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Integrative analysis of large-scale loss-of-function screens identifies robust cancer-associated genetic interactions
Christopher J Lord1, Niall Quinn2, Colm J Ryan2
1Breast Cancer Now Toby Robins Research Centre and Cancer Research UK Gene Function Laboratory, Institute of Cancer Research, London, United Kingdom.
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.
Insights
Researchers identified 220 robust genetic interactions in cancer cell lines using a novel computational method. These findings highlight the importance of protein interactions for robust synthetic lethality and prioritizing cancer drug targets.
Area of Science:
- Cancer Genomics
- Computational Biology
- Systems Biology
Background:
- High-throughput genetic screens identify cancer-related genetic interactions, including synthetic lethality.
- Reproducibility and context-specificity remain challenges for many identified genetic interactions.
Purpose of the Study:
- To develop a computational approach for identifying robust, reproducible genetic interactions across independent cancer cell line experiments.
- To analyze the characteristics of robust genetic interactions and their relationship with protein-protein interaction networks.
- To identify and validate novel synthetic lethal interactions associated with passenger gene alterations.
Main Methods:
- Development of a novel computational method to identify robust genetic interactions.
- Analysis of genetic interactions across independent cancer cell line panels.
- Utilizing protein-protein interaction networks to predict and validate synthetic lethal effects.
Main Results:
- Identification of 220 robust driver-gene associated genetic interactions reproducible across studies.
- Demonstration that oncogene addiction effects are more robust than oncogene-related synthetic lethal effects.
- Confirmation that robust genetic interactions are enriched among physically interacting protein pairs.
- Validation of two novel synthetic lethal effects associated with passenger gene alterations.
Conclusions:
- A new computational approach enables the identification of robust genetic interactions in cancer.
- Protein-protein interaction networks are valuable for discovering robust synthetic lethal interactions.
- Prioritizing therapeutic targets based on robust genetic interactions may overcome tumor heterogeneity.
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