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Clinical characteristics and determinants of primary refractory metastatic renal cell carcinoma (mRCC): An International Metastatic Database Consortium (IMDC) study.

2025· article· en· W4410822468 on OpenAlexaff
Karl Semaan, Marc Eid, Wanling Xie, Razane El Hajj Chehade, Liliana Ascione, David Maj, Martín Zarbá, J. Connor Wells, Ulka N. Vaishampayan, Martín Ángel, Jae‐Lyun Lee, Kosuke Takemura, Christian Kollmannsberger, Georg A. Bjarnason, José Manuel Ruiz-Morales, Daniel Yick Chin Heng, Toni K. Choueiri

Bibliographic record

VenueJournal of Clinical Oncology · 2025
Typearticle
Languageen
FieldMedicine
TopicRenal cell carcinoma treatment
Canadian institutionsSunnybrook Health Science CentreBC Cancer AgencyUniversity of Calgary
Fundersnot available
KeywordsMedicineRenal cell carcinomaRefractory (planetary science)OncologyInternal medicineCancer research

Abstract

fetched live from OpenAlex

4549 Background: Immune checkpoint inhibitor (ICI)-based regimens, including ICI + ICI and ICI+VEGF-targeted therapy (VEGF-TT), represent the current standard of care for first line (1L) in mRCC. A subset of patients (pts) experiences primary refractory disease, defined as progressive disease (PD) as best response. The aim of this study is to investigate the clinical characteristics and determinants of pts with primary refractory mRCC. Methods: Pts with mRCC treated with 1L ICI-based regimens from the IMDC were included. Pts were categorized as primary refractory (PD as best response evaluated per RECIST 1.1 criteria) and non-primary refractory (stable disease or partial/complete response as best response). Baseline characteristics were compared using a Chi-Square test. Independent factors associated with primary refractory mRCC were identified using a logistic regression. Results: In total, 2001 pts were included, of which 1301 (65%) were treated with dual ICI, and 701 (35%) with ICI+VEGF-TT. Of 2001 pts, 494 (24%) experienced PD at first restaging. Primary refractory and non-primary refractory groups did not differ by age or gender. The primary refractory group had more pts treated with dual ICI (76 vs. 62%), more pts with poor IMDC risk (29 vs. 19%), and more pts with non-clear cell RCC (27 vs. 19%; all p < 0.001). The primary refractory group had shorter diagnosis to treatment interval (mean: 1.6 vs. 2.3 years), lower KPS (median: 80 vs. 90), higher rate of anemia (65% vs. 52%), neutrophilia (11% vs. 9%), and thrombocytosis (30 vs 22%; all p < 0.001). The primary refractory group had more liver (24 vs. 17%; p < 0.001), bone (39 vs 32%; p < 0.001) and lymph nodes metastasis (52 vs. 47%; p = 0.03) at the start of 1L therapy. On multivariable analysis, independent factors associated with primary refractory RCC were low KPS, and the presence of liver metastasis or bone metastasis (Table). Dual ICI regimen was associated with a 1.8-fold increase of primary refractory RCC. Conclusions: In pts with mRCC, low KPS and the presence of liver or bone metastasis are independent risk factors for the development of primary refractory disease. Primary refractory disease is more commonly observed in pts receiving dual ICI compared to those on ICI+VEGF-TT regimen. Multivariable analysis for independent factors associated with primary refractory RCC. OR Lower 95% CI Upper 95% CI p-value Diagnosis to treatment interval < 1 year (yes vs. no) 1.2 0.9 1.6 0.2 Anemia (yes vs. no) 1.37 1.1 1.8 0.03 Low KPS(<80: yes vs. no) 1.9 1.4 2.5 <0.001 Neutrophilia (yes vs. no) 1.3 1 1.8 0.09 Thrombocytosis (yes vs. no) 0.9 0.7 1.3 0.6 Liver metastasis (yes vs. no) 1.7 1.3 2.3 <0.001 Lymph Nodes metastasis (yes vs. no) 1.3 1 1.6 0.07 Bone metastasis (yes vs. no) 1.3 1.03 1.7 0.03 Dual ICI (vs. ICI+VEGF-TT) 1.8 1.37 2.4 <0.001 Non-clear cell RCC (vs. clear cell) 1.3 1 1.8 0.06 Nephrectomy (yes vs. no) 0.9 0.7 1.2 0.6

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 score0.006
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.165
GPT teacher head0.474
Teacher spread0.309 · 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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