Biomarker analyses in patients with advanced renal cell carcinoma (aRCC) from the phase 3 CLEAR trial.
Notice bibliographique
Résumé
4504 Background: In the primary analysis of CLEAR, lenvatinib + pembrolizumab (L+P) significantly improved efficacy vs sunitinib (S) in treatment-naïve patients with aRCC (Motzer 2021). Results were confirmed at the final prespecified OS analysis (Motzer 2024). We report biomarker analyses from CLEAR. Methods: PD-L1 IHC 22C3 pharmDx and NGS assays (ImmunoID NeXT platform: WES and RNA-Seq) were performed on archival tumor specimens. To identify somatic alterations including mutations and copy-number variations, paired PBMC samples were used as reference. For RNA-Seq/IHC-derived analyses, a continuous value analysis was performed adjusting by KPS score for: each gene-signature score (T-cell inflamed gene-expression profile [GEP], and non-GEP signatures including proliferation, angiogenesis, hypoxia, MYC, WNT, and other signatures [Cristescu 2022]) vs best overall response (BOR); non-GEP signatures vs BOR adjusted by GEP; and PD-L1 CPS vs BOR. Cutoff analyses were performed for biomarkers that showed significant association in the continuous value analysis. Cutoff values (1st tertile of GEP, or median of non-GEP, signatures) were determined based on combined L+P and S arms. WES analyses were descriptively summarized if TMB/INDEL burden and mutation status of key RCC driver genes were associated with BOR. Results: There were no notable differences in baseline characteristics and tumor responses in biomarker analysis sets vs the ITT population. In the L+P arm, the continuous GEP signature score was not associated with BOR. The MYC signature score was negatively associated with BOR (2-sided test, significance criteria 0.1; FDR-adjusted p=0.013/0.012 with/without adjustment by GEP signature score, respectively). The ORRs (95% CI) for the MYC-high and -low groups were 66.3% (56.1-75.6) and 84.0% (75.0-90.8), respectively. In the S arm, the continuous GEP signature score was positively associated with BOR (2-sided test, significance criteria 0.05; p=0.010). The ORRs (95% CI) for the GEP-high and -low groups were 46.9% (38.1-55.9) and 28.8% (18.3-41.3), respectively. The angiogenesis signature was positively associated with BOR (2-sided test, significance criteria 0.1; FDR-adjusted p=0.046/0.088 with/without adjustment by GEP signature score, respectively). The ORRs (95% CI) for the angiogenesis-high and -low groups were 52.1% (41.6-62.5) and 30.4% (21.7-40.3), respectively. PD-L1 CPS and TMB/INDEL burden were not associated with BOR in L+P or S arms. ORR was higher with L+P vs S, regardless of the deleterious mutation status of BAP1, VHL, PBRM1, SETD2, and KDM5C—frequently mutated genes in RCC. Conclusions: The superiority of L+P vs S in ORR does not appear to be impacted by gene-expression signatures for tumor-induced proliferation, angiogenesis, hypoxia, MYC, or WNT, or by PD-L1 status, TMB/INDEL burden or mutation status of RCC driver genes. Clinical trial information: NCT02811861 .
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».