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Enregistrement W2983059016 · doi:10.1182/blood-2019-129078

Allogeneic Stem Cell Transplantation Has Limited Benefit in Older Patients with Mixed Phenotype Acute Leukemia

2019· article· en· W2983059016 sur OpenAlexaffabout
Claire Andrews, Eshetu G. Atenafu, Tracy Murphy, Zeyad Al‐Shaibani, Steven M. Chan, Vikas Gupta, Dennis Dong Hwan Kim, Rajat Kumar, Wilson Lam, Jeffrey H. Lipton, Jonas Mattsson, Dawn Maze, Fotios V. Michelis, Caroline McNamara, Aaron D. Schimmer, Andre C. Schuh, Hassan Sibai, Auro Viswabandya, Karen Yee, Mark D. Minden, Arjun Law

Notice bibliographique

RevueBlood · 2019
Typearticle
Langueen
DomaineMedicine
ThématiqueAcute Myeloid Leukemia Research
Établissements canadiensPrincess Margaret Cancer CentreUniversity Health Network
Organismes subventionnairesnon disponible
Mots-clésMedicineInternal medicineTransplantationHematopoietic stem cell transplantationOncologyCohortLog-rank testChemotherapyChemotherapy regimenSurvival analysis

Résumé

récupéré en direct d'OpenAlex

Mixed phenotype acute leukemia (MPAL) is a heterogeneous disease consisting of acute leukemia expressing markers of both myeloid and lymphoid lineage. The role of hematopoietic stem cell transplantation (alloHSCT) remains uncertain due to lack of prospective trials . Several studies have retrospectively examined its role in pediatric and younger adult subgroups. Myeloablative conditioning (MAC) can result in an overall survival (OS) of 56% at 3 years, but is not suitable for older and frail patients. With expanding transplant availability and alternative donors, we sought to evaluate the outcomes of our cohort of MPAL patients, including older adults unfit for MAC, in a heterogeneous, real-world scenario compared with the outcomes of patients who were consolidated with chemotherapy only. Methods: Seventy four patients, aged 18 years or older, at the Princess Margaret Cancer Center between January 1, 2000 and December 31, 2018 were evaluated. All patients included in analysis met the WHO 2016 classification criteria for MPAL. Overall survival and relapse free survival rates were calculated using the Kaplan-Meier product-limit method. The log-rank test was used to compare the two groups with respect to OS and relapse free survival. Results: Baseline characteristics of the patient cohorts are shown in Table 1. Among the 74 MPAL patients included in this study, 59 (79%) received induction chemotherapy. Complete remission (CR) was achieved in 47 (79%) treated patients. Twenty-five of 36(80%) achieved a CR using ALL protocols (DFCI Protocol, Hyper-CVAD), while 9 of 23 (39%) achieved a CR using AML protocols (3+7, FLAG-Ida). Consolidation treatment post CR was evenly split, with 24(51%) receiving chemotherapy followed by an alloHSCT and 23 (49%) receiving chemotherapy only. In the alloHSCT group, RIC was used in 20 (81%) patients while MAC was used in four younger patients with a median age of 51 and 30 respectively. Donor types included sixteen matched unrelated donors, five sibling donors, two haploidentical donors, and one double umbilical cord. The median age of the transplant cohort was 50 years with a median OS of 21 months. In the chemotherapy only group, the median age was 57 with a median OS of 18 months. ALL-type treatment was used for consolidation in 74% of patients with 83% using the asparaginase-containing DFCI protocol. Univariate analysis demonstrated no difference in outcomes with several parameters including age, induction chemotherapy, donor source, and conditioning intensity. The sole predictor of poor OS on univariate analysis was acute graft versus host disease (aGVHD) with a hazard ratio of 3.3 (95% CI 0.9-11). In total, 12 patients (50%) had aGVHD with a median OS of 16 months. However, when Grade 1/2 and Grade 3/4 aGVHD were compared, median OS was not reached in patients with G1/2, compared with an OS of 11 months in those patients with G3/4 aGVHD. Chronic GVHD was present in 6 patients and did not impact OS (HR 0.9892 CI 95% 0.2-2.9). Post-transplant relapses occurred solely in the RIC group and were early, occurring within a median of 124 days (range 87-188). Although, there was no relapses in the MAC group, the non-relapse mortality (NRM) was 75%, with patients dying from either aGVHD or infection. The NRM in the RIC group was 17.65%. The 3 year OS and relapse free survival (RFS) for the entire cohort were 30% and 32%, respectively. When comparing those patients that had undergone alloSCT and those that who received chemo alone, survival in the two groups were similar with a 1 year OS of 75% vs. 68% and a 3 year OS of 29% vs 32%, respectively (Figure 1). The 3 year RFS was also similar at 29% and 34%, respectively. Subgroup analysis between the two groups was performed for patients with specific poor prognostic factors (older age, complex cytogenetics, higher WCC, conditioning regimen used), but results showed no significant survival differences. Conclusions Older patients with MPAL have an inferior prognosis and worse outcomes with alloHSCT compared with those of younger adults or children. Despite the increase in access to alloHSCT and an expanding pool of alternative donors, outcomes with RIC remain similar to consolidation with chemotherapy alone. There may be some evidence to suggest that the graft versus leukemia effect can be harnessed to improve outcomes in selected patients. Further research towards optimizing patient outcomes is clearly required to address the needs of older patients unfit for MAC. Disclosures Gupta: Sierra Oncology: Honoraria, Membership on an entity's Board of Directors or advisory committees; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Incyte: Honoraria, Research Funding. Mattsson:Gilead: Honoraria; Celgene: Honoraria; Therakos: Honoraria. Maze:Pfizer Inc: Consultancy; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees. Michelis:CSL Behring: Other: Financial Support. McNamara:Novartis Pharmaceutical Canada Inc.: Consultancy. Schimmer:Jazz Pharmaceuticals: Consultancy; Otsuka Pharmaceuticals: Consultancy; Medivir Pharmaceuticals: Research Funding; Novartis Pharmaceuticals: Consultancy. Schuh:Teva Canada Innovation: Honoraria, Membership on an entity's Board of Directors or advisory committees; Agios: Honoraria; AbbVie: Honoraria, Membership on an entity's Board of Directors or advisory committees; Astellas: Honoraria, Membership on an entity's Board of Directors or advisory committees; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees; Jazz: Honoraria, Membership on an entity's Board of Directors or advisory committees; Pfizer: Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Teva Canada Innovation: Honoraria, Membership on an entity's Board of Directors or advisory committees. Yee:Astex: Research Funding; MedImmune: Research Funding; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Hoffman La Roche: Research Funding; Takeda: Membership on an entity's Board of Directors or advisory committees; Millennium: Research Funding; Astellas: Membership on an entity's Board of Directors or advisory committees; Pfizer: Honoraria, Membership on an entity's Board of Directors or advisory committees; Merck: Research Funding. Minden:Trillium Therapetuics: Other: licensing agreement.

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: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,001
Score d'incertitude au seuil0,003

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
Bibliométrie0,0000,000
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)0,0010,000

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 Opus0,011
Tête enseignante GPT0,222
Écart entre enseignants0,211 · 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

Citations1
Publié2019
Routes d'admission2
Résumé présentoui

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