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Enregistrement W2926169207 · doi:10.1182/blood.v130.suppl_1.883.883

Efficacy of ALL Therapy for WHO2016-Defined Mixed Phenotype Acute Leukemia: A Report from the Children's Oncology Group

2017· article· en· W2926169207 sur OpenAlexaff
Etan Orgel, Thomas Alexander, Brent L. Wood, Samir B. Kahwash, Meenakshi Devidas, Yunfeng Dai, Todd A. Alonzo, Charles G. Mullighan, Hiroto Inaba, Stephen P. Hunger, Alan S. Gamis, Andrew J. Carroll, Nyla A. Heerema, Jason N. Berman, William G. Woods, Mignon L. Loh, Patrick A. Zweidler‐McKay, John Horan

Notice bibliographique

RevueBlood · 2017
Typearticle
Langueen
DomaineMedicine
ThématiqueAcute Lymphoblastic Leukemia research
Établissements canadiensIzaak Walton Killam Health Centre
Organismes subventionnairesnon disponible
Mots-clésMedicineOncologyClinical trialInternal medicineLeukemiaPediatricsImmunology

Résumé

récupéré en direct d'OpenAlex

Abstract There remains uncertainty regarding the optimal treatment for mixed phenotype acute leukemia (MPAL), an uncommon leukemia that represents ~3% of de novo acuteleukemia. Because of the dearth of clinical trials, varying approaches are employed: acute lymphoblastic leukemia (ALL) regimens as well as more intensive acute myeloid leukemia (AML) and hybrid therapies. The evolution in the prevailing diagnostic criteria from the EGIL classification to the more narrowly defined WHO2008 and WHO2016 criteria has made the existing literature difficult to interpret. Increasing insight into the molecular heterogeneity of MPAL adds further complexity. Recently published studies (retrospective clinical case series), however, are beginning to suggest that ALL therapy may be the most appropriate form of initial therapy for MPAL. To further assess the efficacy of ALL therapy for pediatric MPAL, we reviewed the Children's Oncology Group (COG) experience. Possible MPAL cases were identified through the ALL biology studies (both AALL03B1 and AALL08B1 were open to MPAL) and from AAML0531, the phase 3 trial for de novo AML (patients enrolled on this trial subsequently found to have MPAL were taken off study). All identified cases were reviewed centrally. In cases where the original flow cytometry testing was inadequate to establish the diagnosis, flow cytometry was repeated with an expanded antibody panel using banked diagnostic specimens. Only cases meeting WHO2016 criteria were retained. Patients were treated per physician discretion and not on a therapeutic clinical trial. Supplemental data on treatment and outcomes were collected. From the study databases, 97 potential MPAL cases were identified; of these, 54 cases (56%) were confirmed to be MPAL via central review. Patients were diagnosed between 2003 and 2016. The majority of patients were <10 years of age and B/Myeloid MPAL (B/My) was the most common phenotype. Fewer than a third of patients presented with hyperleukocytosis, adverse recurrent cytogenetics, or central nervous system involvement (Table 1). In the cohort, 38 patients (70%) received ALL induction therapy, 12 (22%) AML induction, and 3 hybrid induction; 14 (28%) patients received hematopoietic stem cell transplantation (HSCT). The CR rate was 71.1% [27/38], 66.7% (8/12) and 100% (3/3) in patients receiving ALL, AML and hybrid induction, respectively (p=0.761). CR was achieved in 78.8% of B/My cases [26/33], 68.4% T/My cases [13/19], and no B/T cases (0/2, p=0.074). Treatment became more heterogeneous further into therapy due to varying incorporation of ALL, AML and hybrid elements and the varying use of HSCT. Three-year EFS was 65±17% in patients started on ALL therapy and 53±26% in patients started on AML therapy (p=0.169, Fig. 1A); 3-year OS was 79±15% and 60±27% respectively (p=0.510, Fig. 1B). SCT was not associated with a difference in EFS (p=0.935) or OS (p=0.726). In 20 patients receiving ALL therapy alone without SCT, 3-year EFS was 59±22% and OS 79±18%. This series represents a large cohort of centrally-reviewed pediatric MPAL cases diagnosed according to the most recent WHO2016 criteria and treated with COG-style chemotherapy. Our results add to the growing body of literature suggesting that ALL regimens may be effective for pediatric MPAL. It also suggests, however, that induction failure and relapse are still relatively common with ALL therapy and that alternative forms of treatment are necessary for some. The limited size of the sample and its heterogeneity preclude any conclusions regarding the relative efficacy of AML therapy and role for HSCT. Given the greater morbidity and mortality associated with these forms of therapy, consideration should be given to reserving their use for patients with a suboptimal response to ALL therapy. Continued variability in accurately diagnosing and treating MPAL as seen here also underscores the necessity of well-delineated clinical trials with integrated central review to advance MPAL therapy. Our results therefore further support the basis for the first-ever prospective evaluation of high risk ALL therapy for pediatric MPAL in a trial now being planned within the COG. In this trial, minimal residual disease testing with multiparameter flow cytometry will be employed to identify patients responding well to ALL therapy versus those who may benefit from more intensive AML therapy and transplantation. Disclosures Mullighan: Loxo Oncology: Research Funding; Amgen: Consultancy. Hunger: Erytech Pharmaceuticals: Consultancy; Novartis: Consultancy; Jazz Pharmaceuticals: Honoraria; Amgen: Consultancy, Equity Ownership. Berman: AGADA Therapeutics: Research Funding. Zweidler-McKay: ImmunoGen: Employment.

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: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,003
Score d'incertitude au seuil0,008

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
Bibliométrie0,0010,001
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 Opus0,028
Tête enseignante GPT0,326
Écart entre enseignants0,298 · 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
Publié2017
Routes d'admission1
Résumé présentoui

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