Mitochondria Transfer by Platelet-Derived Microparticles Regulates Breast Cancer Bioenergetic States and Malignant Features
Bibliographic record
Abstract
An increasing number of studies show that platelets as well as platelet-derived microparticles (PMP) play significant roles in cancer malignancy and disease progression. Particularly, PMPs have the capacity to interact and internalize within target cells resulting in the transfer of their bioactive cargo, which can modulate the signaling and activation processes of recipient cells. We recently identified a new subpopulation of these vesicles (termed mitoMPs), which contain functional mitochondria. Given the predominant role of mitochondria in cancer cell metabolism and disease progression, we set out to investigate the impact of mitoMPs on breast cancer metabolic reprograming and phenotypic processes leading to malignancy. Interestingly, we observed that recipient cell permeability to PMP internalization varied among the breast cancer cell types evaluated in our study. Specifically, cells permissive to mitoMPs acquire mitochondrial-dependent functions, which stimulate increased cellular oxygen consumption rates and intracellular ATP levels. In addition, cancer cells co-incubated with PMPs display enhanced malignant features in terms of migration and invasion. Most importantly, the cancer aggressive processes and notable metabolic plasticity induced by PMPs were highly dependent on the functional status of the mitoMP-packaged mitochondria. These findings characterize a new mechanism by which breast cancer cells acquire foreign mitochondria resulting in the gain of metabolic processes and malignant features. A better understanding of these mechanisms may provide therapeutic opportunities through PMP blockade to deprive cancer cells from resources vital in disease progression. IMPLICATIONS: We show that the transfer of foreign mitochondria by microparticles modulates recipient cancer cell metabolic plasticity, leading to greater malignant processes.
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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".