Large extracellular vesicles carry most of the tumour DNA circulating in prostate cancer patient plasma
Bibliographic record
Abstract
ABSTRACT Cancer‐derived extracellular vesicles (EVs) are membrane‐enclosed structures of highly variable size. EVs contain a myriad of substances (proteins, lipid, RNA, DNA) that provide a reservoir of circulating molecules, thus offering a good source of biomarkers. We demonstrate here that large EVs (L‐EV) (large oncosomes) isolated from prostate cancer (PCa) cells and patient plasma are an EV population that is enriched in chromosomal DNA, including large fragments up to 2 million base pair long. While L‐EVs and small EVs (S‐EV) (exosomes) isolated from the same cells contained similar amounts of protein, the DNA was more abundant in L‐EVs, despite S‐EVs being more numerous. Consistent with in vitro observations, the abundance of DNA in L‐EV obtained from PCa patient plasma was variable but frequently high. Conversely, negligible amounts of DNA were present in the S‐EVs from the same patients. Controlled experimental conditions, with spike‐ins of L‐EVs and S‐EVs from cancer cells in human plasma from healthy subjects, showed that circulating DNA is almost exclusively enclosed in L‐EVs. Whole genome sequencing revealed that the DNA in L‐EVs reflects genetic aberrations of the cell of origin, including copy number variations of genes frequently altered in metastatic PCa (i.e. MYC, AKT1, PTK2, KLF10 and PTEN ). These results demonstrate that L‐EV‐derived DNA reflects the genomic make‐up of the tumour of origin. They also support the conclusion that L‐EVs are the fraction of plasma EVs with DNA content that should be interrogated for tumour‐derived genomic alterations.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".