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Enregistrement W4417001845 · doi:10.1182/blood-2025-761

Clinical characteristics and outcomes of children with T-cell acute lymphoblastic leukemia harboring ABL-class fusions: A report from the ponte di legno consortium

2025· article· en· W4417001845 sur OpenAlexaff
Thai Hoa Tran, Meenakshi Devidas, Leyna Xoi, Andishe Attarbaschi, Judith M. Boer, Gunnar Cario, Giovanni Cazzaniga, Gabriele Escherich, Yoshihiro Gocho, Mats Heyman, Stephen P. Hunger, Hiroto Inaba, Gábor Kovács, Anthony V. Moorman, Charles G. Mullighan, Kjeld Schmiegelow, Jan Starý, Tomasz Szczepański, David T. Teachey, Andrea Biondi, Mignon L. Loh, Rob Pieters, Sarah Elitzur

Notice bibliographique

RevueBlood · 2025
Typearticle
Langueen
DomaineMedicine
ThématiqueAcute Lymphoblastic Leukemia research
Établissements canadiensCentre Hospitalier Universitaire Sainte-Justine
Organismes subventionnairesnon disponible
Mots-clésCohortPDGFRBRetrospective cohort studyLymphoblastic LeukemiaAcute lymphocytic leukemiaDasatinibWhite blood cellTyrosine kinaseETV6

Résumé

récupéré en direct d'OpenAlex

Abstract Introduction: ABL-class acute lymphoblastic leukemia (ALL) is characterized by rearrangements of ABL1 or other ABL-family tyrosine kinases that drive uncontrolled cell growth. Children with ABL-class B-ALL treated without tyrosine kinase inhibitors (TKIs) achieve 5-year event-free survival (EFS) of 59% (Den Boer et al., Lancet Haematology, 2021). The addition of TKIs to chemotherapy-based therapy has substantially improved outcomes for BCR::ABL1-positive B-ALL. ABL-class fusions occur in 5-10% of T-cell ALL (T-ALL) cases, and small case series report aggressive disease with poor outcomes. However, comprehensive outcome data for pediatric ABL-class T-ALL remain limited. We describe the clinical characteristics, outcomes, and prognostic factors of children with T-ALL harboring ABL-class fusions. Methods: We conducted a multinational retrospective cohort study encompassing 14 collaborative groups from the Ponte di Legno (PdL) consortium. Demographic, clinical, treatment, and outcome data were collected for newly-diagnosed pediatric T-ALL patients harboring ABL-class fusions between 1987 and 2023. Results: The cohort comprised 196 patients with ABL-class T-ALL: 16 (8.2%) with BCR::ABL1 fusions, 136 (69.4%) with NUP214::ABL1 fusions and the remaining 44 (22.4%) with other ABL-class fusions “other-ABL” involving non-NUP214 ABL1 (n=28), ABL2 (n=2), PDGFRA (n=6) and PDGFRB (n=8). The median age at diagnosis was 8 years (range: 1 – 22 years). The median white blood cell (WBC) count was 82,600 cells/mm³ (range: 2,000-1,471,000). Patients with other-ABL had higher median WBC compared to those with BCR::ABL1 or NUP214::ABL1 fusions (217,500 vs. 39,000 vs. 72,400; p=0.0129). Of those with available treatment dates, 77.5% were treated in 2010 or later. Central nervous system (CNS) involvement was classified as CNS-2 in 35 patients (18.7%) and CNS-3 in 7 patients (3.7%). Cranial radiotherapy was administered to 35.9% of patients. Consolidation with hematopoietic stem cell transplantation (HSCT) in first complete remission (CR1) was performed in 18.6% of patients. TKIs were administered to 27 of 157 (17.2%) patients with available treatment data: BCR::ABL1 (n=9), NUP214::ABL1 (n=9) and other-ABL (n=9). Overall, 149/167 (89.2%) patients achieved morphologic complete remission (CR). Failure to achieve CR was highest among patients with other-ABL (25.0%) compared to those with BCR::ABL1 (15.4%) and NUP214::ABL1 (4.5%) fusions; p=0.0025. Among 140 patients with available end of induction (EOI) minimal residual disease (MRD) data, 45 (32.1%) patients were EOI MRD negative at the 0.01% threshold, 50 (35.7%) patients had MRD levels of 0.01% to <1% and 45 (32.1%) patients had levels ≥1%. With a median follow-up of 6.1 years for the 162 patients with outcome data, the 5-year EFS and overall survival (OS) for the entire cohort were 78.1% (95% CI 71.7% - 85.2%) and 83.6% (95% CI 77.7% - 90.1%), respectively. By fusion subtype, 5-year EFS and OS were 67.7% (95% CI 46.0% - 99.7%) and 75.5% (95% CI 54.9% - 100%) for BCR::ABL1; 80.8% (95% CI 73.2% - 89.3%) and 85.7% (95% CI 78.7% - 93.3%) for NUP214::ABL1; and 74.6% (95% CI 62.2% - 89.6%) and 81.2% (95% CI 69.5% - 94.9%) for other-ABL. The 5-year cumulative incidence of relapse was 16.2±10.5% for patients with BCR::ABL1, 14.1±3.6% for patients with NUP214::ABL1 and 13.0± 5.4% for patients with other-ABL. Among patients with non-BCR::ABL1 ABL-class T-ALL, the 5-year EFS was 70.0% (95% CI 51.0%- 96.2%) for those treated with TKIs (n=18) versus 79.8% (95% CI 72.5% - 87.7%) for those not treated with TKIs (n=117); p=0.5082. Among the 18 TKI-treated patients, there were 2 induction failures, 2 relapses and 1 treatment-related death. In univariate Cox regression analyses, only HSCT in CR1 (HR=3.21; p=0.006) was associated with inferior EFS for patients with non-BCR::ABL1 ABL-class T-ALL. In univariate analyses, none of the variables were significantly associated with OS. Conclusion: This large international cohort study has established the baseline characteristics and outcomes of children with ABL-class T-ALL. In contrast to ABL-class B-ALL, ABL-class T-ALL demonstrated comparable outcomes to non-ABL-class T-ALL. We could not demonstrate the benefit of TKI treatment among patients with non-BCR::ABL1 ABL-class T-ALL, although numbers in the TKI group were small. These findings provide important baseline data for future prospective studies evaluating novel therapies in pediatric ABL-class T-ALL.

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)0,002
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 enseignants0,015
Score d'incertitude au seuil0,031

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,002
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,002
Études des sciences et des technologies0,0000,001
Communication savante0,0010,000
Science ouverte0,0010,001
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 Opus0,012
Tête enseignante GPT0,290
Écart entre enseignants0,278 · 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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