The Role of Extracellular Vesicles in the Control of Vascular Checkpoints for Cancer Metastasis
Bibliographic record
Abstract
Systemic cancer progression culminating in metastatic disease is implicitly dependent on tumour cell interactions with the vascular system. Indeed, different facets of the micro- and macro-vasculature can be regarded as rate-limiting 'vascular checkpoints' in the process of cancer dissemination. The underlying complex communication networks drive tumour neovascularization, angiogenesis, immunoregulation, activation of the coagulation system, angiocrine interactions, and non-angiogenic vascular responses across multiple cancer types. Yet, each cancer may represent a unique vascular interaction scenario raising a prospect of targeted modulation of blood and lymphatic vessels for therapeutic purposes, beyond the traditional notion of tumour anti-angiogenesis. While the emphasis of studies aiming to understand this circuitry has traditionally been on soluble, or 'mono-molecular' mediators, the rise of the particulate secretome encompassing heterogeneous subpopulations of extracellular vesicles (EVs; including exosomes) and particles (EPs) brings another dimension into the tumour-vascular communication web during the process of metastasis. EVs and EPs are nanosized cellular fragments, the unique nature of which lies in their ability to encapsulate, protect and deliver to target cells a range of bioactive molecular entities (proteins, RNA, DNA) assembled in ways that enable them to exert a wide spectrum of biological activities. EVs and EPs penetrate through biological barriers and are capable of intracellular uptake. Their emerging vascular functions in metastatic or infiltrative cancers are exemplified by their roles in pre-metastatic niche formation, thrombosis, vasectasia or angiocrine regulation of cancer stem cells. Here, we survey some of the related evidence supporting the biological, diagnostic and interventional significance of EVs/EPs (EVPs) in disseminated neoplastic disease.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".