Establishing contemporary benchmarks for Acute Myeloid Leukemia outcomes and measurable residual disease: Real-world data utilization from the ELN-david MRD international working group
Notice bibliographique
Résumé
Abstract Background The treatment landscape of acute myeloid leukemia (AML) is rapidly evolving due to advances in novel targeted agents, lower-intensity treatments, and allogeneic stem cell transplantation. Simultaneously, technologic advances are allowing increasingly sensitive detection of measurable residual disease (MRD) to guide treatment decision-making. However, our knowledge of AML patient characteristics, treatments, clinical outcomes and utility of multiparameter flow cytometry and molecular MRD techniques is primarily based on data from clinical trials. There is a need for real-world data to establish AML outcomes and MRD testing in routine practice. Thus, ELN-DAVID, an international European LeukemiaNet (ELN) working group focused on the assessment and validation of MRD in AML, launched the BENCHMARK initiative to establish a regularly updated platform for real-world clinical data and MRD assessment practices in ELN participating centers. Methods Centers provided de-identified, aggregated data from 100 or more unselected, consecutively seen patients with AML starting backwards from December 2022. Centers could include patients in clinical trials, if allowed by the clinical trial protocol. Descriptive data on patient characteristics, treatment strategies, response (morphologic and MRD) and survival were combined for collective analysis. As pooled survival analysis was not possible, the range of the median survival between centers is reported. European LeukemiaNet risk classification is defined as either the 2017 or 2022 version, depending on what the center used and entered. Results To date, data were provided for 1457 patients from 14 international centers treated between 2016 and 2022. Of these patients, 801 (55%) were male and a total of 974 (68%) patients received intensive chemotherapy, 323 (23%) non-intensive treatment, and 136 (9%) supportive care only. Across all treatment groups, most patients were not treated on clinical trials (86%), targeted (gemtuzumab-ozogamicin, FLT3 or IDH inhibitors) treatment was given to 36%, and the majority underwent allogeneic stem cell transplantation (64%). In patients treated with intensive chemotherapy the ELN risk group was 33% favorable, 29% intermediate, and 38% adverse. Of the non-intensively treated patients, most received venetoclax-based therapy (62%). Response evaluations showed complete remission (CR), or CR with incomplete count recovery (CRi) or CR with incomplete platelet recovery (CRp) in 80% of patients that were treated with intensive chemotherapy and in 54% of patients that received non-intensive treatment. Median follow-up time ranges from 4 months to 51 months and the median overall survival in each of the intensively treated ELN risk categories ranges from 16 months to not reached (NR), 8 months to NR, and 5.5 months to NR for favorable, intermediate and adverse risk patients, respectively. MRD data were available for 598 (61%) intensively treated patients after 2 cycles of treatment, 360 (37%) at the end of treatment and 295 (30%) during follow-up. Flow cytometry was the most frequently used MRD technology across all time points (cycle 2: 76%; end of treatment: 64%; follow-up: 62%), followed by quantitative PCR (cycle 2: 40%; end of treatment: 48%; follow-up: 43%). Next-generation sequencing was performed in 8% of patients after two cycles, 12% at end of treatment and 16% during follow-up. Among non-intensively treated patients, 107 (33%) had MRD data available, the majority (73%) was analyzed by flow cytometry. Conclusions The survival data from BENCHMARK-2025, reflecting international practices, are comparable to those expected from recent AML clinical trials. Most centers performed MRD assessment using flow cytometry for patients treated with intensive chemotherapy. MRD was infrequently performed in non-intensively treated patients. A standardized database has been developed to allow future analyses of individual patient data. It is anticipated that data will be updated regularly, giving the opportunity to construct synthetic control cohorts, allowing longitudinal correlation between evolving MRD practices and AML outcomes, and serving as benchmark for contemporary experience.
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 | 0,066 | 0,072 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,006 | 0,009 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,005 | 0,002 |
| Science ouverte | 0,003 | 0,005 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,001 |
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 ».