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Enregistrement W2907087254 · doi:10.1182/blood-2018-99-116618

Quantification of Fusion Transcripts Reveals Slower Treatment Kinetics As Compared with Multiparameter Flow Cytometry during Induction Treatment of Acute Myeloid Leukemia in Children

2018· article· en· W2907087254 sur OpenAlexaff
Lene Karlsson, Charlotte Guldborg Nyvold, Lars Palmqvist, Anne Tierens, Henrik Hasle, Birgitte Lausen, Ólafur G. Jónsson, Gitte Wulff ‐Juergensen, Lene Hyldahl Ebbesen, Florian Kuchenbauer, Erik Forestier, Jonas Abrahamsson, Linda Fogelstrand

Notice bibliographique

RevueBlood · 2018
Typearticle
Langueen
DomaineMedicine
ThématiqueAcute Myeloid Leukemia Research
Établissements canadiensTerry Fox Research InstituteUniversity Health Network
Organismes subventionnairesnon disponible
Mots-clésMinimal residual diseaseMyeloid leukemiaMedicineBone marrowLeukemiaOncologyFlow cytometryInternal medicineFusion transcriptImmunologyFusion geneCancer researchBiologyGenetics

Résumé

récupéré en direct d'OpenAlex

Abstract The prognosis of children with acute myeloid leukemia (AML) is improving but still unsatisfactory. We and others have shown that measurable residual disease (MRD) as measured with multiparameter flow cytometry (MFC) is a very strong prognostic factor, however this technique cannot reliably identify low-risk cases. An alternative approach for MRD analysis is quantification of leukemia-specific transcripts using reverse transcription followed by quantitative polymerase chain reaction (RT-qPCR). This approach has shown strong prognostic value in adult AML but its significance in childhood AML is less studied. In this retrospective study, we evaluated early treatment response with RT-qPCR and MFC in parallel to determine treatment kinetics and the relationship between results of the two methods. The study included 15 children (7 females, 8 males, median age 6 years (range 1-16)), diagnosed 2004-2011 with de novo AML with a quantifiable fusion transcript (8 with RUNX1-RUNX1T1, one with CBFB-MYH11 and 6 with KMT2A-MLLT3), all treated in the NOPHO AML-2004 trial. MRD analyses were performed on samples from day 15 from start of the first induction course and just before the first consolidation course. MRD analysis with MFC was performed in bone marrow samples by identifying leukemia-associated immunophenotypes (LAIP), and reported as percent of viable cells. Fusion transcripts were analyzed with RT-qPCR in bone marrow and blood according to the Europe against cancer program, and reported as percentage of diagnostic level in bone marrow. Results of all MRD analyses were reported to clinicians but not used for treatment decisions. All patients achieved complete remission. 9 patients relapsed (median time from diagnosis 12 months, range 8-16); 5 with RUNX1-RUNX1T1 and 4 with KMT2A-MLLT3. When we compared results of RT-qPCR in blood and bone marrow day 15 and before consolidation, there was a high correlation; 0.93, p<0.01. When 0.1% was used as cut-off for positivity, 20/22 samples were concordant (Cohens kappa test K value 0.818, P<0.001) with the two disconcordant samples positive in bone marrow but negative in blood before consolidation. Of the concordant samples, 9 were ≥0.1% and 11 were <0.1% in both bone marrow and blood. There was no significant correlation between results obtained with MFC and RT-qPCR, neither in bone marrow nor in blood. When comparing MFC and RT-qPCR results in bone marrow using the 0.1% cut-off, only 12 of 22 samples were concordant (4/10 at day 15, 8/12 before consolidation, in total 55% agreement; K value 0.141, P=0.36). Most discordant results (9/10) were ≥0.1% with RT-qPCR but negative (<0.1%) with MFC. There was a pattern of slower treatment kinetics with higher MRD results obtained with RT-qPCR than MFC. In cases with RUNX1-RUNX1T1 fusion transcript levels were 22% (median, range 12-63%) of diagnostic value day 15 and 0.03% (0-1.4%) before consolidation. In cases with KMT2A/MLLT3, the corresponding levels were 15% (0 - 40%) and 0 (0-0.038%). There was no difference in relapse frequency between children with fusion transcripts ≥0.1% or <0.1% day 15. On the other hand, two patients with RUNX1-RUNX1T1 fusion transcript level ≥0.1% but negative with MFC before consolidation both relapsed. In order to find the reason for remaining high levels of fusion transcripts day 15 when the bone marrow is usually hypoplastic, we reanalyzed flow cytometry files from four cases with remaining fusion transcript >0.1%. These analyses confirmed the absence of immature cells with LAIP and detected either more mature myeloid cells or immature cells without LAIP. We hypothesize that fusion transcripts can be present in mature cells not detected as MRD using MFC. This was supported by analyzing sorted immature and mature cells from bone marrow samples at diagnosis of AML (n=4, adults). In CD34-CD117+ cells, leukemic transcripts (RUNX1-RUNX1T1/CBFB-MYH11/mutated NPM1) were 67±10% (mean±SEM) of the level in the most immature cells, CD34+CD117+, from the same patients and in mature granulocytes (CD34-CD117-, high SSC) 88±9%. In conclusion, RT-qPCR and MFC provide different information during induction treatment. RT-qPCR has a stronger ability to detect remaining leukemic burden, but whether this burden is clinically relevant remains to be shown. The impact of fusion transcript levels on relapse risk is currently investigated in the NOPHO-DBH AML-2012 trial in which MFC is used for risk stratification. Disclosures No relevant conflicts of interest to declare.

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: aucune
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
Métarecherche0,0010,001
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 savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
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 Opus0,025
Tête enseignante GPT0,294
Écart entre enseignants0,268 · 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
Publié2018
Routes d'admission1
Résumé présentoui

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