Long-Term Subgroup Analyses from Azacitidine Vs. Decitabine in Unfit Newly Diagnosed Acute Myeloid Leukemia Patients: Results from the Pethema Registry
Notice bibliographique
Résumé
Abstract INTRODUCTION The hypomethylating agents (HMAs), decitabine (DEC) and azacitidine (AZA), have made it possible to treat more elderly patients with acute myeloid leukemia (AML). Both HMAs have demonstrated efficacy in monotherapy and in combination with targeted therapies. However, there is little direct comparative data on AZA and DEC in first-line treatment, and we do not know which group of patients might benefit from each drug. Results of the full analysis set (FAS) were presented previously (Labrador J, et al. ASH 2020). Here, we report long-term clinical efficacy from prespecified patient subgroup analyses. METHODS We conducted a retrospective study to compare real-life clinical outcomes between AZA and DEC in patients with AML ineligible for intensive chemotherapy included in the PETHEMA registry, and analyzed clinical variables associated with response and overall survival (OS) between AZA and DEC. RESULTS A total of 626 patients were included for the FAS between 2006 and 2019. 487 (78%) received AZA and 139 (22%) received DEC. Baseline characteristics were comparable in both groups, except for the percentage of bone marrow blasts (44% vs. 34% in the DEC group compared to AZA, p=0.010). In the FAS, there was no difference in the CR, CR/CRi or ORR (CR/RCi + PR) rate: 18%, 20.5% and 32% with AZA vs. 23%, 25% and 39.5% with DEC (p=0.20, p=0.27 and p=0.12). In the subgroup analysis, DEC was associated with higher CR/CRi rate than AZA in patients with ECOG ≥ 2 (95% CI: 0.088 - 0.801), bone marrow blast count < 50% (95% CI: 0.293 - 0.965), secondary AML (95% CI: 0.223 - 0.918) and adverse cytogenetics (95% CI: 0.171 - 0.857) (Figure 1A). DEC was associated with higher ORR rate than AZA in patients with ECOG ≥ 2 (95% CI: 0.116 - 0.782), leukocytes < 10 x10 9/L (95% CI: 0.321 - 0.920) and bone marrow blasts < 50% (95% CI: 0.321 - 0.920) (Figure 1B) 120 days-mortality was 25.4% after AZA and 27.1% after DEC, p=0.70. Patients who did not achieve at least a PR had significantly higher 120-day mortality with both HMAs (OR 8.85 and 8.22 for AZA and DEC, respectively). In the subgroup analysis, patients with leukocytes ≥ 10 x10 9/L (95% CI: 1.069 - 4.157) and those with estimated glomerular filtration rate (eGFR) ≥ 45 mL/min/1.73m 2 (95% CI: 1.249 - 4.664) had higher 120-day mortality with DEC than with AZA (Figure 1C) With a median follow-up of 12 months, median OS was 10.4 months (95% CI: 9.2 - 11.7) for AZA vs. 8.8 months (95% CI: 6.7 - 11.0) for DEC (p = 0.455). The subgroup analysis revealed that patients ≥ 80 years (95%: CI 1.005 - 2.341), with leukocytes ≥ 10 x10 9/L (95% CI 1.039 - 2.062), platelet count <20 x10 9/L (95% CI: 1.150 - 3.422) and those with eGFR ≥ 45 mL/min/1.73m 2 (95% CI: 1.040 - 2.059) did benefit for treatment with AZA compared to DEC (Figure 1D). CONCLUSIONS Our study provides real-life data on the outcomes of AML patients treated with AZA compared to DEC in a large retrospective cohort with long-term follow-up. In addition, we identify for the first time some baseline characteristics that could benefit from AZA or DEC in terms of responses, 120-day mortality and OS. These findings could help us to choose the most appropriate HMA in monotherapy or for the development of new combinations. Figure 1 Figure 1. Disclosures de la Fuente: Novartis: Research Funding; Abbie: Consultancy, Speakers Bureau; Incyte: Consultancy, Speakers Bureau; BMS: Consultancy, Speakers Bureau. Tormo: Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Jazz Pharmaceuticals: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Pfizer: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Astellas: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Pérez-Simón: Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau. Montesinos: Forma Therapeutics: Consultancy; Tolero Pharmaceutical: Consultancy; Karyopharm: Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Pfizer: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Sanofi: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Janssen: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Incyte: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Daiichi Sankyo: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Glycomimetics: Consultancy; Agios: Consultancy; Stemline/Menarini: Consultancy; Teva: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; AbbVie: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Astellas Pharma, Inc.: Consultancy, Honoraria, Other: Advisory board, Research Funding, Speakers Bureau.
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 | 0,004 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,004 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 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 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 ».