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Enregistrement W2531315909 · doi:10.1182/blood.v126.23.800.800

Prevalence and Prognostic Significance of KIT Mutations in Pediatric Core Binding Factor AML Patients Treated with Gemtuzumab Ozogamicin: Results from the Randomized Phase III Children's Oncology Group Trial AAML0531

2015· article· en· W2531315909 sur OpenAlexaff
Jessica A. Pollard, Todd A. Alonzo, Robert B. Gerbing, Yi‐Cheng Wang, Jason Joaquin, Susana C. Raimondi, Betsy Hirsch, Lillian Sung, Richard Aplenc, Irwin D. Bernstein, Alan S. Gamis, Soheil Meshinchi

Notice bibliographique

RevueBlood · 2015
Typearticle
Langueen
DomaineMedicine
ThématiqueAcute Myeloid Leukemia Research
Établissements canadiensSickKids FoundationPopulation Health Research InstituteHospital for Sick Children
Organismes subventionnairesnon disponible
Mots-clésGemtuzumab ozogamicinMedicineCore binding factorInternal medicineExonOncologyMissense mutationCD33MutationGastroenterologyBiologyGenetics

Résumé

récupéré en direct d'OpenAlex

Abstract Mutations of KIT (KIT +) occur in children and adults with core binding factor (CBF) acute myeloid leukemia (AML) and cluster within exons 8 and 17. We previously reported a 19% prevalence of KIT mutations in pediatric CBF AML and lack of prognostic significance in serial pediatric cooperative trials. We also determined that gemtuzumab ozogamicin (GO) improves outcomes for a subset of CBF AML patients with higher CD33 expression enrolled on AAML0531, a randomized trial of conventional chemotherapy with or without GO. Thus, in this study, we determined whether the clinical outcome of patients with KIT + CBF AML is affected by GO treatment. COG AAML0531 enrolled 1022 eligible pediatric de novo AML patients of which 247 had CBF AML [137 t(8;21) and 110 inv(16)/t(16;16)] based on central cytogenetic review. Of these 247 patients, 218 had evaluable samples for KIT mutational analysis. Analysis included PCR amplification of exons 8 and 17 and fragment length analysis and direct sequencing to identify all missense and size mutations. Mutations were detected in 55 patient samples (25%); 27 (49%) involved exon 8, 26 (47%) involved exon 17 and 2 (4%) involved both exons. Breakdown by exon and CBF translocation type demonstrated exon 8 mutations in 12/121 (10%) t(8;21) samples and 17/97 (18%) inv(16)/t(16;16)patient samples. Exon 17 mutations were found in 18/121 (15%) t(8;21) and 10/97 (10%) inv(16)/t(16;16) patient samples. Overall outcome analysis among the 218 CBF AML samples analyzed for KIT mutations revealed similar complete remission (CR) rates after induction I for KIT + vs. KIT- patients (83% vs. 82%, p=0.796). Five-year event-free survival (EFS) from study entry for KIT + vs. KIT- was 54% and 70%, respectively (p=0.029) with a corresponding overall survival (OS) of 76% vs. 83% (p=0.380). Notably, KIT + patients who achieved CR had a relapse risk (RR) of 45% vs. 23% for KIT- patients (p=0.010). Disease-free survival (DFS) for KIT + vs. KIT- was 51% and 72%, respectively (p=0.021). We also compared the clinical impact of exon 8 vs. exon 17 mutations. Outcomes of CBF AML patients with exon 8 mutations were similar to CBF AML patients without these mutations (OS 90% vs. 80%, p=0.277, EFS 55% vs. 68%, p=0.224, DFS 58% vs. 68%, p= 0.419, RR 42% vs. 26%, p= 0.112). In contrast, outcomes of patients with exon 17 mutations were inferior to those CBF AML patients without exon 17 mutations [OS 64% vs. 84%, p=0.035; DFS 43% vs. 70%, p=0.016) and higher RR was observed (48% vs. 26%, p=0.057). The impact of GO treatment on outcome was subsequently evaluated. KIT + CBF AML patients who did not receive GO had inferior OS and EFS from study entry compared to KIT-patients (OS 64% vs. 86%, p= 0.034, EFS: 46% vs. 69%, p=0.037). Higher RR (55% vs. 31%, p= 0.046) and inferior DFS (45% vs. 66%, p= 0.094) were also observed. In contrast, KIT + and KIT-patients receiving GO treatment had comparable outcomes (OS 88% vs. 80%, p=0.393; EFS 62% vs. 72%, p=0.438) as well as RR (33% vs. 15%, p=0.103) and DFS (57% vs. 77%, p=0.109). Analysis by mutation subtype revealed that outcomes of patients with exon 8 mutations were similar to exon 8 wild-type (WT) patients when treatment did not include GO (OS 81% vs. 80%, p=0.910; EFS 50% vs. 65%, p= 0.185). DFS and RR were also similar (DFS 57% vs. 62%, p= 0.752, RR 43% vs. 36%, p= 0.632). Treatment of exon 8 mutations with GO resulted in significant improvement in OS at 5 years from study entry compared to those without exon 8 mutations (100% vs. 80%, landmark p value <0.001) but other outcome parameters were not significantly improved (EFS 62% vs. 71%, p= 0.707; DFS 58% vs. 75%, p=0.382; RR 42% vs. 16%, p=0.056). For patients with exon 17 mutations, treatment without GO resulted in inferior outcomes when compared to CBF AML patients without exon 17 mutations (OS 56% vs. 85%, p= 0.019; EFS 44% vs. 66%, p=0.154; DFS 33% vs. 65%, p=0.049; RR 67% vs. 32%, p=0.031). Adding GO abrogated this negative impact. Specifically, OS, EFS, DFS and RR for patients with exon 17 mutations were comparable to that of CBF AML patients with WT exon 17 when treated with GO (OS 77% vs. 83%, p= 0.542; EFS 62% vs. 71%, p=0.516; DFS: 56% vs. 74%, p=0.195; RR 22% vs.19%, p=0.898). This analysis suggests that pediatric KIT + CBF AML has negative prognostic impact within the context of AAML0531. This effect was abrogated, particularly for patients with exon 17 mutations, with GO treatment. CD33-targeted agents may be beneficial, at least for a subset of these patients, in future clinical trials. Disclosures Aplenc: Sigma Tau: Honoraria.

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)0,002
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: Essai randomisé · Signal consensuel: Essai randomisé
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,002
Score d'incertitude au seuil0,011

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,002
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,001
Communication savante0,0010,001
Science ouverte0,0000,000
Intégrité de la recherche0,0010,001
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 Opus0,032
Tête enseignante GPT0,305
Écart entre enseignants0,273 · 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'étudeEssai randomisé
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

Citations1
Publié2015
Routes d'admission1
Résumé présentoui

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