Binding of Anti-GRP78 Autoantibodies to Cell Surface GRP78 Increases Tissue Factor Procoagulant Activity via the Release of Calcium from Endoplasmic Reticulum Stores
Bibliographic record
Abstract
The increased risk of venous thromboembolism in cancer patients has been attributed to enhanced tissue factor (TF) procoagulant activity (PCA) on the surface of cancer cells. Recent studies have shown that TF PCA can be modulated by GRP78, an endoplasmic reticulum (ER)-resident molecular chaperone. In this study, we investigated the role of cell surface GRP78 in modulating TF PCA in several human cancer cell lines. Although both GRP78 and TF are present on the cell surface of cancer cells, there was no evidence of a stable interaction between recombinant human GRP78 and TF, nor was there any effect of exogenously added recombinant GRP78 on cell surface TF PCA. Treatment of cells with the ER stress-inducing agent thapsigargin, an inhibitor of the sarco(endo)plasmic reticulum Ca(2+) pump that causes Ca(2+) efflux from ER stores, increased cytosolic [Ca(2+)] and induced TF PCA. Consistent with these findings, anti-GRP78 autoantibodies that were isolated from the serum of patients with prostate cancer and bind to a specific N-terminal epitope (Leu(98)-Leu(115)) on cell surface GRP78, caused a dose-dependent increase in cytosolic [Ca(2+)] and enhanced TF PCA. The ability to interfere with cell surface GRP78 binding, block phospholipase C activity, sequester ER Ca(2+), or prevent plasma membrane phosphatidylserine exposure resulted in a significant decrease in the TF PCA induced by anti-GRP78 autoantibodies. Taken together, these findings provide evidence that engagement of the anti-GRP78 autoantibodies with cell surface GRP78 increases TF PCA through a mechanism that involves the release of Ca(2+) from ER stores. Furthermore, blocking GRP78 signaling on the surface of cancer cells attenuates TF PCA and has the potential to reduce the risk of cancer-related venous thromboembolism.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".