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.

中文翻译：

---

使用功能变异学对癌症突变进行基础分辨率分层。

对人类癌症变异的全面了解需要新的方法，以系统，有效地大规模评估基因组突变的功能效应。在这里，我们描述了一套以基本分辨率快速克隆和分层数千种癌症突变的工具。该协议提供了大规模并行管道以实现高严格性和吞吐量。该方法包括通过Gateway高通量生成突变体克隆，通过条形码和下一代测序确认变异体同一性，以及通过多重相互作用分析对癌症变异体进行分层。与其他定点诱变方法相比，我们的协议所需的测序工作更少，并且能够可靠地统计调用等位基因特异性效应。为确保变体交互分析的准确性，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高斯普林斯萤光素荧光素酶蛋白片段互补分析（GPCA）。这些具有标准对照的独立测定法可高质量验证突变相互作用谱。该协议提供了实验得出的指导方针，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“驱动者”或“乘客”。对于约100个基因组突变，可在短短2-3周内完成包括目标引物设计，变体文库构建和序列验证在内的方案，而癌症变体分层可在2周内完成。