Chemotherapy, but Not Bevacizumab, Increases Tissue Factor Activity Levels In Endothelial Cells, Blood Monocytes, and Non-Small Cell Lung Cancer Cells
Bibliographic record
Abstract
Abstract Abstract 5117 Introduction: Venous Thromboembolism (VTE) is a complication commonly seen in patients with lung cancer. The risk of VTE is further increased in patients undergoing standard chemotherapy as well as anti-angiogenesis therapies such as bevacizumab. However, the molecular mechanisms between lung cancer, anti-cancer agents, and thrombosis remain poorly understood. In vitro studies suggest that treatment of endothelial cells with particular lung cancer chemotherapy drugs such as cisplatin, gemcitabine and paclitaxel promote coagulation through an increase in tissue factor (TF) activity. However, the effect of these drugs on non-small cell lung cancer cells and monocytes remains largely unknown. It is also unknown if blockage of VEGF function by bevacizumab affects TF activity in endothelial cells and A549 lung cancer cells, both of which express VEGFR-1 and VEGFR-2 receptors. Objective: The purpose of this study was to determine the effects of standard lung cancer chemotherapy agents cisplatin, carboplatin, paclitaxel and gemcitabine on TF activity levels on vascular endothelial cells, blood monocytes, and the non-small cell lung cancer cell line A549. We studied the effects of these chemotherapy agents individually as well as in established combination chemotherapy (cisplatin/gemcitabine and carboplatin/paclitaxel). We also studied the effects of the anti-angiogenesis agent bevacizumab on TF activity levels in endothelial cells and A549 cells. Methods: Human umbilical vein endothelial cells (HUVECs), blood monocytes, and A549 cells were exposed for 24 hours to clinically relevant concentrations of chemotherapy agents or bevacizumab. At the end of treatment time, cell surface TF activity was measured by the generation of factor Xa in the presence of factor VIIa and CaCl2 on drug-treated cells. Cell viability was examined by Trypan Blue exclusion assays. Factor Xa generation was adjusted to exclude non-viable cells (at most, 15% of the cells were non-viable after 24-hour exposure to the anti-cancer agents). Results: Treatment of all cell lines with single chemotherapy agents increased cell surface TF activity. The combinations of cisplatin/gemcitabine and carboplatin/paclitaxel on HUVECs resulted in synergistic and additive effects on TF activity levels, respectively. The increase in TF activity levels were observed in the absence of cell death. In contrast, treatment of HUVECs and A549ccells with bevacizumab did not affect TF activity levels on HUVECs and A549 cells. To confirm that chemotherapy-induced factor Xa generation was TF dependent, we repeated the same assay in the absence of factor VIIa, an essential component of the extrinsic tenase complex. Absence of factor VIIa resulted in no cell surface generation of factor Xa confirming that the generation of factor Xa is due to cell surface TF activity. Conclusions: Our studies are the first to explore the effects of chemotherapy agents on TF activity levels in A549 cells and monocytes, as well as the effects of bevacizumab on TF activity levels in endothelial cells and A549 cells. Our studies suggest that, unlike chemotherapy agents, bevacizumab does not induce a procoagulant phenotype on endothelial cells and A549 cells via the upregulation of TF activity. Disclosures: No relevant conflicts of interest to declare.
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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 | 0.000 |
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".