MétaCan
Menu
← Retour à la cohorte
Enregistrement W3096217495 · doi:10.1182/blood-2020-137589

Prognostic Impact of a Modified European LeukemiaNet (ELN) Genetic Risk Stratification in Predicting Outcomes for Adults with Acute Myeloid Leukemia (AML) Undergoing Allogeneic Hematopoietic Stem Cell Transplantation (HCT). a Center for International Blood and Marrow Transplant Research (CIBMTR) Analysis for the CIBMTR Acute Leukemia Writing Committee

2020· article· en· W3096217495 sur OpenAlexaff
Antonio Jiménez, Trent Wang, Marcos de Lima, Krishna V. Komanduri, Partow Kebriaei, Mark R. Litzow, Vijaya Raj Bhatt, Frédéric Baron, Ayman Saad, Nandita Khera, Joseph Maakaron, Hemant S. Murthy, Edward A. Copelan, Zachariah DeFilipp, Christopher Bredeson, Rodrigo Martino, Maxwell M. Krem, Taiga Nishihori, Mei‐Jie Zhang, Daniel J. Weisdorf, Karen Chen, Wael Saber

Notice bibliographique

RevueBlood · 2020
Typearticle
Langueen
DomaineMedicine
ThématiqueAcute Myeloid Leukemia Research
Établissements canadiensOttawa Hospital
Organismes subventionnairesnon disponible
Mots-clésMedicineInternal medicineCumulative incidenceTransplantationHazard ratioUnivariate analysisOncologyHematopoietic stem cell transplantationClinical endpointProportional hazards modelGraft-versus-host diseaseMultivariate analysisClinical trialConfidence interval

