Original Article
Characteristics of fusion gene expression in acute lymphoblastic leukemia
Huang Xianqi, Lin Yani, Liu Enbin, Xing Fei, Wang Zhe, Chen Xuejing, Chen Long, Ma Jingting, Mi Yingchang, Ru Kun
Published 2022-04-08
Cite as Chin J Pathol, 2022, 51(4): 307-313. DOI: 10.3760/cma.j.cn112151-20211028-00781
Abstract
ObjectiveTo analyze the genetic landscape of 52 fusion genes in patients with de novo acute lymphoblastic leukemia (ALL) and to investigate the characteristics of other laboratory results.
MethodsThe fusion gene expression was retrospectively analyzed in the 1 994 patients with de novo ALL diagnosed from September 2016 to December 2020. In addition, their mutational, immunophenotypical and karyotypical profiles were investigated.
ResultsIn the 1 994 patients with ALL, the median age was 12 years (from 15 days to 89 years). In the panel of targeted genes, 15 different types of fusion genes were detected in 884 patients (44.33%) and demonstrated a Power law distribution. The frequency of detectable fusion genes in B-cell ALL was significantly higher than that in T-cell ALL (48.48% vs 18.71%), and fusion genes were almost exclusively expressed in B-cell ALL or T-cell ALL. The number of fusion genes showed peaks at<1 year, 3-5 years and 35-44 years, respectively. More fusion genes were identified in children than in adults. MLL-FG was most frequently seen in infants and TEL-AML1 was most commonly seen in children, while BCR-ABL1 was dominant in adults. The majority of fusion gene mutations involved signaling pathway and the most frequent mutations were observed in NRAS and KRAS genes. The expression of early-stage B-cell antigens varied in B-cell ALL patients. The complex karyotypes were more common in BCR-ABL1 positive patients than others.
ConclusionThe distribution of fusion genes in ALL patients differs by ages and cell lineages. It also corresponds to various gene mutations, immunophenotypes, and karyotypes.
Key words:
Leukemia; Oncogene proteins, fusion; Mutation; Immunophenotyping
Contributor Information
Huang Xianqi
State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology &
Blood Diseases Hospital, Chinese Academy of Medical Sciences &
Peking Union Medical College, Tianjin 300020, China
SINO-US Diagnostics Lab Tianjin Enterprise Key Laboratory of AI-aided Hematopathology Diagnosis, Tianjin 300385, China
Lin Yani
SINO-US Diagnostics Lab Tianjin Enterprise Key Laboratory of AI-aided Hematopathology Diagnosis, Tianjin 300385, China
Liu Enbin
SINO-US Diagnostics Lab Tianjin Enterprise Key Laboratory of AI-aided Hematopathology Diagnosis, Tianjin 300385, China
Xing Fei
SINO-US Diagnostics Lab Tianjin Enterprise Key Laboratory of AI-aided Hematopathology Diagnosis, Tianjin 300385, China
Wang Zhe
State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology &
Blood Diseases Hospital, Chinese Academy of Medical Sciences &
Peking Union Medical College, Tianjin 300020, China
Chen Xuejing
SINO-US Diagnostics Lab Tianjin Enterprise Key Laboratory of AI-aided Hematopathology Diagnosis, Tianjin 300385, China
Chen Long
SINO-US Diagnostics Lab Tianjin Enterprise Key Laboratory of AI-aided Hematopathology Diagnosis, Tianjin 300385, China
Ma Jingting
SINO-US Diagnostics Lab Tianjin Enterprise Key Laboratory of AI-aided Hematopathology Diagnosis, Tianjin 300385, China
Mi Yingchang
State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology &
Blood Diseases Hospital, Chinese Academy of Medical Sciences &
Peking Union Medical College, Tianjin 300020, China
Ru Kun
SINO-US Diagnostics Lab Tianjin Enterprise Key Laboratory of AI-aided Hematopathology Diagnosis, Tianjin 300385, China
