CABOSEQ: The Effectiveness of Cabozantinib in Patients With Treatment Refractory Advanced Renal Cell Carcinoma: Results From the International Metastatic Renal Cell Carcinoma Database Consortium (IMDC)
Bibliographic record
Abstract
BACKGROUND: There are limited data evaluating the activity of cabozantinib (CABO) as second line (2L) therapy post standard of care ipilimumab-nivolumab (IPI-NIVO) or immuno-oncology(IO)/vascular endothelial growth factor inhibitor (VEGFi) combinations (IOVE). MATERIALS AND METHODS: Using the IMDC database, we sought to identify the objective response rate, time to treatment failure (TTF) and overall survival (OS) of 2L CABO after IPI-NIVO, IOVE combinations, pazopanib or sunitinib (PAZ/SUN) or other first line (1L) therapies. Multivariable Cox regression, adjusted for underlying differences in IMDC groups, was used to compare differences in OS for 2L CABO based on preceding therapy. RESULTS: Three hundred and forty-six patients received 2L CABO (78 post IPI NIVO, 46 post IOVE, 161 post PAZ/SUN, 61 post Other). Of the entire cohort, 12.6%, 62.6%, and 24.8% were IMDC favourable, intermediate, and poor risk, respectively. Patients that received 1L IPI-NIVO had a median OS of 21.4 (95% CI, 12.1 - NE [Not evaluable]) months compared to 15.7 (95% CI, 9.3 - NE) months in 1L IOVE and 20.7 (95% CI, 15.6 - 35.6) months in 1L PAZ/SUN, P = .28. Median TTF from the initiation of 2L CABO in the overall population was 7.6 (95% CI, 6.6 - 9.0) months. We were unable to detect a significant difference in 2L CABO OS based on type of 1L therapy received: 1L IPI-NIVO (reference group) vs. 1L IOVE HR 1.73 (95% CI, 0.83 - 3.62 P = .14), 1L PAZ/SUN 1.16 (95% CI, 0.67 - 2.00 P = .60), however given the retrospective observational nature of this work a lack of sufficient power may contribute to this. CONCLUSION: In a large real world dataset, we identified clinically meaningful activity of 2L CABO after all evaluated contemporary 1L therapies, irrespective of whether the 1L regimen included a VEGFi. These are real world benchmarks with which to counsel our patients.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".