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超越变异:癌症精准医学下一代测序数据中的突变模式

Beyond the Variants: Mutational Patterns in Next-Generation Sequencing Data for Cancer Precision Medicine.

作者信息

Parilla Megan, Ritterhouse Lauren L

机构信息

Department of Pathology, University of Chicago Medicine, Chicago, IL, United States.

Center for Integrated Diagnostics, Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States.

出版信息

Front Cell Dev Biol. 2020 May 19;8:370. doi: 10.3389/fcell.2020.00370. eCollection 2020.

Abstract

Massively parallel sequencing, also referred to as "next-generation sequencing" (NGS) provides not only information about simple, single nucleotide alterations, but it can also provide information on complex variations, such as insertions and deletions, copy number alterations, and structural variants. In addition to identifying individual alterations, genome-wide biomarkers can be discerned from somatic cancer NGS data, broadly termed mutational patterns and signatures. This review will focus on several of the most common genome-wide biomarkers such as tumor mutational burden, microsatellite instability, homologous recombination deficiency, and mutational signatures.

摘要

大规模平行测序,也被称为“下一代测序”(NGS),不仅能提供关于简单单核苷酸改变的信息,还能提供关于复杂变异的信息,如插入和缺失、拷贝数改变及结构变异。除了识别个体改变外,还能从体细胞癌NGS数据中辨别全基因组生物标志物,广义上称为突变模式和特征。本综述将聚焦于几种最常见的全基因组生物标志物,如肿瘤突变负荷、微卫星不稳定性、同源重组缺陷和突变特征。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0ed2/7248322/9d795a9d5a35/fcell-08-00370-g001.jpg

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