Mutational landscape and clinical outcome of pediatric acute myeloid leukemia with 11q23/KMT2A rearrangements

11q23/KMT2A重排儿童急性髓系白血病的突变图谱和临床结局

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Abstract

BACKGROUND: Alterations of 11q23/KMT2A are the most prevalent cytogenetic abnormalities in acute myeloid leukemia (AML) and the prognostic significance of 11q23/KMT2A-rearranged AML based on various translocation partners varies among different studies. However, few studies evaluated the molecular characteristics of 11q23/KMT2A-rearranged pediatric AML. We aim to analyze the mutational landscape of 11q23/KMT2A-rearranged AML and assess their prognostic value in outcomes. METHODS: The mutational landscape and clinical prognosis of 105 children with 11q23/KMT2A-rearranged AML in comparison with 277 children with non-11q23/KMT2A-rearranged AML were analyzed using publicly accessible next-generation sequencing data from Therapeutically Applicable Research to Generate Effective Treatments (TARGET) dataset. RESULTS: Pediatric AML patients with 11q23/KMT2A-rearrangements harbored a low number of mutations (Median, 1 mutation/patient, range, 1-22), 58% of which involved in RAS pathway mutations (KRAS, NRAS, and PTPN11) and 10.5% of which comprised of SETD2 mutations. Compared with non-11q23/KMT2A-rearranged AML, the incidence of KRAS (32.4% vs. 10.1%, P〈0.001) and SETD2 (10.5% vs. 1.4%, P=0.001) gene mutations in 11q23/KMT2A-rearranged AML was significantly higher. Both KRAS and SETD2 mutations occurred more often in t(10;11)(p12;q23). KRAS mutations were correlated with worse 5-year event-free survival [EFS] (Plog-rank = 0.001) and 5-year overall survival [OS] (Plog-rank = 0.009) and the presence of SETD2 mutations increases the 5-year relapse rate (PGray = 0.004). Multivariate analyses confirmed KRAS mutations in 11q23/KMT2A-rearranged AML as an independent predictor for poor EFS (hazard ratio [HR] = 2.10, P=0.05) and OS (HR = 2.39, P=0.054). CONCLUSION: Our findings show that pediatric patients with 11q23/KMT2A rearrangements have characteristic mutation patterns and varying clinical outcomes depending on different translocation partners, which could be utilized to develop more accurate risk stratification and tailored therapies.

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