Anti‐proliferative and anti‐tumour effects of lymphocyte‐derived microparticles are neither species‐ nor tumour‐type specific
Bibliographic record
Abstract
BACKGROUND: Unregulated cell proliferation or growth is a prominent characteristic of cancer. We have previously demonstrated that LMPs (cell membrane microparticles derived from apoptotic human CEM T lymphoma cells stimulated with actinomycin D) strongly suppress the proliferation of not only human endothelial cells but also mouse Lewis lung carcinoma cells. METHODS: LMPs were generated either from CEM T cells using different stimuli or from 3 different types of lymphocytes. The effects of LMPs on cancer cell proliferation were examined using cell lines from different species and tissues. The cell cycle kinetics was evaluated by FACS and the expression of cell cycle-related genes was determined using quantitative RT-PCR. The in vivo anti-tumor effect of LMPs was investigated using xenografts and allografts. RESULTS: LMPs at doses far above physiological levels dramatically suppressed the proliferation of cancer cells in a non species-specific manner. LMPs selectively target high proliferating cells and their anti-proliferative effect is not dependent on parental cell origin or stimuli. The anti-proliferative effect of LMPs was due to induction of cell-cycle arrest in G0/G1, with associated increases in expression of the cyclin-dependent kinase inhibitors p15(INK4b), p16(INK4a), and p21(Cip1). In vivo, LMPs significantly suppressed tumor growth in animal tumor models. CONCLUSION: These results highlight the potential role of LMPs in modulating the growth of high proliferating cells. Given that cell-based therapies are considered less toxic than pharmacologic approaches and have the potential to target multiple pathways in a synergistic manner, LMPs may serve as a veritable option for cancer treatment.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".