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Enregistrement W4417016685 · doi:10.1182/blood-2025-37

Therapy-related core binding factor Acute Myeloid Leukemia – a Study of the french acute leukemia intergroup

2025· article· en· W4417016685 sur OpenAlexaff
Mathilde Chanut, Loïc Vasseur, Matthieu Duchmann, Pierre Hirsch, Céline Berthon, Maël Heiblig, Thorsten Braun, Mathieu Leclerc, Jean Valère Malfuson, Delphine Lebon, Madalina Uzunov, Jean Bastié, Gian Matteo Pica, Thomas Cluzeau, Juliette Lambert, Lauris Gastaud, Karine Celli‐Lebras, Arnaud Pigneux, Stéphane de Botton, Christian Récher, Claude Preudhomme, Éric Jourdan, Hervé Dombret, Nicolas Boissel, Nicolas Duployez, Raphaël Itzykson, Jean-Baptiste Micol

Notice bibliographique

RevueBlood · 2025
Typearticle
Langueen
DomaineMedicine
ThématiqueAcute Myeloid Leukemia Research
Établissements canadiensChild, Adolescent and Family Mental Health
Organismes subventionnairesnon disponible
Mots-clésAcute leukemiaMyeloid leukemiaRadiation therapyChemotherapyWhite blood cellComplete remissionRetrospective cohort studyChemotherapy regimen

Résumé

récupéré en direct d'OpenAlex

Abstract Introduction Therapy-related CBF-AMLs represent approximately 10% of all CBF-AMLs and are associated with poorer outcomes, partly due to the associated solid tumor. Existing studies are small, and more data—especially NGS and cytogenetics—are needed to better define their characteristics and prognosis. Methods We analyzed data from a retrospective multicenter study (NCT05070208) and the prospective CBF-2006 trial (NCT00428558, Jourdan et al. 2014) from the French AML intergroup, including CBF-AML patients diagnosed between 2007–2020. Cases with prior chemo- or radiotherapy were classified as therapy-related (t-AML). Baseline characteristics were compared in the overall cohort. Outcomes were assessed in a sub-population of fit patients (≤80 years, cancer in remission, intensive chemotherapy). Centralized NGS was performed using 40- and 68-gene panels (36 genes in common), and MRD was assessed by RT-qPCR for RUNX1::RUNX1T1 and CBFB::MYH11. Results Among 749 CBF-AML patients included between 2007 and 2021, 78 had t-AML. t-AML patients were older (median age: 59 vs. 45 years, p<0.001), more likely to be female (68% vs. 44%, p<0.001), and had lower white blood cell counts (WBC) at diagnosis (median: 6 vs. 15 G/L, p<0.001). was the most common prior neoplasm (42%). Median interval from chemo/radiotherapy to t-AML onset was 38 months (IQR [interquartile range]: 25–69). Neoplasms were in complete remission after chemotherapy (74%) and/or radiotherapy (49%) at CBF-AML diagnosis in 83% pts and remained in sustained remission in 78% after a median follow up of 1.8 years. The distribution of RUNX1::RUNX1T1 and CBFB::MYH11 subtypes was similar between therapy-related and de novo cases (44% and 43% for RUNX1::RUNX1T1, p>0.9). Cytogenetically, X chromosome deletions were more frequent in t-AML (16% vs. 8%, p=0.016). t-AMLs showed fewer on NGS (63% vs. 75%, p = 0.019), and fewer FLT3 mutations (10% vs. 24%, p = 0.03). No other difference was observed in the mutational landscape of t-AML, including KIT and TP53 alterations. In the selected population of patients without active cancer at AML diagnosis and treated with intensive chemotherapy (n=693, including 57 t-AML), induction regimens were mainly based on 7+3 ( in t-AML vs 72% in non-t-AML, p=0.07) and consolidation courses on intermediate/high dose cytarabine courses (93% in t-AML vs 90% in non-t-AML, p=0.64). Gemtuzumab-ozogamycin was added to Cx in 9% of t-AML (vs. 10% of non-t-AML, p=1.0). Allogeneic transplant in first complete remission was performed in 9% of t-AML and 5% of non-t-AML (p=0.21). Therapy-related AML patients had a CR/CRp rate of 95%, not different from de novo patients (95%, p=0.74). MRD after one cycle of induction was not different in t-AML in bone marrow (median: 0.20% [IQR: 0.04-0.82%] vs. 0.17% [IQR: 0.03-0.49%], p=0.50) nor in peripheral blood (median: 0.02% [IQR: 0.002-0.14%] vs. 0.03% [IQR: 0.001-0.19%], p=0.85). With a median follow-up of 5.3 years (IQR :3.9-6.8), the 3-year cumulative incidence (CI) of relapse was 41% (95%CI [confidence interval]:27%-54%) in t-AML patients and 38% (95%CI:34%-42%) in non-t-AML patients (csHR=1.07 [95%CI:0.69-1.68], p=0.76). No difference was also observed for non-relapse mortality (NRM) (3-year CI-NRM: 2% [95%CI:0%-9%] for t-AML vs. 3% [95%CI:2%-5%] for non-t-AML, csHR=0.87 [95%CI:0.21-3.66], p=0.85). Overall survival (OS) was lower in t-AML in univariable analysis (3-year OS: 62% [95%CI:50%-77%] in t-AML vs 79% [95%CI:75%-82%] in non-t-AML, p=0.03). Nevertheless, this was not confirmed in multivariable analysis (HR=1.36, 95%CI:0.84-2.18, p=0.21 for t-AML) when accounting for age (per 10 years of age HR=1.27, 95%CI:1.14-1.42, p<0.001), WBC count (log10 scale HR=1.25, 95%CI:0.94-1.65, p=0.12), and CBF subtype (HR=1.19, 95%CI:0.87-1.62, p=0.29 for RUNX1::RUNX1T1). In patients who experienced relapse (n=249 including 21 t-AML), OS after relapse was dismal in t-AML (16% [95%CI:6%-45%] vs 56% [95%CI:50%-64%], p<0.0001). Conclusion This study shows that therapy-related CBF-AML patients are older, have lower WBC at diagnosis, and present with similar molecular profiles compared to de novo cases. Among a homogenous cohort of patients without active cancer and treated with intensive chemotherapy, there were no differences in CR/CRp rates, relapse incidence, or non-relapse mortality. Unlike previous studies, overall survival was not significantly different after multivariable adjustment for age, WBC count, and CBF subtype.

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)0,002
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 enseignants0,024
Score d'incertitude au seuil0,047

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,003
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,000
Communication savante0,0010,000
Science ouverte0,0000,001
Intégrité de la recherche0,0010,000
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

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 GPT0,303
Écart entre enseignants0,280 · 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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