Efficacy of tyrosine kinase inhibitors (TKI) after combination ipilimumab plus nivolumab (I/N) in metastatic clear cell renal cell carcinoma (ccmRCC) patients: Results from the Canadian Kidney Cancer Information System (CKCis).
Bibliographic record
Abstract
346 Background: The use of I/N is a proven first-line option for patients with intermediate/poor IMDC prognostic criteria. The use of vascular endothelial growth factor inhibitors such as sunitinib have shown activity in the treatment of ccmRCC, but their effectiveness post I/N needs better characterization. This study aims to demonstrate the efficacy of sunitinib, and other TKI agents post I/N in ccmRCC in a real world setting. Methods: Patients with ccmRCC who had received I/N and were subsequently treated with TKI between Jan 1, 2011 and December 31, 2019 were identified from CKCis. Time to treatment failure (TTF – time from start of first subsequent TKI to discontinuation for any reason) and overall survival (OS) – time from first subsequent TKI to death) were calculated using the Kaplan-Meier method. Cox regression was performed to adjust for IMDC criteria. RECIST criteria was used to determine best overall response (ORR) of TKI radiographically. Results: 64 patients were treated with TKI post I/N. Characteristics and outcomes are listed in the table. Of the second-line TKI patients, 51 received sunitinib, 10 received pazopanib and 3 received other TKI. Reasons for second-line TKI discontinuation are: 28% toxicity, 34% progression, 7% other reasons while 31% remain on treatment. Median follow-up time was 12.9m. ORR for second-line TKI overall and second-line sunitinib was 30.0% and 29.4%, respectively. Conclusions: These data show that TKI are active after I/N in ccmRCC. TTF may underestimate PFS due to the large number of patients discontinuing treatment for toxicity and not progression. Efficacy of second-line TKI post I/N in this dataset is similar that of first-line sunitinib from recent randomized phase III trials, suggesting that there may be no significant loss of TKI activity after having received first-line I/N. Overall, these data support the use of TKI after I/N.[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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".