Variant Allele Frequency Status in Elderly Patients with Acute Myeloid Leukemia Can be Early Predictors of Responsiveness to Decitabine Treatment
Notice bibliographique
Résumé
Abstract As interest in elderly Acute Myeloid Leukemia (AML) patients increases, American society of hematology (ASH) 2020 guidelines for treating newly diagnosed AML in older adults suggested diverse treatment options. The guidelines suggest using monotherapy over combination of hypomethylation agent (HMAs) with other agents in newly diagnosed AML in older adults due to similar efficacy and the potential for more toxicity. HMAs alone is still used widely as an alternative treatment for patients who cannot use venetoclax due to the high cost and poor performance score. If there are early predictors of responsiveness to Decitabine mono therapy, it will be helpful to decide whether to combine Novel agents. This retrospective cohort study from a single institution aimed to evaluate the prognostic significance of Variant allele frequency (VAF) changes in elderly patients after 4 th cycle of decitabine. Total 123 patients with elderly AML were eligible. 57 patients performed follow-up bone marrow biopsy and 49 patients were available of follow up targeted NGS samples from biopsy after 4th cycle of decitabine. To clarify the immortal timed bias, landmark analyses were performed with patients (n=84) who remained at least the median time to perform follow-up bone marrow biopsy after 4th cycle of decitabine treatment. 24 patients (54.5%, 24 of 44) showed more than 50% decrease of VAF after 4 th cycle of decitabine (figure 1a). DMNT3A, TET2, IDH1, IDH2, and SETBP1 and SMC1A showed less than 50% of the decreases of VAF. Patients with DNA methylation genes showed significantly reduced VAF less than 50% (figure 1b). A significant difference of ∆VAF was observed depending on CR status (p=0.021). The survival outcome of patients who showed more than 50% decrease of initial VAF after 4th cycle of decitabine was significantly better than that that with less than 50% decrease of VAF(1-year OS VAF decrease ≥ 50% (n=23), 75.0%; VAF decrease < 50% (n=20), 38.5%; no mutation (n=12), 45.5%; not available of follow up targeted NGS sample (n=29), 16.6%; p < 0.001, figure 2a). Mutations in DNMT3A, TET2, and ASXL1 (DTA genes) were detected in samples from 19 patients at diagnosis. After the exclusion of DTA mutations, the survival outcome improved prognostic risk stratification power of NGS-based MRD assessment in AML. The survival outcome of patients who showed more than 50% decrease of initial VAF after 4th cycle of decitabine was significantly better than that that with less than 50% decrease of VAF(1-year OS VAF decrease ≥ 50% (n=24), 75.0%; VAF decrease < 50% (n=19), 35.1%; no mutation (n=12), 50.1%; not available of follow up targeted NGS sample (n=29), 16.6%; p<0.001, figure 2b). In conclusion, more than 50% decrease of VAF was important negative prognostic factors by improving overall response rate and OS. In case of patients with older adults who received decitabine treatment, if follow up BM biopsy after 4 th cycles of decitabine treatment showed more than 50% reduction of VAF, it may suggest to maintain decitabine treatment. However, if VAF is reduced by less than 50% in follow up BM biopsy, the residual disease burden is considered for the selection of combination treatment to improve survival outcome. Figure 1 Figure 1. Disclosures Kim: Bristol-Meier Squibb: Research Funding; Paladin: Honoraria, Research Funding; Pfizer: Honoraria, Research Funding; Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| 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 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 source (Gemma direct ou Codex distillé), 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 ».