Response to systemic therapy in non-clear cell renal cell carcinomas: A systematic review and meta-analysis.
Bibliographic record
Abstract
425 Background: Clinical data supporting the efficacy of systemic therapy in non-clear cell renal cell carcinoma (non-ccRCC) are limited and based on retrospective analyses, expanded access programs and single arm phase II trials. Therefore the optimal treatment for this subgroup remains uncertain. Methods: A systematic review of electronic databases was conducted to identify publications evaluating the outcomes of patients with non-ccRCC (excluding those with sarcomatoid tumors) treated with different systemic approaches (immunotherapy, chemotherapy, targeted agents, small molecules). The primary endpoint was response rate and secondary endpoints were median progression free (PFS) and overall survival (OS). Where possible, data were pooled in a meta-analysis using the Mantel-Haenszel random-effect modeling. For studies comprising of unselected patients, outcomes of those with non-ccRCC were compared with clear cell renal cell carcinoma (ccRCC). Results: Forty-nine studies comprising 7,799 patients were included: 471 patients were enrolled on studies conducted exclusively in non-ccRCC and 7,328 patients on studies of unselected renal cell carcinoma. Among these, 903 (12%) had non-ccRCC and 6,425 (88%) had ccRCC. For non-ccRCC, overall response rate, median PFS and median OS were 9%, 7.9 and 13.4 months, respectively. By comparison, the overall response rate for ccRCC was 15% (Risk Ratio for response [RR] 0.67, 95% CI 0.52-0.86, p=0.002). This association was independent of type of treatment administered. Among the different novel agents (bevacizumab, lenalidomide, linefanib, sorafenib, sunitinib, pazopanib, everolimus and temsirolimus), sunitinib was significantly less efficacious in non-ccRCC than ccRCC (RR 0.56, 95% CI 0.42-0.72), but there was no significant difference in response rates for sorafenib (RR 0.64, 95% CI 0.31-1.35) or other agents (RR 1.10, 95% CI 0.50-2.44), However, confidence intervals were wide. Results of further analyses will be presented at the meeting. Conclusions: Patients with non-ccRCC have lower response rates than those with ccRCC, but the absolute difference between them is modest. Further study of targeted therapy in non-ccRCC is warranted.
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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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.030 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".