arrest of TF-expressing tumor cells in high TFPI-expressing vessels under conditions of low shear during metastasis.
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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".