P411: LONGITUDINAL CHARACTERIZATION OF MOLECULAR VARIANTS AT REMISSION AND RELAPSE: SUBANALYSIS OF THE QUAZAR AML-001 TRIAL
Notice bibliographique
Résumé
Background: Acute myeloid leukemia (AML) is characteristically heterogeneous, with outcomes impacted by age, cytogenetics and molecular factors. Despite achieving remission with frontline intensive chemotherapy (IC), most patients (pts) eventually relapse. In the QUAZAR trial (NCT01757535), oral azacitidine (Oral-AZA) prolonged OS and RFS vs placebo (PBO) in older pts with AML in remission post IC, including pts with investigator-reported NPM1-mutant (mut) AML at diagnosis (Dx; Wei et al., N Engl J Med 2020; Döhner et al., Blood 2022). The prognostic relevance of gene variants characterized at screening, prior to Oral-AZA maintenance, and the molecular architecture at relapse after Oral-AZA has not been reported. Aims: To 1) evaluate the association between baseline (BL; post IC) gene mutations on survival outcomes after Oral-AZA vs PBO; 2) compare the gene mutation profile at relapse in the two treatment (Tx) arms. Methods: In QUAZAR, 472 pts (≥55 years) with AML with intermediate- or poor-risk cytogenetics in remission after IC were randomized 1:1 to Oral-AZA or PBO. Among pts who consented, targeted NGS was performed using 37 myeloid-related genes in DNA isolated from bone marrow (BM) mononuclear cells at BL (n=310) and relapse (n=160). Mean NGS coverage was 13K reads and the median locus-specific minimal detectable variant allele frequency (VAF) was 0.12% (range: 0.02–2.79). RFS was time from randomization until relapse (≥5% BM blasts) or death, estimated by Kaplan–Meier methods. Nominal P values were derived from log-rank tests. Results: In the NGS biomarker population (n=310), median RFS (mRFS) for Oral-AZA vs PBO was 10.2 vs 4.7 months (mo), respectively. Of these, 221 (71.3%) had detectable mutations at BL, most frequently in DNMT3A (28.4%), TP53 (15.5%), IDH2 (12.3%), TET2 (11.9%), SRSF2 (11.0%), IDH1 (6.1%) and ASXL1 (5.5%). The frequency of gene mutations detected at BL was similar between Tx arms. The presence of ≥3 mutations was associated with shorter RFS in both Tx arms (≥3 vs <3 mutations: mRFS 7.4 vs 12.9 mo [P=0.008], respectively). RFS favored Oral-AZA in pts with low mutational burden at BL (<3 mutations: mRFS 12.9 vs 4.9 mo [P<0.001]; n=143 vs 128 [PBO]), but there was no significant difference between Tx arms in pts with higher mutational burden (≥3 mutations: mRFS 7.4 vs 1.9 mo [P=0.12]; n=22 vs 17 [PBO]). Analysis of BL gene variants showed RFS benefit for Oral-AZA (n=51) vs PBO (n=37) among pts with DNMT3A mutations (Figure). To determine if this effect was related to concurrent NPM1 mutations at Dx, we compared RFS between Tx arms in pts who had either single DNMT3Amut or co-mutated DNMT3Amut/NPM1mut AML (23.7% of the NPM1mut cohort). In pts with DNMT3Amut/NPM1mut AML, mRFS was not reached vs 6.2 mo for Oral-AZA vs PBO, respectively (HR [CI]: 0.04 [0.008, 0.235]; P<0.001). In pts with DNMT3Amut/NPM1wildtype AML, mRFS was 10.0 vs 4.6 mo for Oral-AZA vs PBO, respectively (HR [CI]: 0.40 [0.225, 0.724]; P=0.002; Figure). At relapse, the frequency of mutations was comparable between Tx arms (not shown). Summary/Conclusion: In pts with AML in remission post IC, post hoc analyses showed that Oral-AZA improved RFS vs PBO in pts with lower mutational burden at BL. RFS was improved by Oral-AZA in pts with DNMT3A mutations independent of NPM1 status at Dx. As DNMT3A mutations at BL could either be preleukemic or related to clonal hematopoiesis, factors responsible for improved outcome after Oral-AZA independent of NPM1 mutations remain to be determined. The spectrum of mutations at relapse was similar between Tx arms. Figure. RFS according to gene mutation status at BLKeywords: Acute myeloid leukemia, Genomics, AML, Clinical trial
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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 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 | 0,000 |
| 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 ».