Related Experiment Video
Updated: May 8, 2026
A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions
Published on: May 27, 2021
Identification of potential synthetic lethal genes to p53 using a computational biology approach
Xiaosheng Wang1, Richard Simon
1Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, NE, USA. xiaosheng.wang@unmc.edu.
Background:
Identification of genes that are synthetic lethal to p53 is an important strategy for anticancer therapy as p53 mutations have been reported to occur in more than half of all human cancer cases. Although genome-wide RNAi screening is an effective approach to finding synthetic lethal genes, it is costly and labor-intensive.
Methods:
To illustrate this approach, we identified potentially druggable genes synthetically lethal for p53 using three microarray datasets for gene expression profiles of the NCI-60 cancer cell lines, one next-generation sequencing (RNA-Seq) dataset from the Cancer Genome Atlas (TCGA) project, and one gene expression data from the Cancer Cell Line Encyclopedia (CCLE) project. We selected the genes which encoded kinases and had significantly higher expression in the tumors with functional p53 mutations (somatic mutations) than in the tumors without functional p53 mutations as the candidates of druggable synthetic lethal genes for p53. We identified important regulatory networks and functional categories pertinent to these genes, and performed an extensive survey of literature to find experimental evidence that support the synthetic lethality relationships between the genes identified and p53. We also examined the drug sensitivity difference between NCI-60 cell lines with functional p53 mutations and NCI-60 cell lines without functional p53 mutations for the compounds that target the kinases encoded by the genes identified.
Results:
Our results indicated that some of the candidate genes we identified had been experimentally verified to be synthetic lethal for p53 and promising targets for anticancer therapy while some other genes were putative targets for development of cancer therapeutic agents.
Conclusions:
Our study indicated that pre-screening of potential synthetic lethal genes using gene expression profiles is a promising approach for improving the efficiency of synthetic lethal RNAi screening.
Insights
Identifying genes synthetically lethal to p53 is crucial for cancer therapy. This study efficiently pre-screens potential synthetic lethal genes using gene expression profiles, improving RNAi screening efficiency for novel anticancer targets.
Area of Science:
- Oncology
- Genetics
- Bioinformatics
Background:
- p53 mutations occur in over half of human cancers, making synthetic lethal gene identification a key anticancer strategy.
- Genome-wide RNAi screening is effective but costly and labor-intensive for finding these genes.
Purpose of the Study:
- To identify potentially druggable genes synthetically lethal to p53.
- To develop a more efficient pre-screening method for synthetic lethality using gene expression data.
Main Methods:
- Utilized microarray and RNA-Seq datasets (NCI-60, TCGA, CCLE) to analyze gene expression profiles.
- Selected kinase-encoding genes with significantly higher expression in tumors with functional p53 mutations.
- Performed literature surveys and analyzed drug sensitivity for identified candidate genes.
Main Results:
- Identified candidate genes experimentally verified as synthetic lethal to p53, showing promise for anticancer therapy.
- Discovered additional putative targets for future cancer therapeutic development.
- Found differences in drug sensitivity in cell lines with and without functional p53 mutations.
Conclusions:
- Pre-screening potential synthetic lethal genes using gene expression profiles is a promising approach.
- This method enhances the efficiency of synthetic lethal RNAi screening for anticancer drug discovery.
