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Enregistrement W4405043407 · doi:10.1182/blood-2024-208622

Multicenter, Real-World Experience Study of FLT3-Inhibitor Post-Transplant Maintenance Therapy Following Allogeneic Hematopoietic Stem Cell Transplantation in Acute Myeloid Leukemia with FLT3-ITD

2024· article· en· W4405043407 sur OpenAlexaff
Yomna Eissa, Hyeoung Joon Kim, Joon Ho Moon, Robert Zeiser, Francesca Biavasco, Christopher J. Lemieux, Mohamed Elemary, Varun Mehra, Mili Shah, Ahmed Alotaibi, Yu Cai, Xianmin Song, Jae-Sook Ahn, Sang Kyun Sohn, Mohsen Al Zahrani, Muhned Alhumaid, Hee‐Je Kim, Dennis Dong Hwan Kim

Notice bibliographique

RevueBlood · 2024
Typearticle
Langueen
DomaineMedicine
ThématiqueAcute Myeloid Leukemia Research
Établissements canadiensSaskatchewan Cancer AgencyUniversity of SaskatchewanUniversité LavalPrincess Margaret Cancer CentreUniversity of TorontoUniversity Health Network
Organismes subventionnairesnon disponible
Mots-clésMyeloid leukemiaStem cellMedicineTransplantationMaintenance therapyHaematopoiesisHematopoietic stem cell transplantationFms-Like Tyrosine Kinase 3ImmunologyLeukemiaAcute leukemiaOncologyCancer researchInternal medicineChemotherapyBiology

Résumé

récupéré en direct d'OpenAlex

Introduction Patients of acute myeloid leukemia (AML) with FLT3-internal tandem duplication (ITD) carry a considerably higher risk of relapse, even after allogeneic hematopoietic stem cell transplantation (HCT). Post-transplant maintenance (PTM) using FLT3 inhibitors (FLT3i) is known to reduce the risk of relapse and to improve long-term outcomes significantly. Although there are randomized trials analyzing the outcomes of the different FLT3i PTM, real-world experience data is still lacking. Accordingly, we performed a multicenter retrospective study to analyze the survival benefit of FLT3i PTM, its tolerability and treatment duration. This is the largest real-world experience data on FLT3i PTM to date. Patients and method We conducted a retrospective multicenter study on 440 patients from 12 centers who received an allogeneic HCT from 2007 to 2024 for AML with FLT3-ITD. The primary endpoint was relapse-free survival (RFS). The use of FLT3i PTM was treated as a time-dependent covariate. The Mantel-Byar test (MBT), which will avoid immortal bias, was conducted to compare outcomes between those who received FLT3i PTM vs those who did not. Kaplan-Meier method was used to analyze RFS, overall survival (OS) and graft-versus-host disease-free, relapse-free survival (GRFS), while cumulative incidence of competing event function was implemented for the analysis of cumulative incidence of relapse (CIR), non-relapse mortality (NRM) and chronic graft-versus-host disease (cGvHD). Multivariate analysis was conducted using either Cox's or Fine-gray model, appropriately. Results Out of 440 patients, 389 (87.7%) were in complete remission (CR) and 52 (11.8%) had relapsed/refractory disease prior to HCT. PTM with FLT3i was started in 171 (38.9%) patients either with Sorafenib (n=138, 80.7%), Gilteritinib (n=32, 18.7%) or Midostaurin (n=1). Four pts received Sorafenib PTM subsequently received Gilteritinib, while 4 pts received Gilteritinib subsequently received Sorafenib, mainly due to toxicity. The median time to start treatment with PTM was 91 days (17-685). In most of the patients, Sorafenib was mainly started either 200mg daily (n=72) or 400mg daily (n=61), while Gilteritinib was started 80 mg daily (n=14), followed by 120mg daily (n=8) and 40mg daily (n=6). Dose modification was required in 46% of pts. With a median follow-up duration of 17.5 months following PTM in all the patients who received PTM, 97 (56.7%) patients had discontinued FLT3i PTM with the most common cause being planned treatment completion (usually within 2 years, n=39), followed by FLT3i-related toxicity (n=32) and relapse (n=21). Out of the 171 PTM patients, 30 pts (17.5%) relapsed at a median of 17 months after starting PTM. The median duration of FLT3i PTM was around 2.2 years. The incidence of relapse was 21.8% (14.2-30.3%) at 3 years after PTM with the relapse curve plateauing after 3 years. In univariate analysis, the RFS rate in patients on PTM was 79.8% vs 48.2%for no PTM (p<0.0001), while the OS rate was 82.6% vs 54.8% (p<0.0001) at 3 years. The CIR was 15.3% vs 38.5% (p<0.0001), while the NRM was 4.9% vs 12.7% (p=0.005) at 3 years. However, these analyses did not take into account FLT3i PTM starts as a time-dependent covariate; we conducted a time-dependent analysis and compared PTM vs no PTM using MBT. FLT3i PTM was found to reduce the risk of death by 63% (HR 0.371 [0.243, 0.567], p<0.0001) and reduced the risk of RFS by 42% (HR 0.578 [0.395, 0.847], p=0.004). There was no difference in OS (p=0.5) or RFS (p=0.453) between the Sorafenib and Gilteritinib PTM groups. The incidence of relapse post Sorafenib PTM was 13%, whereas that of post Gilteritinib PTM was 9.4%. Multivariate analysis also confirmed the clinical benefit of FLT3i PTM for OS (HR 0.315, p=0.0005), RFS (HR 0.540, p=0.029) and GRFS (HR 0.605, p=0.010), but not for NRM or cGvHD. Conclusion The present study confirmed the clinical benefit of FLT3i PTM in the patients of AML with FLT3-ITD following allogeneic HCT. Following FLT3i PTM, the incidence of relapse was 21.8% at 3 years, which looked to plateau after 3 years, suggesting that FLT3i PTM can be stopped after 3 years following FLT3i PTM started. Both Sorafenib and Gilteritinib seems similar outcomes although toxicity profiles are different. Further study is warranted to identify the high-risk patient for relapse following FLT3i PTM, for which additional therapeutic intervention would be required 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)0,002
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 enseignants0,002
Score d'incertitude au seuil0,010

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,002
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,001
Communication savante0,0010,001
Science ouverte0,0000,001
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 Opus0,014
Tête enseignante GPT0,281
Écart entre enseignants0,267 · 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

Citations1
Publié2024
Routes d'admission1
Résumé présentoui

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