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Enregistrement W2961690031 · doi:10.1182/blood.v128.22.1687.1687

Down Syndrome AML Is Unique in Phenotype Both at Diagnosis and in Post Chemotherapy Regeneration

2016· article· en· W2961690031 sur OpenAlexaff
Elisabeth R. Wilson, Lisa Eidenschink Brodersen, Barbara K. Zehentner, Andrew J. Menssen, Andrew P. Voigt, Laura Pardo, Denise A. Wells, Johann Hitzler, Jason N. Berman, Todd A. Alonzo, Samir B. Kahwash, Soheil Meshinchi, Michael R. Loken

Notice bibliographique

RevueBlood · 2016
Typearticle
Langueen
DomaineMedicine
ThématiqueAcute Myeloid Leukemia Research
Établissements canadiensIzaak Walton Killam Health CentreHospital for Sick Children
Organismes subventionnairesnon disponible
Mots-clésMedicineInternal medicineDown syndromeChemotherapyOncologyMyeloid leukemiaImmunophenotypingMinimal residual diseaseBone marrowLeukemiaImmunologyGastroenterologyPathologyFlow cytometry

Résumé

récupéré en direct d'OpenAlex

Abstract Background: Down syndrome (DS) acute myeloid leukemia (DS-AML) is predominantly found in young children (under the age of 4) and responds well to chemotherapy. The difference in clinical features suggests that despite the predominance of megakaryoblastic disease, DS-AML blasts are different from non-DS AML blasts with megakaryoblastic morphology (AMKL). The immunophenotypes of DS-AML blasts at diagnosis were compared to those of non-DS AML patients using unsupervised hierarchical clustering analysis (HCA) and assessed for their immunophenotypic overlap with known morphologic subgroups. In addition, residual disease monitoring of non-DS AML patients using "Difference from Normal" flow cytometry has been applied to DS-AML with unexpected results. Methods: A standardized antibody panel was used to define quantitative gene product (antigen) expression in bone marrow (BM) specimens from patients with AML at diagnosis and following chemotherapy. The quantitative phenotypic expression of multiple cell surface markers and physical characteristics defined a position in 15 dimensional data space for each patient. HCA was performed to mathematically cluster DS-AML patients enrolled on Children's Oncology Group study AAML1531 with non-DS patients from study AAML0531 based on 15 dimensional profiles. Five patients with DS who were older than 4 years of age and therefore treated on AAML0531 were also included. Data regarding morphology were appended to the data set. The same antibody panel was used to assess measurable residual disease (MRD) in 147 follow up BM aspirates from 50 DS patients ages 1 to 21 undergoing treatment for either AML or B-ALL (n=46, n=4 respectively) who were not included in the clinical studies. When possible, cell sorting was performed for FISH or SNP/CGH microarray studies. Results: Diagnostic Clustering: HCA of 17 DS-AML diagnostic phenotypes with 769 non-DS AML patients revealed a tight immunophenotypic correlation between 12 (71%) DS-AML patients (Figure 1). Further analysis of this diagnostic subset showed a consistent mean antigen expression profile that was distinct from non-DS AML diagnostic phenotypes. Similarly, the DS-AML patients clustered separately from the 5 older DS patients (>4 years of age) with AML, who were treated on AAML0531 and exhibited consistently different identifying diagnostic markers despite the shared constitutional trisomy 21. Additionally, the phenotype associated with the DS-AML cluster does not match that of patients with AMKL morphology. The largest immunophenotypic cluster of non-DS patients with AMKL morphology were classified with the RAM phenotype[1], which segregate into a mathematically unique branch of the dendogram distinct from the DS-cluster. Observation of a consistent DS-specific non-leukemic myeloid regenerative phenotype: Of the 147 post chemotherapy specimens submitted for MRD analysis off clinical study, 141 (96%) had a clearly identifiable CD34+/CD56+ population present between 0.06-6.0% total non-erythroid composition. A second abnormal population expressing CD34+ but not HLA-DR was also observed in these patients but at a lower level (0.02-2.66%) compared to the CD34+/CD56+ cell population. Two patients had a reproducible absence of 56+, DR- progenitors across all follow up specimens (6/147) and may represent unique clinical cases with disease features beyond our knowledge. In four DS patients undergoing treatment for B-ALL, the same immunophenotypic features (CD34+/CD56+ and negative HLA-DR) were observed. Genetic data: In 4 cases, cell sorting of CD34+/CD56+ post treatment progenitors revealed a lack of cytogenetic markers present in the clone of leukemic blasts at diagnosis. Conclusions: The blasts of patient with typical DS-AML (i.e under the age of 4 years at diagnosis) are immunophenotypically distinct from those of non-DS-AML patients and exhibit consistent immunophenotypic features that differ from DS patients with AML who are older than 4 years of age. The normal myeloid progenitors in DS patients who have undergone chemotherapy for AML or ALL express an immunophenotype that is different from normal BM of non-DS patients recovering from chemotherapy. [1] Brodersen, Lisa Eidenschink, et al. "A Recurrent Immunophenotype at Diagnosis Independently Identifies High Risk Pediatric AML: A Report from the Children's Oncology Group Trial AAML0531." Disclosures Wilson: Hematologics: Employment. Brodersen:Hematologics: Employment. Zehentner:Hematologics: Employment, Equity Ownership. Pardo:Hematologics, Inc: Employment. Wells:Hematologics: Employment, Equity Ownership. Loken:Hematologics: Employment, Equity Ownership.

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)0,000
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 seuil0,006

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
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)0,0020,000

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 GPT0,266
Écart entre enseignants0,252 · 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

Citations4
Publié2016
Routes d'admission1
Résumé présentoui

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