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Base-resolution stratification of cancer mutations using functional variomics

Nature Protocols volume 12, pages 23232341 (2017) | Download Citation

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.

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Acknowledgements

We acknowledge the following research funds: Cancer Prevention and Research Institute of Texas (CPRIT) grant RR160021 (N.S.); a University of Texas Systems Rising STARs award (N.S.); NIH/NCI award no. P30CA016672 (N.S.); and the University Center Foundation via the Institutional Research Grant program (to N.S.) at the University of Texas MD Anderson Cancer Center.

Author information

Author notes

    • Song Yi
    •  & Ning-Ning Liu

    These authors contributed equally to this work.

Affiliations

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

    • Song Yi
    • , Limei Hu
    •  & Nidhi Sahni
  2. School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

    • Ning-Ning Liu
    •  & Hui Wang
  3. Graduate Program in Structural and Computational Biology and Molecular Biophysics, Baylor College of Medicine, Houston, TX, USA.

    • Nidhi Sahni

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Contributions

S.Y., N.-N.L., L.H., and N.S. performed the experiments. S.Y., N.-N.L., H.W., and N.S. analyzed the data. S.Y. and N.S. wrote the manuscript.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to Song Yi or Nidhi Sahni.

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DOI

https://doi.org/10.1038/nprot.2017.086

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