Exploration of the role of gene mutations in myelodysplastic syndromes through a sequencing design involving a small number of target genes

• Published in: Scientific reports Vol. 7; no. 1; p. 43113
• Main Authors: Xu, Feng, Wu, Ling-Yun, He, Qi, Wu, Dong, Zhang, Zheng, Song, Lu-Xi, Zhao, You-Shan, Su, Ji-Ying, Zhou, Li-Yu, Guo, Juan, Chang, Chun-Kang, Li, Xiao
• Format: Journal Article
• Language: English
• Published: England Nature Publishing Group 21.02.2017
• Subjects: Adolescent; Adult; Aged; Aged, 80 and over; Asian Continental Ancestry Group - genetics; Child; Chromosome 7; DNA Mutational Analysis; Female; Genes; Genetic transformation; High-Throughput Nucleotide Sequencing; Humans; Karyotypes; Male; Middle Aged; Mutation; Myelodysplastic syndrome; Myelodysplastic syndromes; Myelodysplastic Syndromes - genetics; p53 Protein; Runx1 protein; Trisomy; Young Adult
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/srep43113

Novel sequencing designs are necessary to supplement the recognized knowledge of myelodysplastic syndrome (MDS)-related genomic alterations. In this study, we sequenced 28 target genes in 320 Chinese MDS patients but obtained 77.2% of recall factors and 82.8% of genetic abnormalities (including karyotype abnormalities). In addition to known relationships among mutations, some specific chromosomal abnormalities were found to link to specific gene mutations. Trisomy 8 tended to be linked to U2AF1 and ZRSR2 mutations, and 20q- exhibited higher SRSF2/WT1 and U2AF1 mutation frequency. Chromosome 7 involvement accounted for up to 50% of RUNX1 mutations and 37.5% of SETBP1 mutations. Patients carrying a complex karyotype were prone to present TP53 mutations (36.1%). However, individuals with normal karyotypes rarely possessed mutations in the TP53, RUNX1 and U2AF1. Moreover, DNMT3A, TP53, SRSF2, STAG2, ROBO1/2 and WT1 predicted poor survival and high AML transformation. By integrating these predictors into international prognostic scoring system (IPSS) or revised IPSS, we built a set of mutation-based prognostic risk models. These models could layer different degrees of risk in patients more satisfactorily. In summary, this sequencing design was able to detect a number of gene mutations and could be used to stratify patients with varied prognostic risk.