S1613 ALLOGENEIC HEMATOPOIETIC CELL TRANSPLANTATION CAN ABROGATE INCREASING RISK OF RELAPSE FROM PERSISTENT MUTATIONS MEASURED BY TARGETED SEQUENCING AT REMISSION IN NORMAL KARYOTYPE ACUTE MYELOID LEUKEMIA
Notice bibliographique
Résumé
Background: Normal karyotype acute myeloid leukemia (NK AML) currently does not have a universal measurable residual disease (MRD) target, thus making next generation sequencing (NGS) more attractive as an MRD monitoring tool. Also, there is limited knowledge on selecting consolidation therapy based on the MRD status at time of first achievement of complete remission (CR1) in NK AML between allogeneic hematopoietic cell transplantation (allo‐HCT) and chemotherapy. Aims: Current study aims to investigate the impact of type of consolidation therapy on the long‐term outcomes according to the NGS‐based MRD status at CR1 in NK AML Methods: A total of 139 patients diagnosed with NK‐AML, treated, and achieved CR1 were included in this study. Paired bone marrow or peripheral blood samples were obtained at diagnosis and at CR1. Using a panel of 45 genes commonly mutated in AML, 278 samples were sequenced using Illumina Hiseq 2500 followed by variant calling procedures (Avg. on‐target coverage 895x).Treatment outcomes were evaluated in terms of overall survival (OS), relapse free survival (RFS), and cumulative incidence of relapse (CIR). The prognostic impact of risk factors for OS, RFS, and CIR was determined using Mantel‐Byar test, which is a Cox proportional hazard model considering time‐dependent covariates, and Fine‐Gray proportional hazard regression model considering competing events. Variant allele frequency (VAF) of 0.2% was defined as minimum sensitivity to detect MRD and as MRD pos given the mutation was present at diagnosis. Results: Of 139 patients, 124 patients (89.2%) carried 363 mutations at diagnosis and 15 patients without detected mutations were excluded from further analyses. At CR1, 72/124 patients carried 121 persistent mutations over 0.2% (Fig A). Mutations in genes associated with DNA methylation such as DNMT3A (n = 33/40), followed by IDH2 (n = 13/21), and TET2 (n = 10/15) were highly persistent. In contrary, mutations in NPM1 and signaling related genes show lower frequency of persistence (n = 4/49 and n = 12/70). As mutations in DNMT3A , TET2 , and ASXL1 are often associated with age‐related clonal hematopoiesis, they were excluded in MRD assessment. Based on MRD status (50 positive and 74 negative cases) and the type of consolidation therapy they are treated with (65 with allo‐HCT and 59 with chemotherapy only), we performed subgroup analyses (Fig B).In univariate analyses on the entire cohort, MRD status at CR1 did not affect long‐term outcome, while allo‐HCT was associated with a better RFS (p = 0.009), a lower CIR (p = 0.008) and, a trend of better in OS. We further stratified the cohort into 4 subgroups based on the two factors (i.e. MRD status at CR1 and the type of consolidation therapy). Most notably, for MRD pos patients, the benefit of allo‐HCT became more apparent in terms of RFS (p = 0.002), CIR (p = 0.002), and OS ( p = 0.011) (Fig C). In contrast, this benefit from allo‐HCT over chemotherapy was not observed in MRD neg group (Fig D). Multivariate analyses confirmed that allo‐HCT was an independent favorable prognostic factor for OS (Hazard Ratio (HR); 0.33, p = 0.011), RFS (HR; 0.28, p = 0.002) and CIR (HR; 0.20, p = 0.002) in the MRD pos group. Summary/Conclusion: NGS‐based MRD pos patients at CR1 show better long‐term outcome when received allo‐HCT as consolidation therapy while no survival difference was observed regardless of type of consolidation therapy in MRD neg patients. The present study highlights urgent demand for the development of a decision‐making algorithm for selection of consolidation therapy according to NGS‐based MRD status at CR1 in NK AML. image
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».