Literature DB >> 27260157

CRISPR Screens Provide a Comprehensive Assessment of Cancer Vulnerabilities but Generate False-Positive Hits for Highly Amplified Genomic Regions.

Diana M Munoz1, Pamela J Cassiani1, Li Li1, Eric Billy2, Joshua M Korn1, Michael D Jones1, Javad Golji1, David A Ruddy1, Kristine Yu1, Gregory McAllister3, Antoine DeWeck2, Dorothee Abramowski2, Jessica Wan1, Matthew D Shirley1, Sarah Y Neshat1, Daniel Rakiec1, Rosalie de Beaumont1, Odile Weber2, Audrey Kauffmann2, E Robert McDonald1, Nicholas Keen1, Francesco Hofmann2, William R Sellers1, Tobias Schmelzle2, Frank Stegmeier1, Michael R Schlabach4.   

Abstract

UNLABELLED: CRISPR/Cas9 has emerged as a powerful new tool to systematically probe gene function. We compared the performance of CRISPR to RNAi-based loss-of-function screens for the identification of cancer dependencies across multiple cancer cell lines. CRISPR dropout screens consistently identified more lethal genes than RNAi, implying that the identification of many cellular dependencies may require full gene inactivation. However, in two aneuploid cancer models, we found that all genes within highly amplified regions, including nonexpressed genes, scored as lethal by CRISPR, revealing an unanticipated class of false-positive hits. In addition, using a CRISPR tiling screen, we found that sgRNAs targeting essential domains generate the strongest lethality phenotypes and thus provide a strategy to rapidly define the protein domains required for cancer dependence. Collectively, these findings not only demonstrate the utility of CRISPR screens in the identification of cancer-essential genes, but also reveal the need to carefully control for false-positive results in chromosomally unstable cancer lines. SIGNIFICANCE: We show in this study that CRISPR-based screens have a significantly lower false-negative rate compared with RNAi-based screens, but have specific liabilities particularly in the interrogation of regions of genome amplification. Therefore, this study provides critical insights for applying CRISPR-based screens toward the systematic identification of new cancer targets. Cancer Discov; 6(8); 900-13. ©2016 AACR.See related commentary by Sheel and Xue, p. 824See related article by Aguirre et al., p. 914This article is highlighted in the In This Issue feature, p. 803. ©2016 American Association for Cancer Research.

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Year:  2016        PMID: 27260157     DOI: 10.1158/2159-8290.CD-16-0178

Source DB:  PubMed          Journal:  Cancer Discov        ISSN: 2159-8274            Impact factor:   39.397


  136 in total

1.  Genome-wide CRISPR screen identifies HNRNPL as a prostate cancer dependency regulating RNA splicing.

Authors:  Teng Fei; Yiwen Chen; Tengfei Xiao; Wei Li; Laura Cato; Peng Zhang; Maura B Cotter; Michaela Bowden; Rosina T Lis; Shuang G Zhao; Qiu Wu; Felix Y Feng; Massimo Loda; Housheng Hansen He; X Shirley Liu; Myles Brown
Journal:  Proc Natl Acad Sci U S A       Date:  2017-06-13       Impact factor: 11.205

Review 2.  Genomic evolution of cancer models: perils and opportunities.

Authors:  Uri Ben-David; Rameen Beroukhim; Todd R Golub
Journal:  Nat Rev Cancer       Date:  2019-02       Impact factor: 60.716

3.  Common pitfalls in preclinical cancer target validation.

Authors:  William G Kaelin
Journal:  Nat Rev Cancer       Date:  2017-05-19       Impact factor: 60.716

4.  Improved design and analysis of CRISPR knockout screens.

Authors:  Chen-Hao Chen; Tengfei Xiao; Han Xu; Peng Jiang; Clifford A Meyer; Wei Li; Myles Brown; X Shirley Liu
Journal:  Bioinformatics       Date:  2018-12-01       Impact factor: 6.937

Review 5.  Genetic interaction networks in cancer cells.

Authors:  Barbara Mair; Jason Moffat; Charles Boone; Brenda J Andrews
Journal:  Curr Opin Genet Dev       Date:  2019-04-08       Impact factor: 5.578

6.  Defining a Cancer Dependency Map.

Authors:  Aviad Tsherniak; Francisca Vazquez; Phil G Montgomery; Barbara A Weir; Gregory Kryukov; Glenn S Cowley; Stanley Gill; William F Harrington; Sasha Pantel; John M Krill-Burger; Robin M Meyers; Levi Ali; Amy Goodale; Yenarae Lee; Guozhi Jiang; Jessica Hsiao; William F J Gerath; Sara Howell; Erin Merkel; Mahmoud Ghandi; Levi A Garraway; David E Root; Todd R Golub; Jesse S Boehm; William C Hahn
Journal:  Cell       Date:  2017-07-27       Impact factor: 41.582

Review 7.  Technical considerations for the use of CRISPR/Cas9 in hematology research.

Authors:  Michael C Gundry; Daniel P Dever; David Yudovich; Daniel E Bauer; Simon Haas; Adam C Wilkinson; Sofie Singbrant
Journal:  Exp Hematol       Date:  2017-07-27       Impact factor: 3.084

8.  Genomic Amplifications Cause False Positives in CRISPR Screens.

Authors:  Ankur Sheel; Wen Xue
Journal:  Cancer Discov       Date:  2016-08       Impact factor: 39.397

Review 9.  Design and analysis of CRISPR-Cas experiments.

Authors:  Ruth E Hanna; John G Doench
Journal:  Nat Biotechnol       Date:  2020-04-13       Impact factor: 54.908

10.  CRISPR Technology for Breast Cancer: Diagnostics, Modeling, and Therapy.

Authors:  Rachel L Mintz; Madeleine A Gao; Kahmun Lo; Yeh-Hsing Lao; Mingqiang Li; Kam W Leong
Journal:  Adv Biosyst       Date:  2018-08-17
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