Fibroblast Growth Factor Receptor-Dependent and -Independent Paracrine Signaling by Sunitinib-Resistant Renal Cell Carcinoma
Bibliographic record
Abstract
Antiangiogenic therapies, such as sunitinib, have revolutionized renal cell carcinoma (RCC) treatment. However, a precarious understanding of how resistance emerges and a lack of tractable experimental systems hinder progress. We evaluated the potential of primary RCC cultures (derived from tumors and tumor grafts) to signal to endothelial cells (EC) and fibroblasts in vitro and to stimulate angiogenesis ex vivo in chorioallantoic membrane (CAM) assays. From 65 patients, 27 primary cultures, including several from patients with sunitinib-resistant RCC, were established. RCC cells supported EC survival in coculture assays and induced angiogenesis in CAM assays. RCC-induced EC survival was sensitive to sunitinib in half of the tumors and was refractory in tumors from resistant patients. Sunitinib sensitivity correlated with vascular endothelial growth factor (VEGF) production. RCC induced paracrine extracellular signal-regulated kinase (ERK) activation in EC which was inhibited by sunitinib in sensitive but not in resistant tumors. As determined by fibroblast growth factor receptor substrate 2 (FRS2) phosphorylation in fibroblasts, RCC broadly induced low-level fibroblast growth factor receptor (FGFR) signaling. Whereas ERK activation in EC was uniformly inhibited by combined VEGF/platelet-derived growth factor (PDGF)/FGF receptor inhibitors, paracrine ERK activation in fibroblasts was blocked in only a fraction of tumors. Our data show that RCC activates EC through VEGF-dependent and -independent pathways, that sunitinib sensitivity correlates with VEGF-mediated ERK activation, and that combined inhibition of VEGF/PDGF/FGF receptors is sufficient to inhibit mitogenic signaling in EC but not in fibroblasts.
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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) | 0.001 | 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".