Activity of cabozantinib (cabo) after PD-1/PD-L1 immune checkpoint blockade (ICB) in metastatic clear cell renal cell carcinoma (mccRCC)
Bibliographic record
Abstract
Background: Cabo is approved for mccRCC based on trials in which the vast majority of patients were ICB-naive. We analyzed the activity of cabo in mccRCC patients who had progressed on ICB. Methods: We included 69 patients with mccRCC who received cabo after progression on ICB alone or in combination with VEGF or other therapies. Baseline characteristics, best response (BR, investigator-assessed), time to treatment failure (TTF) and overall survival (OS) were analyzed. Results: Median age was 62 years (range 37-78). Median number of prior therapies was 2 (range 1-10). Median time on prior ICB was 3.9 months (range 0.5-38). Type of prior therapy was ICB single agent (54%) or in combination with a VEGF inhibitor (35%) or other therapies (12%). At time of cabo initiation, IMDC risk groups were 6% good, 67% intermediate and 27% poor. BR was 33% PR, 46% SD, 17% PD, 3% unevaluable. Median follow up after cabo initiation was 12 months. At time of analysis, 35% (n = 24) remained on cabo and median TTF was 6.6 (95%CI: 5.3-8.5) months. Of those discontinuing cabo, 58% (n = 26) received additional therapy. At time of analysis, 62% (n = 43) were alive with 1-year OS rate of 53% (95%CI: 37%-66%).Table: 879PBest Response to CaboNPRSDPDUnevaluableAll patients6923(33%)32(46%)12(17%)2(3%)By prior ICB typeICB alone3716(43%)15(41%)5(14%)1(3%)ICB+VEGF246(25%)12(50%)5(21%)1(4%)ICB+Other81(13%)5(63%)2(25%)By prior ICB duration<6mos4212(29%)22(52%)8(19%)>6mos2711(41%)10(37%)4(15%)2(7%) Open table in a new tab Conclusions: Cabo is active in patients treated after PD-1/PD-L1 based ICB independent of prior combination therapy with VEGF inhibitors, with 79% achieving disease control at minimum. These results support the continued use of cabo irrespective of ICB timing. Equal contribution: BAM, AAL. Legal entity responsible for the study: Dana Farber Cancer institute. Funding: Has not received any funding. Disclosure: B.A. McGregor: Consulting: Exelixis, Genentech, Astellas, Seattle-Genetics, Bayer, Jannsen, AstraZeneca, Pfizer Institutional research: Bristol-Myers-Squibb. L.C. Harshman: Advisory: Bayer, Genentech, Dendreon, Pfizer, Medivation/ Astellas, Kew Group, Theragene, Corvus, Merck, Exelixis; Novartis; Research to the institution: Bayer, Sotio, Bristol-Myers Squib, Merck, Takeda, Dendreon/Valient, Jannsen, Medivation/Astellas, Genentech, Pfizer. M.A. Bilen: Consulting: Exelixis. T.K. Choueiri: Research funding: AstraZeneca, B Bristol-Myers-Squibb MS, Exelixis, Genentech, GSK, Merck, Novartis, Peloton, Pfizer, Roche, Tracon, Eisai; Consulting and Advisory role: AstraZeneca, Bayer, Bristol-Myers-Squibb, Cerulean, Eisai, Foundation Medicine Inc., Exelixis, Genentech, Roche, GlaxoSmithKline, Merck, Novartis, Peloton, Pfizer, Prometheus Labs, Corvus, Ipsen. All other authors have declared no conflicts of interest.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".