Genetic Determinants of Response to Guadecitabine (SGI-110) in AML
Notice bibliographique
Résumé
Abstract Background: Guadecitabine is a second generation hypomethylating drug with improved pharmacokinetics and pharmacodynamics compared to decitabine and demonstrated clinical activity in both treatment naïve (tn) and relapsed-refractory (rr) AML. Previous studies reported similar response rates to guadecitabine in different cytogenetic subsets but it remains unknown whether this extends to genetic changes. Methods: Pre-treatment blood or bone marrow derived DNA was available for analysis from a total of 220 patients with AML enrolled on guadecitabine phase I-II trials (121 rrAML and 99 tnAML). We included only patients treated at therapeutic doses. Mutations in a panel of 54 genes was studied by the TruSight Myeloid Sequencing Panel (Illumina) and deep sequencingon the Illumina HiSeq platform. FLT3 mutations were separately determined by PCR analysis. The sequence data was analyzed for mutations using the TruSeq Amplicon Application in the Illumina BaseSpace Suite. Putative mutation calls were further filtered by sequencing read quality, minimum variant allelic fraction, and presence in the dbSNP and COSMIC databases. Results: In aggregate, responses to guadecitabine in rrAML were 15 CR (12%), 12 PR/CRi/CRp and 94 non responders (NR) and in tnAML, responses were 34 CR (34%), 21 PR/CRi/CRp and 44 NR. Overall, a median of 1 (range 0 - 5) mutation was present in each patient, with no significant differences between tn and rr AML. The most frequently mutated genes were ASXL1 (16.8%), TET2 (14.1%), IDH2 (10.9%), NPM1 (10%), RUNX1 (9.5%), DNMT3A (9.1%), NRAS (9.1%), FLT3-ITD (8.6%), U2AF1 (8.2%), IDH1 (6.8%), TP53 (5%), and KRAS (4.5%). The distribution of mutations was as expected for a group of patients with rrAML and elderly tnAML. We used Fisher's exact tests to compare mutation frequencies between patients who achieved CR and those who did not achieve CR. When we evaluated rrAML and tnAML separately, none of the genes showed a significantly different mutation rate between response subgroups. We then examined the population as a whole (N=220) and found that mutations in NRAS were significantly lower in patients who achieved CR (0/49) compared to those who did not (20/171, p=0.009). NRAS and KRAS mutations were inversely correlated, and when we considered the two genes together, mutations were present in 1/49 CR patients compared to 28/171 non-CR patients (p=0.007). Overall, CR rate was 3.4% in patients with RAS mutations compared to 25.1% in patients without such mutations. There was a similar significant trend for IDH2 mutations to be lower in CR patients (1/48) compared to non-CR patients (23/172, p=0.02) but this was not seen for IDH1. None of the mutations in other epigenetic regulators (DNMT3A, ASXL1, EZH2, TET2, U2AF1 or WT1) were significantly different between CR and non-CR patients individually or when we considered mutations in any of 8 epigenetic regulators (mutated in 22/49 CR patients vs. 93/171 non-CR patients, p=0.26). RAS mutations were higher in rrAML (22/121, 18.2%) than in tnAML (7/99, 7.1%, p=0.017) which may explain the lower CR rate in this group. Patients with PR/CRi/CRp were genetically similar to NR. Conclusions: In patients with AML treated with guadecitabine, RAS pathway mutations and IDH2 mutations are associated with a lower likelihood of achieving a CR. Disclosures Kropf: Celgene: Consultancy; Takeda: Consultancy. Roboz:Cellectis: Research Funding; Agios, Amgen, Amphivena, Astex, AstraZeneca, Boehringer Ingelheim, Celator, Celgene, Genoptix, Janssen, Juno, MEI Pharma, MedImmune, Novartis, Onconova, Pfizer, Roche/Genentech, Sunesis, Teva: Consultancy. Yee:Novartis Canada: Membership on an entity's Board of Directors or advisory committees, Research Funding. Taverna:Astex Pharmaceuticals: Employment. Naim:Astex Pharmaceuticals: Employment. Azab:Astex Pharmaceuticals, Inc.: Employment. Issa:Teva Pharmaceutical Industries: Consultancy; Astex Pharmaceuticals: Consultancy.
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 | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| 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 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 ».