Angiokines Associated with Targeted Therapy Outcomes in Patients with Non–Clear Cell Renal Cell Carcinoma
Bibliographic record
Abstract
PURPOSE: Biomarkers are needed in patients with non-clear cell renal cell carcinomas (NC-RCC) to inform treatment selection but also to identify novel therapeutic targets. We thus sought to profile circulating angiokines in the context of a randomized treatment trial of everolimus versus sunitinib. PATIENTS AND METHODS: ASPEN (NCT01108445) was an international, randomized, open-label phase II trial of patients with metastatic papillary, chromophobe, or unclassified NC-RCC with no prior systemic therapy. Patients were randomized to everolimus or sunitinib and treated until disease progression or unacceptable toxicity. The primary endpoint was radiographic progression-free survival (PFS) defined by RECIST 1.1. Plasma angiokines were collected at baseline, cycle 3, and progression and associated with PFS and overall survival (OS). RESULTS: We enrolled 108 patients, 51 received sunitinib and 57 everolimus; of these, 99 patients had evaluable plasma for 23 angiokines. At the final data cutoff, 94 PFS and 64 mortality events had occurred. Angiokines that were independently adversely prognostic for OS were osteopontin (OPN), TIMP-1, thrombospondin-2 (TSP-2), hepatocyte growth factor (HGF), and VCAM-1, and these were also associated with poor-risk disease. Stromal derived factor 1 (SDF-1) was associated with improved survival. OPN was also significantly associated with worse PFS. No statistically significant angiokine-treatment outcome interactions were observed for sunitinib or everolimus. Angiopoeitin-2 (Ang-2), CD-73, HER-3, HGF, IL6, OPN, PIGF, PDGF-AA, PDGF-BB, SDF-1, TGF-b1-b2, TGFb-R3, TIMP-1, TSP-2, VCAM-1, VEGF, and VEGF-R1 levels increased with progression on everolimus, while CD-73, ICAM-1, IL6, OPN, PlGF, SDF-1, TGF-b2, TGFb-R3, TIMP-1, TSP-2, VEGF, VEGF-D, and VCAM-1 increased with progression on sunitinib. CONCLUSIONS: In patients with metastatic NC-RCC, we identified several poor prognosis angiokines and immunomodulatory chemokines during treatment with sunitinib or everolimus, particularly OPN.
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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 | 0.001 | 0.001 |
| 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".