Oncogene-dependent survival of highly transformed cancer cells under conditions of extreme centrifugal force – implications for studies on extracellular vesicles
Bibliographic record
Abstract
Extracellular vesicles (EVs), including exosomes, are a subject of intense interest due to their emission by cancer cells and role in intercellular communication. Earlier reports suggested that oncogenes, such as RAS, MET or EGFR, drive cellular vesiculation. Interestingly, these oncogenes may also traffic between cells using the EV-mediated emission and uptake processes. One of the main tools in the analysis of EVs are ultracentrifugation protocols designed to efficiently separate parental cells from vesicles through a sequence of steps involving increasing g-force. Here we report that ultracentrifugationonly EV preparations from highly transformed cancer cells, driven by the overexpression of oncogenic H-ras (RAS-3) and v-src (SRC-3), may contain clonogenic cancer cells, while preparations of normal or less aggressive human cell lines are generally free from such contamination. Introduction of a filtration step eliminates clonogenic cells from the ultracentrifugate. The survival of RAS-3 and SRC-3 cells under extreme conditions of centrifugal force (110,000 g) is oncogene-induced, as EV preparations of their parental non-tumourigenic cell line (IEC-18) contain negligible numbers of clonogenic cells. Moreover, treatment of SRC-3 cells with the SRC inhibitor (PP2) markedly reduces the presence of such cells in the unfiltered ultracentrifugate. These observations enforce the notion that EV preparations require careful filtration steps, especially in the case of material produced by highly transformed cancer cell types. We also suggest that oncogenic transformation may render cells unexpectedly resistant to extreme physical forces, which may affect their biological properties in vivo.
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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) | 0.000 | 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".