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Mutational patterns and their correlation to CHIP-related mutations and age in hematological malignancies

Acquired somatic mutations are crucial for the development of most cancers. We performed a comprehensive comparative analysis of the mutational landscapes and their correlation with CHIP-related (clonal hematopoiesis of indeterminate potential) mutations and patient age of 122 genes in 3096 cases of...

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Detalles Bibliográficos
Autores principales: Stengel, Anna, Baer, Constance, Walter, Wencke, Meggendorfer, Manja, Kern, Wolfgang, Haferlach, Torsten, Haferlach, Claudia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Society of Hematology 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8579257/
https://www.ncbi.nlm.nih.gov/pubmed/34570179
http://dx.doi.org/10.1182/bloodadvances.2021004668
Descripción
Sumario:Acquired somatic mutations are crucial for the development of most cancers. We performed a comprehensive comparative analysis of the mutational landscapes and their correlation with CHIP-related (clonal hematopoiesis of indeterminate potential) mutations and patient age of 122 genes in 3096 cases of 28 different hematological malignancies. Differences were observed regarding (1) the median number of mutations (highest, median n = 4; lowest, n = 0); (2) specificity of certain mutations (high frequencies in atypical chronic myeloid leukemia [aCML; ASXL1, 86%], follicular lymphoma [FL; KMT2D, 87%; CREBBP, 73%], hairy cell lymphoma [BRAF, 100%], lymphoplasmacytic lymphoma [MYD88, 98%; CXCR4, 51%], myeloproliferative neoplasm [MPN; AK2, 68%]); (3) distribution of mutations (broad distribution within/across the myeloid/lymphoid lineage for TET2, ASXL1, DNMT3A, TP53, BCOR, and ETV6); (4) correlation of mutations with patient’s age (correlated with older age across entities: TET2, DNMT3A, ASXL1, TP53, EZH2, BCOR, GATA2, and IDH2; younger age: KIT, POT1, RAD21, U2AF2, and WT1); (5) correlation of mutation number per patient with age. Moreover, we observed high frequencies of mutations in RUNX1, SRSF2, IDH2, NRAS, and EZH2 in cases comprising at least 1 DTA (DNMT3A, TET2, ASXL1) mutation, whereas in cases without DTA mutations, TP53, KRAS, WT1, and SF3B1 were more frequent across entities, suggesting differences in pathophysiology. These results give further insight into the complex genetic landscape and the role of DTA mutations in hematological neoplasms and define mutation-driven entities (myelodysplastic syndrome/MPN overlap; secondary acute myeloid) in comparison with entities defined by chromosomal fusions (chronic myeloid leukemia; myeloid/lymphoid neoplasm with eosinophilia).