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Enregistrement W3096269916 · doi:10.1182/blood-2020-138862

Prognostic Role of Multiparameter Flow Cytometry-Based Measurable Residual Disease Assessment in Patients with Acute Myeloid Leukemia Harboring DNMT3A/TET2/ASXL1 Mutation

2020· article· en· W3096269916 sur OpenAlexaff
Muhned Alhumaid, Georgina S. Daher-Reyes, Aaron D. Schimmer, Andre C. Schuh, Anne Tierens, Caroline McNamara, Dawn Maze, Igor Novitzky‐Basso, Karen Yee, Mark D. Minden, Steven M. Chan, Tracy Murphy, Vikas Gupta, Dennis Dong Hwan Kim, Hassan Sibai

Notice bibliographique

RevueBlood · 2020
Typearticle
Langueen
DomaineMedicine
ThématiqueAcute Myeloid Leukemia Research
Établissements canadiensPrincess Margaret Cancer CentreUniversity of TorontoUniversity Health Network
Organismes subventionnairesnon disponible
Mots-clésMinimal residual diseaseMedicineMyeloid leukemiaclone (Java method)Internal medicineOncologyLeukemiaPopulationFlow cytometryMyeloidAcute leukemiaMutationImmunologyCancer researchBiologyGeneGenetics

Résumé

récupéré en direct d'OpenAlex

BACKGROUND: Multiparameter flow cytometry (MFC) has increasingly been used for measurable residual disease (MRD) assessment in patients with acute myeloid leukemia (AML), while next-generation sequencing (NGS)-based MRD monitoring tool is in clinical development for its application. Clonal hematopoiesis (CH), in which leukemia-associated somatic mutations gene are present in individuals with no apparent hematologic disease, adds a challenge in the detection of MRD. In patients with AML, CH could be potentially pre-leukemic, while persistent mutations in DNMT3A, TET2 orASXL1 (DTA) in remission marrow are usually removed from the analysis of residual leukemic cells. However, reports suggest that persistent DTA mutations in remission may be correlated with an increased relapse risk. In the patients with DTA mutations, the use of NGS for MRD monitoring is limited or modified due to the presence of CH clone in the remission marrow. We evaluated whether MFC-MRD can be adjunctive to predict the risk of AML relapse in this population of 221 patients with DTA mutation (DNMT3A (n=123), ASXL1 (n=56) or TET2 (n=100). METHODS: The present study evaluated long-term outcomes in AML patients who achieved first complete remission (CR1) and compared outcomes according to MFC-based MRD status (was defined as negative if patients achieved 0.1 or less) assessed at the time of CR1. A total of 435 patients diagnosed with AML and treated with induction chemotherapy between 2015 and 2018 were included. MFC-MRD was assessed in 336 patients in CR1 (77%). NGS was performed using samples obtained at the time of initial diagnosis and used for mutational subgroup classification. Overall survival (OS) was calculated as the date of CR1 to the date of death and censored on the date of the last follow-up. Relapse-free survival (RFS) was defined as the time from the date of CR1 to the date of relapse or death from any cause. Cumulative incidence of relapse (CIR) and non-relapse mortality (NRM) were calculated considering competing risk. The Kaplan-Meier method using a log-rank test and a multivariate Cox proportional hazard model was used for analyses of time-to-event endpoints. For CIR and NRM, Gray test was performed for the risk factors and the Fine-Gray model was adopted for the multivariate model. RESULTS: According to the MFC-MRD status, i.e., the group with positive MRD (MRDpos; n=118, 35%) vs. those with negative MRD (MRDneg; n=218, 65%), we evaluated OS, RFS, and CIR. The MFC-MRDneg group showed better OS at 2 years 67.0% than the MFC-MRDpos group 40.7% (p<0.001). The MFC-MRDneg group also showed a higher RFS rate at 2 years (58.7%) than the MFC-MRDpos group (40.6%) (p=0.001). The CIR was higher in the MFC-MRDpos group, 26.9%, than in the MFC-MRDneg group 21.1%, but with borderline statistical significance (p=0.083). NRM was slightly higher in the MFC-MRDpos group, 32.5%, than in the MFC-MRDneg group, 20.2%, but with borderline statistical significance (p=0.057). We divided the groups according to the number of induction treatment courses, AML type, cytogenetics risk, and age (<60 vs ≥60), and compared OS, RFS, CIR and NRM between MFC-MRDpos vs MRDneg groups, which showed that MFC-MRD is relevant for risk stratification regardless of above-mentioned clinical variables Tab1. Also, we evaluated MFC-MRD status at CR by mutational profile subgroup. Long-term outcomes such as OS, RFS, CIR or NRM were compared by the mutational subgroup. It consistently showed a trend of superior OS, RFS and lower risk of CIR in patients with MFC-MRDneg compared to MFC-MRDposTab1. Of interest, in the subgroup of patients carrying any DTA mutations (n=221), those with MFC-MRDneg (n=103) showed better OS (HR 1.61 [1.01-2.55%]; p=0.042), RFS (HR 1.66 [1.06-2.61%]; p=0.026) and CIR (HR 1.99[1.03-3.83%]; p=0.04) compared to those MFC-MRDpos (n=64; Fig 1). Multivariate analysis confirmed that the MFC-MRDneg is an independent prognostic factor in patients with DTAmutwith respect to OS: MFC-MRDpos (HR 1.63, p=0.04) and age (≥60; HR 2.04, p=0.008) for OS; for RFS, MFC-MRDpos (HR 1.71, p=0.02) and age (≥60; HR 2.32, p= 0.001); for CIR, MFC-MRDpos (HR 2.31, p=0.01) and HCT (HR 0.14, p=<0.001). Conclusion: These findings suggest that in AML patients with DTAmut, MFC-MRD status at the time of remission assessment can be a tool for MRD assessment when NGS-based MRD assessment is limited. Further study is strongly warranted to reach a clearer conclusion with multiple cohorts. Disclosures Schimmer: Takeda: Honoraria, Research Funding; Novartis: Honoraria; Jazz: Honoraria; Otsuka: Honoraria; Medivir AB: Research Funding; AbbVie Pharmaceuticals: Other: owns stock . Tierens:Amgen: Membership on an entity's Board of Directors or advisory committees; Jazz Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees; Astellas Pharma: Membership on an entity's Board of Directors or advisory committees. McNamara:Novartis: Honoraria. Maze:Pfizer: Consultancy; Novartis: Honoraria; Takeda: Research Funding. Gupta:Pfizer: Consultancy; Bristol MyersSquibb: Honoraria, Membership on an entity's Board of Directors or advisory committees; Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Sierra Oncology: Consultancy, Membership on an entity's Board of Directors or advisory committees; Incyte: Honoraria, Research Funding.

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)0,0000,000
Bibliométrie0,0010,000
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)0,0010,000

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 Opus0,013
Tête enseignante GPT0,260
Écart entre enseignants0,247 · 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
Publié2020
Routes d'admission1
Résumé présentoui

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