SETBP1 and NRAS Mutations Are Frequent Events in Post-Myeloproliferative Neoplasm Acute Myeloid Leukemia (post-MPN AML) Lacking JAK-STAT Activating Mutations
Notice bibliographique
Résumé
Abstract Background: The BCR/ABL-negative myeloproliferative neoplasms (MPNs) include essential thrombocythemia (ET), polycythemia vera (PV), and myelofibrosis (MF) are characterized by JAK-STAT activating mutations (mutations in JAK2, CALR, and MPL) . Approximately 10% of MPN patients in chronic phase are “triple-negative” for mutations in any of the three genes. Leukemic transformation (LT) of chronic-phase MPNs carries a poor prognosis, and the biologic and genetic mechanisms underlying transformation are poorly understood. Further, although no standard therapeutic approach to LT exists, conventional anti-leukemic therapies such as induction chemotherapy and the use of hypomethylating agents have been utilized. However, there is a paucity of data comparing these approaches, as well as evaluating the impact of genomic alterations on treatment outcomes. We have sought to address these issues through detailed analysis of a well-annotated multi-center cohort of post-MPN AML patients. Methods: Next-generation sequencing was performed on 114 patients at the time of LT. Sequencing of the entire coding sequence of 585 cancer-associated genes on 84 samples collected from Memorial Sloan Kettering Cancer Center, Princess Margaret Cancer Centre, and the Myeloproliferative Diseases Research Consortium (MPD-RC) was performed. Mutational calls were made in comparison to curated matched normals. Tumor-only mutational data was obtained from an additional 30 patients sequenced using the Foundation One Heme platform. Results: Of the 114 patients analyzed, 66 (58%) had a JAK2 mutation, 8 (7%) had a MPL mutation, and 9 (8%) had a CALR mutation (Figure 1). 32 patients (28%) had TN disease. Notably, SETBP1 and NRAS mutations occurred exclusively in the TN cohort, each occurring at a frequency of 18.8% (6/32). TP53 mutations were identified in 18.8% (6/32) of TN cases and 24% (20/82) of cases with JAK-STAT mutations. ASXL1 mutations occurred frequently in both the TN cohort and the JAK-STAT mutant cohort [40% (13/32%) and 27% (22/82), respectively]. Splicing factor mutations (SF3B1, SRSF2, U2AF1, and ZRSR2) occurred in 43.8% (14/32) and 30.5% (25/82) of TN and JAKT-STAT mutant cohorts respectively; these were found to be highly prevalent in comparison to prior reports of 11% frequency in de novo AML. Further, FLT3 and NPM1 mutations, two of the most common mutations in de novo AML, occurred relatively infrequently (3.5% and Clinical factors were evaluable in 84 patients, of whom 22 had ET, 23 had PV, 19 had primary MF (PMF) prior to LT. An additional 16 patients had secondary MF or an unknown antecedent MPN defined by the presence of a JAK2 mutation. Evaluation of these four groups, without pairwise interactions, found a statistically significant one-year OS difference among them (p=0.041, Figure 2). Patients were treated with induction chemotherapy, single-agent hypomethylating agent, ruxolitinib and decitabine combination therapy on a phase I/II trial (NCT02076191), or best supportive care. A one-year OS difference was noted for patients who received anti-leukemic therapy compared to supportive care (p = 0.008, Figure 3), with improved outcomes for all three treatment modalities in comparison to supportive care alone. Overall survival did not differ between TN and JAK-STAT mutated patients (p=0.374). The presence of 3 or more mutations also did not impact OS (p=0.339). Data on remission status, allogeneic stem cell transplant status, and the impact of clinical factors on outcome on this cohort will be presented. Analysis of variant allele frequency of RAS mutant cases shows that in some cases these mutations are likely subclonal events (Figure 4). Further evaluation of clonal architecture will be presented. Conclusions: LT is characterized by a distinct mutational profile as compared with de novo AML. Further, within post-MPN AML, patients with TN disease appear to have distinct mutational events such as SETBP1 and NRAS mutations, which may have important implications for the biology of transformation. Finally, our data indicate similar OS in patients treated with induction chemotherapy and non-induction chemotherapy approaches, both of which were superior to supportive care alone. Further validation of theses observations is required in other data sets. Download : Download high-res image (311KB) Download : Download full-size image Disclosures Mascarenhas: Janssen: Research Funding; CTI Biopharma: Research Funding; Merck: Research Funding; Novartis: Other: DSMB member , Research Funding; Promedior: Research Funding; Incyte: Other: Clinical Trial Steering Committee , Research Funding. Gupta: Incyte: Consultancy, Research Funding; Novartis: Consultancy, Honoraria, Research Funding. Verstovsek: Seattle Genetics: Research Funding; Genentech: Research Funding; CTI BioPharma Corp: Research Funding; NS Pharma: Research Funding; Astrazeneca: Research Funding; Seattle Genetics: Research Funding; Blueprint Medicines Corp: Research Funding; Lilly Oncology: Research Funding; NS Pharma: Research Funding; Lilly Oncology: Research Funding; Bristol Myers Squibb: Research Funding; Astrazeneca: Research Funding; Roche: Research Funding; Celgene: Research Funding; Pfizer: Research Funding; Galena BioPharma: Research Funding; Blueprint Medicines Corp: Research Funding; CTI BioPharma Corp: Research Funding; Genentech: Research Funding; Gilead: Research Funding; Pfizer: Research Funding; Promedior: Research Funding; Galena BioPharma: Research Funding; Celgene: Research Funding; Gilead: Research Funding; Bristol Myers Squibb: Research Funding; Promedior: Research Funding; Incyte: Research Funding; Incyte: Research Funding; Roche: Research Funding. Levine: Roche: Research Funding; Roche: Research Funding; Qiagen: Equity Ownership; Celgene: Research Funding; Celgene: Research Funding; Qiagen: Equity Ownership.
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 | 0,001 | 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) | 0,002 | 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 ».