Breast Cancer Processes Are Modulated by Platelet‐Derived Microparticles
Bibliographic record
Abstract
Cancer is one of the leading causes of morbidity and mortality among women worldwide. Although mortality rates have been declining for years, metastasis accounts for the majority of deaths associated with the disease. An increasing number of studies show that platelets as well as platelet‐derived microparticles (PMPs) play a significant role in cancer malignancy and disease progression. PMPs are small vesicles released into the circulatory system and the extracellular environment during platelet activation. PMP generation involves a process where bioactive material is transferred from platelets to the PMPs. Interestingly, PMPs have the capacity to interact with target recipient cells and transfer their bioactive cargo into these cells upon internalization. Accordingly, studies have shown that PMPs allow intercellular exchange and trafficking of bioactive material to modulate signaling and activation processes of recipient cells. We believe that PMPs represent an important source of breast cancer modulators leading to malignant features and disease progression. The objective of this study was to evaluate the impact of PMPs on breast cancer processes. More specifically, we investigated the modulating effects of PMPs on breast cancer metabolism and phenotypic processes involved in breast cancer metastasis. First, we characterized and validated the capacity of breast cancer models to internalize PMPs and their cargo using confocal microscopy and flow cytometry. Interestingly, we observed that the levels of PMPs internalization vary and are dependent of the type of breast cancer recipient cells. Using a series of biochemical and cell‐based assays, we also demonstrated that the cargo of PMPs is biologically active which results in the modulation of breast cancer cell metabolism, viability and migration properties of recipient cells. Overall, we demonstrate that PMPs modulate cancer cell processes reminiscent of disease malignancy. These findings provide a better understanding of the role of PMPs and their influence as cancer disease modulators. The knowledge gained from these studies will thus foster the development of potentially new strategic interventions to help mitigate the morbidity and mortality associated with cancer disease.
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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".