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利用功能变异组学对癌症突变进行碱基分辨率分层。

Base-resolution stratification of cancer mutations using functional variomics.

作者信息

Yi Song, Liu Ning-Ning, Hu Limei, Wang Hui, Sahni Nidhi

机构信息

Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.

School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

出版信息

Nat Protoc. 2017 Nov;12(11):2323-2341. doi: 10.1038/nprot.2017.086. Epub 2017 Oct 5.

Abstract

A complete understanding of human cancer variants requires new methods to systematically and efficiently assess the functional effects of genomic mutations at a large scale. Here, we describe a set of tools to rapidly clone and stratify thousands of cancer mutations at base resolution. This protocol provides a massively parallel pipeline to achieve high stringency and throughput. The approach includes high-throughput generation of mutant clones by Gateway, confirmation of variant identity by barcoding and next-generation sequencing, and stratification of cancer variants by multiplexed interaction profiling. Compared with alternative site-directed mutagenesis methods, our protocol requires less sequencing effort and enables robust statistical calling of allele-specific effects. To ensure the precision of variant interaction profiling, we further describe two complementary methods-a high-throughput enhanced yeast two-hybrid (HT-eY2H) assay and a mammalian-cell-based Gaussia princeps luciferase protein-fragment complementation assay (GPCA). These independent assays with standard controls validate mutational interaction profiles with high quality. This protocol provides experimentally derived guidelines for classifying candidate cancer alleles emerging from whole-genome or whole-exome sequencing projects as 'drivers' or 'passengers'. For ∼100 genomic mutations, the protocol-including target primer design, variant library construction, and sequence verification-can be completed within as little as 2-3 weeks, and cancer variant stratification can be completed within 2 weeks.

摘要

要全面了解人类癌症变异,需要新的方法来大规模系统且高效地评估基因组突变的功能效应。在此,我们描述了一套工具,可在碱基分辨率水平上快速克隆和分层分析数千个癌症突变。本方案提供了一个大规模并行流程,以实现高严谨性和高吞吐量。该方法包括通过Gateway高通量生成突变克隆、通过条形码和下一代测序确认变异身份,以及通过多重相互作用分析对癌症变异进行分层。与其他定点诱变方法相比,我们的方案所需的测序工作量更少,并且能够对等位基因特异性效应进行可靠的统计学判定。为确保变异相互作用分析的准确性,我们进一步描述了两种互补方法——高通量增强型酵母双杂交(HT-eY2H)分析和基于哺乳动物细胞的海肾荧光素酶蛋白片段互补分析(GPCA)。这些带有标准对照的独立分析可高质量地验证突变相互作用谱。本方案提供了基于实验得出的指导原则,用于将全基因组或全外显子测序项目中出现的候选癌症等位基因分类为“驱动基因”或“乘客基因”。对于约100个基因组突变,该方案——包括靶引物设计、变异文库构建和序列验证——可在短短2至3周内完成,癌症变异分层可在2周内完成。

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