PS968 GENETIC LANDSCAPE OF FLT3‐MUTATED ACUTE MYELOID LEUKEMIA (AML) PATIENTS TREATED WITHIN THE RATIFY TRIAL: CALGB 10603 (ALLIANCE)
Notice bibliographique
Résumé
Background: Recently, the oral multi‐targeted FLT3 inhibitor midostaurin (M) was approved in combination with standard chemotherapy for treatment of FLT3 mutated (FLT3 mut) AML. In the international RATIFY (NCT00651261) trial, M significantly improved overall (OS) and event‐free survival (EFS) compared to placebo, thus defining a new standard of care in this AML subset (Stone RM et al. NEJM 2017). Aims: To comprehensively delineate the mutational profile of FLT3 mut AML patients (pts) treated within the RATIFY trial and to correlate genetic findings with clinical outcome. Methods: Targeted sequencing was performed of 262 candidate genes including 20 kinases targeted by M (M kinome, MK) using SureSelectXT (Agilent). Diagnostic specimens were available from 475 (66%) of 717 FLT3 mut pts. Sequencing was carried out on a HiSeq (Illumina). FLT3 mutation (mut) status was available for all 475 pts [tyrosine kinase domain (TKD: 24%); internal tandem duplication (ITD: 76%)]; cytogenetic data were present for 371 pts (78%). Results: Overall, 1813 mut were identified (median coverage: 978x) with an average of 3.8 mut per pt (FLT3 strata; TKD: 3.9; ITD: 3.8). Mut ≥5% were found for NPM1 (61%), DNMT3A (39%), WT1 (21%), TET2 (12%), RUNX1 (11%), NRAS (11%), PTPN11 (9%), ASXL1 (8%), IDH1 (8%), IDH2 (7%; #R140 only), and SMC1A (6%). In contrast, MK mut were rare events (7% in total) with KIT (2%), MAP3K11 (1%), and NTRK3 (1%) being most frequently mutated. First, we categorized all pts to the recently defined 11 genomic AML classes (Papaemmanuil E et al. NEJM 2016). The majority of cases fell into two classes, namely the NPM1 (N; 60%), and the chromatin‐spliceosome (C‐S; 15%) classes. Core‐binding factor (CBF) AML [t(8;21) and inv(16)] accounted for 4% only; in 13% no class‐defining lesion was identified (no class, NC). In terms of clinical outcome, CBF and N classes showed better response to induction therapy than C‐S and NC (Complete remission (CR) rate: 83%, 70%, 60%, 38%; p = .0005), which translated into significantly superior OS (p < .0001) and EFS (p < .0001) for CBF and N compared to C‐S and NC. When stratified according to treatment arm, addition of M resulted in improved OS in N, C‐S, as well as NC, irrespective of the genetic background. Next, we categorized pts according to the 2017 European LeukemiaNet (ELN) genetic risk groups (Döhner H et al. Blood 2017). CR rate was inferior within the adverse (ADV) and intermediate (INT) as compared to the favorable (FAV) risk group (52%, 62%, 74%; p = .002) translating into significantly shorter EFS (p < .0001) and OS (p < .0001) in INT and ADV than FAV. Of note, M showed consistent beneficial effects across all ELN risk groups. When comparing pts with MK gene mut vs. pts with wildtype MK, no differences in outcome were observed. Within the group of MK mut pts there was no benefit for M; however, the results have to be interpreted cautiously due to the small sample size. Summary/Conclusion: In this comprehensive targeted sequencing approach of 475 FLT3 mut AML pts treated within the RATIFY trial, remarkable differences in the underlying AML genomic classes were found. Predominant classes were N, C‐S, CBF, showing significant differences with regard to response to treatment and survival. Of note, treatment with M showed beneficial effects across all genomic AML classes. In addition, M was associated with a better outcome in all 2017 ELN risk categories, demonstrating a consistent benefit of M across various genetic AML subgroups. Support: U10CA180821, ‐ U10CA180882, U24CA196171; Novartis; https://acknowledgments.alliancefound.org
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 | 0,001 | 0,001 |
| 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 | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 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 ».