Résumé

récupéré en direct d'OpenAlex

Background: Allogeneic HCT continues to be the optimal consolidation strategy for many patients with AML. Cytogenetic and molecular abnormalities are known to influence post-transplant outcomes. We tested the prognostic ability of a modified (mELN) classification system based on available CIBMTR genetic data, to predict post-transplant outcomes. Methods: Adult patients with a diagnosis of AML in first complete remission (CR1) with available pre-transplant cytogenetic and molecular mutational data, receiving a first allogeneic HCT from 2013-2017, were included. Patients were stratified according to mELN genetic classification in three distinct groups: favorable (Fav), intermediate (IM) and adverse (Adv). Clinical outcomes following HCT were compared among groups after adjusting for significant patient, disease, and transplant-related variables. The primary endpoint was disease free survival (DFS). Secondary endpoints were overall survival (OS), non-relapse mortality (NRM), cumulative incidence of relapse, acute GVHD, and chronic GVHD. Cox proportional hazard models were used to compare endpoints among mELN risk groups, age groups and genetic subsets within the adverse-risk group. Results: Demographic characteristics are summarized in Table 1. 2289 patients (Fav, n=181; IM n=1185; and Adv n=923) met the inclusion criteria. Median follow-up for survivors was 35 months. Importantly, 41% of transplant recipients (n=936) were >60 years, 76% (n=1743) had de novo AML and 48% (n=1111) received a myeloablative conditioning regimen. Univariate analysis (UVA) demonstrated significant differences in 2-year OS (Fav: 67.7%, IM: 64.9% and Adv: 53.9%; p<0.001); DFS (Fav: 57.8%, IM: 55.5% and Adv: 45.3; p<0.001) and relapse (Fav: 28%, IM: 27.5% and Adv: 37.5%; p<0.001) There were no significant differences in NRM (p=0.467) or the incidence of acute (p=0.423) and chronic GVHD (p=0.442) among mELN groups. Initial multivariate analysis (MVA) of mELN risk groups indicated that there was no significant difference in clinical outcomes between the Fav and IM risk groups. Thus, these groups were combined for subsequent analyses. Adv risk (vs. Fav/IM) led to significantly worse OS (HR 1.39 [1.24-1.57] p=<0.001), DFS (HR 1.32 [1.18-1.48] p=<0.001), and relapse (HR 1.42 [1.23-1.63] p=<0.001) (Table 2). This mELN classification effectively stratified both younger (<60 y/o) and older (>60 y/o) patients for OS (Adv vs. Fav/IM HR for <60: 1.43 [1.21-1.69] p<0.001; >60: 1.44 [1.21-1.72] p<0.001), DFS (Adv HR for <60: 1.31 [1.12-1.53] p<0.001; >60: 1.41 [1.19-1.66] p<0.001) and relapse (Adv HR for <60: 1.44 [1.20-1.74] p<0.001, >60: 1.42 [1.15-1.76] p=0.001). NRM was higher for older patients (>60 y/o) in both the Fav/IM (HR 1.40 [1.10-1.78] p=0.007) and Adv-risk cohorts (HR 1.59 [1.18-2.13] p=0.002). Genetic subset comparisons within the adverse-risk group showed that patients carrying monosomy 5, del(5q) or monosomy 7 had inferior 2-year OS (42.6%, p<0.001) and DFS (35.2%, p<0.001), as well as higher rates of relapse (45%, p= 0.002) when compared to other patients within the Adv-risk cohort (OS 60%, DFS 50.8%, relapse 33.5%). Conclusion: Stratification using mELN criteria resulted in clear prognostic separation of OS, DFS and relapse in this large cohort of AML patients undergoing allogeneic HCT. While Fav and IM groups had similar OS, DFS and relapse rates; patients in the Adv risk group had the highest risk of relapse and inferior DFS/OS. However, the majority of patients in all cohorts had favorable outcomes for HCT in CR1. Our findings confirm the value of a combined genetic prognostic model in the AML HCT setting and justify the use of this stratification system in future HCT trials. Correlation of genetic subtypes with other important transplant variables, such as conditioning intensity and pre-transplant MRD status deserves further evaluation. There remains a subset of Adv-risk patients for which post-transplant outcomes continue to be poor, even when transplanted in CR1. Novel peri-transplant pre-emptive/therapeutic strategies are urgently needed for this high-risk cohort. Disclosures de Lima: Celgene: Research Funding; Pfizer: Other: Personal fees, advisory board, Research Funding; BMS: Other: Personal Fees, advisory board; Incyte: Other: Personal Fees, advisory board; Kadmon: Other: Personal Fees, Advisory board. Komanduri:Kiadis: Consultancy; Takeda: Consultancy; Celgene: Consultancy; Atara: Consultancy, Membership on an entity's Board of Directors or advisory committees; Adaptimmune: Membership on an entity's Board of Directors or advisory committees; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees; Kite/Gilead: Consultancy, Membership on an entity's Board of Directors or advisory committees. Kebriaei:Ziopharm: Other: Research Support; Jazz: Consultancy; Kite: Other: Served on advisory board; Amgen: Other: Research Support; Novartis: Other: Served on advisory board; Pfizer: Other: Served on advisory board. Bhatt:National Marrow Donor Program: Research Funding; Rigel Pharmaceuticals: Other; Jazz: Research Funding; Agios: Other: Personal Fees; Incyte: Other: Personal Fees, Research Funding; Takeda: Other: Personal Fees; Partner Therapeutics: Other: Personal Fees; Pfizer: Other, Research Funding; CSL Behring: Other; Tolero Pharmaceuticals: Research Funding; Oncoceutics: Other: Drug support for a trial; Partnership for health analytic research, LLC: Other: Personal Fees; Omeros: Other: Personal Fees; Abbvie: Other: Personal Fees, Research Funding. Saad:Orcabio: Other: research support; Kadmon: Other: research support; Amgen: Other: research support; Incyte Pharmaceuticals: Other: Personal Fees; Magenta Therapeutics: Other: Personal Fees. Copelan:Amgen: Membership on an entity's Board of Directors or advisory committees. Defilipp:Incyte: Research Funding; Regimmune: Research Funding; Syndax Pharmaceuticals: Consultancy. Nishihori:Karyopharm: Other: Research support to institution; Novartis: Other: Research support to institution. Weisdorf:Incyte: Research Funding; FATE Therapeutics: Consultancy.

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)0,003
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 seuil0,007

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,003
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 recherche0,0000,000
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 Opus0,029
Tête enseignante GPT0,297
Écart entre enseignants0,267 · 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
Publié2020
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueBlood→Même sujetAcute Myeloid Leukemia Research→Travaux en français237 207→