Cabozantinib real-world effectiveness in the first through fourth-line settings for the treatment of metastatic renal cell carcinoma (mRCC): Results from the International mRCC Database Consortium (IMDC).
Bibliographic record
Abstract
639 Background: Cabozantinib (Cabo) is approved for mRCC patients based on the METEOR and CABOSUN trials. The real-world effectiveness of Cabo in mRCC patients in the first- (1L), second- (2L), third- (3L) and fourth-line (4L) settings requires characterization. Methods: This retrospective analysis included mRCC patients who were treated with Cabo and stratified using IMDC risk groups. Overall response rate (ORR), time to treatment failure (TTF), and overall survival (OS) were calculated. Results: A total of 413 patients (82.6% with clear cell and 17.4% non-clear cell) were identified. The median age was 57 years. Overall, 63% of patients had a Karnofsky performance status score of >80 and 82.6% had prior nephrectomy. 23.1%, 75.4% and 88.3% of patients received immunotherapy as a prior line of treatment, before receiving Cabo in the 2L, 3L and 4L settings, respectively. For patients treated with 1L PD(L)1 combination or monotherapy (n=31), 2nd line Cabo had ORR of 20.8%, median TTF of 5.4 months and median OS of 17.4 months. When segregated into IMDC favorable, intermediate, and poor risk groups, the median OS was 34.8 months (95% CI 5.52-NR), 18.0 months (12.3-35.6) and 9.8 months (7.4-20.8), p=0.0088, respectively for 2L Cabo; and 31.5 months (23.6-39.3), 20.5 months (10.1-21.8), and 6.9 months (4.1-10.9), p=<0.0001), respectively for 3L Cabo. Conclusions: The ORR and TTF of Cabo were maintained from the 1L to the 4L therapy settings. In the 2L and 3L settings, the IMDC criteria appropriately stratified patients into favorable, intermediate and poor risk groups for OS. Cabo has activity after first line immunotherapy.[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 | 0.005 | 0.006 |
| 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 | 0.000 |
| 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".