Durable complete responses and near complete responses to sunitinib in metastatic renal cell carcinoma
Bibliographic record
Abstract
15514 Introduction: Sunitinib is a tyrosine kinase inhibitor with activity against VEGFR and PDGFR recently approved by the FDA for the treatment of advanced renal cell carcinoma (RCC). There is no existing literature that details complete responses (CRs) in patients taking sunitinib for metastatic RCC. Methods: Seventy-four patients with metastatic RCC receiving sunitinib at the Cleveland Clinic Taussig Cancer Center on clinical trials were reviewed to determine the number of patients with RECIST-defined CRs. Additionally, patients who achieved near-CRs defined as a greater than 90% reduction in composite tumor volume or residual disease of less than or equal to 1 cm were reviewed. Results: Two patients (2.7%) achieved a RECIST-defined CR lasting >15 months. The patients who obtained CRs had non-bulky pulmonary metastases, favorable or intermediate MSKCC risk profiles, were treated with sunitinib in the first-line setting and had a significant reduction in composite tumor measurements within the first two cycles. An additional 2 patients achieved near-CRs, including one patient that previously progressed on bevacizumab. These 2 near-CR patients remain progression-free for more than 19 months. Finally, 1 patient achieved sufficient downstaging and reduction of tumor volume such that the remaining lesion could be excised, resulting in a surgical CR. This patient is currently off sunitinib and remains progression-free 4 months after surgery. Conclusion: Sunitinib is capable of producing durable CRs in cytokine-naïve metastatic RCC patients with non-bulky pulmonary metastases. Additionally, near-CRs can be seen despite non-pulmonary metastatic sites and prior VEGF-targeted therapy. No significant financial relationships to disclose.
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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".