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Enregistrement W4417020718 · doi:10.1182/blood-2025-4273

Relapse with post-transplant tyrosine kinase inhibitor (TKI) maintenance therapy in phliladelphia chromosome positive acute lymphoblastic leukemia (Ph+ALL) after allogeneic hematopoietic stem cell transplantation: Incidence and risk factor analysis

2025· article· en· W4417020718 sur OpenAlexaffabout
Eshrak Al‐Shaibani, Jonas Mattsson, Yu Cai, Xianmin Song, Mohsen Al Zahrani, Mohamed Elemary, Hanan Alkhaldi, Muhned Alhumaid, Ayman Saad, Francesca Biavasco, Robert Zeiser, Dennis Dong Hwan Kim

Notice bibliographique

RevueBlood · 2025
Typearticle
Langueen
DomaineMedicine
ThématiqueChronic Myeloid Leukemia Treatments
Établissements canadiensSaskatchewan Cancer AgencyPrincess Margaret Cancer Centre
Organismes subventionnairesnon disponible
Mots-clésCumulative incidenceMaintenance therapyHematopoietic stem cell transplantationIncidence (geometry)Clinical endpointAcute lymphocytic leukemiaTransplantationTyrosine-kinase inhibitorRetrospective cohort studyStem cell

Résumé

récupéré en direct d'OpenAlex

Abstract Introduction Post-transplant maintenance (PTM) therapy with tyrosine kinase inhibitors (TKIs) is increasingly utilized in contemporary clinical practice for Philadelphia chromosome-positive acute lymphoblastic leukemia (Ph+ALL) following allogeneic hematopoietic stem cell transplantation (HCT). Despite widespread use, the efficacy of TKI-based PTM remains uncertain, with relapse rates post-HCT around 20–30%. TKI may lower relapse risk but does not eliminate it. We previously reported that TKI-based PTM in Ph+ ALL was associated with improved relapse-free survival (RFS) following allogeneic HCT in 245 Ph+ ALL patients (ASH 2024). The current study further evaluated the incidence of relapse in 126 Ph+ ALL patients received TKI-based PTM, analysed the risk factor for relapse, and explored the potential for additional therapeutic interventions beyond TKI maintenance to further improve outcomes in this population. Patients and method We conducted a multicenter, retrospective analysis in 126 patients (51.4%) received PTM with TKIs, from 245 patients with Ph+ ALL who underwent their first allogeneic HCT in complete remission from 4 countries (Canada, Germany, Saudi Arabia and China). The primary endpoint was the cumulative incidence of relapse (CIR) following TKI-based PTM. Secondary endpoints included RFS, overall survival (OS), non-relapse mortality (NRM), and chronic graft-versus-host disease (cGvHD), after initiation of TKI-based PTM. The OS and RFS were calculated using Kaplan-Meier method and compared with log-rank test. CIR, NRM, and cGvHD were assessed accounting for competing events and analyzed using Fine-Gray model. Results Out of 126 pts received PTM, ABL1 kinase-domain mutation (KDM) was detected in 13 pts prior to HCT: T315I (n=7) and others (n=6). The median time to start PTM was 3 months (range: 0.5-115 months) after HCT. Dasatinib was most frequently used (n=56) followed by imatinib (n=32), ponatinib (n=31), or other TKIs (n=7). The median dose of TKI for maintenance was dasatinib 50 mg (range 50-140 mg), imatinib 400 mg (100-600 mg) and ponatinib 15 mg (15-45 mg), while PTM was maintained for a median duration of 23 months (range: 0-163 months). Out of 126 pts who received TKI-PTM, TKI-PTM was discontinued in 63 (50%) pts, of whom 22 pts completed the planned treatment of 2 years, while 29 pts discontinued due to either relapse (n=25), TKI-related toxicity (n=13) and death (n=3). With a median follow-up duration of 32 months, there was 7pts (8%) relapsed in the PTM group, among whom 6 pts relapsed during PTM therapy and 1 pts relapsed after PTM stopped due to toxicity. The incidence of relapse after PTM starts was 11.5%, 12.8%, 16.8% and 16.8% at 2, 3, 4 and 5 years, respectively, showing a plateau of relapse incidence after 4 years. Between the pts relapse during PTM and those not, the patient and disease characteristics did not show any difference in age (median 40 vs 38 years, p=0.78), sex (p=0.33), BCR-ABL qPCR level prior to HCT (p=0.19), remission status prior to HCT (CR1 vs CR2/beyond, p=1.0) or type of TKI (p=0.6). However, a significant difference was observed with respect to the presence of ABL kinase mutation (T315I vs. other mutations vs no ABL1 mutation, n=5 (18.5%), n=5 (18.5%) and n=17 (63%) vs n=2 (2%), n=1 (1%), 96 (97%), p≤0.001). A total of 34 (27%) deaths were observed out of 126 pts with 74.4% of OS rate, 67.9% of RFS rate, 12.8% of CIR, 14% of NRM and 10% of cGvHD incidence at 3 years. In the multivariate analysis, ABL1-KDM status was significantly associated with increased relapse risk (HR 2.755, [95% CI 1.520–4.993], p = 0.00084). Of interest, cGvHD development during PTM-TKI did not show to reduce the risk of relapse without statistical significance (HR 0.63 [0.08-4.75], p = 0.65). Also, there is no difference in the risk of relapse (p=0.799), OS (p=0.876), RFS (p=0.754), NRM (p=0.806) or cGvHD incidence (p=0.307) according to their policy for TKI-PTM duration (2 years vs 5 years). Conclusion The incidence of relapse during TKI-based PTM was plateaued up to 16.8% at 4 years. Increased risk of relapse was noted especially in patients with ABL1-KDM, notably T315I, highlighting the need for ABL1-KDM-guided therapies. Of note, reduced risk of relapse with cGvHD development was not observed, thus not supporting addition of prophylactic donor lymphocyte infusion in the group receiving TKI-PTM. Further studies with larger number of patients are needed to reach a clearer conclusion on this.

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 enseignants0,005
Score d'incertitude au seuil0,010

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
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 Opus0,005
Tête enseignante GPT0,219
Écart entre enseignants0,214 · 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'admission2
Résumé présentoui

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