MétaCan
Menu
Back to cohort

Biomarker analysis of the phase 3 KEYNOTE-426 study of pembrolizumab (P) plus axitinib (A) versus sunitinib (S) for advanced renal cell carcinoma (RCC).

2024· article· en· W4399393998 on OpenAlexaff
Brian I. Rini, Elizabeth R. Plimack, V.P. Stus, Rustem Gafanov, Tom Waddell, Dmitry Nosov, Frédéric Pouliot, B. Yа. Alekseev, Denis Soulières, Sérgio Jobim Azevedo, Delphine Borchiellini, Rodolfo F. Perini, Julia F. Markensohn, L. Rhoda Molife, Yiwei Zhang, Michael Nebozhyn, Andrey Loboda, Amir Vajdi, Thomas Powles

Bibliographic record

VenueJournal of Clinical Oncology · 2024
Typearticle
Languageen
FieldMedicine
TopicRenal cell carcinoma treatment
Canadian institutionsCentre Hospitalier de l’Université de MontréalUniversité Laval
Fundersnot available
KeywordsMedicineAxitinibSunitinibRenal cell carcinomaPembrolizumabBiomarkerOncologyInternal medicineKidney cancerCancerImmunotherapy

Abstract

fetched live from OpenAlex

4505 Background: P + A improved OS, PFS, and ORR over S in 1L advanced RCC in KEYNOTE-426 (NCT02853331).Here, we present exploratory biomarker results including RNAseq, WES, and PD-L1. Methods: Patients (pts) with treatment-naive advanced RCC were randomly assigned 1:1 to P + A or S. Association between T-cell–inflamed gene signature (TcellinfGEP), angiogenesis gene signature (RNAseq), and PD-L1 CPS (22C3 IHC) with clinical outcomes were tested at prespecified α=0.05. Other RNA signatures (Cristescu et al. Clin Cancer Res. 2022. 2022;28:1680) and molecular subtypes, based on clustering identified from IMmotion151 (Motzer et al. Cancer Cell. 2020;38:803), were tested at prespecified α = 0.10 after multiplicity adjustment. DNA mutations ( VHL, PBRM1, SETD2, and BAP1) by WES were tested at prespecified α = 0.10 after multiplicity adjustment. Results: Of 861 pts, 369 (P + A) and 361 (S) had archival samples for RNAseq; 347 (P + A) and 351 (S) had WES samples. PD-L1 CPS was negatively associated with OS ( P=0.013) for S. There was a strong positive association of TcellinfGEP with OS ( P=0.003), PFS ( P<0.0001), and ORR ( P<0.0001) for P + A. Angiogenesis was positively associated with OS ( P=0.013) for P + A; there was a strong positive association with OS ( P<0.0001), PFS ( P<0.001), and ORR ( P=0.002) for S. For other RNA signatures, positive association with mMDSC was found for PFS ( P=0.018) and ORR ( P=0.093) with P + A. For S, positive association was found with hypoxia (OS, P=0.034; ORR, P=0.071) and negative associations with MYC (OS, P<0.001; PFS, P=0.012) and proliferation (OS, P=0.002). Across all molecular clusters, ORR favored P + A over S, with the highest P + A ORR in the immune/proliferative cluster (Table). By WES, PBRM1 mutation had positive association with ORR ( P=0.004) and PFS ( P=0.079) for P + A. For S, positive associations were observed with OS for VHL ( P=0.073) and PBRM1 ( P=0.001) mutations and a negative one observed for BAP1 mutation ( P=0.046). P + A improved ORR over S regardless of mutational status. Conclusions: There was a strong relationship of TcellinfGEP with clinical outcomes with P + A. Angiogenesis was positively associated with outcomes with S and only with OS with P + A. Further understanding the role of the immune microenvironment in combination therapy will be critical to advance treatment strategies. Clinical trial information: NCT02853331 . [Table: see text]

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)0.003
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: Meta-analysis · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.204
GPT teacher head0.497
Teacher spread0.293 · 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 designMeta-analysis
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".

Quick stats

Citations12
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Clinical OncologySame topicRenal cell carcinoma treatmentFrench-language works237,207