Second-line VEGF TKI after IO combination therapy: Results from the International Metastatic Renal Cell Carcinoma Database Consortium (IMDC).
Bibliographic record
Abstract
684 Background: Immuno-Oncology (IO) combinations are standard of care first-line treatment for metastatic renal cell carcinoma (mRCC). Data on therapy with vascular endothelial growth factor (VEGF) tyrosine kinase inhibitors (TKI) post-progression on IO-combination therapy are limited. Methods: Using the IMDC, a retrospective analysis was done on mRCC patients treated with second-line VEGF TKIs after receiving IO combination therapy. Patients received first-line ipilimumab+nivolumab (IOIO) or anti-PD(L)1+anti-VEGF (IOVE). Baseline variables and second-line IMDC risk factors were collected. Overall response rates (ORR), time to treatment failure (TTF) and overall survival (OS) were determined. Multivariable Cox regression analysis was performed. Results: 142 patients were included. 75 patients received IOIO and 67 received IOVE pretreatment. The ORR of 2nd line therapy was 17/46 (37%) and 7/57 (12%) in the IOIO and IOVE pretreated groups, respectively (p<0.01). 2nd-line TTF was 5.4 months (95% CI 4.1-8.3) for the IOIO- and 4.6 months (95% CI 3.7-5.8) for the IOVE-pretreated group (p=0.37). 2nd-line median OS was 17.2 months (95% CI 10.8-35.1) and 11.8 months (95% CI 9.9-21.3) for the prior IOIO and IOVE groups, respectively (p=0.13). The hazard ratio adjusted by IMDC for IOVE vs IOIO pretreatment was 1.22 (95% CI 0.73-2.07, p=0.45) for 2nd line TTF and 1.43 (95% CI 0.74-2.8, p=0.29) for 2nd line OS. Conclusions: VEGF TKIs show activity after combination IO therapy. Response rates are higher in patients treated with VEGF TKIs after first-line IOIO compared to after IOVE. In patients with VEGF TKI after IOIO or IOVE, no difference in OS and TTF was observed.[Table: see text]
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".