Association of Molecular Subtypes With Differential Outcome to Apalutamide Treatment in Nonmetastatic Castration-Resistant Prostate Cancer
Notice bibliographique
Résumé
IMPORTANCE: There is a need to identify prognostic biomarkers to guide treatment intensification in patients with nonmetastatic castration-resistant prostate cancer (nmCRPC). OBJECTIVE: To examine whether molecular subtypes predict response to apalutamide, using archived primary tumor samples from the randomized, double-blind, phase 3 SPARTAN trial. DESIGN, SETTING, AND PARTICIPANTS: In this cohort study, gene expression data from 233 archived samples from patients with nmCRPC enrolled in the SPARTAN trial were generated using a human exon microarray. The present analysis was conducted from May 10, 2018, to October 15, 2020. INTERVENTIONS: Patients were randomized (2:1) to apalutamide, 240 mg/d, with androgen deprivation therapy (apalutamide+ADT) or placebo+ADT. MAIN OUTCOMES AND MEASURES: Patients were stratified into high-risk and low-risk categories for developing metastases based on genomic classifier (GC) scores for high (GC >0.6) and low to average (GC≤0.6) and into basal and luminal subtypes; associations between these molecular subtypes and metastasis-free survival (MFS), overall survival (OS), and progression-free survival 2 (PFS2) were evaluated using Cox proportional hazards regression and Kaplan-Meier analysis. RESULTS: Median age of the 233 included patients was 73 (range, 49-91) years. A total of 116 of 233 patients (50%) in the SPARTAN biomarker subset had high GC scores. Although all patients receiving apalutamide+ADT had improved outcomes, having high GC scores was associated with the greatest improvement in MFS (hazard ratio [HR], 0.21; 95% CI, 0.11-0.40; P < .001), OS (HR, 0.52; 95% CI, 0.29-0.94; P = .03), and PFS2 (HR, 0.39; 95% CI, 0.23-0.67; P = .001) vs placebo+ADT. In total, 152 of 233 patients (65%) had the basal molecular subtype. Although there were no significant differences in MFS, PFS2, or OS between patients with the luminal vs basal subtype in the placebo+ADT arm, patients with the luminal subtype in the apalutamide+ADT arm had a significantly longer MFS (apalutamide+ADT: HR, 0.40; 95% CI, 0.18-0.91; P = .03; placebo+ADT: HR, 0.66; 95% CI, 0.33-1.31; P = .23) compared with patients with basal subtype; similar trends were observed for OS (apalutamide+ADT: HR, 0.50; 95% CI, 0.25-0.98; P = .04; placebo+ADT: HR, 0.78; 95% CI, 0.38-1.60; P = .50), and PFS2 (apalutamide+ADT: HR, 0.71; 95% CI, 0.42-1.22; P = .22; placebo+ADT: HR, 0.72; 95% CI, 0.38-1.39; P = .33). In regression analysis, the luminal-basal subtype score was significantly associated with MFS in patients receiving apalutamide+ADT (HR, 2.65; 95% CI, 1.15-6.08; P = .02), whereas GC score was significantly associated with MFS in placebo+ADT recipients (HR, 2.09; 95% CI, 1.02-4.27; P = .04). CONCLUSIONS AND RELEVANCE: The findings of this study suggest that the GC score and basal-luminal subtype derived from archived tumor specimens may be biomarkers of response to apalutamide+ADT in the nmCRPC setting. Although overall, the addition of apalutamide to ADT was beneficial, higher-risk and luminal subtypes appeared to benefit most. Obtaining GC scores may be useful for identifying patients for early treatment intensification with apalutamide, and basal-luminal subtyping may be a beneficial approach for patient selection for further treatment intensification in trials combining novel therapies with apalutamide.
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 | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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 ».