Association of cabozantinib dose reductions for toxicity with clinical effectiveness in metastatic renal cell carcinoma (mRCC): Results from the Canadian Kidney Cancer Information System (CKCis).
Bibliographic record
Abstract
316 Background: Cabozantinib (cabo) is an oral multi-targeted tyrosine kinase inhibitor (TKI) with activity in mRCC. TKI toxicity, an indicator of adequate drug exposure, has been associated with clinical effectiveness for sunitinib, pazopanib, and axitinib. We explored whether cabo dose reductions (a surrogate for toxicity) were associated with improved clinical outcomes in mRCC. Methods: Using the CKCis database, we performed an analysis of patients treated with cabo in the second-line or later between 2011-2021. We divided the cohort into those needing a dose reduction (DR, defined as less than the starting dose at time of treatment discontinuation) and those who did not (no-DR). We compared outcomes by dose reduction status, including objective response rate (ORR), time to treatment failure (TTF), and overall survival (OS). Results: We identified 260 patients who received cabo, of which 103 (41.0%) needed a DR. Across all lines, the ORR was similar between the DR and non-DR groups: 19.6% vs. 18.9% (p = 0.903) respectively. The median TTF was 12.75 months (95% CI 10.38 – 17.64) in the DR group vs. 6.44 months (95% CI 5.49 – 8.67) in the no-DR group. After adjusting for IMDC risk, the hazard ratio (HR) for TTF comparing DR vs. no-DR was 0.69 (95% CI 0.50 - 0.97, p-value = 0.03). The median OS was 29.6 months (95% CI 19.58 – 42.64) in the DR group vs. 15.28 (95% CI 11.04 – 22.64) in the no-DR group. After adjusting for IMDC risk, the HR for OS comparing DR vs. no-DR was 0.65 (95% CI 0.43 - 0.98, p = 0.04). Conclusions: Cabozantinib dose reductions, a surrogate for toxicity and adequate drug exposure, appear to be associated with improved TTF and OS in mRCC. Toxicity driven/individualized dosing strategies for cabo alone and in combination with immunotherapy, warrant further investigation.[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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".