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Enregistrement W4405095992 · doi:10.1182/blood-2024-211434

Multiparameter Flow Cytometry-Based Measurable Residual Disease Assessment in the Patients with CCAAT Enhancer-Binding Protein a Gene (CEBPA) Mutated Acute Myeloid Leukemia

2024· article· en· W4405095992 sur OpenAlexaff
Swe Mar Linn, Nihar Desai, Steven M. Chan, Andre C. Schuh, Aniket Banker, Marta Davidson, Guillaume Richard‐Carpentier, Aron Shimmer, Dawn Maze, Karen Yee, Mark D. Minden, Vikas A. Gupta, Tracy Stockley, Hassan Sibai, José‐Mario Capo‐Chichi, Anne Tierens, Dennis Dong Hwan Kim

Notice bibliographique

RevueBlood · 2024
Typearticle
Langueen
DomaineMedicine
ThématiqueAcute Myeloid Leukemia Research
Établissements canadiensToronto General HospitalUniversity of TorontoPrincess Margaret Cancer CentreUniversity Health Network
Organismes subventionnairesnon disponible
Mots-clésCEBPACcaat-enhancer-binding proteinsFlow cytometryMyeloid leukemiaCancer researchMolecular biologyMyeloidMinimal residual diseaseBiologyGeneLeukemiaMutationMedicineImmunologyGeneticsNuclear proteinTranscription factor

Résumé

récupéré en direct d'OpenAlex

Introduction Acute myeloid leukemia (AML) with mutations in CCAAT enhancer-binding protein A gene (CEBPA) is a unique AML subtype with heterogeneous molecular features such as CEBPA biallelic or double mutations (CEBPAdm) and in-frame basic leucine zipper region (CEBPAinf-bZIP) mutations. CEBPAdm and CEBPAinf-bZIP mutations can be monitored by next-generation sequencing (NGS)-based residual disease (RD) monitoring but with limitation due to guanine-cytosine (GC) rich content of the CEBPA gene. Alternatively, multiparameter flow cytometry (MFC) can be used for RD assessment in these CEBPA mutated AML subtype. Patients and methods The study cohort consisted of patients diagnosed with AML at our institution between 2015 and 2022. NGS was conducted using a custom hybrid-capture NGS panel (Oxford Gene Technology, Kidlington, UK) targeting 49 myeloid genes regions including the entire consensus DNA sequence of CEBPA. Following sequencing (Illumina Miseq v2), CEBPA variants analysis included alignment to the GRCh37/hg19 human genome reference (Burrows-Wheeler Aligner v 0.7.12), base calling (GATK v3.3.0). and variant calling (Varscan v2.3.8), and the mean depth of coverage of the KMT2A exon interval was calculated using Picard v1.130. The analytical sensitivity of this NGS panel is 3-5% for the detection of small nucleotide variants as well as larger insertion deletion and duplications in CEBPA. The present study evaluated treatment outcomes in 150 patients with CEBPA mutated AML (CEBPA-AML) who achieved first complete remission (CR1), and compared the outcomes according to MFC-based RD status at CR1 assessment. Presentation marrow aspirate samples were screened for leukemia-associated phenotypes (LAIP) using 10 color flow. RD assessment has been a standard of care in our institution since 2015 based on the LAIP and/or “different from normal” (DfN), which was performed in parallel with morphologic assessment at CR1 after induction chemotherapy. The limit of detection (LOD) of the RD assay was 0.1%. Relapse-free survival (RFS) was calculated from CR1 to the date of relapse or death from any cause. Overall survival (OS) was calculated from diagnosis to the date of death. Cumulative incidence of relapse (CIR) was calculated considering competing events. Cox's proportional hazard and Fine-gray model were appropriately applied for univariate and multivariate analysis. Results In total NGS detected 211 CEBPA variants in 150 patients. 44 patients CEBPAinf-bZIPwere determined based on the presence of an in-frame variant with the CEBPA bZIP domain. 37 patients were determined CEBPAdm due a CEBPAinf-bZIP combined to a truncating variant (frameshift, nonsense or splicing) in the N-terminal end of CEBPA. The remaining patients 106 patients although harboring a CEBPA variant could not be identified as CEBPAinf-bZIP or CEBPAdm. Based on NGS results, 150 pts were diagnosed with CEBPA-AML. According to the MRC 2010 risk group, 104 (69.4%) was stratified as intermediate risk, 25 (16.7%) as adverse and 6 (4.0%) as favorable. Out of 98 pts who received intensive chemotherapy, 84 pts (90.3%) achieved remission. Out of 74 pts in CR who has available MFC-RD result, 22 (29.7%) showed detectable MFC-RD (RDpos), while 52 pts (70.3%) showed undetectable RD (RDneg). With a median follow-up duration of 43 months, 20 pts (24%) died, 12 (14%) relapsed, and 29 (34%) underwent HCT in CR1 of whom 4 pts (4%) relapsed after HCT. At 2 years, the RDneg group showed a higher RFS (97.8%) and OS rate (86.7%) compared to the RDpos group (77.3%, p=0.06; 56.5%, p=0.009). With respect to CIR, the RDpos group showed higher CIR (20.3%) than the RDneg group (9.5%; p=0.10),. In the CEBPAb-Zip subgroup those with RDneg showed a higher OS rate at 2 years (91.6%) and lower CIR (3.6%) compared to the RDneg group (71.7%; p=0.006 for OS; 19.2%, p=0.05 for CIR). Multivariate analysis confirmed that MFC-RDneg at CR1 is a favorable prognostic factor in the overall population with respect to RFS (HR 0.43, p=0.04), OS (HR 0.399, p=0.032) and CIR (HR 0.39, p=0.09), particularly in the bZIP subgroup for RFS (HR 0.19, p=0.007), OS (HR 0.31, p=0.006) and CIR (HR 0.17, p=0.002). Conclusion These findings suggest that in CEBPA mutated AML, MFC-based RD assessment is feasible and will provide an excellent predictive measure when NGS-based RD assessment is limited. Further studies are warranted to reach a clearer conclusion with a larger number of replication cohorts.

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)0,001
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 seuil0,003

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,001
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 savante0,0010,000
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 Opus0,016
Tête enseignante GPT0,287
Écart entre enseignants0,271 · 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
Publié2024
Routes d'admission1
Résumé présentoui

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