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Real-world outcomes of first-line dual immunotherapy versus combination VEGF immunotherapy in intermediate-poor risk metastatic renal cell carcinoma: Results from the International Metastatic Renal Cell Carcinoma Data Consortium (IMDC).

2025· article· en· W4407678084 on OpenAlexaff
David Maj, Martín Zarbá, J. Connor Wells, Razane El Hajj Chehade, Zeynep İrem Özay, Sumanta Kumar Pal, Benoit Beuselinck, Elizabeth Liow, Ajjai Alva, Thomas Powles, Georg A. Bjarnason, Lori Wood, Ravindran Kanesvaran, Guillermo de Velasco, Jae‐Lyun Lee, Arnoud J. Templeton, Rana R. McKay, Cristina Suárez, Toni K. Choueiri, Daniel Yick Chin Heng

Bibliographic record

VenueJournal of Clinical Oncology · 2025
Typearticle
Languageen
FieldMedicine
TopicRenal cell carcinoma treatment
Canadian institutionsDalhousie UniversityQueen Elizabeth II Health Sciences CentreSunnybrook HospitalUniversity of Calgary
Fundersnot available
KeywordsMedicineRenal cell carcinomaImmunotherapyOncologyInternal medicineSecond lineCancer researchFirst lineCancer

Abstract

fetched live from OpenAlex

477 Background: Multiple phase 3 trials have established either dual immunotherapy with ipilimumab and nivolumab (IPI-NIVO) or immunotherapy with a VEGFR inhibitor (IO-VE) as standard-of-care first-line therapy for metastatic clear cell renal cell carcinoma (mRCC). We focused this analysis on patients with IMDC intermediate or poor risk where either IPI-NIVO or IO-VE would be standard. Methods: Using the IMDC database, we performed a retrospective analysis of patients with intermediate or poor risk disease (1 or more IMDC risk factors) who received first-line therapy with IPI-NIVO or an approved IO-VE combination (avelumab-axitinib, nivolumab-cabozantinib, pembrolizumab-axitinib, or pembrolizumab-lenvatinib). Baseline characteristics, objective response rates (ORR), time to next therapy (TTNT), and overall survival (OS) were compared between IPI-NIVO and IO-VE regimens by Cox regression analyses. Results: A total of 1,523 patients were identified of whom 72.9% received IPI-NIVO and 28.2% received IO-VE. Baseline characteristics of our cohort are summarized in the table. Median follow-up was 24 months. The ORR was lower with IPI-NIVO compared with IO-VE (39.1% vs 48.0%; p = 0.004). Median TTNT was shorter in IPI-NIVO than IO-VE (10.4 months, 95% CI 9.4-11.7 vs 18.6 months, 95% CI 16.3-24.6; p <0.0001). Median OS was similar between groups at 35.4 months (95% CI: 31.4-41.9) and 35.6 months (95% CI: 28.9-44.1) for IPI-NIVO and IO-VE respectively (p = 0.277), and there were no significant differences when intermediate and poor risk groups were analyzed separately. Median OS in IO-VE vs IPI-NIVO was not significantly different when adjusted by IMDC criteria, brain, bone and liver metastases (HR 0.87, 95% CI 0.71-1.06; p=0.165). Conclusions: In a real-world setting amongst patients with IMDC intermediate-poor risk disease, IPI-NIVO and IO-VE strategies show similar survival outcomes although longer follow-up will be necessary to assess the tails of these survival curves. IO-VE is associated with a greater response rate compared with IPI-NIVO. Baseline characteristics. IPI-NIVON = 1110 IO-VEN = 413 p-value Male 808 (72.8) 303 (73.4) 0.823 IMDC Intermediate/Poor Risk 730/280 (65.8/34.2) 274/139 (66.3/33.7) 0.832 Pre-existing autoimmune condition 16 (2.6) 12 (4.6) 0.133 Non-clear cell histology 139 (15.9) 57 (16.4) 0.844 Sarcomatoid 147 (21.3) 50 (17.2) 0.1437 Nephrectomy 613 (55.3) 239 (57.9) 0.374 Brain metastasis 90 (8.4) 23 (5.7) 0.084 Bone metastasis 373 (34.3) 173 (42.5) 0.003 Liver metastasis 218 (20.2) 65 (16.0) 0.063

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 score0.004
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.118
GPT teacher head0.416
Teacher spread0.299 · 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
Has abstractyes

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