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Updated outcomes of patients with metastatic non-clear cell renal cell carcinoma (mnccRCC) treated with first-line (1L) therapies: Results from the International Metastatic Renal Cell Carcinoma Database Consortium (IMDC).

2025· article· en· W4407721050 on OpenAlexaff
Kosuke Takemura, Jeffrey Graham, David Maj, Martín Zarbá, J. Connor Wells, Razane El Hajj Chehade, Marc Eid, Eddy Saad, Renée Maria Saliby, Jae‐Lyun Lee, Frede Donskov, Benoit Beuselinck, Evon Jude, Rana R. McKay, Naveen S. Basappa, Sumanta K. Pal, Camillo Porta, Neeraj Agarwal, Toni K. Choueiri, Daniel Yick Chin Heng

Bibliographic record

VenueJournal of Clinical Oncology · 2025
Typearticle
Languageen
FieldMedicine
TopicRenal cell carcinoma treatment
Canadian institutionsUniversity of AlbertaUniversity of CalgaryUniversity of ManitobaCancerCare Manitoba
Fundersnot available
KeywordsMedicineRenal cell carcinomaClear cell renal cell carcinomaOncologyInternal medicineCarcinoma

Abstract

fetched live from OpenAlex

494 Background: Immuno-oncology (IO)-based combination therapy with or without anti-vascular endothelial growth factor (VE) has become a standard of care for mnccRCC. However, real-world evidence on the effectiveness of contemporary therapies over traditional targeted therapies against mnccRCC is limited. Methods: Using the IMDC, patients with mnccRCC were classified into five subgroups based on 1L therapies: IOIO, IOVE, CABO, SUN/PAZ, and mammalian target of rapamycin (mTOR). Baseline patient characteristics, clinician assessment of objective response rates (ORRs) as per RECIST 1.1, and overall survival (OS) were compared across 1L therapies. Results: Of 1551 patients with mnccRCC, 180 (11.6%), 90 (5.8%), 45 (2.9%), 1039 (70.0%), and 197 (12.7%) received IOIO, IOVE, CABO, SUN/PAZ, and mTOR, respectively. The most common histology was papillary in 725 (46.7%), followed by unclassified in 287 (18.5%), chromophobe in 200 (12.9%), and translocation in 84 (5.4%), while sarcomatoid dedifferentiation was found in 236 (15.2%). The IMDC prognostic categories (favourable/intermediate/poor) differed significantly across 1L therapies: IOIO (6.7%/52.3%/40.9%), IOVE (26.9%/44.9%/28.2%), CABO (16.1%/53.2%/30.7%), SUN/PAZ (16.1%/53.2%/30.7%), and mTOR (9.6%/51.4%/39.0%). For the papillary subtype, ORRs and median OS were better in IOIO (26.1% and 31.9 months), IOVE (31.0% and 33.2 months), and CABO (36.8% and 30.7 months) than in SUN/PAZ (12.8% and 17.2 months) and mTOR (3.4% and 13.1 months), whereas for the unclassified subtype, CABO did not appear to be as effective as IOIO and IOVE. For the chromophobe and translocation subtypes, there was no significant relationship between 1L therapies and the outcomes. IOIO was associated with the highest ORR and the longest median OS for mnccRCC with sarcomatoid dedifferentiation. Conclusions: Contemporary therapies seem to be effective against mnccRCC, although histology-specific strategies may guide personalized treatment selection. Histologic subtype IOIO IOVE CABO SUN/PAZ mTOR p-value Papillary (n = 54) (n = 31) (n = 25) (n = 499) (n = 116) ORR, n (%) 12/46 (26.1%) 9/29 (31.0%) 7/19 (36.8%) 52/407 (12.8%) 3/87 (3.4%) <0.001 Median OS (95% CI), months 31.9 (20.3–NA) 33.2 (18.6–NA) 30.7 (17.5–48.7) 17.2 (15.3–19.6) 13.1 (11.1–15.4) 0.002 Unclassified (n = 50) (n = 20) (n = 10) (n = 179) (n = 28) ORR, n (%) 14/45 (31.1%) 5/17 (29.4%) 0/7 (0%) 22/149 (14.8%) 1/22 (4.5%) 0.018 Median OS (95% CI), months 18.8 (13.8–29.0) 15.5 (11.1–NA) 7.6 (2.1–NA) 13.6 (11.0–16.7) 6.1 (3.6–9.5) <0.001 mnccRCC with sarcomatoid dedifferentiation (n = 47) (n = 12) (n = 2) (n = 141) (n = 34) ORR, n (%) 16/41 (39.0%) 2/10 (20.0%) 0/2 (0%) 16/106 (15.1%) 1/26 (3.8%) 0.003 Median OS (95% CI), months 31.9 (19.3–NA) 14.0 (2.1–NA) 14.3 (7.6–NA) 12.8 (7.0–13.9) 6.6 (3.6–12.5) <0.001

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)0.001
metaresearch head score (Gemma)0.002
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 score0.011
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
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 head0.060
GPT teacher head0.352
Teacher spread0.292 · 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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Citations2
Published2025
Admission routes1
Has abstractyes

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