Lung Cancer Chemotherapy Agents Increase Procoagulant Activity In Vitro and In Vivo
Bibliographic record
Abstract
Abstract Abstract 35 Introduction: Cancer patients have an elevated risk for thrombosis, with venous thromboembolism (VTE) being the second leading cause of death in cancer patients. The risk of VTE is further increased in patients undergoing standard chemotherapy. However, the mechanism(s) by which chemotherapy increases the risk for thrombosis in lung cancer patients is not well understood. Previous studies have shown that treatment of endothelial cells with chemotherapy agents (such as cisplatin, gemcitabine, and paclitaxel) promotes coagulation through an increase in tissue factor (TF) procoagulant activity. Objective: The purpose of this study was to determine in vitro and in vivo procoagulant effects of anti-lung cancer therapy agents cisplatin, carboplatin, paclitaxel, gemcitabine and bevacizumab. We studied the effects of these agents individually as well as in combinations (cisplatin/gemcitabine and carboplatin/paclitaxel) on cell surface TF activity, TF antigen levels, and phosphatidylserine (PS) exposure levels. We also examined the effects of lung cancer chemotherapy agents on thrombin generation in healthy mice. Methods: Human umbilical vein endothelial cells (HUVECs), blood monocytes, and A549 cells were treated for 24 hours with clinically relevant concentrations of chemotherapy agents alone as well as in combination. Cell surface TF activity was measured by factor Xa (FXa) generation in the presence or absence of factor VIIa and CaCl2 on drug-treated cells. To demonstrate that FXa generation was TF-dependent, Xa generation assays were performed on chemotherapy-treated cells incubated with a TF-inhibitory antibody. Cell surface TF antigen and PS levels were measured by flow cytometry. Healthy Balb/C mice received intraperitoneal injections of lung cancer chemotherapy agents alone or in combination. Blood was collected 24 hours post-chemotherapy by carotid artery cannulation. Plasma levels of thrombin-antithrombin (TAT) complex were measured by ELISA. Results: Treatment of all cell lines with single agent and combination chemotherapy significantly increased FXa generation on HUVEC, A459 cells and monocytes. To confirm that chemotherapy-induced FXa generation was TF dependent, FXa generation was repeated in the presence or absence of factor VIIa, an essential component of the extrinsic tenase complex. We also performed the FXa generation assay in the presence or absence of a TF inhibiting antibody HTF-1. Absence of factor VIIa as well as pre-incubation of cells with HTF-1 resulted in ablation of FXa generation, confirming that generation of FXa is due to cell surface TF activity. Through flow cytometry methods we determined that cell surface TF antigen levels remain unchanged on chemotherapy treated HUVEC and A459 cells. In contrast to HUVEC and A549 cells, monocytes treated with high-dose cisplatin alone or in combination with gemcitabine, expressed a significant increase in TF antigen levels. Measurement of cell surface PS exposure levels revealed a significant increase in PS levels only on HUVEC treated with high-dose cisplatin and gemcitabine combinations. However, inhibition of PS with Annexin V on HUVEC treated with high-dose cisplatin/gemcitabine combination did not decrease FXa generation. Furthermore, inhibition of protein disulfide isomerase (PDI) on A549 cells treated with cisplatin/gemcitabine combination but not carboplatin/paclitaxel combination resulted in moderately diminished levels of FXa. Our in vivo results demonstrated that treatment of healthy mice with bevacizumab, paclitaxel and carboplatin moderately increased plasma TAT levels in healthy mice. Conclusions: Our studies are the first to explore the effects of lung cancer chemotherapy agents on monocyte and A549 cell TF activity levels, as well as the first to investigate the mechanisms by which lung cancer agents may promote TF decryption on these cell lines. We are also the first to report in vivo increases in thrombin generation in response to treatment of healthy mice with anti-lung cancer agents. These studies reveal potential mechanisms by which lung cancer chemotherapy induce a hypercoagulable state. 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".