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A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions
Published on: May 27, 2021
Synthetic lethality in large-scale integrated metabolic and regulatory network models of human cells
Naroa Barrena1, Luis V Valcárcel1,2,3, Danel Olaverri-Mendizabal1
1University of Navarra, Tecnun School of Engineering, Manuel de Lardizábal 13, 20018, San Sebastián, Spain.
Abstract:
Synthetic lethality (SL) is a promising concept in cancer research. A wide array of computational tools has been developed to predict and exploit synthetic lethality for the identification of tumour-specific vulnerabilities. Previously, we introduced the concept of genetic Minimal Cut Sets (gMCSs), a theoretical approach to SL developed for genome-scale metabolic networks. The major challenge in our gMCS framework is to go beyond metabolic networks and extend existing algorithms to more complex protein-protein interactions. In this article, we take a step further and incorporate linear regulatory pathways into our gMCS approach. Extensive algorithmic modifications to compute gMCSs in integrated metabolic and regulatory models are presented in detail. Our extended approach is applied to calculate gMCSs in integrated models of human cells. In particular, we integrate the most recent genome-scale metabolic network, Human1, with 3 different regulatory network databases: Omnipath, Dorothea and TRRUST. Based on the computed gMCSs and transcriptomic data, we discovered new essential genes and their associated synthetic lethal for different cancer cell lines. The performance of the different integrated models is assessed with available large-scale in-vitro gene silencing data. Finally, we discuss the most relevant gene essentiality predictions based on published literature in cancer research.
Insights
This study extends computational methods for synthetic lethality (SL) by integrating metabolic and regulatory networks. The approach identifies new essential genes and synthetic lethal partners for cancer therapy.
Area of Science:
- Computational biology
- Systems biology
- Cancer research
Background:
- Synthetic lethality (SL) offers a promising avenue for targeted cancer therapy by exploiting tumor-specific vulnerabilities.
- Genetic Minimal Cut Sets (gMCSs) provide a theoretical framework for predicting SL in metabolic networks.
- Extending gMCSs beyond metabolic networks to incorporate regulatory interactions is a significant challenge.
Purpose of the Study:
- To extend the gMCS framework to integrate linear regulatory pathways with metabolic networks.
- To develop and present detailed algorithmic modifications for computing gMCSs in integrated models.
- To identify novel essential genes and synthetic lethal interactions in human cancer cell lines.
Main Methods:
- Integration of the Human1 genome-scale metabolic network with regulatory network databases (Omnipath, Dorothea, TRRUST).
- Development of novel algorithms for computing gMCSs in combined metabolic and regulatory models.
- Application of computed gMCSs and transcriptomic data to predict gene essentiality and synthetic lethality.
Main Results:
- Successfully computed gMCSs in integrated human cell models, combining metabolic and regulatory information.
- Discovered new essential genes and their synthetic lethal partners across various cancer cell lines.
- Assessed the performance of integrated models using large-scale in-vitro gene silencing data.
Conclusions:
- The extended gMCS approach effectively integrates metabolic and regulatory data for enhanced prediction of synthetic lethality.
- This framework facilitates the discovery of novel therapeutic targets and synthetic lethal interactions in cancer.
- The findings provide a foundation for developing more precise and effective cancer treatment strategies.
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