Cell surface GRP78 activation by anti-GRP78 autoantibodies in relation to prostate tumour growth via tissue factor activation.
Bibliographic record
Abstract
31 Background: Prostate cancer (PC) is characterized by increased prothrombotic state due to enhanced tissue factor (TF) expression/procoagulant activity (PCA). We and others observed that GRP78, a molecular chaperone, is expressed on the surface of PC cells where it functions as a signaling receptor to promote cell proliferation and survival. Further, exposure of GRP78 on the surface of PC cells stimulates the production of anti-GRP78 autoantibodies in PC patients. We have reported that binding of these autoantibodies to cell surface GRP78 enhances TF PCA. We hypothesize that this autoantibody/cell surface GRP78 complex interaction increases PC progression and that disruption of this complex would represent a viable target for the treatment of advanced PC. Methods: Wild type, TF knockdown DU145 cells, and NOD/SCID mouse model system was used to investigate the effect of anti-GRP78 autoantibodies on tumor growth. Protein expression was determined using western blotting and qRT-PCR. TF activity was determined using the TF PCA continuous assay. Blood samples from patients diagnosed with PC were obtained from the Ontario Tumour Bank and St. Joseph’s Hamilton. Results: Pre-prostatectomy patients demonstrated high levels of anti-GRP78 autoantibodies (60-100µg/ml) vs. healthy individuals (5-10µg/ml). These titers were significantly reduced 24-weeks post prostatectomy. We show here that anti-GRP78 autoantibodies upregulate TF and its PCA through a mechanism involving depletion of ER Ca2+ stores. This very effect on ER Ca2+ stores also caused activation of the unfolded protein response, a pro-survival cellular pathway. Furthermore, these autoantibodies were shown to accelerate tumor growth in a NOD/SCID mouse model. Finally, heparin and low molecular weight heparin were shown to interfere with the binding of these antibodies to cancer cells and prevent PC cell activation. Conclusions: We have identified the function of an agent in patients' blood, anti-GRP78 autoantibodies, that correlate with PC stage in patients and increase TF PCA and promote PC progression in mice. The effect of this autoantibody can be reversed using heparin, thus, this acts as a new potential therapeutic target for PC.
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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".