Doyle Maria A, Li Jason, Doig Ken, Fellowes Andrew, Wong Stephen Q
Peter MacCallum Cancer Centre, St Andrews Place, East Melbourne, VIC, 3002, Australia,
Methods Mol Biol. 2014;1168:83-98. doi: 10.1007/978-1-4939-0847-9_6.
Cancer is a complex disease driven by multiple mutations acquired over the lifetime of the cancer cells. These alterations, termed somatic mutations to distinguish them from inherited germline mutations, can include single-nucleotide substitutions, insertions, deletions, copy number alterations, and structural rearrangements. A patient's cancer can contain a combination of these aberrations, and the ability to generate a comprehensive genetic profile should greatly improve patient diagnosis and treatment. Next-generation sequencing has become the tool of choice to uncover multiple cancer mutations from a single tumor source, and the falling costs of this rapid high-throughput technology are encouraging its transition from basic research into a clinical setting. However, the detection of mutations in sequencing data is still an evolving area and cancer genomic data requires some special considerations. This chapter discusses these aspects and gives an overview of current bioinformatics methods for the detection of somatic mutations in cancer sequencing data.
癌症是一种复杂的疾病,由癌细胞在其生命周期中获得的多种突变驱动。这些改变被称为体细胞突变,以区别于遗传性种系突变,可包括单核苷酸替换、插入、缺失、拷贝数改变和结构重排。患者的癌症可能包含这些畸变的组合,生成全面基因图谱的能力应能极大地改善患者的诊断和治疗。新一代测序已成为从单一肿瘤来源发现多种癌症突变的首选工具,这种快速高通量技术成本的下降正促使其从基础研究转向临床应用。然而,测序数据中突变的检测仍是一个不断发展的领域,癌症基因组数据需要一些特殊的考虑。本章将讨论这些方面,并概述当前用于检测癌症测序数据中体细胞突变的生物信息学方法。