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Enregistrement W4405040029 · doi:10.1182/blood-2024-199996

Presence of Leukemia-Related Basophils and Mast Cells with Atypical Immunophenotype during Induction Should Not be Interpreted As Measurable Residual Disease in Children with Acute Myeloid Leukemia with <i>RUNX1</i>::<i>RUNX1T1</i>

2024· article· en· W4405040029 sur OpenAlexaff
Anastasia Soboli, Anne Tierens, Leo Escano, Mathias Jerkfelt, Anna Rehammar, Liv Osnes, Sanna Siitonen, Hanne Vibeke Marquart, Rock Y. Y. Leung, Helly Vernitsky, Josefine Palle, Bernward Zeller, Kirsi Jahnukainen, Henrik Hasle, Birgitte Lausen, Daniel KL Cheuk, Nira Arad‐Cohen, Florian Kuchenbauer, Jonas Abrahamsson, Linda Fogelstrand

Notice bibliographique

RevueBlood · 2024
Typearticle
Langueen
DomaineMedicine
ThématiqueChronic Myeloid Leukemia Treatments
Établissements canadiensVancouver General HospitalTerry Fox Research InstituteUniversity of British ColumbiaUniversity Health Network
Organismes subventionnairesnon disponible
Mots-clésImmunophenotypingMyeloid leukemiaRUNX1LeukemiaImmunologyMedicinePreleukemiaBiologyHaematopoiesisFlow cytometryGeneticsStem cell

Résumé

récupéré en direct d'OpenAlex

In most study protocols for children with acute myeloid leukemia (AML), treatment response is assessed with flow cytometry (FCM) after one and/or two courses of induction treatment, impacting risk stratification. In AML with RUNX1::RUNX1T1, which is the most common subtype of pediatric AML and generally associated with good prognosis, assessment of treatment response is complicated by two factors: 1) Reverse transcription quantitative PCR (RT-qPCR-MRD) often shows high levels of RUNX1::RUNX1T1 fusion transcripts indicating residual disease. 2) FCM-MRD sometimes detects cells with immature markers such as CD34 or CD117 and atypical immunophenotype, leading to an interpretation as MRD. However, we have observed that such cells lack the diagnostic immunophenotype and instead display basophilic or mast cell markers. In this study, we aimed to improve interpretation of FCM-MRD by characterization of AML with RUNX1::RUNX1T1 with focus on basophils and mast cells. Our study included 45 of 46 children with AML with RUNX1::RUNX1T1 in Sweden, Finland, Norway, Denmark, Hong Kong and Israel during 2013-2020, comprising 22 females and 23 males with median age of 10 years (range 4-17). They were treated according to the NOPHO-DBH AML2012 protocol. FCM-MRD analysis utilized antibodies allowing for identification of basophils as CD123+, HLA-DR-, CD33+ and mast cells as CD117++, HLA-DR-, CD33+. FCM-MRD was performed at diagnosis, day 22 after induction 1, before start of induction 2, and before start of consolidation. Results were compared with 70 children treated in Sweden with the same protocol during the same time period for AML without RUNX1::RUNX1T1. When analyzing children with AML with RUNX1::RUNX1T1 on day 22 after induction 1, both basophils (median 0.64%, range 0-40%) and mast cells (0.44%, 0-29%) were elevated compared with regenerating bone marrow (basophils 0.32%, 0.04-0.69%; mast cells 0.01%, 0.006-0.06%). In 80% of cases, the basophils displayed atypical immunophenotype with lower expression of CD123, CD38, CD11b and CD13 and higher CD34 and CD117, and in 38% mast cells exhibited lower CD117. Levels normalized before consolidation treatment (basophils 0.12%, 0-0.7%; mast cells 0.04%, 0-1.2%). To investigate the impact of these cells, we divided children into those with notably high basophils on day 22, defined as ≥2% (n=16), and those with low basophils, <2% (n=29). The group with high basophils day 22 had more atypical basophils and mast cells already at diagnosis, as well as high levels of mast cells day 22 (median 1.87% vs 0.27%, p=0.007). Leukemic origin of atypical basophils and mast cells at diagnosis was verified using FCM cell sorting and RT-qPCR of RUNX1::RUNX1T1. Children with high basophils did not differ from low basophil cases regarding age, white blood cell count, leukemia-associated immunophenotype, KIT or FLT3 mutation status, or treatment intensity. The clinical outcome was favorable, with only five relapses (11%), with no difference between children with high and low basophils. Similar findings were seen when children were divided into groups of high (≥0.9%, n=18) and low (<0.9%, n=27) mast cells day 22, partly overlapping with basophil groups. This suggests that both basophils and mast cells have leukemic origin but not relapse potential. A comparison with children with other AML (n=70) showed that elevated levels and atypical immunophenotype of basophils and mast cells day 22 was unique to AML with RUNX1::RUNX1T1. To understand the reason for this phenomenon, we investigated the gene expression profile of cases with AML with RUNX1::RUNX1T1 using the TARGET (children), Beat AML 1.0 and TCGA-LAML (both adults) datasets. In all three datasets, AML with RUNX1::RUNX1T1 showed an enrichment of the signature genes of the common myeloid progenitor with eosinophil/mast cell/basophil potential, namely CSF2RB (CD131), CLC, HDC, EPX and IL5RA, compared to other types of AML. This suggests that RUNX1:::RUNX1T1 leukemia can originate in this progenitor. In conclusion, during induction treatment of children with AML with RUNX1::RUNX1T1, basophils and mast cells are often increased. Since they are leukemia-related and often have atypical immature immunophenotype, they might cause concern. However, such cells do not seem to be associated with a worse prognosis but rather reflect an inherent feature of this leukemia and should not be interpreted as MRD.

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 enseignants0,002
Score d'incertitude au seuil0,005

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
Bibliométrie0,0010,001
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 Opus0,010
Tête enseignante GPT0,227
Écart entre enseignants0,218 · 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

Citations2
Publié2024
Routes d'admission1
Résumé présentoui

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