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Landscape of driver mutations and their clinical effects on Down syndrome-related myeloid neoplasms.
- Source :
-
Blood [Blood] 2024 Jun 20; Vol. 143 (25), pp. 2627-2643. - Publication Year :
- 2024
-
Abstract
- Abstract: Transient abnormal myelopoiesis (TAM) is a common complication in newborns with Down syndrome (DS). It commonly progresses to myeloid leukemia (ML-DS) after spontaneous regression. In contrast to the favorable prognosis of primary ML-DS, patients with refractory/relapsed ML-DS have poor outcomes. However, the molecular basis for refractoriness and relapse and the full spectrum of driver mutations in ML-DS remain largely unknown. We conducted a genomic profiling study of 143 TAM, 204 ML-DS, and 34 non-DS acute megakaryoblastic leukemia cases, including 39 ML-DS cases analyzed by exome sequencing. Sixteen novel mutational targets were identified in ML-DS samples. Of these, inactivations of IRX1 (16.2%) and ZBTB7A (13.2%) were commonly implicated in the upregulation of the MYC pathway and were potential targets for ML-DS treatment with bromodomain-containing protein 4 inhibitors. Partial tandem duplications of RUNX1 on chromosome 21 were also found, specifically in ML-DS samples (13.7%), presenting its essential role in DS leukemia progression. Finally, in 177 patients with ML-DS treated following the same ML-DS protocol (the Japanese Pediatric Leukemia and Lymphoma Study Group acute myeloid leukemia -D05/D11), CDKN2A, TP53, ZBTB7A, and JAK2 alterations were associated with a poor prognosis. Patients with CDKN2A deletions (n = 7) or TP53 mutations (n = 4) had substantially lower 3-year event-free survival (28.6% vs 90.5%; P < .001; 25.0% vs 89.5%; P < .001) than those without these mutations. These findings considerably change the mutational landscape of ML-DS, provide new insights into the mechanisms of progression from TAM to ML-DS, and help identify new therapeutic targets and strategies for ML-DS.<br /> (© 2024 American Society of Hematology. Published by Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.)
Details
- Language :
- English
- ISSN :
- 1528-0020
- Volume :
- 143
- Issue :
- 25
- Database :
- MEDLINE
- Journal :
- Blood
- Publication Type :
- Academic Journal
- Accession number :
- 38513239
- Full Text :
- https://doi.org/10.1182/blood.2023022247