Effects of adjuvant chemotherapy on the protein C anticoagulant pathway in patients with early stage breast cancer
Bibliographic record
Abstract
8563 Background: Although chemotherapy treatment is associated with an increased risk of thrombosis, the pathogenic mechanisms are poorly understood. We have previously shown that treatment of endothelial cells with adriamycin and epirubicin impairs the protein C anticoagulant pathway by downregulating EPCR (endothelial protein C receptor), an endothelial receptor required for the conversion of protein C to the anticoagulant enzyme activated protein C (APC). In this study, we examined the effects of adriamycin- and epirubicin-containing chemotherapy on the protein C anticoagulant pathway in early stage breast cancer patients. Methods: We collected blood samples from 20 patients patients with early stage breast cancer undergoing adjuvant CEF (cyclophosphamide, epirubicin and 5-fluorouracil) or CMF (cyclophosphamide, methotrexate and 5-fluorouracil) chemotherapy on days 1, 2 and 8 for the first 2 cycles of treatment. Markers of protein C generation (protein C, thrombin-antithrombin (TAT) complexes, and APC) were measured on these days. Results: Plasma protein C levels were significantly lower at cycle 2 day 8 (0.88 ± 0.14 U/ml) compared to pre-chemotherapy levels (1.07 ± 0.23 U/ml) (p=0.0036). Plasma TAT levels were significantly higher at cycle 2 day 8 (2.87 ± 0.79 μg/L) compared to pre-chemotherapy levels (2.01 ± 1.24 μg/L) (p=0.02). Based on the APC levels, the patients appear to fall into two categories in terms of their ability to generate APC. The first group (n=16) has elevated APC levels that parallel the elevated TAT levels, whereas the second group (n=4) has low APC levels despite elevated TAT levels. Conclusions: Impaired ability to generate APC in a subpopulation of patients may reflect chemotherapy-induced impairment of the protein C pathway through the downregulation of EPCR. This pilot study provides new mechanistic insights into the prothrombotic effects of chemotherapy agents. No significant financial relationships to disclose.
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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 | 0.001 |
| 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) | 0.001 | 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".