Abstract

The CRISPR–Cas9 system has revolutionized gene editing both at single genes and in multiplexed loss-of-function screens, thus enabling precise genome-scale identification of genes essential for proliferation and survival of cancer cells1,2. However, previous studies have reported that a gene-independent antiproliferative effect of Cas9-mediated DNA cleavage confounds such measurement of genetic dependency, thereby leading to false-positive results in copy number–amplified regions3,4. We developed CERES, a computational method to estimate gene-dependency levels from CRISPR–Cas9 essentiality screens while accounting for the copy number–specific effect. In our efforts to define a cancer dependency map, we performed genome-scale CRISPR–Cas9 essentiality screens across 342 cancer cell lines and applied CERES to this data set. We found that CERES decreased false-positive results and estimated sgRNA activity for both this data set and previously published screens performed with different sgRNA libraries. We further demonstrate the utility of this collection of screens, after CERES correction, for identifying cancer-type-specific vulnerabilities.

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Acknowledgements

This work was supported by grants U01 CA176058, U01 CA199253, and P01 CA154303 (W.C.H.) and by the Slim Initiative for Genomic Medicine, a project funded by the Carlos Slim Foundation and the H.L. Snyder Foundation.

Author information

Author notes

    • Robin M Meyers
    •  & Jordan G Bryan

    These authors contributed equally to this work.

Affiliations

  1. Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.

    • Robin M Meyers
    • , Jordan G Bryan
    • , James M McFarland
    • , Barbara A Weir
    • , Ann E Sizemore
    • , Han Xu
    • , Neekesh V Dharia
    • , Phillip G Montgomery
    • , Glenn S Cowley
    • , Sasha Pantel
    • , Amy Goodale
    • , Yenarae Lee
    • , Levi D Ali
    • , Guozhi Jiang
    • , Rakela Lubonja
    • , William F Harrington
    • , Matthew Strickland
    • , Ting Wu
    • , Derek C Hawes
    • , Victor A Zhivich
    • , Meghan R Wyatt
    • , Zohra Kalani
    • , Jaime J Chang
    • , Michael Okamoto
    • , Kimberly Stegmaier
    • , Todd R Golub
    • , Jesse S Boehm
    • , Francisca Vazquez
    • , David E Root
    • , William C Hahn
    •  & Aviad Tsherniak
  2. Dana-Farber Cancer Institute, Boston, Massachusetts, USA.

    • Neekesh V Dharia
    • , Kimberly Stegmaier
    • , Todd R Golub
    • , Francisca Vazquez
    •  & William C Hahn
  3. Boston Children's Hospital, Boston, Massachusetts, USA.

    • Neekesh V Dharia
    • , Kimberly Stegmaier
    •  & Todd R Golub
  4. Harvard Medical School, Boston, Massachusetts, USA.

    • Neekesh V Dharia
    • , Kimberly Stegmaier
    • , Todd R Golub
    •  & William C Hahn
  5. Howard Hughes Medical Institute, Chevy Chase, Maryland, USA.

    • Todd R Golub
  6. Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA.

    • William C Hahn

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Contributions

R.M.M., J.G.B., and A.T. conceived and designed the study. R.M.M., J.G.B., and J.M.M. performed computational analysis and interpretation of results. J.G.B. wrote and implemented the modeling software. R.M.M., B.A.W., and A.E.S. processed and managed data. H.X. and N.V.D. assisted with computational analysis. P.G.M. provided computational tools. G.S.C., S.P., and F.V. provided project management. A.G., Y.L., L.D.A., G.J., R.L., W.F.H., M.S., T.W., D.C.H., V.A.Z., M.R.W., Z.K., J.J.C., and M.O. assisted with data generation. R.M.M., J.G.B., J.M.M., W.C.H., and A.T. wrote and/or revised the manuscript with assistance from other authors. K.S., T.R.G., J.S.B., F.V., D.E.R., W.C.H., and A.T. supervised the study and performed an advisory role.

Competing interests

W.C.H. reports receiving a commercial research grant from Novartis and serving as a consultant/advisory-board member for Novartis as well as for KSQ Therapeutics. No potential conflicts of interest are disclosed by the other authors.

Corresponding authors

Correspondence to William C Hahn or Aviad Tsherniak.

Integrated supplementary information

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Supplementary Figures 1–11

  2. 2.

    Life Sciences Reporting Summary

Text files

  1. 1.

    Supplementary Table 1

    Cancer cell line informationSample information for the 342 cancer cell lines used in this study

  2. 2.

    Supplementary Table 2

    sgRNA sequences and targetssgRNA barcode sequences with genome alignments and coding sequence mappings for the Avana library

  3. 3.

    Supplementary Table 6

    Avana guide activity scoresCERES-estimated guide activity scores for sgRNAs in the Avana dataset

  4. 4.

    Supplementary Table 7

    GeCKOv2 guide activity scoresCERES-estimated guide activity scores for sgRNAs in the GeCKOv2 dataset

  5. 5.

    Supplementary Table 8

    Wang guide activity scoresCERES-estimated guide activity scores for sgRNAs in the Wang2017 dataset

CSV files

  1. 1.

    Supplementary Table 3

    Avana gene-knockout effectsCERES-estimated gene-knockout effects for 342 cancer cell lines screened with the Avana sgRNA library

  2. 2.

    Supplementary Table 4

    GeCKOv2 gene-knockout effectsCERES-estimated gene-knockout effects for 33 cancer cell lines screened with the GeCKOv2 sgRNA library published in Aguirre et al. (2016)

  3. 3.

    Supplementary Table 5

    Wang2017 gene-knockout effectsCERES-estimated gene-knockout effects for 14 AML cell lines screened with the Wang2017 sgRNA library published in Wang et al. (2017)

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DOI

https://doi.org/10.1038/ng.3984

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