Integrative analysis of large-scale loss-of-function screens identifies robust cancer-associated genetic interactions

  1. Christopher J Lord
  2. Niall Quinn
  3. Colm J Ryan  Is a corresponding author
  1. Institute of Cancer Research, United Kingdom
  2. University College Dublin, Ireland

Abstract

Genetic interactions, including synthetic lethal effects, can now be systematically identified in cancer cell lines using high-throughput genetic perturbation screens. Despite this advance, few genetic interactions have been reproduced across multiple studies and many appear highly context-specific. Here, by developing a new computational approach, we identified 220 robust driver-gene associated genetic interactions that can be reproduced across independent experiments and across non-overlapping cell line panels. Analysis of these interactions demonstrated that: (i) oncogene addiction effects are more robust than oncogene-related synthetic lethal effects; and (ii) robust genetic interactions are enriched among gene pairs whose protein products physically interact. Exploiting the latter observation, we used a protein-protein interaction network to identify robust synthetic lethal effects associated with passenger gene alterations and validated two new synthetic lethal effects. Our results suggest that protein-protein interaction networks can be used to prioritise therapeutic targets that will be more robust to tumour heterogeneity.

Data availability

All data generated during this study are included in the manuscript and supporting files

Article and author information

Author details

  1. Christopher J Lord

    Breast Cancer Now Toby Robins Research Centre and Cancer Research UK Gene Function Laboratory, Institute of Cancer Research, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  2. Niall Quinn

    School of Computer Science and Systems Biology Ireland, University College Dublin, Dublin, Ireland
    Competing interests
    The authors declare that no competing interests exist.
  3. Colm J Ryan

    School of Computer Science and Systems Biology Ireland, University College Dublin, Dublin, Ireland
    For correspondence
    colm.ryan@ucd.ie
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2750-9854

Funding

Irish Research Council (Laureate Awards 2017/2018)

  • Colm J Ryan

Wellcome (103049/Z/13/Z)

  • Colm J Ryan

Cancer Research UK (CRUK/A14276)

  • Christopher J Lord

Breast Cancer Now (CTR-Q4-Y2)

  • Christopher J Lord

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Maureen E Murphy, The Wistar Institute, United States

Version history

  1. Received: May 15, 2020
  2. Accepted: May 18, 2020
  3. Accepted Manuscript published: May 28, 2020 (version 1)
  4. Version of Record published: June 11, 2020 (version 2)
  5. Version of Record updated: June 30, 2020 (version 3)

Copyright

© 2020, Lord et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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  1. Christopher J Lord
  2. Niall Quinn
  3. Colm J Ryan
(2020)
Integrative analysis of large-scale loss-of-function screens identifies robust cancer-associated genetic interactions
eLife 9:e58925.
https://doi.org/10.7554/eLife.58925

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https://doi.org/10.7554/eLife.58925

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