Platelet phenotype changes associated with breast cancer and its treatment
Bibliographic record
Abstract
Platelets and their granular contents influence both angiogenesis and breast cancer progression. This study was performed to assess the effect of breast cancer and its treatment on platelet biology and the response to inhibition of the platelet P2Y12 receptor. Receptor-specific platelet activation and inhibition was studied for three platelet-associated proteins important in cancer angiogenesis and progression, vascular endothelial growth factor (VEGF), thrombospondin1 (TSP1), and transforming growth factor beta 1 (TGF-β1). Twenty-four women with active breast cancer and 10 healthy controls not receiving antiplatelet therapy participated in the study. Ex vivo activation of platelets in whole blood was accomplished using PAR1AP, PAR4AP, convulxin, and ADP. Platelet inhibition was accomplished using the P2Y12 receptor antagonist cangrelor (the in vitro equivalent of clopidogrel). VEGF, TSP1, and TGF-β1 were measured using standard ELISA. Platelet activation by ADP, PAR1, PAR4, and collagen receptors increased VEGF, TSP1, and TGF-β1 secretion in patients with breast cancer. Agonist-induced release of VEGF was greater in cancer patients as compared to healthy controls (p = 0.02 for ADP, p < 0.001 for PAR1AP, PAR4AP, and convulxin) despite a decrease in the efficiency of VEGF secretion in patients with breast cancer. These differences were not observed for TSP1 and TGF-β1 secretion. P2Y12 receptor inhibition decreased VEGF, TSP1, and TGF-β1 secretion. In patients with cancer, cangrelor inhibited TSP1 release to a greater extent than VEGF and TGF-β1 release. In patients with breast cancer, the magnitude of platelet inhibition achieved by cangrelor was greater than that achieved with healthy controls for all agonists and platelet proteins studied. While platelets are known to influence progression of breast cancer, our results show that breast cancer and its treatment influence the platelet phenotype by increasing the secretion of pro-angiogenic proteins following platelet activation, modulating the efficiency of platelet protein release as well as increasing the response to antiplatelet therapy.
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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) | 0.001 | 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".