Abstract 431: Conversion of Tumor Microvessels into a Hierarchical and Vasoreactive Network, and Suppression of Metastases, by Fibroblast Growth Factor 9
Bibliographic record
Abstract
Normalizing the tumor vasculature has been proposed as a therapeutic paradigm. However, to function normally, microvessels must exist as a vasoreactive and hierarchical network with red blood cells flowing single file through capillaries. Such a network has not been identified in malignant tumors. We previously found that fibroblast growth factor 9 (FGF9) could stabilize new blood vessels in ischemic muscle. To determine if FGF9 impacted tumors vessels, renal carcinoma (Renca) cells, expressing GFP or FGF9, were implanted into the subcapsular space of female Balb/c mice. After 14 days, the resulting FGF9-tumors had 17% fewer microvessels than control tumors (p=0.003) but the vessels had a collagen-fortified basement membrane and were more extensively covered with pericytes (4-fold, p=0.015) and smooth muscle cells (14-fold, p=0.002). Notably, this was associated with reduced pulmonary metastases (p=0.029). Intravital video microscopy revealed that FGF9 converted a haphazard web of channels into a hierarchal network with arterioles, capillaries, and venules. There was also a 33% reduction in vessel length density (p=0.034), a 67% reduction in mean lumen diameter (p<0.001), and 57% fewer bifurcations (p=0.019). Moreover, whereas vasoreactivity was absent in control tumors, arterioles in FGF9-tumors could constrict and dilate in response to adrenergic and nitric oxide releasing agents, respectively. Pimonidazole infusion revealed a 33% reduction of hypoxia in the tumor core (p=0.031) with a 35% reduction in VEGFA expression (p=0.031). Immunostaining and selective cell harvesting revealed that FGF9 selectively amplified a population of PDGFRß-positive stromal cells in the tumor (p=0.045). Furthermore, in vivo blocking of PDGFRß prevented microvascular differentiation by FGF9 and worsened metastases (p=0.002). Conclusion: FGF9 can impart an otherwise dysfunctional tumor microvasculature with hierarchy, vasoreactivity, and improved oxygen delivery, via selective amplification of PDGFRß-expressing mesenchymal stromal cells. These findings suggest an approach to driving microvascular network differentiation, to an extent not observed previously, to pacify the tumor.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".