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Improved estimation of cancer dependencies from large-scale RNAi screens using model-based normalization and data integration

Author

Listed:
  • James M. McFarland

    (Broad Institute of MIT and Harvard)

  • Zandra V. Ho

    (Broad Institute of MIT and Harvard)

  • Guillaume Kugener

    (Broad Institute of MIT and Harvard)

  • Joshua M. Dempster

    (Broad Institute of MIT and Harvard)

  • Phillip G. Montgomery

    (Broad Institute of MIT and Harvard)

  • Jordan G. Bryan

    (Broad Institute of MIT and Harvard)

  • John M. Krill-Burger

    (Broad Institute of MIT and Harvard)

  • Thomas M. Green

    (Broad Institute of MIT and Harvard)

  • Francisca Vazquez

    (Broad Institute of MIT and Harvard
    Dana-Farber Cancer Institute)

  • Jesse S. Boehm

    (Broad Institute of MIT and Harvard)

  • Todd R. Golub

    (Broad Institute of MIT and Harvard
    Dana-Farber Cancer Institute
    Harvard Medical School
    Boston Children’s Hospital)

  • William C. Hahn

    (Broad Institute of MIT and Harvard
    Dana-Farber Cancer Institute
    Harvard Medical School
    Brigham and Women’s Hospital)

  • David E. Root

    (Broad Institute of MIT and Harvard)

  • Aviad Tsherniak

    (Broad Institute of MIT and Harvard)

Abstract

The availability of multiple datasets comprising genome-scale RNAi viability screens in hundreds of diverse cancer cell lines presents new opportunities for understanding cancer vulnerabilities. Integrated analyses of these data to assess differential dependency across genes and cell lines are challenging due to confounding factors such as batch effects and variable screen quality, as well as difficulty assessing gene dependency on an absolute scale. To address these issues, we incorporated cell line screen-quality parameters and hierarchical Bayesian inference into DEMETER2, an analytical framework for analyzing RNAi screens ( https://depmap.org/R2-D2 ). This model substantially improves estimates of gene dependency across a range of performance measures, including identification of gold-standard essential genes and agreement with CRISPR/Cas9-based viability screens. It also allows us to integrate information across three large RNAi screening datasets, providing a unified resource representing the most extensive compilation of cancer cell line genetic dependencies to date.

Suggested Citation

  • James M. McFarland & Zandra V. Ho & Guillaume Kugener & Joshua M. Dempster & Phillip G. Montgomery & Jordan G. Bryan & John M. Krill-Burger & Thomas M. Green & Francisca Vazquez & Jesse S. Boehm & Tod, 2018. "Improved estimation of cancer dependencies from large-scale RNAi screens using model-based normalization and data integration," Nature Communications, Nature, vol. 9(1), pages 1-13, December.
    • Handle: RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-018-06916-5
    DOI: 10.1038/s41467-018-06916-5
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    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. Abdallah Gaballa & Anneli Gebhardt-Wolf & Bastian Krenz & Greta Mattavelli & Mara John & Giacomo Cossa & Silvia Andreani & Christina Schülein-Völk & Francisco Montesinos & Raphael Vidal & Carolin Kast, 2024. "PAF1c links S-phase progression to immune evasion and MYC function in pancreatic carcinoma," Nature Communications, Nature, vol. 15(1), pages 1-18, December.
    3. Michelle M. Kameda-Smith & Helen Zhu & En-Ching Luo & Yujin Suk & Agata Xella & Brian Yee & Chirayu Chokshi & Sansi Xing & Frederick Tan & Raymond G. Fox & Ashley A. Adile & David Bakhshinyan & Kevin , 2022. "Characterization of an RNA binding protein interactome reveals a context-specific post-transcriptional landscape of MYC-amplified medulloblastoma," Nature Communications, Nature, vol. 13(1), pages 1-19, December.
    4. Tea Pemovska & Johannes W. Bigenzahn & Ismet Srndic & Alexander Lercher & Andreas Bergthaler & Adrián César-Razquin & Felix Kartnig & Christoph Kornauth & Peter Valent & Philipp B. Staber & Giulio Sup, 2021. "Metabolic drug survey highlights cancer cell dependencies and vulnerabilities," Nature Communications, Nature, vol. 12(1), pages 1-19, December.
    5. Kaja Kostyrko & Marta Román & Alex G. Lee & David R. Simpson & Phuong T. Dinh & Stanley G. Leung & Kieren D. Marini & Marcus R. Kelly & Joshua Broyde & Andrea Califano & Peter K. Jackson & E. Alejandr, 2023. "UHRF1 is a mediator of KRAS driven oncogenesis in lung adenocarcinoma," Nature Communications, Nature, vol. 14(1), pages 1-18, December.
    6. 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.
    7. Yuan Liu & Jingwen Yang & Tianlu Wang & Mei Luo & Yamei Chen & Chengxuan Chen & Ze’ev Ronai & Yubin Zhou & Eytan Ruppin & Leng Han, 2023. "Expanding PROTACtable genome universe of E3 ligases," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
    8. Sanju Sinha & Karina Barbosa & Kuoyuan Cheng & Mark D. M. Leiserson & Prashant Jain & Anagha Deshpande & David M. Wilson & Bríd M. Ryan & Ji Luo & Ze’ev A. Ronai & Joo Sang Lee & Aniruddha J. Deshpand, 2021. "A systematic genome-wide mapping of oncogenic mutation selection during CRISPR-Cas9 genome editing," Nature Communications, Nature, vol. 12(1), pages 1-13, December.

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