Platelet-derived microparticles provoke chronic lymphocytic leukemia malignancy through metabolic reprogramming
Bibliographic record
Abstract
Background: It is well established that inflammation and platelets promote multiple processes of cancer malignancy. Recently, platelets have received attention for their role in carcinogenesis through the production of microvesicles or platelet-derived microparticles (PMPs), which transfer their biological content to cancer cells. We have previously characterized a new subpopulation of these microparticles (termed mito-microparticles), which package functional mitochondria. The potential of mitochondria transfer to cancer cells is particularly impactful as many aspects of mitochondrial biology (i.e., cell growth, apoptosis inhibition, and drug resistance) coincide with cancer hallmarks and disease progression. These metabolic aspects are particularly notable in chronic lymphocytic leukemia (CLL), which is characterized by a relentless accumulation of proliferating, immunologically dysfunctional, mature B-lymphocytes that fail to undergo apoptosis. The present study aimed to investigate the role of PMPs on CLL metabolic plasticity leading to cancer cell phenotypic changes. Methods: CLL cell lines were co-incubated with different concentrations of human PMPs, and their impact on cell proliferation, mitochondrial DNA copy number, OCR level, ATP production, and ROS content was evaluated. Essential genes involved in metabolic-reprogramming were identified using the bioinformatics tools, examined between patients with early and advanced CLL stages, and then validated in PMP-recipient CLLs. Finally, the impact of the induced metabolic reprogramming on CLLs' growth, survival, mobility, and invasiveness was tested against anti-cancer drugs Cytarabine, Venetoclax, and Plumbagin. Results: The data demonstrated the potency of PMPs in inducing tumoral growth and invasiveness in CLLs through mitochondrial internalization and OXPHOS stimulation which was in line with metabolic shift reported in CLL patients from early to advanced stages. This metabolic rewiring also improved CLL cells' resistance to Cytarabine, Venetoclax, and Plumbagin chemo drugs. Conclusion: Altogether, these findings depict a new platelet-mediated pathway of cancer pathogenesis. We also highlight the impact of PMPs in CLL metabolic reprogramming and disease progression.
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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".