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Enregistrement W2911441731 · doi:10.1182/blood-2018-99-115842

Comprehensive Molecular Profiling of FLT3-Mutated Acute Myeloid Leukemia (AML) Patients Treated within the Ratify Trial (Alliance C10603)

2018· article· en· W2911441731 sur OpenAlexaff
Nikolaus Jahn, Ekaterina Panina, Lars Bullinger, Anna Dolnik, Julia Herzig, Tamara J. Blätte, Axel Benner, Julia Krzykalla, Insa Gathmann, Richard A. Larson, Francesco Lo‐Coco, Sergio Amadori, Thomas W. Prior, Joseph Brandwein, Frederick R. Appelbaum, Bruno C. Medeiros, Martin S. Tallman, Eva Tiecke, Céline Pallaud, Gerhard Ehninger, Michael Heuser, Arnold Ganser, Richard M. Stone, Christian Thiede, Hartmut Döhner, Clara D. Bloomfield, Konstanze Döhner

Notice bibliographique

RevueBlood · 2018
Typearticle
Langueen
DomaineMedicine
ThématiqueAcute Myeloid Leukemia Research
Établissements canadiensPrincess Margaret Cancer Centre
Organismes subventionnairesnon disponible
Mots-clésMyeloid leukemiaMedicineOncologyLeukemiaInternal medicineCancer researchImmunology

