克隆(Java方法)
表型
突变
生物
慢性粒单核细胞白血病
骨髓纤维化
骨髓增生异常综合症
遗传学
髓样
种系突变
骨髓增生性肿瘤
单核细胞增多
人口
癌症研究
免疫学
医学
基因
骨髓
环境卫生
作者
Gabriele Todisco,Maria Creignou,Anna Gallí,Paola Guglielmelli,Elisa Rumi,Marco Roncador,Ettore Rizzo,Yasuhito Nannya,Daniela Pietra,Chiara Elena,Elisa Bono,Elisabetta Molteni,Vittorio Rosti,Silvia Catricalà,Martina Sarchi,Marios Dimitriou,Johanna Ungerstedt,Alessandro M. Vannucchi,Eva Hellström‐Lindberg,Seishi Ogawa
出处
期刊:Leukemia
[Springer Nature]
日期:2020-12-21
卷期号:35 (8): 2371-2381
被引量:24
标识
DOI:10.1038/s41375-020-01106-z
摘要
Somatic mutations in splicing factor genes frequently occur in myeloid neoplasms. While SF3B1 mutations are associated with myelodysplastic syndromes (MDS) with ring sideroblasts, SRSF2P95 mutations are found in different disease categories, including MDS, myeloproliferative neoplasms (MPN), myelodysplastic/myeloproliferative neoplasms (MDS/MPN), and acute myeloid leukemia (AML). To identify molecular determinants of this phenotypic heterogeneity, we explored molecular and clinical features of a prospective cohort of 279 SRSF2P95-mutated cases selected from a population of 2663 patients with myeloid neoplasms. Median number of somatic mutations per subject was 3. Multivariate regression analysis showed associations between co-mutated genes and clinical phenotype, including JAK2 or MPL with myelofibrosis (OR = 26.9); TET2 with monocytosis (OR = 5.2); RAS-pathway genes with leukocytosis (OR = 5.1); and STAG2, RUNX1, or IDH1/2 with blast phenotype (MDS or AML) (OR = 3.4, 1.9, and 2.1, respectively). Within patients with SRSF2-JAK2 co-mutation, JAK2 dominance was invariably associated with clinical feature of MPN, whereas SRSF2 mutation was dominant in MDS/MPN. Within patients with SRSF2-TET2 co-mutation, clinical expressivity of monocytosis was positively associated with co-mutated clone size. This study provides evidence that co-mutation pattern, clone size, and hierarchy concur to determine clinical phenotype, tracing relevant genotype-phenotype associations across disease entities and giving insight on unaccountable clinical heterogeneity within current WHO classification categories.
科研通智能强力驱动
Strongly Powered by AbleSci AI