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Enregistrement W7108439879 · doi:10.1182/blood-2025-3386

Differences in outcomes with intensive chemotherapy in AML patients according to mutational status – recent real-world results

2025· article· en· W7108439879 sur OpenAlexaff

Notice bibliographique

RevueBlood · 2025
Typearticle
Langueen
DomaineMedicine
ThématiqueAcute Myeloid Leukemia Research
Établissements canadiensUniversity of Alberta
Organismes subventionnairesnon disponible
Mots-clésCytarabineGemtuzumab ozogamicinMidostaurinAdverse effectChemotherapyInduction chemotherapyRetrospective cohort studyHematopoietic stem cell transplantation

Résumé

récupéré en direct d'OpenAlex

Abstract Background Most studies have found that European LeukemiaNet (ELN) Intermediate risk AML patients treated with intensive chemotherapy (IC) have an inferior prognosis compared with ELN favorable risk patients, but this has generally been based on older data. Within the ELN Adverse risk group it is unclear how outcomes compare among the different mutations. Methods We retrospectively evaluated real world outcomes of all newly diagnosed AML patients treated with IC at a single center between Jan. 2018 – Apr. 2025. All pts underwent cytogenetic analysis and molecular profiling by NGS and classified according to ELN2022. ELN favorable (fav) and intermediate (int) risk patients received induction with 7+3 (+ midostaurin for FLT3-mutated pts, and gemtuzumab ozogamicin for some favorable risk pts). ELN adverse (adv) risk pts received either 7+3, CPX-351 or FLAG-Ida at physician discretion. High dose cytarabine was used as consolidation therapy. ELN int and adv risk patients were referred for allogeneic stem cell transplant (HSCT) in CR1, while fav risk patients were only referred if high risk features were present (e.g. inadequate MRD response after 2 cycles of IC or CBF with KIT mutation). Results were correlated with ELN2022 risk group, age and de novo vs. secondary AML. Results A total of 229 patients received IC; the breakdown by ELN2022 risk groups was fav 69, int 67, adv 88 (of which 24 were TP53 mutated), and 5 unknown. The CR/CRi rates with 1-2 inductions for fav, int, adv non-TP53 mutated and TP53mut pts were 88%, 82%, 64% and 46%, respectively (p<0.001 comparing all 4, p=NS comparing fav vs. int). HSCT rates for patients achieving CR/CRi/MLFS were 17% fav, 72% int, 85% adv non-TP53 and 64% TP53mut, respectively. The estimated 5-year OS for fav, int, adv non-TP53 mutated and TP53mut was 80%, 74%, 33% and 0%, respectively (p<0.001). The 5-year RFS was 71%, 66%, 52% and 0%, respectively (p=0.003). There was no significant difference in OS or RFS between ELN fav vs. int risk patients. For patients who underwent HSCT in CR1 the 5-year OS for ELN fav, int, adv nonTP53 and TP53mut was 90%, 90%, 48% and 0%, respectively (p<0.001). Within the ELN int group with FLT3-ITD mutations the 5-year OS was 77%; for patient with NPM1 mutations the 5-year OS and RFS were 80% and 63%, respectively. Other predictors of OS on univariate analysis included age < 60 vs. 60+ (p<0.001) and secondary vs. de novo AML (p<0.001). On multivariate Cox regression analysis, ELN2022 risk group was independently predictive of OS (HR 2.184, 95% CI 1.687-2.828, p<0.001), while age group was only weakly predictive (HR 1.663, p=0.028) and secondary vs. de novo was not predictive (HR 0.638, p=0.095). Among the adv risk non-TP53 mutated group, the OS was higher in the RUNX1 mutated group, with an est. 5-year OS of 54%, compared with 16% for those with other myelodysplasia-related (MR) gene mutations (p=0.017). For those undergoing HSCT in CR1, the est. 5-year OS for RUNX1 mutated patients was 80% vs. 24% for those with other MR mutations (p=0.055). Conclusions The OS and RFS for ELN2022 int risk patients treated with IC has improved in recent years and is now approaching that of fav risk pts, likely related to the high HSCT rate achievable in int risk patients. ELN adv risk patients continue to have inferior outcomes, even with transplant; however, within this group, RUNX1 mutated patients have a better outcome with IC followed by HSCT than those with other MR gene mutations. Revised prognostic scoring systems should take into account this heterogeneity within the adv risk group, as well as more recent outcome data.

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)0,003
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 enseignants0,001
Score d'incertitude au seuil0,007

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,003
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 recherche0,0000,000
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 Opus0,022
Tête enseignante GPT0,322
Écart entre enseignants0,300 · 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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