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Enregistrement W4405041605 · doi:10.1182/blood-2024-209687

Survival Outcomes for Patients with Acute Myeloid Leukemia with Myelodysplasia Related Changes (AML-MRC) in a Real-World, Retrospective Cohort Receiving Intensive Chemotherapy

2024· article· en· W4405041605 sur OpenAlexaff
Nicholas L.J. Chornenki, Afraa Fadul, Analiza Supan, Yasser Abou Mourad, Hannah Cherniawsky, Shanee Chung, Donna L. Forrest, Deepesh Lad, Aly Karsan, Florian Kuchenbauer, Eric McGinnis, Stephen H. Nantel, Sujaatha Narayanan, Thomas J. Nevill, Judith Anula Rodrigo, Claudie Roy, David Sanford, Kevin Song, Cynthia L. Toze, Jennifer White, Ryan J. Stubbins

Notice bibliographique

RevueBlood · 2024
Typearticle
Langueen
DomaineMedicine
ThématiqueAcute Myeloid Leukemia Research
Établissements canadiensVancouver General HospitalCanada's Michael Smith Genome Sciences CentreBC Cancer AgencyUniversity of British Columbia
Organismes subventionnairesnon disponible
Mots-clésMedicineMyeloid leukemiaRetrospective cohort studyInternal medicineChemotherapy regimenChemotherapyCohortOncologyOverall survivalLeukemiaHematologic NeoplasmsCancer

Résumé

récupéré en direct d'OpenAlex

Introduction Acute myeloid leukemia with myelodysplasia-related changes (AML-MRC) is defined by the presence of myelodysplasia-related cytogenetic abnormalities (MRC-cyt) or gene mutations (MRC-mut) in the 2022 International Consensus Classification (ICC) guidelines. This replaced previous definitions from the 2016 World Health Organization (WHO) guidelines which included only morphologic, clinical, and cytogenetic criteria. AML with mutated TP53 (AML-TP53) was also recognized as a separate entity, whereas many TP53-mutated (mt) patients were AML-MRC by WHO 2016. Patients with MRC-mut and some with MRC-cyt are classified as adverse (adv)-risk in the European LeukemiaNet (ELN) 2022, unless occurring in the context of favorable (fav)-risk subtypes. However, many validation studies for the AML-MRC cytogenetic and mutation criteria were based on historical induction chemotherapy (IC) cohorts, which may not reflect current practice. We assessed the number of patients reclassified as AML-MRC by 2016 vs. 2022 criteria and defined survival outcomes for AML-MRC and AML-TP53 patients in a recent, real-world cohort of AML patients receiving IC. Methods Sequential patients with newly diagnosed AML, excluding acute promyelocytic leukemia, receiving IC were retrospectively identified at a single institution from 2016-2022. Patients received 7+3 (cytarabine 100mg/m2, daunorubicin 60mg/m2), CPX-351 (44mg/m2), or other cytarabine-based IC. Cytogenetics and next-generation sequencing were done in a clinical lab; patients without these were excluded. Patients were retrospectively classified as AML-MRC or AML-TP53 by both WHO 2016 and ICC 2022, with hierarchical classification of patients as AML-TP53, MRC-mut, or MRC-cyt. Patients met WHO 2016 clinical criteria if they had morphologic dysplasia or prior MDS or MDS/MPN, without mutated NPM1 or CEBPA. Therapy-related AML was included as a 2016 clinical criteria. Survival analysis was by Kaplan-Meier, between-group comparisons by t-test. Results We identified 420 AML patients who received IC. Median age was 58.7 years (yrs) (range 21-77 yrs). IC was 7+3 in 89% (374/420), CPX-351 in 8% (34/420), and other cytarabine-based IC in 3% (12/420); 19% (78/420) received FLT3-inhibitor and 12% (52/420) GO. Overall, 22% (92/420) of the cohort met MRC 2016 criteria and 29% (122/420) met MRC 2022 criteria. Only 15% (63/420) of patients met both MRC 2016 and 2022 criteria. AML-MRC patients were older when considered by 2016 (61.4 vs. 57.9 yrs, P=0.043) or 2022 (60.8 vs. 57.8 yrs, P=0.031) criteria. MRC 2016 patients had a median overall survival (mOS) of 19.7 months (mo) vs. 46.4 mo (P=0.003). However, 25% (23/92) of MRC 2016 patients had TP53-mt; removing these patients resulted in a mOS of 27.8 mo for MRC 2016 patients vs. 46.4 mo (P=0.200). AML-TP53 per ICC 2022 comprised 5% (23/420) of all patients; these patients had a mOS of 7.0 mo vs. 45.9 mo (P<0.001). Amongst ICC 2022 AML-MRC patients, 83% (102/122) were MRC-mut (median 2 mt, range 0-5) and 16% (20/122) were MRC-cyt. AML-MRC patients had a mOS of 46.5 mo vs. 33.6 mo in the overall cohort (P=0.940). Comparing the AML-MRC group to ELN 2022 int-risk patients, we observed a mOS of 46.5 mo vs. 25.9 mo (P=0.450). MRC-mut patients had a mOS of 49.5 mo vs. 33.6 mo in the overall cohort (P=0.730). MRC-cyt patients had a mOS of 24.4 mo vs. 33.6 mo in the overall cohort (P=0.340) Within the MRC-mut group, no difference in mOS was seen for chromatin modifier (P=0.260), splicing (P=0.390), transcription factor (P=0.270) or mixed (P=0.720) mutation patterns. There was no difference for patients with >1 vs. 1 MRC-mut (P=0.660). Among ELN fav-risk patients, 18% (23/129) had a MRC-mut with a mOS of 33.6 mo vs. not reached for those without (P=0.280). Patients meeting 2016 criteria receiving CPX-351 (26%, 24/92) had a mOS of 36.5 mo vs. other IC regimens (74%, 68/92) at 16.7 mo (P=0.290). Patients meeting 2022 criteria receiving CPX-351 (18%, 19/122) had a mOS of 63.3 mo vs. other IC regimens (84%, 103/122) at 46.5 mo (P=0.930). Conclusion Patients with ICC 2022 AML-MRC are a substantively different group than older MRC cohorts. Patients with MRC-defining cytogenetic abnormalities or mutations had comparable mOS to ELN int-risk patients. MRC-defining mutations did not modify mOS for ELN fav-risk patients. Patients with AML-TP53 have dismal outcomes. Further study is needed to clarify risk stratification of AML-MRC in the modern treatment era.

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)0,001
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 enseignants0,002
Score d'incertitude au seuil0,005

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0010,001
Science ouverte0,0000,001
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 Opus0,011
Tête enseignante GPT0,279
Écart entre enseignants0,268 · 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
Publié2024
Routes d'admission1
Résumé présentoui

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