Age‐related properties of the tumour vasculature in renal cell carcinoma
Bibliographic record
Abstract
Study Type – Aetiology (case series) Level of Evidence 4 What’s known on the subject? and What does the study add? Little is known about the impact of vascular ageing on the angiogenic features of clear cell renal cell carcinoma, a disease in which antiangiogenic therapy currently has a well established role. It is also rather surprising that this question has not been raised in a disease context where patients’ age may differ by several decades. We provide the first glimpse in to the related vasclar changes, including morphology and some of the molecular features. OBJECTIVE To assess whether ageing processes influence angiogenesis in renal cell carcinoma (RCC) we carried out a pilot study of vascular properties in a series of archival primary kidney tumours in patients of different ages. PATIENTS AND METHODS A cohort of patients with RCC was identified restrospectively, with an age range of 35–84 years. Paraffin‐embedded, formalin‐fixed sections of surgical tumour specimens were stained for endothelial (CD31, von Willebrand factor [vWF]), pericyte (alpha smooth muscle actin [SMA]) and leucocytic (CD45) markers, as well as for proliferative (Ki67) and angiogenic activity (tumour endothelial markers [TEMs], delta‐like 4 [Dll4], Dll1, endothelial nitric oxide synthase [eNOS]). Vascular properties were compared between patients above and below 65 years of age. RESULTS Microvascular density (MVD) within capillary hot spots was generally higher in patients with non‐metastatic clear‐cell RCC (ccRCC; n = 21) than in those with metastatic RCC (mRCC; n = 9). Patients with ccRCC who were more than 65 years old showed significantly higher MVD than their younger (<65 years) counterparts. There were dividing (Ki67‐positive) endothelial and mural cells in both small (<20 µm) capillary and large (>20 µm), pre‐capillary vessels, suggesting the involvement of both angiogenic and remodelling/arteriogenic processes. Tumour endothelial markers (TEM1, TEM7, TEM8), Notch ligands (Dll1, Dll4), and other molecular characteristics (eNOS) were analysed. Age‐related differences were observed in the frequency of pre‐capillary vessels expressing Dll1, which was significantly higher in tumours of younger patients (<65 years), while eNOS was more prevalent among capillaries associated with ccRCC in older patients (>65 years). CONCLUSIONS The results of the present study suggest that age influences the structural and molecular properties of the tumour vasculature in ccRCC. We postulate that vascular ageing could also be relevant in the context of anti‐angiogenic therapy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".