Oncogenic Regulation of Extracellular Vesicle Proteome and Heterogeneity
Bibliographic record
Abstract
Mutational and epigenetic driver events profoundly alter intercellular communication pathways in cancer. This effect includes deregulated release, molecular composition, and biological activity of extracellular vesicles (EVs), membranous cellular fragments ranging from a few microns to less than 100 nm in diameter and filled with bioactive molecular cargo (proteins, lipids, and nucleic acids). While EVs are usually classified on the basis of their physical properties and biogenetic mechanisms, recent analyses of their proteome suggest a larger than expected molecular diversity, a notion that is also supported by multicolour nano-flow cytometry and other emerging technology platforms designed to analyze single EVs. Both protein composition and EV diversity are markedly altered by oncogenic transformation, epithelial to mesenchymal transition, and differentiation of cancer stem cells. Interestingly, only a subset of EVs released from mutant cells may carry oncogenic proteins (e.g., EGFRvIII), hence, these EVs are often referred to as "oncosomes". Indeed, oncogenic transformation alters the repertoire of EV-associated proteins, increases the presence of pro-invasive cargo, and alters the composition of distinct EV populations. Molecular profiling of single EVs may reveal a more intricate effect of transforming events on the architecture of EV populations in cancer and shed new light on their biological role and diagnostic utility.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".