Autoantibodies against the cell surface–associated chaperone GRP78 stimulate tumor growth via tissue factor
Bibliographic record
Abstract
Tumor cells display on their surface several molecular chaperones that normally reside in the endoplasmic reticulum. Because this display is unique to cancer cells, these chaperones are attractive targets for drug development. Previous epitope-mapping of autoantibodies (AutoAbs) from prostate cancer patients identified the 78-kDa glucose-regulated protein (GRP78) as one such target. Although we previously showed that anti-GRP78 AutoAbs increase tissue factor (TF) procoagulant activity on the surface of tumor cells, the direct effect of TF activation on tumor growth was not examined. In this study, we explore the interplay between the AutoAbs against cell surface-associated GRP78, TF expression/activity, and prostate cancer progression. First, we show that tumor GRP78 expression correlates with disease stage and that anti-GRP78 AutoAb levels parallel prostate-specific antigen concentrations in patient-derived serum samples. Second, we demonstrate that these anti-GRP78 AutoAbs target cell-surface GRP78, activating the unfolded protein response and inducing tumor cell proliferation through a TF-dependent mechanism, a specific effect reversed by neutralization or immunodepletion of the AutoAb pool. Finally, these AutoAbs enhance tumor growth in mice bearing human prostate cancer xenografts, and heparin derivatives specifically abrogate this effect by blocking AutoAb binding to cell-surface GRP78 and decreasing TF expression/activity. Together, these results establish a molecular mechanism in which AutoAbs against cell-surface GRP78 drive TF-mediated tumor progression in an experimental model of prostate cancer. Heparin derivatives counteract this mechanism and, as such, represent potentially appealing compounds to be evaluated in well-designed translational clinical trials.
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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".