PS1123 IMPACT OF MAJOR GENOMIC ALTERATIONS ON OUTCOME OF RELAPSED/REFRACTORY CHRONIC LYMPHOCYTIC LEUKEMIA PATIENTS TREATED WITH VENETOCLAX PLUS RITUXIMAB IN THE PHASE 3 MURANO STUDY
Notice bibliographique
Résumé
Background: We reported superior efficacy for venetoclax + rituximab (VenR) vs bendamustine + rituximab (BR) in relapsed/refractory (R/R) chronic lymphocytic leukemia (CLL) in the MURANO study (NCT02005471), with a significant progression‐free survival (PFS) benefit and sustained undetectable minimal residual disease (uMRD), irrespective of historical risk factors for poor response to chemoimmunotherapy, e.g., unmutated IGHV and del(17p) and/or TP53 mutation. However, the impact of other recurrent somatic mutations and of cytogenetic alterations on outcome of R/R patients (pts) treated with fixed‐duration VenR has not been examined. Aims: To interrogate clinical impact of the major somatic mutations, and of cytogenetic high‐risk features defined by array‐based analysis of genomic complexity (aGC), on outcome in pts treated with VenR and BR in MURANO. Methods: Whole exome sequencing (WES) and aGC by high‐density array‐comparative genomic hybridization were performed on baseline DNA specimens, available from 313/389 enrolled pts. Array‐based genomic complexity was defined as having ≥3 or ≥5 genetic aberrations. Kaplan–Meier estimates and Cox proportional‐hazards models were used to analyze PFS. uMRD was defined as <1 CLL cell in 10,000 leukocytes in peripheral blood (PB). Results: At least one of the 9 mutated driver genes examined here was identified in 234/313 (74.8%) pts from both arms; clonal mutations of ATM occurred in 30.7% of pts, TP53 in 25.6%, SF3B1 in 22.0%, NOTCH1 in 13.7%, BRAF in 8.3%, BIRC3 in 8.0%, and NRAS/KRAS/MYD88 in 1.6% each. Mutation frequency was equally distributed between treatment arms. Prevalence of all CLL key mutated genes will be presented. After 36.0 months’ median follow‐up, a PFS benefit was observed consistently with VenR over BR across mutated (mut) and wildtype (WT) subgroups for ATM , TP53, SF3B1, NOTCH1, ( Figure 1 ) and BIRC3. Median PFS for VenR was not reached in TP53 mut or WT pts, compared with medians of 12.2 months (mo; HR 0.11, 95% CI 0.055–0.24) and 21.6 mo (HR 0.16, 95% CI 0.098–0.25), respectively, for BR. Mutation frequency was too low for assessment in the other subgroups. Within treatment arm, inferior PFS was observed with both VenR and BR for TP53 mut pts, but only with VenR for NOTCH1 mut pts, representing a significant treatment‐dependent prognostic effect (interaction p = 0.007). Two‐year PFS with VenR was 76% for NOTCH1 mut pts vs 89% for WT. The negative impact on PFS in NOTCH1 mut VenR pts was further confirmed by multivariate analysis (MVA) with IGHV, del(17p)/ TP53 , B2 M, stage, and age as covariates (HR 0.54, 95% CI 0.22–1.30). NOTCH1 mutation was enriched in pts with disease progression (47.1%) vs those without (13.9%) in VenR but not BR (69.2% vs 70.8%, respectively). The PFS observations were consistent with PB uMRD rates; the PB uMRD rate was lower in NOTCH1 mut pts (23.5% vs 49.3% in WT pts) at the end of Ven treatment visit (∼24 mo from start of treatment). Assessments of aGC are ongoing and impact on clinical outcomes will be presented. Summary/Conclusion: We assessed the mutational landscape of R/R CLL by WES and confirmed prior mutation frequency reports. A superior PFS benefit was observed for VenR vs BR in all clinical and molecular subgroups assessed, including the key CLL driver mutations reported here. NOTCH1 mutations may define a new high‐risk pt subgroup for VenR. MVA, further validation and deep sequencing for subclones are needed, given the small size of the mutated cohort, and to address the biological basis of the findings. image
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| 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 tête enseignante, 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 ».