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Cabozantinib real-world effectiveness in the second-line setting of metastatic renal cell carcinoma: Results from the International Metastatic Renal Cell Carcinoma Database Consortium (IMDC).

2025· article· en· W4407678390 on OpenAlexaff
Martín Zarbá, Dylan E. O’Sullivan, Razane El Hajj Chehade, Rana R. McKay, Sumanta K. Pal, Benoit Beuselinck, Frede Donskov, Aly‐Khan A. Lalani, Megan Crumbaker, Haoran Li, Ajjai Alva, Thomas Powles, Kosuke Takemura, Christian Kollmannsberger, Shirley Wong, Lori Wood, Guillermo de Velasco, Winson Y. Cheung, Toni K. Choueiri, Daniel Yick Chin Heng

Bibliographic record

VenueJournal of Clinical Oncology · 2025
Typearticle
Languageen
FieldMedicine
TopicRenal cell carcinoma treatment
Canadian institutionsDalhousie UniversityMcMaster UniversityUniversity of Calgary
Fundersnot available
KeywordsCabozantinibMedicineRenal cell carcinomaOncologyInternal medicineUrologyDatabase

Abstract

fetched live from OpenAlex

479 Background: Cabozantinib is approved as a subsequent therapy for patients with metastatic renal cell carcinoma (mRCC) based on the METEOR trial. However, only 5% of patients in this trial received prior immunotherapy. Methods: We identified patients with mRCC from the IMDC who were treated with cabozantinib in the second-line (2L) setting from 2010 to 2023. These patients were stratified by IMDC risk groups and first-line (1L) treatment. We analyzed overall response rate (ORR), time to next treatment (TTNT), treatment duration (TD), overall survival (OS) and performed a multivariable analysis adjusted by IMDC criteria at 2L. Results: A total of 603 patients were identified. Baseline characteristics are summarized in the table. For the entire cohort, the ORR was 25.7%, TTNT was 10.1 months (mo), TD was 8.9 mo and mOS was 19 mo. Among patients treated with 1L ipilimumab/nivolumab (n=190), anti-PD1 + TKI (n=148), and TKI alone (n=207), cabozantinib showed an ORR of 27.2%, 26.4%, and 25%, respectively; a median TTNT of 9.9, 10.3, and 9.7 mo; a median TD of 9.4, 8.2, and 8.3 mo. Median OS was 18.6, 17.6, and 21.3 mo, respectively. A multivariable analysis was unable to demonstrate that first-line ORR (CR/PR vs SD vs PD) or TTNT (< 12 vs ≥ 12 mo) predicts for second-line cabozantinib ORR in the overall cohort and by first-line therapy type. Specifically, patients with stable disease or with partial and complete response in 1L were associated with an OR for a response of 0.99 (95% CI 0.52-1.92) or 1.23 (95% CI 0.61-2.49), respectively. Similarly, a first-line TTNT of ≥12 months had an OR for response of 1.04 (95% CI 0.59-1.82). Conclusions: This study demonstrates that cabozantinib maintains efficacy comparable to that observed in the METEOR trial in a real-world setting, including in patients with prior immunotherapy combination therapies. Efficacy of 1L treatment does not predict efficacy of 2L cabozantinib. Baseline characteristics. Variable Overall (N = 603) IO-IO (N = 190) IO-TKI (N = 148) TKI Alone (N = 207) Other (N = 58) p-value Non clear cell histology 107 (17.7) 33 (17.4) 26 (17.6) 28 (13.5) 20 (34.5) 0.003 Nephrectomy 416 (69.0) 97 (51.1) 110 (74.3) 166 (80.2) 43 (74.1) <0.001 1st line IMDC Risk Fav/Int/Poor 83 (13.8)/296 (49.1)/101 (16.7) 10 (5.3)/100 (52.6) /46 (24.2) 37 (25)/62 (41.9) /20 (13.5) 29 (14)/99 (47.8)/27 (13) 7 (12.1)/35 (60.3)/8 (13.8) <0.001 2nd line IMDC risk Fav/Int/Poor 56 (9.3)/269 (44.6)/108 (17.9) 7 (3.7)/90 (47.4)/45 (23.7) 25 (16.9)/63 (42.6)/24 (16.2) 19 (9.2)/87 (42.0)/32 (15.5) 5 (8.6)/29 (50.0)/7 (12.1) 0.002 Greater than 1 site of Metastasis 473 (78.4) 146 (76.8) 114 (77.0) 161 (77.8) 45 (77.6) 0.722 Brain Metastasis 36 (6.0) 18 (9.5) 5 (3.4) 13 (6.3) 0 (0.0) 0.022 Bone Metastasis 221 (36.7) 74 (38.9) 60 (40.5) 70 (33.8) 17 (29.3) 0.326 Liver Metastasis 106 (17.6) 32 (16.8) 26 (17.6) 39 (18.8) 9 (15.5) 0.926

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.099
GPT teacher head0.418
Teacher spread0.320 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes1
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