IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v12y2021i1d10.1038_s41467-021-21898-7.html
   My bibliography  Save this article

Integrated cross-study datasets of genetic dependencies in cancer

Author

Listed:
  • Clare Pacini

    (Wellcome Genome Campus, Hinxton
    Wellcome Genome Campus, Hinxton)

  • Joshua M. Dempster

    (Broad Institute of MIT and Harvard)

  • Isabella Boyle

    (Broad Institute of MIT and Harvard)

  • Emanuel Gonçalves

    (Wellcome Genome Campus, Hinxton)

  • Hanna Najgebauer

    (Wellcome Genome Campus, Hinxton
    Wellcome Genome Campus, Hinxton
    Wellcome Genome Campus)

  • Emre Karakoc

    (Wellcome Genome Campus, Hinxton
    Wellcome Genome Campus, Hinxton)

  • Dieudonne Meer

    (Wellcome Genome Campus, Hinxton)

  • Andrew Barthorpe

    (Wellcome Genome Campus, Hinxton)

  • Howard Lightfoot

    (Wellcome Genome Campus, Hinxton)

  • Patricia Jaaks

    (Wellcome Genome Campus, Hinxton)

  • James M. McFarland

    (Broad Institute of MIT and Harvard)

  • Mathew J. Garnett

    (Wellcome Genome Campus, Hinxton
    Wellcome Genome Campus, Hinxton)

  • Aviad Tsherniak

    (Broad Institute of MIT and Harvard)

  • Francesco Iorio

    (Wellcome Genome Campus, Hinxton
    Wellcome Genome Campus, Hinxton
    Human Technopole)

Abstract

CRISPR-Cas9 viability screens are increasingly performed at a genome-wide scale across large panels of cell lines to identify new therapeutic targets for precision cancer therapy. Integrating the datasets resulting from these studies is necessary to adequately represent the heterogeneity of human cancers and to assemble a comprehensive map of cancer genetic vulnerabilities. Here, we integrated the two largest public independent CRISPR-Cas9 screens performed to date (at the Broad and Sanger institutes) by assessing, comparing, and selecting methods for correcting biases due to heterogeneous single-guide RNA efficiency, gene-independent responses to CRISPR-Cas9 targeting originated from copy number alterations, and experimental batch effects. Our integrated datasets recapitulate findings from the individual datasets, provide greater statistical power to cancer- and subtype-specific analyses, unveil additional biomarkers of gene dependency, and improve the detection of common essential genes. We provide the largest integrated resources of CRISPR-Cas9 screens to date and the basis for harmonizing existing and future functional genetics datasets.

Suggested Citation

  • Clare Pacini & Joshua M. Dempster & Isabella Boyle & Emanuel Gonçalves & Hanna Najgebauer & Emre Karakoc & Dieudonne Meer & Andrew Barthorpe & Howard Lightfoot & Patricia Jaaks & James M. McFarland & , 2021. "Integrated cross-study datasets of genetic dependencies in cancer," Nature Communications, Nature, vol. 12(1), pages 1-14, December.
  • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-21898-7
    DOI: 10.1038/s41467-021-21898-7
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-021-21898-7
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1038/s41467-021-21898-7?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ruitong Li & Olaf Klingbeil & Davide Monducci & Michael J. Young & Diego J. Rodriguez & Zaid Bayyat & Joshua M. Dempster & Devishi Kesar & Xiaoping Yang & Mahdi Zamanighomi & Christopher R. Vakoc & Ta, 2022. "Comparative optimization of combinatorial CRISPR screens," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    2. Sean A. Misek & Aaron Fultineer & Jeremie Kalfon & Javad Noorbakhsh & Isabella Boyle & Priyanka Roy & Joshua Dempster & Lia Petronio & Katherine Huang & Alham Saadat & Thomas Green & Adam Brown & John, 2024. "Germline variation contributes to false negatives in CRISPR-based experiments with varying burden across ancestries," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    3. George Rosenberger & Wenxue Li & Mikko Turunen & Jing He & Prem S. Subramaniam & Sergey Pampou & Aaron T. Griffin & Charles Karan & Patrick Kerwin & Diana Murray & Barry Honig & Yansheng Liu & Andrea , 2024. "Network-based elucidation of colon cancer drug resistance mechanisms by phosphoproteomic time-series analysis," Nature Communications, Nature, vol. 15(1), pages 1-27, December.
    4. Gisele Nishiguchi & Lauren G. Mascibroda & Sarah M. Young & Elizabeth A. Caine & Sherif Abdelhamed & Jeffrey J. Kooijman & Darcie J. Miller & Sourav Das & Kevin McGowan & Anand Mayasundari & Zhe Shi &, 2024. "Selective CK1α degraders exert antiproliferative activity against a broad range of human cancer cell lines," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    5. Wei Hu & Yangjun Wu & Qili Shi & Jingni Wu & Deping Kong & Xiaohua Wu & Xianghuo He & Teng Liu & Shengli Li, 2022. "Systematic characterization of cancer transcriptome at transcript resolution," Nature Communications, Nature, vol. 13(1), pages 1-16, December.
    6. Tanay Thakar & Ashna Dhoonmoon & Joshua Straka & Emily M. Schleicher & Claudia M. Nicolae & George-Lucian Moldovan, 2022. "Lagging strand gap suppression connects BRCA-mediated fork protection to nucleosome assembly through PCNA-dependent CAF-1 recycling," Nature Communications, Nature, vol. 13(1), pages 1-19, December.
    7. Miguel M. Álvarez & Josep Biayna & Fran Supek, 2022. "TP53-dependent toxicity of CRISPR/Cas9 cuts is differential across genomic loci and can confound genetic screening," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    8. Rani Pallavi & Elena Gatti & Tiphanie Durfort & Massimo Stendardo & Roberto Ravasio & Tommaso Leonardi & Paolo Falvo & Bruno Achutti Duso & Simona Punzi & Aobuli Xieraili & Andrea Polazzi & Doriana Ve, 2024. "Caloric restriction leads to druggable LSD1-dependent cancer stem cells expansion," Nature Communications, Nature, vol. 15(1), pages 1-18, December.
    9. Xiao Chen & Yinglu Li & Fang Zhu & Xinjing Xu & Brian Estrella & Manuel A. Pazos & John T. McGuire & Dimitris Karagiannis & Varun Sahu & Mustafo Mustafokulov & Claudio Scuoppo & Francisco J. Sánchez-R, 2023. "Context-defined cancer co-dependency mapping identifies a functional interplay between PRC2 and MLL-MEN1 complex in lymphoma," Nature Communications, Nature, vol. 14(1), pages 1-17, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-21898-7. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.