Distinct profile of vascular progenitor attachment to extracellular matrix proteins in cancer patients
Bibliographic record
Abstract
BACKGROUND: Vascular progenitor cells (VPCs) facilitate angiogenesis and initiate vascular repair by homing in on sites of damage and adhering to extracellular matrix (ECM) proteins. VPCs also contribute to tumor angiogenesis and induce angiogenic switching in sites of metastatic cancer. In this study, the binding of attaching cells in VPC clusters that form in vitro on specific ECM proteins was investigated. METHODS: VPC cluster assays were performed in vitro on ECM proteins enriched in cancer cells and in remodelling tissue. Profiles of VPC clusters from patients with cancer were compared to healthy controls. The role of VEGF and integrin-specific binding of angiogenic attaching cells was addressed. RESULTS: VPC clusters from cancer patients were markedly increased on fibronectin relative to other ECM proteins tested, in contrast to VPC clusters from control subjects, which formed preferentially on laminin. Specific integrin-mediated binding of attaching cells in VPC clusters was matrix protein-dependent. Furthermore, cancer patients had elevated plasma VEGF levels compared to healthy controls and VEGF facilitated preferential VPC cluster formation on fibronectin. Incubating cells from healthy controls with VEGF induced a switch from the 'healthy' VPC binding profile to the profile observed in cancer patients with a marked increase in VPC cluster formation on fibronectin. CONCLUSION: The ECM proteins laminin and fibronectin support VPC cluster formation via specific integrins on attaching cells and can facilitate patterns of VPC cluster formation that are distinct in cancer patients. Larger studies, however, are needed to gain insight on how tumor angiogenesis may differ from normal repair processes.
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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 | 0.001 | 0.001 |
| 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) | 0.002 | 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".