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Enregistrement W4417006155 · doi:10.1182/blood-2025-4295

Post-transplant monitoring of multiparameter flow-cytometry (MFC)-based measurable residual disease (MRD) assessment combined with T-cell chimerism following allogeneic stem cell transplantation (HCT) in Acute Myeloid Leukemia (AML) and myelodysplastic syndrome (MDS)

2025· article· en· W4417006155 sur OpenAlexaff
Reem Alasbali, Majed Altareb, Carol Chen, Tommy Alfaro Moya, Eshrak Al‐Shaibani, Swe Mar Linn, Ivan Pašić, Igor Novitzky‐Basso, Arjun Law, Fotios V. Michelis, Auro Viswabandya, Rajat Kumar, Jonas Mattsson, Raja Prince-Eladnani, Anne Tierens, Dennis Kim

Notice bibliographique

RevueBlood · 2025
Typearticle
Langueen
DomaineMedicine
ThématiqueAcute Myeloid Leukemia Research
Établissements canadiensToronto General HospitalPrincess Margaret Cancer Centre
Organismes subventionnairesnon disponible
Mots-clésMinimal residual diseaseTransplantationHematopoietic stem cell transplantationMyeloid leukemiaStem cellRisk stratificationMyelodysplastic syndromesLeukemia

Résumé

récupéré en direct d'OpenAlex

Abstract Introduction Post-transplant surveillance using measurable residual disease (MRD) and donor chimerism provides critical information to assess relapse risk and graft stability in acute myeloid leukemia (AML) or myelodysplastic syndromes (MDS) patients (pts) after allogeneic hematopoietic stem cell transplantation (HCT). Multiparameter flow cytometry (MFC)-based MRD assessment has become routinely test after HCT; emerging evidence suggests that the combined use of MFC-based MRD and chimerism analysis may offer improved prognostic stratification for pts underwent HCT. The present study evaluated MFC-MRD assays in both pre-HCT and day 60 post-HCT as well as T-cell chimerism (TCC) and myeloid-cell (MCC) assessments at days 30 and 60, and its prognostic impact on relapse risk and other outcomes following allogeneic HCT in 196 AML or MDS pts. Patients and methods: We retrospectively analyzed 196 pts (AML, n=148; MDS, n=48) underwent allogenic HCT between 2021 and 2024. MFC-based MRD was assessed as pre-transplant evaluation and at D60 after HCT using marrow specimen while TCC and MCC assays were assessed at D30 and/or D60 using peripheral blood samples. MFC-MRD, TCC and MCC at each time points were analyzed longitudinally, recursive partitioning (rpart) method, which can define an optimal threshold providing the best stratification power, was applied to determine the key risk features strongly associated with relapse free survival (RFS). We have incorporated pre- and D60 MFC-MRD values (%) as well as D30/D60 TCC values into it and identified two key nodes determining RFS: 1) D60 MFC-MRD with 0.939% cutoff as the first node and 2) D30 TCC with 83.25% as the second node in the final model. Prognostic impact was analyzed with respect to overall survival (OS), RFS, cumulative relapse incidence (CIR), non-relapse mortality (NRM), and GvHD incidence according to the 2 nodes. Results Myeloablative conditioning (MAC) in 36% (n=71), while reduced-intensity conditioning (RIC) in 64% (n=125). Majorities (93%) received PTCy-based GvHD prophylaxis, including 141 who received dual T-cell depleted with PTCy and ATG. Primary graft failure occurred in 6 pts (2.9%). Pre-HCT MFC-MRD level has further declined at D60 (p= 0.0017 by pairwise t-test). With 0.1% as a definition of MFC-MRD positivity, the proportion of the pts with MFC-MRD positivity was reduced from 52% (n=102/196) at pre-HCT to 38.2% (n=75/196) at D60. TCC level has gone up from 89.7±16.3 at D30 to 92.3±14.9% at D60 (mean±SD, p=0.0064). With the definition of ≥ 95% for full donor chimerism (FDC), the proportion of the pts achieved FDC was noted in 59.0% (n=115) at D30 and 74.5% (n=146) at D60. For MCC, with a definition of 95% for FDC, 98% and 99% of the pts showed FDC at day 30/60. With a median follow-up of 22 months among survivors, 2-years’ OS and RFS rates were 74.2% and 67.5%, while NRM and CIR was 11.2% and 20.9%. Pre-HCT MFC-MRD level was not associated with RFS (p= 0.13), but D60 MFC-MRD level provides strong association with RFS (p= 0.01), identified as the first node in rpart analysis. The pts having MFC-MRD level≥0.939% (i.e. higher disease burden) showed 2 years’ RFS rate of 53.0% (i.e. adverse risk group), while those having undetectable or <0.939% MFC-MRD showed 69.8% of RFS rate at 2 years (p=0.044). The next node was D30 TCC with 83.25%, which stratified the non-adverse-risk group (i.e. D60 MFC-MRD level ≤0.93%) into intermediate (61.2% of 2 yrs’ RFS, ≥83.25% TCC D30) and favorable risk group (83.6% 2 yrs’ of RFS, <83.25% TCC D30). The 3 groups, divided by two nodes (i.e. D60 MFC-MRD 0.93% and D30 TCC with 83.25%), showed excellent risk stratification for RFS (p=0.0064) and for relapse risk (p=0.0061). The CIR was 5.1%, 17.2% and 33.6% at 1 year, respectively. This 3-group system could stratify AML pts for RFS (91.0% vs 61.7% vs 30.5%; p<0.0001) but not for MDS pts (88.9% vs 61.4% vs 59.8%; p=0.397). CIR was significantly different for AML 2.3%, 17.2% and 54.3%; (p<0.0001) at 1 year, respectively, but not for MDS (13.8% vs 17.6% vs 0%; p= 0.257). No significant differences in OS or RFS were observed for T-cell chimerism at D60 or for myeloid chimerism at D30/D60. Conclusion The combination of MFC-MRD at day 60 post HCT and TCC at day 30 could stratify the pts according to the relapse risk following HCT, particularly in AML pts. Novel GvHD prophylaxis including PTCy would require further investigation on its impact on dynamics of post-transplant TCC level and relapse risk.

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)0,000
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 enseignants0,001
Score d'incertitude au seuil0,002

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
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)0,0000,000

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 Opus0,009
Tête enseignante GPT0,254
Écart entre enseignants0,245 · 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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