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Discovering and validating cancer genetic dependencies: approaches and pitfalls

Abstract

Cancer ‘genetic dependencies’ — genes whose products are essential for cancer cell fitness — are promising targets for therapeutic development. However, recent evidence has cast doubt on the validity of several putative dependencies that are currently being targeted in cancer clinical trials, underscoring the challenges inherent in correctly identifying cancer-essential genes. Here we review several common techniques and platforms for discovering and characterizing cancer dependencies. We discuss the strengths and drawbacks of different gene-perturbation approaches, and we highlight the use of poorly validated genetic and pharmacological agents as a common cause of target misidentification. A careful consideration of the limitations of current technologies and cancer models will improve our ability to correctly uncover cancer genetic dependencies and will facilitate the development of improved therapeutic agents.

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Fig. 1: Potential pitfalls when using RNAi or CRISPR–Cas9 technology.
Fig. 2: Comparison of different preclinical models for identifying cancer dependencies.

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Acknowledgements

The authors thank the members of the Sheltzer laboratory for their helpful comments on the manuscript. Research in the Sheltzer laboratory is supported by an NIH Early Independence award (1DP5OD021385), a Breast Cancer Alliance Young Investigator award, a Damon Runyon-Rachleff Innovation award, a Gates Foundation Innovative Technology Solutions grant and a CSHL–Northwell Health Translational Cancer Research grant. A.L. is supported by an NSF Graduate Research Fellowship and a Gabilan Stanford Graduate Fellowship. The figures in this Review were made with Biorender.

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Correspondence to Jason M. Sheltzer.

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A.L. and J.M.S. are co-founders of Meliora Therapeutics. J.M.S. has received consulting fees from Ono Pharmaceutical Co. and is a member of the advisory board of Tyra Biosciences.

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Lin, A., Sheltzer, J.M. Discovering and validating cancer genetic dependencies: approaches and pitfalls. Nat Rev Genet 21, 671–682 (2020). https://doi.org/10.1038/s41576-020-0247-7

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