Genomic Informatics, Human Genetics and Genomic Medicine, Faculty of Medicine, University of Southampton, Southampton, United Kingdom.
Cancer Sciences, Faculty of Medicine, University of Southampton, Southampton, UK.
Sci Rep. 2019 Jul 18;9(1):10444. doi: 10.1038/s41598-019-46906-1.
The aims of this systematic review are to refine the catalogue of somatic variants in splenic marginal zone lymphoma (SMZL) and to provide a well-annotated, manually curated database of high-confidence somatic mutations to facilitate variant interpretation for further biological studies and future clinical implementation. Two independent reviewers systematically searched PubMed and Ovid in January 2019 and included studies that sequenced SMZL cases with confirmed diagnosis. The database included fourteen studies, comprising 2817 variants in over 1000 genes from 475 cases. We confirmed the high prevalence of NOTCH2, KLF2 and TP53 mutations and analysis of targeted genes further implicated TNFAIP3, KMT2D, and TRAF3 as recurrent targets of somatic mutation based on their high incidence across studies. The major limitations we encountered were the low number of patients with whole-genome, unbiased analysis and the relative sensitivities of differing sequencing approaches. Overall, we showed that there is little concordance between whole exome sequencing studies of SMZL. We strongly support the continuing unbiased analysis of the SMZL genome for mutations in all protein-coding genes and provide a valuable database resource to facilitate this endeavour that will ultimately improve our understanding of SMZL pathobiology.
本系统综述的目的是完善脾脏边缘区淋巴瘤(SMZL)的体细胞变异目录,并提供一个注释良好、经过人工整理的高可信度体细胞突变数据库,以促进进一步的生物学研究和未来的临床应用中的变异解释。两名独立的审查员于 2019 年 1 月系统地搜索了 PubMed 和 Ovid,并纳入了对经过确认诊断的 SMZL 病例进行测序的研究。该数据库包含 14 项研究,来自 475 例患者的超过 1000 个基因中的 2817 个变异。我们证实了 NOTCH2、KLF2 和 TP53 突变的高患病率,对靶向基因的分析进一步表明,TNFAIP3、KMT2D 和 TRAF3 是体细胞突变的反复靶标,这基于它们在研究中的高发生率。我们遇到的主要限制是具有全基因组、无偏分析的患者数量较少,以及不同测序方法的相对敏感性。总体而言,我们表明 SMZL 的全外显子组测序研究之间几乎没有一致性。我们强烈支持继续对 SMZL 基因组进行无偏分析,以寻找所有编码蛋白基因中的突变,并提供有价值的数据库资源,以促进这一努力,最终将提高我们对 SMZL 病理生物学的理解。