Fibroblast Growth Factor 9 Imparts Hierarchy and Vasoreactivity to the Microcirculation of Renal Tumors and Suppresses Metastases
Bibliographic record
Abstract
Tumor vessel normalization has been proposed as a therapeutic paradigm. However, normal microvessels are hierarchical and vasoreactive with single file transit of red blood cells through capillaries. Such a network has not been identified in malignant tumors. We tested whether the chaotic tumor microcirculation could be reconfigured by the mesenchyme-selective growth factor, FGF9. Delivery of FGF9 to renal tumors in mice yielded microvessels that were covered by pericytes, smooth muscle cells, and a collagen-fortified basement membrane. This was associated with reduced pulmonary metastases. Intravital microvascular imaging revealed a haphazard web of channels in control tumors but a network of arterioles, bona fide capillaries, and venules in FGF9-expressing tumors. Moreover, whereas vasoreactivity was absent in control tumors, arterioles in FGF9-expressing tumors could constrict and dilate in response to adrenergic and nitric oxide releasing agents, respectively. These changes were accompanied by reduced hypoxia in the tumor core and reduced expression of the angiogenic factor VEGF-A. FGF9 was found to selectively amplify a population of PDGFRβ-positive stromal cells in the tumor and blocking PDGFRβ prevented microvascular differentiation by FGF9 and also worsened metastases. We conclude that harnessing local mesenchymal stromal cells with FGF9 can differentiate the tumor microvasculature to an extent not observed previously.
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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 | 0.000 |
| 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".