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Enregistrement W7108708946 · doi:10.1182/blood-2025-4776

Prognostic impact of molecular genetic and cytogenetic alterations in newly diagnosed acute myeloid leukemia treated with azacitidine and venetoclax: A real-world cohort study

2025· article· en· W7108708946 sur OpenAlexaff

Notice bibliographique

RevueBlood · 2025
Typearticle
Langueen
DomaineMedicine
ThématiqueAcute Myeloid Leukemia Research
Établissements canadiensPrincess Margaret Cancer Centre
Organismes subventionnairesnon disponible
Mots-clésNPM1AzacitidineVenetoclaxIDH1Myeloid leukemiaIDH2Proportional hazards modelCohortCEBPASurvival analysis

Résumé

récupéré en direct d'OpenAlex

Abstract Introduction:Azacitidine and venetoclax (Aza-Ven) is the standard of care for older or unfit patients (pts) with newly diagnosed AML. However, outcomes remain heterogeneous, and the prognostic relevance of genetic abnormalities is not yet fully established in this setting. Risk stratification models have been developed for pts treated with Aza-Ven, such as the European LeukemiaNetwork (ELN) 2024 classification, but validation in independent cohorts remains limited. Methods:We retrospectively analyzed pts treated with Aza-Ven at the Princess Margaret Cancer Centre between 2017 and 2024. Clinical, cytogenetic, and molecular data were collected, andpts were risk stratified using ELN 2024 classification. Overall survival (OS)and response rates were evaluated. Composite complete remission (CRc) rate was defined as a combination of complete remission (CR) and CR with incomplete or hematological recovery (CRi/CRh).Overall response rate (ORR) included CRc, morphological leukemia-free state (MLFS) and partial remission (PR). Kaplan–Meier methodand Cox regression models were used for survival analyses. Results:A total of 132 pts treated with Aza-Ven were included. Median age was 73 years (range, 34–90); 62% were male; 67% had an ECOG of 0-1. Prior MDSor MDS/MPN overlap was noted in 26 (20%) pts and 18 (14%) had prior exposure to cytotoxic therapy.Complex karyotype (CK) was identifiedin 31 (23%) pts. The most common mutations (mut) wereASXL1 (n=34, 26%), SRSF2 (n=33, 25%), TET2 (n=28, 21%), DNMT3A (n=24, 18%), and NPM1 (n=24, 18%). Additionally, 22 (17%) pts had TP53 mut, 21 (16%) pts had FLT3-ITD mut,21 (16%) had IDH2 mut, 15 (11%) hadNRAS/KRASmut, 9 (7%) had IDH1 mut, and 7 (5%) had DDX41 mut. Eighty-one (61%) pts had ≥1 myelodysplasia-related gene mutation (MRGM) as defined by ELN. Among 132 pts, the CRc rate was 61%, includingCR rate of 38%, CRi rate of 22% and CRh rate of 2%. The ORR was 76%, with MLFS in 12%and PR in 2%. With a median follow-up of 21.4 months, the median OS was 13.2 months (95% CI, 9.9–15.8); 1- and 2-year OS rates were 56% (95% CI, 48 - 66) and 35% (95% CI, 27 – 47), respectively. Among VIALE-A–eligible pts (n=83), median OS was 14.5 months (95% CI, 10.4 – 26.3); 2-year OS was 40% (95% CI, 29 – 55). In pts with IDH1and IDH2 mut, the median OS was of 26 months (95% CI, 11 – NR) and 41 months (95% CI, 16 –NR), respectively, and the 2-year OS rate was64% (95% CI, 37 – 100) and 68% (95% CI, 48-97), respectively. (HR 0.37, p<0.01IDH1/2 mut vs others). Pts with NRAS/KRASmut had a median OS of 8.6 months (95% CI, 3.7 –NR) and 1-year OS rate of 35% (95% CI, 16 – 76)(HR 2.16, p=0.02, NRAS/KRAS mut vs others).Pts withTP53 had a median OS and 1-year OS rate of 12.6 months (95% CI, 6.5 – NA) and 55% (95% CI, 36 – 82), respectively(HR 1.61, p=0.10). Pts with FLT3-ITD mutation had a median OS of 11.1 months (95% CI, 6.0 –NA) and 2-year OS rate of 31% (95% CI, 15 – 64) (HR 1.24, p=0.52). Pts with DDX41 mutation had 1-year OS rate of 83% (95% CI, 58 – 100), with median not reached (HR 0.42, p=0.22).Pts with TET2 mutations had worse OS (HR 1.85, p=0.02). BCOR (HR 1.67, p=0.10) and CBL (HR 2.48, p=0.09) mut were marginally associated with worse OS. ASXL1, SRSF2, DNMT3A, NPM1 and RUNX1 were not associated with OS.Presence of ≥ 1 MRGM was not associated with OS (HR 1.18, p=0.50). However, pts with high mutation burden (≥4 mut)had a median OS of 8.6 months (95% CI, 7–16) versus 14 months (95% CI, 12–26) in those with low mutation burden (p=0.05). Patients with CK had a significantly worse OS with median of 9.7 months (95% CI, 8.1 – 14.4) and 1-year rate of 43% (95% CI 27-67) (HR 1.77, p=0.03). According to ELN 2024, median OS was26 months (95% CI, 12–NR), 11 months (95% CI, 8–NR), and 13 months (95% CI, 7–NR) for favorable, intermediate, and adverse groups, respectively (p=0.08). Compared to favorable group, adverse group had significantly worse OS (HR 1.93, p=0.04), with a trend for the intermediate group (HR 1.58, p=0.11). Conclusions:In this real-world cohort of AML pts treated withAza-Ven, we validate the favorable prognosis of pts with IDH1/2 and DDX41 mut, and adverse prognosis of pts with NRAS/KRAS and TP53mut. We identify TET2mut, CK and high mutation burden as potential factors associated with worse OS. Larger multicenter real-world studies are needed to refine and validate risk stratification models in ptswith AML treated withAza-Ven.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,004
Score d'incertitude au seuil0,007

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0010,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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.

Tête enseignante Opus0,010
Tête enseignante GPT0,296
Écart entre enseignants0,286 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission1
Résumé présentoui

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