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A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions
Published on: May 27, 2021
SL-BioDP: Multi-Cancer Interactive Tool for Prediction of Synthetic Lethality and Response to Cancer Treatment
Xiang Deng1,2, Shaoli Das3, Kristin Valdez4,5
1Bioinformatics core facility, Radiation Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA. dengx@mail.nih.gov.
Abstract:
Synthetic lethality exploits the phenomenon that a mutation in a cancer gene is often associated with new vulnerability which can be uniquely targeted therapeutically, leading to a significant increase in favorable outcome. DNA damage and survival pathways are among the most commonly mutated networks in human cancers. Recent data suggest that synthetic lethal interactions between a tumor defect and a DNA repair pathway can be used to preferentially kill tumor cells. We recently published a method, DiscoverSL, using multi-omic cancer data, that can predict synthetic lethal interactions of potential clinical relevance. Here, we apply the generality of our models in a comprehensive web tool called Synthetic Lethality Bio Discovery Portal (SL-BioDP) and extend the cancer types to 18 cancer genome atlas cohorts. SL-BioDP enables a data-driven computational approach to predict synthetic lethal interactions from hallmark cancer pathways by mining cancer's genomic and chemical interactions. Our tool provides queries and visualizations for exploring potentially targetable synthetic lethal interactions, shows Kaplan-Meier plots of clinical relevance, and provides in silico validation using short hairpin RNA (shRNA) and drug efficacy data. Our method would thus shed light on mechanisms of synthetic lethal interactions and lead to the discovery of novel anticancer drugs.
Insights
Synthetic lethality uses cancer's genetic vulnerabilities for targeted therapy. A new tool, SL-BioDP, predicts these interactions across 18 cancer types, aiding novel drug discovery.
Area of Science:
- Oncology
- Computational Biology
- Genomics
Background:
- Synthetic lethality offers targeted cancer therapy by exploiting cancer-specific vulnerabilities.
- Mutations in DNA damage and survival pathways are common in human cancers, presenting therapeutic opportunities.
- Previous methods like DiscoverSL predicted clinically relevant synthetic lethal interactions using multi-omic data.
Purpose of the Study:
- To develop a comprehensive web tool, SL-BioDP, for predicting synthetic lethal interactions.
- To extend the prediction of synthetic lethality to 18 Cancer Genome Atlas cohorts.
- To provide a data-driven platform for discovering novel anticancer drugs.
Main Methods:
- Applied predictive models from DiscoverSL to a broader dataset.
- Integrated genomic and chemical interaction data for pathway analysis.
- Developed a web portal (SL-BioDP) with query, visualization, and validation features.
Main Results:
- SL-BioDP predicts synthetic lethal interactions across 18 cancer types.
- The tool offers visualizations, Kaplan-Meier plots for clinical relevance, and in silico validation.
- Identified potentially targetable synthetic lethal interactions.
Conclusions:
- SL-BioDP provides a valuable resource for data-driven discovery of synthetic lethal interactions.
- The tool facilitates the identification of novel therapeutic targets and anticancer drugs.
- This approach aids in understanding synthetic lethality mechanisms for improved cancer treatment.
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