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Enregistrement W4310096357 · doi:10.1182/blood-2022-168260

Multiparameter Flow Cytometry-Based Residual Disease Analysis Identifies Intermediate-Risk AML Patients Who May Benefit from Allogeneic Hematopoietic Stem Cell Transplantation

2022· article· en· W4310096357 sur OpenAlexaff
Josephine Anne Lucero, Anne Tierens, Muhned Alhumaid, José‐Mario Capo‐Chichi, Tracy Stockley, Vikas Gupta, Steven M. Chan, Andre C. Schuh, Mark D. Minden, Jonas Mattsson, Rajat Kumar, Hassan Sibai, Dennis Dong Hwan Kim

Notice bibliographique

RevueBlood · 2022
Typearticle
Langueen
DomaineMedicine
ThématiqueAcute Myeloid Leukemia Research
Établissements canadiensUniversity Health NetworkUniversity of TorontoPrincess Margaret Cancer Centre
Organismes subventionnairesnon disponible
Mots-clésCumulative incidenceMedicineNPM1Minimal residual diseaseInternal medicineTransplantationOncologyHematopoietic stem cell transplantationStem cellMyeloid leukemiaLeukemiaBiology

Résumé

récupéré en direct d'OpenAlex

Background Allogeneic hematopoietic stem cell transplantation (alloSCT) is a curative therapeutic modality in acute myeloid leukemia (AML). While numerous studies have confirmed the benefit of alloSCT - improved overall survival (OS) and reduced cumulative incidence of relapse (CIR) - restricted mainly to the intermediate and high-risk groups, indications for alloSCT are becoming more restricted over time. Residual disease (RD) monitoring has been proven to be highly predictive of long-term AML outcomes and is now used commonly to guide ongoing therapy decisions. RD-based guidelines in AML propose the use of multiparameter flow cytometry-based RD (FRD) analyses in patients whose disease cannot be followed by molecular means (e.g. CBF AML, APL, NPM1-mutated AML). Methods We performed a retrospective review to evaluate the potential therapeutic benefit of alloSCT in AML patients who achieved first complete remission (CR1) and to further define this benefit based on the FRD status at remission. We performed a time-dependent analysis using the covariate of time-to-alloSCT as the primary endpoint of the study. The Mantel-Byar method was applied to compare time to event between the alloSCT and non-transplant groups, addressing time bias concerns that occur with alloSCT studies, according to the FRD status at the time of remission. Results The FRD assay has been part of our standard of care since 2015 with a sensitivity of 0.1%. The cohort analyzed included 435 patients treated between 2015 and 2018, of which 336 patients had FRD assessment at CR1. The patients were risk classified using the European Leukemia Network (ELN) 2017 classification which integrates cytogenetic and mutational analyses. The proportion of patients with positive FRD (RDpos) at the post-induction time-point was 35.1% (n=118). Negative residual disease (RDneg) status after induction occurred more frequently in de novo, favorable risk, and NPM1-mutated AMLs, than in the adverse risk or secondary AML groups. Overall, across all ELN 2017 groups, the impact of alloSCT was neutral for both OS and relapse free survival (RFS). A potential impact of alloSCT could be discerned, however, when patients were grouped by FRD status at CR assessment. Overall, in the RDpos group, alloSCT improved OS and RFS, while not benefitting the RDneg group. The impact of alloSCT was defined further by considering ELN 2017 risk groups, with an OS/RFS benefit observed in adverse risk patients (n=186; HR for OS 0.55 [0.31-0.99]; p=0.05); HR for RFS 0.57 [0.31-1.05], p=0.07), while an increased risk of death or of an RFS event was seen in favorable risk patients (n=151; HR for OS 4.3 [1.65-11.2], p=0.003; HR for RFS 2.29 [1.07-4.93], p=0.03). In the ELN 2017 intermediate risk patients (n=98), a survival benefit of alloSCT was not seen (HR for OS 1.05 [0.45-2.46], p=0.92; HR for RFS 0.86 [0.42-1.78], p=0.69), although alloSCT was associated with a lower CIR (HR 0.43 [0.20-0.90], p=0.03). However, when only RDpos patients were considered (n=27), trends towards benefit from alloSCT in OS (HR 0.52 [0.11-2.52], p=0.42), RFS (HR 0.38 [0.09-1.62], p=0.19), and CIR (HR 0.44 [0.07-2.72], p=0.38) were observed. In RDneg patients (n=53), alloSCT was associated only with a lower CIR (HR 0.18 [0.05-0.66], p=0.01). We then re-analyzed the data using the older UK-MRC risk classification of AML based exclusively on cytogenetics at diagnosis. This approach increased the patients in the intermediate group from 98 (ELN 2017) to 298 (UK-MRC). When using UK-MRC, an RFS benefit from alloSCT was observed for intermediate risk RDpos patients (n=211; HR 0.44 [0.20-0.96], p=0.04). Trends for benefit in OS (HR 0.45 [0.19-1.03], p=0.06) and CIR (HR 0.45 [0.20-1.04], p=0.06) were observed as well. In RDneg patients, however, an RFS/CIR benefit of alloSCT was not observed, while inferior OS was seen in the alloSCT group from increased non-relapse mortality. Conclusion Our data underscore the established benefit of alloSCT in adverse risk AML and suggest a potential benefit of this treatment modality in RDpos intermediate risk patients, thus confirming the utility of ongoing RD assessment in AML. Our results also indicate that ongoing sophistication in AML risk assignment, based on mutational profile at diagnosis, influences the potential benefit of alloSCT. Further studies are needed to better define the subgroup of RDpos intermediate risk patients who may benefit from alloSCT. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

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)0,003
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 enseignants0,003
Score d'incertitude au seuil0,009

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,003
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0030,001
Études des sciences et des technologies0,0000,000
Communication savante0,0010,000
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 Opus0,011
Tête enseignante GPT0,249
Écart entre enseignants0,238 · 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
Publié2022
Routes d'admission1
Résumé présentoui

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