Identifying cancer driver genes from functional genomics screens

Area of Science:

• Genomics
• Cancer Biology
• Bioinformatics

Background:

• Identifying cancer driver genes is crucial for targeted therapies.
• Integrating diverse genomic datasets improves accuracy in pinpointing driver genes.
• Existing methods often require complex statistical analyses and multiple hypothesis testing.

Purpose of the Study:

• To develop an integrative approach for identifying cancer driver genes using pooled shRNA screening data.
• To introduce DepRanker, a novel scoring system for ranking candidate driver genes.
• To identify and validate potential therapeutic targets in oral cancer.

Main Methods:

• Performed a pooled shRNA screen on 906 human genes in oral cancer cells.
• Integrated screen data with copy number alteration and gene expression data.
• Utilized the DepRanker system to compute a Rank Impact Score (RIS) for gene prioritization.

Main Results:

• DepRanker successfully identified AURKB and TK1 as essential oncogenes for oral cancer cell proliferation.
• Knockdown of TK1 or inhibition of AURKB significantly reduced oral cancer cell proliferation.
• Analysis of TCGA data revealed that alterations in AURKB and TK1 are associated with poor survival in head and neck cancer patients.

Conclusions:

• DepRanker is a robust, dependency-free package for identifying driver genes from functional genomic screens.
• AURKB and TK1 represent promising therapeutic targets for oral and head and neck cancers.
• The integrative approach enhances the reliability of driver gene identification and reduces false positives.