Affiliation:
1. Department of Hematology The First Affiliated Hospital of Chongqing Medical University Chongqing People's Republic of China
2. Department of Hematology The First Affiliated Hospital of Army Medical University(Southwest Hospital) Chongqing China
3. Department of Hematology Shaanxi Provincial People's Hospital Xi'An Shaanxi China
4. Department of Oncology and Hematology Chongqing University Affiliated Center Hospital Chongqing China
Abstract
AbstractBackgroundMyeloid neoplasms (MN) tend to relapse and deteriorate. Exploring the genomic mutation landscape of MN using next‐generation sequencing (NGS) is a great measure to clarify the mechanism of oncogenesis and progression of MN.MethodsThis multicenter retrospective study investigated 303 patients with MN using NGS from 2019 to 2021. The characteristics of the mutation landscape in the MN subgroups and the clinical value of gene variants were analyzed.ResultsAt least one mutation was detected in 88.11% of the patients (267/303). TET2 was the most common mutation in the cohort, followed by GATA2, ASXL1, FLT3, DNMT3A, and TP53. Among patients with myeloid leukemia (ML), multivariate analysis showed that patients aged ≥60 years had lower overall survival (OS, p = 0.004). Further analysis showed TET2, NPM1, SRSF2, and IDH1 gene mutations, and epigenetic genes (p < 0.050) presented significantly higher frequency in older patients. In patients with myelodysplastic syndrome (MDS) and myelodysplastic neoplasms (MPN), univariate analysis showed that BCORL1 had a significant impact on OS (p = 0.040); however, in multivariate analysis, there were no factors significantly associated with OS. Differential analysis of genetic mutations showed FLT3, TP53, MUC16, SRSF2, and KDM5A mutated more frequently (p < 0.050) in secondary acute myeloid leukemia (s‐AML) than in MDS and MPN. TP53, U2AF1, SRSF2, and KDM5A were mutated more frequently (p < 0.050) in s‐AML than in primary AML. KDM5A was observed to be restricted to patients with s‐AML in this study, and only co‐occurred with MUC16 and TP53 (2/2, 100%). Another mutation was MUC16, and its co‐occurrence pattern differed between s‐AML and AML. MUC16 mutations co‐occurred with KDM5A and TP53 in 66.7% (2/3) of patients with s‐AML and co‐occurred with CEBPA in 100% (4/4) of patients with AML.ConclusionsOur results demonstrate different genomic mutation patterns in the MN subgroups and highlight the clinical value of genetic variants.
Subject
Cancer Research,Radiology, Nuclear Medicine and imaging,Oncology