Résumé

récupéré en direct d'OpenAlex

Abstract Background: Recently, the oral multitargeted small molecule FLT3 inhibitor midostaurin (M) was approved for treatment of FLT3-mutated AML in combination with standard chemotherapy. In the international RATIFY (NCT00651261) trial, addition of M led to superior overall and event-free survival compared to placebo, thus defining a new standard of care in this AML subset (Stone RM et al. NEJM 2017). Although not powered for subgroup analyses, M showed consistent effects across all FLT3 mutation strata [tyrosine kinase domain (TKD); internal tandem duplication (ITD) with low (0.05-0.7; ITDlow) or high (>0.7; ITDhigh) allelic ratio] suggesting significant off-target activity beyond FLT3 inhibition. Aim: We aimed to comprehensively profile the mutational landscape of FLT3 mutated (FLT3mut) AML in a large, well characterized cohort of patients (pts) treated within the RATIFY trial using a high-throughput targeted sequencing (HTS) approach. Methods: HTS was performed on the entire coding region of 262 genes involved in hematologic malignancies including 20 genes that encode kinases targeted by M (M kinome, MK). Pretreatment peripheral blood (PB; 14%) or bone marrow (BM; 86%) specimens were available from 475 (66%) of 717 FLT3mut AML RATIFY pts. Libraries were prepared using SureSelectXT custom solutions (Agilent). Paired-end sequencing was carried out on a HiSeq platform (Illumina). FLT3 mutation (mut) status was available for all pts [TKD: 24%; ITD: 76% (ITDlow: 45%; ITDhigh:31%)], and cytogenetic data for 454 pts (96%). Results: An average sequencing depth of 978x was obtained for all pts. In sum, 1815 mut (missense: 49%; indels: 40%; nonsense: 7%; other: 3%) were identified with a mean of 3.8 mut per pt (FLT3 strata; TKD: 4; ITDlow: 4; ITDhigh: 3.6).Overall, recurrent mut (>5% of all pts) were found in 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, TP53 (1%) and biallelic CEPBA (1%) mut were rare events. This was also true for aberrations of the MK (7% in total) with KIT (2%), MAP3K11 (1%), and NTRK3 (1%) being most frequently mutated. When stratified according to FLT3mut type, mut in NRAS (24% vs 7%, p<.0001), SMC1A (10% vs 4%, p=.02), and KIT (4% vs 1%, p=.02) occurred significantly more often in TKD than ITD groups, respectively, whereas WT1 (13% vs 24%, p=.018) was more frequently co-mutated in the ITD group. In general, pts in the TKD group had significantly more mut in genes encoding for cohesin (TKD: 29% vs ITD: 16%, p=.004) and signaling (TKD: 40% vs ITD: 24%, p=.001) proteins compared to ITD pts, who had significantly more mut in transcription genes (TKD: 37% vs ITD: 48%, p=.03). Based on the mut and cytogenetic data, we next sought to assign all FLT3mut pts to the 11 recently defined molecular AML classes (Papaemmanuil E et al. NEJM 2016). The majority fell into two classes, namely the NPM1 (N; 62%) and the chromatin-spliceosome (CS; 15%) classes, underscoring the significance of FLT3mut as the driver in these particular genomic classes. Other class-defining lesions were rare or absent in this cohort [inv(16): 2%; t(8;21): 2%; t(11q23;x): 2%; t(6;9): 1%, TP53-aneuploidy: 1%; CEBPAbiallelic: 1%; IDH2R172: 0%]. In 14% of all pts categorization was not possible (no or >1 class-defining lesion), emphasizing the need for further refinement of this classification. When focusing on these two groups, N and CS had comparable FLT3mut patterns (TKD: 24% vs 21%; ITDlow: 44% vs 45%; ITDhigh: 32% vs 33%), whereas N more frequently correlated with a normal karyotype (N: 91% vs CS: 63%). With respect to clinical characteristics, no differences between N and CS in terms of age, white blood cells, platelets, PB and BM blasts, as well as history of MDS were observed. Conclusion: In this comprehensive sequencing approach, we could further delineate the molecular pattern of FLT3mut AML. Here, FLT3-ITD and -TKD groups exhibited remarkable differences in cooperating pathways, highlighting distinct biologic features in the leukemogenesis of FLT3mut AML. Overall, mut of MK genes were rare events, not fully explaining the complexity of M off-target effects. Understanding the underlying disease mechanism will potentially provide useful information on prognosis and prediction of response to M. Further analyses including correlation with clinical outcome are ongoing. Support: U10CA180821, U10CA180861, U10CA180882, U24CA196171 Disclosures Bullinger: Janssen: Speakers Bureau; Jazz Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Bristol-Myers Squibb: Speakers Bureau; Pfizer: Speakers Bureau; Sanofi: Research Funding, Speakers Bureau; Amgen: Honoraria, Speakers Bureau; Bayer Oncology: Research Funding. Gathmann:Novartis: Employment. Larson:Ariad/Takeda: Consultancy, Research Funding; Pfizer: Consultancy, Research Funding; Novartis: Consultancy, Research Funding; BristolMyers Squibb: Consultancy, Research Funding. Medeiros:Genentech: Employment; Celgene: Consultancy, Research Funding. Tallman:ADC Therapeutics: Research Funding; AROG: Research Funding; BioSight: Other: Advisory board; Orsenix: Other: Advisory board; AbbVie: Research Funding; Daiichi-Sankyo: Other: Advisory board; Cellerant: Research Funding. Tiecke:Novartis: Employment. Pallaud:Novartis: Employment. Ehninger:Cellex Gesellschaft fuer Zellgewinnung mbH: Employment, Equity Ownership; GEMoaB Monoclonals GmbH: Employment, Equity Ownership; Bayer: Research Funding. Ganser:Novartis: Membership on an entity's Board of Directors or advisory committees. Stone:Otsuka: Consultancy; Jazz: Consultancy; Cornerstone: Consultancy; Fujifilm: Consultancy; Arog: Consultancy, Research Funding; Pfizer: Consultancy; Sumitomo: Consultancy; Novartis: Consultancy, Research Funding; Ono: Consultancy; Orsenix: Consultancy; Merck: Consultancy; Argenx: Other: Data and Safety Monitoring Board; AbbVie: Consultancy; Agios: Consultancy, Research Funding; Amgen: Consultancy; Astellas: Consultancy; Celgene: Consultancy, Other: Data and Safety Monitoring Board, Steering Committee. Thiede:AgenDix: Other: Ownership; Novartis: Honoraria, Research Funding. Döhner:AROG Pharmaceuticals: Research Funding; Celgene: Consultancy, Honoraria, Research Funding; AROG Pharmaceuticals: Research Funding; Pfizer: Research Funding; Bristol Myers Squibb: Research Funding; Novartis: Consultancy, Honoraria, Research Funding; Celator: Consultancy, Honoraria; AbbVie: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Astellas: Consultancy, Honoraria; Bristol Myers Squibb: Research Funding; Sunesis: Consultancy, Honoraria, Research Funding; Astellas: Consultancy, Honoraria; Novartis: Consultancy, Honoraria, Research Funding; Astex Pharmaceuticals: Consultancy, Honoraria; Astex Pharmaceuticals: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Jazz: Consultancy, Honoraria; Pfizer: Research Funding; Seattle Genetics: Consultancy, Honoraria; AbbVie: Consultancy, Honoraria; Agios: Consultancy, Honoraria; Celator: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Agios: Consultancy, Honoraria; Seattle Genetics: Consultancy, Honoraria; Sunesis: Consultancy, Honoraria, Research Funding; Janssen: Consultancy, Honoraria; Jazz: Consultancy, Honoraria.

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 enseignants0,001
Score d'incertitude au seuil0,004

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
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
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 Opus0,021
Tête enseignante GPT0,296
Écart entre enseignants0,275 · 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

Citations1
Publié2018
Routes d'admission1
Résumé présentoui

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