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Mouse Models of Frequently Mutated Genes in Acute Myeloid Leukemia

SIMPLE SUMMARY: Acute myeloid leukemia is a genetically heterogeneous disease and shows variable treatment outcomes. Genetic profiling has revealed different driver mutations in AML patients. Therefore, it is important to understand the biological impact of these mutations in leukemia transformation...

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Detalles Bibliográficos
Autores principales: Mohanty, Sagarajit, Heuser, Michael
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8699817/
https://www.ncbi.nlm.nih.gov/pubmed/34944812
http://dx.doi.org/10.3390/cancers13246192
Descripción
Sumario:SIMPLE SUMMARY: Acute myeloid leukemia is a genetically heterogeneous disease and shows variable treatment outcomes. Genetic profiling has revealed different driver mutations in AML patients. Therefore, it is important to understand the biological impact of these mutations in leukemia transformation. In this review, we discuss the individual and synergistic effects of these mutations in the pathogenesis of leukemia based on the available evidence from mouse models. ABSTRACT: Acute myeloid leukemia is a clinically and biologically heterogeneous blood cancer with variable prognosis and response to conventional therapies. Comprehensive sequencing enabled the discovery of recurrent mutations and chromosomal aberrations in AML. Mouse models are essential to study the biological function of these genes and to identify relevant drug targets. This comprehensive review describes the evidence currently available from mouse models for the leukemogenic function of mutations in seven functional gene groups: cell signaling genes, epigenetic modifier genes, nucleophosmin 1 (NPM1), transcription factors, tumor suppressors, spliceosome genes, and cohesin complex genes. Additionally, we provide a synergy map of frequently cooperating mutations in AML development and correlate prognosis of these mutations with leukemogenicity in mouse models to better understand the co-dependence of mutations in AML.