Abstract 580: Lung Cancer Chemotherapy Agents Increase Procoagulant Activity via Protein Disulfide Isomerase-Dependent Tissue Factor Decryption
Bibliographic record
Abstract
Background: Lung cancer patients undergoing chemotherapy have an elevated risk for thrombosis. However, the mechanisms by which chemotherapy agents increase the risk for thrombosis remains unclear. Objectives: To determine the mechanism(s) by which lung cancer chemotherapy agents cisplatin, carboplatin, gemcitabine, and paclitaxel elicit increased tissue factor (TF) activity on endothelial cells, A549 cells, and monocytes. Methods: TF activity, TF antigen, and phosphatidylserine exposure were measured on chemotherapy-treated human umbilical vein endothelial cells (HUVEC), A549 cells, and monocytes. Cell surface protein disulfide isomerase (PDI) and cell surface free-thiol levels were measured on HUVEC and A549 non-small cell lung carcinoma cells. Results: Treatment of HUVECs, A549 cells, and monocytes with lung cancer chemotherapy significantly increased cell surface TF activity. However, elevated TF antigen levels were observed only on cisplatin- and gemcitabine-treated monocytes. Cell surface levels of phosphatidylserine were increased on HUVEC and monocytes treated with cisplatin/gemcitabine combination therapy. Chemotherapy also resulted in increased cell surface levels of PDI and reduced cell surface free thiol levels. Glutathione treatment and PDI inhibition, but not phosphatidylserine inhibition attenuated TF activity. Furthermore, increased TF activity was reversed by reducing cysteines with dithiothreitol. Conclusions: These studies are the first to demonstrate that lung cancer chemotherapy agents increase procoagulant activity on endothelial cells, blood monocytes, and A549 cells by TF decryption through a disulfide bond formation in a PDI-dependent mechanism.
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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) | 0.005 | 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".