Changes in Circulating Pro-Angiogenic Cytokines, other than VEGF, before Progression to Sunitinib Therapy in Advanced Renal Cell Carcinoma Patients
Bibliographic record
Abstract
OBJECTIVES: This study included a cohort of advanced renal cell carcinoma patients treated with sunitinib. Since resistance to sunitinib may be mediated through angiogenic cytokines other than VEGF, we measured the circulating levels of three pro-angiogenic cytokines: basic fibroblast growth factor (bFGF), hepatocyte growth factor (HGF), and interleukin (IL)-6. METHODS: Cytokines were measured at baseline and on the first day of each treatment cycle until progression in 85 advanced kidney cancer patients treated with sunitinib using a quantitative sandwich enzyme immunoassay (ELISA) technique. RESULTS: Even though no statistically significant differences in the titers of the three cytokines were observed between baseline and the time of progression in the whole patient cohort, in 45.3, 46.6, and 37.3% of the patients a more than 50% increase between baseline and the time of progression was shown in circulating IL-6, bFGF, and HGF, respectively. Furthermore, this increase was more than 100% in 37.3, 44, and 30.6% of the patients, respectively. We also demonstrated that, in these patients, cytokines tended to increase and to remain high immediately before progression. CONCLUSIONS: In a large percentage of kidney cancer patients, progression is preceded by a significant increase in pro-angiogenic cytokines other than VEGF.
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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) | 0.001 | 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".