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Enregistrement W4417019060 · doi:10.1182/blood-2025-4284

Risk factor analysis of relapse risk during FLT3-inhibitor post-transplant maintenance therapy following allogeneic hematopoietic stem cell transplantation in Acute Myeloid Leukemia with FLT3-ITD

2025· article· en· W4417019060 sur OpenAlexaff
Yomna Eissa, Yu Cai, Xianmin Song, Ahmed Alotaibi, Mohamed Elemary, Manuel Espinoza-Gutarra, Christopher J. Lemieux, Camille Sylvestre, Hee‐Je Kim, Ali Bazarbachi, Ali Ibrahim, Nour Moukalled, Robert Zeiser, Francesca Biavasco, Jae-Sook Ahn, Hyeoung Joon Kim, Joon Ho Moon, Sang Kyun Sohn, Michael Heuser, Judith Schaffrath, Mili Shah, Varun Mehra, Mohsen Alzahrani, Muhanad Alhumaied, Dennis Dong Hwan Kim

Notice bibliographique

RevueBlood · 2025
Typearticle
Langueen
DomaineMedicine
ThématiqueAcute Myeloid Leukemia Research
Établissements canadiensSaskatchewan Cancer AgencyUniversité LavalPrincess Margaret Cancer Centre
Organismes subventionnairesnon disponible
Mots-clésCumulative incidenceSorafenibClinical endpointHematopoietic stem cell transplantationTransplantationIncidence (geometry)Maintenance therapyMyeloid leukemiaMultivariate analysis

Résumé

récupéré en direct d'OpenAlex

Abstract Background: FLT3-ITD AML is associated with high relapse rates despite allogeneic hematopoietic stem cell transplantation (HCT). Post-transplant maintenance (PTM) with FLT3 inhibitors (FLT3i) can reduce relapse and improve outcomes. However, relapses still occur, prompting this study to assess the incidence and risk factors for relapse during FLT3i PTM. Methods: We conducted a retrospective multicenter study of 233 patients (pts) who received FLT3i PTM out of 577 FLT3-ITD AML pts who underwent HCT from 2007–2024. The primary endpoint was cumulative incidence of relapse (CIR); secondary endpoints included relapse-free survival (RFS), overall survival (OS), non-relapse mortality (NRM), and chronic GVHD (cGVHD). CIR and NRM were analyzed using cumulative incidence methods considering competing risks. Kaplan-Meier was used for RFS/OS. Mantel-Byar test evaluated the effect of cGVHD while avoiding immortal time bias. Multivariate analysis was performed using Cox or Fine-Gray models. A principal component analysis was used to identify the independence of the risk factors for relapse. Results: FLT3i PTM was given to 233 pts (40.4%), of which 120 (51.5%) were females. The median age at HCT was 46 years (14–73). FLT3-ITD allelic ratio (AR) ≥0.5 at diagnosis, was present in 80 pts (34.3%), while 153 pts (65.7%) had AR <0.5. At HCT, 195 pts (83.7%) were in CR1, 15 (6.4%) in CR2, and 23 (9.9%) beyond CR2 (including active disease up to 10% blasts). The most used FLT3i PTM was sorafenib (n=194, 83.3%), followed by gilteritinib (n=50, 21.5%); 1 pt received midostaurin. Seven pts switched from sorafenib to gilteritinib and 4 vice versa, mostly due to toxicity. PTM was started at a median of 90 days (range: 17–661) post-HCT. The starting doses for sorafenib were mainly 400 mg daily (n=90, 46.4%) or 200 mg daily (n=81, 41.8%); gilteritinib 80 mg daily (n=19, 38%), 120 mg daily (n=16, 32%), or 40 mg daily (n=8, 16%). Dose modifications occurred in 30–40%. At a median of 19-month follow-up, 153 (65.6%) had discontinued PTM due to toxicity (n=63, 41.0%), planned completion (n=48, 31.4%; at a median of 2.1 years), or relapse (n=31, 20.2% at a median of 8.6 (0.55-58.9) months). At 3 years from PTM initiation, the OS was 79.1%, RFS was 74.1%, GRFS was 55.8%, CIR was 20.4%, NRM was 7.0%, with cGVHD of 41%. A total of 40 pts (17.2%) relapsed at a median of 17 months post-PTM and mortality was noted in 45 pts (19.3%). Regarding risk factors for relapse, a higher CIR was associated with remission status at HCT (16.3% in CR1/2 vs 56.9% beyond CR2; p<0.001) and disease risk index (DRI) (CIR: 19.8% for DRI-1, 17.2% for DRI-2, and 62.6% for DRI-3; p<0.001). The presence of cGVHD was noted to be protective with a CIR of 7.4% with cGVHD, vs 30.1% without (p=0.002). Of the 87 pts (37.3%) who developed cGVHD, only 8 pts (9.2%) relapsed. With cGVHD as a time-dependent covariate, the CIR was reduced by 66% in the patients who developed cGVHD (HR 0.34; 95% CI 0.17–0.93; p=0.039). The CR status at HCT as well as cGVHD were confirmed to be independent risk factors for post PTM relapse in a MVA. No significant association with relapse was found for FLT3-ITD AR at diagnosis (≥0.5 vs <0.5), cytogenetics risk (adverse vs non-adverse), concurrent mutations (with DNMT3A, NPM1, WT1, TET2, IDH2, RUNX1 or ASXL1), the use of FLT3i pre-transplant (with induction, re-induction or consolidation), the conditioning intensity (myeloablative vs reduced intensity regimen), or the development of acute GVHD. When remission status at transplant, DRI, and cGVHD were incorporated into a composite risk score, the CIR increased with the number of adverse factors. Patients with no risk factors had a CIR of 12.0%, while those with one and two risk factors had CIRs of 29.4% and 47.4%, respectively. The comparison across groups was statistically significant (p = 0.0000014). CIR could not be reliably estimated for the highest-risk group (score = 3) due to small sample size. Conclusion: The current study strongly suggests a significantly increased risk of relapse in the group of FLT3-ITD AML patients, who do not achieve at least CR2 pre-transplant, have a high DRI or do not develop cGVHD, despite receiving FLT3i PTM. This suggests that those pts should continue FLT3i PTM indefinitely and other methods should be incorporated such as prophylactic DLI within the first 6 months post-transplant to reduce the risk of relapse

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 enseignants0,001
Score d'incertitude au seuil0,004

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,002
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,001
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 Opus0,006
Tête enseignante GPT0,238
Écart entre enseignants0,232 · 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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